**Select Population-Based and Translational Research for Improving Pancreatic Cancer Early Detection**

Shiro Urayama

[183] Mallery S, Matlock J, Freeman ML EUS-guided rendezvous drainage of obstructed biliary and pancreatic ducts: Report of 6 cases. Gastrointest Endosc. 2004; 59(1):100-7

[184] Steinberg WM, Barkin JS, Bradley EL 3rd, DiMagno E, Layer P, Canto MI, Levy MJ. Should patients with a strong family history of pancreatic cancer be screened on a

[185] Vasen HF, Wasser M, van Mil A, Tollenaar RA, Konstantinovski M, Gruis NA, Berg‐ man W, Hes FJ, Hommes DW, Offerhaus GJ, Morreau H, Bonsing BA, de Vos tot Ne‐ derveen Cappel WH. Magnetic resonance imaging surveillance detects early-stage pancreatic cancer in carriers of a p16-Leiden mutation. Gastroenterology. 2011;140(3):

[186] Poley JW, Kluijt I, Gouma DJ, Harinck F, Wagner A, Aalfs C, van Eijck CH, Cats A, Kuipers EJ, Nio Y, Fockens P, Bruno MJ. The yield of first-time endoscopic ultraso‐ nography in screening individuals at a high risk of developing pancreatic cancer.Am

[187] Canto MI, Hruban RH, Fishman EK, Kamel IR, Schulick R, Zhang Z, Topazian M, Ta‐ kahashi N, Fletcher J, Petersen G, Klein AP, Axilbund J, Griffin C, Syngal S, Saltzman JR, Mortele KJ, Lee J, Tamm E, Vikram R, Bhosale P, Margolis D, Farrell J, Goggins M; American Cancer of the Pancreas Screening (CAPS) Consortium. Frequent detec‐ tion of pancreatic lesions in asymptomatic high-risk individuals. Gastroenterology.

[188] Zubarik R, Gordon SR, Lidofsky SD, Anderson SR, Pipas JM, Badger G, Ganguly E, Vecchio J. Screening for pancreatic cancer in a high-risk population with serum CA 19-9 and targeted EUS: a feasibility study. Gastrointest Endosc. 2011;74(1):87-95. [189] Langer P, Kann PH, Fendrich V, Habbe N, Schneider M, Sina M, Slater EP, Heverha‐ gen JT, Gress TM, Rothmund M, Bartsch DK. Five years of prospective screening of high-risk individuals from families with familial pancreatic cancer. Gut. 2009;58(10):

[190] Canto MI, Harinck F, Hruban RH, Offerhaus GJ, Poley JW, Kamel I, Nio Y, Schulick RS, Bassi C, Kluijt I, Levy MJ, Chak A, Fockens P, Goggins M, Bruno M; International Cancerof Pancreas Screening (CAPS) Consortium. International Cancer of the Pan‐ creas Screening (CAPS) Consortium summit on the management of patients with in‐

creased risk for familial pancreatic cancer. Gut. 2013;62(3):339-47.

periodic basis for cancer of the pancreas? Pancreas. 2009; 38(5):e137-50

138 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

850–6.

1410-8.

J Gastroenterol. 2009; 104(9):2175-81.

2012 ;142(4):796-804; quiz e14-5.

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57531

## **1. Introduction**

Pancreatic ductal adenocarcinoma (PDAC) comprises majority of pancreatic neoplasm and remains to pose an enormous challenge to patients and clinicians with the worst survival rate among all major malignancies. PDAC is the fourth leading cause overall and second leading cause of gastrointestinal cancer death in the United States. [1] It is estimated that 45,220 new cases and 38,460 deaths would result from pancreatic cancer in the United States in 2013. [2] Worldwide, there were more than 277,668 new cases and 266,029 deaths from this cancer in 2008. [3] In comparison to other major malignancies such as breast, colon, lung and prostate cancers with their respective 89, 64, 16, 99% 5-yr survival rate, PDAC at 6% is conspicuously low[2]. For PDAC, the only curative option is surgical resection, which is applicable in only 10–15% of patients due to the common discovery of late stage at diagnosis. [4] In fact, PDAC is notorious for late stage discovery as evidenced by the low percentage of localized disease at diagnosis, compared to other major malignancies: breast (61%), colon (40%), lung (16%), ovarian (19%), prostate (91%), and pancreatic cancer (7%) [5].

With the high contribution of late-stage discovery and general lack of effective medical therapy, one critical approach in reversing the poor outcome of pancreatic cancer is to develop an early detection scheme for the tumor. Despite the poor prognosis of the disease, for those who have undergone curative resection with negative margins, the 5-year survival rate is 22% in contrast to 2% for the advanced-stage with distant metastasis. [6, 7] An earlier diagnosis with tumor less than 2 cm (T1) is associated with a better 5-yr survival of 58% compared to 17% for stage IIB PDAC. [8] Ariyama, et al showed 100% survival in 79 patients with tumors less than 1 cm undergoing curative resection. [9] Also as the recent report indicates, the estimated time from the transformation to pre-metastatic growths of pancreatic cancer is

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

approximately 15 years [10], there is a wide potential window of opportunity to apply developing technologies in early detection of this cancer.

Elastography is a newer EUS imaging modality used for the real-time visualization of tissue elasticity, and it demonstrates the difference in tissue stiffness between diseased and normal regions. [19, 20] Tumor is commonly stiffer than the normal surrounding tissue, and this characteristic is utilized in the determination of presence of neoplastic lesion, including pancreatic cancer. [21] Giovannini, et al tested this method for the differential diagnosis of benign and malignant lymph nodes and focal pancreatic masses in a small study of 49 patients and showed a sensitivity and specificity of 100% and 67% for the diagnosis of malignant pancreatic lesions. They concluded that this technique could be used to guide biopsy sampling

Select Population-Based and Translational Research for Improving Pancreatic Cancer Early Detection

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141

Contrast enhancing agents such as galactose microparticles (Levovist) and sulfur hexafluoride microparticles (SonoVue, a second-generation agent) have been applied in the diagnosis of pancreatic malignancy by assessing the differential vascular perfusion in the pancreatic mass. [23, 24] Hocke, et al reported the differentiation of inflammation versus pancreatic carcinoma based on perfusion characteristics of the microvessels. [25] By using the contrast-enhanced EUS, the sensitivity of the diagnosis of malignant pancreatic lesion with chronic inflammatory pancreatic disease increased to 91. 1% (in 51 of 56 patients) and the specificity to 93. 3% (in 28 of 30 patients) in comparison to conventional EUS sensitivity and specificity of 73. 2% and 83. 3%, respectively. Applicability of an additional modality such as the low mechanical index contrast-enhanced imaging (wide band harmonic imaging) technique has been reported in 6 patients by Dietrich, et al with good arterial, portal venous and parenchymal contrast en‐ hancement. [26] Further study for accuracy of this particular diagnostic testing is anticipated.

Studies have shown that the accuracy of EUS-FNA is better compared to both ERCP brushings and CT-or transabdominal ultrasound-guided FNA for the PDAC diagnosis. [27, 28] EUS-FNA has reported success rates of 90–95%, with an overall sensitivity and specificity of 90% and near-100%, respectively. [29, 30, 31, 32] The main advantage of EUS-guidance is the ability to visualize and target small pancreatic masses. Lesions of 5 mm or less could be visualized and sampled, which might not have been accessible or identifiable by other imaging modalities. [33] Krishna, et al, in a review of 213 patients, found EUS-guided FNA to be highly accurate for diagnosing malignancy in patients with a focal pancreatic lesion noted on CT scan/MRI without obstructive jaundice. EUS-FNA had 97. 6% accuracy for diagnosing a malignant neoplasm, with 96. 6% sensitivity, 99. 0% specificity, 96. 2% negative predictive value, and 99. 1% positive predictive value. [34] Agarwal, et al compared 81 consecutive patients who underwent EUS, EUS-FNA and spiral CT with a multiphasic pancreatic protocol for clinical suspicion of PDAC. They showed that the accuracy of spiral CT, EUS, and EUS-FNA was 74% (n=60/81, CI 63-83%), 94% (n=76/81, CI 87-98%), and 88% (n=73/81, CI 81-96%), respectively, for detecting pancreatic cancer. In their study, absence of a focal lesion on EUS reliably excluded pancreatic cancer irrespective of clinical presen‐

From a practical standpoint, tumor cell seeding of the FNA tract is rare and only a few EUS cases have been reported. Micames, et al in their study demonstrated that EUS-FNA has a

**2.1. EUS-guided Fine Needle Aspiration (FNA) in pancreatic cancer**

tation (NPV 100% n=5/5, CI 48-100%). [35]

for PDAC diagnosis. [22]

In this article, we will discuss the current status of the PDAC cancer detection/diagnostic modalities and ongoing research endeavors in developing early detection schemes for this devastating disease.

## **2. Current status of PDAC cancer detection and diagnosis — Imaging-based tests**

As clinical symptoms of early stages of PDAC is commonly nonspecific and as currently available clinical markers such as CA19-9, CEA, have low sensitivity and specificity at early stage disease 11, clinicians who are suspecting the occurrence of PDAC in a patient rely heavily on diagnostic imaging tests for assessment of a potential tumor.

Over the past few decades, endoscopic ultrasound (EUS) has proven itself to be a superior imaging modality for detection of a small or early-stage pancreatic neoplasm as compared to others such as transabdominal ultrasound (US), computed tomography (CT), endoscopic retrograde cholangiopancreatography (ERCP), magnetic resonance imaging (MRI), positron emission tomography (PET) and angiography. [12, 13, 14, 15, 16, 17] Yasuda and Rosch had initially demonstrated the superiority of EUS in detection of pancreatic lesions <2 cm in diameter. [12, 18] More recently, De Witt, et al had verified the superiority of EUS as compared to multi-detector CT scan. In their study, the sensitivity of endoscopic ultrasonography (98% [95% CI=91% to 100%]) for detecting a pancreatic mass (of any size) was significantly greater than that of CT images (86% [CI=77% to 93%]; p=0. 012) [13]. In another study, Khashab, et al demonstrated that the sensitivity of EUS in detecting pancreatic tumor was greater than CT (91. 7% vs. 63. 3%; P=. 0002) and particularly for pancreatic neuroendocrine tumors (84. 2% vs. 31. 6%; P=. 001), which commonly consist of smaller pancreatic lesions. Furthermore, EUS detected 20 of 22 CT-negative tumors (91%) in this study. [14] In a retrospective study published by Klapman, et al, EUS diagnosis of pancreatic cancer was found to be highly specific with a negative predictive value (NPV) of 100%. Following the EUS examination, no work-up was required in 119/135 (88%) of patients. [15]

A challenge in imaging-based studies remains to be distinguishing pancreatic malignant lesions from chronic inflammatory changes. Bhutani, et al reviewed 20 cases of missed pancreatic cancer on EUS evaluation in a multicenter retrospective study. They found missed neoplasms in patients with chronic pancreatitis, recent episodes of acute pancreatitis, diffusely infiltrating carcinoma, or a prominent ventral/dorsal split. [16] Conventional power Doppler EUS has some utility in this regard; Sa`ftoiu, et al in a study of 42 patients showed that absence of power Doppler signals inside a suspicious pancreatic mass had a sensitivity of 93% and a specificity of 77%, with an accuracy of 88% in the diagnosis of pancreatic cancer. In the presence of peripancreatic collaterals, the sensitivity and specificity for the diagnosis of pancreatic cancer rose to 97% and 92%, respectively, with an accuracy of 95%. [17]

Elastography is a newer EUS imaging modality used for the real-time visualization of tissue elasticity, and it demonstrates the difference in tissue stiffness between diseased and normal regions. [19, 20] Tumor is commonly stiffer than the normal surrounding tissue, and this characteristic is utilized in the determination of presence of neoplastic lesion, including pancreatic cancer. [21] Giovannini, et al tested this method for the differential diagnosis of benign and malignant lymph nodes and focal pancreatic masses in a small study of 49 patients and showed a sensitivity and specificity of 100% and 67% for the diagnosis of malignant pancreatic lesions. They concluded that this technique could be used to guide biopsy sampling for PDAC diagnosis. [22]

Contrast enhancing agents such as galactose microparticles (Levovist) and sulfur hexafluoride microparticles (SonoVue, a second-generation agent) have been applied in the diagnosis of pancreatic malignancy by assessing the differential vascular perfusion in the pancreatic mass. [23, 24] Hocke, et al reported the differentiation of inflammation versus pancreatic carcinoma based on perfusion characteristics of the microvessels. [25] By using the contrast-enhanced EUS, the sensitivity of the diagnosis of malignant pancreatic lesion with chronic inflammatory pancreatic disease increased to 91. 1% (in 51 of 56 patients) and the specificity to 93. 3% (in 28 of 30 patients) in comparison to conventional EUS sensitivity and specificity of 73. 2% and 83. 3%, respectively. Applicability of an additional modality such as the low mechanical index contrast-enhanced imaging (wide band harmonic imaging) technique has been reported in 6 patients by Dietrich, et al with good arterial, portal venous and parenchymal contrast en‐ hancement. [26] Further study for accuracy of this particular diagnostic testing is anticipated.

#### **2.1. EUS-guided Fine Needle Aspiration (FNA) in pancreatic cancer**

approximately 15 years [10], there is a wide potential window of opportunity to apply

140 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

In this article, we will discuss the current status of the PDAC cancer detection/diagnostic modalities and ongoing research endeavors in developing early detection schemes for this

**2. Current status of PDAC cancer detection and diagnosis — Imaging-based**

As clinical symptoms of early stages of PDAC is commonly nonspecific and as currently available clinical markers such as CA19-9, CEA, have low sensitivity and specificity at early stage disease 11, clinicians who are suspecting the occurrence of PDAC in a patient rely heavily

Over the past few decades, endoscopic ultrasound (EUS) has proven itself to be a superior imaging modality for detection of a small or early-stage pancreatic neoplasm as compared to others such as transabdominal ultrasound (US), computed tomography (CT), endoscopic retrograde cholangiopancreatography (ERCP), magnetic resonance imaging (MRI), positron emission tomography (PET) and angiography. [12, 13, 14, 15, 16, 17] Yasuda and Rosch had initially demonstrated the superiority of EUS in detection of pancreatic lesions <2 cm in diameter. [12, 18] More recently, De Witt, et al had verified the superiority of EUS as compared to multi-detector CT scan. In their study, the sensitivity of endoscopic ultrasonography (98% [95% CI=91% to 100%]) for detecting a pancreatic mass (of any size) was significantly greater than that of CT images (86% [CI=77% to 93%]; p=0. 012) [13]. In another study, Khashab, et al demonstrated that the sensitivity of EUS in detecting pancreatic tumor was greater than CT (91. 7% vs. 63. 3%; P=. 0002) and particularly for pancreatic neuroendocrine tumors (84. 2% vs. 31. 6%; P=. 001), which commonly consist of smaller pancreatic lesions. Furthermore, EUS detected 20 of 22 CT-negative tumors (91%) in this study. [14] In a retrospective study published by Klapman, et al, EUS diagnosis of pancreatic cancer was found to be highly specific with a negative predictive value (NPV) of 100%. Following the EUS examination, no work-up

A challenge in imaging-based studies remains to be distinguishing pancreatic malignant lesions from chronic inflammatory changes. Bhutani, et al reviewed 20 cases of missed pancreatic cancer on EUS evaluation in a multicenter retrospective study. They found missed neoplasms in patients with chronic pancreatitis, recent episodes of acute pancreatitis, diffusely infiltrating carcinoma, or a prominent ventral/dorsal split. [16] Conventional power Doppler EUS has some utility in this regard; Sa`ftoiu, et al in a study of 42 patients showed that absence of power Doppler signals inside a suspicious pancreatic mass had a sensitivity of 93% and a specificity of 77%, with an accuracy of 88% in the diagnosis of pancreatic cancer. In the presence of peripancreatic collaterals, the sensitivity and specificity for the diagnosis of pancreatic

cancer rose to 97% and 92%, respectively, with an accuracy of 95%. [17]

developing technologies in early detection of this cancer.

on diagnostic imaging tests for assessment of a potential tumor.

was required in 119/135 (88%) of patients. [15]

devastating disease.

