*2.3.1.1 Biomarkers*

Predictive BM for immunotherapy differs from typical BM used for targeted therapies in the case of cancer immunotherapy. Because of the complexity of the tumour microenvironment (TME), immune response, and molecular profiling, a more holistic approach is required than using a single analyte BM [3]. To address this issue, researchers have developed a multiplexing strategy, in which numerous BMs are used to provide more precise patient stratification. Histological analysis now includes concomitant analysis of immuno-oncology BMs, such as PD-L1 and immune cell infiltrates (**Figure 1**), as well as more comprehensive immune and tumour-related pathways (**Figure 2**) (the "Cancer Immunogram"). Multiplexed immunoprofiling, which generates a comprehensive biomarker collection that may be associated with clinical parameters, is critical for the effectiveness of PM in cancer immunotherapy [21, 22].

A specific gene or mutation must be linked to a clinical result before a PPM treatment can be created and utilised in patients. This is a significant endeavour; discovering a therapeutically relevant phenotype or polymorphism might take years of research involving many scientists. Furthermore, determining which polymorphisms cause patients to have a good or negative therapy response necessitates additional research. Sequencing DNA from a large number of people is the first step in deciphering the genetic code. This phase is becoming easier with the improvement of sequencing technologies. The most difficult issues are in interpreting these massive data sets, which is where bioinformatics comes in.

#### **Figure 1.**

*Critical checkpoints for host and tumour profiling. A multiplexed biomarker approach is highly integrative and includes both tumour- and immune-related parameters assessed with both molecular and image-based methods for individualised prediction of immunotherapy response. Byassessing patient samples continuously one can collect a dynamic data on tissue-based parameters, such as immune cell infiltration and expressionof immune checkpoints, and pathology methods. These parameters are equally suited for data integration with molecular parameters. TILs: Tumour-infiltrating lymphocytes. PD-L1: Programmed cell death-ligand 1. Immunoscore: A prognostic tool for quantification of in situ immune cell infiltrates. Immunocompetence: body's ability to produce a normal immune response following exposure to an antigen (tumour drawing has been adapted from [19].*

**Figure 2.**

*The cancer immunogram. The schema depicts the seven parameters that characterise aspects of cancer-immune interactions for which biomarkers have been identified or are plausible. Italics represent those potential biomarkers for the different parameters (adapted from [20]).*

Without the enormous achievement of sequencing the human genome, the discipline of PPM would not exist. From 1990 until 2003, the HGP took 13 years to complete. The International Human Genome Sequencing Consortium (IHGSC), which includes over 200 collaborators from 19 nations, was tasked with discovering new knowledge regarding the structure and organisation of the genome. The human genome has around 20,500 genes, and any two persons share 99.99 percent of their genome, implying that genetic individuality can be identified only in the remaining 0.01 percent. Long repeat sequences were also discovered in the genome, and single-base changes (single nucleotide polymorphisms [SNPs]) were found to have the potential to be distinct disease indicators. The use of bacterial artificial chromosomes (BAC) and Sanger sequencing aided in the early data collection. BAC vectors helped with the first stage of genome sequencing by determining the chromosomal location of DNA fragments recovered from a sample. Sanger sequencing, on the other hand, allowed for exact base-by-base identification of a DNA fragment. These approaches were expensive and inefficient, despite their importance in early sequencing attempts [23]. Next Generation Sequencing Technologies (NGSTs)23 have evolved as a result of years of research and development to solve these difficulties. NGSTs are a cost-effective addition to the BAC and Sanger sequencing technologies, allowing for high-dimensional and parallel sequencing [24].

Whole-genome sequencing and whole-exome sequencing are examples of genomics-related technology. There are a variety of commercial technologies for detecting gene mutations, SNPs, and copy number changes. The Cancer Genome Atlas (TCGA) is a joint project of the National Cancer Institute (NCI) and the National Human Genome Research Institute that began in 2005. In thirty-three

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

kinds of cancer, including head and neck SCC, the TCGA has created complete, multidimensional maps of important genetic alterations. Oral and oropharyngeal SCC has two different subgroups, according to thorough genetic profiling: HPVnegative cancers that commonly develop in the oral cavity and lips; and HPV negative cancers of the oral cavity and lips in particular. The molecular changes in these two subgroups of SCC correspond to their clinical behaviour and patient prognosis. The TCGA database demonstrated that the vast majority of HPV negative OSCCs have TP53 loss-of-function mutations and CDKN2A inactivation, which is consistent with previous findings. In addition, HPV negative OSCC showed a high amount of heterogeneity, according to integrated genomic analysis [25, 26]. Whole-exome sequencing, a transcriptomics approach for sequencing all of a genome's expressed genes, revealed new mutations that had been missed in prior studies (known as the exome. NOTCH1 mutations were found in around 15% of the patients, while mutations and focal copies of NOTCH1 were found in about 15% of the cases. NOTCH1 mutations were found in about 15% of cases, and NOTCH2/3 mutations and localised copy-number changes were found in another 11% of OSCC cases [27, 28].

