Targeting Cancer Cell Signaling Using Precision Oncology Towards a Holistic Approach to Cancer Therapeutics

Cancer is a complex disease having a number of composite problems to be considered including cancer immune evasion, therapy resistance, and recurrence for a cure. Fundamentally, it remains a genetic disease as diverse aspects of the complexity of tumor growth and cancer development relate to its genetic machinery and require addressing the problems at the level of genome and epigenome. Importantly, patients with the same cancer types respond differently to cancer therapies indicating the need for patient-specific treatment options. Precision oncology is a form of cancer therapy that focuses on the genetic profiling of tumors to identify molecular alterations involved in cancer development for custom-tailored personalized treatment of the deadly disease. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of ongoing technological advances in this regard to assess its scope and importance in the realization of a proper cure for cancer.

1.  Introduction

       Cancer is a devastating disease causing one in six deaths globally with a huge physical, psychological, and economic impact on people affected by the disease. It continues to be the second most common cause of hospital deaths after heart disease, most of which can be prevented by an early diagnosis and improving prevention and treatment strategies for the disease. It requires an efficient diagnosis of cancer, the development of efficacious treatment options, and a better understanding of the socioeconomic factors that affect cancer incidence, prevalence, and related deaths across the globe [1,2]. More than 100 cancer types with sub-types have been determined based on location, cell of origin, and genetic variations that influence cancer development and therapeutic response. Most cancers appear in epithelial cells as carcinomas, such as lung, skin, breast, liver, colon, prostate, and pancreas cancer, whereas sarcomas arise from mesenchymal tissues, originating in myocytes, adipocytes, fibroblasts, and osteoblasts. Tumors also develop frequently in hematopoietic tissues such as leukemia and lymphomaand in the nervous tissues, e.g., gliomas, and neuroblastomas. They are among the most commoncancer types taking a high toll in terms of lives and property throughout the world [3,4]. Thus, considering the vast number of cancer incidences worldwide, a formal initiative towards fightingthe menace of cancer was needed which first appeared in the United States as the National Cancer Act of 1971 signed by President Richard Nixon, for promoting cancer research and application of the outcomes for minimizing cancer incidences and mortality rates associated withthe disease. The act was euphemistically described as the "War on Cancer", and the National Cancer Program that was borne from this initiative resulted in a concerted effort across the length and breadth of the country to develop the infrastructures required for the treatment, cure, and eradication of cancer [5]. A similar approach was adopted by most other developed and developing nations in the following years to combat the deadly disease which has succeeded in satisfying the purpose involved to a good extent since then despite the fact, as feared and as evidence suggests, that demographic factors played a role in cancer development [6,7]. The findings reveal, overall morbidity from cancer has decreased and net survival rates, both short-term and long-term, for all cancers combined have increased substantially in the past decades. The survival rates for cancer types that are responsive to therapy surpass 90% in developed countries, and the prognosis for several other cancer types that were considered the deadliest diseases earlier has improved noticeably in recent years, thanks to the rapid advances realized in clinical oncology over the years. [8.9]. However, the fight against cancer is far from over as an estimation by the WHO in 2018 revealed that cancer incidence would be doubled to approximately 37 million new cases by 2040 with no confirmed remedy for most cancer types in the sight [10,11]. While researchers continue the endeavors to identify the exact causes of different cancer types and subtypes and develop strategies for prevention, diagnosis, and treatment, cancer remains the leading cause of death and has a major impact on societies throughout the world. There are kinds of therapy available for cancer for quite some time such as chemotherapy, immunotherapy, hormonal therapy, targeted drug therapy, radiation therapy, surgery, stem cell transplant, etc. One can receive a single type of treatment or a combination of therapies, but whatever the treatment regimen, it must bring the much-needed cure that remains largely elusive till now [12].

       Rigorous cancer research in the past few decades supported by advances in cell and molecularbiology has led scientists to clearly understand there are genetic changes associated with cancer incidences that cause the disease to grow and spread to other parts of the body. Cancer is initiated as the result of uncontrolled cell division and proliferation leading to tumor formation which culminates in metastasis that involves the dissemination of tumor cells to new sites in the body forming secondary tumors, and is responsible for about 90% of cancer-related deaths in reality. Cell proliferation requires a balanced rate of cell growth and division to maintain the increase in cell numbers for growth and development, maintenance of tissue homoeostasis and wound healing. The fundamental abnormality leading to cancer development is unwanted cell proliferation due to an absence of balance between cell divisions and cell loss through cell death and differentiation. The division relies on cell cycle regulation that generally involves extracellular growth-regulatory signals as well as internal signaling proteins monitoring the genetic integrity of the cell to ascertain that cellular developments go well in time. It depends on progression through distinct phases of the cell cycle-regulated by several cyclin-dependent kinases (CDKs) that act in association with their cyclin partners. Alterations in the overall expression pattern of cyclins lead the cellular process to go awry resulting in tumor formation. Most of the related events in the transformed cells of the tumor and other cellular activities accompanying cancer progression such as angiogenesis, invasion and metastasis are mainly guided by changes in the concerned genes, and the factors that cause these genetic changes often tend to provoke cancerous development [13,14]. Every single gene in the body is likely to have received deleterious changes in its DNA sequence, i.e., mutations on a number of occasions in the cell’s lifetime while the repair mechanism in place would restrict the noticeable changes. In this way, the generation of cancer must be conclusively linked to the sustained gene mutations caused by some external agents called mutagens that often lead to the appearance of different somatic variants or certain changes that might have been inherited in the body. Importantly, a single mutation will not be enough to transform a normal cell into a cancer cell as it would require a number of changes to accumulate in the cells in the course of time for cancerous development to take place. For example, mutations in the most pronounced cancer-causing genes such as RAS or MYC will not lead to unchecked proliferation until the changes in repressor genes that encode components of the protective mechanisms, such as retinoblastoma gene (RB) or the Tumor protein p53 (TP53) gene have not occurred alongside. Thus, multiple genetic changes will ordinarily be required for cancer manifestation to take place and so it must be seen as an evolutionary process involving both genetic change and selection [15]. There can be multiple rate-limiting steps working against the development of cancer along with persistent changes accelerating the process. Thus, most cancers are thought to derive from a single abnormal cell or a small group of cells with a few deleterious gene mutations followed by accumulation of additional changes in some of their descendants allowing them to outgrow others in number resulting in tumorous growth in the body. Moreover, cancer can also be driven by epigenetic changes that alter the gene expression pattern of cells without the accompanying alteration in the cell's DNA sequence [16]. It is observed because of some physical modificationsin chromatin structure capable of influencing the pattern of gene expression often led by DNA methylation, histone modifications, and miRNA-based alterations inside the cell. Epigenetic regulations of DNA and RNA usually control how genes are turned on or off, and so play important roles in maintaining normal cell behavior whose deregulation causes alterations in gene expression patterns to potentially influence tumorigenesis. The changes are frequently accompanied by sustained exposure of the affected cells to a few stressful external stimuli presented by certain environmental factors and/or lifestyle-related changes that may involve nutrition, toxicants, alcohol, etc. Although epigenetic changes will not alter the sequence of DNA, the process might cause point mutations and disable DNA repair mechanisms frequently involved in cancer development. Traditionally, epigenetic and genetic changes have been seen as two separate mechanisms participating independently in carcinogenesis which may is not the whole regarding cancer development. Recent studies from whole-exome sequencing (WES), the technique for sequencing all of the protein-coding regions of genes in a genome, for thousands of human cancers have revealed the presence of many inactivating mutations in genes that can potentially disrupt DNA methylation patterns, histone modifications, and nucleosome positioning and hence control the epigenome to contribute to cancer progression. Thus, the genome and the epigenome could regulate the progression of cancer through mutations. Interferences between the two are therefore anticipated and can be exploited to provide new possibilities for cancer treatment [17].