**tests**

Studies have shown that the accuracy of EUS-FNA is better compared to both ERCP brushings and CT-or transabdominal ultrasound-guided FNA for the PDAC diagnosis. [27, 28] EUS-FNA has reported success rates of 90–95%, with an overall sensitivity and specificity of 90% and near-100%, respectively. [29, 30, 31, 32] The main advantage of EUS-guidance is the ability to visualize and target small pancreatic masses. Lesions of 5 mm or less could be visualized and sampled, which might not have been accessible or identifiable by other imaging modalities. [33] Krishna, et al, in a review of 213 patients, found EUS-guided FNA to be highly accurate for diagnosing malignancy in patients with a focal pancreatic lesion noted on CT scan/MRI without obstructive jaundice. EUS-FNA had 97. 6% accuracy for diagnosing a malignant neoplasm, with 96. 6% sensitivity, 99. 0% specificity, 96. 2% negative predictive value, and 99. 1% positive predictive value. [34] Agarwal, et al compared 81 consecutive patients who underwent EUS, EUS-FNA and spiral CT with a multiphasic pancreatic protocol for clinical suspicion of PDAC. They showed that the accuracy of spiral CT, EUS, and EUS-FNA was 74% (n=60/81, CI 63-83%), 94% (n=76/81, CI 87-98%), and 88% (n=73/81, CI 81-96%), respectively, for detecting pancreatic cancer. In their study, absence of a focal lesion on EUS reliably excluded pancreatic cancer irrespective of clinical presen‐ tation (NPV 100% n=5/5, CI 48-100%). [35]

From a practical standpoint, tumor cell seeding of the FNA tract is rare and only a few EUS cases have been reported. Micames, et al in their study demonstrated that EUS-FNA has a lower risk of peritoneal contamination with malignancy than CT-guided FNA (2. 2% versus 16. 3%), respectively. [36] This is a potential complication of EUS-FNA that would need to be kept in mind by clinicians when FNA sampling of a lesion is being considered. [37, 38]

the disease or are part of an inherited pancreatic cancer syndrome with a known genetic

Select Population-Based and Translational Research for Improving Pancreatic Cancer Early Detection

STK11/LKB1 Standardized Incidence Ratio (SIR) = 132

SIR = up to 8. 6

CFTR OR = 5. 3-6. 6

Familial pancreatic cancer (FPC) cohort is distinguished by individuals with a strong family history of PDAC-i. e. with the cancer in at least two first-degree relatives and individuals with three or more affected family members (one of whom must be a first-degree relative) – and is considered to be high-risk and a candidate for screening programs. [69, 70, 71] Currently, the genetic basis for most cases of FPC is not fully understood. However, various studies have demonstrated the presence of a germline mutation in the BRCA2 gene [61, 62, 63, 64], associ‐ ation of BRCA1 [72], paladin gene mutation [73] and involvement of other genes: apolipopro‐ tein A4, CEA, keratin 19, stratifin (14-3-3σ), trefoil factor, and calcium binding protein S100 A6 [74, 75] in FPC, and more recently identification of PALB2, [76] as a pancreatic cancer suscept‐ ibility gene. These facts suggest that multiple and heterogeneous factors are likely at play for

Analysis of the PDAC kindred data from Johns Hopkins' National Familial Pancreas Tumor Registry (NFPTR) has demonstrated that the relative risk of PDAC in persons with two affected first-degree relatives is 6. 4% and the cumulative life-time risk is 8%-12%; in individuals with three affected first-degree relatives, the relative risk for PDAC increases to 32% and the cumulative life-time risk to 16%-32%. [77] Tersmette, et al in their analysis of the NFPTR found

Odds ratio (OR) = 69. 9

BRCA2: OR=3. 5-10-fold increased risk BRCA1: OR=2. 26 times average population

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143

**Syndrome Inheritance Gene Mutation Risk of PDAC**

AD PRSS1

AD BRCA2

SPINK1

BRCA1

AD MLH1, MSH2, MSH6 or PMS2

AD CDKN2A SIR=13-38

Autosomal dominant (AD)

mutation. (Table 1)

**Hereditary Pancreatitis**

**Peutz-Jeghers syndrome [53]**

**[54, 55, 56]**

**[57, 58, 59]**

**Familial atypical multiple mole melanoma syndrome**

**Hereditary breastovarian cancer syndrome [60, 61, 62, 63, 64, 65, 66]**

**Lynch syndrome**

Cystic fibrosis [68] Autosomal

**Table 1.** PDAC related genetic syndromes

the genesis of PDAC in this subset.

*4.1.1. Familial pancreatic cancer*

recessive

**[59, 67]**

### **3. Molecular markers & pancreatic cancer**

In order to enhance the diagnostic accuracy of PDAC, molecular markers on EUS-FNA samples have been evaluated in recent years. Utilities of DNA mutations such as *k-ras* and loss of heterozygosity are being reported as potential surrogate markers of the malignancy. [39, 40] In a recent study, Takahashi, et al assessed *k-ras* point mutations in PDAC and chronic focal pancreatitis samples obtained by EUS-FNA. [41, 42, 43] The study revealed the presence of point mutations of *k-ras* in 74% of patients with PDAC compared to no mutations in chronic focal pancreatitis. In another study, Tada, et al reported a high (more than 2% of total *k-ras* gene) mutation rate in 20 of 26 cases of EUS-FNA specimens (77%) and in 12 of 19 cases of pancreatic juice (63%) in PDAC. [44] However, the presence of *k-ras* mutations in chronic pancreatitis and premalignant conditions such as intraductal papillary mucinous neoplasm as well as lack of such mutations in 20% of pancreatic cancer has limitations for using this test solely as a diagnostic tool. Other studies analyzing p53 by immunohistochemistry, [45] telomerase activity with a ribonucleoprotein enzyme, [46] and a broad panel of microsatellite allele loss markers demonstrated similar results. [47] In the presence of inconclusive EUS-FNA cytology, molecular markers could complement EUS-FNA cytology results to help establish the diagnosis of malignancy.

## **4. Select population-based research for early detection scheme development**

#### **4.1. Screening for pancreatic cancer in high-risk individuals**

Currently, a general population-screening program for PDAC is not cost-effective because of low relative disease incidence and non-availability of simple, cheap, highly accurate noninvasive tests. The main aim of the screening is to detect clinically significant precursor lesions or early stage PDAC. However, since the overwhelming majority of premalignant lesions and small pancreatic cancers are asymptomatic, we do not yet have a routinely utilized surrogate marker to identify a subset population for screening. Consequently, as one of the approaches in investigating the genetic risks, research has focused on investigating a subset of individuals with a higher-risk for PDAC development in order to elucidate the genetic predilection. Up to 10% of pancreatic cancer patients have a familial basis and they have increased risk of developing both pancreatic and extra-pancreatic malignancies. [48, 49, 50, 51, 52] Classic categorization of high-risk patients are based on the highly associated genetic risks defined as those who are either members of a family with at least two first-degree relatives affected by the disease or are part of an inherited pancreatic cancer syndrome with a known genetic mutation. (Table 1)


**Table 1.** PDAC related genetic syndromes

#### *4.1.1. Familial pancreatic cancer*

lower risk of peritoneal contamination with malignancy than CT-guided FNA (2. 2% versus 16. 3%), respectively. [36] This is a potential complication of EUS-FNA that would need to be kept in mind by clinicians when FNA sampling of a lesion is being considered. [37, 38]

142 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

In order to enhance the diagnostic accuracy of PDAC, molecular markers on EUS-FNA samples have been evaluated in recent years. Utilities of DNA mutations such as *k-ras* and loss of heterozygosity are being reported as potential surrogate markers of the malignancy. [39, 40] In a recent study, Takahashi, et al assessed *k-ras* point mutations in PDAC and chronic focal pancreatitis samples obtained by EUS-FNA. [41, 42, 43] The study revealed the presence of point mutations of *k-ras* in 74% of patients with PDAC compared to no mutations in chronic focal pancreatitis. In another study, Tada, et al reported a high (more than 2% of total *k-ras* gene) mutation rate in 20 of 26 cases of EUS-FNA specimens (77%) and in 12 of 19 cases of pancreatic juice (63%) in PDAC. [44] However, the presence of *k-ras* mutations in chronic pancreatitis and premalignant conditions such as intraductal papillary mucinous neoplasm as well as lack of such mutations in 20% of pancreatic cancer has limitations for using this test solely as a diagnostic tool. Other studies analyzing p53 by immunohistochemistry, [45] telomerase activity with a ribonucleoprotein enzyme, [46] and a broad panel of microsatellite allele loss markers demonstrated similar results. [47] In the presence of inconclusive EUS-FNA cytology, molecular markers could complement EUS-FNA cytology results to help establish

**4. Select population-based research for early detection scheme**

Currently, a general population-screening program for PDAC is not cost-effective because of low relative disease incidence and non-availability of simple, cheap, highly accurate noninvasive tests. The main aim of the screening is to detect clinically significant precursor lesions or early stage PDAC. However, since the overwhelming majority of premalignant lesions and small pancreatic cancers are asymptomatic, we do not yet have a routinely utilized surrogate marker to identify a subset population for screening. Consequently, as one of the approaches in investigating the genetic risks, research has focused on investigating a subset of individuals with a higher-risk for PDAC development in order to elucidate the genetic predilection. Up to 10% of pancreatic cancer patients have a familial basis and they have increased risk of developing both pancreatic and extra-pancreatic malignancies. [48, 49, 50, 51, 52] Classic categorization of high-risk patients are based on the highly associated genetic risks defined as those who are either members of a family with at least two first-degree relatives affected by

**4.1. Screening for pancreatic cancer in high-risk individuals**

**3. Molecular markers & pancreatic cancer**

the diagnosis of malignancy.

**development**

Familial pancreatic cancer (FPC) cohort is distinguished by individuals with a strong family history of PDAC-i. e. with the cancer in at least two first-degree relatives and individuals with three or more affected family members (one of whom must be a first-degree relative) – and is considered to be high-risk and a candidate for screening programs. [69, 70, 71] Currently, the genetic basis for most cases of FPC is not fully understood. However, various studies have demonstrated the presence of a germline mutation in the BRCA2 gene [61, 62, 63, 64], associ‐ ation of BRCA1 [72], paladin gene mutation [73] and involvement of other genes: apolipopro‐ tein A4, CEA, keratin 19, stratifin (14-3-3σ), trefoil factor, and calcium binding protein S100 A6 [74, 75] in FPC, and more recently identification of PALB2, [76] as a pancreatic cancer suscept‐ ibility gene. These facts suggest that multiple and heterogeneous factors are likely at play for the genesis of PDAC in this subset.

Analysis of the PDAC kindred data from Johns Hopkins' National Familial Pancreas Tumor Registry (NFPTR) has demonstrated that the relative risk of PDAC in persons with two affected first-degree relatives is 6. 4% and the cumulative life-time risk is 8%-12%; in individuals with three affected first-degree relatives, the relative risk for PDAC increases to 32% and the cumulative life-time risk to 16%-32%. [77] Tersmette, et al in their analysis of the NFPTR found an 18-fold increase in risk of PDAC, and an estimated lifetime risk of 9%-18% in the group. [78] Brune, et al in their recent article reported a higher risk of PDAC among members of FPC kindred with a younger age of onset (age < 50 years). [79] Rulyak, et al in another study found smoking as a strong risk factor in FPC kindred, particularly among males and those under age 50. This risk increases by 2. 0-3. 7 times over the inherited predisposition and lowers the age of onset by 10 years. [80] A computer-based risk assessment tool, PancPRO, has been developed and is available for calculating the risk assessment for individuals with familial pancreatic cancer (http://www4. utsouthwestern. edu/breasthealth/cagene/default. asp). [81]

has recently been completed and the results on the detection modality comparison demon‐ strate that the EUS has the highest rate of detection of early neoplastic changes in up to 42. 6%

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145

In another study from the University of Washington, high-risk familial cohorts were screened using EUS and beginning 10 years prior to the earliest PDAC death in the family. If EUS was normal, then they were followed-up with a repeat EUS at 2-3 year intervals. In case of abnormal EUS findings, they were referred for ERCP and if abnormalities were noted, patients were offered surgical intervention. [88] Patients with abnormal EUS, but normal ERCP were offered annual EUS. Out of 75 subjects screened, 15 had abnormalities on EUS and ERCP and went to surgery. The histology revealed premalignant lesions in all: PanIN-3 in 10 cases and PanIN-2 in five. [89] This study gave a diagnostic yield of 13% (10 out of 75) for detecting PanIN-3 premalignant lesions. One patient developed unresectable pancreatic cancer while under

In Europe, the European Registry for Familial Pancreatic Cancer and Hereditary Pancreatitis (EUROPAC) incorporated EUS, ERCP and molecular analysis of the pancreatic juice looking for early mutations (*p53*, *k-ras*, and *p16*), and the results are pending. A German Study (FaPaCa) enrolled 76 patients in a screening program using yearly EUS, MRCP and laboratory tests (genetic analysis of CDKN2a and BRCA2 genes, CA19-9 and CEA). Any suspicious lesion was evaluated with EUS ± FNA after 6 weeks and a close follow-up at 12 weeks. If an abnormality was detected, the patient underwent operative exploration with intraoperative ultrasound, limited pancreatic resection with frozen section, and if cancer was detected, total pancreatec‐ tomy was performed. Ten solid lesions were seen on EUS as compared to only seven detected by MRCP. Out of the seven MRCP-detected lesions, six had limited resections and the histology showed one patient with PanIN-3, one with PanIN-2, one with PanIN-1, and three were benign lesions. These results gave a diagnostic yield of 1. 3% in detecting PanIN-3. [90] A recent study from the Netherlands that used only EUS as the first screening modality in 44 high risk asymptomatic subjects showed a 7% diagnostic yield for asymptomatic cancers and a 16%

**Study CAPS1 CAPS2 CAPS3 U of Washington FaPaCa Dutch Study**

13% (10/75)

1. 3 (1/76

23 (10/44)

42 (92/216)

Questions remain regarding the cost-effectiveness of these screening modalities. Rulyak, et al reported that screening was cost-effective with an incremental cost-effectiveness ratio of \$16,885/life-year saved (assuming a 20% incidence of dysplasia and a 90% sensitivity of EUS and ERCP). [92] Rubenstein, et al performed a systematic review, and created a Markov model

diagnostic yield for premalignant lesions (IPMN-like lesions). [91]

10 (8/78)

\*Represents finding of abnormal imaging such as mass (solid, cyst) or abnormal duct CAPS: Cancer of the Pancreas Screening Study; FaPaCa: Familial Pancreatic Cancer Study

**Table 2.** Results of screening programs for pancreatic cancer in high-risk groups

of the asymptomatic high-risk group. [87]

annual surveillance.

**Diagnostic Yield\***

5. 3 (2/38)

#### *4.1.2. Screening modalities & the current screening programs*

Most of the screening programs have tried to use biomarkers complemented by imaging tests to identify the early lesions. As stated earlier, a commonly used marker, CA19-9, is neither specific nor sensitive independently for reliable detection of early pancreatic cancer or pancreatic precursor lesions. Kim, et al in their studies found only 0. 9% positive predictive value using a cut-off value of 37 U/mL. [82] Recently, many biomarkers have been investigated including MIC-1, CEACAM-1, SPan1, DUPAN, Alpha4GNT, and PAM4, but none is validated for routine clinical use. [83] In another approach, elevated fasting-glucose level has been shown to be a marker for early cancer in sporadic cases [84] and is currently used by the EUROPAC study in high-risk individuals with molecular analysis of pancreatic juice for the *k-ras* and *p53* mutations in addition to *p16* promoter methylation status.

Multiple international programs exist that screen for pancreatic cancer in high-risk individuals in a research setting. "Cancer of the Pancreas Screening Study" (CAPS study), led by John Hopkins University, is the largest screening program that involves 24 Ameri‐ can Centers of Excellence. To date, three studies, CAPS 1, CAPS 2 and CAPS 3, have been completed. (Table 2)

In the CAPS 1 study, thirty-eight patients were studied; 31 (mean age, 58-yr; 42% men) from a kindred with > 3 affected with pancreatic cancer; 6 from a kindred with 2 affected relatives, and 1 was a patient with Peutz-Jeghers syndrome (PJS). Six pancreatic masses were found by EUS: 1 invasive ductal adenocarcinoma, 1 benign intraductal papillary mucinous neoplasm, 2 serous cystadenomas, and 2 nonneoplastic masses. In this study, the diagnostic yield of screening was 5. 3%. [85] In the CAPS 2 study a 10% diagnostic yield of screening for preinvasive malignant lesions was found. [86] In this study, screening was performed using annual EUS and CT. If an abnormality was detected, ERCP was offered. Seventy-eight highrisk patients (72 from a FPC kindred, 6 PJS) and 149 control patients were studied. Of these, eight patients had confirmed pancreatic neoplasia by surgery or FNA (10% yield of screening); 6 patients had benign intraductal papillary mucinous neoplasms (IPMNs), 1 had an IPMN that progressed to invasive ductal adenocarcinoma, and 1 had high-grade pancreatic intraepithelial neoplasia (PanIN-3). The CAPS 3 study was a multicenter prospective, controlled cohort study that involved annual screening using EUS and MRCP, MRI with secretin and a panel of candidate DNA and protein markers in serum and pancreatic juice (CA19-9, macrophage inhibitory cytokine-1 (MIC-1), DNA hypermethylation, and *k-ras* gene mutations) as indicators of pancreatic neoplasm. Over 200 patients were enrolled over a three-year period. The study has recently been completed and the results on the detection modality comparison demon‐ strate that the EUS has the highest rate of detection of early neoplastic changes in up to 42. 6% of the asymptomatic high-risk group. [87]

an 18-fold increase in risk of PDAC, and an estimated lifetime risk of 9%-18% in the group. [78] Brune, et al in their recent article reported a higher risk of PDAC among members of FPC kindred with a younger age of onset (age < 50 years). [79] Rulyak, et al in another study found smoking as a strong risk factor in FPC kindred, particularly among males and those under age 50. This risk increases by 2. 0-3. 7 times over the inherited predisposition and lowers the age of onset by 10 years. [80] A computer-based risk assessment tool, PancPRO, has been developed and is available for calculating the risk assessment for individuals with familial pancreatic

144 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

Most of the screening programs have tried to use biomarkers complemented by imaging tests to identify the early lesions. As stated earlier, a commonly used marker, CA19-9, is neither specific nor sensitive independently for reliable detection of early pancreatic cancer or pancreatic precursor lesions. Kim, et al in their studies found only 0. 9% positive predictive value using a cut-off value of 37 U/mL. [82] Recently, many biomarkers have been investigated including MIC-1, CEACAM-1, SPan1, DUPAN, Alpha4GNT, and PAM4, but none is validated for routine clinical use. [83] In another approach, elevated fasting-glucose level has been shown to be a marker for early cancer in sporadic cases [84] and is currently used by the EUROPAC study in high-risk individuals with molecular analysis of pancreatic juice for the *k-ras* and *p53*

