Translational Advances in Oncogene and Tumor-Suppressor Gene Research.

1. Introduction

Cancer is defined as an abnormal growth of cells that can originate from any organ or body structure where immune cells are unable to destroy overly dividing cells [ 1 ]. Typically, cancer must reach a size of 1 cm or be comprised of 1 million cells before it can be detected and is then often labeled as a “mass”, “growth”, “tumor”, “nodule”, or “lesion” [ 1 ]. Most recent estimates provided by the World Health Organization (WHO)’s cancer agency, the International Agency for Research on Cancer (IARC), indicate that, in 2022, there were around 20 million new cases and 9.7 million deaths attributable to cancer [ 2 ]. Moreover, it was found that about one in five people will develop cancer in their lifetime, with approximately one in nine men and one in twelve women dying from the disease [ 2 ].

Since the inception of revolutionary screening tests and therapeutic interventions, clinicians and researchers have become quite proficient at classifying cancers and subsequently diagnosing them based on their distinct stages and morphology [ 3 ]. However, despite this, there have been challenges in identifying specific biological drivers that account for cancer heterogeneity. The genomic revolution offers a potential route to better understand the fundamental origins of various cancers by analyzing genomic profiles and identifying patterns of gene expression, genetic alterations, and mutation. These genetic patterns represent the sub-cellular mechanisms that are responsible for the development of cancer, and understanding them may lead to more precise and personalized approaches to cancer diagnosis and treatment [ 3 ]. Somatic mitochondrial DNA (mtDNA) mutations are another major contributor to tumorigenesis and have also been observed to influence apoptosis or controlled cell death, which further impacts the progression of cancer [ 4 ]. These mtDNA mutations tend to build up over time, eventually causing an inability for the DNA to repair itself and subsequently dysregulating cell proliferation [ 4 ]. While there is a functional understanding of mtDNA mutations, they often serve as secondary or synergistic contributors to cancer biology, along with driver genes, including both oncogenes and tumor-suppressor genes [ 5 ].

The main cancer-driver genes were sequenced several decades ago. However, there is little known about the significance of many variants and the interplay between multiple genes and other factors surrounding nuclear DNA damage [ 6 ]. Driver genes are genes in which mutations cause cancer to develop or progress, and their affiliated cancer-causing mutations are known as driver mutations [ 6 ]. One prevalent driver gene is the TP53 gene, which encodes the tumor protein p53, a tumor suppressor that is essential for regulating cell division. Alterations in the p53 pathway are found in more than 50% of all human tumors [ 6 ]. Cancer-related genes can be classified into two categories: tumor suppressors, which normally inhibit cellular proliferation and promote apoptosis, and oncogenes, those that regulate cell proliferation and survival [ 7 ]. DNA repair genes, responsible for maintaining DNA repair and chromosome integrity, work together with these other driver genes to maintain cellular homeostasis, and their disruption can lead to tumorigenesis.

2. Molecular Mechanisms

2.1. Tumor-Suppressor Gene Inactivation and Oncogene Activation

Tumor-suppressor genes work to regulate cell division and promote controlled apoptosis under normal physiological conditions [ 8 ]. Dysfunction of these genes results in a loss-of-function (LOF) effect, subsequently causing uncontrolled cell growth—a hallmark of cancer [ 8 ]. The silencing of autosomal tumor-suppressor genes is explained through Alfred G. Knudson’s two-hit theory [ 8 ]. Knudson studied the molecular mechanisms of the RB1 gene, which is responsible for retinoblastoma, and investigated its role in both hereditary and nonhereditary forms of the disease. In his research, Knudson proposed that cancer initiation requires two mutational events, where in the dominantly inherited form, one mutation is inherited via the germinal cells, and the second occurs in the somatic cells [ 9 ]. Meanwhile, in the nonhereditary form, both mutations occur in the somatic cells. Tumors are then initiated when these cells contain two damaged or “hit” alleles [ 9 ]. This mechanism involves LOF mutations followed by a loss of heterozygosity (LOH) at specific loci of the affected tumor-suppressor genes [ 8 , 10 ]. Heterozygosity refers to the presence of one normal and one abnormal allele, where the loss of the normal allele produces a locus with abnormal function, as observed in certain inherited autosomal dominant cancer susceptibility disorders [ 10 , 11 ]. If the altered gene is a tumor-suppressor gene, the LOH and its polymorphic markers leave only the abnormal allele, thus disabling the normal function of the gene and making the cell more susceptible to malignant transformation.

While tumor-suppressor genes are responsible for inhibiting cell division, proto-oncogenes are involved in four basic mechanisms that regulate normal cell growth: growth factors, growth factor receptors, signal transduction molecules, and nuclear transcription factors [ 12 ]. These regulators are essential for normal proliferation and differentiation, and when mutated, they can cause uncontrolled cell growth and neoplastic transformation [ 7 , 12 ]. The conversion of proto-oncogenes to oncogenes, also known as activation, involves gain-of-function (GOF) mutations and can be caused by a variety of mechanisms such as transduction, insertional mutagenesis, amplification, point mutations, and chromosomal translocations [ 8 , 12 , 13 ]. Unlike tumor-suppressor genes that require “two-hit” mutations for inactivation, only “one-hit” mutation can be sufficient to activate a proto-oncogene and cause carcinogenesis [ 14 , 15 ].

Transduction is the process by which retroviruses capture and insert a full or partial proto-oncogene from the host into their viral genome, which leads to deregulation [ 12 ]. The c- SRC proto-oncogene can be seen to be transduced into v- Src oncogene in the Rous sarcoma virus (RSV), causing it to lose its regulatory phosphorylation site. This loss results in unregulated tyrosine kinase activity and enhances the oncogenic potential, transforming the infected cells and forming tumors [ 12 , 16 ]. Insertional mutagenesis is a similar oncogene activator mechanism, as it involves the integration of a retrovirus’ promoter gene into its host’s genome near a proto-oncogene, resulting in its overexpression. An example of this process is the avian leukosis virus (ALV), which integrates upstream of the cellular myelocytomatosis oncogene (c- MYC ), leading to an overexpression of the c-MYC transcriptional factor and the development of lymphomas [ 17 , 18 ]. In gene amplification, the creation of multiple copies of proto-oncogene leads to excessive protein production [ 17 ]. For example, excess c- MYC is observed in neuroblastoma, while human epidermal growth factor receptor 2 ( HER2 ) amplification is linked to aggressive breast cancers [ 19 , 20 ]. Meanwhile, in point mutations, a single nucleotide change in a proto-oncogene can alter its amino acid sequence, creating overly activated proteins [ 17 , 21 ]. The most common example is the Ras gene family, where mutations at the 12, 13, or 61 codons impair GTPase activity, keeping Ras constantly active and promoting uncontrolled proliferation [ 17 , 22 , 23 ]. Chromosomal translocation occurs when a proto-oncogene is moved from its original location to a new chromosomal context, where it can become aberrantly regulated. A classic example of this is the formation of the Philadelphia chromosome in chronic myelogenous leukemia (CML) arising from a translocation between chromosomes 9 and 22, creating a fusion gene known as BCR-ABL1 that results in upregulated tyrosine kinase activity [ 12 , 17 , 24 ].

2.2. Key Signaling Pathways Involved

Cell division is driven and regulated by complex molecular pathways designated to prevent uncontrolled cell proliferation. In the scope of cancer, the main mode of post-translational modifications comes in the form of protein kinases such as cyclin-dependent kinases (CDKs) and Polo-like kinases [ 25 , 26 ]. CDKs are a group of serine/threonine kinases that regulate cell division. They act as catalytic subunits and require association with proteins called cyclins to become fully active. This association forms a heterodimer complex, with the cyclins serving as regulatory subunits [ 27 , 28 ]. In human cells, there are 20 CDKs and 29 cyclins, where CDK1, CDK2, CDK3, CDK4, CDK6, and CDK7 directly regulate cell-cycle transitions and cell division, while CDK7-11 oversees gene transcription [ 29 , 30 ]. Moreover, cyclin-dependent kinase inhibitors (CKIs) are responsible for the negative regulation of the cell cycle in tandem with CDKs, working to prevent premature entry into the next phase of cell division [ 7 , 27 , 31 ].

The resting phase, G 0 , is the state in which most cells are arrested prior to replication. Here, growth and arrest signals determine whether cells remain quiescent or enter the cell cycle ( Figure 1 ) [ 7 , 30 ].

The transition from the G 0 to G 1 and G 1 to S phase, where DNA replication occurs, is facilitated by the activation of CDK4 and CDK6 in complex with D-type cyclins (cyclin D1, cyclin D2, and cyclin D3) [ 32 , 33 ]. CDK4/CDK6-cyclin D complexes help push the cell past the restriction point in G 1 , committing to a new division cycle ( Figure 1 ) [ 34 ]. These CDKs–cyclins complexes are regulated by the INK4 family of inhibitors, which include p16 INK4A , p15 INK4B , p18 INK4C , and p19 INK4D , which bind CDK4 and CDK6, preventing their association with D-type cyclins, thus suppressing their kinase activity [ 35 ]. As the cell progresses through G 1 , the CDK2–cyclin–E complex becomes active, further driving the cell’s progression into the S phase ( Figure 1 ) [ 36 ]. During the S phase, CDK2 pairs with cyclin A to support replication, and later, CDK1 binds cyclin A in G 2 and then cyclin B to initiate mitosis in the G 2 /M phase [ 33 , 37 ]. These CDK2 and CDK1 activities are modulated by the CIP/KIP family of inhibitors (p21 CIP1 , p27 KIP1 , and p57 KIP2 ) to ensure proper progression or arrest ( Figure 1 ) [ 35 ]. This complex regulation ensures that the process of cell division is well-controlled, preventing uncontrolled cell proliferation that may lead to cancer.

The CDK-cyclin complexes, particularly CDK4 and CDK6 with D-type cyclins, regulate the cell cycle by phosphorylating the retinoblastoma family of proteins Rb, p107, and p130, which act like tumor suppressors [ 32 , 33 ]. In its active, hypophosphorylated state, Rb binds E2F transcription factors, which inhibit the expression of genes required for the G 1 /S transition, such as cyclin E ( CCNE ), cyclin A ( CCNA ), thereby preventing premature cell-cycle progression in G 1 [ 32 , 37 , 38 ]. On the other hand, when phosphorylated by CDK4/6 complexes, Rb loses its affinity for E2F, which allows E2F to activate these genes and drive the cell cycle into the S phase ( Figure 1 ) [ 39 ]. Following mitosis, the Rb’s growth-suppressing function is restored through dephosphorylation by protein phosphatase 1 (PP1), returning it to its active, hypophosphorylated state [ 40 ].