         Cancer in general remains a multi-step process triggered by mutations leading to the activation of specific oncogenic pathways with the concurrent inactivation of tumor suppressor genes that act as sentinels to control unwanted cell growth and proliferation. Scientists have been trying to analyze the totality of cancer-causing gene mutations regarded as the “mutational landscape’, of different types of cancer types and to target them effectively for cancer cure. Most of these biochemical processes are conserved in model organisms such as the free-living transparent nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster alongsidemice and other large animal models, and are widely used due to the ease of their genetic manipulation to study the complex biology of cancer. The somatic cell mutations, called somatic structural variants (SVs), have been shown to account for more than half of all cancer- causing mutations. These are the variants or mutations different from the hereditary or germline variants that have passed from parents to offspring and become incorporated into the DNA of every cell in the body [18]. The somatic SVs can be noticed in the transformed cells and in their daughter cells that may continue to grow because of errors in DNA copying and their repair mechanisms during cell division thereby altering the genomic structure which will become more numerous with time. Although somatic SVs play a crucial role in cancer development, relatively little has been known about their mode of action in cancer development. Methods to detect and identify the functional effects of these SVs are sure to enable researchers to understand the molecular consequences of individual somatic mutations in cancer. The findings related to the mutation-specific alterations could be used to develop therapies that target the mutated cells, opening new possibilities in cancer therapy. Furthermore, most of the human genome consists of noncoding regions, and studies on variations in the noncoding regions of the cancer cells reveal additional mechanisms underlying cancer progression. For example, changes in noncoding regions such as point mutations and complex genomic rearrangements can disrupt or create transcription factor-binding sites or even affect non-coding RNA loci leaving options for unwanted changes in the gene expression pattern of the cell. Cancer whole-genome sequencing (WGS) remains the most comprehensive method for identifying variants in non-coding regions as targeted approaches like exome sequencing (WES) may miss certain variants residing outside the coding regions [19]. Pieces of evidence suggest oncogenesis typically involves interplay between germline and somatic variants and different modes of action of non-coding variants could further potentiate these developments. Thus, a systematic approach to unraveling the roles of the non-coding genome in cancer progression should help improve cancer diagnosis and therapy [20].

2. Cancer Genomics and the Emergence of Precision Oncology

As a matter of fact, changes in vulnerable genes involved in cell growth, proliferation, death, or differentiation appear to be essential for all the changes in cell behaviors and remain the most fundamental feature of all cancers, so cancer has to be seen as a genetic disease to be treated accordingly for better outcomes. Over the years, technological advances in the field of molecular biology have been exploited to unravel genomic changes to fully understand the pathogenesis of human cancer. The range of cancer-causing mutations is known to be huge, and the mutational landscape differs from one another depending on the type of cancer and even people suffering from the same cancer type are found to have considerably different mutation patterns. Moreover, it has long been observed that every patient responds differently to particular treatments despite having the same type and stage of cancer. These observations have been compelling and led researchers to adopt a precision medicine approach to cancer therapy necessitating the study of genetic features of vulnerable individuals for a patient-specific treatment regimen towards the most effective treatment of cancer [21]. Biometricians since the nineteenth century have been interested in decoding the relationship between genetics and diseases and attempted to understand the roles of "constitutional" and environmental factors in the distribution of diseases. Werner Kalow's 1962 textbook 'Pharmacogenetics' published on the issue of heredity and the response to drugs, emphatically tried to set the agenda of relating the response of therapeutic drugs to their biochemistry and the role of genetics and evolution in shaping individual-level differences in and the idea seems to be of practical use in cancer research. The advances in genetic technologies and consequent understanding of clinically relevant genetic variations over the years are revolutionizing how a range of diseases can be diagnosed and treated in clinics exploiting genetic peculiarities of the individuals and it applies to cancer research adequately. It has been deliberated accordingly in recent years for cancer treatment leading to the emergence of precision oncology as the field of cancer research that takes into account the genetic specificities of the individuals for a possible cure. [22]. The term, precision oncology has been coined for the specific clinical oncology practice that relies upon genomic profiling of individual tumors for a complete molecular characterization of the transformed cells and tissues to identify and target specific molecular alterations for efficient cancer therapy [23]. Thus, precision oncology intends to bring a perfectly planned cancer therapy by designing a custom-tailored treatment regimen for vulnerable individuals by identifying their unique needs for the best possible results. The effectiveness of precision oncology has been tested through progressive clinical trials on different tumor types and recent precision oncology trials that include the NCI-MATCH (Molecular Analysis for Therapy Choice) or the NCI- MPACT (Molecular Profiling-based Assignment of Cancer Therapy) have helped shift the focus from cancer treatment based on type and origin to targeting cancer-specific genetic mutations for cure [24]. The discovery of imatinib for the treatment of chronic myeloid leukemia virtually marks the beginning of precision oncology management. Thus, the good use of precision oncology in clinics began about 25 years ago, but has significantly improved the effectiveness of cancer treatment and is about to enter the mainstream of clinical practice. [25].

