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New Frontiers in Cancer Therapy and Diagnostics
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Cancer remains one of the most pressing challenges in modern medicine, but recent advancements are revolutionizing both therapeutic and diagnostic landscapes. This exploration of new frontiers in cancer therapy and diagnostics highlights a diverse array of innovative strategies that target the molecular mechanisms of tumorigenesis while enhancing early detection and personalized care. Cutting-edge therapies, such as small-molecule inhibitors and monoclonal antibodies, specifically target oncogene-driven pathways, offering selective toxicity over traditional chemotherapy. Immunotherapies, including immune checkpoint inhibitors, radioimmunotherapy, antibody-drug conjugates (ADCs), and chimeric antigen receptor (CAR) T-cell therapy, activate the immune system to combat malignancies, showing remarkable efficacy in oncogene-addicted cancers and hematological malignancies. Emerging approaches like cancer vaccines and oncolytic viruses further amplify immune responses, while liquid biopsy transforms diagnostics by analyzing circulating tumor markers for early detection, treatment monitoring, and resistance profiling. Artificial intelligence (AI) and machine learning amplify these advances, refining diagnosis through image analysis, predicting oncogenic mutations, and guiding personalized treatment plans. Together, these breakthroughs—including targeted therapies, immunotherapies, and technology-driven diagnostics—represent a major progress in oncology, though challenges like drug resistance, tumor heterogeneity, and accessibility persist. This summary highlights the promise and complexity of these new frontiers, paving the way for more effective, tailored cancer management.

Small-molecule inhibitors Monoclonal antibodies Immune checkpoint inhibitors Radioimmunotherapy Antibody-drug conjugates Chimeric antigen receptor (CAR) T cell therapy Liquid biopsy Cancer vaccines Oncolytic viruses Artificial inteligence (AI)

1. Targeted Therapy and Immunotherapy: Precision Approaches to Cancer

Among the most promising novel therapeutic strategies designed to combat cancer are targeted therapy and immunotherapy, which leverage precision medicine that targets specific proteins and genetic changes driving tumor heterogeneity [1]. Unlike traditional chemotherapy, which nonselectively harms all cells, targeted therapy and immunotherapy focus on abnormal proteins or immune responses, minimizing toxicity to healthy cells [1][2]. These approaches encompass a range of techniques, including small-molecule inhibitors, monoclonal antibodies, immune checkpoint inhibitors, radioimmunotherapy, antibody-drug conjugates (ADCs), chimeric antigen receptor (CAR) T-cell therapy, cancer vaccines, and oncolytic viruses [3][4][5].

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 [6] (Figure 1). Their low molecular weight allows them to diffuse through cells and target intracellular drivers that regulate proliferation and apoptosis [2]. The list of various U.S. Food and Drug Administration (FDA)-approved small-molecule inhibitors for cancer treatment is shown in (Table 1). 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 [7]. Recent breakthroughs have shown promising applications of small-molecule inhibitors in treating oncogene-driven mutations [2]. 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 [2][8]. 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 [8][9]. 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 [10][11].

Table 1. FDA-approved small-molecule inhibitors in use for treatment of various cancers.
Inhibitor Target Mechanism of Action FDA Approval
Erlotinib EGFR (RTK) Competitively inhibits ATP binding to EGFR; Blocks downstream signaling 2004 (NSCLC),
2005 (Pancreatic cancer)
Gefitinib EGFR (RTK) Inhibits EGFR tyrosine kinase activity;
reduces cell proliferation
2003 (NSCLC)
Lapatinib HER2, EGFR (RTKs) Dual inhibitor; prevents phosphorylation and signaling 2007 (HER+ Breast cancer)
Sunitinib VEGFR, PDGFR, KIT (RTKs) Inhibits multiple RTKs; leads to antiangiogenic and antiproliferative effects 2006 (pNET, RCC, GIST)
Sorafenib VEGFR, PDGFR, KIT (RTKs) Inhibits RTKs and RAF kinase;
blocks angiogenesis and tumorigenesis
2005 (HCC, RCC,
Thyroid cancer)
Vemurafenib BRAF V600E
(MAPK Pathway)
Selectively inhibits mutant BRAF; prevents aberrant MAPK activation 2011 (Melanoma,
Erdheim–Chester disease)
Dabrafenib BRAF V600E
(MAPK Pathway)
Inhibits mutant BRAF kinase;
reduces MAPK-driven cell proliferation
2013 (Melanoma, NSCLC, Anaplastic thyroid cancer)
Encorafenib BRAF V600E
(MAPK Pathway)
BRAF kinase inhibitor; blocks mutant BRAF kinase; inhibits MAPK pathway signaling. 2018 (Melanoma),
2020 (Colorectal cancer), 2023 (NSCLC)
Binimetinib MEK1/2
(MAPK Pathway)
Blocks MEK1/2 activity; inhibits downstream MAPK pathway signaling. 2018 (Melanoma),
2023 (NSCLC)
Trametinib MEK1/2
(MAPK Pathway)
Inhibits MEK1/2; blocks MAPK activation downstream of BRAF 2013 (Melanoma, NSCLC, Anaplastic thyroid cancer)
Cobimetinib MEK1/2
(MAPK Pathway)
Selectively inhibits MEK;
suppresses MAPK signaling
2015 (Melanoma)