Dysregulation of the Rb pathway, often through mutations in the Rb1 gene, leads to uncontrolled E2F activity, resulting in aberrant cell proliferation and tumorigenesis. In human cancers, the key regulators of G 1 phase, such as CDK4/6, their positive regulator cyclin D1, negative regulators p16 INK4A and p15 INK4B , and their substrate Rb, are frequently altered [ 33 ]. Amplification of cyclin D1 or CDK4, translocation of CDK6, and deletions of INK4 proteins are key events disrupting cell-cycle control and contributing to carcinogenesis [ 33 ]. The CDK4/6-mediated phosphorylation is essential for the normal cell division process, and its hyperactivation results in excessive Rb protein phosphorylation, impairing its tumor-suppressor abilities and contributing to uncontrolled proliferation [ 33 , 41 ]. Recent findings by Zhang et al. [ 42 ] suggest that Rb can also be deactivated independently of CDK phosphorylation, such as through altered upstream signaling pathways or viral oncoprotein interactions, further inducing LOF mutations and nullifying its tumor-suppressor capabilities.

Regarded as the “guardian of the genome”, understanding the inactivation of the TP53 tumor-suppressor gene is crucial, as it is mutated in over 50% of human cancers and in the other 50% is compromised through biological inactivation by its negative regulators like murine double minute 2 (MDM2) and X (MDMX) or through mutations in upstream kinases [ 43 ]. Under normal conditions, p53 acts as a transcription factor that induces the expression of CDKN1A (p21), a cyclin-dependent kinase inhibitor that blocks CDK4/6 and CDK2 to maintain the active hypophosphorylated state of Rb [ 44 , 45 ]. This allows Rb to form a complex with E2F, thus blocking the G 1 phase and enforcing cell-cycle arrest by suppressing the genes required for S-phase entry [ 45 , 46 ]. MDM2, an E3 ubiquitin ligase, and MDMX, its cofactor, negatively regulate p53, targeting it for ubiquitination and degradation by proteasomes in the cytoplasm [ 44 , 47 , 48 ]. During cellular stress like DNA damage, the ubiquitination of p53 is inhibited, triggering a rapid increase in intracellular p53 levels. This enables p53 to promote the expression of genes for apoptosis, cell-cycle arrest, or DNA repair, contributing significantly to its tumor-suppressor role [ 49 , 50 , 51 , 52 ].

The genomic amplification of the MDM2 locus, located on chromosome 12q15 in humans, is a common alteration in most cancers [ 43 , 53 ]. This overexpression of the MDM2 protein is often caused by chromosomal aberrations such as gene duplication, aneuploidy, or focal amplification [ 54 , 55 ]. Upregulation of MDM2 transcription may also result from oncogenic signals, such as epidermal growth factor (EGF) and insulin-like growth factor-1 (IGF-1), which activate the PI3K/AKT and MAPK pathways and phosphorylate MDM2 at residues Ser166 and Ser186 [ 56 , 57 , 58 , 59 ]. MDM2 amplification has broad consequences, primarily in the rapid ubiquitination and proteasomal degradation of p53, thereby effectively silencing p53-mediated transcriptional programs and neutralizing its tumor-suppressor properties [ 44 , 60 ]. Furthermore, MDM2 amplification downregulates p21, which allows cyclin–CDK complexes to hyperphosphorylate Rb at serine and threonine residues [ 45 , 61 , 62 ]. This hyperphosphorylation causes conformational changes in Rb, disrupting its ability to inhibit E2F, which ultimately accelerates cell-cycle progression [ 33 , 41 ]. Additionally, MDM2 amplification impairs p53-regulated processes, such as apoptosis and genomic stability. p53 is known to activate pro-apoptotic genes such as BAX , NOXA , and PUMA , in which NOXA and PUMA act as upstream regulators that inhibit anti-apoptotic proteins, enabling BAX to permeabilize the mitochondrial membrane and trigger apoptosis [ 44 , 63 , 64 , 65 , 66 ]. Moreover, p53 maintains genomic stability by inducing growth arrest and DNA damage-inducible 45 ( GADD45 ) and various DNA repair enzymes. Therefore, MDM2 amplification, by inhibiting p53, results in mutation accumulation and promotes tumorigenesis [ 67 ].

Oncogenes follow signaling pathways that operate both independently and in conjunction with those of tumor-suppressor genes. While tumor suppressors primarily follow the p53 and Rb pathways, one of the most common oncogenic events is the dysregulation of the Ras/Raf/MEK/ERK MAPK pathway ( Figure 2 ) [ 68 ]. The genes in the Ras oncogene family are among the first oncogenes to be discovered forty years ago, and mutations in this pathway prevail in 25% of all tumors [ 69 ]. The pathway begins when receptor tyrosine kinases (RTKs) are activated by extracellular signals, causing them to activate guanine nucleotide exchange factors (GEFs) and, subsequently, Ras, a family of small GTP-binding proteins (HRAS, KRAS, and NRAS) [ 70 , 71 ]. Activated Ras then recruits Raf kinases to the cell membrane, specifically ARAF, BRAF, and CRAF (also known as RAF-1), which are serine/threonine kinase enzymes and isoforms of each other [ 72 , 73 ]. BRAF is the most active of these kinases and has a role in the phosphorylation of mitogen-activated protein kinase kinase (MEK) at Ser166 and Ser186, which then activates mitogen-activated protein kinase (MAPK), also known as extracellular signal-regulated kinase (ERK) [ 73 , 74 , 75 ].

The regulation of MAPK/ERK by MEK is a crucial intracellular process that allows ERK to translocate to the nucleus, where it regulates the activity of key transcription factors, including Elk-1 and c-MYC, promoting cell proliferation [ 68 , 73 , 76 ]. Dysregulation of this pathway is a major driver of oncogenic events, particularly through mutations in the KRAS and BRAF genes. KRAS mutations are a predominant factor in the activation of the MAPK pathway in tumors. These mutations are characterized by single-base missense changes, with approximately 98% occurring at codons 12 (G12), 13 (G13), or 61 (Q61) [ 77 ]. These alterations lock KRAS in an active GTP-bound state, continuously signaling via the MAPK and PI3K pathways to promote uncontrolled cell growth and survival. Recent developments have introduced KRAS G12C-specific inhibitors, such as sotorasib and adagrasib, that effectively target and bind the mutant protein, blocking its activity and offering promising therapeutic advances [ 78 ]. The BRAF V600E mutation, prevalent in melanomas, and also observed in thyroid, ovarian, and lung cancers, occurs when valine (V) is replaced by glutamic acid (E) at position 600 [ 72 , 79 , 80 ]. This mutation forces a constant Ras-independent activation of ERK/MAPK, thus promoting uncontrolled cell migration and proliferation [ 81 ]. Furthermore, hyperactivation of this pathway leads to the overexpression of anti-apoptotic genes like B-cell lymphoma-2 ( Bcl-2 ), decreasing pro-apoptotic signals and allowing cell survival in tumor microenvironments despite various stresses [ 79 , 80 ]. Moreover, the BRAF V600E mutation increases vascular endothelial growth factor (VEGF) expression as a downstream effect, facilitating invasion by activating matrix metalloproteinases (MMPs) and, thus, inducing angiogenesis [ 82 , 83 ].

Much like the MAPK pathway, the phosphoinositide 3-kinase (PI3K)/AKT pathway is crucial for regulating cellular growth, survival, and proliferation ( Figure 2 ) [ 84 ]. This pathway is activated when RTKs trigger PI3K, which then converts phosphatidylinositol-4,5-bisphosphate (PIP2) into phosphatidylinositol-3,4,5-trisphosphate (PIP3), providing a docking site for 3-phosphoinositide-dependent protein kinase 1 (PDK1) and mTOR complex 2 (mTORC2), which then phosphorylate and activate protein kinase B (AKT) [ 85 ]. AKT is a serine/threonine kinase similar to the Raf family, which phosphorylates several proteins with different outcomes. It activates the mechanistic target of rapamycin (mTOR), a kinase that regulates protein synthesis; inhibits Bcl-2-associated death promoter (BAD), a pro-apoptotic protein; and inhibits forkhead box O (FOXO), a family of transcription factors that promote apoptosis and cell-cycle arrest [ 86 , 87 , 88 , 89 ]. mTOR signaling is modulated by the PI3K/AKT pathway through the phosphorylation of its negative regulator, TSC2, which indirectly activates mTORC1 [ 90 , 91 ]. This activation of mTORC1 leads to a subsequent cascade in downstream pathways such as 4E-BP1 and S6K1, which are both involved in mRNA translation and protein synthesis [ 92 ]. Disruption of the PI3K/ATK pathway can occur from mutations in PIK3CA , the PI3K catalytic subunit, or by the loss of phosphate and tensin homolog (PTEN), a tumor suppressor that dephosphorylates PIP3 to inhibit AKT activation, resulting in oncogenesis through uncontrolled proliferation, metabolic reprogramming, resistance to apoptosis, and GOF missense mutations [ 89 , 93 ]. While disruption of the PI3K/ATK pathway significantly contributes to oncogenesis, its effects are further amplified by extensive crosstalk with the MAPK pathway [ 94 ]. Both signaling pathways share RTKs as activators, creating a feedback loop that interconnects them, where ERK can activate mTOR and mTORC1 modulates MAPK signaling [ 91 , 95 ]. As a direct result, failure in either or both mechanisms causes oncogenesis through rapid cell division.

The Wnt/β-catenin is another crucial signaling pathway that governs cell proliferation, differentiation, and stem-cell maintenance ( Figure 2 ) [ 96 ]. In the absence of Wnt ligands, the destruction complex, consisting of adenomatous polyposis coli (APC), glycogen synthase kinase-3β (GSK-3β), and Axin, is active, which is responsible for the phosphorylation of β-catenin, targeting it for degradation [ 96 , 97 , 98 , 99 ]. When Wnt ligands bind to a frizzled receptor and LRP5/6 co-receptors on the cell surface, this activates the recruitment of the Dishevelled protein (DVL), which deactivates the destruction complex, thus inhibiting the degradation of β-catenin. Accumulated β-catenin then translocates in the nucleus, activating transcription factors like TCF and LEF and promoting cell proliferation [ 97 , 100 ]. Wnt/β-catenin is most often disrupted through mutations in catenin-Beta-1 ( CTNNB1 ), which encodes β-catenin, or in APC , subsequently reducing β-catenin degradation and leading to persistent nuclear localization and activation of Wnt target genes [ 101 , 102 ]. Moreover, dysregulation of the Wnt pathway promotes cancer metastasis by inducing epithelial-to-mesenchymal transition (EMT), where β-catenin activates transcription factors like Snail and Slug, leading to downregulation of epithelial markers (e.g., E-cadherin) and upregulation of mesenchymal markers (e.g., vimentin and N-cadherin) [ 96 , 103 , 104 ]. The Wnt/β-Catenin pathway also exhibits crosstalk with MAPK and PI3K, causing a synergistic effect of oncogenesis and metastatic development [ 105 ]. Wnt signaling can upregulate PI3K/AKT activity by increasing PIP3 levels via downstream targets or modulating PTEN expression to enhance cell survival and metabolism [ 106 , 107 ]. Conversely, AKT can stabilize β-catenin by phosphorylating and inhibiting GSK-3β, thus amplifying Wnt signaling [ 108 , 109 ]. Similarly, the MAPK pathway interacts with Wnt signaling, where β-catenin can influence Ras activity and Raf/MEK/ERK signaling downstream of Ras to promote proliferation, making these three pathways the most significant and prominent players in the processes of oncogenesis [ 110 , 111 ].