3. Molecular Basis of Cellular Reprogramming and Cancer Therapy

The important part of tumorigenesis is that cancers of different tissues utilize somewhat different patterns to finally converge to a common path of cancer development witnessed in the form of tumor growth followed by angiogenesis, invasion, and metastases. All such developments are ultimately guided by genetic and epigenetic changes associated with cancer cells and supported by certain tissue-specific factors that enable the tissue to exploit these changes to its specific needs resulting in reprogramming of the molecular events utilized by different cancer cells, and no gene change is thought to be common to all cancers [27]. Because the realization of uncontrolled cell growth and proliferation remain the most evident cause of cancer, certain alterations in the pattern of cell death and differentiation promoting overall cell survival could further aggravate the gradual transformation of tissue from normal to tumorous and from benign to metastatic. Certain disruptions of the physiologic balance between cell proliferation and cell death prolonging cell survival and proliferation are thought to be an important step in carcinogenesis. Expectedly, observations confirm that the evasion of cell death by apoptosis and autophagy is the hallmark property of most if not all cancers actively contributing to cell growth and proliferation. Apoptosis, the process of programmed cell death, also known as type 1 cell death, is mediated through caspase degradation activated by mitochondria. It is employed for removing damaged cells and is crucial to the early development and overall maintenance of tissue homeostasis. Loss of apoptotic control enables cancer cells to survive longer allowing more time for the accumulation of mutations which can deregulate cell proliferation and differentiation and stimulate angiogenesis and metastasis. Autophagy is the major intracellular degradation system mediated by lysosomes that involve the engulfment of unwanted proteins and damaged organelles in double-membraned vesicles called autophagosomes, for their destruction and recycling. Autophagy can play a protective role in promoting cell survival, but excessive autophagy plays a suppressive role by inducing autophagic cell death, known as type 2 cell death. Autophagy has universally been accepted to play a tumor-suppressive role at the early stage, while defective autophagy is associated with tumorigenesis. Deregulation of these essential catabolic pathways contributes to the development of a tumor andis often involved in promoting invasion and metastasis Cancer cells can develop novel mechanisms for evading apoptosis and autophagy and new discoveries direct toward the possible interrelationship between these two catabolic pathways. Evidence suggests that inhibition of apoptosis causes autophagy, while autophagy inhibition induces apoptosis. It may help the key proteins and intermediates involved with these pathways to be exploited in cancer therapeutics successfully. Furthermore, cancer cells maintaining constant proliferative capacity may be guided by their transformation into everlasting non-senescent cells. In this regard, telomeres are the specific repeating DNA structures found at the ends of the chromosome of the cell, which protect the genome against unnecessary nucleolytic degradation, recombination, repair, and interchromosomal interactions. Telomeres are maintained by telomerase which adds nucleotides to telomeres to keep them from getting shorter. Germ cells typically express high levels of telomerase to maintain telomere length. In somatic cells, telomere length usually decreases with the lapse of time, leading cells to undergo senescence with age. Loss of cells in this way generally acts as a barrier to tumor growth which the transformed cells escape as they maintaintheir telomeres despite repeated cell divisions because these cells are able to express a lot of active telomerase. Telomerase has become a potential target in cancer therapeutics as they are over-expressed in transformed cancer cells and cancer stem cells in diverse forms of malignancies. Telomere maintenance mechanisms (TMM) are used by cancer cells through telomerase activation and sometimes by alternate means called alternative lengthening of telomeres (ALT).to avoid apoptosis. Anti-telomerase therapeutics have been developed to selectively target cancer cells to induce cell death by apoptosis without affecting normal cells [28].

An important feature of cancer is that the population of cells that make up cancer is profoundly heterogeneous at the genetic, and epigenetic levels. Tumors usually represent a heterogeneous mass of distinctly differentiated cells that include connective tissue cells, immune cells, cancer stem cells, and vasculature, and these subpopulations of cells can be further distinguished by a variety of features impacting their phenotype that generally involve genetic alterations. Tumors develop this feature mainly because the cancer genome is unstable due to accumulating numbers of cancer-causing gene mutations. Genomic instability further promotes genetic diversity by providing the raw material for the generation of tumor heterogeneity [29]. There are transposable elements (TEs) present in the cells called 'jumping genes', the repetitive sequences of DNA that move from place to place in the genome by different means. and represent almost half of the human genome. They represent a powerful means of genetic modification and have played an important role in the evolution of genomes. TEs are typically regulated since the early stage of development and throughout the lifespan by epigenetic mechanisms such as DNA methylation and histone modifications and are crucial for maintaining genomic stability through the regulation of transcriptomic and proteomic profiles of the cell. Dysregulation of TEs has been implicated in different types of human cancers, with the possibility of chromosomal aberrations, oncogenic activation, transcriptional dysregulation, and non-coding RNA aberrations as potential mechanisms underlying the development of cancer.

       Further, there are fragile points in every genome where the DNA is more likely to be mutated when the genome is replicated. These breakage points have frequently been linked to genetic and heritable disorders like cancer. Moreover, there can be mutations present in certain genes, known as mutator mutations, that further increase the inherent rate of genomic changes, resulting in even greater genetic instability that leads to the accumulation of multiple oncogenic mutations within a cellular lineage. Not all such changes are "malignant", but the rate of such development could translate into cancer manifestation at different stages in a lifetime. Mutator mutations and genetic instability are generalized concepts in cancer genetics, referred to as mutator hypothesis, that relates to those few mutations that lead to an enhanced rate of the gene mutations leading to chromosomal instability, microsatellite instability, and deregulation of activities related to DNA damage and repair [31]. Furthermore, the gradual accumulation of oxidative damage to critical biomolecules such as DNA, due to persistent metabolic oxidative stress and inflammation also contributes to genomic instability and related diseases, including cancer indicating relevant measures for prevention and cure. This feature of cancer cells has also guided researchers to kill vulnerable cells by inducing lethal genomic instability in the cells through radiation therapy and chemotherapy. It has been a rather nonselective means of killing cancer cells with associated side effects which could be perfected by devising methods to selectively target the affected cells inside the body. Researchers have begun examining the genomic data of vulnerable individualsto allow clinicians to embark on the path of personalized radiation therapy.

