# Exploring and bypassing resistance to targeted therapies in colorectal cancer

Solène Favier, Wei Xiong, Mounira Chalabi-Dchar, Kamel Chettab, Oliver Piercey, Ting Wu, Tanguy Fenouil, Marie-Alexandra Albaret, Lars Petter Jordheim, Jean-Jacques Diaz, et al.

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## To cite this version:

Solène Favier, Wei Xiong, Mounira Chalabi-Dchar, Kamel Chettab, Oliver Piercey, et al. Exploring and bypassing resistance to targeted therapies in colorectal cancer. Biomedicine and Pharmacotherapy, 2025, 193, pp.118741. 10.1016/j.biopha.2025.118741. hal-05472051

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Review

Exploring and bypassing resistance to targeted therapies in
colorectal cancer

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Solène Favier<sup>a</sup>, Wei Xiong<sup>b</sup>, Mounira Chalabi-Dchar<sup>b</sup>, Kamel Chettab<sup>a,c</sup>, Oliver Piercey<sup>d,e</sup>, Ting Wu<sup>b</sup>, Tanguy Fenouil<sup>b,c</sup>, Marie-Alexandra Albaret<sup>b</sup>, Lars Petter Jordheim<sup>a</sup>, Jean-Jacques Diaz<sup>b</sup>, Frédéric Hollande<sup>d,f,g</sup>, Charles Dumontet<sup>b,c</sup>, Hichem C. Mertani<sup>b,g,*</sup>

ARTICLE INFO

Keywords:

Colorectal cancer

Targeted therapy

Drug-tolerant persister cells (DTP)

Combination therapy

Antibody-drug conjugates (ADC)

Precision oncology

A B S T R A C T

Colorectal cancer (CRC) remains a leading cause of cancer-related deaths globally, though targeted therapies have transformed treatment and brought renewed hope. However, resistance to these therapies is a persistent challenge, driven by genetic mutations, epigenetic changes, tumor microenvironment (TME) adaptations, and disruptions in key signaling pathways. These mechanisms allow tumor cells to evade treatment, causing disease progression and limiting long-term success. This review highlights recent breakthroughs and innovative strategies aimed at overcoming resistance in CRC. A deeper understanding of tumor heterogeneity, exosome-mediated communication, and the evolving TME is central to this effort. Promising approaches include biomarker-guided combination therapies and the use of epigenetic modulators to disrupt resistance mechanisms and restore treatment efficacy. Emerging studies also reveal the vulnerabilities of cancer persister cells. Targeting these cells,

Abbreviations: 4E-BPs, eIF4E-binding proteins; 5-FU, 5-fluorouracil; ABC, ATP-binding cassette; ADC, Antibody-drug conjugate; AI, Artificial intelligence; ALDH, Aldehyde dehydrogenase; AMT, Adipose Manipulation Transplantation; BRAF, B Rapidly accelerated fibrosarcoma; CAA, Cancer-Associated Adipocyte; CAFs, Cancer-associated fibroblasts; CapOX, Capecitabine and oxaliplatin; CAR, Chimeric antigen receptor; CDK, Cyclin-dependent kinase; CIAP2, Cellular inhibitor of apoptosis 2; CMS, Consensus Molecular Subtypes; CRC, Colorectal cancer; CSC, Cancer stem cell; CtDNA, Circulating tumor DNA; DCR, Disease control rate; DNMT, DNA methyltransferases; DTC, Drug-tolerant cells; DTP, Drug tolerant persister; E2F, Translation factors; ECM, Extracellular matrix; EGF, Epidermal growth factor; EGFR, Epidermal growth factor receptor; EIF, Translation initiation factors; EMT, Epithelial to mesenchymal transition; ER, Endoplasmic reticulum; FDA, Food and drug administration; FGF, Fibroblast growth factor; FGFR4, Fibroblast growth factor receptor 4; FOLFIRI, Leucovorin, 5-fluorouracil, and irinotecan; FOLFOX, Leucovorin, 5-fluorouracil, and Oxaliplatin; FSP1, Ferroptosis suppressor protein 1; GPX4, Glutathione peroxidase 4; HDACs, Histone deacetylases; HER2, Human Epidermal Growth Factor Receptor-2; HGF, Hepatocyte growth factor; HR, Homologous recombination; IGF1/2, Insulin-like growth factor 1 and 2; IL, Interleukin; ICGC, International Cancer Genome Consortium; ISM1, Isthmin 1; KRAS, Kirsten rat sarcoma viral oncogene homolog; KDM, Histone lysine demethylase; MAPK, Mitogen-activated protein kinase; MCRC, Metastatic colorectal cancer; MDSCs, Myeloid-derived suppressor cells; MET, Mesenchymal to epithelial transition; MiRNA, microRNA; ML162/210, Molecular Libraries 162/210; MMR, Mismatch repair; MOS, Median overall survival; MPFS, Median progression-free survival; MRD, Minimal residual disease; MSC, Mesenchymal stem cell; MSI, Microsatellite instability; MSI-H, High microsatellite instability; MSS, Microsatellite stable; NcRNA, non-condensed RNA; NER, Nucleotide excision repair; NK, Natural killer; ORR, Overall response rate; PDGF, Platelet-derived growth factor; PD-1, Programmed cell death protein; PD-L1, Programmed death-ligand 1; PFS, Progression-free survival; PlGF, Placental growth factor; PI3K, Phosphatidylinositol 3-kinase; PLC, Phospholipase C; PGE-2, Prostaglandin E2; ROS, Reactive oxygen specie; RSL3, Ras-selective lethal 3; RTKI, Receptor tyrosine kinase inhibitor; S6K1, ribosomal S6 kinase; SMC4, Chromosome 4 structural maintenance gene; TAMs, Tumor-associated macrophages; TCGA, The Cancer Genome Atlas; TET, Translocation enzymes seventeen; TGF-beta, Transforming growth factor beta; TKI, Tyrosine kinase inhibitors; TME, Tumor microenvironment; TSA, Trichostatin A; VEGF, Vascular endothelial growth factor; VM, Vascular mimicry; WRN, Werner helicase; ZEB, Zinc finger E box-binding homeobox.

<footnote><sup>a</sup> Université Claude Bernard Lyon 1, INSERM U-1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, Lyon 69008, France</footnote>

<footnote><sup>b</sup> LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, Lyon CEDEX 08, France</footnote>

<footnote><sup>c</sup> Hospices Civils de Lyon, Lyon, France</footnote>

<footnote><sup>d</sup> Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia</footnote>

<footnote><sup>e</sup> Peter MacCallum Cancer Centre, Department of Medical Oncology, Melbourne, Victoria, Australia</footnote>

<footnote><sup>f</sup> Collaborative Centre for Genomic Cancer Medicine, University of Melbourne, Melbourne, Victoria, Australia</footnote>

<footnote><sup>g</sup> Plasticity, Heterogeneity and Tumour Microenvironment International Research (PHANTOM) Laboratory, CNRS and The University of Melbourne, Melbourne, Victoria, Australia</footnote>

<footnote>* Corresponding author at: LabEx Dev2CAN, Institut Convergence Plascan, Centre de Recherche en Cancérologie de Lyon, Inserm U1052, CNRS UMR5286, Université de Lyon, Université Claude Bernard Lyon, Centre Léon Bérard, Lyon CEDEX 08, France.</footnote>

<footnote>E-mail address: hichem.mertani@lyon.unicancer.fr (H.C. Mertani).</footnote>
along with addressing cell cycle dysregulation and DNA damage repair (DDR) pathways, offers new opportunities for durable responses. This review serves as a roadmap for tackling resistance in CRC, with the ultimate goal of improving patient outcomes.