2.3. Epigenetic Regulation

Epigenetics is modernly defined, according to Sharma et al. [ 112 ], as the study of heritable changes in gene expression that occur independently of changes in the primary DNA sequence. Disruption of these epigenetic processes can result in inappropriate activation or inhibition of various signaling pathways that alter gene functions and cause malignant cellular transformation [ 113 , 114 ]. DNA methylation, a critical epigenetic process, involves the addition of methyl groups to specific cytosine residues at CpG sites, regulating gene expression by silencing certain genes and suppressing repetitive genomic elements [ 115 , 116 , 117 ]. This process is regulated by DNA methyltransferases (DNMTs), which alter chromatin’s structure to modulate expression [ 118 ]. Aberrant DNA methylation is a hallmark of carcinogenesis, as irregular methylation leads to the silencing of tumor-suppressor genes and the induction of oncogenesis [ 116 ]. Hypermethylation and global hypomethylation are the two types of aberrant DNA methylation that affect tumor suppressors and proto-oncogenes, respectively [ 119 ]. DNA promoter hypermethylation is often an early event in tumorigenesis and is commonly observed in tumor-suppressor genes, such as cyclin-dependent kinase inhibitor 2A ( CDKN2A ), breast cancer gene 1 ( BRCA1 ), and MutL protein homolog 1 ( MLH1 ), among others, where excessive methylation represses transcription, ultimately disabling cell-cycle barriers that normally limit cell proliferation and compromising the cellular functions that are crucial for maintaining genomic stability and repair [ 120 , 121 , 122 , 123 ]. On the other hand, global hypomethylation refers to a decrease in methylation across the genome, which in repetitive sequences can lead to chromosomal rearrangements, translocations, and aneuploidy, facilitating proto-oncogene activation [ 124 , 125 ]. The most notable example of global hypomethylation in proto-oncogenes is the activation of the Ras family genes, particularly the H-Ras proto-oncogene, where hypomethylation leads to its upregulated expression through long-range interactions between distant chromatin regions, allowing enhancers to activate the H-Ras promoter more effectively [ 126 ]. Recent studies have also shown that hypermethylation and hypomethylation can coexist in cancers, resulting in a dual mechanism that amplifies the accumulation of aberrant DNA mutations in both tumor suppressors and oncogenes [ 127 , 128 ].

Histone modifications and chromatin remodeling are another group of essential epigenetic processes whose malfunction can contribute to carcinogenesis [ 129 ]. Histones are small, basic proteins rich in arginine and lysine residues, around which DNA wraps itself to form nucleosome cores, leading to the formation of chromatin and, ultimately, mitotic chromosomes [ 130 , 131 ]. Post-translational modifications of histones include acetylation, methylation, phosphorylation, and ubiquitination, which play a role in regulating chromatin dynamics and gene expression [ 132 ]. Aberrant histone modifications disrupt chromatin accessibility, causing dysregulation of both tumor-suppressor genes and oncogenes [ 129 ]. For instance, the BRCA1 tumor-suppressor gene, which can undergo DNA hypermethylation, is also found to be silenced in breast and ovarian cancers due to histone deacetylation mediated by histone deacetylases (HDACs) [ 132 , 133 ]. Moreover, trimethylation of histone H3 at the lysine 27 position (H3K27me3), catalyzed by polycomb-repressive complex 2 (PRC2) subunit EZH2, has been found to silence tumor-suppressor genes such as INK4a / ARF [ 134 , 135 ]. These aberrant histone modifications have also been linked to oncogenesis, as exemplified by H3K4me3, which promotes the transcription of oncogenes, including CCND1 and MYC [ 136 ].

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level, and, like DNA methylation and histone modifications, their abnormal expression can lead to cancer progression [ 133 ]. miRNAs exhibit pleiotropic functions, acting differently depending on their cellular and tissue-specific context [ 137 ]. The first miRNAs to be associated with cancer, miR-15 and miR-16, were identified in B-cell leukemia, and since then, strong links have been established between the biogenesis of miRNA and their target genes, including tumor suppressors and oncogenes [ 133 , 138 ]. These miRNAs have been found to play a critical role in the regulation of the key signaling pathways involved in cell proliferation. For instance, miR-99a has been found to inhibit the target of mTOR, whereas miR-19 and miR-501-5p activate the Wnt/β-catenin signaling pathway [ 139 , 140 ]. Additionally, miR-21 and miR-221/222 stimulate the development of cancer stem cells through the inhibition of PTEN [ 141 ]. Even more, the Let-7 family of miRNAs has major implications in other pathways, such as activating Ras oncoproteins and deregulating signal transduction pathways, including PI3K/AKT [ 142 ]. The interplay of DNA methylation, histone modifications, and miRNA expression creates a complex epigenetic network that controls the silencing of tumor suppressors and the activation of oncogenes. These epigenetic alterations do not simply drive the development and progression of cancers but also hold great promise for therapeutic intervention.

3. Challenges and Controversies

3.1. Tumor Heterogeneity

Due to rapid mutation rates during cell proliferation, cancer is characterized by its ability to evolve in composition over time. Throughout the progression of the disease, cancer becomes increasingly heterogeneous in nature, developing subsets of genetically distinct cell populations. These populations can exhibit variations in cell surface markers, levels of expression of tumor-driver genes, and copy number alterations depending on the tumor microenvironment [ 143 ]. Tumor heterogeneity can be observed both within a singular tumor, referred to as intratumoral heterogeneity, and between a primary tumor and its distant metastases, known as intertumoral heterogeneity [ 144 ]. As a heterogeneous tumor accumulates mutations and chromosomal aberrations, it gains the potential to resist pharmacological treatment, leading to poor clinical outcomes and tumor relapse [ 144 , 145 ]. A multiregional sampling study conducted by Jamal-Hanjini examined one hundred early-stage non-small cell lung cancer (NSCLC) tumors and found that a median of 30% of somatic mutations were subclonal in nature, indicating a high level of tumor heterogeneity [ 146 ]. The study determined that more extensive copy number heterogeneity is associated with worse clinical outcomes in patients, as it is representative of underlying genomic instability [ 144 ].

Proposed models for the evolution of intratumoral heterogeneity offer insight into how cancers continue to persist despite treatment. Evidence shows that many solid tumors undergo a branched pattern of evolution, wherein multiple different subclonal populations arise from a common ancestor [ 147 ]. These subclonal populations emerge simultaneously and do not completely outcompete and replace the preceding clones. Instead, the clones coexist in a mosaic-like pattern. This dynamic process allows the tumor to develop a higher degree of heterogeneity, increasing its chance of survival against the selective pressures imposed by pharmacologic treatment [ 144 ]. High rates of intratumoral heterogeneity are associated with many malignant and aggressive cancers. For example, a single-cell sequencing study conducted by Shi et al. found that recurrent bladder cancer tumors displayed greater local cell-type heterogeneity than primary bladder cancer tumors [ 148 ]. The recurrent tumors exhibited more complex immune and stromal cell populations, differing in composition from the primary tumor, thus contributing to their ability to interact with the immune response [ 148 ]. A similar pattern was observed in multi-region biopsies of medulloblastoma, malignant glioma, and renal cell carcinoma, where genes targeted for therapy were highly heterogeneous within tumor samples [ 149 ]. Morrisy et al. [ 149 ] argue that monotherapies aimed at a single target are unlikely to be effective, as the target is not ubiquitously present, allowing the untargeted clones to remain and repopulate the tumor. The presence of multiple distinct cell lineages within a single tumor has been linked to poor prognosis in patient outcomes, highlighting that intratumoral heterogeneity poses great challenges for targeted therapies [ 150 ].

Intertumoral heterogeneity between a primary tumor and its metastases develops in a similar manner to intratumoral heterogeneity, but the evolution is markedly shaped by the tumor microenvironment of the metastases. The tumor microenvironment includes all of the unique conditions under which a primary tumor or its metastasis develops. Tumor micro-environments may differ in their vasculature, susceptibility to immune attack, and other growth conditions, each creating specific challenges and opportunities for cell populations to thrive and evolve [ 151 ]. For instance, it has been shown that a tumor microenvironment with disordered blood vessel formation leads to an uneven distribution of cancer therapy drugs, thus leading to the proliferation of drug-resistant clonal populations [ 152 ]. The tumor microenvironment can differ vastly from site to site, explaining why intertumoral heterogeneity can be observed in both local and distant metastasis sites. In the case of clear-cell renal cell carcinoma, local metastasis is initiated with the formation of a tumor thrombus in the proximal vasculature [ 153 ]. Evidence shows that this thrombus originates from advanced subclonal cells of the primary tumor, which exhibits a loss of the 9p chromosome, a significant marker for tumor progression and malignancy that leads to poor prognosis in this type of cancer [ 154 , 155 ]. Intertumoral heterogeneity between a primary tumor and distant metastases is also common and can be explained by polyclonal seeding, in which different metastatic sites are established by genetically distinct subclones that originated from the same primary tumor [ 144 ]. The metastases are then subjected to the selective pressures presented by the respective tumor microenvironments in which they develop [ 144 ].

3.2. Drug-Resistance Mechanisms

Clonal evolution poses significant challenges in the pharmacological treatment of cancer due to the acquisition of mutations that confer drug resistance. Chemotherapy presents yet another selective pressure that drives clonal evolution, ultimately resulting in a population of multidrug-resistant cancer cells. A key mechanism of resistance relies on the multidrug resistance-associated proteins (MRPs), a family of transmembrane efflux pumps within the ATP-binding cassette (ABC) transporter group [ 156 ]. MRPs utilize the energy from ATP hydrolysis to expel cytotoxic drugs from cancer cells, decreasing the intracellular drug concentration and diminishing the general chemotherapeutic effect [ 157 ]. MRP1, MRP3, MRP4, and MRP5, for instance, have often been found to be overexpressed in gastric adenocarcinoma (GAC), a highly chemoresistant cancer driven by oncogene activation, particularly through the upregulation of efflux pumps via the EGFR, Ras, or MYC pathways [ 158 , 159 , 160 ]. In a study by Al-Abdulla et al., inhibition of MRP1 and MRP4 using drugs like diclofenac and probenecid increased the responsiveness of GAC to chemotherapeutic agents [ 160 ]. These findings indicate that targeting MRP efflux pumps with specific inhibitor drugs can improve the efficacy of chemotherapy. Breast cancer resistance protein (BCRP) is another MRP within the ABC transporter superfamily, which transports substrates, including chemotherapeutic agents such as mitoxantrone and methotrexate [ 161 ]. Although the chemotherapeutic treatment is initially successful, its efficacy diminishes as drug resistance develops through clonal evolution. However, a study has found BCRP to be overexpressed in chemotherapy-naive breast cancer brain metastases, indicating intrinsic drug resistance properties [ 162 ].