Multiple international programs exist that screen for pancreatic cancer in high-risk individuals in a research setting. "Cancer of the Pancreas Screening Study" (CAPS study), led by John Hopkins University, is the largest screening program that involves 24 Ameri‐ can Centers of Excellence. To date, three studies, CAPS 1, CAPS 2 and CAPS 3, have been

In the CAPS 1 study, thirty-eight patients were studied; 31 (mean age, 58-yr; 42% men) from a kindred with > 3 affected with pancreatic cancer; 6 from a kindred with 2 affected relatives, and 1 was a patient with Peutz-Jeghers syndrome (PJS). Six pancreatic masses were found by EUS: 1 invasive ductal adenocarcinoma, 1 benign intraductal papillary mucinous neoplasm, 2 serous cystadenomas, and 2 nonneoplastic masses. In this study, the diagnostic yield of screening was 5. 3%. [85] In the CAPS 2 study a 10% diagnostic yield of screening for preinvasive malignant lesions was found. [86] In this study, screening was performed using annual EUS and CT. If an abnormality was detected, ERCP was offered. Seventy-eight highrisk patients (72 from a FPC kindred, 6 PJS) and 149 control patients were studied. Of these, eight patients had confirmed pancreatic neoplasia by surgery or FNA (10% yield of screening); 6 patients had benign intraductal papillary mucinous neoplasms (IPMNs), 1 had an IPMN that progressed to invasive ductal adenocarcinoma, and 1 had high-grade pancreatic intraepithelial neoplasia (PanIN-3). The CAPS 3 study was a multicenter prospective, controlled cohort study that involved annual screening using EUS and MRCP, MRI with secretin and a panel of candidate DNA and protein markers in serum and pancreatic juice (CA19-9, macrophage inhibitory cytokine-1 (MIC-1), DNA hypermethylation, and *k-ras* gene mutations) as indicators of pancreatic neoplasm. Over 200 patients were enrolled over a three-year period. The study

cancer (http://www4. utsouthwestern. edu/breasthealth/cagene/default. asp). [81]

*4.1.2. Screening modalities & the current screening programs*

mutations in addition to *p16* promoter methylation status.

completed. (Table 2)

In another study from the University of Washington, high-risk familial cohorts were screened using EUS and beginning 10 years prior to the earliest PDAC death in the family. If EUS was normal, then they were followed-up with a repeat EUS at 2-3 year intervals. In case of abnormal EUS findings, they were referred for ERCP and if abnormalities were noted, patients were offered surgical intervention. [88] Patients with abnormal EUS, but normal ERCP were offered annual EUS. Out of 75 subjects screened, 15 had abnormalities on EUS and ERCP and went to surgery. The histology revealed premalignant lesions in all: PanIN-3 in 10 cases and PanIN-2 in five. [89] This study gave a diagnostic yield of 13% (10 out of 75) for detecting PanIN-3 premalignant lesions. One patient developed unresectable pancreatic cancer while under annual surveillance.

In Europe, the European Registry for Familial Pancreatic Cancer and Hereditary Pancreatitis (EUROPAC) incorporated EUS, ERCP and molecular analysis of the pancreatic juice looking for early mutations (*p53*, *k-ras*, and *p16*), and the results are pending. A German Study (FaPaCa) enrolled 76 patients in a screening program using yearly EUS, MRCP and laboratory tests (genetic analysis of CDKN2a and BRCA2 genes, CA19-9 and CEA). Any suspicious lesion was evaluated with EUS ± FNA after 6 weeks and a close follow-up at 12 weeks. If an abnormality was detected, the patient underwent operative exploration with intraoperative ultrasound, limited pancreatic resection with frozen section, and if cancer was detected, total pancreatec‐ tomy was performed. Ten solid lesions were seen on EUS as compared to only seven detected by MRCP. Out of the seven MRCP-detected lesions, six had limited resections and the histology showed one patient with PanIN-3, one with PanIN-2, one with PanIN-1, and three were benign lesions. These results gave a diagnostic yield of 1. 3% in detecting PanIN-3. [90] A recent study from the Netherlands that used only EUS as the first screening modality in 44 high risk asymptomatic subjects showed a 7% diagnostic yield for asymptomatic cancers and a 16% diagnostic yield for premalignant lesions (IPMN-like lesions). [91]


\*Represents finding of abnormal imaging such as mass (solid, cyst) or abnormal duct

CAPS: Cancer of the Pancreas Screening Study; FaPaCa: Familial Pancreatic Cancer Study

**Table 2.** Results of screening programs for pancreatic cancer in high-risk groups

Questions remain regarding the cost-effectiveness of these screening modalities. Rulyak, et al reported that screening was cost-effective with an incremental cost-effectiveness ratio of \$16,885/life-year saved (assuming a 20% incidence of dysplasia and a 90% sensitivity of EUS and ERCP). [92] Rubenstein, et al performed a systematic review, and created a Markov model for 45-year-old male first-degree relatives, with findings of chronic pancreatitis on screening by EUS. They compared 4 strategies: do-nothing, prophylactic total pancreatectomy (PTP), annual surveillance by EUS, and annual surveillance with EUS and fine needle aspiration. In the do-nothing strategy, the lifetime risk of cancer was 20% and it provided the greatest remaining years of life, the lowest cost, and the greatest remaining quality-adjusted life years (QALYs). PTP provided the fewest remaining years of life and QALYs. Screening with EUS provided nearly identical results to PTP, and screening with EUS/FNA provided intermediate results between PTP and the do-nothing approach. Total pancreatectomy provided the longest life expectancy if the lifetime risk of PDAC was at least 46% and provided the most QALYs if the risk was at least 68%. [93] Further assessment of the models in other clinical scenarios with developing technology would be in order.

**5.2. Translational research — Application of systems biology approach**

As we continue to translate the advancement of biological understanding of PDAC, we strongly anticipate that better biomarkers will become available in the near future that would identify higher-risk individuals within the general population for developing early-stage PDAC. Aside from the previously referenced reports, many genetic, epigenetic, proteomics, metabolomics, glycomics findings-utilizing systems biology approaches-are being considered for biomarker identifications for PDAC detection. In transcriptomics analysis of blood biomarkers in PDAC-associated diabetes mellitus, for example, gene expression analysis in blood from PDAC patients with new-onset diabetes versus long-term or no history of diabetes revealed a set of differentially expressed genes such as vanin-1 and matrix metalloproteinase 9, which are able to discriminate the PDAC group with sensitivity of 92% and specificity of 84%. [107] From proteomics analyses, shotgun approaches with highly accurate mass spec‐ trometric assays demonstrated such proteins as apolipoprotein CIII [108], mannose-binding lectin 2, myosin light chain kinase 2 [109], CXC chemokine ligand 7 [110], TIMP1-ICAM1 [111], and alpha-1 antitrypsin [112] as candidate biomarkers of PDAC. These and other candidate biomarkers need to be validated with larger populations with appropriate control groups.

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147

With the technological advancement in the mass spectrometric techniques over the recent decades and resumed interest in the cancer-associated metabolic abnormality, [113, 114] application of metabolomics in the cancer field has attracted more attention. Metabolomics allows for elucidating the complete set of metabolites or low-molecular-weight intermediates in the physiological, developmental or pathological state of the cell, tissue, organ, or organism. [115] And metabolomics study of PDAC detection biomarkers will seek identification of a set of small molecules or metabolites (or chemical intermediates) that are potential discriminators of developing PDAC and the controls. Recent reports from our group as well as others have demonstrated specific small molecules such as amino acids, bile acids, and various lipids and fatty acids as potential candidates for PDAC biomarkers. [116, 117, 118, 119] Since a metabo‐ lome represents a current physiological readout of the biochemical state in an individual's biofluid or tissue space and as the functional end-product of the varying signals from the genome and proteome, it reflects the up-to-date phenotypic state of an individual in the presence of environmental stimuli. Thus, metabolomics data potentially provides additional temporal information to cancer risks derived from gene-based PDAC risk data alone. Since many enzymes in a metabolic network determine metabolites' concentrations and nonlinear quantitative relationship from the genes to the proteome and metabolome levels exist, a metabolome cannot be easily decomposed to a specific single marker, which will designate the disease state. [120] So, in order to delineate a physiological or pathological state, multiple metabolomic features might be required for accurate depiction of such a state as a developing cancer. In addition, future studies are anticipated to incorporate further cancer systems' biological knowledge, including multi-omics-based analyses for optimal designation of PDAC biomarkers, which would be utilized in conjunction with a clinical-parameter-derived population subset for establishing the PDAC screening population. Subsequently, further

validation studies for the PDAC biomarkers need to be performed.

## **5. Future of pancreatic cancer screening**

Current EUS screening programs have demonstrated that the endoscopic evaluation can detect premalignant lesions and early cancers in certain subsets of high-risk groups, although costeffectiveness still remains an issue. However, as the majority of PDAC diagnosis is given to patients who develop the disease sporadically without a recognized genetic abnormality, the application of this modality for PDAC detection screening is very limited for the general adult population. In order to further delineate and expand the at-risk subset, there is a strong need for novel surrogate markers which allow identification of the group with increased PDAC risk for whom the endoscopic/imaging-based screening strategy could be applied.

#### **5.1. Select population based research — Identification of a higher-PDAC-risk group**

A practical approach for further selection of the potential screening population is to focus on selective clinical parameters that would be used to characterize the subset of the general population at increased PDAC risk. For instance, based on the epidemiological evidence, such clinical parameters include incidence of hyperglycemia or diabetes, which are being noted in 50-80% of pancreatic cancer patients [94, 95, 96, 97, 98]. Though this subset does not encompass all PDAC patients, this group includes a much larger proportion of PDAC patients whom we may select further to screen for PDAC. Similarly, patients with a history of chronic pancreatitis or obesity are reported to have increased PDAC risk during their lifetime [99, 100, 101, 102, 103, 104]. Animal studies investigating effects of diet-induced obesity in a PDAC mouse model demonstrated increased occurrence of pancreatic inflammation and accelerated pancreatic neoplastic changes, supporting the association of obesity and pancreatic inflammation and PDAC risks. [105, 106] Considering the millions of patients who are being diagnosed with diabetes, chronic pancreatitis, or obesity annually as opposed to PDAC, further refinement of screening of these patient groups is critically needed to justify developing a larger scale screening protocol in the future.

#### **5.2. Translational research — Application of systems biology approach**

for 45-year-old male first-degree relatives, with findings of chronic pancreatitis on screening by EUS. They compared 4 strategies: do-nothing, prophylactic total pancreatectomy (PTP), annual surveillance by EUS, and annual surveillance with EUS and fine needle aspiration. In the do-nothing strategy, the lifetime risk of cancer was 20% and it provided the greatest remaining years of life, the lowest cost, and the greatest remaining quality-adjusted life years (QALYs). PTP provided the fewest remaining years of life and QALYs. Screening with EUS provided nearly identical results to PTP, and screening with EUS/FNA provided intermediate results between PTP and the do-nothing approach. Total pancreatectomy provided the longest life expectancy if the lifetime risk of PDAC was at least 46% and provided the most QALYs if the risk was at least 68%. [93] Further assessment of the models in other clinical scenarios with

146 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

Current EUS screening programs have demonstrated that the endoscopic evaluation can detect premalignant lesions and early cancers in certain subsets of high-risk groups, although costeffectiveness still remains an issue. However, as the majority of PDAC diagnosis is given to patients who develop the disease sporadically without a recognized genetic abnormality, the application of this modality for PDAC detection screening is very limited for the general adult population. In order to further delineate and expand the at-risk subset, there is a strong need for novel surrogate markers which allow identification of the group with increased PDAC risk

for whom the endoscopic/imaging-based screening strategy could be applied.

**5.1. Select population based research — Identification of a higher-PDAC-risk group**

A practical approach for further selection of the potential screening population is to focus on selective clinical parameters that would be used to characterize the subset of the general population at increased PDAC risk. For instance, based on the epidemiological evidence, such clinical parameters include incidence of hyperglycemia or diabetes, which are being noted in 50-80% of pancreatic cancer patients [94, 95, 96, 97, 98]. Though this subset does not encompass all PDAC patients, this group includes a much larger proportion of PDAC patients whom we may select further to screen for PDAC. Similarly, patients with a history of chronic pancreatitis or obesity are reported to have increased PDAC risk during their lifetime [99, 100, 101, 102, 103, 104]. Animal studies investigating effects of diet-induced obesity in a PDAC mouse model demonstrated increased occurrence of pancreatic inflammation and accelerated pancreatic neoplastic changes, supporting the association of obesity and pancreatic inflammation and PDAC risks. [105, 106] Considering the millions of patients who are being diagnosed with diabetes, chronic pancreatitis, or obesity annually as opposed to PDAC, further refinement of screening of these patient groups is critically needed to justify developing a larger scale

developing technology would be in order.

screening protocol in the future.

**5. Future of pancreatic cancer screening**

As we continue to translate the advancement of biological understanding of PDAC, we strongly anticipate that better biomarkers will become available in the near future that would identify higher-risk individuals within the general population for developing early-stage PDAC. Aside from the previously referenced reports, many genetic, epigenetic, proteomics, metabolomics, glycomics findings-utilizing systems biology approaches-are being considered for biomarker identifications for PDAC detection. In transcriptomics analysis of blood biomarkers in PDAC-associated diabetes mellitus, for example, gene expression analysis in blood from PDAC patients with new-onset diabetes versus long-term or no history of diabetes revealed a set of differentially expressed genes such as vanin-1 and matrix metalloproteinase 9, which are able to discriminate the PDAC group with sensitivity of 92% and specificity of 84%. [107] From proteomics analyses, shotgun approaches with highly accurate mass spec‐ trometric assays demonstrated such proteins as apolipoprotein CIII [108], mannose-binding lectin 2, myosin light chain kinase 2 [109], CXC chemokine ligand 7 [110], TIMP1-ICAM1 [111], and alpha-1 antitrypsin [112] as candidate biomarkers of PDAC. These and other candidate biomarkers need to be validated with larger populations with appropriate control groups.

With the technological advancement in the mass spectrometric techniques over the recent decades and resumed interest in the cancer-associated metabolic abnormality, [113, 114] application of metabolomics in the cancer field has attracted more attention. Metabolomics allows for elucidating the complete set of metabolites or low-molecular-weight intermediates in the physiological, developmental or pathological state of the cell, tissue, organ, or organism. [115] And metabolomics study of PDAC detection biomarkers will seek identification of a set of small molecules or metabolites (or chemical intermediates) that are potential discriminators of developing PDAC and the controls. Recent reports from our group as well as others have demonstrated specific small molecules such as amino acids, bile acids, and various lipids and fatty acids as potential candidates for PDAC biomarkers. [116, 117, 118, 119] Since a metabo‐ lome represents a current physiological readout of the biochemical state in an individual's biofluid or tissue space and as the functional end-product of the varying signals from the genome and proteome, it reflects the up-to-date phenotypic state of an individual in the presence of environmental stimuli. Thus, metabolomics data potentially provides additional temporal information to cancer risks derived from gene-based PDAC risk data alone. Since many enzymes in a metabolic network determine metabolites' concentrations and nonlinear quantitative relationship from the genes to the proteome and metabolome levels exist, a metabolome cannot be easily decomposed to a specific single marker, which will designate the disease state. [120] So, in order to delineate a physiological or pathological state, multiple metabolomic features might be required for accurate depiction of such a state as a developing cancer. In addition, future studies are anticipated to incorporate further cancer systems' biological knowledge, including multi-omics-based analyses for optimal designation of PDAC biomarkers, which would be utilized in conjunction with a clinical-parameter-derived population subset for establishing the PDAC screening population. Subsequently, further validation studies for the PDAC biomarkers need to be performed.

#### **6. Conclusion**

Current imaging-based detection and diagnostic methods for PDAC is effectively providing answers to clinical questions raised for patients with signs or symptoms of suspected pancre‐ atic lesions. However, the endoscopic/imaging-based schemes are currently limited in applications to early PDAC detection in asymptomatic patients, aside from a relatively small group of known genetically high-risk groups. There is a high demand for developing a method of selecting distinct subsets among the general population for implementing the endoscopic/ imaging screening test effectively. Application of combinations of clinical risk parameters/ factors with the developing molecular biomarkers from translational science brings high hopes of providing us with early PDAC detection markers, and developing effective early detection screening scheme for the patients in the near future.

[8] Egawa S, Takeda K, Fukuyama S, Motoi F, Sunamura M, Matsuno S. Clinicopatho‐

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149

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## **Author details**

#### Shiro Urayama

Division of Gastroenterology & Hepatology, Department of Internal Medicine, University of California, Davis, USA

#### **References**


[8] Egawa S, Takeda K, Fukuyama S, Motoi F, Sunamura M, Matsuno S. Clinicopatho‐ logical aspects of small pancreatic cancer. Pancreas 2004; 28: 235-240.