     The nuclear factor erythroid 2 (NFE2)-related factor 2 (Nrf2) belongs to CNC (cap‘n’collar) family proteins, a group of basic leucine zipper (bZip) transcription factors encoded by basic leucine zipper (bZIP) genes, which serves as the master regulator of the cellular antioxidant response. Recent studies have revealed many new roles for Nrf2 in the regulation of essential cellular processes through interacting with other pathways within the cells, thus establishing it as a truly pleiotropic transcription factor involved in carcinogenesis. Originally recognized as a target of chemopreventive agents to help prevent cancer, its protective role is found altered in 6- 7% of cancer cases. A growing body of evidence has established the Nrf2 pathway's involvement in the deregulation of cell metabolism, apoptosis, and self‐renewal capacity of cancer stem cells, and thus an important driver of cancer progression, metastasis, and cancer drug resistance [48].

      The insulin-like growth factor receptor (IGF-1R), is an RTK that binds IGF1 with a high affinity and is an important factor in the growth, differentiation, and survival of cells in health and disease. IGF-1R plays an important role in the anchorage-independent growth of cells, which may enable cancer cells to survive and grow in the absence of anchorage to the extracellular matrix (ECM) and the neighboring cells. High gene expression level for IGF-1 and IGF-1R have been associated with the upregulation of pathways supporting cell growth and survival, cell cycle progression, angiogenesis, and metastatic activities during cancer development, and is considered essential in many cancer types [49].

       B-cell lymphoma-2 (Bcl-2) oncoprotein is primarily a cell death regulatory protein that controls whether a cell lives or dies by apoptosis. It is a member of a family of regulatory proteins actively involved in the regulation of cell death by all major pathways, including apoptosis, autophagy, and necrosis, serving at the critical junction of multiple pathways with crucial roles in oncogenesis. An aberrant expression of the BCL2 gene may keep cancer cells from dying and is frequently implicated in prolonged cell survival and therapy resistance in human cancer. The Bcl-2 family proteins form subgroups, one of which may inhibit cell death and prolong cell survival by limiting apoptosis while others induce cell death by inducing apoptosis, autophagy, etc. [50]. The gene for the Bcl-2 protein is found on chromosome 18 but can be transferred to different chromosomes as can be seen in many cancer types. An increased expression of pro-survival proteins or abnormal reduction of death-inducing regulatory proteins, resulting in sharp inhibition of apoptosis and other related catabolic activities are frequently seen in many cancers. Resistance to apoptosis is a key development in several hematological malignancies and has been attributed to the upregulation of pro-survival Bcl-2 proteins. The important role played by Bcl-2 family proteins in cancer development renders them potential targets for the therapy of different cancers, including solid tumors and hematological disorders. Alterations in Bcl-2 activities with concurrent changes in other important regulators such as c- Myc or p53 appear to be great combinations in cancer progression [51]. The recent development of inhibitors of pro-survival Bcl-2 proteins, termed BH3-mimetic drugs may prove to be novel agents for cancer therapy.

        The earliest information regarding the relationship between cancer and growth factors camefrom the observation that normal cells in culture often required serum for proliferation, while cancer cells had a much less requirement for serum. The serum is known for providing growth factors among other ingredients needed for the overall regulation of the cell cycle. The other hints came from gene mutations found in cancer cells observed to cause changes in cell behaviors very similar to those related to the activities of growth factors and their receptors. Theoncogenic mutations disrupt the cellular circuits that control cell adhesion and signaling, enabling cells that carry them to over-proliferate and invade the other tissues in an uncontrolled fashion. Many of these mutations have been directly linked to the growth factors and their receptor proteins involved with tumor growth, angiogenesis, invasion, and metastases [55,56].

     A critically important finding of cell signaling is that one kind of cell membrane receptor can mediate many different downstream intracellular pathways and one pathway can also be activated by several of the upstream surface receptors revealing common signaling components in multiple signaling pathways. For example, the RTKs, like EGFR, FGFR, IGFR, VEGFR, PDGFR, and the GPCRs, can all activate the MAPK cascade while the widely studied RTKs such as EGFR/HER family receptor can initiate different signaling pathways including mitogen- activated protein kinase (MAPK), phosphoinositide-3-kinase (PI3K)/AKT, and mammalian target of rapamycin (mTOR) pathways involved in regulations of cell growth, proliferation, differentiation, and survival. This feature of the signaling process evidently presents the option for crosstalk between components of different signaling pathways at different stages of the cellular process. A molecule participating in crosstalk can affect the activation of alternate signaling pathways, and receptors can also have an altered ability to bind to the ligands which can swiftly lead to cancer manifestation. As generally observed, most of the cell signaling pathways contribute to the development of cancer and seldom does a cancer type arise from the deregulation of a single pathway. Breast cancer can arise due to elevated expression of the estrogen receptor (ER), EGFR/HER, or IGFR, but in many cases, molecules and intermediates of multiple signaling pathways can be interactively involved in the process. In this way, the many signaling molecules affecting cancer cells together could beconsidered to create elaborate integrated circuits within the cell, derived from the usual signaling circuits that operate in normal cells. The transformed intracellular circuit could be divided into distinct subcircuits specialized in specific cellular activities to promote hallmark features of cancer [57] (Fig. 1). Signal transduction leading to tumor growth, cancer cell migration, metastasis, and drug resistance are often complex processes, as cancer cells typically develop abnormalities in multiple signaling pathways or rely on crosstalk between different pathways and some redundant pathways for maintenance of growth and survival.

Figure 1. Intracellular Signaling Networks Regulate the Operations of the Cancer Cell.