Cancers have also been shown to upregulate key DNA repair mechanism enzymes, therefore decreasing the efficacy of chemotherapeutic drugs that aim to disrupt the S phase of the cell cycle. One such enzyme is DNA polymerase theta (POLQ), which repairs double-stranded DNA breaks through microhomology-mediated end-joining [ 163 ]. POLQ is overexpressed in cancer cells with deficient repair mechanisms to fill single-stranded gaps, allowing continued replication and proliferation of cancer cells [ 163 ]. Moreover, POLQ is found exclusively in cancer cells, suggesting that it acts as a compensatory mechanism that ensures cancer cell survival [ 163 ]. Multiple studies suggest that upregulation in POLQ is associated with poor prognosis in patients with breast cancer, with around a 4-fold increased risk of death when the enzyme is overexpressed [ 164 , 165 ]. Similarly, another key regulatory enzyme associated with the repair of single-stranded breaks (SSBs), poly (ADP-ribose) polymerase 1 (PARP1), has been found to be overexpressed in multiple cancers. It rapidly detects and binds to SSBs, playing a vital role in the activation of DNA polymerases and ligases to fix these lesions [ 166 ]. Furthermore, nuclear PARP1 overexpression has been observed in various breast cancers, including ductal and highly malignant triple-negative breast carcinomas, and even in breast cancer stem cells [ 167 , 168 , 169 ]. PARP1 is key for maintaining genomic stability and facilitating DNA repair. Thus, PARP1 overexpression promotes the upholding of the cancer integrity and its continued proliferation. Consequently, high levels of nuclear PARP1 correlate with poor prognosis, contributing to lower disease-free and overall survival rates in breast cancer patients [ 170 ]. Additionally, PARP1 overexpression has also been observed in recurrent oral cancers that have developed resistance to chemotherapy and radiation, linking PARP1 function to progressed malignancies [ 171 ]. In BRCA1 -deficient cells, POLQ and PARP play important roles in DNA repair, with POLQ mediating error-prone repair via microhomology-mediated end-joining (MMEJ), while PARP1 addresses single-strand break repair. Simultaneously targeting both POLQ and PARP1 may increase synthetic lethality, leading to improved therapeutic effects by impairing compensatory repair mechanisms [ 172 , 173 ].

Cancers have also been found to have an increased expression of glutathione, a powerful antioxidant that aids in cancer’s resistance to chemotherapeutic drugs designed to induce cytotoxicity via reactive oxygen species (ROS) [ 174 ]. Glutathione, a tripeptide containing cysteine and sulfur, exists predominantly in its reduced form, enabling it to bind and neutralize ROS. This antioxidant role is particularly important in cancer cells, where ROS levels are generally elevated due to the extensive metabolism needed for cell proliferation [ 175 ]. Upregulated glutathione function has been observed in lung, breast, ovarian, and head and neck cancers, with cancer cells often expressing nearly ten times the glutathione levels of healthy cells [ 176 , 177 ]. Although prooxidant chemotherapies have been developed to increase ROS production and induce tumor cell death, their effectiveness is limited due to the tumor cells’ ability to mitigate excessive oxidative stress [ 178 ]. The overexpression of glutathione may be linked to the deregulation of the RAS/MAP signaling pathway due to mutations in the genes activating KRAS [ 179 ]. For example, a study on lung cancer tumors found that cells with mutant KRAS exhibited increased channeling of metabolites into glutathione biosynthesis, enhancing their antioxidant capacity [ 179 ]. Similarly, colorectal cancer patients with KRAS mutations exhibited increased glutathione levels, which suppressed ferroptosis, an iron-dependent form of cell death triggered by ROS and lipid accumulation [ 180 , 181 ]. Moreover, KRAS mutations have been shown to alter amino acids, fatty acids, and nucleotide biosynthesis in cancer cells, further highlighting the role of oncogenic mutations in promoting drug resistance across various tumor types [ 182 ].

3.3. The “Gray” Area Between Oncogenes and Tumor-Suppressor Genes

The traditional classification of oncogenes and tumor-suppressor genes has provided a basis for targeted cancer therapies. However, current studies reveal that certain genes can exhibit both oncogenic and tumor-suppressive roles depending on specific conditions, thus complicating the development of effective therapeutic interventions [ 183 ]. Oncogenic transformations that drive uncontrolled cell proliferation can also affect tumor suppressors. For example, while the TP53 gene is inherently a tumor suppressor, it can also undergo GOF mutations (e.g., R175, R248), acquiring oncogenic properties independent of its LOF effects [ 184 ]. Mutations in six “hot spot” amino acids of its coding sequence have been linked to increased proliferation, inhibition of apoptosis, enhanced inflammation, and the induction of angiogenesis [ 184 , 185 ]. Conversely, uncontrolled MYC expression can trigger an oncogenic-stress response via MDM2 to stimulate p53-mediated apoptosis, mimicking tumor-suppressor behavior [ 186 ]. Similarly, transforming growth factor beta 1 ( TGFβ1 ) normally acts as a tumor suppressor by inhibiting epithelial growth and promoting apoptosis [ 187 ], but pathway defects (e.g., SMAD loss) can lead to a cascade of downstream events that drive multiple oncogenic processes like sustained angiogenesis, loss of apoptosis, and immune evasion [ 187 ]. These examples, summarized in Table 1 , illustrate how context-dependent conditions and mechanisms, such as mutation-induced protein changes, stress responses, and disrupted signaling, drive these shifts and reshape gene function. Recognizing the complexities involved in this area deepens our understanding of cancer pathogenesis and emphasizes the need for personalized therapeutic approaches that are capable of addressing mutations at the genomic level within tumor heterogeneity

4. Clinical Implications

4.1. Diagnostic Approaches

Next-generation sequencing (NGS) is a modern tool in genomics research that can rapidly sequence millions of DNA fragments simultaneously [ 188 ]. This transformative technology is highly regarded for its high throughput and cost-effectiveness in analyzing DNA and RNA, producing unparalleled comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications [ 188 , 189 ]. NGS techniques significantly differ from the widely used traditional Sanger sequencing approach [ 188 ]. The Sanger method, named after its creator Fred Sanger, was a revolutionary DNA sequencing technique that involved the separation and analysis of DNA strands using gel and later capillary electrophoresis [ 190 ]. While it was largely considered the gold standard in genomics research and was used in the Human Genome Project, the technique eventually reached its ceiling due to its ability to analyze only one sequence at a time, thus limiting its scalability and efficiency [ 191 ]. These limitations of the Sanger method paved the way for the development of NGS, where techniques such as whole-genome sequencing (WGS), whole-exome sequencing (WES), and epigenome or targeted sequencing (TS) can achieve more comprehensive results using less time and fewer resources [ 191 , 192 , 193 , 194 ].

WGS is an application of NGS used to determine the complete DNA sequence of an organism’s genome at once. WGS covers coding (exons) and noncoding (introns) sequences, circulating DNA, copy number variations, and genomic structural rearrangements [ 188 , 195 ]. By sequencing the entire genome, WGS enables the identification of genetic variations, such as single-nucleotide polymorphisms (SNPs), and structural changes, like insertions and deletions [ 188 ]. WGS utilizes two sequencing approaches: short-read and long-read protocols [ 196 , 197 ]. Short-read protocols produce reads of less than 300 base pairs (bp), whereas long-read protocols can range from 1000 bp or kilobase pair (kbp) to several megabase pairs (mbp) [ 196 ]. Both protocols hold their merits as short-reads are especially useful for providing high accuracy and depth for detecting smaller variants at a low cost per base, while long-reads improve sequence phasing and are better suited for identifying more significant haplotypes and complex structural variants [ 198 , 199 ]. This technology is especially promising when applied to newborn screening, cancer detection, genetic diseases, and personalized medicine [ 200 ]. WGS finds its primary clinical applications in diagnosing rare diseases and identifying actionable somatic variants in tumors, making it especially useful for studying the activation of proto-oncogenes and the silencing of tumor-suppressor genes at the molecular level [ 199 , 201 ]. A recent study by Kinnersley et al. [ 202 ] showed the extensive potential of WGS for tumor genomic profiling, where they sequenced data from 10,478 patients spanning 35 different cancer types, identifying 330 candidate-driver genes, of which 74 were newly associated with cancer.

Similar to WGS, WES is an efficient strategy for selectively sequencing the coding regions of a genome to discover variants [ 188 ]. The method captures the protein-coding regions of the genome, known as the exome, and identifies single-nucleotide variants (SNVs), insertions, deletions, and copy number variations (CNVs) within these genes [ 203 , 204 ]. Regions outside this focus, such as introns, promoters, and intergenic regions, are not sequenced [ 205 ]. Like WGS, WES has extensive clinical applications, particularly in cancer genomics [ 206 ]. WES is crucial for detecting highly susceptible genes, offering opportunities for preventive measures [ 206 ]. A study by Hamdi et al. [ 207 ] performed WES on seven Tunisian breast cancer families to detect shared genetic variations that may enable early intervention strategies, identifying four novel candidate genes associated with breast cancer risk: MMS19 , DNAH3 , POLK, and KAT6B . In reference to the “two-hit theory”, this early screening could enable the detection of an inherited mutated allele, providing a window for therapeutic strategies. WES has also been valuable in post-cancer diagnosis and during treatment phases, where it can provide comprehensive genetic profiling of tumors and help identify targetable mutations in oncogenes and tumor suppressors [ 206 ].

TS is the third technique stemming from NGS, and while WGS and WES focus on comprehensive and broad coverage, TS panels focus on very specific genomic regions, requiring less computational demand [ 208 , 209 ]. Like other NGS methods, TS can detect genetic variations, including SNVs, small gene deletions, duplications, insertions, and rearrangements, but on a smaller scale, making it more cost-effective and easier for clinicians to manage [ 188 , 209 ]. TS offers greater sequencing depth for various tumor contents, such as circulating tumor DNA (ctDNA) and formalin-fixed paraffin-embedded (FFPE) samples, compared to non-NGS-based techniques like Sanger sequencing and allele-specific PCR (AS-PCR) [ 208 ]. Like WES, TS is particularly useful in the post-cancer phase, as it can isolate the mutations present in a small portion of malignant cells and detect variant allele frequency (VAF) as low as 0.1–0.2% [ 210 , 211 , 212 ]. Moreover, TS can identify any minor aberration of tumor-suppressor genes that may be clinically significant, highlighting its potential for guiding treatments targeting damaged tumor-related genes.