Monoclonal antibodies are immunoglobulins designed to bind specific antigens and represent the second most common form of targeted therapy [1][6]. 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 [2][12] (Figure 1). This approach is primarily used to target the antigens associated with oncogene signaling, thus inhibiting the pathways that promote cancer cell growth and survival [13]. The most common clinical applications of monoclonal antibodies are trastuzumab (targeting HER2), cetuximab (targeting EGFR), and pembrolizumab (targeting the PD-1/PD-L1 axis) [14][15][16]. 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 [17][18]. Similarly, cetuximab and panitumumab bind to EGFR, preventing ligand binding and receptor dimerization, which inhibits oncogene signaling [19][20]. 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[19][21].

Immunotherapy is a 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[22]. 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 [23]. 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 [4][24]. 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 [22][25][5]. Immunotherapy may also improve outcomes for tumors with genomic instability, including mutations in tumor-suppressor genes such as TP53, STK11, and KEAP1[24][26]. Moreover, immunotherapy has also shown success when combined with chemotherapy and anti-angiogenesis agents like bevacizumab, which work synergistically to change the tumor microenvironment[25][27].

Radioimmunotherapy (RIT) is an extension of immunotherapy that combines targeted radiation with monoclonal antibodies to selectively target and destroy cancer cells[28]. This approach delivers a high dose of therapeutic or tracer radiation while minimizing exposure to normal cells[29]. 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 [30][31]. Beyond its cytotoxic capabilities, RIT can influence oncogene and tumor-suppressor gene activity. For instance, a study by Guo et al. [32] 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 [32].

Figure 1. Mechanisms of action of small-molecule inhibitors, monoclonal antibodies, immune checkpoint inhibitors, and radionuclides in cancer therapy. Small-molecule inhibitors target key receptors and kinases to disrupt signaling pathways and block tumor progression. Monoclonal antibodies recognize and bind specific antigens on the tumor cell surface, leading to immune-mediated destruction. Immune checkpoint inhibitors enhance T-cell activity by blocking inhibitory immune signals, restoring the immune system’s ability to recognize and eliminate tumor cells. Radioimmunotherapy combines monoclonal antibodies with radionuclides to selectively deliver cytotoxic radiation to tumor cells, increasing treatment precision while minimizing damage to normal tissues. Created in BioRender. Stojchevski, R. (2025) https://BioRender.com/k49h851.

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 [33]. 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[34]. The first FDA-approved ADC was the anti-CD33-targeted agent gemtuzumab ozogamicin in 2000, to treat patients with acute myeloid leukemia[35]. Since then, there have been eleven FDA-approved ADCs (Table 2) 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[36], and many more are in advanced stages of clinical trials.