**6. Conclusion**

**Author details**

California, Davis, USA

Shiro Urayama

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**Section 3**

**A Novel Approach to Treatment**


**A Novel Approach to Treatment**

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**Chapter 7**

**Metformin and Pancreatic Cancer Metabolism**

Numerous epidemiological studies have reported that metformin, a well-known and widely used anti-diabetic drug, may provide protective benefits in decreasing pancreatic cancer risk among the diabetic population. Following a brief introduction regarding metformin's history and pharmacological properties, this book chapter presents epidemiological findings showing how metformin is associated with protection against pancreatic cancer. We also introduce the anti-cancer effects of metformin through AMPK-independent and AMPK-dependent manners [1-6]. These mechanisms include its inhibitory effects on the insulin growth factor-1 (IGF-1), G protein-coupled receptor (GPCR) and mTORC1 signaling pathways [3-10]. We then discuss the metabolic effects of metformin in cancer. For example, metformin has been shown to inhibit glycolysis in various cancer cell lines [11-13]. Metformin is a known inhibitor of complex I of the electron transport chain [14-18], potentially limiting the intact oxidative respiration capabilities of the cancer cell. We also discuss in depth the anti-cancer mechanisms of action of metformin in the context of lipid metabolism as reported in numerous models. These include metformin's ability to increase fatty acid β-oxidation in adipocytes [19] and its ability to inhibit hepatic lipogenesis [2]. As shown by numerous studies [20-23], metformin also possesses antilipogenic properties, potentially limiting this critical metabolic pathway that confers cancer

We provide preclinical and clinical evidences of the potential utility of metformin in pancreatic cancer. For example, a very recent report has shown tumor growth inhibition *in vitro* and *in vivo* by metformin through down-regulation of Sp (specificity protein) transcription factors and consequent down-regulation of the Sp-regulated genes.[24]. Metformin has been also shown to impair tumor development in pancreatic cancer in xenografts models [25]. Finally, we also explore the role of lipid metabolism in the context-specific ability of metformin to act as a chemopreventive/therapeutic agent. As early as 2001, it has been reported that metformin significantly impairs the formation of pancreatic lesions induced by the pancreatic carcinogen

> © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Mary Jo Cantoria, Hitendra Patel,

http://dx.doi.org/10.5772/57432

pancreatic cell survival advantage.

**1. Introduction**

Laszlo G. Boros and Emmanuelle J. Meuillet

Additional information is available at the end of the chapter

## **Metformin and Pancreatic Cancer Metabolism**

Mary Jo Cantoria, Hitendra Patel, Laszlo G. Boros and Emmanuelle J. Meuillet

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57432

## **1. Introduction**

Numerous epidemiological studies have reported that metformin, a well-known and widely used anti-diabetic drug, may provide protective benefits in decreasing pancreatic cancer risk among the diabetic population. Following a brief introduction regarding metformin's history and pharmacological properties, this book chapter presents epidemiological findings showing how metformin is associated with protection against pancreatic cancer. We also introduce the anti-cancer effects of metformin through AMPK-independent and AMPK-dependent manners [1-6]. These mechanisms include its inhibitory effects on the insulin growth factor-1 (IGF-1), G protein-coupled receptor (GPCR) and mTORC1 signaling pathways [3-10]. We then discuss the metabolic effects of metformin in cancer. For example, metformin has been shown to inhibit glycolysis in various cancer cell lines [11-13]. Metformin is a known inhibitor of complex I of the electron transport chain [14-18], potentially limiting the intact oxidative respiration capabilities of the cancer cell. We also discuss in depth the anti-cancer mechanisms of action of metformin in the context of lipid metabolism as reported in numerous models. These include metformin's ability to increase fatty acid β-oxidation in adipocytes [19] and its ability to inhibit hepatic lipogenesis [2]. As shown by numerous studies [20-23], metformin also possesses antilipogenic properties, potentially limiting this critical metabolic pathway that confers cancer pancreatic cell survival advantage.

We provide preclinical and clinical evidences of the potential utility of metformin in pancreatic cancer. For example, a very recent report has shown tumor growth inhibition *in vitro* and *in vivo* by metformin through down-regulation of Sp (specificity protein) transcription factors and consequent down-regulation of the Sp-regulated genes.[24]. Metformin has been also shown to impair tumor development in pancreatic cancer in xenografts models [25]. Finally, we also explore the role of lipid metabolism in the context-specific ability of metformin to act as a chemopreventive/therapeutic agent. As early as 2001, it has been reported that metformin significantly impairs the formation of pancreatic lesions induced by the pancreatic carcinogen

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

N-nitrosobis-(2-oxopropyl)amine in hamsters fed a high fat diet [26]. We will argue that the in order for metformin to exert its anti-cancer properties, consideration of the genetic and metabolic status of the model system is critical.

We conclude this chapter by discussing our most recent findings that show how metformin inhibits glucose-derived fatty acid synthesis in the context of available acetyl-CoA and the presence of K-*ras* mutation in pancreatic cancer cells in the context of obesity, the metabolic syndrome and diabetes [21]. These results strongly suggest that metformin, being an antilipogenic drug, may be useful when combined with lipid lowering and chemotherapeutic agents. Finally, because up-regulation of fatty acid synthase (FAS), the enzyme that catalyzes the terminal step in palmitate synthesis, is associated with increased resistance to gemcitabine and radiation treatments in human pancreatic cancer tissues [27], we argue that the use of metformin could synergize with these treatments.

However, an important question remains on whether or not metformin really has chemopre‐ ventive and/or therapeutic use for pancreatic cancer. This chapter argues that metformin does have anti-cancer properties by examining numerous experimental studies on metformin's potential mechanisms of action along with the metabolic and genetic context by which metformin may act as an anti-cancer drug.

### **2. The history behind metformin**

*Galega officinalis* (also known as the French lilac or Goat's Rue) is a plant that has been used for the treatment of *diabetes mellitus* in traditional medicine for centuries. At the end of the 19th century, guanidine compounds were discovered in *Galega* extracts (Figure 1). Shortly after, in 1918 animal studies showed that these compounds lowered blood glucose levels [28]. How‐ ever, guanidines are fairly toxic. Following this discovery, some less toxic derivatives, named synthalin A and synthalin B (Figure 1), were synthesized based on the structure of galegine and used for diabetes treatment under the marketed name of Synthalin in the 1940s. The discovery of insulin overshadowed the use and further development of synthalin compounds and they were forgotten for the next several decades. When chemists found that they could also make the guanidine compounds more tolerable by bonding two guanidines together, forming a biguanide, interest in these molecules was regained and attention was focused on metformin, phenformin and buformin (Figure 1). Finally, the interest in metformin, synthe‐ sized by K. Slotta, was further renewed in the late 1950s after several reports that it could reduce blood sugar levels in people. The French physician Jean Sterne published the first clinical trial of metformin as a treatment for diabetes in 1957 [29].

## **3. Synthesis, structure and pharmacology of metformin**

*Synthesis:* Metformin is synthesized by equimolar fusion of hydrochloric dimethylamine and dicyandinamide at 130-150°C for 0.5 to 2 hours [30].

**Figure 2.** Synthesis of N,N'-dimethyl biguanide.

**Figure 1.** Structures of guanidine and biguanides.

*Structure:* Metformin is globally charged positively and does not or poorly permeates the plasma membrane. The structure was represented in a wrong tautomeric form until it was corrected in 2005 by a group of chemists from India [31]. Metformin is given to patients as a

Metformin and Pancreatic Cancer Metabolism

http://dx.doi.org/10.5772/57432

161

**Figure 1.** Structures of guanidine and biguanides.

N-nitrosobis-(2-oxopropyl)amine in hamsters fed a high fat diet [26]. We will argue that the in order for metformin to exert its anti-cancer properties, consideration of the genetic and

160 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

We conclude this chapter by discussing our most recent findings that show how metformin inhibits glucose-derived fatty acid synthesis in the context of available acetyl-CoA and the presence of K-*ras* mutation in pancreatic cancer cells in the context of obesity, the metabolic syndrome and diabetes [21]. These results strongly suggest that metformin, being an antilipogenic drug, may be useful when combined with lipid lowering and chemotherapeutic agents. Finally, because up-regulation of fatty acid synthase (FAS), the enzyme that catalyzes the terminal step in palmitate synthesis, is associated with increased resistance to gemcitabine and radiation treatments in human pancreatic cancer tissues [27], we argue that the use of

However, an important question remains on whether or not metformin really has chemopre‐ ventive and/or therapeutic use for pancreatic cancer. This chapter argues that metformin does have anti-cancer properties by examining numerous experimental studies on metformin's potential mechanisms of action along with the metabolic and genetic context by which

*Galega officinalis* (also known as the French lilac or Goat's Rue) is a plant that has been used for the treatment of *diabetes mellitus* in traditional medicine for centuries. At the end of the 19th century, guanidine compounds were discovered in *Galega* extracts (Figure 1). Shortly after, in 1918 animal studies showed that these compounds lowered blood glucose levels [28]. How‐ ever, guanidines are fairly toxic. Following this discovery, some less toxic derivatives, named synthalin A and synthalin B (Figure 1), were synthesized based on the structure of galegine and used for diabetes treatment under the marketed name of Synthalin in the 1940s. The discovery of insulin overshadowed the use and further development of synthalin compounds and they were forgotten for the next several decades. When chemists found that they could also make the guanidine compounds more tolerable by bonding two guanidines together, forming a biguanide, interest in these molecules was regained and attention was focused on metformin, phenformin and buformin (Figure 1). Finally, the interest in metformin, synthe‐ sized by K. Slotta, was further renewed in the late 1950s after several reports that it could reduce blood sugar levels in people. The French physician Jean Sterne published the first

*Synthesis:* Metformin is synthesized by equimolar fusion of hydrochloric dimethylamine and

metabolic status of the model system is critical.

metformin could synergize with these treatments.

metformin may act as an anti-cancer drug.

**2. The history behind metformin**

clinical trial of metformin as a treatment for diabetes in 1957 [29].

**3. Synthesis, structure and pharmacology of metformin**

dicyandinamide at 130-150°C for 0.5 to 2 hours [30].

**Figure 2.** Synthesis of N,N'-dimethyl biguanide.

*Structure:* Metformin is globally charged positively and does not or poorly permeates the plasma membrane. The structure was represented in a wrong tautomeric form until it was corrected in 2005 by a group of chemists from India [31]. Metformin is given to patients as a hydrochloride from. Several studies have demonstrated an affinity of biguanides for phos‐ pholipids at the plasma membrane [32] as well as some protein binding [33]. The interaction of the biguanide with the polar head group of phospholipids induces a diminution of the plasma membrane fluidity leading to the rigidification of the plasma membrane [32]. However, this reduction of the fluidity has not been reproduced by Wiernsperger and collaborators [33] who showed an increase in fluidity of red blood cells membranes.

**5. Chemotherapeutic properties of metformin**

e) inhibition of general transcription factors.

2007 [46] United States Cohort Pancreatic

[47] United Kingdom Cohort

Li et al., 2009 [35] United States Case-

2011 [36] United Kingdom Case-

[48] United States Cohort Pancreatic

control

control

**Year (reference) Location Design Outcome Comparison Risk**

cancer risk

Progression to pancreatic cancer

Pancreatic

Pancreatic cancer risk

cancer risk

Metformin vs no Metformin

Sulfonylureas vs Metformin Metformin + Sulfonylureas vs Metformin Insulin-based therapies

cancer risk Metformin **OR: 0.38**

Metformin (both sexes)

Metformin and pioglitazone

only) Sulfonylureas Insulin

Metformin (females

RR: 1.26 (0.80-1.99)

**HR: 4.95 (2.74-8.96)** HR: 0.38 (0.13-1.12) **HR: 4.63 (2.64-8.10)**

**(0.22-0.69)**

OR: 0.87 (0.59-1.29) **OR: 0.43 (0.23-0.80) OR: 1.90 (1.32-2.74) OR: 2.29 (1.34-3.92)**

**HR: 1.2 (1.0-1.5)**

**First Author/**

Oliveria et al.,

Currie et al., 2009

Bodmer et al.,

Ferrara et al., 2011

Experimental studies show that metformin possesses anti-cancer effects in various cancer types. As the metabolic effects of metformin are discussed in the section "Metformin as an Anti-lipogenic Drug," figure 2 provides a summary of metformin's effects exclusively on cancer signaling pathways. Overall, we present metformin's effects on cancer cells as AMPKdependent (pathways 2, 4, 5, 6 and 10) and independent (pathways 1, 3, 7- 9 and 11). Although there is an overlap between cell signaling and metabolic alterations due to metformin treatment (e.g., via ETC complex I and ATP production, AMPK/mTORC1 axis and metabolic control), metformin's anti-cancer effects can be grouped into: a) inhibition of ATP and ROS production, b) inhibition of IRS-1/Akt/mTORC1 axis, c) anti-inflammatory effects, d) cell cycle arrest and

**(95% CI) Confounding Factors**

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163

Age, gender, gastrectomy, chronic pancreatitis, deep venous thrombosis, dermatomyositis/ polymyositis, alcoholism, hepatitis B/C, history of polyps

Age, sex, smoking status, diagnosis of a previous cancer

Age, sex, race smoking, alcohol, BMI, family history of cancer, diabetes duration, use of insulin

BMI, smoking, alcohol consumption, diabetes

Age, ever use of other diabetes medications, year of cohort entry, sex, race/ethnicity, income, current

duration

Metformin has an oral bioavailability of 50-60% under fasting conditions and the peak plasma concentration is reached within a couple of hours. The plasma protein binding is negligible. Metformin is not metabolized but it is accumulated in tissues such as the liver, the kidneys, the salivary glands and the gastrointestinal tract [34]. Eighty percent of the elimination of metformin occurs by the urinary tract. The average elimination half-life in plasma is 6.2 hours. The half-life of biguanides is approximately 2 hours [34]. Interestingly, metformin is distrib‐ uted to (and appears to accumulate in) red blood cells with a much longer elimination halflife: 17.6 hours.

#### **4. Chemopreventive properties of metformin**

Numerous observational studies show that metformin use, when compared against other diabetic agents such as insulin and sulfonylureas, decreases cancer risk and overall cancer mortality among the diabetic population. These protective associations have been reported across different cancer types and among various diabetic populations.

Table 1A summarizes epidemiological studies that show pancreatic cancer risk and cancer mortality associated with metformin use while Table 1B presents observational studies and clinical trials on overall cancer risk and mortality in relation to metformin use. While some studies show a reduction in pancreatic cancer [35-39] and overall cancer risk [37, 39-45] among diabetic metformin users, there are also studies that report no significant difference in cancer risk among diabetics who take metformin compared to patients who take other anti-diabetic treatments [36, 46-51]. These conflicting results may be explained by differences in the study population, the confounding factors accounted for during statistical analysis and the selected study design (e.g., cohort *versus* case-control). For example, patients who were prescribed metformin may generally have better glucose control compared to those prescribed insulin hence, the risk for future diseases such as cancer may be lower at baseline for metformin users versus diabetics treated with other modalities. As shown in Tables 1A and 1B, different studies account for different confounding factors which can largely influence the results of disease risk calculations. There is no standardized procedure for the selection of which potential confounding factors are to be included in a statistical model hence; this can lead to large variations among observational study outcomes. Overall, although these epidemiological studies are correlative in nature and hence, cannot establish causality between metformin use and cancer risk and mortality, they provide a biological basis to further explore whether metformin possesses chemopreventive and/or chemotherapeutic properties.

## **5. Chemotherapeutic properties of metformin**

hydrochloride from. Several studies have demonstrated an affinity of biguanides for phos‐ pholipids at the plasma membrane [32] as well as some protein binding [33]. The interaction of the biguanide with the polar head group of phospholipids induces a diminution of the plasma membrane fluidity leading to the rigidification of the plasma membrane [32]. However, this reduction of the fluidity has not been reproduced by Wiernsperger and collaborators [33]

162 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

Metformin has an oral bioavailability of 50-60% under fasting conditions and the peak plasma concentration is reached within a couple of hours. The plasma protein binding is negligible. Metformin is not metabolized but it is accumulated in tissues such as the liver, the kidneys, the salivary glands and the gastrointestinal tract [34]. Eighty percent of the elimination of metformin occurs by the urinary tract. The average elimination half-life in plasma is 6.2 hours. The half-life of biguanides is approximately 2 hours [34]. Interestingly, metformin is distrib‐ uted to (and appears to accumulate in) red blood cells with a much longer elimination half-

Numerous observational studies show that metformin use, when compared against other diabetic agents such as insulin and sulfonylureas, decreases cancer risk and overall cancer mortality among the diabetic population. These protective associations have been reported

Table 1A summarizes epidemiological studies that show pancreatic cancer risk and cancer mortality associated with metformin use while Table 1B presents observational studies and clinical trials on overall cancer risk and mortality in relation to metformin use. While some studies show a reduction in pancreatic cancer [35-39] and overall cancer risk [37, 39-45] among diabetic metformin users, there are also studies that report no significant difference in cancer risk among diabetics who take metformin compared to patients who take other anti-diabetic treatments [36, 46-51]. These conflicting results may be explained by differences in the study population, the confounding factors accounted for during statistical analysis and the selected study design (e.g., cohort *versus* case-control). For example, patients who were prescribed metformin may generally have better glucose control compared to those prescribed insulin hence, the risk for future diseases such as cancer may be lower at baseline for metformin users versus diabetics treated with other modalities. As shown in Tables 1A and 1B, different studies account for different confounding factors which can largely influence the results of disease risk calculations. There is no standardized procedure for the selection of which potential confounding factors are to be included in a statistical model hence; this can lead to large variations among observational study outcomes. Overall, although these epidemiological studies are correlative in nature and hence, cannot establish causality between metformin use and cancer risk and mortality, they provide a biological basis to further explore whether

who showed an increase in fluidity of red blood cells membranes.

**4. Chemopreventive properties of metformin**

across different cancer types and among various diabetic populations.

metformin possesses chemopreventive and/or chemotherapeutic properties.

life: 17.6 hours.

