**1. Introduction**

Oral cancer is a type of head and neck cancer (HNC), which encompasses a wide range of tumour types that arise from a variety of anatomic structures, including the oral cavity, oropharynx, larynx, hypopharynx, and nasopharynx. Squamous cell carcinomas (OSCCs) account for over 90% of these malignancies histopathologically, with over 50% occurring in the oral cavity [1]. Tobacco usage (smoked or chewed), arecanut, excessive alcohol use, and/or human papillomavirus (HPV) infection are the most major and well-established risk factors associated with this neoplasm [2–4].

Cancer is a major public health concern around the world. According to the International Agency for Research on Cancer's GLOBOCAN project, there were approximately 14.1 million newly diagnosed cancer cases and 8.2 million deaths worldwide in 2012.Oral cancer is one of the most common cancers worldwide, accounting for 2% of all cancer cases and having a nearly 50% mortality rate [5]. Oral and pharyngeal tumours are the sixth most common cancer worldwide [6]. Internationally, South Asian countries such as Sri Lanka, India, and Taiwan have the highest rates of oral cancer, which can be attributed to high rates of cigarette smoking and areca nut use in these countries [7].

Despite technological advancements and improvements in OSCC diagnosis and treatment modalities, the 5-year survival rate remains low, hovering around 50–60%, ranging from 80% for stage I cancers to 40% for stage IV cancers. This disparity can be explained by the delay in diagnosis as well as the relatively high tumour recurrence rates found in these patients. In general, only one-third of OSCC patients have the disease in its early stages at the time of diagnosis (I and II) [8].

Treatment strategies for OSCC differ depending on the stage of the disease at the time of diagnosis. Patients with localised disease are typically treated with surgery and/or radiotherapy, which results in a high chance of long-term survival but significant morbidity. Chemotherapy and radiotherapy and recently immunotherapy are the mainstays of treatment for metastatic OSCC [9]. Despite advances in understanding the pathobiological mechanisms of OSCC, the prognosis has not improved over the last few decades. This is largely due to the high morbidity and mortality rates associated with local and regional OSCC recurrences. The clinical challenge remains in detecting regional metastasis accurately and efficiently treating second primary OSCC and recurrent tumours [10].

The practise of medicine is still primarily empirical today, with doctors relying on pattern matching to make diagnoses based on a patient's medical history, physical examination, and test data. As a result, a prescribed treatment is frequently dependent on a physician's previous experience with patients with comparable symptoms. As a result, the best drug may be given for a "typical patient" with a certain condition. The treatment decisions are made through trial and error, and patients may experience unforeseen adverse effects or poor or no efficacy for a medicine that theoretically works in some people with that disease [11].

Traditional therapeutic procedures have a poor prognosis and are associated with negative side effects. Immunotherapy adoption has moved the field of cancer treatment toward the concept of precision and personalised medicine (PPM), which tailors' treatments to each individual. For cancer treatment, there are two options: the traditional approach and the PPM model. The fundamental distinctions between the classic cancer treatment approach and the developing precision and personalised medicine (PPM) concept are compared. Traditionally, cancer has been treated with "one-size-fits-all" treatments including chemotherapy, radiation, and surgical tumour removal. These treatments have a wide range of efficacy in different people, and they frequently destroy healthy, noncancerous organs and tissues. Individualised therapy customised to specific tissues, gene alterations, and personal characteristics relevant to each unique case of cancer characterise the PPM approach [12].

This article discusses the role of precision medicine in OC prevention, detection, and management by reviewing our understanding of OC from both genetic and OMICS perspectives.

## **2. Why personalised precision medicine (PPM)**

Traditionally Surgery, Radiotherapy and chemotherapy have been used in the treatment of OC. Some people will only need one treatment, but most people will need a combination of medicines to combat cancer's resistance. When there are solid tumours that have not metastasized and are in easily accessible places of the

#### *Personalised Precision Medicine - A Novel Approach for Oral Cancer Management DOI: http://dx.doi.org/10.5772/intechopen.99558*

body, surgery can be utilised; nevertheless, many cancers do metastasis, necessitating more harsh therapies such as radiotherapy and chemotherapy. High doses of radiation and medicines are used in these methods to kill cancer cells and shrink tumours, but they often inflict additional damage to healthy cells [13]. It is stated that the given class of cancer medications is projected to be useless in up to 75% of patients The success of these treatments is influenced by a variety of factors, including the type, stage, and location of the cancer, as well as the patient's age and overall condition. This shows that before choosing a cancer treatment, various personal aspects should be examined [14]. Over the last decade, it has been increasingly obvious that no two patients' malignancies are exactly the same, and therefore generic treatments like chemotherapy and radiation may have varying outcomes. This standard cancer treatment strategy is extremely simple, resulting in ineffective, expensive treatments and unwanted side effects for patients [15]. It is well understood that a treatment's response varies across the variability of a population, including good and poor responders. Variables such as genetic predisposition, cohort heterogeneity, ethnicity, slow vs. quick metabolizers, epigenetic factors, and early vs. late stage of disease affect patient and therapy response. These variables influence whether or not a person will respond well to a certain treatment [11].