# 1. Introduction

CRC represents the third most prevalent malignancy globally and constitutes the second leading cause of cancer-related mortality according to the World Health Organization, accounting for 1.9 million incident cases and exceeding 930,000 deaths in 2020 [1]. Risk factors encompass dietary patterns including processed meat consumption, sedentary lifestyle, obesity, tobacco use, and excessive alcohol intake [2]. Although predominantly diagnosed in patients over 50 years, epidemiological data demonstrate increasing incidence in younger demographics [3,4]. Current therapeutic modalities include surgical resection and systemic chemotherapy regimens such as FOLFOX, FOLFIRI, and CapOX, which exhibit suboptimal efficacy due to systemic toxicity and limited tumor selectivity [5]. The advent of next-generation sequencing technologies has facilitated comprehensive genomic characterization through large-scale initiatives including The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium Accelerating Research in Genomic Oncology (ICGC ARGO), enabling identification of molecular subtypes and therapeutic vulnerabilities [6, 7]. This genomic understanding has catalyzed the development of targeted therapeutics comprising small molecule inhibitors and monoclonal antibodies designed to selectively modulate aberrantly activated oncogenic pathways [5,8]. Despite these advancements, a major challenge persists: the development of therapeutic resistance, particularly in advanced-stage disease. Emerging evidence highlight the role of persistent drug-tolerant cells (DTPs), rare and phenotypically distinct subpopulations that survive initial treatment and contribute to disease relapse. These cells evade targeted monotherapies through adaptive, non-genetic mechanisms that allow temporary drug tolerance, posing a significant barrier to durable clinical responses [9]. Understanding the biology of DTPs is therefore critical to overcoming resistance and improving long-term outcomes in CRC.

# 2. Targeted therapies

## 2.1. Targeting the erythroblastosis oncogene b growth factor receptor and the vascular endothelial growth factor

The epidermal growth factor receptor (EGFR/ERBB1) exhibits overexpression in 25-77% of CRC cases and constitutes a validated therapeutic target [8]. Anti-EGFR monoclonal antibodies cetuximab and panitumumab demonstrate clinical efficacy in RAS wild-type tumors; however, resistance mechanisms manifest within 3-12 months of treatment initiation [10-13]. Human epidermal growth factor receptor 2 (HER2) amplification occurs in 5-7% of CRC patients. Contemporary HER2-directed strategies demonstrate enhanced therapeutic potential, including trastuzumab-tucatinib combination therapy validated in the MOUNTAINEER trial [10]. The antibody-drug conjugate trastuzumab-deruxtecan exhibits robust antitumor activity with favorable safety profiles in the DESTINY-CRC02 study, leveraging membrane-permeable payloads to overcome HER2 expression heterogeneity through bystander effects [11]. Angiogenesis mediated by vascular endothelial growth factor (VEGF) pathway activation promotes tumor vascularization, nutrient supply, and metastatic dissemination [12]. VEGF-A overexpression activates downstream effectors including PLCγ, MAPK, and PI3K/AKT signaling cascades, facilitating cellular proliferation, migration, and apoptosis resistance [13]. Anti-angiogenic agents bevacizumab, aflibercept, and ramucirumab target VEGF signaling components [14-16]. However, monotherapeutic approaches demonstrate limited clinical efficacy in advanced disease [17].

## 2.2. Targeting the RAS/RAF/MEK/ERK and PI3K/AKT/mTOR pathways

The RAS/RAF/MEK/ERK cascade mediates cellular proliferation, survival, and differentiation, with RAS mutations occurring in approximately 50% of CRC cases [8]. KRAS mutations predominate (~40%), affecting codons G12D (45%), G12V (31%), and G12C (11%) [18]. In the KRYSTAL-1 study, covalent KRAS G12C inhibitors sotorasib and adagrasib demonstrate targeted therapeutic efficacy, while investigational agent MRT1133 exhibits preclinical activity against KRAS G12D mutations (Fig. 1) [5,8,18,19]. BRAF V600E mutations (~10% of CRC) constitutively activate MAPK signaling independent of upstream RAS regulation. Single-agent BRAF inhibition with vemurafenib fails in CRC due to compensatory EGFR pathway activation. MEK inhibitors trametinib, binimetinib, and cobimetinib exhibit modest monotherapeutic activity (Fig. 1) [8,12]. The PI3K/AKT/mTOR pathway orchestrates cellular metabolism, survival, and autophagy, with dysregulation occurring in ~15% of CRC cases [8]. PTEN tumor suppressor loss (20-40% of patients) results in PI3K pathway hyperactivation [8,12]. FDA-approved targeted therapies currently used in CRC management are summarized in Table 1.

# 3. Resistance mechanisms

## 3.1. Genetic mechanisms

Resistance to targeted therapies arises through oncogenic "gain-of-function" mutations conferring constitutive pathway activation and tumor suppressor "loss-of-function" mutations. TP53 mutations occur in 60% of metastatic CRC, with missense variants (TP53R273H) promoting invasion and metastasis [12]. Receptor mutations prevent drug binding while preserving downstream signaling; EGFR S492R mutations abrogate cetuximab binding [8,12]. KRAS mutations at codon 12 exhibit differential drug susceptibility: G12C retains cysteine for sotorasib covalent binding, whereas G12D substitution eliminates this interaction [18,34]. BRAF V600E mutations (~10% CRC) maintain constitutive MAPK activation, rendering pathway inhibitors ineffective [12]. Resistance mutations occur in parallel pathways (PIK3CA, AKT1/2, MTOR), sustaining PI3K/AKT signaling under MAPK inhibition [8,12]. In addition to these molecular alterations, resistance may be intrinsic-stemming from pre-existing mutations that cause primary treatment failure, or acquired, developing through clonal selection (Darwinian) or therapy-induced mutagenesis [12].

## 3.2. Non genetic mechanisms of resistance

Cancer cells exhibit phenotypic plasticity, transitioning reversibly between proliferative, invasive, and drug-tolerant states without mutation [35,36]. This encompasses dedifferentiation, transdifferentiation, epithelial-mesenchymal transition (EMT) with cancer stem cell (CSC) characteristics [12,37] and the permissive role of the TME [38,39].

### 3.2.1. Translational deregulations and resistance

The regulation of mRNA translation is a critical mechanism governing protein synthesis and cell survival, and its dysregulation has emerged as a key driver in the tumorigenesis and progression of multiple cancers, including CRC [40]. APC loss perpetuates wingless/integrated (WNT) signaling and c-Myc upregulation, enhancing ribosome biogenesis [40]. Oncogenic WNT/MAPK/PI3K activation triggers mTORC1-mediated translational reprogramming via translation
initiation factors (eIF) 4E-binding proteins and the ribosomal S6 kinase (S6K1) [40]. Approximately 45% of CRC patients exhibit elevated MNK/mTOR signaling, correlating with poor survival. MNK1/2-driven eIF4E phosphorylation promotes selective mRNA translation and chemoresistance [41]. eIF4A2 upregulation predicts poor survival and confers oxaliplatin resistance through c-Myc upregulation and silvestrol inhibition restores chemosensitivity [42]. eIF4E drives 5-FU resistance, reversible by ribavirin or siRNA [43]. E2F1 mediates proliferation, invasion, and oxaliplatin resistance [43]. In BRAFV600E tumors, tRNA modifications at the wobble position (U34) regulate codon recognition and translational reprogramming, crucial for therapeutic resistance [44]. Enzymes ELP3, CTU1, and CTU2 are essential for BRAFV600E cell survival by controlling key protein synthesis. PI3K pathway activation, a common resistance mechanism following MAPK inhibition, amplifies U34-modifying enzyme expression [44]. This drives codon-dependent HIF1A mRNA translation, sustaining HIF1α levels that promote glycolysis and treatment resistance [44]. Ribosome biogenesis dysregulation increasingly contributes to CRC progression through altered rRNA processing and ribosomal protein assembly [45]. Overexpression of biogenesis factors PES1, BOP1, and RRS1 associates with tumorigenesis, progression, and chemoresistance [45]. Despite advances in understanding translational regulation, targeting the translational machinery remains a therapeutic priority [40].

### 3.2.2. Epithelial to mesenchymal transition and resistance

EMT involves epithelial-to-mesenchymal transition characterized by loss of apical-basal polarity and intercellular interactions, with acquisition of front-back polarity and cytoskeletal remodeling [46]. EMT transcription factors (Snail, Slug, ZEB1/2, Twist) under TGF-β/WNT/Notch signaling induce hybrid epithelial-mesenchymal states [46]. Over 90% of CRC cell lines undergo partial EMT, promoting dissemination and CSC properties [12,47].

<caption>**Table 1**</caption>

FDA-approved targeted therapies for colorectal cancer: treatment type, molecular target, and year of approval.