Experimental studies show that metformin possesses anti-cancer effects in various cancer types. As the metabolic effects of metformin are discussed in the section "Metformin as an Anti-lipogenic Drug," figure 2 provides a summary of metformin's effects exclusively on cancer signaling pathways. Overall, we present metformin's effects on cancer cells as AMPKdependent (pathways 2, 4, 5, 6 and 10) and independent (pathways 1, 3, 7- 9 and 11). Although there is an overlap between cell signaling and metabolic alterations due to metformin treatment (e.g., via ETC complex I and ATP production, AMPK/mTORC1 axis and metabolic control), metformin's anti-cancer effects can be grouped into: a) inhibition of ATP and ROS production, b) inhibition of IRS-1/Akt/mTORC1 axis, c) anti-inflammatory effects, d) cell cycle arrest and e) inhibition of general transcription factors.


**First Author/**

Nakai et al., 2013

Singh et al., 2013 [53] Various

Zhang et al., 2013 [39] Various

SRR, summary relative risk. **First Author/ Year**

[41] Scotland Case-

2009 [42] Italy Case-

Evans et al., 2005

Libby et al., 2009 [40] Scotland

Monami et al.,

[51] Japan Cohort

**Year (reference) Location Design Outcome Comparison Risk**

Prognostic factors of overall survival in pancreatic cancer

Pancreatic

Cancer incidence and mortality

Cancer incidence

Cancer incidence

cancer risk Metformin use

users

**(a)** Bold type under "Risk" column indicates statistical significance using 95% confidence interval. HR, hazard ratio; OR, odds ratio,

**Location Design Outcome Comparison Relative Risk**

Cancer deaths Metformin vs. no metformin

Metaanalysis

Metaanalysis

control

Nested casecontrol

control

Univariate Analysis: Biguanide Sulfonylureas Insulin

HR: 0.61 (0.19-1.44) **HR: 0.60 (0.39-0.88) HR: 0.83 (0.59-1.15) HR: 0.19 (0.01-0.84)**

OR: 0.76 (0.57-1.03)

SRR: Incidence **0.54 (0.35-0.83) Mortality 0.64 (0.48-0.86)**

**(95% CI)**

OR: 0.77 (0.64-0.92)

HR: 0.63 (0.53-0.75)

OR: 0.28 (0.13-0.57)

Thiazolidine-dione

Metformin (and in combination with other drugs) vs. non-

Metformin vs. no metformin

36 mo metformin vs no metformin

**(95% CI) Confounding Factors**

http://dx.doi.org/10.5772/57432

Metformin and Pancreatic Cancer Metabolism

or no)

**Confounding Factors Accounted for**

Smoking, body mass index, blood pressure, and postcode rank for material deprivation

Sex, age, BMI, A1C, deprivation, smoking, other drug use

Concomitant therapies, exposure to

Age (G65 or Q65 years old), sex (male or female), performance status (PS; 0Y1 or 2Y3), primary tumor size (G30 or Q30 mm), distant metastasis (yes or no), body mass index (G22 or Q22 kg/ m2), chemotherapy (combination therapy with gemcitabine and S-1 vs others), DM (yes or no), insulin (yes or no), sulfonylurea (yes or no), biguanide (yes or no), thiazolidine (yes or no), hypertension (yes or no), ACEI or ARB (yes or no), Ca-blocker (yes or no), A-blocker (yes or no), and statin (yes

165



**First Author/**

Liao et al., 2011

Lee et al., 2011

Morden et al.,

Ruiter et al., 2012

Sadeghi et al.,

2012 [52] United States Cohort

[49] Taiwan Cohort Pancreatic

[37] Taiwan Cohort Pancreatic

2011 [50] United States Cohort Pancreatic

[38] Netherlands Cohort Pancreatic

**Year (reference) Location Design Outcome Comparison Risk**

164 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

cancer risk

cancer risk

Median survival in pancreatic cancer, prognostic factors of overall survival in pancreatic cancer

cancer risk Metformin HR: 0.85

Metformin vs. potential use of other

cancer risk Metformin HR: 1.25

Metformin vs. sulfonylureas

Metformin vs. Nonmetformin Univariate Analysis: Metformin Multivariate Analysis: Metformin

oral antihyperglycemic medications

**(95% CI) Confounding Factors** smoking, baseline HbA1c, diabetes duration, new diabetes

failure

(0.39-1.89) Crude/ Unadjusted

**HR: 0.15 (0.03-0.79)**

(0.89-1.75)

**HR: 0.73 (0.66-0.80)**

15.2 months vs. 11.1 months **(P = 0.009) HR: 0.68 (0.52-0.89) HR: 0.64 (0.48-0.86)**

diagnosis, creatinine, and congestive heart

Age, gender, other oral antihyperglycemic medication, Charlson comorbidity index score, time-dependent metformin use

Age category, race/ ethnicity, diabetes complications, obesity diagnosis, oral estrogen use, Part D low income subsidy (a poverty indicator), 14

Charlson comorbidities, and tobacco exposure diagnosis

Age at first oral glucose-lowering drug (OGLD) prescription, sex, year in which the first OGLD prescription was dispensed, number of unique drugs used in the year,

number of

No significant differences in BMI, age, sex, race, diabetes duration, disease stage, tumor size, performance status, serum CA-19-9 between metformin and non-metformin

OGLD

group

hospitalizations in the year before the start of Bold type under "Risk" column indicates statistical significance using 95% confidence interval. HR, hazard ratio; OR, odds ratio, SRR, summary relative risk.



1] Metformin is a known inhibitor of complex I of the electron transport chain (ETC) [14, 15]. 2) The resulting decrease in adenosine triphosphate (ATP) production and increase in adenosine monophosphate (AMP) levels activate the kin‐ ase AMP-activated protein kinase (AMPK), a regulator of cellular energy status. Besides inhibition of energy-consum‐ ing biosynthetic processes (e.g., lipid synthesis and gluconeogenesis) and up-regulation of energy-generating catabolic metabolic pathways (e.g., β-oxidation of fatty acids and glycolysis), AMPK also signals to numerous proteins involved in cell survival, senescence, autophagy and death. For example, both high AMP and adenosine diphosphate (ADP) levels (from ETC complex I inhibition) is permissive for AMPK activation. AMP promotes AMPK phosphorylation at its catalytic α-subunit (Thr-172) by its upstream kinases liver kinase B1 (LKB1) and calcium/calmodulin-dependent protein kinase kinase-beta (CaMKKβ), its allosteric activation and prevents dephosphorylation by protein phosphatase type 2a (PP2a) and protein type 2c (PP2c) phosphatases [55-57]. ADP also protects AMPK from dephosphorylation [58]. 3) The metformin-induced decline in endogenous reactive oxygen species (ROS) levels has been implicated to be involved in cancer risk reduction owing to its ability to reduce ROS-induced DNA damage [59]. 4) AMPK has also been shown to activate the tumor suppressor protein 53 (p53) (Ser-15) in inducing cancer cell cycle arrest and senescence [60]. The reversible arrows between p53 and pAMPK indicate that p53 has been shown to increase AMPK activity which, ultimately leads to mammalian target of rapamycin (mTOR) inhibition *in vitro* [61]. 5) Metformin has been shown to cause a G0/G1 cell cycle arrest by decreasing the expression of cyclin D1 and preventing the phosphoryla‐ tion of pRb and hence, its inactivation [62]. 6) Metformin-induced AMPK activation has been shown to phosphorylate insulin receptor substrate-1 (IRS-1) at Ser-794 which results in decreased recruitment of the p85 subunit of phosphoi‐ nositide-3-kinase (PI3K), thus, impairing the insulin-like growth factor (IGF)-stimulated PI3K/protein kinase B/ mam‐ malian target of rapamycin complex 1 (PI3K/Akt/mTORC1) signaling pathway [63]. Metformin has also been shown to inhibit the crosstalk between the insulin/IGF receptor and G protein-coupled receptor (GPCR) signaling, resulting in the inhibition of mTORC1 [4, 5]. 7) Biguanides are implicated in the inhibition of the Rag-dependent mTORC1 signal‐ ing [8], by preventing the co-localization of mTORC1 with its activator Ras homolog enriched in brain (Rheb). Rags are GTPases comprised of four proteins RagA, RagB, RagC and RagD that heterodimerize to activate mTORC1 upon amino acid stimulation [10]. Rags bind to the Ragulator complex made up of mitogen-activated protein kinase scaffold pro‐ tein 1 (MP1), p14 and p18 trimeric proteins, localizing mTORC1 from the perinulcear compartment (where Rheb is lo‐ cated) into the cytoplasm, preventing Rheb activation of mTORC1 [64]. 8) Metformin also increases the expression of the mTOR inhibitor, regulated in development and DNA damage responses (REDD1), consequently down-regulating mTOR signaling [65]. 9) In human monocytes, metformin prevents lipopolysaccharide (LPS) and oxidized low density lipoprotein (LDL)-induced tumor necrosis factor (TNF) production at micromolar concentrations [66]. 10) Activation

Metformin and Pancreatic Cancer Metabolism

http://dx.doi.org/10.5772/57432

167

Bold type under "Risk" column indicates statistical significance using 95% confidence interval. HR, hazard ratio; OR, odds ratio, SRR, summary relative risk.

**Table 1.** A. Human Studies on Pancreatic Cancer Risk and Mortality with Metformin Use Among Diabetics, B. Human Studies on Overall Cancer Risk and Mortality with Metformin Use Among Diabetics

**First Author/**

Bowker et al.,

Home et al., 2010 [54] Various

Landman et al.,

Lee et al., 2011

Monami et al.,

Zhang et al., 2013 [39] Various

SRR, summary relative risk.

2010 [45] Netherlands Cohort Cancer

[37] Taiwan Cohort Cancer

control

Metaanalysis

Studies on Overall Cancer Risk and Mortality with Metformin Use Among Diabetics

2011 [43] Italy Case-

**Year (reference) Location Design Outcome Comparison Risk**

166 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

Cancer incidence

mortality

incidence

Cancer incidence

Cancer incidence and mortality

sulfonylurea

Metformin vs. rosiglitazone Metformin vs. glibenclamide Metformin + sulfonylurea vs. Rosiglitazone + sulfonylurea

Metformin vs. no metformin

Metformin vs. no metformin

Metformin vs. no metformin in patients under insulin treatment,

Metformin (and in combination with other drugs) vs. non-

users

**(b)** Bold type under "Risk" column indicates statistical significance using 95% confidence interval. HR, hazard ratio; OR, odds ratio,

**Table 1.** A. Human Studies on Pancreatic Cancer Risk and Mortality with Metformin Use Among Diabetics, B. Human

2010 [44] Canada Cohort Cancer death Metformin vs.

Randomize d control trials

**(95% CI) Confounding Factors** metformin and gliclazide

> Age, sex and chronic disease score

Not reported

no)

exposure

of insulin

Smoking (yes or no), age, sex, diabetes duration, A1C, serum creatinine, BMI, blood pressure, total cholesterol–to–HDL ratio, albuminuria, insulin use, sulfonylurea use, and macrovascular complications (yes or

age, gender, other oral anti-hyperglycemic medication usage, CCI score and dose and duration of metformin

Charlson comorbidity score (CCS), glargine mean daily dose (MDD), and total MDD

HR: 0.80 (0.65-0.98)

HR: 0.92 (0.63-1.35) HR: 0.78 (0.53-1.14) HR: 1.22 (0.86-1.74)

HR: 0.43 (0.23-0.80)

HR: 0.12 (0.08-0.19)

OR: 0.46 (0.25-0.85)

SRR: Incidence 0.73 (0.64-0.83) Mortality 0.82 (0.76-0.89

1] Metformin is a known inhibitor of complex I of the electron transport chain (ETC) [14, 15]. 2) The resulting decrease in adenosine triphosphate (ATP) production and increase in adenosine monophosphate (AMP) levels activate the kin‐ ase AMP-activated protein kinase (AMPK), a regulator of cellular energy status. Besides inhibition of energy-consum‐ ing biosynthetic processes (e.g., lipid synthesis and gluconeogenesis) and up-regulation of energy-generating catabolic metabolic pathways (e.g., β-oxidation of fatty acids and glycolysis), AMPK also signals to numerous proteins involved in cell survival, senescence, autophagy and death. For example, both high AMP and adenosine diphosphate (ADP) levels (from ETC complex I inhibition) is permissive for AMPK activation. AMP promotes AMPK phosphorylation at its catalytic α-subunit (Thr-172) by its upstream kinases liver kinase B1 (LKB1) and calcium/calmodulin-dependent protein kinase kinase-beta (CaMKKβ), its allosteric activation and prevents dephosphorylation by protein phosphatase type 2a (PP2a) and protein type 2c (PP2c) phosphatases [55-57]. ADP also protects AMPK from dephosphorylation [58]. 3) The metformin-induced decline in endogenous reactive oxygen species (ROS) levels has been implicated to be involved in cancer risk reduction owing to its ability to reduce ROS-induced DNA damage [59]. 4) AMPK has also been shown to activate the tumor suppressor protein 53 (p53) (Ser-15) in inducing cancer cell cycle arrest and senescence [60]. The reversible arrows between p53 and pAMPK indicate that p53 has been shown to increase AMPK activity which, ultimately leads to mammalian target of rapamycin (mTOR) inhibition *in vitro* [61]. 5) Metformin has been shown to cause a G0/G1 cell cycle arrest by decreasing the expression of cyclin D1 and preventing the phosphoryla‐ tion of pRb and hence, its inactivation [62]. 6) Metformin-induced AMPK activation has been shown to phosphorylate insulin receptor substrate-1 (IRS-1) at Ser-794 which results in decreased recruitment of the p85 subunit of phosphoi‐ nositide-3-kinase (PI3K), thus, impairing the insulin-like growth factor (IGF)-stimulated PI3K/protein kinase B/ mam‐ malian target of rapamycin complex 1 (PI3K/Akt/mTORC1) signaling pathway [63]. Metformin has also been shown to inhibit the crosstalk between the insulin/IGF receptor and G protein-coupled receptor (GPCR) signaling, resulting in the inhibition of mTORC1 [4, 5]. 7) Biguanides are implicated in the inhibition of the Rag-dependent mTORC1 signal‐ ing [8], by preventing the co-localization of mTORC1 with its activator Ras homolog enriched in brain (Rheb). Rags are GTPases comprised of four proteins RagA, RagB, RagC and RagD that heterodimerize to activate mTORC1 upon amino acid stimulation [10]. Rags bind to the Ragulator complex made up of mitogen-activated protein kinase scaffold pro‐ tein 1 (MP1), p14 and p18 trimeric proteins, localizing mTORC1 from the perinulcear compartment (where Rheb is lo‐ cated) into the cytoplasm, preventing Rheb activation of mTORC1 [64]. 8) Metformin also increases the expression of the mTOR inhibitor, regulated in development and DNA damage responses (REDD1), consequently down-regulating mTOR signaling [65]. 9) In human monocytes, metformin prevents lipopolysaccharide (LPS) and oxidized low density lipoprotein (LDL)-induced tumor necrosis factor (TNF) production at micromolar concentrations [66]. 10) Activation and phosphorylation of AMPK is dependent on the serine-threonine kinase, ataxia telangiectasia mutated (ATM), a checkpoint that responds to double-strand breaks and oxidative stress by activating the DNA damage response involv‐ ing numerous downstream targets such as p53, checkpoint kinase 2 (Chk2), breast cancer 1 (BRCA1), Fanconi anemia, complementation group 2 (FANCD2), Nijmegen breakage syndrome 1 (Nbs1), p53 upregulated modulator of apopto‐ sis (Puma) - Phorbol-12-myristate-13-acetate-induced protein 1 (Noxa) and BCL2-associated X protein (Bax) [67, 68] thus, preventing further DNA insult. 11) Metformin induces nuclear degradation and decreased expression of Sp pro‐ teins, transcription factors for genes involved in cell proliferation (cyclin D1), metabolism (FAS), apoptosis ((B-cell lym‐ phoma 2 (bcl-2) and survivin)) and angiogenesis ((vascular endothelial growth factor (VEGF) and its receptor VEGFR1)) [24].

a treatment dosage. The usual anti-diabetic dose of metformin is 500 - 1250 mg PO BID. Maximum recommended dose in patients with diabetes in 1250 mg PO BID. There seem to be some difference in data regarding prevention or actual treatment of cancer with metformin. The typical serum concentration from 1000- 2000 mg/day of metformin in diabetic patients is about 0.5 - 2 mg/L. The retrospective data so far have primarily looked at these doses and have shown that metformin prevents some cancers (including pancreatic cancer) [52, 77]. If we look at data regarding treatment of pancreatic cancer with metformin, it is different in terms of dosing. The pre-clinical data show that much higher doses are used (10-100 times the clinical used doses). If we look at data in humans using metformin for treatment of cancers, it shows some benefit at clinically used doses but it certainly is not as impressive as pre-clinical high dose metformin [78-80]. We did not find any ongoing human study using very high doses (beyond 3000 mg/day) primarily to treat cancers. Further studies need to be performed addressing this issue within the same animal model or within a similar patient population.