4.2. Therapeutic Strategies

As diagnostic approaches advance, so do the therapeutic strategies designed to combat the genomic changes that drive tumorigenesis. Among the most promising novel techniques is targeted therapy, which is a form of precision medicine that targets the specific proteins and genetic changes responsible for tumor heterogeneity [ 213 ]. The key advantage of targeted therapy is its ability to affect only abnormal proteins, unlike traditional chemotherapy, which is nonselective and can be indiscriminately toxic to all cells [ 213 , 214 ]. Targeted therapy includes various approaches, including immunotherapy, small-molecule inhibitors, and monoclonal antibodies ( Figure 3 ) [ 215 , 216 , 217 ].

Small-molecule inhibitors are broadly used in targeted therapy designed to slow or kill tumor cells by primarily targeting protein kinases, which are highly active pro-growth signaling initiators [ 218 ]. Their low molecular weight allows them to diffuse through cells and target intracellular drivers that regulate proliferation and apoptosis [ 214 ]. The list of various U.S. Food and Drug Administration (FDA)-approved small-molecule inhibitors for cancer treatment is shown in Table 2 .

The predominant and widely accepted class of small-molecule inhibitors includes those targeting RTKs and VEGF receptors, such as Erlotinib, Sunitinib, and others, which exert antiangiogenic and antiproliferative effects [ 219 ]. Recent breakthroughs have shown promising applications of small-molecule inhibitors in treating oncogene-driven mutations [ 214 ]. Serval inhibitors for BRAF of the MAPK pathway, such as Vemurafenib and Dabrafenib, have shown effective results against melanomas. Moreover, these inhibitors are particularly potent in treating patients with Ras and BRAF V600E mutations when used in combination with general MAPK inhibitors like Trametinib [ 214 , 220 ]. Despite their promise, small-molecule inhibitors have limitations, as they lead to the development of drug resistance through mechanisms that may include their influence on tumor microenvironments and the potential reactivation of both MAPK and PI3K/AKT signaling pathways [ 220 , 221 ]. Additionally, resistance may arise from the changes occurring in the genes coding for target proteins, deviation in signaling pathways that activate different proteins with similar functions, or mutations in the genes coding for the proteins associated with the target molecule [ 222 , 223 ].

Monoclonal antibodies are immunoglobulins designed to bind specific antigens and represent the second most common form of targeted therapy [ 213 , 218 ]. Unlike small-molecule inhibitors, monoclonal antibodies are larger molecules that cannot enter cells. Instead, they work by targeting receptors on the surfaces of cancer cells, thereby blocking the molecules that signal proliferation or angiogenesis [ 214 , 224 ]. This approach is primarily used to target the antigens associated with oncogene signaling, thus inhibiting the pathways that promote cancer cell growth and survival [ 225 ]. The most common clinical applications of monoclonal antibodies are trastuzumab (targeting HER2), cetuximab (targeting EGFR), and pembrolizumab (targeting the PD-1/PD-L1 axis) [ 226 , 227 , 228 ]. Trastuzumab, for example, effectively disrupts oncogenic signaling by downregulating HER2, an RTK that is commonly overexpressed in HER2 -positive breast cancer, thereby promoting its internalization and degradation [ 229 , 230 ]. Similarly, cetuximab and panitumumab bind to EGFR, preventing ligand binding and receptor dimerization, which inhibits oncogene signaling [ 231 , 232 ]. Moreover, the use of antibody therapy extends to tumor suppressors, as treatments with pembrolizumab or nivolumab have been shown to restore T-cell functionality against tumors, compensating for the LOF mutations in tumor suppressors [ 231 , 233 ].

Immunotherapy is the third major treatment method and a promising therapeutic approach, especially in people with oncogene-addicted cancers—cancers that depend heavily on a single oncogene or pathway [ 234 ]. This treatment increases the ability of the immune system to recognize and eliminate cancer cells, mainly through immune checkpoint inhibitors that block pathways like PD-1, PD-L1, and CTLA-4, which tumors exploit to decrease immune responses [ 235 ]. Notably, pembrolizumab and nivolumab, previously discussed as monoclonal antibodies, overlap with this category, as they release the brakes on T cells and are very effective against oncogene-driven cancers that have mutations in genes like KRAS and EGFR [ 236 , 237 ]. Tumors with mutated KRAS are likely to respond better due to upregulated PD-L1 expression and immune cell infiltration, as opposed to EGFR and ALK -driven tumors, which typically have “cold” tumor microenvironments with fewer immune cells [ 234 , 238 , 239 ]. Immunotherapy may also improve outcomes for tumors with genomic instability, including mutations in tumor-suppressor genes such as TP53 , STK11 , and KEAP1 [ 237 , 240 ]. Moreover, immunotherapy has also shown success when combined with chemotherapy and anti-angiogenesis agents like bevacizumab, which work synergistically to change the tumor microenvironment [ 238 , 241 ].

Radioimmunotherapy (RIT) is an extension of immunotherapy that combines targeted radiation with monoclonal antibodies to selectively target and destroy cancer cells [ 242 ]. This approach delivers a high dose of therapeutic or tracer radiation while minimizing exposure to normal cells [ 243 ]. Recent studies have focused on optimizing the combination of targeted radiation and immunotherapy, particularly in treatments that use alpha (radium-223 and actinium-225) and beta radionuclides (90y-ibritumomab tiuxetan), which have shown cytotoxic effects in treating leukemia, prostate cancer, and non-Hodgkin lymphoma [ 244 , 245 ]. Beyond its cytotoxic capabilities, RIT can influence oncogene and tumor-suppressor gene activity. For instance, a study by Guo et al. [ 246 ] identifies the correlation between p53 and RIT efficacy in tumors with wild-type TP53 . The study found that, in response to RIT, the activity of p53 was upregulated, which led to increased apoptosis and better regulation of DNA damage in cancer cells. This suggests that RIT could benefit patients with functional tumor-suppressor pathways, serving as an alternative therapy in cases resistant to conventional treatments [ 246 ].

Antibody–drug conjugates (ADCs) are a class of targeted cancer therapy that connects monoclonal antibodies with a potent cytotoxic drug (payload) through a chemical linker [ 247 ]. The monoclonal antibody offers a highly specific targeting capability, thus binding to a target antigen on the cancer cell’s surface. The presence of a chemical linker ensures that the payload, which has a highly potent cytotoxic effect, is released only inside the cancer cell, therefore minimizing the damage to healthy tissues [ 248 ]. The first FDA-approved ADC was the anti-CD33-targeted agent gemtuzumab ozogamicin in 2000, to treat patients with acute myeloid leukemia [ 249 ]. Since then, there have been eleven FDA-approved ADCs ( Table 3 ) for targeting various tumor antigens, such as CD19, CD22, CD30, CD33, and CD79b in blood cancers (myeloma, lymphoma, and leukemia), and HER2, tissue factor, folate factor alpha, Nectin-4, and Trop-2 in solid cancers (NSCLC, breast cancer, gastric cancer, and ovarian cancer, among others [ 250 ], and many more are in advanced stages of clinical trials.

Chimeric antigen receptor (CAR) T-cell therapy is a novel cancer therapy that uses patients’ own T cells to fight cancer [ 251 ]. Usually, the T cells do not present receptors specific to the cancer cells’ antigens, which prevents them from attaching to the antigens and destroying the cancer cells. In CAR T-cell therapy, T cells are extracted from the patient’s blood and undergo genetic modification, which introduces a gene that encodes a cancer-specific antigen receptor on their cellular membrane, enabling them to recognize and attach to the cancer cell [ 252 ]. Then, CAR T cells are infused back into the patients, where they circulate and attack cancer cells. This therapy has shown a significantly greater promise in targeting and combating circulating blood cancers like leukemia, lymphomas, and myelomas compared to solid tumors, mostly because of the solid tumors’ inaccessibility due to their complex microenvironment [ 253 ]. So far, there are six FDA-approved CAR T-cell products for treating hematological malignancies ( Table 4 ), and many more are in active clinical trials [ 254 ].

Despite the significant advancements and successes of targeted therapies and immune therapy, these approaches have many persistent limitations that hinder their success. Cancer cells frequently adapt, developing resistance that reduces the effectiveness of treatments like monoclonal antibodies, small-molecule inhibitors, ADCs, CAR T-cell products, and immune checkpoint inhibitors over time [ 255 ]. The resistance often stems from genetic mutations, altered signaling pathways, or changes in the tumor microenvironment, such as variable vasculature and immune suppression [ 256 ]. Additionally, these therapies can cause a spectrum of secondary effects that impact patients’ quality of life, including but not limited to skin toxicity, high blood pressure, and heart damage to severe autoimmune reactions like cytokine release syndrome (CRS), neurotoxicity, swelling, nausea, vomiting, diarrhea or constipation, allergic reactions, and hair loss [ 257 ]. Moreover, the complexity of these treatments, especially ADCs and CAR T-cell products, demands specialized manufacturing and delivery, increasing the costs and limiting their widespread availability and accessibility [ 258 , 259 ]. Addressing these challenges and limitations of targeted cancer therapies is crucial for improving therapeutic outcomes and treatment strategies to overcome these challenges.

4.3. Personalized Medicine Applications

Personalized medicine, also known as precision medicine, is a novel emerging approach that aims to determine disease patterns and develop devices and drugs tailored to individual patients [ 260 ]. This approach has been particularly intriguing in oncology, as it considers the inter- and intra-tumor variability in genetic profiles, tumor environments, and the oncogenic drivers specific to each tumor [ 261 ]. Personalized medicine aims to understand tumor biology and the development of resistance mechanisms, thus helping identify the tumor-specific biomarkers that factor into therapy-induced toxicities [ 261 , 262 ]. The majority of these techniques rely on NGS for cost- and time-effective tumor DNA sequencing to allow clinicians and researchers to better understand the unique genomic composition of each malignancy [ 263 ]. Previously discussed targeted therapies are at the forefront of emerging personalized medicine technologies [ 264 ]. However, personalized medicine includes other approaches like pharmacogenomics that not only focus on targeting oncogenic mutations but also aim to enhance drug metabolism and efficiency [ 265 ].