Table 2. FDA-approved antibody–drug conjugates (ADCs) for treatment of various cancers.
ADC Generic Name Target Antigen Cytotoxic Payload FDA Approval
Loncastuximab tesirine CD19 SG3199, alkylating agent
(DNA targeting)
2021 (Diffuse Large B-Cell
Lymphoma—DLBCL)
Inotuzumab ozogamicin CD22 Calicheamicin
(cytotoxic antibiotic)
2017 (B-cell Acute Lymphoblastic
Leukemia—ALL)
Brentuximab vedotin CD30 Monomethyl auristatin E
(microtubule targeting)
2011, 2015, 2018
(Hodgkin lymphoma—HL;
2011, 2017, 2018 (Anaplastic Large Cell
Lymphoma—ALCL);
2018 (Peripheral T-Cell Lymphoma—PTCL)
Gemtuzumab ozogamicin CD33 Calicheamicin
(cytotoxic antibiotic)
2017 (Acute Myeloid Leukemia—AML)
Polatuzumab vedotin CD79b Monomethyl auristatin E
(microtubule targeting)
2019, 2023 (DLBCL)
Trastuzumab emtansine HER2 DM1
(microtubule targeting)
2013, 2019 (HER2+ Breast Cancer)
Trastuzumab deruxtecan HER2 Topoisomerase I inhibitor
(DNA targeting)
2019, 2022 (HER2+ Breast Cancer);
2021 (Gastric Adenocarcinoma—GAC
or Gastroesophageal Junction—GEJ
Adenocarcinoma);
2022 (NSCLC)
Tisotumab vedotin Tissue Factor Monomethyl auristatin E
(microtubule targeting)
2021 (Cervical Cancer)
Mirvetuximab soravtansine–gynx Folate Receptor
Alpha
DM4
(microtubule targeting)
2022 (Ovarian Cancer, Fallopian Tube Cancer, and Peritoneal Cancer)
Enfortumab vedotin Nectin-4 Monomethyl auristatin E
(microtubule targeting)
2019, 2023 (Urothelial Cancer)
Sacituzumab govitecan Trop-2 SN-38
topoisomerase-1 inhibitor
(DNA targeting)
2020 (Triple-Negative Breast Cancer—TNBC);
2021 (Urothelial Cancer);
2023 (HER2- Breast Cancer,
HR+ Breast Cancer)

Chimeric antigen receptor (CAR) T-cell therapy is a novel cancer therapy that uses patients’ own T cells to fight cancer[37]. 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 [38]. 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 [39]. So far, there are six FDA-approved CAR T-cell products for treating hematological malignancies (Table 3), and many more are in active clinical trials[40].

Table 3. FDA-approved CAR T-cell products for treatment of hematological malignancies.
CAR T-Cell Product
Generic Name
Target Antigen FDA Approval
Tisagenlecleucel CD19 2017 (ALL); 2018 (DLBCL);
2022 (Follicular lymphoma—FL)
Axicabtagene ciloleucel CD19 2017, 2022 (DLBCL, PMBCL);
2021 (FL)
Brexucabtagene autoleucel CD19 2020 (Mantle Cell Lymphoma—MCL);
2021 (ALL)
Lisocabtagene maraleucel CD19 2021, 2022, 2024 (DLBCL, PMBCL)
Idecabtagene vicleucel BCMA 2021, 2024 (Multiple Myeloma—MM)
Ciltacabtagene autoleucel BCMA 2022, 2023 (MM)

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[41]. The resistance often stems from genetic mutations, altered signaling pathways, or changes in the tumor microenvironment, such as variable vasculature and immune suppression [42]. 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 [43]. 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 [44][45]. Addressing these challenges and limitations of targeted cancer therapies is crucial for improving therapeutic outcomes and treatment strategies to overcome these challenges.

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 2) [46][47]. 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 [47][48]. 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 [47][49]. Viral/bacterial-based vaccines are naturally immunogenic, and their genetic material can be engineered to express tumor antigens [47]. Peptide-based vaccines contain biosynthetic peptides that represent known tumor antigens to stimulate the immune system to attack particular tumor sites [47]. 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 [47][50].

Figure 2. Types of cancer vaccines. There are four types of cancer vaccines: cell-based, viral/bacterial-based, peptide-based, and nucleic acid-based vaccines. Cell-based vaccines are prepared using whole tumor cells or tumor cell fragments. which can be injected directly or loaded onto dendritic cells along with adjuvants to enhance their immunogenicity and stimulate a stronger anti-tumor immune response. Viral/bacterial-based vaccines are designed using recombinant viral or bacterial vectors to deliver genetic material encoding cancer-specific proteins or antigens. These vectors infect host cells, enabling the expression of the target antigens and stimulating an immune response against cancer cells. Peptide-based vaccines use short biosynthetic peptides that mimic specific tumor epitopes of tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs) to stimulate the immune system to recognize and attack cancer cells at specific tumor sites where the target antigens are expressed. Nucleic acid-based vaccines deliver genetic material (RNA or DNA) that encodes tumor-specific antigens. The RNA or DNA is typically encapsulated in carriers to protect it from degradation and facilitate efficient delivery into the host cells. Once inside, the genetic material is expressed, producing the target antigens, which are then presented to the immune system. This stimulates T and B cells to recognize and attack cancer cells that express these antigens. Created in BioRender. Stojchevski, R. (2025) https://BioRender.com/c04n703.