An elaborate integrated circuit operates within normal cells and is reprogrammed to regulate hallmark capabilities within cancer cells. Separate sub-circuits, depicted here in differently colored fields, are specialized to orchestrate the various capabilities. At one level, this depiction is simplistic, as there is considerable crosstalk between such sub-circuits. In addition, because each cancer cell is exposed to a complex mixture of signals from its microenvironment, each of these sub-circuits is connected with signals originating from other cells in the tumor microenvironment. (Hanahan and Wienberg [57]. With permission from Elsevier)

6. Integrating Artificial Intelligence (AI) with Multi-Omics in Precision Oncology

Multiomics: High-throughput sequencing technologies, also known as next-generation sequencing (NGS), are a comprehensive term used to describe technologies that sequence DNA and RNA rapidly and cost-effectively. It has revolutionized the field of genetics and molecular biology and aided in the study of biological sciences as never before [100]. Technologies using NGS have been developed that measure some characteristics of a whole family of cellular molecules, such as genes, proteins, or metabolites, and have been named by appending the term "-omics. Multiomics refers to the approach where the data sets of different omics groups are combined during sample analysis to allow scientists to read the more complex and transient molecular changes that underpin the course of disease progression and response to treatment and to select the right drug target for desired results [101]. It forms the basis of precision medicine in general and is at the core of the development of precision oncology. The breakthroughs in high- throughput technologies in recent years have led to the rapid accumulation of large-scale omics cancer data and brought an evolving concept of “big data” in cancer the analysis of which requires huge computational resources with the potential to bring new insights into critical problems. The combination of big data, bioinformatics, and artificial intelligence is thought to lead to notable advances in translational research in cancer [102,103].

Artificial intelligence: Artificial intelligence (AI) encompasses multiple technologies with the common aim of computationally simulating human intelligence to solve complex problems. It is based on the principle that human intelligence can be defined in a way that a machine can easily mimic and execute tasks from the simpler to far more complex ones successfully [104]. Broadly referred to as computer programming enabled to perform specific tasks, the term may be applied to any machine that displays traits associated with human understanding, such as learning and problem-solving. In regular programming, data are processed with well-defined rules to bring solutions, whereas AI relies on the learning process to devise rules for the efficient processing of data to yield smart results. AI and related technologies have increasingly been prevalent in finance, security, and society, and are now being applied to healthcare as well [105]. It has been widely applied in precision medicine-based healthcare practices and is found to be greatly useful in medical oncology practice. Many artificial intelligence algorithms have been developed and applied in cancer research in recent years. An exact understanding of the structure of a protein remains the first step to knowing all about its roles in cancer progression and therapeutic drugs are also designed using structural information of the target proteins where AI-based techniques can be used for the solutions. The advances in NGS have led multi-omics data on cancer to become available to researchers providing them with opportunities to explore the genetic risk and reveal underlying cancer mechanisms to help early diagnosis, exact prognosis, and the discovery, design, and application of specific targeted drugs against cancer. Thus, integrating multi-omics-related studies with artificial intelligence is the need of the hour and is likely to serve the purpose well with time. Taking the help of large datasets from multi-omics platforms, imaging techniques, and biomarkers found and mined by artificial intelligence algorithms, oncologists can diagnose cancer early at its onset and help direct treatment options for individualized cancer therapy for anticipated results. Thus, the advances in AI present an opportunity to perfect the methods of diagnosis and prognosis and develop strategies for personalized treatment using large datasets, and future developments in AI technologies are mostlikely to help many more problems in this direction to be resolved swiftly. In this way, AI is thought to be the future of precision oncology towards the prevention, detection, risk assessment,and treatment of cancer [106,107].

Machine learning: Machine learning (ML) is a branch of artificial intelligence that aims to develop computational systems with advanced analytical capabilities. It is concerned with the development of domain-specific programming algorithms with the ability to learn from data to solve a class of problems [108]. Therefore, the most common and purposeful application of traditional machine learning in healthcare seems to be in the area of precision medicine and is most suited for the data-driven identification of cancer states and designing treatment options that is crucial to precision oncology-based cancer treatment [109].

Deep Learning: Deep learning (DL) is a sub-branch of ML that uses statistics and predictive modeling to extract patterns from large data sets to precisely predict a result. A variety of data have been appearing in modern biomedical research, including electronic health records, imaging, multi-omics-based reports, sensor data, etc., which are complex, heterogeneous, and poorly defined and need to be mined efficiently to bring correct results. To meet this end, DL uses a machine learning program called artificial neural networks modeled on the human brain that forms a diverse family of computational models consisting of many deep data processing layers for automated feature extraction and pattern recognition in large datasets to efficiently answer the problems. The human brain consists of neurons arranged together as a network of nerves processing several pieces of information received from many different sources to translate into a particular reflex action. In DL, the same concept of a network of neurons is imitated on a machine learning platform to emulate human understanding to bring perfect solutions. The neurons are created artificially in a computer system and the data processing layers work together to create an artificial neural network where the working of an artificial neuron could be taken as like that of a neuron present in the brain. Thus, DL is designed to use a complex set of algorithms enabling it to process unstructured data such as documents, images, and text to find efficient results [110].

       The effective development of drugs for the treatment of cancer is a major problem in cancer research and DL provides immense help to researchers in this regard. Changes in the genetic composition of tumors translate into structural changes in cellular subsystems that require to be integrated into drug design to predict therapy response and concurrently learn about the mechanism underlying a particular drug response. A proper understanding of the mechanism of drug action can lead researchers to understand the importance of the different signaling pathways, including some new and uncommon pathways associated with tumors to help develop novel drugs for the therapeutic targeting of diverse forms of cancer. Drug combinations targeting multiple pathways are thought to be the answers to the incidences of drug resistance in cancer therapy where computational models could be used to find solutions. Occupation-oriented pharmacology is the dominant paradigm of drug discovery for the treatment of cancer. It relies on the use of inhibitors that occupy the functional binding site of a protein and can disrupt protein interactions and their functions. New advances in AI have enabled researchers to develop DL-based models to predict tumor cell response to synergistic drug combinations to be employed effectively in precision oncology [111]. Researchers continue to discover proteins that may be the key drivers of cancer and need a fuller understanding of the 3D shape, or structure, of these proteins to decide their exact functions in the cell. A recent development in the DL system is AlphaFold, which is being used to predict the structures of different proteins, and the tool has already determined the structures of around 200 million proteins, from almost every known organism on the planet [112,113]. This revolutionary new development in DL is going to be of great use in understanding the roles of suspected proteins in cancer development and in anticancer drug design. A newly developed DL system called PocketMiner is an efficient tool for predicting the locations of bonding sites on proteins. Proteins exist in a state of dynamic equilibrium with their different conformational structures, including experimentally determined structures that may not have targetable pockets. PocketMiner uses graph neural networks to find hidden areas or pocket formation from a single protein and is thought to be 1,000 times faster than existing methods of finding binding sites on proteins. This technology has made researchers understand that around half of proteins that were earlier considered undruggable might have ‘cryptic pockets’ that could be targeted successfully by anticancer agents. The AI-based system finds multiple uses in cancer management like the prediction of treatment response, estimation of survival analysis, risk estimation, and treatment planning, and is becoming the central approach in precision oncology [114].