Pharmacogenomics offers a very compelling approach in the realm of personalized medicine through its implementation of genotyping technology to select and tailor drugs, predict drug delivery and dosage, anticipate adverse reactions, and ultimately improve treatment efficacy and prevention [ 265 , 266 ]. This field covers proteomics, transcriptomics, metabolomics, and metagenomics to develop single-gene drug therapies [ 267 ]. Rapalogs are currently the leading experimental candidates in advanced clinical trials that work by inhibiting the mTOR protein kinase. Notably, rapamycin and its analogs, temsirolimus, everolimus, and AP23573, are being tested and have generally shown good tolerance despite dose-dependent toxicity in some studies [ 91 , 268 ]. mTORC1 is sensitive to rapamycin, and the drug works by inhibiting downstream targets like 4E-BP1 and S6K1, which are involved in protein synthesis and cell growth, thus slowing down tumor progression [ 269 ]. However, rapamycin’s effect paradoxically leads to AKT activation by disrupting the mTOR/S6K-mediated feedback loop, which can unintentionally promote survival signals in cancer cells [ 90 , 91 , 268 ]. Despite the promising prospects of this approach, further research is needed to assess the drugs’ interference with metabolism and to address the resistance mechanisms that arise from the activation of the mTOR pathway [ 268 , 270 ]. Precision medicine still relies on co-administration with other therapies, involving the integration of NGS techniques, pharmacogenomics, and targeted therapies to minimize the adverse effects and setbacks in patient outcomes. The ongoing development and integration of emerging technologies such as artificial intelligence (AI) models in diagnosis, new cell-sequencing methods, and liquid biopsy techniques may open new avenues for providing comprehensive treatments for cancers that are driven by oncogenes and tumor-suppressor genes [ 260 , 271 , 272 , 273 ].

5. Emerging Technologies and Future Perspectives

5.1. Single-Cell Sequencing

Single-cell sequencing has emerged as a vital tool in analyzing tumor heterogeneity by offering the opportunity to sequence individual cells within a neoplastic lesion. The technique allows for the understanding of temporal heterogeneity and the evolution of cancer over time, showing the development of oncogenic mutations that lead to drug resistance. This technology allows for not only genomic, epigenomic, and transcriptional analysis but also offers insights into single-nucleotide variations, copy number variations, and structural variations within tumors [ 274 , 275 , 276 ]. The most widely used method of single-cell sequencing, single-cell RNA sequencing (scRNA-seq), begins with the isolation of a single cell from a tumor lesion using laser capture microdissection, where a laser precisely extracts individual cells from the tissue [ 277 , 278 ]. Cells are then kept in a lysis buffer and sorted into microtiter plates using fluorescence-activated cell sorting (FACS) [ 279 ]. The process of quickly and accurately isolating single cells without compromising their integrity is the biggest challenge for single-cell sequencing. For instance, using trypsin or collagenase in the buffer can impair cell viability and change the transcriptional profile. Following isolation, cells are treated with reverse transcriptase alongside a poly(t) primer, resulting in the creation of complementary DNA (cDNA), which is then amplified using a polymerase chain reaction or in vitro transcription [ 279 , 280 , 281 , 282 ]. Finally, sequencing is achieved using a sequencing platform such as Illumina.

Investigating tumor heterogeneity using single-cell sequencing offers valuable insight into the expression of oncogenes underlying drug resistance mechanisms. Using single-cell transcriptome sequencing, A study by Zhao et al. [ 283 ] utilized scRNA-seq to uncover increased tumor heterogeneity in a patient with recurring diffuse large B-cell lymphoma (DLBCL). The study identified malignant B-cell groups with upregulated genes coding for proteins involved in numerous oncogenic pathways, including MYC , BCL2 , and E2F targets [ 283 ]. Zhao et al. [ 283 ] isolated two distinct malignant B-cell subgroups from a singular tumor, one of which revealed co-expression of MYC and BCL2 . The co-expression of MYC and BCL2 is a hallmark of double-expression lymphoma (DEL) and is linked to poor overall prognosis in patients [ 284 , 285 ]. Further analysis by Zhao et al. [ 283 ] showed that cell populations with a double expression of MYC / BCL2 exhibited upregulation of inflammatory and immune-related signaling pathways, which were previously shown to promote tumor invasion and metastasis [ 286 ]. Meanwhile, the second isolated cell population of malignant B-cells lacked the double expression of MYC / BCL2 , therefore lacking the pro-inflammatory capabilities [ 283 ]. However, this population showed a marked reduction in MHC Class I and II expression [ 283 ]. Low MHC expression in tumor cells results in low immunogenicity and immune evasion capabilities, which also leads to a poor patient outcome [ 287 ]. scRNA-seq of this tumor revealed the heterogeneous nature of the present cells, where despite differing in MYC / BCL2 expression, each malignant B-cell population contained adaptations that would promote multi-drug resistance and tumorigenesis, particularly in diffuse large B-cell lymphoma (DLBCL) [ 283 ]. A similar study employed scRNA-seq to monitor the expression of the KIT F522C mutation in mast cell leukemia before and after 10 months of treatment with midostaurin, a chemotherapeutic agent that inhibits KIT signaling [ 288 ]. The KIT gene encodes a receptor tyrosine kinase involved in cell proliferation and migration, which gains oncogenic function when mutated or upregulated in cancer cells [ 289 ]. Pathogenic mutation in KIT is found in 80% of systemic mastocytosis cases, including mast cell leukemia [ 290 ]. Single sequencing of peripheral blood after treatment with midostaurin showed a decrease in cells expressing a KIT GOF mutation. However, an increase in the cells exhibiting a mutation in the runt-related transcription factor 1 gene ( RUNX1 ) was also observed. RUNX1 acts as an oncogene by enhancing the Wnt/β-catenin signaling pathway, promoting metastasis and poor prognosis [ 291 , 292 ].

Multi-omics is an integrative approach that combines multiple layers of biological data, such as genomics, transcriptomics, proteomics, metabolomics, etc. ( Table 5 ) [ 293 ].

The term “omics” refers to a comprehensive quantitative analysis of molecules in a sample to understand its underlying molecular mechanisms [ 294 ]. This approach is especially powerful in oncogene and tumor-suppressor research when combined with single-cell sequencing, as it allows researchers to observe molecular changes at a single-cell resolution [ 295 ]. The availability of a multi-omics approach represents a new era in cancer therapy. It uses algorithmic frameworks to identify crucial genomic alterations, making it a necessary tool for tumor classification, diagnostics, and prognostication [ 296 ]. One of the key applications of multi-omics is its role in the classification of tumor-suppressor genes [ 297 ]. Large-scale initiatives of multi-omics data, such as The Cancer Genome Atlas (TCGA) datasets, have made identifying tumor suppressors and other biomarkers in various cancers much more convenient [ 298 , 299 ]. By utilizing transcriptomics and functional genomic data to analyze breast cancer, one study revealed the significant role of the ribosome pathway in gene regulation of tumor suppressors. The study also highlighted that certain processes involved in mRNA processing are more active and are connected to key functions linked with angiogenesis and proliferation, which are very important for tumor development [ 297 ]. Furthermore, multi-omics can enhance the analysis of intratumoral heterogeneity when combined with NGS techniques, which help reveal the nuclear aberrations in tumors [ 300 ]. Multi-omics produces an extensive quantity of data that can be difficult to interpret. Maximizing the full potential of the high-throughput data from different molecular layers forces high costs and demands for more sophisticated analytical technologies [ 301 ].

5.2. CRISPR-Cas9 Screening

Recent advances in gene-editing technologies have significantly improved our understanding of the roles of oncogenes and tumor-suppressor genes in cancer pathology. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated endonuclease 9 (Cas9) protein has been developed as a powerful tool to rapidly and precisely induce oncogenic mutations within a cell population [ 302 ]. The Cas9 protein functions as an endonuclease that cleaves DNA to produce double-stranded breaks or single-strand nicks, creating new genetic sequences to study the resulting phenotypic changes [ 303 ]. While previous technologies, such as zinc-finger nucleases and transcription activator-like effector nucleases, used specific customizable proteins that offered advantages in gene editing, they were difficult to apply across whole-genome investigations.

CRISPR-Cas9 screening begins with recognizing a target gene sequence by using a single guide RNA (sgRNA), which then activates the Cas9 endonuclease to create a double-stranded break [ 304 ]. Following the introduction of the desired sequence, the double-stranded break is then repaired using the cell’s native non-homologous end joining or homology-directed repair system, depending on the cell-cycle stage in which the break was introduced [ 304 ]. Variations of Cas9 have been developed to improve its endonuclease activity. For example, the Cas9 mutant nickase (Cas9n) has been developed to introduce more site-specific double-stranded breaks while retaining the efficiency of the wild-type Cas9 protein [ 305 ]. By targeting specific gene sequences and observing phenotypic effects, vast libraries of sgRNA targets have been compiled to screen for essential genes in cancer and those associated with drug resistance [ 302 ].

Therapeutic targeting of oncogenes and tumor-suppressor genes using CRISPR-Cas9 technology has successfully identified their roles in affecting downstream mechanisms. One recent study used CRISPR-Cas9 to introduce a mutation in the BRAF gene in stem cells carrying a fusion mutation characteristic of angiomatoid fibrous histiocytoma (AFH) [ 306 ]. AFH tumors are known to be driven by the oncogenic fusion of the EWS RNA-binding protein 1 (EWSR1) and cAMP-responsive element-binding protein 1 (CREB1), making them a primary focus of targeted therapies [ 307 ]. By using CRISPR-Cas9 to induce BRAF c.1799 T > A point mutation in human embryonic stem cells with the EWSR1 / CREB1 fusion, Vanoli et al. [ 306 ] confirmed an upregulation of the downstream pathways that facilitate cancer proliferation. Upregulated genes included protein tyrosine phosphatase receptor type N ( PTPRN ), which is associated with cell growth, mitosis, and oncogenic transformation, as well as tissue-type plasminogen activator ( PLAT ), versican ( VCAN ), and transforming growth factor beta 1 ( TGF-β1 ), which play roles in tumor cell migration, adhesion, and proliferation [ 306 ]. CRISPR-Cas9 methods were vital in elucidating the role of BRAF in promoting the proliferation of AFH, as these effects were not observed in human stem cells that exhibited only the EWSR1 / CREB1 fusion [ 306 ].

Beyond manipulating and analyzing tumor cell samples, CRISPR-Cas9 holds potential for somatic gene therapy, aiming to introduce new genetic material in vivo to make the cancer more susceptible to treatment [ 308 ]. Current studies show limitations in achieving clinical success due to gene silencing, host immune response, and off-target effects [ 309 ]. However, nanotechnologies are being developed to reduce some of these adverse effects by using nanocarriers to deliver CRISPR-Cas9 components into cells, offering the potential for in vivo gene therapy in the future [ 310 ].