OSCC's incredible diversity exemplifies how precision medicine may actually help patients and enhance medical care. The Pan Cancer Analysis of Whole Genomes project (PCAWG) is now steered to reveal noncoding driver mutations, such as alternative promoter usage, splicing, expression, editing, fusion, allele specific expression, and nonsynonymous variants, as it progresses from whole-exome sequencing to whole-genome sequencing [29]. MiRNAs and long noncoding RNAs (lncRNAs) are two types of noncoding transcripts. These noncoding transcripts, including miRNAs and long noncoding RNAs (lncRNAs), have a lot of potential for clinical research [30, 31].

#### *2.3.1.2 Omics*

While genomic data is crucial for establishing a full understanding of disease progression and therapeutic effects in physiological systems, intermediate omics levels such as the transcriptome, proteome, and metabolome are used to bridge the gap between genotypic effect and phenotypic event.

#### *2.3.1.3 Transcriptomics*

The transcriptome is the total mRNA within an individual or sample. Microarray and RNA sequencing (RNA-Seq) are two modern high-throughput sequencing approaches for collecting transcriptome data. Microarray analysis measures the amount of hybridization between a sample and corresponding probe to determine mRNA expression. The quantity of fluorescence seen within each well of the array corresponding to a given probe indicates the abundance of gene expression within a sample. Microarray analysis is constrained by the fact that designing probes requires prior knowledge of the gene's sequence. This approach is similar to Sanger sequencing in that it determines the mRNA sequence by adding fluorescently tagged nucleotide bases one by one. During each loop, fluorescent pictures are recorded, and their analysis shows the exact sequence as well as its expression level. Microarray analysis takes less time to prepare samples than RNA-Seq, although RNA-Seq does not require prior knowledge of gene sequences and may handle fewer samples. Both technologies have tremendous throughput capacities, but microarray has a higher cost-value at the moment [32, 33].

Genomic profiling enables modern medication development, which often includes either microarray analysis or RNA-Seq for transcriptome profiling. Both microarray and RNA-Seq analyses allow for the identification of disease phenotype and medication effect within a system (single cell or bigger), which is crucial for the development of genome-specific therapeutics. Although RNA-Seq looks to be more advantageous for discovering novel genomic medication effects and disease characteristics, microarray analyses are less expensive and have more standardised techniques. In general, RNA-Seq results are better for clinical research since they have a lower signal-to-noise ratio than microarray results. Furthermore, as compared to microarray approaches, RNA-Seq results can be obtained from smaller sample quantities — nanogram versus microgram masses, respectively. As NGSTs become more widely used in clinical diagnostics, RNA-Seq methods are expected to become more standardised, eventually replacing microarray diagnostics [33, 34].

With transcriptomics technology, extensive attempts have been made to define OSCC at the molecular level. Reliable biomarkers are necessary to ease the prediction of clinical outcome and evaluate therapy efficacy in order to optimise therapeutic regimens for the management of OSCC. Dysregulation of several pathways (e.g., mRNA processing, cytoskeletal organisation, metabolic processes, cell cycle regulation, and apoptosis) was discovered when assessing a cohort of OSCC transcriptomes [35]. OSCC has also been recommended for molecular characterisation, similar to lung SCC [36]. Dysregulation of the KEAP1/ NFE2L2 oxidative stress pathway is one of the signalling pathways that has been impacted, SOX2 and TP63 lineage markers, as well as PIK3CA and EGFR mutations, were used differently. Different activation patterns of the EGFR pathway are linked to clinically diverse behaviours [37]. A molecular signature has also been proposed to help with OSCC treatment planning by predicting the existence of lymph node metastases using the primary tumour at the time of diagnosis [38]. Furthermore, microarray results demonstrated BGH3, MMP9, and PDIA3 upregulation in more than 80% of OSCC tumours, implying the relevance of ECM-cell receptor interactions in OSCC progression [39]. These transcriptional markers may be useful in the development of customised therapy regimens for the treatment of OSCC in the future.

#### *2.3.1.4 Proteomics*

The term "proteomics" refers to the process of identifying and cataloguing all proteins in a biological system, as well as their relationships. Protein structure, quantities, and cellular localizations, protein–protein interactions, and protein production and breakdown rates are all revealed by proteomic analysis. This data is utilised to figure out how the proteome changes throughout various biological activities and to spot disease patterns. Data on post-transcriptional alterations, or the quantity of proteins in a tissue, may be useful for illness diagnosis, progression, and treatment in the case of PPM. Mass spectrometry (MS) has been the primary instrument for gathering proteomic data for the past two decades, particularly to assess protein expression, identify protein modification sites, and analyse protein– protein interactions [40, 41].

The cellular abundance of proteins is primarily controlled by the quantity of translation, according to a landmark study published in 2011 that measured absolute mRNA and protein abundance and turnover using parallel metabolic pulse labelling [42]. Despite the fact that mRNA and protein levels are related to some extent, genome-wide protein abundance remains an important metric in determining cellular state and function. Intracellular and secreted proteins in body fluid specimens (e.g., serum, plasma, urine, and saliva) can be investigated using high-throughput total and phosphorylated protein analysis [43]. Alterations in protein expression in cell metabolism, adhesion, motility, and signal transduction have been discovered using proteomic analysis combined with in situ hybridization or immunohistochemistry [44, 45]. Promising results have been seen in studies.

Outcomes of salivary or serum proteomics in identifying OSCC and normal samples Analyses with a sensitivity and specificity of up to 90% [46, 47].