Metformin and Pancreatic Cancer Metabolism

http://dx.doi.org/10.5772/57432

169

Finally, the fact that metformin prevents tumor development and growth in nude mice [5], supports a potential priming effect of metformin on the host potentially limiting the availability of 'onco' metabolites for which the tumor is 'addicted'. These types of studies investigating the processes of carcinogenesis, may address important gaps in current knowledge regarding the role of tumor metabolism in drug response. We strongly believe that mechanistic insight on these issues will have exceptionally high impact and potentially re-shape current paradigms about anti-metabolic drugs, pancreatic cancer treatment and personalized medicine. Indeed, we speculate that the efficacy of metformin – and possibly drugs with similar mechanism – depends on the metabolic context in which the tumor exists. This is potentially, a paradigmchanging concept as it suggests that host/tissue metabolic factors play a role in tumor condi‐ tioning and influence treatment response; a hypothesis that has not previously been considered

Metformin works by decreasing hepatic gluconeogenesis [81], activating insulin receptor tyrosine phosphorylation [82], decreasing intestinal glucose absorption and increasing skeletal and adipose tissue glucose uptake [82]. One mechanistic study conducted in mice demon‐ strates that metformin (250 mg/kg/day for three consecutive days) increases the association of the glycolytic enzymes hexokinase to the mitochondria and phosphofuctokinase to F-actin in mice hearts [83]. These associations result in their activation and up-regulation in glycolysis, increasing cardiac glucose utilization which may partly explain the cardio protective effects of the drug [84, 85]. Since 1995, metformin has been a widely prescribed glucose lowering agent in the United States for type 2 diabetic and polycystic ovary syndrome patients. It is a welltolerated drug with lactic acidosis as a reported serious side effect [86]. However, the link

Dating back to the 1920's, Otto Warburg published his observations on the metabolic aberra‐ tions of cancer cells. In the seminal paper entitled "The Metabolism of Tumors in the Body,"

between lactic acidosis and metformin use has recently been questioned [87].

in the clinical evaluation of metformin.

**6.1. Metformin as a glucose lowering drug**

**6.2. Metformin as an anti-lipogenic drug**

**Figure 3.** Metformin impairs signaling molecules for cancer survival.

## **6. Overall physiological and cellular effects of metformin in cancer models**

Contrary to sulfonylureas, which act at the level of the pancreatic secretion of insulin, bigua‐ nides act at the level of sensitivity of the target tissues for insulin. Moreover, the biguanides can reduce the hyperglycemia without leading to incidental hypoglycemia. Hence, the term "anti-glycemic" agent was coined for metformin.

In the late 90s, amongst many studies published on the cellular effects of metformin, we showed that metformin is able to modulate the insulin receptor (IR) in cholesterol (chol) treated human hepatoma cells, HepG2 [69]. In that study, we used a cellular model in which insulin sensitivity was altered by supplementing the culture medium of HepG2 cells with a derivative of CHOL, cholesteryl hemisuccinate (CHS) [70, 71]. Overall, metformin did not affect IR phosphorylation in control cells. However, metformin affected IR autophosphoryla‐ tion in CHS-treated cells. At 1 and 5 min of insulin stimulation, metformin increased IR phosphorylation in these cells, restoring IR phosphorylation in CHS-treated cells towards control levels. As mentioned earlier, metformin is a charged biguanide, requiring cell surface transport protein for its influx [72] and exhibits membrane effects as well as cellular effects [73]. Pertinent to our early work, recent studies from Algire et al. [74] demonstrated that a high energy diet promotes tumor growth and that metformin decreases tumor volume only in highenergy fed animals. The authors suggest that, *"the inhibitory effect of metformin on tumor growth was restricted to animals on the high-energy diet. These results suggested that any benefits of this drug in reducing cancer aggressiveness may be restricted to a metabolically defined subset of cancer pa‐ tients."* [74].

After nearly two decades of research and approval of metformin by the FDA in 1994, the target of the compound has yet to be identified. Arguably, as mentioned above, metformin is a charged biguanide, requiring cell surface transport protein for its influx [75] and exhibits membrane effects as well as cellular effects [73]. While Algire et al found that the anti-tumor effect of metformin was limited in animals on high-energy diets using *in vivo* models of lung and colorectal xenografts [76]; very recently, the work from Rozengurt and Eibl (from UCLA) demonstrates a strong tumor growth delay effect of metformin in pancreatic cancer xenograft models [5]. However, the doses used (>200mg/kg, i.p.) may not be clinically relevant. The ongoing debate on metformin dosages in animal models and human clinical trials has yet to define clearly the anti-diabetic dose *versus* the anti-cancer dose as well as a preventive *versus* a treatment dosage. The usual anti-diabetic dose of metformin is 500 - 1250 mg PO BID. Maximum recommended dose in patients with diabetes in 1250 mg PO BID. There seem to be some difference in data regarding prevention or actual treatment of cancer with metformin. The typical serum concentration from 1000- 2000 mg/day of metformin in diabetic patients is about 0.5 - 2 mg/L. The retrospective data so far have primarily looked at these doses and have shown that metformin prevents some cancers (including pancreatic cancer) [52, 77]. If we look at data regarding treatment of pancreatic cancer with metformin, it is different in terms of dosing. The pre-clinical data show that much higher doses are used (10-100 times the clinical used doses). If we look at data in humans using metformin for treatment of cancers, it shows some benefit at clinically used doses but it certainly is not as impressive as pre-clinical high dose metformin [78-80]. We did not find any ongoing human study using very high doses (beyond 3000 mg/day) primarily to treat cancers. Further studies need to be performed addressing this issue within the same animal model or within a similar patient population.

Finally, the fact that metformin prevents tumor development and growth in nude mice [5], supports a potential priming effect of metformin on the host potentially limiting the availability of 'onco' metabolites for which the tumor is 'addicted'. These types of studies investigating the processes of carcinogenesis, may address important gaps in current knowledge regarding the role of tumor metabolism in drug response. We strongly believe that mechanistic insight on these issues will have exceptionally high impact and potentially re-shape current paradigms about anti-metabolic drugs, pancreatic cancer treatment and personalized medicine. Indeed, we speculate that the efficacy of metformin – and possibly drugs with similar mechanism – depends on the metabolic context in which the tumor exists. This is potentially, a paradigmchanging concept as it suggests that host/tissue metabolic factors play a role in tumor condi‐ tioning and influence treatment response; a hypothesis that has not previously been considered in the clinical evaluation of metformin.

#### **6.1. Metformin as a glucose lowering drug**

and phosphorylation of AMPK is dependent on the serine-threonine kinase, ataxia telangiectasia mutated (ATM), a checkpoint that responds to double-strand breaks and oxidative stress by activating the DNA damage response involv‐ ing numerous downstream targets such as p53, checkpoint kinase 2 (Chk2), breast cancer 1 (BRCA1), Fanconi anemia, complementation group 2 (FANCD2), Nijmegen breakage syndrome 1 (Nbs1), p53 upregulated modulator of apopto‐ sis (Puma) - Phorbol-12-myristate-13-acetate-induced protein 1 (Noxa) and BCL2-associated X protein (Bax) [67, 68] thus, preventing further DNA insult. 11) Metformin induces nuclear degradation and decreased expression of Sp pro‐ teins, transcription factors for genes involved in cell proliferation (cyclin D1), metabolism (FAS), apoptosis ((B-cell lym‐ phoma 2 (bcl-2) and survivin)) and angiogenesis ((vascular endothelial growth factor (VEGF) and its receptor VEGFR1))

168 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

**6. Overall physiological and cellular effects of metformin in cancer models**

Contrary to sulfonylureas, which act at the level of the pancreatic secretion of insulin, bigua‐ nides act at the level of sensitivity of the target tissues for insulin. Moreover, the biguanides can reduce the hyperglycemia without leading to incidental hypoglycemia. Hence, the term

In the late 90s, amongst many studies published on the cellular effects of metformin, we showed that metformin is able to modulate the insulin receptor (IR) in cholesterol (chol) treated human hepatoma cells, HepG2 [69]. In that study, we used a cellular model in which insulin sensitivity was altered by supplementing the culture medium of HepG2 cells with a derivative of CHOL, cholesteryl hemisuccinate (CHS) [70, 71]. Overall, metformin did not affect IR phosphorylation in control cells. However, metformin affected IR autophosphoryla‐ tion in CHS-treated cells. At 1 and 5 min of insulin stimulation, metformin increased IR phosphorylation in these cells, restoring IR phosphorylation in CHS-treated cells towards control levels. As mentioned earlier, metformin is a charged biguanide, requiring cell surface transport protein for its influx [72] and exhibits membrane effects as well as cellular effects [73]. Pertinent to our early work, recent studies from Algire et al. [74] demonstrated that a high energy diet promotes tumor growth and that metformin decreases tumor volume only in highenergy fed animals. The authors suggest that, *"the inhibitory effect of metformin on tumor growth was restricted to animals on the high-energy diet. These results suggested that any benefits of this drug in reducing cancer aggressiveness may be restricted to a metabolically defined subset of cancer pa‐*

After nearly two decades of research and approval of metformin by the FDA in 1994, the target of the compound has yet to be identified. Arguably, as mentioned above, metformin is a charged biguanide, requiring cell surface transport protein for its influx [75] and exhibits membrane effects as well as cellular effects [73]. While Algire et al found that the anti-tumor effect of metformin was limited in animals on high-energy diets using *in vivo* models of lung and colorectal xenografts [76]; very recently, the work from Rozengurt and Eibl (from UCLA) demonstrates a strong tumor growth delay effect of metformin in pancreatic cancer xenograft models [5]. However, the doses used (>200mg/kg, i.p.) may not be clinically relevant. The ongoing debate on metformin dosages in animal models and human clinical trials has yet to define clearly the anti-diabetic dose *versus* the anti-cancer dose as well as a preventive *versus*

**Figure 3.** Metformin impairs signaling molecules for cancer survival.

"anti-glycemic" agent was coined for metformin.

[24].

*tients."* [74].

Metformin works by decreasing hepatic gluconeogenesis [81], activating insulin receptor tyrosine phosphorylation [82], decreasing intestinal glucose absorption and increasing skeletal and adipose tissue glucose uptake [82]. One mechanistic study conducted in mice demon‐ strates that metformin (250 mg/kg/day for three consecutive days) increases the association of the glycolytic enzymes hexokinase to the mitochondria and phosphofuctokinase to F-actin in mice hearts [83]. These associations result in their activation and up-regulation in glycolysis, increasing cardiac glucose utilization which may partly explain the cardio protective effects of the drug [84, 85]. Since 1995, metformin has been a widely prescribed glucose lowering agent in the United States for type 2 diabetic and polycystic ovary syndrome patients. It is a welltolerated drug with lactic acidosis as a reported serious side effect [86]. However, the link between lactic acidosis and metformin use has recently been questioned [87].

#### **6.2. Metformin as an anti-lipogenic drug**

Dating back to the 1920's, Otto Warburg published his observations on the metabolic aberra‐ tions of cancer cells. In the seminal paper entitled "The Metabolism of Tumors in the Body," Warburg and colleagues showed the absence of lactic acid accumulation in the blood of normal animals (no cancer) [88]. Whereas, animals with tumors accrued greater concentration of lactic acid in venous compared to arterial blood as well as in the tumor cavity, indicating the formation of lactic acid from glucose fermentation as blood goes through the tumor [88].

The reliance of cancer cells on glucose metabolism stems from their need to generate metab‐ olites and reducing equivalents that are used to support crucial biosynthetic reactions that make lipids, nucleotides and amino acids. These biomolecules are rate-limiting for cell proliferation and survival. Glycolysis yields glucose-6-phosphate that enters the oxidative arm of the Pentose Phosphate Pathway (PPP). The oxidative PPP produces NADPH which, together with acetyl-CoA, fuels lipid synthesis in the cytosol. The non-oxidative branch of the PPP yields ribose-5-phosphate that is the precursor for nucleotides. In fact, as early as 1998, it has been argued that the both PPP branches (but primarily the non-oxidative branch) serve to produce ribose to sustain the increased needs of the cancer cell for DNA and RNA [89]. Fructose-6-phosphate and glyceraldehyde-3-phosphate are by-products of the glycolytic and the non-oxidative pentose phosphate pathways, providing an intimate link between glucose metabolism and nucleotide generation. Acetyl-CoA produced from the pyruvate dehydro‐ genase reaction enters the tricarboxylic acid (TCA) cycle in the mitochondria. Citrate can be exported from the mitochondria into the cytosol and converted back to acetyl-CoA (catalyzed by ATP citrate lyase) for lipid synthesis. Malate, an intermediate in the TCA cycle, can be converted into pyruvate by malic enzyme with the production of NADPH, a reducing equivalent that is used to generate reduced glutathione, allowing cancer cells greater tolerance to free radical-induced damage [90]. Glutaminase catalyses the hydrolysis of the amine group of glutamine to form glutamate and ammonia. Glutamate equilibrates with α-ketoglutarate via glutamate dehydrogenase. In a process termed reductive carboxylation, glutamine-derived citrate provides the acetyl-CoA for lipid synthesis and TCA cycle intermediates [91]. Hence, the glutamine addiction of cancer cells is another mechanism by which the metabolism is rewired to support biosynthesis [90, 91]. Please refer to Figure 3 for an integrated visual of

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Diabetes and cancer are both metabolic diseases. It is therefore, not surprising that the mechanisms of action of metformin against type 2 diabetes and cancer include the drug's ability to alter critical metabolic circuits that lead to the normalization of blood glucose in diabetes and the impairment of biosynthetic pathways in cancer cells. For example, it is wellestablished that metformin is an inhibitor of complex I of the ETC. In 2000, two research groups have independently shown that dimethylbiguanide selectively blocks complex I of the ETC [14, 15]. In intact isolated hepatocytes, dimethylbiguanide has been reported to dose-depend‐ ently (0.1 to 10mM) inhibit oxygen consumption maximally at 20-30min [15]. The inhibition of respiration only occurred when glutamate-malate (complex I substrates) were used as substrates versus when succinate (complex II substrate)-rotenone or *N, N, N', N'*-tetrameth‐ yl-1,4-phenylenediamine dihydrochloride (TMPD)-ascorbate (complex IV substrates) were added during the assessment of oxygen uptake. It is interesting to note that El-Mir and colleagues did not observe these changes when oxygen uptake experiments were performed in digitonin-permeabilized hepatocytes or in isolated liver mitochondria [15]. This is in contradiction to what Owen and others published when they showed that lower metformin concentrations of 50 and 100 μM were able to significantly decrease state 3 respiration rate in digitonin-permeabilized rat hepatoma cells [14]. Metformin has slow permeation properties across the inner mitochondrial membrane [14] and longer incubation periods (30 min in the El-Mir group versus 24-60 hours in the Owen group) may have eventually yielded comparable

cancer metabolism.

Metformin inhibits the gene expression of carnitine palmitoyltransferase 1 (CPT1), a mitochondrial enzyme that is the rate-limiting step in long-chain fatty acid β-oxidation. CPT1 catalyzes the transfer of acyl-CoA to the carnitine hydroxyl group, forming acyl-carnitine which is then transported into the mitochondrial matrix via translocase. Carnitine palmi‐ toyltransferase 2 (CPT2) catalyzes the formation of acyl-CoA from acyl-carnitine. Acyl-CoA then undergoes β-oxida‐ tion. Metformin prevents the nuclear activation of sterol-regulatory element-binding protein 1c isoform (SREBP-1c) and SREBP-2 sterol-regulatory element-binding protein 2 isoform (SREBP-2), transcription factors that induce the ex‐ pression of enzymatic genes involved in fatty acid and cholesterol synthesis, respectively. Metformin decreases the ac‐ tivities of 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR) and acyl coenzyme A:cholesterol acyltransferase (ACAT). HMGCR is the rate-limiting enzyme of the mevalonate pathway, that catalyzes the reduction of 3-hydroxy-3 methylglutaryl-coenzyme A (HMGCoA) to mevalonate. The mevalonate pathway synthesizes isoprenoids and choles‐ terol. ACAT is an endoplasmic reticulum protein that catalyzes the formation of cholesterol esters from acyl-CoA and cholesterol. Metformin decreases the gene expression of steroyl-CoA desaturase 1 (SCD1), the enzyme responsible for desaturation of stearic acid (18:0) into oleic acid (18:1 n-9) and of palmitic acid (16:0) to palmitoleic acid (16:1 n-7). MET decreases the protein expression of FAS, acetyl-CoA carboxylase (ACC) and ATP citrate lyase (ACLY), which are enzymes involved in fatty acid synthesis.

**Figure 4.** Metformin inhibits key metabolic steps in lipogenesis.

The reliance of cancer cells on glucose metabolism stems from their need to generate metab‐ olites and reducing equivalents that are used to support crucial biosynthetic reactions that make lipids, nucleotides and amino acids. These biomolecules are rate-limiting for cell proliferation and survival. Glycolysis yields glucose-6-phosphate that enters the oxidative arm of the Pentose Phosphate Pathway (PPP). The oxidative PPP produces NADPH which, together with acetyl-CoA, fuels lipid synthesis in the cytosol. The non-oxidative branch of the PPP yields ribose-5-phosphate that is the precursor for nucleotides. In fact, as early as 1998, it has been argued that the both PPP branches (but primarily the non-oxidative branch) serve to produce ribose to sustain the increased needs of the cancer cell for DNA and RNA [89]. Fructose-6-phosphate and glyceraldehyde-3-phosphate are by-products of the glycolytic and the non-oxidative pentose phosphate pathways, providing an intimate link between glucose metabolism and nucleotide generation. Acetyl-CoA produced from the pyruvate dehydro‐ genase reaction enters the tricarboxylic acid (TCA) cycle in the mitochondria. Citrate can be exported from the mitochondria into the cytosol and converted back to acetyl-CoA (catalyzed by ATP citrate lyase) for lipid synthesis. Malate, an intermediate in the TCA cycle, can be converted into pyruvate by malic enzyme with the production of NADPH, a reducing equivalent that is used to generate reduced glutathione, allowing cancer cells greater tolerance to free radical-induced damage [90]. Glutaminase catalyses the hydrolysis of the amine group of glutamine to form glutamate and ammonia. Glutamate equilibrates with α-ketoglutarate via glutamate dehydrogenase. In a process termed reductive carboxylation, glutamine-derived citrate provides the acetyl-CoA for lipid synthesis and TCA cycle intermediates [91]. Hence, the glutamine addiction of cancer cells is another mechanism by which the metabolism is rewired to support biosynthesis [90, 91]. Please refer to Figure 3 for an integrated visual of cancer metabolism.