5.3. Artificial Intelligence and Machine-Learning Applications

Artificial intelligence (AI) has quickly become an emerging technology that has transformed the personalized approach to cancer medicine, including screening, diagnosis, early detection, and treatment [ 260 ]. For example, AI classification systems have been shown to be successful in differentiating between carcinoma and non-lactating metastasis in breast cancer, with the convolutional neural network (CNN) being particularly effective in detecting breast cancer with high accuracy [ 273 ]. AI technologies rely on algorithms such as machine learning (ML) and deep learning to analyze patients’ clinical variables and medical data, assisting in the treatment of lung cancer cases [ 311 ]. The diagnosis of lung cancer relies on early screening for lung nodules, which have a high likelihood of malignancy if larger than 3 cm [ 312 , 313 ]. Computer-aided diagnoses (CAD) tools have been developed to work with lung cancer databases like the Lung Image Database Consortium (LIDC) and the Early Lung Cancer Action Program (ELCAP) to diagnose malignancy in lung nodules [ 314 , 315 , 316 ]. In one study, 6400 images of 978 nodules were analyzed using the CAD system, achieving an accuracy, sensitivity, and specificity of 93.2%, 92.4%, and 94.8%, respectively, for malignancy diagnosis [ 317 ]. Studies have demonstrated that AI can also predict oncogenic mutations in lesions based on the morphological characteristics of NSCLC in hematoxylin–eosin-stained images [ 311 ]. Genetic alterations in the epidermal growth factor receptor (EFGR) and anaplastic lymphoma kinase (ALK) have been used as valuable predictive markers in NSCLC, with EFGR mutation prediction playing a vital role in selecting treatment [ 318 ]. AI analysis has been successful in predicting the mutations present in six common oncogenes ( STK11 , EGFR , FAT1 , SETBP1 , KRAS , and TP53 ), offering an accurate and cost-effective method for identifying the genetic markers that are important for treatment planning [ 319 ].

ML is another important tool in the genomic analysis of cancers, which opens new opportunities for personalized medicine. For instance, molecular signatures from gene expression profiling have been analyzed in prostate cancers and applied to clinical decision-making in treatment [ 320 ]. One study used ML models to identify 30 genes with downregulation and hypermethylation at their promoter region, which were used to predict metastasis in patients with prostate cancer [ 321 ]. Additionally, the ML model identified five significantly mutated genes in patients with metastasis, which included POLR3K (RNA polymerase III subunit K), EEF1D (eukaryotic translation elongation factor 1 delta), IGFALS (insulin-like growth factor-binding protein acid labile subunit), H2AW (H2A.W histone), and FASTK (Fas-activated serine/threonine kinase) [ 321 ]. Developments in AI offer many exciting opportunities for accurate, noninvasive diagnosis and a deeper understanding of the expression of oncogenes and tumor-suppressor genes underlying the pathology of cancer.

5.4. Liquid Biopsy

Tissue biopsies are a standard method for detecting and profiling tumors. While reliable, many limitations demand less invasive and more accurate approaches [ 322 ]. Unlike the surgical excision of malignant tissue samples, the analysis of cancer-related signals in biological fluids, known as liquid biopsy, has emerged as an alternative diagnostic tool [ 323 ]. The term “liquid biopsy” refers to the analysis of bodily fluids, such as blood, urine, cerebrospinal fluid, and saliva, to detect cancer-related biomarkers ( Figure 4 ). Analytes collected from these fluids include circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), cell-free RNA (miRNA, lncRNA, and circRNA), extracellular vesicles (EVs), immune cells, and proteins [ 271 , 322 ]. By analyzing these analytes, liquid biopsies can uncover genetic mutations, chromosomal abnormalities, DNA methylation patterns, tumor-associated protein expressions, immune cell activity profiles, etc. The method relies on advanced detection techniques such as PCR, NGS, and immunoassays to identify traditional and specific markers that are in low abundance in bodily fluids, making it especially useful in early screening, unlike traditional biopsies that occur at later stages of tumor development [ 322 ]. PCR tests with primers targeting tumor-specific transcriptions, mutations, translocations, and methylation patterns can detect CTCs, even in cases of low mRNA expression [ 324 , 325 ]. Additionally, NGS techniques like WES provide a more comprehensive cancer-gene panel, allowing for the characterization of ctDNA and exosomes, addressing the limitations of traditional laboratory techniques [ 322 , 326 ].

Liquid biopsies cover a broad range of clinical applications, including assessing immunotherapy response at checkpoint blockades, prognostication, early cancer detection, evaluating residual disease after treatment, early evaluation of response and resistance, and understanding tumor heterogeneity [ 327 , 328 , 329 , 330 , 331 ]. The diagnostic methodologies within liquid biopsy have shown promise in the isolation of oncogene mutations. Siravegna et al. [ 332 ] identified alterations in KRAS , NRAS , MET , ERBB2 , FLT3 , EGFR , and MAP2K1 by studying patients’ ctDNA, highlighting their role in oncogenic signaling pathways. Moreover, using liquid biopsy, alterations in BRAF , HER2 , AKT , and ROS1 can be identified, proving it to be a useful way for studying oncogene-addicted cancers [ 333 , 334 ]. Beyond primary diagnosis, liquid biopsies can be employed throughout a patient’s treatment course to measure the progression and responsiveness of the treatment [ 335 ]. Minimal residual disease (MRD) refers to cancer cells persisting at undetectable levels in a patient after the treatment, posing a risk for recurrence [ 336 , 337 ]. The application of liquid biopsy analyses can identify very low concentrations of CTCs and ctDNA in blood samples, thus enabling MRD detection in patients with various malignancies [ 335 , 336 , 337 ]. However, it is important to note that liquid biopsies are still majorly limited due to their lack of sensitivity and precision in identifying tumor types to the same degree as tissue biopsy. These limitations also raise concerns about whether liquid biopsy samples are representative of all genomic clones within a tumor or just a specific sub-region [ 338 , 339 ].

5.5. Tumor Microenvironment, Cancer Vaccines, and Oncolytic Viruses

The tumor microenvironment (TME) is a complex network of immune cells, stromal cells, blood vessels, extracellular matrix (ECM), and signaling molecules that interact with the tumor cells [ 340 ]. The effectiveness of antitumor immunity is determined by the collaboration between innate and adaptive immune cells within the TME [ 341 ]. In this environment, natural killer (NK) cells, neutrophils, and macrophages drive rapid tumoricidal activity, while dendritic cells (DCs) present and process antigens for the activation of CD4 + and CD8 + T cells [ 341 , 342 ]. Once activated, T cells migrate to the TME, where they inhibit tumor cell proliferation and induce cell death via interferon γ (IFNγ), interleukin 2 (IL-2), and tumor necrosis factor (TNF)-dependent mechanisms [ 341 ]. Cancer-associated fibroblasts (CAFs) are a type of cell found in the TME that is activated in response to cancer, contributing to the overproduction of ECM, growth factors (VEGF), and cytokines (TGF-B), which promote tumor progression [ 343 , 344 ]. CAFs, along with tumor-associated macrophages (TAMs), have been shown to contribute to the extrinsic immunoresistance of cancers [ 341 ]. Recent research suggests that using MMP inhibitors or collagen crosslinking inhibitors can disrupt the formation of ECM by CAFSs, facilitating the migration of T cells and, thus, reinforcing the effect of immunotherapies [ 341 , 345 ].

TME-associated cancer vaccination therapies are a novel extension of immunotherapy that offers revolutionary potential in oncology [ 346 ]. Cancer vaccines can be clinically used therapeutically or preventively and are delivered in four forms: cell-based, viral/bacterial-based, peptide-based, and nucleic acid-based vaccines ( Figure 5 ) [ 347 , 348 ]. These vaccines use tumor-associated antigens (TAAs) and tumor-specific antigens (TSAs) to elicit an immune response in patients that would provoke both cellular and humoral immune responses to eradicate tumors and prevent tumorigenesis [ 348 , 349 ]. Cell-based vaccines are prepared using whole tumor cells or cell fragments, which can be injected directly or loaded on DCs with adjuvants to enhance immunogenicity [ 348 , 350 ]. Viral/bacterial-based vaccines are naturally immunogenic, and their genetic material can be engineered to express tumor antigens [ 348 ]. Peptide-based vaccines contain biosynthetic peptides that represent known tumor antigens to stimulate the immune system to attack particular tumor sites [ 348 ]. Lastly, nucleic acid-based vaccines deliver genetic material that encodes tumor antigens, thus inducing MHC I-mediated CD8 + T-cell responses, making it one of the more promising approaches [ 348 , 351 ].

Therapeutic cancer vaccines have shown great success in clinical trials [ 347 ]. Several therapeutic vaccines that have been approved by the FDA are already in use against various cancers ( Table 6 ) [ 347 ].

Sipuleucel-T was the first FDA-approved therapeutic cell-based vaccine for metastatic prostate cancer [ 352 ]. The prolonged disease course of advanced prostate cancer creates a window where the body can generate an immune response against the cancer cells [ 353 ]. Another example is the bacillus Calmette–Guerin (BCG) vaccine, which is a bacterial-based vaccine used to treat early-stage bladder cancer [ 347 ]. BCG uses inactivated tuberculosis bacteria, which is administered through a catheter to stimulate an immune response, causing apoptosis, necrocytosis, and oxidative stress [ 354 , 355 ]. Despite recent successes, therapeutic cancer vaccines face several challenges, such as difficulties in finding cancer-specific antigens, as some antigens can be present on both normal and cancerous cells, which could cause unwanted side effects, or the production of molecules by the cancer cells that suppress the immune response [ 347 ].

While therapeutic vaccines target existing tumors, prophylactic/preventive cancer vaccines aim to reduce the initial risk of cancer development, primarily protecting against virus-induced cancers [ 347 ]. One of the two currently approved and common prophylactic cancer vaccines is the Human Papillomavirus (HPV) vaccine ( Table 6 ), which utilizes a virus-like particle of the non-oncogenic and non-infectious papillomavirus capsid protein L1 to build an immune response that would prevent HPV from inserting itself into the host’s genome and cause nuclear aberrations [ 356 , 357 ]. The other prophylactic vaccine in use is the Hepatitis B vaccine, which is a common liver infection that leads to liver cirrhosis and hepatocellular carcinoma (HCC) ( Table 6 ) [ 358 ]. A study by Cao et al. [ 359 ] showed that the Hepatitis B vaccine offers 72% protection against liver cancer post-infection.