7. The Cancer Genome Atlas (TCGA) Program and Related Cancer Initiatives

         The National Institutes of Health (NIH) has taken the lead role in cancer research and is the largest funder of cancer research in the world. The National Cancer Institute (NCI), the leading cancer research enterprise is part of NIH and is committed to exploiting basic cancer research into efficacious cancer therapies. In this regard, the Cancer Genome Atlas (TCGA) Program is the landmark cancer genomics program initiated by the NIH, and has contributed immensely to realizing the importance of genomics in cancer research and treatment in the last decade and has begun to change the way the disease has been treated in the clinic. It is a joint effort by the NCI and the National Human Genome Research Institute (NHGRI), also a part of NIH, that began working in 2006 and has brought together researchers from diverse disciplines and multiple institutions to work on the characterization and analysis of cancer at the molecular level for a complete understanding of the genetic basis of human cancer [115,116]. Considering the genes and pathways affecting different cancer types and individual tumors vary considerably, a complete understanding of these alterations becomes essential to identify vulnerabilities and discover precise therapeutic solutions. A comprehensive analysis of tumors based on their genomic studies must reveal the alterations in signaling pathways indicating patterns of vulnerabilities and the means to identify prospective targets for the development of personalized treatments and new combination therapies. The TCGA ResearchNetwork has profiled and analyzed a large number of human tumors to discover molecular aberrations at the DNA, RNA, protein, and epigenetic levels and thereby provided reliable diagnostic and prognostic biomarkers for different cancer types since then.

       As our understanding of biochemical signaling has grown and the range of possible treatment options expands, it is essentially required to have biomarkers to accurately predict how patients will respond to specific treatment regimens, which is a vital need for precision oncology. Circulating DNA and extracellular vesicles are abundantly released by cancer cells that can be obtained by liquid biopsies and are excellent sources of a variety of molecular markers. Molecular profiling of these markers can be used to gain crucial information regarding cancer development including tumor heterogeneity. Genomic analysis of tumors has certainly become the mainstay in cancer care, and applying it to oncological practice needed a clinical support system that could swiftly predict the clinical implications associated with specific mutations. It led to the development of OncoKB, an expert-guided precision oncology knowledge base developed at Memorial Sloan Kettering Cancer Center (MSKCC), in New York which is among the first to have been recognized as the NCI-Designated Cancer Centers as part of the national cancer program of the federal govt. that started in 1971. OncoKB's curated list of cancer genes with detailed comments is available on its public web resource (http://oncokb.org, which has been incorporated into the cBioPortal for Cancer Genomics (https://www.cbioportal.org/ ) to provide visualization, analysis, and download of large-scale cancer genomics data sets allowing researchers to gain a thorough understanding of the genomic alterations involved in cancer development. The public cBioPortal site is hosted by the Center for Molecular Oncology at MSKCC and maintained by a multi-institutional team consisting of MSK and others. A vast number of mutations contribute to cancer and the use of next-generation sequencing-based approaches in clinical diagnostics is leading to a tremendous increase in data with an enormous number of variants of uncertain significance requiring further analysis and validation by means of precise techniques to fulfill the purpose involved with the big-data studies satisfactorily [117,118].

        Predicting the effects of mutations using in silico tools has become a frequently used approach, but these data cannot be analyzed by simply using traditional tools and techniques that have been available to scientists, but even more advanced computational methods are supposed to be coming to help gain insights into the molecular basis of the origin and evolution of cancer. To meet this end, a cancer hallmark framework through modeling genome sequencing data has been proposed for the systematic identification of representative driver networks to convincingly predict cancer evolution and associated clinical phenotypes [119,120]. It is based on the consideration that possible observable combinations of those mutations must converge to a few hallmark signaling pathways and associated networks responsible for cancer development. In this way, the proposed framework aims to analyze the available data to explain how the different gene mutations in different patients bring the same downstream effects on the protein networks, ultimately leading to the common path of cancer progression and direct treatment planning accordingly. In this regard, researchers funded by the NIH have separately completed a detailed genomic analysis of data available through the TCGA program known as the 'PanCancer Atlas', providing an independent view of the oncogenic processes that contribute to the development of human cancer [121,122]. Analyzing over 11,000 tumors from the most prevalent forms of cancer, and focusing on how germline and somatic variants collaborate in cancer progression, the Pan-Cancer Atlas has so far provided a most comprehensive and in-depth understanding of how and why tumors arise in humans [123,124]. 

      The synchronizing view of oncogenic processes based on PanCancer Atlas analyses tries to elucidate the possible consequences of genome alterations on the different signaling pathways involved with human cancers, also reflecting on their influence on tumor microenvironment and immune cell responses, to provide new insights into the development of new forms of targeted drugs and immunotherapies. Further, the stemness features extracted from transcriptomic and epigenetic data from TCGA tumors also present novel biological and clinical insight for cancer stem cell-targeted therapies [125,126]. The challenge to identify the relevant genes and signaling molecules for different cancer types using cutting-edge technologies will remain an essential part of cancer research and is most likely to help vulnerable people receive precisely designed treatment for cancer. As a singular and unified point of reference, the Pan-Cancer Atlas can be taken as a vital resource to explore the influence of mutation on cancer cell signaling for the development of new treatments in the pursuit of precision oncology.