<table><thead><tr><td>Targeted therapies</td><td>Type of treatment</td><td>Molecular target/ pathway</td><td>Year of FDA approval</td></tr></thead><tbody><tr><td>Bevacizumab (Avastin)</td><td>Angiogenesis inhibitor</td><td>VEGF</td><td>2004 [20]</td></tr><tr><td>Cetuximab (Erbitux)</td><td>Monoclonal antibody</td><td>EGFR (KRAS/NRAS wild-type)</td><td>2004 [21]</td></tr><tr><td>Panitumumab (Vectibix)</td><td>Monoclonal antibody</td><td>EGFR (KRAS/NRAS wild-type)</td><td>2006 [22]</td></tr><tr><td>Regorafenib (Stivarga)</td><td>Multi-kinase inhibitor</td><td>VEGFR2, TIE2, RAF kinases</td><td>2012 [23]</td></tr><tr><td>Ziv-aflibercept (Zaltrap)</td><td>Angiogenesis inhibitor</td><td>VEGF-A/B, PIGF</td><td>2012 [24]</td></tr><tr><td>Ramucirumab (Cyramza)</td><td>Angiogenesis inhibitor</td><td>VEGFR2</td><td>2015 [25]</td></tr><tr><td>Nivolumab (Opdivo)</td><td>Anti-PD-1 immunotherapy</td><td>PD-1 (MSI-H/ dMMR)</td><td>2017 [26]</td></tr><tr><td>Ipilimumab + Nivolumab</td><td>Combined immunotherapy</td><td>CTLA-4 + PD-1 (MSI-H/dMMR)</td><td>2018 [27]</td></tr><tr><td>Encorafenib + Cetuximab</td><td>BRAF inhibitor + Anti-EGFR</td><td>BRAF V600E + EGFR</td><td>2020 [28]</td></tr><tr><td>Tucatinib + trastuzumab</td><td>TKI + Monoclonal antibody</td><td>HER2</td><td>2023 [29]</td></tr><tr><td>Fruquintinib (Fruzaqla)</td><td>Tyrosine kinase inhibitor</td><td>VEGFR1/2/3</td><td>2023 [30]</td></tr><tr><td>Trastuzumab deruxtecan (Enhertu)</td><td>ADC</td><td>HER2</td><td>2024 [31]</td></tr><tr><td>Adagrasib + Cetuximab</td><td>KRAS G12C inhibitor + Anti-EGFR</td><td>KRAS G12C + EGFR</td><td>2024 [32]</td></tr><tr><td>Sotorasib + Panitumumab</td><td>KRAS G12C inhibitor + Anti-EGFR</td><td>KRAS G12C + EGFR</td><td>2025 [33]</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Overview of currently used anti-CRC drugs (blue) and emerging targeted therapy strategies (red). Treatments represented by dotted blue lines indicate DNA-targeted chemotherapies, while solid blue lines denote targeted therapies. Targeted therapies for CRC primarily act on key receptors and pathways, including EGFR, VEGFR, HER2, and immune checkpoint regulators (PD-1/PD-L1 and CTLA-4/B7).</figcaption></figure>
### 3.2.3. CSCs and resistance

CSCs drive tumor initiation and heterogeneity through proliferation, renewal, and differentiation capabilities, exhibiting adaptive plasticity in response to TME changes. CD133 + /CD44 + /ALDH1 + CSCs resist therapy via ABC transporter-mediated efflux, DNA repair, and protective TME niches [48]. These niches exhibit immunosuppressive elements and the downregulation of cell death pathways, with a notable emphasis on PDL1 activity [37]. TGF-β induces TWIST1 expression, promoting dedifferentiation [49]. MEK inhibitors paradoxically activate Wnt signaling, enhancing stemness [50].

### 3.2.4. Epigenetic modifications and resistance

DNA/histone methylation and non-coding RNAs (ncRNA) dysregulation confer reversible resistance, such as cetuximab resistance [51]. EGFR hypermethylation like reduces inhibitor efficacy [52,53]. Histone deacetylases (HDA) C2 upregulate ABC transporters; HDAC2 inhibition restores doxorubicin sensitivity [35,54]. Histone H3 methylation and deacetylation upregulate lncRNAs that suppress AP-2α, activating Wnt/β-catenin and increasing ABCB1 expression [55]. Epigenetic dysregulation plays a pivotal role in both intrinsic and acquired resistance to therapy CRC, influencing transcriptional plasticity, DNA repair, and apoptosis. Aberrant DNA methylation and histone modifications are among the most frequent alterations driving therapeutic escape. In particular, hypermethylation of tumor suppressor genes such as *MLH1*, *CDKN2A*, and *APC* leads to transcriptional silencing and reduced drug sensitivity [56–58]. DNA methyltransferase (DNMT) inhibitors, including azacitidine and decitabine, have demonstrated the ability to reverse these epigenetic marks, reactivate silenced genes, and resensitize CRC cells to chemotherapy and targeted agents [59]. Similarly, HDA inhibitors, notably vorinostat, panobinostat, and entinostat, suppress EMT, induce apoptosis, and enhance the cytotoxicity of 5-FU and oxaliplatin in preclinical CRC models [60]. Recent mechanistic studies also highlight the potential of histone lysine methylation modifiers as therapeutic targets. Specifically, inhibition of enzymes such as EZH2, KDM5, and MLL family methyltransferases can disrupt transcriptional programs sustaining drug tolerance and stemness, restoring chemosensitivity [61].

### 3.2.5. Tumor micro-environment and resistance

The TME diversity impacts treatment efficacy and is linked to therapeutic resistance [62]. It comprises cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), immune cells, and extracellular components [12,35]. Adipocytes, neurons, endothelial cells, secreted factors, and the extracellular matrix form a complex TME now mapped in detail by high-resolution techniques such as single cell RNA-sequencing (scRNA-seq), spatial transcriptomics, and imaging mass spectrometry revealing cellular interactions driving resistance [63]. CAFs secrete growth factors (EGF, HGF, IGF1/2, VEGF) activating MAPK and PI3K/AKT pathways, promoting proliferation and survival [64–66]. TAMs create immunosuppression via PD-L1/L2 and cytokines (IL-6/10, TGF-β), induce EMT through TNF-α/NF-κB-Snail/ZEB1, and promote angiogenesis [67–70]. TAM-CSC crosstalk maintains stemness via STAT3/NF-κB/AKT activation [71,72].

Exosomes mediate intercellular communication by transporting proteins, oncoproteins, and nucleic acids (DNA, mRNA, miRNAs, circRNAs), driving tumor progression, immunosuppression, angiogenesis, and metastasis [73,74]. These extracellular vesicles establish treatment resistance through direct drug interaction or transcriptomic modulation of sensitive cells [75]. In CRC, tumor-derived exosomes confer specific resistance mechanisms: PTEN downregulation with enhanced Akt phosphorylation mediates cetuximab resistance, while miRNA-210-containing exosomes promote 5-FU resistance and metastatic cell adhesion [76,77]. Exosomes from metastatic CRC cells transfer miRNA-335-5p to recipient cells, inducing migration, invasion, and EMT by targeting RAS p21 protein activator 1 [78]. Bidirectional exosome-mediated crosstalk modulates the tumor microenvironment: CRC cells transfer miRNAs to macrophages, particularly

miRNA-106b-5p, which activates PI3Kγ/AKT/mTOR signaling and promotes M2 polarization, subsequently enhancing EMT and facilitating metastasis [79]. Beyond mediating intercellular communication, tumor-derived exosomes have emerged as both key effectors of therapeutic resistance and valuable prognostic tools in CRC. Exosomal cargo, including miRNAs, proteins, and lipids, modulates signaling pathways that sustain tumor survival under therapeutic pressure. For example, exosomal miR-106b-5p promotes M2 macrophage polarization and enhances immunosuppressive signalling through the PI3Kγ/AKT/mTOR axis, thereby facilitating resistance and metastatic progression [80]. Clinically, the baseline expression of circulating exosomal miR-92a-3p and miR-221-3p has been shown to predict response to first-line chemotherapy and overall survival in metastatic CRC patients [81]. Broader analyses confirm that exosomal miRNAs (e.g., miR-10b, miR-21, miR-210) contribute to resistance by activating pro-survival signaling and remodeling the tumor stroma [82]. Recent comprehensive reviews highlight that exosome cargo profiling could support liquid-biopsy-based monitoring and personalized therapy selection in CRC, offering new translational avenues to overcome resistance [83].