Immunotherapy, which uses a patient's own immune system to combat cancer, is another type of cancer treatment that has cleared the way for more specific and successful treatments. Monoclonal antibodies (mAbs), checkpoint inhibitors, cytokines, vaccinations, and adoptive cell transfer, most notably in the form of haematopoietic stem cell transplants (HSCTs) and chimeric antigen receptor (CAR) T-cell therapies, are examples of immunotherapy treatments [16]. Targeted therapeutics, such as cetuximab (monoclonal epidermal growth factor receptor [EGFR] antibody), bevacizumab (monoclonal vascular endothelial growth factor [VEGF] antibody), and mechanistic target of rapamycin (mTOR) inhibitors, have recently been introduced into treatment regimens or ongoing clinical trials to improve survival rate and reduce toxicity. With the advancement of immunotherapy, the Food and Drug Administration (FDA) has approved monoclonal antibodies that target programmed cell death protein-1 (PD-1), a receptor of the immune escape pathway, such as nivolumab and pembrolizumab, for recurrent and/or metastatic head and neck SCC [9]. Immunotherapy adoption has moved the field of cancer treatment toward the concept of personalised precision medicine (PPM), which tailors' treatments to each individual.

The purpose of PPM is to allow doctors to forecast the best course of action for a patient promptly, effectively, and precisely. Clinicians will require tools that are both compatible with their clinical workflow and cost-effective in order to achieve this. These techniques can make managing the biological complexity that underpins human diseases a lot easier. A PPM ecosystem is under constant development to enable the creation and refining of such tools, and it is the solution to the problem. Precision medicine emphasises the need of combining established clinical indicators with molecular profiling to provide diagnostic, prognostic, and therapeutic techniques tailored to the individual needs of each patient group. For the optimal utilisation of the PM ecosystem, accurate data interpretation is required. The PM ecosystem brings together omics and clinical data to solve problems [11].

A move from empirical treatment to PPM is now possible thanks to increased usage of Biomarkers and companion diagnostics (CDX) (the right medicine, for the right patient, at the right dose, at the right time) [17]. PPM is a more effective model that is ready to disrupt the "one size fits all" approach. Based on the measurement and manipulation of essential patient genetic and omic data, this perspective

promotes the creation of customised treatments for each individual subtype of cancer (transcriptomics, metabolomics, proteomics, etc.) [12].

Based on the definition provided by the National Cancer Institute, Personalised Precision Medicine, (PPM) is "an approach to patient care that allows doctors to select treatments that are most likely to help patients based on a genetic understanding of their disease."

#### **2.1 The PPM method and its use to cancer therapy**

Patients with a cancer are enrolled randomly to prevent bias in traditional drug development, employing a "all comers" method with the assumption that the enrolled patients are nearly homogeneous. The purpose of random enrollment is to guarantee that the general population is well represented. In practise, we never conduct clinical trials on patients who are randomly chosen; instead, we apply various forms of enrichments to patients' enrolment by using particular inclusion and exclusion criteria. Despite all of the efforts to enrich the community, the population that is ultimately chosen for the study can be quite diverse in terms of drug-metabolising capacity, environmental factors (e.g., nutrition, smoking habits, lifestyle, etc.), prior medication(s) exposure, and an individual's genetic and epigenetic make-up are all factors to consider. BMs are being used to better define molecular, genetic, and environmental changes. Drug developers have been studying the epigenetic makeup of patients and attempting to take a more objective stance.

Patient stratification is used to distinguish between likely responders and non-responders. When compared to randomly selected individuals, prospective stratification can result in a smaller and shorter clinical study. At a bare minimum, stratification can expedite approval for medication candidates targeted at a subset of patients while providing room for additional testing and market development in the more heterogeneous patient group. In the best-case scenario, it can reveal an effective therapeutic agent that would otherwise be lost in the noise generated by non-responders, as was the case with trastuzumab and gefitinib. This will not only decrease the duration of the clinical trial but will also be cost effective [18].

Scientists were able to read and understand an individual's genetic code, as well as detect hereditary predispositions to particular diseases, when the Human Genome Project (HGP) was completed. This watershed moment shifted the focus of health care from reactive to proactive. Scientists are currently striving to gain a detailed understanding of the body's function at numerous omics levels, as well as characterise how environmental exposures alter genetic predispositions. When all of this data is combined, scientists and doctors will be able to better anticipate how patients will respond to a particular treatment. CDx assays patients for genetic features that determine whether or not they will respond to a specific medication. This technique has the potential to have a significant impact on the patient's care. The transformation from a clinician choosing a generic medicine that is more or less experimental for the patient to one that effectively addresses the disease with PPM is the revolution [12].

#### **2.2 Steps in PPM**


*Personalised Precision Medicine - A Novel Approach for Oral Cancer Management DOI: http://dx.doi.org/10.5772/intechopen.99558*