       Besides that, the Cancer Cell Mapping Initiative (CCMI), originally founded in 2015 by researchers from the University of California, San Francisco, and the University of California, San Diego, has been dedicated to generating complete maps of major protein-based genetic interactions underlying cancer progression and attempts to develop computational methods using these maps to identify novel drug targets and patient groups with common outcomes. It has been successful in charting how hundreds of genetic mutations involved in breast cancer and cancers of the head and neck affect the activity of certain proteins that ultimately lead to cancer progression. As there exists a vast amount of sequence data from many different cancer types, efforts are being made to extract mechanistic insight from the available information, and an integrated computational and experimental strategy will have to be employed to help place these alterations into the context of the higher order signaling mechanisms in cancer cells [127]. This is the defined goal of the CCMI and is likely to create a resource that will be used for cancer genome interpretation, allowing the identification of key complexes and pathways to be studied in greater mechanistic detail to gain insight into the biology underlying different types and stages of cancer [128]. Furthermore, the Broad Institute of MIT and Harvard's Cancer Dependency Map (DepMap)initiative, an academic-industrial partnership program formally announced in 2019, is devoting its research to accelerate precision cancer medicine by creating a comprehensive map of tumor vulnerabilities and identifying key biomarkers of cancer. DeepMap initiative is focused on screening thousands of cancer cell lines by the use of RNA interference (RNAi) and CRISPR- Cas9 loss-of-function gene-editing strategies to identify genes whose expression may have been found to be essential for cancer cell development. CRISPR-Cas9 gene editing is an efficient method for genome modification for nearly all cell types. CRISPR editing and screening have emerged as powerful tools for investigating almost all aspects of cellular behaviors and have greatly impacted our understanding of cancer biology and continue to contribute to new discoveries.

        A related project called, Cancer Cell Line Encyclopedia (CCLE) project was initiated as a collaboration between the Broad Institute, and the Novartis Institutes for Biomedical Research in 2008 aimed at large-scale genetic characterization of thousands of cancer cell lines to link characteristic genetic alterations with distinct pharmacologic vulnerabilities, and to translate cell line integrative genomics into cancer patient stratification. By access to critical genomic data such as gene mutation, copy number variation, gene expression, and methylation profiles from the CCLE, scientists can now predict novel synthetic lethality and identify new molecular markers whose selective targeting can control cells that possess specific genetic mutations. In this way, the initiative has provided a rigorous foundation on which to study genetic variants, and candidate targets, design anticancer agents and identify new markers-driven cancer diagnoses and therapies [129]. By all such means, the field of cancer genomics can be seen as constantly evolving to help cancer-causing changes be identified to gain a better understanding of the molecular basis of cancer growth, metastasis, and drug resistance, and translate cancer research into new cancer therapeutics.

8. Single-cell Technology to Unmask Tumor Heterogeneity

The tumor is an abnormal mass of tissue that appears due to unregulated growth and division ofcells which successfully avoid senescence. A tumor is benign till it is limited to its original position and becomes malignant or cancerous when capable of growing and spreading to other parts ofthe body. Tumor heterogeneity is a hallmark property of cancer development and broadly refersto the differences between tumors of the same type in different patients, the differences betweena primary and a secondary tumor, and the differences in genomic and phenotypic profiles displayed by cells within a single tumor. Heterogeneity within a single tumor, referred to as genetic intratumoral heterogeneity (ITH), has been documented across most cancers as an outcome of genome instability and clonal evolution [130,131]. Tumor heterogeneity appears to be a critical phenomenon in the history of individual cancers, as its translational significance may reflect on tumor progression, disease recurrence, treatment response, and resistance [132]. Recent investigations on drug resistance and tumor heterogeneity have confirmed the clonal organization of tumors as the underlying basis for drug resistance, thus indicating the need to fully understand the structure and dynamics of ITH to develop advanced treatment strategies for cancer [133,134]. More precisely the cellular composition of a tumor is known, the underlying mechanism of disease progression is understood, and/or molecules and pathways involved in the process are identified, and more specific therapeutic strategies could be devised to get the desiredresult. It is the stated goal of precision oncology and the emergence of single-cell technologies for biological analysis has become the crucial tool in this regard as they can carry out accurate single-cell measurements to provide a clear picture of tumor heterogeneity and reveal how structural changes in chromosomes can lead to the complex biological processes involved with carcinogenesis [135,136]. The rapid progress in the development of NGS in recent years has provided many valuable insights into cancer genomics, and NGS-based technologies for genomics, transcriptomics, and epigenomics have enabled laboratories to carry out related single-cell measurements efficiently. Single-cell genomics now facilitates the simultaneous measurement of thousands of genes in thousands of ‘single’ cells from a single specimen, allowing researchers to compare genomes of individual cells to determine the mutational profile of the affected cells to better understand the molecular consequences of different variants presentin the tumor. The single-cell template strand sequencing (Strand-seq), a special single-cell sequencing technology now enables independent and efficient analysis of the two parental DNA strands resolving homologous chromosomes similar in shape and structure but not identical within single cells which is crucial to identifying somatic SVs, understanding genomic rearrangements and unmask tissue heterogeneity. Moreover, single-cell sequencing can also be combined with CRISPR knockout screening that exploits the efficiency and flexibility of CRISPR–Cas9 genome editing to enable large-scale studies regarding how genetic modification can affect cell behavior or gain insights into a specific physiological condition required to fully understand the underlying cellular events [137]. Combining the CRISPR-Cas system with single-cell techniques for studying gene functions with the concurrent use of single-cell resolution techniques, such as flow cytometry, microfluidics, manual cell picking, or micromanipulation, can be exploited in cancer research in many ways, including identifying novel drug targets, studying unknown mechanisms of action of drugs and designing treatment regimen [138].

Therapeutic targeting of DNA damage response (DDR) signaling is another emerging field of targeted cancer therapy that exploits the options of targeting cancer cells with exceeding deficiencies in homologous recombination (HR) signaling which includes BRCA-mutated cancers. Poly(ADP-ribose) polymerase (PARP) and Inhibitors of poly(ADP- ribose)glycohydrolase (PARG) are the most important DNA repair enzymes that work synergistically in many different DDR pathways, including base excision repair, non- homologous end joining, nucleotide excision repair, homologous recombination (HR), maintenance of replication fork stability and nucleosome remodeling. These enzymes are essentially involved in the process of single-strand break (SSB) repair whose failure leads to the conversion of SSB into double-strand breaks (DSB) requiring repair by HR to prevent cell death. Such lethal genetic interactions, known as synthetic lethality, can be exploited to develop anticancer therapeutics and the enzymes of DDR signaling fit the needs satisfactorily. Overexpression of these proteins has been witnessed in different cancer types such as pancreatic, prostate, breast, ovarian, and oral cancers, providing scope for inhibiting PARP activity as an effective therapeutic strategy. PARP and PARG inhibitors have shown improved results in different forms of tumors, and are under investigation for being used in combination therapy safely. [150,151].