Cancer-Associated Adipocytes provide metabolic substrates and induce hypoxia via YAP/TAZ-mediated dedifferentiation, limiting angiogenic efficacy [84,85]. Hypoxia-driven vascular mimicry (VM) enables CRC cells to form perfusable, matrix-embedded vasculature independently of endothelial cells via HIF-1α-driven upregulation of Claudin-4, suppression of E-cadherin, and induction of vimentin, bypassing VEGF-targeted therapies [86,87]. ECM remodeling through laminin-5γ2/MMP-14 interactions further sustains nutrient delivery under anti-angiogenic pressure; targeting HIF-1α or metabolic pathways such as PDK4 abrogates VM by impairing hypoxia adaptation and EMT plasticity, warranting combination strategies to overcome resistance [87]. Finally, the gut microbiome has been shown to modulate chemotherapy efficacy in CRC, notably through enrichment of *Fusobacterium nucleatum*, which promotes 5-FU resistance by upregulating BIRC3 and activating autophagy networks in tumor cells [88]. Combinatorial strategies targeting genetic, epigenetic, and microenvironmental mechanisms are essential to overcome multifaceted resistance in CRC.

## 4. Targeted interventions against resistance in CRC

### 4.1. Biomarker-driven and emerging approaches in resistant CRC

Identification of predictive biomarkers remains critical for addressing resistance. C-Met, with approximately 85–90% of mutated patients developing resistance [5,18]. Liquid biopsies detecting circulating tumor DNA (ctDNA) enable early identification of emerging mutations, guiding therapy adjustments in real time [5]. Exosomal biomarkers, such as elevated TAG72, are emerging as predictors of 5-FU resistance, offering a potential tool for dynamic treatment monitoring [89].

Genomic and molecular profiling has already yielded clinically actionable biomarkers that guide therapeutic selection. In the Phase III PRIME trial, panitumumab improved progression-free survival in patients harboring EGFR S492R mutations following cetuximab failure. Multi-epitope antibodies (MM-151) demonstrated 40% tumor growth inhibition in cetuximab-resistant xenografts, while the bispecific EGFR/MET agent amivantamab achieved 60% tumor regression in preclinical (Fig. 1) [90–92]. Similarly, angiopoietin-2 has emerged as a prognostic and predictive biomarker for angiogenesis inhibition, with elevated levels correlating with improved OS by 5.5 months outcomes in the vanucizumab/mFOLFOX6 Phase II trial (vs. bevacizumab/mFOLFOX6) in KRAS wild-type mCRC [93]. Emerging biomarker-guided therapies are playing a transformative role in early-phase clinical development for CRC. For example, overexpression of c-Met, a receptor tyrosine kinase frequently implicated in tumor progression and metastasis, has been identified as a predictive biomarker for response to ABBV-400, a c-Met-directed antibody-drug conjugate (ADC). In Phase I clinical trials
involving advanced solid tumors, including CRC, ABBV-400 achieved an objective response rate (ORR) of 15-20%, demonstrating encouraging early efficacy and manageable safety, warranting further investigation in biomarker-selected patient cohorts [94,95]. Similarly, expression of carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) and epithelial cell adhesion molecule (EpCAM) has emerged as a potential predictive marker for ADC sensitivity. In early clinical studies, the anti-CEACAM5 ADC M9140 achieved 10% partial response (PR) and 42.5% stable disease (SD) rates, while the anti-EpCAM ADC CX-2051 demonstrated disease control in approximately 50% of patients with advanced CRC [96,97]. Additionally, targeting the Wnt/β-catenin signaling pathway with cinobufacini, a natural compound with anti-tumor activity, has shown promising preclinical results—suppressing EMT and reducing tumor growth by 65% in murine CRC models [98].

## 4.2. Combination therapy to overcome resistance

Monotherapy rapidly induces resistance; thus, combinations are essential. In the TRIBE trial, FOLFOXIRI/bevacizumab extended median OS to 19.0 months versus 10.7 months with FOLFIRI/bevacizumab in BRAF-mutant mCRC, although subsequent meta-analysis failed to confirm superiority [99]. In contrast, the Phase III BEACON CRC trial established encorafenib plus cetuximab as NCCN-recommended therapy for BRAF V600E-mutant mCRC, improving median OS to 9.3 months versus 5.9 months with standard chemotherapy and doubling response rates (20% vs 4%) [100]. Resistance driven by secondary RAS, EGFR, and MET alterations underlies ongoing trials combining chemotherapy and immunotherapy with targeted agents [101,102]. Selective PI3Kα inhibition with alpelisib has shown a favorable safety profile and preliminary anti-tumor efficacy in PIK3CA-mutant CRC, with phase I data establishing 400 mg once daily as the recommended dose and reporting objective partial responses in colorectal cohorts [103]. Combining alpelisib with capecitabine in a phase Ib/II trial for PIK3CA-mutant mCRC patients yielded manageable toxicities, most notably hyperglycemia, but did not show significant clinical benefits in CRC patients compared to breast cancer warranting further evaluation [104].

## 4.3. Immunotherapy to overcome resistance

Immune checkpoint inhibitors (ICIs) represent a recent breakthrough for CRC, particularly in patients with DNA mismatch repair deficiency (MMRd) and high microsatellite instability (MSI-H) (Fig. 1) [105-107]. KEYNOTE-177 demonstrated sustained benefit with pembrolizumab in MSI-H/dMMR mCRC, achieving median OS of 77.5 versus 36.7 months compared to chemotherapy at 5-year follow-up, with durable responses (median 75.4 months) despite 62% crossover rate [108]. Combination regimens, nivolumab/ipilimumab combination achieved 71% PFS and 85% OS at 12 months in mCRC MMRd/MSI-H patients, leading to regulatory approvals [109]. However, mechanisms of resistance to these immunotherapies, such as loss of the target antigen or immunosuppression mechanisms, can occur [105]. One way of combating this resistance, and in particular the mechanisms of immunosuppression, is to develop combinations comprising different classes of anticancer drugs [105]. Surprisingly, studies have shown that radiotherapy and chemotherapy can enhance the efficacy of the immune system by increasing T-cell infiltration, remodeling the microenvironment [110]. A single-arm, multicenter Phase 1b trial (NCT02375672) involving patients with predominantly pMMR/MSS advanced CRC, was designed to investigate the effect of combining pembrolizumab with mFOLFOX6. Efficacy was observed, with an objective remission rate of 57%, and a mPFS of 8.8 months [111]. The CAVE trial (NCT04561336) evaluated cetuximab (anti-EGFR) + avelumab (anti-PD-L1) in RAS/BRAF wild-type metastatic CRC patients previously treated with anti-EGFR therapies, demonstrating a mPFS of 3.6 months and an ORR of 8% (6/77 partial responses), with disease control achieved in 65% of

patients. These results highlight limited efficacy of EGFR/PD-L1 dual blockade in re-exposure settings despite preclinical synergy hypotheses (Fig. 1) [112]. To prevent the recruitment of immunosuppressive cells, SX-682 a small molecule inhibitor of CXCR1 and CXCR2 has been developed [113]. In mice, SX-682 proved effective in preventing the recruitment of MDSCs, which inhibit the anti-tumor activity of NK cells, in head and neck squamous cell carcinoma [113]. Clinical trials are currently underway to test the combination of SX-682 and nivolumab in patients with RAS-mutated CRC [114]. In addition, the use of chimeric antigen receptor (CAR) T cells represents a major advance in the active immunotherapy of mCRC. A study of the efficacy and toxicity of this technique showed that around 70% of treated patients achieved stable tumor control (Fig. 2) [115]. GCC19CART, a novel CAR T-cell therapy targeting guanylate cyclase-C (GCC) and CD19, has demonstrated promising clinical activity in patients with refractory mCRC [116]. In a phase 1 dose-escalation study (NCT0531931), GCC19CART showed an overall response rate of 56% across both dose levels, with 80% of patients in the higher dose group (2 × 10⁶ cells/kg) achieving partial or complete responses, suggesting a dose-dependent efficacy profile [116]. However, major limitations persist, including inefficient CAR T-cell trafficking, immunosuppressive TME, and the lack of specific tumor targets, which restrict clinical efficacy and lead to challenges in

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Therapeutic landscape of colorectal cancer. Current clinical strategies and emerging targeted approaches to overcome resistance. In blue: Established clinical strategies, including localized treatments (surgery, radiotherapy) and systemic therapies (chemotherapy, targeted therapies, and immunotherapies). In red: Novel targeted therapy approaches addressing resistance, focusing on the tumor microenvironment, immune system, cell cycle, and DNA repair mechanisms. Examples of innovative therapies discussed in this review are provided in brackets.</figcaption></figure>
managing on-target, off-tumor toxicities [117]. Future directions include exploring advanced combination therapies, such as integrating CAR T-cells with checkpoint inhibitors, dendritic cell vaccines, or small-molecule drugs, as well as developing hybrid approaches like CAR NK-T cells to enhance therapeutic outcomes while minimizing toxicity [118,119].