Warburg and colleagues showed the absence of lactic acid accumulation in the blood of normal animals (no cancer) [88]. Whereas, animals with tumors accrued greater concentration of lactic acid in venous compared to arterial blood as well as in the tumor cavity, indicating the formation of lactic acid from glucose fermentation as blood goes through the tumor [88].

170 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

Metformin inhibits the gene expression of carnitine palmitoyltransferase 1 (CPT1), a mitochondrial enzyme that is the rate-limiting step in long-chain fatty acid β-oxidation. CPT1 catalyzes the transfer of acyl-CoA to the carnitine hydroxyl group, forming acyl-carnitine which is then transported into the mitochondrial matrix via translocase. Carnitine palmi‐ toyltransferase 2 (CPT2) catalyzes the formation of acyl-CoA from acyl-carnitine. Acyl-CoA then undergoes β-oxida‐ tion. Metformin prevents the nuclear activation of sterol-regulatory element-binding protein 1c isoform (SREBP-1c) and SREBP-2 sterol-regulatory element-binding protein 2 isoform (SREBP-2), transcription factors that induce the ex‐ pression of enzymatic genes involved in fatty acid and cholesterol synthesis, respectively. Metformin decreases the ac‐ tivities of 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR) and acyl coenzyme A:cholesterol acyltransferase (ACAT). HMGCR is the rate-limiting enzyme of the mevalonate pathway, that catalyzes the reduction of 3-hydroxy-3 methylglutaryl-coenzyme A (HMGCoA) to mevalonate. The mevalonate pathway synthesizes isoprenoids and choles‐ terol. ACAT is an endoplasmic reticulum protein that catalyzes the formation of cholesterol esters from acyl-CoA and cholesterol. Metformin decreases the gene expression of steroyl-CoA desaturase 1 (SCD1), the enzyme responsible for desaturation of stearic acid (18:0) into oleic acid (18:1 n-9) and of palmitic acid (16:0) to palmitoleic acid (16:1 n-7). MET decreases the protein expression of FAS, acetyl-CoA carboxylase (ACC) and ATP citrate lyase (ACLY), which are

enzymes involved in fatty acid synthesis.

**Figure 4.** Metformin inhibits key metabolic steps in lipogenesis.

Diabetes and cancer are both metabolic diseases. It is therefore, not surprising that the mechanisms of action of metformin against type 2 diabetes and cancer include the drug's ability to alter critical metabolic circuits that lead to the normalization of blood glucose in diabetes and the impairment of biosynthetic pathways in cancer cells. For example, it is wellestablished that metformin is an inhibitor of complex I of the ETC. In 2000, two research groups have independently shown that dimethylbiguanide selectively blocks complex I of the ETC [14, 15]. In intact isolated hepatocytes, dimethylbiguanide has been reported to dose-depend‐ ently (0.1 to 10mM) inhibit oxygen consumption maximally at 20-30min [15]. The inhibition of respiration only occurred when glutamate-malate (complex I substrates) were used as substrates versus when succinate (complex II substrate)-rotenone or *N, N, N', N'*-tetrameth‐ yl-1,4-phenylenediamine dihydrochloride (TMPD)-ascorbate (complex IV substrates) were added during the assessment of oxygen uptake. It is interesting to note that El-Mir and colleagues did not observe these changes when oxygen uptake experiments were performed in digitonin-permeabilized hepatocytes or in isolated liver mitochondria [15]. This is in contradiction to what Owen and others published when they showed that lower metformin concentrations of 50 and 100 μM were able to significantly decrease state 3 respiration rate in digitonin-permeabilized rat hepatoma cells [14]. Metformin has slow permeation properties across the inner mitochondrial membrane [14] and longer incubation periods (30 min in the El-Mir group versus 24-60 hours in the Owen group) may have eventually yielded comparable results. In support of the previous findings, recent reports confirm that metformin is a specific inhibitor of the ETC complex I which leads to some impairment in mitochondrial function in human-derived non-malignant and in cancer cells [16-18, 92, 93]. This potentially limits the intact oxidative respiration capabilities of the cancer cell [16].

activity of the enzyme resulting in impairment in long fatty acid chain transport from the mitochondrial outer membrane into the matrix where β-oxidation takes place. Current publications also render support to the lipid-inhibitory effects of metformin. Metformin (0.2 to 1.0 mM for 16 h) has been shown to activate AMPK and decrease the mRNA, nuclear translocation and consequent activation via cleavage of the nuclear portion and the promoter activity of SREBP-1c in rat hepatoma McA-RH7777 cells [22, 23]. The mRNA and nuclear protein levels of SREBP-2 were also reduced after metformin treatment. This AMPK-mediated suppression of SREBP-1c has also been reported to prevent lipogenesis in an insulin resistant mouse model [20] and is consistent with a decrease in hepatic SREBP-1 expression in mice fed a high fat (60% lipids) diet for 10 weeks and then metformin (0.48mg% of the diet) for another six weeks [100]. Since SREBP-1c and SREBP-2 are transcription factors that promote the expression of enzymatic genes involved in fatty acid and cholesterol synthesis, respectively [101] we would expect diminished lipid synthesis as a biological endpoint of their downregulation. In accordance, MRC5 human fetal lung fibroblasts incubated for 72 h with met‐ formin (5 x 10-5 to 5 x 10-4 M) decreased 1-14acetate incorporation into sterols, fatty acids and triglycerides compared to control, accompanied by a reduction in the activities of HMGCR and ACAT, enzymes that catalyze the formation of mevalonic acid from HMGCoA and the esterification of cholesterol, respectively [102]. Also, metformin has been shown to induce the phosphorylation (Ser-351) of the nuclear receptor TR4 via AMPK, leading to decreased TR4 transactivation and a decrease in the gene expression of its target, steroyl-CoA desaturase 1 (*SCD1*) gene expression. SCD is an enzyme that catalyzes the synthesis of monounsaturated fatty acids palmitoleic acid (16:1 n-7) and oleic acid (18:1 n-9) from saturated fatty acids obtained from *de novo* lipogenesis or from the diet [103]. SCD1 has been shown to be associated to numerous diseases including but not limited to obesity, hepatic steatosis, hypertriglyceri‐

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demia, insulin resistance, low grade inflammation and bone fractures [103].

The role of SCD1 in cancer has been gaining more attention as a potential pharmacological target in cancer interventions [104]. SCD is an endoplasmic reticulum-bound protein encoded by the *SCD1* and *SCD5* genes in humans [105]. They are highly expressed in liver and adipose tissue (SCD1) and in the brain and the pancreas (SCD5) [103]. Observational studies have reported a positive association between saturation index (18:0 to 18:1 n-9 ratio used by investigators as a marker for SCD activity) with cancer risk [106-110]. The first cDNA of human SCD published in 1994 revealed that the mRNA levels of this enzyme were elevated in tissues derived from esophageal carcinoma, colorectal cancer and hepatocellular adenoma [111]. Its protein levels are highly expressed in SV40-transformed fibroblasts compared to their wild type counterpart [112]. However, decreased transcript expression was reported in prostate cancer when compared to normal epithelium [113]. These seemingly conflicting results may reflect variation in expression depending on the tissue type or some malignancy-induced metabolic changes in lipid synthesis and/or lipid profile which would overall guarantee cancer cell survival advantage. Indeed, Moore and others speculated that the down-regulation of SCD cDNA in prostate carcinoma may be due to: a) the need of the cancer cell to increase the levels of palmitate which can be done by decreasing SCD activity, b) eliminate the SCD-induced

Besides inhibition of complex I and effects on glucose metabolism, numerous studies also show metformin-induced metabolic changes in non-cancer and cancer cells. One of the most notable effects of metformin is inhibition of lipogenesis, a metabolic pathway that is critical for a cancer cell's survival advantage. Under lipogenic conditions, surplus glucose in the cell is converted to pyruvate via glycolysis in the cytoplasm. Pyruvate is converted to acetyl-CoA and trans‐ ported as citrate from the mitochondria into the cytoplasm. ATP citrate lyase (ACLY) converts citrate back to acetyl-CoA. Acetyl-CoA carboxylase (ACC) catalyzes the carboxylation of acetyl-CoA to malonyl-CoA in an ATP-dependent manner. Acetyl-CoA and malonyl-CoA are then used as substrates for the production of palmitate by the seven enzymatic reactions catalyzed by FAS. In cancer, *de novo* fatty acid (FA) synthesis is up-regulated mainly for membrane production (usage of FA for phospholipids) and post-translational modification of proteins [94]. ACLY, ACC and FAS expression and activity have been shown to be upregulated in cancers including pancreatic cancer. Thus, metabolic enzymes involved in FA synthesis have emerged as therapeutic targets in cancer [94]. The effects of MET on energy homeostasis in normal hepatocytes and breast and colon cancer cells have been characterized by the blocked activation or expression of key lipid biosynthesis enzymes such as ACC, FAS, HMGCR and enhanced expression of regulators of mitochondrial biogenesis, peroxisome proliferator-activated receptor-gamma co-activator 1 (PGC-1) [1, 74, 95].

Suppression of anabolic pathways (metformin is anti-lipogenic) is in keeping with the expected consequences of AMPK activation [1]. HMGCR may also play an important role in human malignancies. Indeed, recent transcriptional profiling demonstrated that cholesterol and lipid metabolisms are linked to cellular transformation [96]. Interestingly, high HMGCR mRNA levels correlated with poor patient prognosis and reduced survival. The levels of additional mevalonate (MVA) pathway genes were also significantly correlated with poor prognosis of breast cancer patients, suggesting the entire pathway may be deregulated in these cases [97]. It is interesting to note that the metformin-induced inhibition of respiration is blocked by the addition of palmitate in 3T3-L1 adipocytes [19]. Adipocytes treated with palmitate complexed to albumin in the presence of carnitine had comparable oxygen consumption rates when compared to control. These results indicate that the metformin-induced inhibition of respira‐ tion can be reversed by the addition of fatty acids, which led the authors to conclude that the mechanism of action of metformin may be linked to fatty acid metabolism [19]. Although indirect, this article presented a link between metformin and its effects on lipid metabolism or *vice versa*.

The normoglycemic effects of metformin has also been attributed to its ability to prevent fatty acid oxidation which decreases acetyl-CoA, ATP and reducing equivalents' availability for hepatic gluconeogenesis [98], an effect likely mediated by a reduction in the expression of the carnitine palmitoyltransferase I gene [99] and eventually, a decrease in protein expression and activity of the enzyme resulting in impairment in long fatty acid chain transport from the mitochondrial outer membrane into the matrix where β-oxidation takes place. Current publications also render support to the lipid-inhibitory effects of metformin. Metformin (0.2 to 1.0 mM for 16 h) has been shown to activate AMPK and decrease the mRNA, nuclear translocation and consequent activation via cleavage of the nuclear portion and the promoter activity of SREBP-1c in rat hepatoma McA-RH7777 cells [22, 23]. The mRNA and nuclear protein levels of SREBP-2 were also reduced after metformin treatment. This AMPK-mediated suppression of SREBP-1c has also been reported to prevent lipogenesis in an insulin resistant mouse model [20] and is consistent with a decrease in hepatic SREBP-1 expression in mice fed a high fat (60% lipids) diet for 10 weeks and then metformin (0.48mg% of the diet) for another six weeks [100]. Since SREBP-1c and SREBP-2 are transcription factors that promote the expression of enzymatic genes involved in fatty acid and cholesterol synthesis, respectively [101] we would expect diminished lipid synthesis as a biological endpoint of their downregulation. In accordance, MRC5 human fetal lung fibroblasts incubated for 72 h with met‐ formin (5 x 10-5 to 5 x 10-4 M) decreased 1-14acetate incorporation into sterols, fatty acids and triglycerides compared to control, accompanied by a reduction in the activities of HMGCR and ACAT, enzymes that catalyze the formation of mevalonic acid from HMGCoA and the esterification of cholesterol, respectively [102]. Also, metformin has been shown to induce the phosphorylation (Ser-351) of the nuclear receptor TR4 via AMPK, leading to decreased TR4 transactivation and a decrease in the gene expression of its target, steroyl-CoA desaturase 1 (*SCD1*) gene expression. SCD is an enzyme that catalyzes the synthesis of monounsaturated fatty acids palmitoleic acid (16:1 n-7) and oleic acid (18:1 n-9) from saturated fatty acids obtained from *de novo* lipogenesis or from the diet [103]. SCD1 has been shown to be associated to numerous diseases including but not limited to obesity, hepatic steatosis, hypertriglyceri‐ demia, insulin resistance, low grade inflammation and bone fractures [103].

results. In support of the previous findings, recent reports confirm that metformin is a specific inhibitor of the ETC complex I which leads to some impairment in mitochondrial function in human-derived non-malignant and in cancer cells [16-18, 92, 93]. This potentially limits the

172 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

Besides inhibition of complex I and effects on glucose metabolism, numerous studies also show metformin-induced metabolic changes in non-cancer and cancer cells. One of the most notable effects of metformin is inhibition of lipogenesis, a metabolic pathway that is critical for a cancer cell's survival advantage. Under lipogenic conditions, surplus glucose in the cell is converted to pyruvate via glycolysis in the cytoplasm. Pyruvate is converted to acetyl-CoA and trans‐ ported as citrate from the mitochondria into the cytoplasm. ATP citrate lyase (ACLY) converts citrate back to acetyl-CoA. Acetyl-CoA carboxylase (ACC) catalyzes the carboxylation of acetyl-CoA to malonyl-CoA in an ATP-dependent manner. Acetyl-CoA and malonyl-CoA are then used as substrates for the production of palmitate by the seven enzymatic reactions catalyzed by FAS. In cancer, *de novo* fatty acid (FA) synthesis is up-regulated mainly for membrane production (usage of FA for phospholipids) and post-translational modification of proteins [94]. ACLY, ACC and FAS expression and activity have been shown to be upregulated in cancers including pancreatic cancer. Thus, metabolic enzymes involved in FA synthesis have emerged as therapeutic targets in cancer [94]. The effects of MET on energy homeostasis in normal hepatocytes and breast and colon cancer cells have been characterized by the blocked activation or expression of key lipid biosynthesis enzymes such as ACC, FAS, HMGCR and enhanced expression of regulators of mitochondrial biogenesis, peroxisome

intact oxidative respiration capabilities of the cancer cell [16].

proliferator-activated receptor-gamma co-activator 1 (PGC-1) [1, 74, 95].

*vice versa*.

Suppression of anabolic pathways (metformin is anti-lipogenic) is in keeping with the expected consequences of AMPK activation [1]. HMGCR may also play an important role in human malignancies. Indeed, recent transcriptional profiling demonstrated that cholesterol and lipid metabolisms are linked to cellular transformation [96]. Interestingly, high HMGCR mRNA levels correlated with poor patient prognosis and reduced survival. The levels of additional mevalonate (MVA) pathway genes were also significantly correlated with poor prognosis of breast cancer patients, suggesting the entire pathway may be deregulated in these cases [97]. It is interesting to note that the metformin-induced inhibition of respiration is blocked by the addition of palmitate in 3T3-L1 adipocytes [19]. Adipocytes treated with palmitate complexed to albumin in the presence of carnitine had comparable oxygen consumption rates when compared to control. These results indicate that the metformin-induced inhibition of respira‐ tion can be reversed by the addition of fatty acids, which led the authors to conclude that the mechanism of action of metformin may be linked to fatty acid metabolism [19]. Although indirect, this article presented a link between metformin and its effects on lipid metabolism or

The normoglycemic effects of metformin has also been attributed to its ability to prevent fatty acid oxidation which decreases acetyl-CoA, ATP and reducing equivalents' availability for hepatic gluconeogenesis [98], an effect likely mediated by a reduction in the expression of the carnitine palmitoyltransferase I gene [99] and eventually, a decrease in protein expression and

The role of SCD1 in cancer has been gaining more attention as a potential pharmacological target in cancer interventions [104]. SCD is an endoplasmic reticulum-bound protein encoded by the *SCD1* and *SCD5* genes in humans [105]. They are highly expressed in liver and adipose tissue (SCD1) and in the brain and the pancreas (SCD5) [103]. Observational studies have reported a positive association between saturation index (18:0 to 18:1 n-9 ratio used by investigators as a marker for SCD activity) with cancer risk [106-110]. The first cDNA of human SCD published in 1994 revealed that the mRNA levels of this enzyme were elevated in tissues derived from esophageal carcinoma, colorectal cancer and hepatocellular adenoma [111]. Its protein levels are highly expressed in SV40-transformed fibroblasts compared to their wild type counterpart [112]. However, decreased transcript expression was reported in prostate cancer when compared to normal epithelium [113]. These seemingly conflicting results may reflect variation in expression depending on the tissue type or some malignancy-induced metabolic changes in lipid synthesis and/or lipid profile which would overall guarantee cancer cell survival advantage. Indeed, Moore and others speculated that the down-regulation of SCD cDNA in prostate carcinoma may be due to: a) the need of the cancer cell to increase the levels of palmitate which can be done by decreasing SCD activity, b) eliminate the SCD-induced down-regulation of lipid membrane rafts and c) down-regulate susceptibility of tumor cells containing more unsaturated fatty acids to TNF-induced free radical attack [113]. Nevertheless, knockdown of human SCD in transformed human fibroblasts resulted in decreased oleic acid synthesis, lowered desaturation index profiles of the main polar lipids phosphatidylcholine and phosphatidylethanolamine, and decreased *de novo* synthesis of 14C-labeled phospholipids, cholesterol and cholesterol esters, free fatty acids and triacylglycerols [114]. Interestingly, there was an inhibition in cellular proliferation and anchorage-independent growth in the SCD knockdown cells [114]. Other studies confirm that SCD inhibition by chemical or genetic manipulations resulted in inhibition of cancer cell proliferation and/or death [115-118]. Thus, SCD appears to be involved in modulating lipid metabolism and signaling processes crucial for cancer cell replication and anchorage-independent growth, effects are likely influenced by the effects of SCD loss on membrane integrity [114].

synthesis pathways utilize acetyl-CoA as a common substrate [126], the addition of cholesterol (in the form of the more water-soluble cholesteryl hemisuccinate) inhibits the thiolasecatalyzed cholesterol pathway and shifts the glucose-derived acetyl CoA- towards the acetyl-CoA carboxylase-catalyzed fatty acid synthesis pathway. These effects are observed only in MiaPaCa-2-cells, which harbor the GGT(Gly) to TGT(Cys) codon 12 K-*ras* mutation. Further, non-lipogenic cancer cells harboring a K-*ras*G12C mutation [127] with suppressed cholesterol synthesis were significantly more sensitive to the growth inhibiting effects of metformin than tumor cells containing wild-type K-*ras* with normal cholesterol synthesis. These results are

consistent with expected modulation of AMPK [1] and/or mTOR [128].

