**7. Conclusion**

APC reserves unpredictable mechanisms to maintain a highly resistant phenotype. The genetic and epigenetic alterations of the APC lead to the resistance of the chemotherapy.

Nowadays, many biomarkers have been on board to improve the clinical treatment outcome of advanced pancreatic cancer. Although these successful biomarkers have provided notable therapeutic effects on advanced pancreatic cancer, the outcomes remain unsatisfactory to the patients and health providers. With the development of the biology of advanced pancreatic cancer, we now expect better biomarkers and conduct therapy by unveiling the tumor microenvironment and the mechanism of the mutations (**Figure 1**).

We can assume that with the development of truly effective treatments and clinically useful markers for early detection of the disease, better combination of markers to advanced pancreatic cancer. In the meanwhile, researchers are trying to detect magnificently predictive biomarkers to decide the treatment strategy and permit practitioners to adequately evaluate and propose individualized treatment protocols which would give a greater survival rate.

Clearly, there is a need to better understand the underlying signaling networks that drive pancreatic cancer progression and potential escape mechanisms. In addition, it is necessary to improve the role of preclinical models of pancreatic cancer

**Figure 1.** *Difficulties in decision making both for R&D and doctors.*

the expression of non-mutant KRAS and have achieved some success in preclinical

**Year Author N M F Drug OS PFS Biomarkers** 2011 Kindler [43] 314 191 123 Gemcitabine; AG-013736 8.5 4.4 VEGF

2013 Rougier [45] 275 157 118 Placebo; gemcitabine 7.8 3.7 VEGF

2013 Wu [61] 30 16 14 Etanercept; gemcitabine 5.43 0.3 TNF-α

2014 Propper [58] 104 59 45 Erlotinib 4.0 1.5 EGFR

2014 Infante [62] 80 39 41 Gemcitabine; GSK1120212 8.4 16.1 cfDNA

2014 Wolpin [63] 10 5 5 Hydroxychloroquine 400 mg 1.8 1.8 LC3-II

53 31 22 Gemcitabine hydrochloride;

2017 Chung [65] 62 22 40 Fluorouracil; oxaliplatin 6.7 2.0 KRAS

2017 Faivre [66] 86 42 44 Sunitinib malate 38.6 12.6 VEGF;

2017 Ko [67] 66 38 28 OGX-427 6.9 3.8 Hsp27

2017 Laquente [68] 65 42 23 LY2603618; gemcitabine 7.8 3.5 CHK1

85 40 45 Placebo 29.1 5.8

66 37 29 Placebo 5.3 2.7

34 20 14 Gemcitabine 8.3 5.6

44 27 17 Gemcitabine; pimasertib 7.3 3.7

44 22 22 Gemcitabine; placebo 7.6 2.8 ERK 1/2

2012 Ko [44] 29 18 11 Cetuximab; bevacizumab

*Current Cancer Treatment*

2015 Catenacci [64] 53 27 26 Gemcitabine hydrochloride;

2016 Noonan [46] 36 22 14 wild-type reovirus; carboplatin;

316 188 128 Gemcitabine; placebo 8.3 4.4

gemcitabine

29 14 15 Cetuximab; bevacizumab 3.55 1.91

271 160 111 Aflibercept; gemcitabine 6.5 3.7

8 3 5 Gemcitabine 8.1 1.8

103 59 44 Placebo 3.1 1.5

80 46 34 Placebo; gemcitabine 6.7 15.1

10 6 4 Hydroxychloroquine 600 mg 3.0 1.6

Placebo

vismodegib

paclitaxel

IL-8 37 19 18 Carboplatin; paclitaxel 8.77 5.2

protein 58 35 23 Akt inhibitor MK2206; selumetinib

5.41 4.17 EGFR

6.1 2.5 SHH

7.31 4.94 VEGF;

IL-6;

PDGF

6.9 4.0

3.9 1.9

VEGF

In recent years, many studies have suggested that the oncogene KRAS plays a major role in controlling cancer metabolism by coordinating multiple metabolic changes [71]. Furthermore, combined inhibition of therapeutic effects and feedback pathways is promising in KRAS mutant cancers. Moreover, it is unclear what spe-

cific pathways should be used to optimize treatment response [72].

trials [70].

**136**

**Table 4.**

2018 Van Cutsem [69]

*Clinical trial failed outcome on pancreatic cancer.*

and the optimal transformation of preclinical success into experimental design. The genetic and proteomic technologies show great potential to detect the novel biomarkers in cancer research. We place great expectations on these technologies to personalize treatment for advanced pancreatic cancer patients.