The latest advance in immunotherapy is the vaccine strategy, which have shown promise in enhancing tumor antigen presentation and boosting immune responses [120]. However, current limitations include the short duration of antitumor immunity, challenges in overcoming the immunosuppressive tumor microenvironment, and high costs and complexity in personalized vaccine production [120]. On average, the time required to produce and deliver a personalized vaccine to the patient ranges from 3 to 5 months, presenting a significant challenge for metastatic patients [120]. Future advancements in CRC vaccines emphasize synergistic combinations and innovative platforms [120]. Nanovaccine platforms are being developed to enhance antigen delivery and immune activation [120]. There are four types of nanovaccine: lipid-based, polymer-based, inorganic vector-based and biologically derived. Notably, the BanNV vaccine has demonstrated efficacy, achieving up to 70 % remission in preclinical models when paired with anti-PD-1 therapy [121]. Moreover, neoantigen-based vaccines, like Nous-209, target shared MSI-induced mutations and are undergoing trials combined with checkpoint inhibitors for mCRC (NCT04041310) [122]. Additionally, prophylactic approaches, such as MUC1-targeting vaccines, are being explored to establish durable immune protection in high-risk individuals, marking a significant step toward preventative immunotherapy [120,123].

## 4.4. Tumor micro-environment modulation to overcome resistance

Building on the crucial role of the TME in driving therapeutic resistance discussed earlier, it is increasingly clear that targeting TME components offers promising strategies to overcome drug insensitivity [12,124,125]. For example, fibroblasts can secrete certain growth factors such as HGF, which bind to cancer cells and activate the MAPK and PI3K/AKT pathways, leading to resistance to cetuximab [126]. FGFR4 has been validated as a prognostic biomarker in CRC, with over-expression correlating with advanced tumor stages and reduced survival outcomes [127]. The FGFR4-selective inhibitor BLU9931, when combined with cetuximab, demonstrated synergistic anti-tumor effects in preclinical models by suppressing cell viability and EMT pathways in FGFR4-high CRC cell lines (Fig. 2) [127]. Targeting TAM-CSC interactions in the CRC TME represents a promising therapeutic strategy, encompassing approaches such as TAM repolarization towards an anti-tumoral M1 phenotype, inhibition of TAM recruitment, and disruption of TAM-CSC signaling pathways [71]. Preclinical studies have demonstrated the potential of this approach, with CSF1 receptor inhibition reducing chemoresistance and improving survival in pancreatic model, while IL-6 signaling blockade reverses the protective effect of TAMs and enhances chemotherapy efficacy [128,129]. Emerging therapeutic strategies targeting CAAs, such as combining anti-angiogenic drugs with β-oxidation inhibitors or modulating lipid metabolism, offer promising approaches to enhance CRC treatment efficacy [84]. Treatments based on YAP/TAZ inhibitors, such as verteporfin, have shown promising results in preventing CAA transformation in the context of TME [85]. A recent study introduced adipose manipulation transplantation (AMT), a technology using modified adipocytes designed to outcompete tumors for nutrients, thereby significantly suppressing cancer progression [130]. By enhancing glucose and fatty acid utilization through UCP1 regulation, these engineered adipocytes reduced tumor growth, angiogenesis and hypoxia in CRC cell line, murine models and human-derived organoids, demonstrating their potential as a therapeutic strategy against multiple cancer types [130]. Targeting HIF-1α or metabolic reprogramming (e.g., glucagon-mediated PDK4 inhibition) disrupts VM by suppressing hypoxia adaptation and

EMT plasticity, suggesting combinatorial strategies to overcome this resistance mechanism [131]. Dysbiosis, meaning qualitative and/or quantitative imbalance in the host's intimate microbial community, also influences immunotherapy outcomes: preclinical and clinical data demonstrate that specific bacterial taxa (e.g., *Bacteroides fragilis*, *Akkermansia muciniphila*) enhance anti-PD-1/PD-L1 and anti-CTLA-4 efficacy by stimulating dendritic cell maturation and CD8+ T-cell infiltration [132]. Fecal microbiota transplantation (FMT) from ICI responders into germ-free CRC models reversed resistance to checkpoint blockade, underscoring the therapeutic potential of microbiome transfer to sensitize refractory tumors to immunotherapy [133]. Emerging strategies, such as next-generation probiotics, prebiotic dietary fibers, and engineered bacterial consortia, aim to selectively enrich immunostimulatory microbes and deplete pro-resistant species, offering a precision-microbiome approach to enhance both chemo- and immunotherapeutic efficacy in CRC [134].

## 4.5. Targeting cell cycle to overcome resistance

Targeting the cell cycle has recently become a focus of research [12,135]. Studies have shown that when certain genes are lost, cells become more dependent on another gene family for survival [136]. This phenomenon, where the loss of one gene makes the cell reliant on another, is known as synthetic lethality [136,137]. The proposed therapy for tumors exhibiting loss of tumor suppressor gene involves targeting and inhibiting the protein that has become essential for cell survival [136]. For example, a recent randomized trial, FOCUS4-C, tested adavosertib (AZD1775), an inhibitor of WEE1, a protein that inhibits the Cyclin-dependent kinase (CDK) 1, at the G2/M checkpoint (Fig. 2). Despite the absence of improved OS, mCRC patients with TP53 mutations treated with adavosertib demonstrated a superior PFS of 3.61 months compared to 1.87 months in the control group [138].

Inhibitors of CDK4/6, involved in the G1/S transition, have also been developed to target the cell cycle [135]. As targeted therapy with a single agent rapidly leads to the development of resistance, the strategy adopted is to use this type of inhibitors in combination with other specific inhibitors [139]. Preclinical data indicate that dual targeting of the PI3K/AKT axis with alpelisib alongside CDK4/6 inhibitors or immune checkpoint blockade may overcome adaptive resistance mechanisms, supporting ongoing exploration of combination regimens in CRC [140].

A recent preclinical study showed a strong synergistic anti-proliferative effect for the combination of palbociclib (CDK4/6 inhibitor) and gedatolisib (PI3K and mTOR inhibitor) in wild-type CRC lines as well as in KRAS mutated and KRAS-PI3K mutated cell lines (Fig. 2) [141]. Furthermore, this anti-proliferative effect was observed in correlation with a suppression of S6rp in the cell lines, making it a promising marker for predicting response to this combination therapy in the clinic [141]. In addition, the combination of palbociclib and gedatolisib has already shown encouraging results of efficacy in breast cancer patients in clinical trials [142,143]. Palbociclib is currently in a phase II clinical trial for the treatment of mCRC in combination with cetuximab (NCT03446157) [144]. Despite the promising efficacy of combinations using palbociclib, increased PDL1-expression in tumors was observed during treatment with this compound, reducing the efficacy of the immunotherapy. To counteract the immunosuppressive effects of CD73, the researchers developed AB680 (quemliclustat), a novel small molecule inhibitor of CD73, which restores T cell functionality by blocking adenosine generation in the TME [145]. The combination of AB680 and palbociclib appears to improve anti-tumor efficacy in animal models of CRC and represents a promising new strategy for immunotherapy [145].