       The therapeutic mAbs are modified monoclonal antibodies that target antigens found on the cancer cells or cytotoxic T-lymphocytes in targeted cancer therapy. mAbs are important in cancer treatment as they may be exploited for potentiating the natural immune system by successfully mutualizing changes in immunogenicity of the affected cells during oncogenesis. The mAbs may be designed to coat the cancer cells to be opsonized and destroyed by the immune cell, block the activity of different cancer-specific antigens called neoantigens, generated by cancer cells, or inhibit the activities of immune checkpoint proteins that promote immune evasion in cancer development [152,153]. Several immune checkpoint proteins are expressed by immune cells, such as T cells, and cancer cells capable of binding with other partner proteins to help cancer cells escape immune responses. Their activation limits vital immune cell activities like T-cell infiltration and other effector cell functions resulting in tumor formation. CTLA-4 is a checkpoint protein present on the T-cell surface that binds to another protein called B7, preventing T cells from killing other target cells, including cancer cells. Certain mAbs, also called anti-CTLA4 monoclonal antibodies, are used to block CTLA-4 and are widely used as immune checkpoint inhibitors in a variety of human cancers. Different forms of monoclonal antibody-based therapy have proven to be efficacious in cancer treatment and are becoming increasingly important tools in targeted cancer therapy [154,155]. Importantly, cancer cells express a number of protein antigens that can be recognized by cytotoxic T lymphocyte (CTL) T cells, thus providing means for CTL-mediated cancer therapy. Targeting transformed cells by CTL may be crucial to the prevention of both hematological and solid tumors and its roles are being explored in cancer immunotherapy. T-cell transfer therapy, also called adoptive immunotherapy or immune cell therapy is a new form of cancer treatment designed to exploit enhanced anti-tumor immune response of the tumor antigen-specific CTL found in the tumors, and has been tried against neoantigen-possessing cells effectively in recent times. Two types of T-cell transfer therapy, tumor-infiltrating lymphocytes or TIL therapy and CAR T-cell therapy are in use, and both involve harvesting autologous T cells infiltrated into the tumor, growing largenumbers of these cells in vitro, and administering to the patient for desired results. CAR T- cell therapy is similar to TIL therapy except that the T cells are designed to express a type of protein known as CAR (CAR for chimeric antigen receptor) to target specific antigens expressed in cancer cells in the body. Although CAR T cells have significantly improved the landscape for hematological malignancies, it has shown limited results in solid tumors as the solid tumors present certain obvious barriers to adoptive T-cell transfer and localization, but a variety of approaches are being deliberated to overcome these barriers to increase its specificity, efficacy, and safety in the treatment of different malignancies. The development of CAR T cell therapy forsolid tumors has been impaired also because most target antigens are common with normal cells.Research is being directed to develop a ‘toolbox’ of novel chimeric antigen receptors (CARs) that could be programmed to use logic to discriminate between normal and cancerous cells to prevent toxicity. This development could help to overcome some of the barriers to the application of CAR-T cells against solid tumors.

       Furthermore, therapeutic cancer vaccines, such as the dendritic cell (DC) vaccine, peptide vaccine, and RNA-based neoantigen vaccines have been developed for inducing CTLs against the antigens in cancer patients and have shown encouraging results. These vaccines can be designed to induce the production of biomolecules capable of targeting the shared antigens expressed by cancer cells through appropriate immune response and, are being investigated for their efficacy as neoantigen-targeted individualized cancer vaccines. Dendritic cells (DCs) are specialized Antigen-presenting cells (APCs) known for their ability to present antigens to T cells, and this property of DCs has been exploited for their application in therapeutic cancer vaccines which have been shown to induce protective anti-tumor activities. [156,157]. Besides that, the transposable elements (REs) usually present in the tumor microenvironment are of potential therapeutic importance to create a pan-cancer vaccine that can aid in the prevention of a range of cancers. There is an enumerable number of regions with TEs involved with the expression of proteins in the cancer cell. Many of these are shared across tumors of the same type and could provide means for destruction by the immune system. The goal of immunotherapy remains to activate the individual's own immune system against the evolving tumors to successfully target the transformed cells with high selectivity, low toxicity, and appropriate results. Thus, immunotherapy remains the frontline area of cancer research, and precision oncology will be focused on immunotherapy accordingly.

Figure 2. Therapeutic Targeting of the Hallmarks of Cancer

Therapeutic agents that can mitigate the acquired capabilities necessary for tumor growth and cancer progression are being developed for clinical use in treating different cancer types. These drugs are being developed in clinical trials to target each of the emerging neoplastic characteristics and the enabling hallmarks capabilities towards effective cancer therapy. The listed drugs are just illustrative examples; there is a deep pipeline of investigational drugs in development to target different signaling molecules that lead to the hallmark capabilities. (Hanahan and Wienberg [57]. With permission from Elsevier)

        Recent advances in cancer genomics and single-cell technologies have certainly made targeted therapy the accepted form of cancer treatment, and yet a huge amount of investment willbe needed for future research, drug discovery, and diagnostics to fully unlock its potential and for their application in the management of cancer. Let us not forget that the socioeconomic burden of cancer remains high as the treatment options for most common cancers have been limited so far and is an indication for a renewed approach to expedite drug development to bring effective anticancer agents from bench to bedside in a cost-effective manner. The lack of understanding of the genetic heterogeneity of individual cancers has traditionally been limiting the search for efficacious agents for cancer treatment and missing a wide range of possibly suitable agents from other disease areas. The use of molecular characterization of different cancer types through cancer genomics can help resolve drug-related issues to a reasonable extentby repurposing the use of certain existing drugs as anticancer agents for a wide range of applications, and it will remain at the forefront of precision oncology [162,163]. Moreover, the move from tissue-based cancer-specific treatments to genome-based targeted treatments entails the reuse of anticancer drugs prescribed for one type of cancer to treat other cancer types as well.It is envisaged that, with the ever-greater understanding of cell signaling mechanisms and geneticalterations in carcinogenesis, considerable progress in cancer treatment will be realized in the near future. Considering that academia, industries, and civil society will be working in tandem tocater to the contemporary needs of the system, it is hoped that a wide range of people with cancer will benefit from this new development in cancer research in the future to benefit the system as a whole [164,165].