Not yet recruiting

Recruiting

Recruiting

Recruiting

Recruiting

Recruiting

**Table 2.** Ongoing Clinical Trials on Metformin and Pancreatic Cancer

Metformin Hydrochloride in Treating Patients With Pancreatic Cancer That Can be Removed by Surgery

Metformin Combined With Chemotherapy for Pancreatic

Metformin Plus Modified FOLFOX 6 in Metastatic Pancreatic Cancer

Combination Chemotherapy With or Without Metformin Hydrochloride in Treating Patients With Metastatic Pancreatic Cancer

Treatment of Patients With Advanced Pancreatic Cancer After Gemcitabine Failure

Gemcitabine+Nab-paclitaxel and FOLFIRINOX and Molecular Profiling for Patients With Advanced Pancreatic Cancer

Cancer

**Title Recruitment Results Conditions Interventions**

No Results Available

No Results Available

No Results Available

No Results Available

No Results Available

No Results Available

*Pancreatic Cancer*: Stage IA Stage IB Stage IIA Stage IIB

Locally Advanced Pancreatic Cancer/ Metastatic Pancreatic

Adenocarcinoma of the

Adenocarcinoma of the

Recurrent Pancreatic

Stage IV Pancreatic

Pancreatic Cancer

Stage IV Pancreatic

Pancreatic Adenocarcinoma Advanced or Metastatic

Cancer

Cancer

Acinar Cell

Pancreas, Duct Cell,

Pancreas,

Cancer,

Cancer

*Drug*: metformin hydrochloride *Other*: pharmacological study

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*Drug*: gemcitabine, erlotinib, metformin, placebo

*Drug*: metformin hydrochloride, oxaliplatin, leucovorin calcium,

laboratory biomarker analysis

*Drug*: capecitabine, cisplatin, epirubicin, gemcitabine,

*Drug*: paclitaxel, metformin

*Drug*: Gemcitabine, nab-paclitaxel, FOLFIRINOX, metformin *Genetic*: Immunohistochemistry

fluorouracil *Other*:

metformin

(IHC) Analysis

In support of the anti-lipogenic effects of metformin, Bhalla and others [2] have reported that metformin decreased the gene and protein expression of enzymes involved in fatty acid synthesis namely, ACC, FAS and ACLY which was accompanied by a reduction in hepatic triglycerides in a mouse model of hepatocellular cancer fed metformin at a dose of 250 mg/kg for 24-36 weeks.

Obesity is a known risk factor for cancers of the pancreas, colon and rectum, esophagus, kidney, prostate, breast, uterus and ovaries [119-123]. In order to recapitulate this condition in the preclinical setting, animal models are fed high energy (HE), high fat (HF) diets to induce the metabolic syndrome and/or obesity. In an *in vivo* model of colon carcinoma by Algire and colleagues [124], metformin (50mg/kg/day for five weeks) significantly decreased tumor volume only in mice fed the HF/HE diet. Of note, these concentrations are more likely to be physiologically relevant to what a diabetic patient would have in their system. They also found that metformin reduced the expression of SREBP-1 and one of its target enzymes, FAS, regardless of the type of diet. Interestingly, a previous study from the same group using an *in vivo* model of lung cancer back in 2008 showed that the tumor growth inhibitory impact of metformin is exclusive to the mice under the HF/HE diet [76]. These two studies suggest that metformin may retard cancer growth depending on a particular metabolic state of the organism which in this case, is the abundance of circulating lipids from the HE/HF diet.

We recently found that the *in vitro* response to metformin depends on the level of intracellular cholesterol synthesis of the tumor [125]. We were the first to demonstrate that a physiologically relevant dose of metformin impairs glucose utilization in pancreatic cancer by inhibiting FAS when cholesterol synthesis is limited. Specifically, we found that pancreatic cells that have a K-*ras* mutation and that require *de novo* fatty acid (FA) synthesis for lipids ('lipogenic cells') were unable to synthesize FA from acetyl-CoA in the presence of an inhibitor of cholesterol synthesis and metformin. Our *in vitro* model shows that a physiologically relevant dose of metformin (100 μM) using an acute treatment of 24 h decreases *de novo* lipid synthesis via the FAS pathway in pancreatic adenocarcinoma only when: a) the glucose-derived acetyl-CoA is made available for fatty acid synthesis by inhibition of cholesterol synthesis (addition of exogenous cholesterol) and b) K-*ras* mutation is present [21]. As the fatty acid and cholesterol synthesis pathways utilize acetyl-CoA as a common substrate [126], the addition of cholesterol (in the form of the more water-soluble cholesteryl hemisuccinate) inhibits the thiolasecatalyzed cholesterol pathway and shifts the glucose-derived acetyl CoA- towards the acetyl-CoA carboxylase-catalyzed fatty acid synthesis pathway. These effects are observed only in MiaPaCa-2-cells, which harbor the GGT(Gly) to TGT(Cys) codon 12 K-*ras* mutation. Further, non-lipogenic cancer cells harboring a K-*ras*G12C mutation [127] with suppressed cholesterol synthesis were significantly more sensitive to the growth inhibiting effects of metformin than tumor cells containing wild-type K-*ras* with normal cholesterol synthesis. These results are consistent with expected modulation of AMPK [1] and/or mTOR [128].

down-regulation of lipid membrane rafts and c) down-regulate susceptibility of tumor cells containing more unsaturated fatty acids to TNF-induced free radical attack [113]. Nevertheless, knockdown of human SCD in transformed human fibroblasts resulted in decreased oleic acid synthesis, lowered desaturation index profiles of the main polar lipids phosphatidylcholine and phosphatidylethanolamine, and decreased *de novo* synthesis of 14C-labeled phospholipids, cholesterol and cholesterol esters, free fatty acids and triacylglycerols [114]. Interestingly, there was an inhibition in cellular proliferation and anchorage-independent growth in the SCD knockdown cells [114]. Other studies confirm that SCD inhibition by chemical or genetic manipulations resulted in inhibition of cancer cell proliferation and/or death [115-118]. Thus, SCD appears to be involved in modulating lipid metabolism and signaling processes crucial for cancer cell replication and anchorage-independent growth, effects are likely influenced by

174 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

In support of the anti-lipogenic effects of metformin, Bhalla and others [2] have reported that metformin decreased the gene and protein expression of enzymes involved in fatty acid synthesis namely, ACC, FAS and ACLY which was accompanied by a reduction in hepatic triglycerides in a mouse model of hepatocellular cancer fed metformin at a dose of 250 mg/kg

Obesity is a known risk factor for cancers of the pancreas, colon and rectum, esophagus, kidney, prostate, breast, uterus and ovaries [119-123]. In order to recapitulate this condition in the preclinical setting, animal models are fed high energy (HE), high fat (HF) diets to induce the metabolic syndrome and/or obesity. In an *in vivo* model of colon carcinoma by Algire and colleagues [124], metformin (50mg/kg/day for five weeks) significantly decreased tumor volume only in mice fed the HF/HE diet. Of note, these concentrations are more likely to be physiologically relevant to what a diabetic patient would have in their system. They also found that metformin reduced the expression of SREBP-1 and one of its target enzymes, FAS, regardless of the type of diet. Interestingly, a previous study from the same group using an *in vivo* model of lung cancer back in 2008 showed that the tumor growth inhibitory impact of metformin is exclusive to the mice under the HF/HE diet [76]. These two studies suggest that metformin may retard cancer growth depending on a particular metabolic state of the organism which in this case, is the abundance of circulating lipids from the HE/HF diet.

We recently found that the *in vitro* response to metformin depends on the level of intracellular cholesterol synthesis of the tumor [125]. We were the first to demonstrate that a physiologically relevant dose of metformin impairs glucose utilization in pancreatic cancer by inhibiting FAS when cholesterol synthesis is limited. Specifically, we found that pancreatic cells that have a K-*ras* mutation and that require *de novo* fatty acid (FA) synthesis for lipids ('lipogenic cells') were unable to synthesize FA from acetyl-CoA in the presence of an inhibitor of cholesterol synthesis and metformin. Our *in vitro* model shows that a physiologically relevant dose of metformin (100 μM) using an acute treatment of 24 h decreases *de novo* lipid synthesis via the FAS pathway in pancreatic adenocarcinoma only when: a) the glucose-derived acetyl-CoA is made available for fatty acid synthesis by inhibition of cholesterol synthesis (addition of exogenous cholesterol) and b) K-*ras* mutation is present [21]. As the fatty acid and cholesterol

the effects of SCD loss on membrane integrity [114].

for 24-36 weeks.


**Table 2.** Ongoing Clinical Trials on Metformin and Pancreatic Cancer

In summary, metformin's anti-cancer properties rest on its ability to impair cancer cell lipogenesis, a critical mechanism by which cancer cells maintain their survival advantage over normal cells. Metformin is able to control lipogenesis through inhibition of the transcription factors SREBP-1 and SREBP-2, inhibition of activities and/or expression of enzymes involved in cholesterol and fatty acid synthesis. We have shown that metformin's anti-cancer role is effective in select metabolic phenotype and likely, a particulate cancer genotype. Thus, it is important to understand the metabolic context by which metformin exerts anti-cancer effects so that the correct patient population can be selected for therapeutic purposes.

**Author details**

**References**

Mary Jo Cantoria1,2, Hitendra Patel2,3, Laszlo G. Boros4,5 and Emmanuelle J. Meuillet2,6\*

Metformin and Pancreatic Cancer Metabolism

http://dx.doi.org/10.5772/57432

177

1 Department of Nutritional Sciences, The University of Arizona, Tucson, USA

\*Address all correspondence to: emeuillet@azcc.arizona.edu

2 The University of Arizona Cancer Center, Tucson, AZ, USA

3 College of Medicine, The University of Arizona, Tucson, USA

5 Department of Pediatrics, Los Angeles Biomedical Research, USA

6 Institute at the Harbor-UCLA Medical Center, Torrance, CA, USA

Department of Nutritional Sciences, The University of Arizona, Tucson, USA

[1] Zhou G, Myers R, Li Y, Chen Y, Shen X, Fenyk-Melody J, et al. Role of AMP-activat‐ ed protein kinase in mechanism of metformin action. The Journal of clinical investi‐

[2] Bhalla K, Hwang BJ, Dewi RE, Twaddel W, Goloubeva OG, Wong KK, et al. Metfor‐ min prevents liver tumorigenesis by inhibiting pathways driving hepatic lipogenesis.

[3] Sinnett-Smith J, Kisfalvi K, Kui R, Rozengurt E. Metformin inhibition of mTORC1 ac‐ tivation, DNA synthesis and proliferation in pancreatic cancer cells: dependence on glucose concentration and role of AMPK. Biochemical and biophysical research com‐

[4] Rozengurt E, Sinnett-Smith J, Kisfalvi K. Crosstalk between insulin/insulin-like growth factor-1 receptors and G protein-coupled receptor signaling systems: a novel target for the antidiabetic drug metformin in pancreatic cancer. Clinical cancer re‐ search : an official journal of the American Association for Cancer Research.

[5] Kisfalvi K, Eibl G, Sinnett-Smith J, Rozengurt E. Metformin disrupts crosstalk be‐ tween G protein-coupled receptor and insulin receptor signaling systems and inhib‐

its pancreatic cancer growth. Cancer research. 2009;69(16):6539-45.

4 SiDMAP, LLC, Los Angeles, CA, USA

gation. 2001;108(8):1167-74.

munications. 2013;430(1):352-7.

2010;16(9):2505-11.

Cancer Prev Res (Phila). 2012;5(4):544-52.

## **7. Ongoing clinical trials on metformin as a chemotherapeutic drug for pancreatic cancer**

There is considerable interest in the anti-tumor action of the commonly used anti-diabetic drug metformin for the treatment and management of patients with pancreatic cancer. Enthusiasm for metformin has been significantly strengthened by *in vitro* and *in vivo* experimental findings of potent anti-tumor activity of metformin at therapeutically safe doses. As a result, a number of early phase trials are now being conducted to assess the efficacy of metformin in combination with standard and experimental therapeutics in pancreatic cancer patients. Although there are numerous studies that show the cancer preventive and cancer therapeutic actions of metformin in preclinical models, there is a need to conduct adequately powered clinical trials on the therapeutic effects of metformin that include prognosis and survival markers. At the time that this book chapter was being written, there are six ongoing clinical trials specifically on pancreatic cancer and metformin from the ClinicalTrials.gov website (Table 2).

#### **8. Conclusions and perspectives**

Metformin is an inexpensive and well-tolerated drug and its utility as a chemopreventive and/ or chemotherapeutic agent can be harnessed when we identify the drug's target/s, optimal dosage, and the correct patient sub-population who will benefit from metformin treatment. Until then, metformin remains the most widely prescribed anti-diabetic drug in the world with an unknown mechanism of action. In the era of targeted cancer therapy, one may cautiously link gene mutations and oncogenes up and down-regulation to cancer and involve metabolic phenotyping of the patient for better selection and truly personalized medicine.

## **Acknowledgements**

MJC was supported by the USDA National Needs Fellowship training grant (Grant 2010-38420-20369). Part of the work presented in this chapter was supported by the Hirshberg Foundation for Pancreatic Cancer Research to EJM and LGB.

## **Author details**

In summary, metformin's anti-cancer properties rest on its ability to impair cancer cell lipogenesis, a critical mechanism by which cancer cells maintain their survival advantage over normal cells. Metformin is able to control lipogenesis through inhibition of the transcription factors SREBP-1 and SREBP-2, inhibition of activities and/or expression of enzymes involved in cholesterol and fatty acid synthesis. We have shown that metformin's anti-cancer role is effective in select metabolic phenotype and likely, a particulate cancer genotype. Thus, it is important to understand the metabolic context by which metformin exerts anti-cancer effects

176 Pancreatic Cancer - Insights into Molecular Mechanisms and Novel Approaches to Early Detection and Treatment

so that the correct patient population can be selected for therapeutic purposes.

pancreatic cancer and metformin from the ClinicalTrials.gov website (Table 2).

phenotyping of the patient for better selection and truly personalized medicine.

Foundation for Pancreatic Cancer Research to EJM and LGB.

**pancreatic cancer**

**8. Conclusions and perspectives**

**Acknowledgements**

**7. Ongoing clinical trials on metformin as a chemotherapeutic drug for**

There is considerable interest in the anti-tumor action of the commonly used anti-diabetic drug metformin for the treatment and management of patients with pancreatic cancer. Enthusiasm for metformin has been significantly strengthened by *in vitro* and *in vivo* experimental findings of potent anti-tumor activity of metformin at therapeutically safe doses. As a result, a number of early phase trials are now being conducted to assess the efficacy of metformin in combination with standard and experimental therapeutics in pancreatic cancer patients. Although there are numerous studies that show the cancer preventive and cancer therapeutic actions of metformin in preclinical models, there is a need to conduct adequately powered clinical trials on the therapeutic effects of metformin that include prognosis and survival markers. At the time that this book chapter was being written, there are six ongoing clinical trials specifically on

Metformin is an inexpensive and well-tolerated drug and its utility as a chemopreventive and/ or chemotherapeutic agent can be harnessed when we identify the drug's target/s, optimal dosage, and the correct patient sub-population who will benefit from metformin treatment. Until then, metformin remains the most widely prescribed anti-diabetic drug in the world with an unknown mechanism of action. In the era of targeted cancer therapy, one may cautiously link gene mutations and oncogenes up and down-regulation to cancer and involve metabolic

MJC was supported by the USDA National Needs Fellowship training grant (Grant 2010-38420-20369). Part of the work presented in this chapter was supported by the Hirshberg Mary Jo Cantoria1,2, Hitendra Patel2,3, Laszlo G. Boros4,5 and Emmanuelle J. Meuillet2,6\*


4 SiDMAP, LLC, Los Angeles, CA, USA

5 Department of Pediatrics, Los Angeles Biomedical Research, USA

6 Institute at the Harbor-UCLA Medical Center, Torrance, CA, USA

Department of Nutritional Sciences, The University of Arizona, Tucson, USA

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