## 4.6. Targeting DNA damage repair dysregulation to overcome resistance

In healthy cells, there are DNA repair mechanisms that can be maintained, such as homologous recombination (HR), a mechanism for repairing DNA double-strand breaks, and DNA mismatch repair (MMR),
which concerns base errors during replication or recombination. In cancer cells, these damage repair processes are deregulated, increasing genomic instability and hence the appearance of mutations [146]. A study of CRC cell models treated with targeted therapy showed inhibition of HR and MMR activity. At the same time, an increase in DNA damage was observed in resistant cells [147]. A recent high-content screening study, along with the analysis of patient tissues, identified the SENP5 gene as being involved in DNA damage repair mechanisms through HR. This gene is overexpressed in CRC cases that are resistant to radiotherapy, suggesting it could serve as a prognostic marker for radiotherapy outcomes. Additionally, knocking down SENP5 increased the sensitivity of cancer cells to radiotherapy treatment [148]. PARP1, another crucial enzyme involved in DNA repair, has been targeted by specific inhibitors that have proven effective in treating breast and ovarian tumors with BRCA1/BRCA2 deficiencies. These inhibitors can be used either as standalone treatments or in combination with other therapies [149]. In contrast, inhibitors such as olaparib and veliparib have been tested in clinical trials for CRC, either in combination or on their own. However, the results have been mixed, with limited efficacy observed in unselected patient populations [150–152]. When the MMR system fails, microsatellites are particularly affected, leading to the microsatellite instability (MSI) phenotype. This phenotype is observed in 15 % of sporadic CRC, most often due to the inactivation of the key gene MLH1 [153]. In MSI and MMR-deficient CRC, cancer cell survival is dependent on Werner helicase (WRN) [154]. Inhibiting BLM helicase activity with the potent inhibitor ML216 leads to the accumulation of DNA damage, which increases p53 expression and ultimately triggers cell apoptosis (Fig. 2) [136]. The nucleotide excision repair (NER) pathway, mediated by the ERCC1-XPF endonuclease complex, represents a key resistance mechanism to DNA crosslinking agents like cisplatin and mitomycin C. Small-molecule inhibitors (e.g., F06, B9) disrupting ERCC1-XPF heterodimerization impair DNA adduct repair, restoring cisplatin sensitivity in p53-proficient HCT-116 CRC cells by inducing lethal replication stress and apoptosis [155,156]. Pharmacodynamic studies demonstrate that these inhibitors reduce ERCC1-XPF protein stability by > 70 %, synergizing with cisplatin to decrease tumor viability and suppress in vivo xenograft [157].

### 4.7. Targeting drug-tolerant persister (DTP) cells to overcome resistance

A major challenge in improving outcomes for patients undergoing chemotherapy and targeted therapy is the ability of tumor cells to tolerate treatment [158]. Research has shown that a subpopulation of cancer cells, known as drug-tolerant cells (DTCs), can survive the initial phases of drug treatment by entering a reversible state of tolerance without acquiring genetic mutations [9]. In CRC, this concept was demonstrated by Rehman et al. in a landmark study from Catherine O'Brien's group [9]. Using patient-derived models, they provided direct evidence that DTPs can survive drug exposure and later give rise to recurrent tumors. Their work also highlighted autophagy as a key mechanism helping these cells endure therapy, suggesting that targeting autophagy could be a promising strategy to eliminate DTPs and reduce the risk of relapse. DTP cells are characterized by their low proliferation rate and phenotypic plasticity, enabling them to escape drug-induced cytotoxicity (Fig. 3) [9]. DTCs can reside in the TME and produce a reservoir of stable drug-resistant clones, also known as DTP cells contributing to minimal residual disease (MRD) and eventual relapse [159]. These cells exhibit a transient phenotype, meaning they temporarily halt proliferation during treatment but resume growth once the treatment is stopped, becoming sensitive to other therapies [159]. The main hypothesis that could explain this concept of drug tolerance is similar to that used in microbiology concerning the resistance of bacteria to antibiotics [160]. After treatment with an antibiotic a sub-population of bacteria which do not proliferate have become resistant and are selected [160]. Once the treatment has been stopped, these bacteria resume their proliferation, reconstituting a population sensitive to the treatment [160]. This phenotypic plasticity leading to the renewal of an aggressive, treatment-sensitive population shows that the changes are transient and therefore do not seem to involve genetic variations [160]. The concept of drug-tolerant cells was first proposed by Settleman's team in 2010 [161]. The study identified a subpopulation of PC9 non-small cell lung cancer cells exhibiting over 100-fold reduced sensitivity to EGFR inhibitor, and resistance to its lethal doses [161]. These cells exhibit a low proliferation rate without detectable genetic mutations and are characterized by an altered reversible chromatin state dependent on the histone lysine demethylase KDM5A [161]. The

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Biological characteristics of DTP cells and key regulatory mechanisms. Left panel: Epigenomic modifications, such as histone alterations mediated by regulators like KDM, HDAC, and DNMT (purple), drive gene expression changes that sustain the DTP cell phenotype. Novel therapeutic strategies aim to inhibit these regulators (red) to counteract this process and overcome treatment resistance. Right panel: Metabolic modifications enhance energy production in DTP cells via autophagy-driven mitochondrial respiration. Key proteins (purple) play a protective role against oxidative stress-induced cytotoxicity. Several inhibitors targeting these proteins (red) are currently under investigation.</figcaption></figure>
relationship between reversibly drug-tolerant cells and CSCs is complex, as drug-tolerant cells express CSC-associated markers such as CD133 and CD24 but achieve treatment resistance through mechanisms independent of drug efflux, a hallmark of many CSCs [161]. Other DTCs were subsequently identified in colorectal, lung, breast and melanoma cancers that were tolerant to targeted therapies such as EGFR, RAF and MET inhibitors [161]. The survival of these DTP cells is driven by a complex network of molecular mechanisms, including epigenetic modifications, transcriptional and translational reprogramming, metabolic adaptations, anti-apoptotic signals, and interactions with the TME (Fig. 3) [146]. Understanding these mechanisms is essential for developing therapeutic strategies aimed at eradicating DTP cells and overcoming drug resistance in cancer treatment [162].

Gene expression analysis in gastric cancer cell models from patients treated with 5-FU or SN38 (the active metabolite of irinotecan CPT-11) revealed overexpression of aldehyde dehydrogenase 1A3 (ALDH1A3) [163]. Reducing its expression using RNA interference eliminated DTC cells and decreased cell proliferation in murine xenograft models [163]. ALDH1A3 appears to be linked to the mTOR cell survival pathway, and its specific mTOR inhibition using temsirolimus also reduced the number of 5-FU-resistant cells [163]. Epigenetic reprogramming, involving DNA methylation, histone modifications, and chromatin remodeling, plays a pivotal role in the survival of DTCs [162]. In CRC, KDM5A/B upregulation in drug-tolerant cells (DTCs) drives genome-wide H3K4me3 demethylation, specifically repressing the CDKN1A/p21 promoter to silence this pro-apoptotic checkpoint, thereby sustaining cell cycle progression and conferring cisplatin resistance through evasion of DNA damage-induced apoptosis [164,165]. Similarly, KDM6A and KDM6B, which demethylate H3K27, are critical for maintaining the DTC phenotype and drug resistance [166]. These enzymes can be targeted to prevent the formation of DTCs. Small molecule inhibitors are being developed and tested, such as CPI-455, which specifically inhibits KDM5 to restore cell sensitivity to chemotherapy or targeted therapies [167]. Other studies have identified a subpopulation of cisplatin-resistant DTP cells that exhibit an altered chromatin state through the intervention of RBP2, KDM5A and Jarid1A demethylases [161]. The results show that this resistant state can be reversed, and the cells eliminated using a specific inhibitor of the IGF-1 receptor or specific inhibitors of histone deacetylases (HDACs) such as trichostatin A (TSA) [161]. Inhibitors of histone demethylases (KDM5A and KDM5B) and DNA methyltransferases (DNMT) can reverse the epigenetic changes that support DTC survival [162]. Combining these agents with standard chemotherapy may enhance treatment efficacy (Fig. 3) [162]. Building upon this, recent studies have revealed that DTP eradication may also be enhanced by combining autophagy inhibition with immune checkpoint blockade, thereby reactivating antitumor immunity and preventing immune evasion within minimal residual disease niches [168]. It was additionally reported that DTP cells modify their mRNA translation, enabling them to rapidly adapt their phenotype in response to treatment [146]. The eIF4A initiation factor has been identified as critical for the survival of DTP cells. Hence, inhibiting this complex could disrupt DTP cells ability to adapt, making them more vulnerable to treatment [169, 170]. Live genetic lineage-tracing systems tracking individual cells in xenotransplanted human CRC organoids identified a subpopulation of LGR5 +p27 + cells that persist during chemotherapy through dormancy mechanisms mediated by COL17A1-dependent cell-matrix interactions, and subsequently undergo clonal expansion to regenerate tumors following treatment cessation [171]. Following exposure to chemotherapy agents (5-FU, oxaliplatin, and SN38), chemo-resistant DTP cells were isolated and characterized [172]. Transcriptomic analyses revealed that these DTP cells express the *MEX3A* gene while simultaneously downregulating genes associated with stem cell pathways, including WNT signaling [172]. Notably, these DTP cells retained the ability to resume proliferation and regenerate colonic organoids. Since Mex3a has been linked to chemotherapy resistance, its role in targeted therapy resistance remains unclear. However, identifying DTP cells