Therapeutic cancer vaccines have shown great success in clinical trials [46]. Several therapeutic vaccines that have been approved by the FDA are already in use against various cancers (Table 4) [46]. Sipuleucel-T was the first FDA-approved therapeutic cell-based vaccine for metastatic prostate cancer [51]. The prolonged disease course of advanced prostate cancer creates a window where the body can generate an immune response against the cancer cells [52]. Another example is the bacillus Calmette–Guerin (BCG) vaccine, which is a bacterial-based vaccine used to treat early-stage bladder cancer [46]. BCG uses inactivated tuberculosis bacteria, which is administered through a catheter to stimulate an immune response, causing apoptosis, necrocytosis, and oxidative stress [53][54].

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 [46]. 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 [55][56]. 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 4) [57]. A study by Cao et al. [58] showed that the Hepatitis B vaccine offers 72% protection against liver cancer post-infection.

Table 4. FDA-approved cancer vaccines.
Vaccine Name Type Key Details Prophylactic/
Therapeutics
FDA Approval
Sipuleucel-T
(Provenge)
Cell-based Autologous dendritic cells activated with PAP-GM-CSF fusion protein Therapeutic 2010 (Metastatic castration-
resistant prostate cancer mCRPC)
Bacillus Calmette-Guerin (BCG) Bacterial-based Live attenuated bacterium; stimulates immune response against bladder tumors Therapeutic 1990 (Non-muscle-invasive bladder cancer NMIBC)
Talimogene Laherparepvec
(T-VEC, Imlygic)
Viral-based
(Oncolytic)
Modified herpes virus;
lyses tumors and enhances
antitumor immunity
Therapeutic 2015 (Melanoma)
Hepatitis B (HBV) Vaccine
(Recombivax HB, Energix-B)
Viral-based
(Recombinant protein)
Prevents HBV infection, indirectly reducing HCC Prophylactic 1986 Recombivax HB
1989 Energix-B,
(Hepatitis B virus—prevents HCC)
HPV Vaccines
(Cervarix, Gardasil 9)
Viral-based
(Virus replicon particle)
Targets HPV strains (i.e., 16/18) directly linked to HPV-related cancer and prevents infection Prophylactic 2009 Cervarix, 2014 Gardasil 9
(HPV-related cancers—
prevents cervical, anal, and other types of cancers)
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 [47]. The OV infects the tumor cells, inducing ROS production and cytokine release to stimulate immune cells and subsequently release TAAs [47][59]. 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 [60][61][62][63]. 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 [63][64]. Some OVs that are considered possible vehicles for oncoviral therapy are the herpes simplex virus, measles, mumps, adenovirus, retrovirus, and parvovirus, among others [65]. Talimogene laherparepvec (T-VEC), the first approved OV immunotherapy, treats metastatic melanoma by successfully activating tumor-specifi[74]c effector T cells and TAAs (Table 4) [66]. 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 [67][68][69].
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 [70][71]. Similarly, oncolytic viruses are limited by delivery challenges, immune clearance, and variable tumor susceptibility [72]. These hurdles highlight the need for further research to optimize their clinical impact.