Oncolytic viruses (OVs) are a novel immunotherapy that functions similarly to viral-based cancer vaccines to specifically target and kill tumor cells and promote anti-tumor immune responses [ 348 ]. The OV infects the tumor cells, inducing ROS production and cytokine release to stimulate immune cells and subsequently release TAAs [ 348 , 360 ]. OVs can induce various forms of immunogenic cell death, such as apoptosis, necroptosis, and pyroptosis, stimulating the dying host cells to release damage-associated molecular patterns (DAMPs), creating a pro-inflammatory environment that advances the maturation of antigen-presenting cells to further activate the immune response [ 361 , 362 , 363 , 364 ]. Moreover, OVs can be synergistically used with immune checkpoint inhibitors, such as anti-PD-1/PD-L1 and anti-CTLA-4 antibodies, to modulate the TME and strengthen the immune response [ 364 , 365 ]. Some OVs that are considered possible vehicles for oncoviral therapy are the herpes simplex virus, measles, mumps, adenovirus, retrovirus, and parvovirus, among others [ 366 ]. Talimogene laherparepvec (T-VEC), the first approved OV immunotherapy, treats metastatic melanoma by successfully activating tumor-specific effector T cells and TAAs ( Table 6 ) [ 367 ]. Adenoviruses and herpes simplex viruses are the most promising OVs, as they are easy to manipulate, have a clear genetic structure, can easily achieve gene transfer and tumor antigen expression, have a broad spectrum of host cell tropism, and can be prepared in large quantities [ 368 , 369 , 370 ].

Cancer vaccines and OVs hold promise as emerging cancer therapies, but they face significant limitations. Cancer vaccines often struggle with weak immunogenicity and tumor heterogeneity, reducing their effectiveness [ 346 , 371 ]. Similarly, oncolytic viruses are limited by delivery challenges, immune clearance, and variable tumor susceptibility [ 372 ]. These hurdles highlight the need for further research to optimize their clinical impact.

6. Conclusions

The interaction between tumor-suppressor genes and oncogenes represents an essential area of research for enhancing our understanding of genomic treatments for cancer. By clarifying the roles of these genes in regulating the key signaling pathways involved in cell proliferation—such as p53, Rb, Ras/Raf/ERK/MAPK, PI3K/AKT, and Wnt/β-catenin—clinicians and researchers will be better equipped to develop therapeutic strategies that target specific genomic alterations in patients. As studies reveal how these genes interact with one another and fulfill various tumorigenic roles, they provide a more comprehensive perspective on the complexities of tumor heterogeneity due to multiple genetic alterations. This understanding is further improved by exploring the tumor microenvironment, where innate and adaptive immune cells (NK cells and DCs) promote antitumor responses. Most importantly, as recent research progresses, these insights will support the development of novel technologies for sequencing, diagnosing, and treating cancer at a genomic level in a personalized manner, thereby potentially revolutionizing the field of oncology and genomics.

Advancements in sequencing technologies, such as NGS methods and, particularly, single-cell sequencing methods, stand out as the most significant developments in genomics and have significant implications in diagnostics, preventative screening, and progression monitoring. By focusing on short- and long-coverage genome sequencing, these emerging techniques can efficiently identify a broad spectrum of genetic alterations, ranging from SNVs, CNVs, insertions, and deletions. These methods can spot existing mutations and discover genomic regions that are susceptible to mutation. NGS unlocks the potential for less-invasive procedures, such as liquid biopsy, that can monitor tumorigenesis more efficiently than traditional methods by detecting and sequencing cancer biomarkers, establishing transcriptional profiles, evaluating residual disease after treatment, and predicting and identifying morphological variations between tumors. While liquid biopsy offers depth and quantity in identifying tumorigenic manifestations, it still lacks the sensitivity and precision of traditional tissue biopsy. Additionally, the sequencing techniques generate a large amount of data for laboratories to process and interpret, which calls for advancements in bioinformatics to improve analytical efficiency. The integration of AI and ML into oncology can overcome these limitations to improve the existing and develop new diagnostic approaches. Using advanced algorithms, AI models can identify the predictive markers and epigenetic changes of tumor suppressors and oncogenes and differentiate between the pathology and morphology associated with tumor heterogeneity. Moreover, CRISPR-Cas9 offers a revolutionary tool for directly manipulating the specific gene sequences that are responsible for tumorigenesis and drug resistance mechanisms, further improving the precision of genomic interventions. Finally, the insights into the tumor microenvironment enable innovations in cancer vaccines and oncolytic virus therapies by enhancing the ability of the immune system to detect and target tumor cells, thus edging closer to truly personalized and effective prevention, detection, diagnosis, and treatment of cancer.

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Author Contributions

Conceptualization, R.S. (Radoslav Stojchevski) and D.A.; methodology, R.S. (Radoslav Stojchevski) and D.A.; validation, R.S. (Radoslav Stojchevski) and D.A.; investigation, R.S. (Radoslav Stojchevski), E.A.S. and R.S. (Rinni Sutanto); visualization, R.S. (Radoslav Stojchevski); resources, D.A.; writing—original draft preparation, R.S. (Radoslav Stojchevski), E.A.S. and R.S. (Rinni Sutanto); writing—review and editing, R.S. (Radoslav Stojchevski), N.H.-P., M.M., S.R.S., R.B., S.G., B.Y., K.K.S., P.V., S.S., J.K.S. and D.A. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript: ABC ATP-Binding Cassette ADC Antibody–Drug Conjugate AFH Angiomatoid Fibrous Histiocytoma AI Artificial Intelligence AKT Protein Kinase B ALL Acute Lymphoblastic Leukemia ALK Anaplastic Lymphoma Kinase ALV Avian Leukosis Virus AML Acute Myeloid Leukemia APC Adenomatous Polyposis Coli AS-PCR Allele-Specific Polymerase Chain Reaction BAD Bcl-2-Associated Death Promoter BCG Bacillus Calmette–Guerin BCRP Breast Cancer Resistance Protein Bcl-2 B-cell Lymphoma-2 bp Base Pairs BRCA1 Breast Cancer Gene 1 CAF Cancer-Associated Fibroblasts CAD Computer-Aided Diagnoses CAR Chimeric Antigen Receptor Cas9 CRISPR-associated Endonuclease 9 Cas9n Cas9 Mutant Nickase CCNE Cyclin E CDK Cyclin-Dependent Kinase CDKN2A Cyclin-Dependent Kinase Inhibitor 2A cDNA Complementary DNA c-MYC Cellular Myelocytomatosis Oncogene CKI Cyclin-Dependent Kinase Inhibitors CNN Convolutional Neural Network CML Chronic Myelogenous Leukemia CNV Copy Number Variations CRS Cytokine Release Syndrome CRISPR Clustered Regularly Interspaced Short Palindromic Repeats CTNNB1 Catenin-Beta-1 CTCs Circulating Tumor Cells ctDNA Circulating Tumor DNA DAMP Damage-Associated Molecular Patterns DC Dendritic Cell DEL Double-Expression Lymphoma DLBCL Large B-cell Lymphoma DNMT DNA Methyltransferase DVL Disheveled Protein ECM Extracellular Matrix EGF Epidermal Growth Factor EFGR Epidermal Growth Factor ELCAP Early Lung Cancer Action Program EMT Epithelial to Mesenchymal Transition ERK Extracellular Signal-Regulated Kinase EWSR1 EWS RNA-Binding Protein 1 EVs Extracellular Vesicles FACS Fluorescence-Activated Cell Sorting FASTK Fas-Activated Serine/Threonine Kinase FDA U.S. Food and Drug Administration FFPE Formalin-Fixed Paraffin Embedded FL Follicular Lymphoma FOXO Forkhead Box O GAC Gastric Adenocarcinoma GADD45 Growth Arrest and DNA Damage-Inducible 45 GEF Guanine Nucleotide Exchange Factors GEJ Gastroesophageal Junction Adenocarcinoma GIST Gastrointestinal Stromal Tumor GOF Gain of Function GSK-3β Glycogen Synthase Kinase-3β H2AW H2A.W Histone H3K27me3 Trimethylation of Histone H3 at Lysine 27 HCC Hepatocellular Carcinoma HER2 Human Epidermal Growth Factor Receptor 2 HL Hodgkin Lymphoma HPV Human Papillomavirus HR+ Hormone Receptor Positive (Estrogen and Progesterone) IARC International Agency for Research on Cancer EEF1D Eukaryotic Translation Elongation Factor 1 Delta IFNγ Interferon gamma IGF-1 Insulin-Like Growth Factor-1 IGFALS Insulin-Like Growth Factor Binding Protein Acid Labile Subunit IL-2 Interleukin 2 kbp Kilobase Pairs LIDC Lung Image Database Consortium LOF Loss of Function LOH Loss of Heterozygosity MAPK Mitogen-Activated Protein Kinase mbp Megabase Pairs MCL Mantle Cell Lymphoma mCRPC Metastatic Castration-Resistant Prostate Cancer MDM2 Murine Double Minute 2 MDMX Murine Double Minute X MEK Mitogen-Activated Protein Kinase Kinase ML Machine Learning MMEJ Microhomology-Mediated End Joining miRNA MicroRNAs MLH1 MutL Protein Homolog 1 MM Multiple Myeloma MMPs Matrix Metalloproteinases MRP Multidrug Resistance-Associated Proteins MRD Minimal Residual Disease mtDNA Mitochondrial DNA mTORC2 mTOR Complex 2 NGS Next-Generation Sequencing NK Natural Killer NMIBC Non-Muscle-Invasive Bladder Cancer NSCLC Non-Small Cell Lung Cancer PARP1 Poly (ADP-ribose) Polymerase 1 PI3K Phosphoinositide 3-Kinase PIP2 Phosphatidylinositol-4,5-bisphosphate PIP3 Phosphatidylinositol-3,4,5-trisphosphate PLAT Tissue Type Plasminogen Activator PDK1 3-Phosphoinositide-Dependent Protein Kinase 1 POLQ DNA Polymerase Theta POLR3K RNA Polymerase III Subunit K PP1 Protein Phosphatase PRC2 Polycomb Repressive Complex 2 PTCL Peripheral T-Cell Lymphoma PTEN Phosphate and Tensin Homolog PTPRN Protein Tyrosine Phosphatase Receptor Type N p21 CDKN1A pNET Primitive Neuro-Ectodermal Tumor RCC Renal Cell Carcinoma RIT Radioimmunotherapy ROS Reactive Oxygen Species RSV Rous Sarcoma Virus RTK Receptor Tyrosine Kinases sgRNA Single Guide RNA SSB Single-Stranded Breaks SNP Single-Nucleotide Polymorphism SNV Single Nucleotide Variants TAM Tumor-Associated Macrophage TAA Tumor-Associated Antigen TCGA The Cancer Genome Atlas TGFβ1 Transforming Growth Factor Beta 1 TNBC Triple-Negative Breast Cancer TNF Tumor Necrosis Factor TSA Tumor-Specific Antigen TS Targeted Sequencing TME Tumor Microenvironment T-VEC Talimogene Laherparepvec VAF Variant Allele Frequency VCAN Versican VEGF Vascular Endothelial Growth Factor WES Whole Exome Sequencing WGS Whole Genome Sequencing WHO World Health Organization

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