through Mex3a expression could be a promising strategy for preventing relapse in CRC treatment [172]. Metabolic reprogramming is also a hallmark of DTP cells in CRC, as evidenced by genetic barcoding and mathematical modeling analyses, which have highlighted functional and transcriptional parallels between these cells and embryonic diapause [9,173,174]. Indeed, diapause is a defining feature that distinguishes DTPs from genetically resistant clones, reflecting their phenotypic plasticity and reversible adaptability under therapeutic stress. Unlike genetically resistant cells, which acquire stable mutations that permanently rewire oncogenic pathways, DTPs persist through non-mutational mechanisms involving epigenetic remodeling, transcriptional reprogramming, and metabolic adaptations that confer a flexible and dynamic state of drug tolerance. This transient survival mode allows DTPs to enter a reversible quiescent, diapause-like state, enabling them to evade apoptosis, conserve energy, and maintain viability until treatment pressure is relieved [174]. Mechanistically, DTPs display chromatin remodeling and transcriptional reprogramming mediated by histone modifiers such as KDM5A/B and KDM6A/B, which reversibly repress or activate stress-response genes, facilitating rapid shifts between dormancy and proliferation [175]. Furthermore, DTPs exhibit metabolic flexibility, characterized by mTOR suppression, autophagy induction, and oxidative stress modulation, promoting survival in hostile conditions while maintaining the capacity to resume growth when therapy pressure is relieved [174,176]. This unique non-genetic and reversible nature of DTPs makes them a critical reservoir for tumor relapse and a prime target for novel therapeutic strategies aimed at eradicating residual disease before genetic resistance emerges. Diapause, a reversible state of arrested embryonic development triggered by external stress such as nutrient deprivation, shares striking similarities with the DTP state [173]. RNA-sequencing analysis of DTP cells derived from CRC xenografts demonstrates significant down-regulation of Myc and mTORC1 signaling pathways, coupled with the suppression of cellular growth processes, further supporting the resemblance to a diapause-like state [174,177]. The mTOR pathway, essential for regulating autophagy, a cellular process responsible for degrading damaged or redundant components, plays a pivotal role in this adaptive mechanism [178]. Notably, the downregulation of mTOR led to the upregulation of key autophagy-related genes [179]. RNA-seq data and *in vitro* experiments revealed upregulation of autophagy-related genes in diapause-like DTP cells, including phosphorylation of ULK1 and increased expression of ATG13 and LC3, underscoring the importance of autophagy in maintaining this state [174]. This diapause-like adaptation presents a promising therapeutic target. Strategies could involve maintaining the dormant state through Myc inhibition or disrupting it via CDK9 inhibition, thereby combating drug-tolerant cancer cells [174,177]. Supporting this approach, studies have shown that combining irinotecan (CPT-11) with autophagy inhibitors such as chloroquine or the ULK1 inhibitor SBI-0206965, significantly enhances apoptosis induction compared to irinotecan alone, offering a potential avenue to overcome chemoresistance (Fig. 3) [174,179]. In the CRC MC38 murine line, the chromosome 4 structural maintenance gene (SMC4) has been identified as an intervener in diapause state. The inhibition of this gene, involved in the mechanisms of cell proliferation and aerobic glycolysis, was studied using a knock-out, showing a decreased cell proliferation and increased resistance to irinotecan [180]. Another mechanism detected in DTP cells is anastasis, a cell survival mechanism in the face of apoptotic stress due to activation of the caspase pathway. One study revealed activation of the cellular inhibitor of apoptosis 2 (cIAP2) / NFκB pathway in response to chemotherapy (5-FU and paclitaxel). A lineage tracing system and RNA-seq analyses demonstrated that activation of this pathway enables cell survival in the face of executive caspase activation, as well as enhancing cell migration and metastasis [181]. Furthermore, DTP cells are known to enter a state of cell cycle arrest or exhibit slow or halted proliferation in response to oxidative stress caused by therapeutic agents [165,182]. Redox signaling could regulate this process by activating
epigenetic reprogramming through histone modification by KDMs [183,184]. In addition, oxidative stress modulates the activity of other enzymes such as ten eleven translocation (TET) enzyme family [185]. Studies have shown that TET2 activity is involved in the reduced proliferation rate of DTP cells and resistance to treatment in several cancers such as melanoma, glioblastoma and CRC [186]. The enzymatic oxidation product of 5-methylcytosine by TET2, 5-hydroxymethylcytosine, was found in higher quantities in 5-FU resistant colon cancer cells, acting as a potent antioxidant [187]. Overall, high levels of reactive oxygen species (ROS) metabolites induce oxidative stress in the cell, damaging cellular macromolecules and ultimately leading to cell senescence and cell death [188,189]. One example is ferroptosis, a form of cell death characterized by iron-dependent accumulation of lipid peroxides and ROS [190]. Cancer cells use several mechanisms to escape this cell death, such as activation of glutathione peroxidase 4 (GPX4), a central regulator of ferroptosis that prevents the accumulation of ROS and reduces hydroperoxides [176]. Initially GPX4 inhibitors, such as Ras-selective lethal 3 (RSL3), Molecular Libraries 162 (ML162) and ML210, have been shown to be effective in inducing ferroptosis in DTP cells [176,191,192]. Furthermore, efforts to enhance stability and optimize pharmacokinetic properties have driven the development of advanced formulations, including arachidonic acid micelles encapsulating RSL3 [193]. These micelles facilitate targeted drug release in the presence of free radicals within the tumor microenvironment, thereby improving therapeutic efficacy and promoting a more robust induction of ferroptosis [193]. Recently, a trastuzumab-RSL3 ADC has been developed with a drug to antibody ratio (DAR) of 8 (8 RSL3 molecules per antibody). This ADC has been shown to induce cell death by accumulation of ROS and to enhance the activity of certain therapies such as doxorubicin in combination therapy [194]. However, recent studies suggest a re-evaluation of RSL3 and ML162, showing that they have no capacity to inhibit the enzymatic activity of recombinant GPX4 selenoprotein [195]. Surprisingly, these compounds proved to be effective inhibitors of another selenoprotein, TXNRD1 [195]. Other known TXNRD1 inhibitors, notably auranofine (Ridaura®), TRi-1 and TRi-2, are also effective inducers of cell death, but this could not be suppressed by ferrostatin-1 and would be interesting to study for their anti-cancer effect [196,197]. Other targets of ferroptosis, such as ferroptosis suppressor protein 1 (FSP1), have been identified as being overexpressed in KRAS mutant CRC cells, thus protecting them from ferroptosis [198]. Targeting mitochondrial oxidative respiration is another promising strategy for overcoming treatment resistance in CRC. For example, tigecycline, an antibiotic that inhibits the translation of mitochondrial proteins, has been used to disrupt this mitochondrial respiration in chronic myeloid leukaemia [199]. The imatinib-tigecycline combination could eradicate leukaemic stem cells (Fig. 3) [199]. Tigecycline exhibits potent anti-tumor effects in CRC by targeting the Wnt/β-catenin pathway, inducing apoptosis through intrinsic, extrinsic, and endoplasmic reticulum-mediated pathways, and restoring gut microbiota composition [200,201]. It also modulates the immune response, enhancing cytotoxic T lymphocyte activity and reducing tumor-associated inflammation, ultimately attenuating tumorigenesis *in vivo* [200]. In mCRC, tigecycline disrupts mitochondrial function by impairing biogenesis and complex I activity, leading to reduced ATP production and heightened oxidative stress [201]. These effects sensitize metastatic cancer cells to the combination of this tigecycline with standard chemotherapies [201].