2. Diagnostic and Analytical Innovations: Advanced Tools for Cancer Care

Among the most transformative advancements in cancer management are diagnostic and analytical tools like liquid biopsy, artificial intelligence (AI), and machine learning, which enable precise detection and profiling of tumor characteristics [73][74]. Tissue biopsies are a standard method for detecting and profiling tumors. While reliable, many limitations demand less invasive and more accurate approaches [74]. 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 [75]. 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 3). 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 [76][74]. 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 [74]. PCR tests with primers targeting tumor-specific transcriptions, mutations, translocations, and methylation patterns can detect CTCs, even in cases of low mRNA expression [77][78]. 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 [74][79].
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 [80][81][82][83][84]. The diagnostic methodologies within liquid biopsy have shown promise in the isolation of oncogene mutations. Siravegna et al. [85] 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 [86][87]. Beyond primary diagnosis, liquid biopsies can be employed throughout a patient’s treatment course to measure the progression and responsiveness of the treatment [88]. Minimal residual disease (MRD) refers to cancer cells persisting at undetectable levels in a patient after the treatment, posing a risk for recurrence [89][88]. 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 [88][89][90]. 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 [91][92].
Figure 3. Liquid biopsy of bodily fluids (blood). Analytes collected through liquid biopsy include circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), cell-free RNA (e.g., miRNA, lncRNA, circRNA), extracellular vesicles (EVs), immune cells, and proteins, which are then analyzed using advanced detection techniques such as PCR and next-generation sequencing (NGS) to identify specific cancer markers, even at low abundance. Liquid biopsy enables the discovery of various cancer-related biomarkers, including various genetic mutations, chromosomal abnormalities, DNA methylation patterns, tumor-associated protein expression, and post-translational modifications, such as phosphorylation. Created in BioRender. Stojchevski, R. (2025) https://BioRender.com/h75n317.
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 [93]. 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 [73]. 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 [94]. The diagnosis of lung cancer relies on early screening for lung nodules, which have a high likelihood of malignancy if larger than 3 cm [95][96]. 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 [97][98][99]. 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 [100]. Studies have demonstrated that AI can also predict oncogenic mutations in lesions based on the morphological characteristics of NSCLC in hematoxylin–eosin-stained images [94]. Genetic alterations in the epidermal growth factor receptor (EFGR) and anaplastic lymphoma kinase (ALK) have been used as valuable predictive markers in NSCLC, with EGFR mutation prediction playing a vital role in selecting treatment [101]. 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 [102].
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 [103]. 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 [104]. 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) [104]. 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.
Abbreviations

ADC – Antibody–Drug Conjugate

AI – Artificial Intelligence

AKT – Protein Kinase B

ALK – Anaplastic Lymphoma Kinase

AML – Acute Myeloid Leukemia

BCG – Bacillus Calmette–Guérin

BRAF – v-Raf Murine Sarcoma Viral Oncogene Homolog B

CAD – Computer-Aided Diagnoses

CAR – Chimeric Antigen Receptor

CNN – Convolutional Neural Network

CRS – Cytokine Release Syndrome

CTCs – Circulating Tumor Cells

ctDNA – Circulating Tumor DNA

CTLA-4 – Cytotoxic T-Lymphocyte-Associated Protein 4

DAMP – Damage-Associated Molecular Patterns

DC – Dendritic Cell

EGFR – Epidermal Growth Factor Receptor

ELCAP – Early Lung Cancer Action Program

ERBB2 – Erb-B2 Receptor Tyrosine Kinase 2 (synonym for HER2)

EVs – Extracellular Vesicles

FASTK – Fas-Activated Serine/Threonine Kinase

FAT1 – FAT Atypical Cadherin 1

FDA – U.S. Food and Drug Administration

FLT3 – Fms-Like Tyrosine Kinase 3

H2AW – H2A.W Histone

HCC – Hepatocellular Carcinoma

HER2 – Human Epidermal Growth Factor Receptor 2

HPV – Human Papillomavirus

IGFALS – Insulin-Like Growth Factor Binding Protein Acid Labile Subunit

KEAP1 – Kelch-Like ECH-Associated Protein 1

KRAS – Kirsten Rat Sarcoma Viral Oncogene Homolog

LIDC – Lung Image Database Consortium

LOF – Loss of Function

MAP2K1 – Mitogen-Activated Protein Kinase Kinase 1 (MEK1)

MAPK – Mitogen-Activated Protein Kinase

MET – Mesenchymal-Epithelial Transition Factor

miRNA – MicroRNAs

ML – Machine Learning

MM – Multiple Myeloma

MRD – Minimal Residual Disease

NGS – Next-Generation Sequencing

NRAS – Neuroblastoma RAS Viral Oncogene Homolog

NSCLC – Non-Small Cell Lung Cancer

PCR – Polymerase Chain Reaction

PD-1 – Programmed Cell Death Protein 1

PD-L1 – Programmed Death-Ligand 1

PI3K – Phosphoinositide 3-Kinase

POLR3K – RNA Polymerase III Subunit K

RIT – Radioimmunotherapy

ROS – Reactive Oxygen Species

ROS1 – ROS Proto-Oncogene 1, Receptor Tyrosine Kinase

RTK – Receptor Tyrosine Kinases

SETBP1 – SET Binding Protein 1

STK11 – Serine/Threonine Kinase 11

TAA – Tumor-Associated Antigen

TME – Tumor Microenvironment

TP53 – Tumor Protein p53

TSA – Tumor-Specific Antigen

T-VEC – Talimogene Laherparepvec

VEGF – Vascular Endothelial Growth Factor

WES – Whole Exome Sequencing

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