Prospective clinical studies examining minimal residual disease in Stage III colon cancer patients receiving adjuvant FOLFOX chemotherapy have demonstrated that approximately 25 % experience relapse within 5 years despite curative intent treatment, with circulating tumor cells (CTCs) serving as surrogate markers of persister [202]. In a cohort of 76 Stage III patients, CTC-positive individuals after adjuvant chemotherapy exhibited markedly inferior outcomes compared to CTC-negative patients, with 5-year survival rates of 4 % versus 87 % and median restricted mean survival times of 1.5 years versus 4.6 years,

respectively, underscoring the clinical significance of therapy-resistant persister populations [202]. The GALAXY study (n = 1039 patients with resectable Stage II-IV CRC) identified postsurgical ctDNA positivity as the most significant prognostic factor for recurrence (hazard ratio 10.82, *P* < 0.001), with improved outcomes observed only in ctDNA-positive patients achieving molecular clearance following adjuvant chemotherapy, thereby providing molecular evidence that DTP cells surviving initial treatment constitute the cellular reservoir for disease relapse [203].

Despite the compelling rationale for eradicating DTP cells to prevent relapse, clinical translation of persister-targeting strategies faces significant therapeutic challenges, particularly the absence of robust and specific biomarkers for DTP identification, as current detection methods rely on indirect markers (KDM5A, ALDH, AXL) that lack sufficient specificity and overlap considerably with other cellular phenotypes such as CSCs or senescent cells [204]. Drug delivery to DTPs represents an additional obstacle, as these cells reside within poorly vascularized tumor regions characterized by dense extracellular matrix and elevated interstitial fluid pressure, creating substantial penetration barriers that are further compounded by the conflicting nanoparticle size requirements for systemic circulation (100–150 nm) versus deep tumor penetration (<20 nm), as well as the inherently quiescent metabolic state and reduced drug uptake capacity of persister cells [205].

Selective targeting of DTP cells presents substantial toxicity concerns, as molecular vulnerabilities exploited in DTPs, including ferroptosis susceptibility through GPX4 inhibition and dependence on specific epigenetic regulators such as KDM5 demethylases, are shared by normal quiescent stem cells in rapidly renewing tissues, thereby limiting the therapeutic window for current ferroptosis inducers such as RSL3 and ML210 despite their demonstrated preclinical efficacy [204,206,207]. Although emerging approaches employing tumor-specific GPX4egraders using PROTAC technology or selective delivery systems show promise, these strategies remain hindered by fundamental challenges including poor water solubility, low stability, and the requirement for precise targeting ligands capable of discriminating between malignant and normal persister populations [208].

### 4.8. Precision stratification and AI integration in CRC therapy

Advances in molecular characterization have redefined CRC as a highly heterogeneous disease requiring precision-guided strategies. The Consensus Molecular Subtypes (CMS1-CMS4) framework offers a robust basis for patient stratification, as each subtype displays distinct biological and therapeutic behaviors [209,210]. CMS1 (MSI-high, immune-active) tumors are highly responsive to immune checkpoint inhibitors, whereas CMS2 (canonical) tumors benefit most from anti-EGFR therapies when RAS and BRAF are wild type. CMS3 (metabolic) cancers, frequently harboring KRAS mutations, often require combinations targeting MEK or PI3K/mTOR pathways, while CMS4 (mesenchymal) tumors, characterized by TGF-β activation and stromal infiltration, remain resistant to most agents and are under investigation stromal-targeted therapies [211,212].

Current biomarker-guided approaches rely on detecting individual alterations such as KRAS, BRAF, Micro Satellite Instability, and Tumor Mutational Burden to predict prognosis or therapeutic benefit [213]. However, as treatment options expand, biomarker interactions and their cumulative effects on therapeutic outcomes have become increasingly complex. Comprehensive molecular profiling is now essential to capture these interactions and guide rational combinations. For example, targeting the MAPK pathway through multi-gene profiling has improved response rates and enabled re-treatment with anti-EGFR antibodies after monitoring resistant subclones [214]. Moreover, patients with multiple targetable lesions, such as MSI-H/RAS-wild-type tumors co-harboring BRAFV600E mutations or kinase fusions, illustrate both the potential and the challenge of sequencing targeted therapies. Distinguishing prognostic from predictive biomarker roles remains difficult,
emphasizing the need for integrative analytics to optimize treatment sequencing [215]. Beyond static biomarkers, AI-driven multi-omics integration is reshaping how patient stratification and resistance prediction are achieved. Platforms such as AI-HOPE-RTK-RAS integrate clinical and genomic data to map RTK-RAS pathway heterogeneity, revealing new therapeutic vulnerabilities [216].

AI-based models using transcriptomic and proteomic data also improve prediction of immunotherapy response, with baseline PD-L1 levels serving as accurate AI-derived predictors of anti-PD-1 efficacy [217]. In parallel, AI-enhanced liquid biopsy analysis combines ctDNA and exosomal RNA to detect resistance-associated signatures before clinical relapse, allowing for adaptive treatment adjustment [218]. Integrated genomic-transcriptomic modeling further uncovers transcriptional reprogramming and immune evolution during anti-EGFR therapy, clarifying mechanisms of acquired resistance [219]. Together, molecular subtyping, biomarker-guided selection, and AI-driven multi-omics analysis constitute the foundation of next-generation precision oncology. These converging approaches are transforming CRC management from reactive treatment to proactive, adaptive precision

therapy, improving both response durability and long-term disease control.

## 5. Conclusion

The intricate molecular diversity and heterogeneity of CRC contribute to drug resistance and less favorable outcomes. The last decades have seen significant progress in understanding the initiation and development of CRC and the mechanisms of resistance to targeted therapies (Fig. 4). This review provides an in-depth analysis of existing targeted therapies, mechanisms of drug resistance, and potential treatment strategies to overcome resistance in CRC. The major signaling pathways involved in CRC development, such as the RAS/RAF/MEK/ERK and PI3K/AKT/mTOR pathways, are often linked to oncogenic mutations that contribute to both intrinsic and acquired resistance to targeted therapies. Mutations in the RAS and BRAF genes are particularly concerning due to their central role in resistance to EGFR inhibitors and other therapies. Additionally, EMT and the stem cell-like phenotype adopted by cancer cells are key factors in tumor plasticity, enabling

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Mechanisms of drug resistance, approaches for detection and therapeutic targeting. Drug resistance in cancer (lower left) arises through a complex network of mechanisms, including oncogenic mutations (e.g., RAS, BRAF), influences from the tumor microenvironment—such as cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs)—epigenetic modifications, immune evasion, and tumor plasticity. At the core of this resistance landscape are DTP cells (highlighted inset), a transient, non-genetic subpopulation that plays a pivotal role in tumor survival and relapse. These cells can evade therapy by entering a low-proliferative, quiescent state supported by autophagy, metabolic reprogramming, and epigenetic plasticity. Unlike resistant clones driven by permanent mutations, DTP cells are reversible and highly adaptable, allowing them to survive initial treatment and later give rise to resistant populations. Their presence is a major contributor to minimal residual disease and long-term recurrence, making them a critical therapeutic target. Detecting and monitoring such cells relies heavily on advanced molecular profiling technologies (upper left), including next-generation sequencing (NGS), liquid biopsies, and multi-omics platforms, which provide early insights into resistance emergence and enable real-time therapy adjustments. In response, emerging therapies (upper right)—such as next-generation targeted inhibitors, immune-modulating agents, and rational drug combinations—are being developed to eliminate both genetic and non-genetic resistance drivers, with a growing focus on eradicating DTP cells. Finally, key open questions and perspectives remain: identifying reliable biomarkers of DTP states, mapping tumor evolution under therapy, integrating AI-driven treatment strategies, and designing adaptive interventions capable of preventing DTP-driven relapse.</figcaption></figure>
therapeutic escape and recurrence. This review also emphasizes the crucial role of the TME in treatment resistance, with cancer-associated fibroblasts and tumor-associated macrophages creating conditions that promote tumor survival and reinforce resistance to both targeted therapies and immunotherapy. Furthermore, epigenetic modifications, such as DNA methylation and histone alterations, are key mechanisms that allow cancer cells to maintain a drug-tolerant state. In response to these challenges, innovative strategies have been developed, including next-generation inhibitors targeting molecular targets of tumor pathways, the cell cycle or DNA damage repair mechanisms. Other strategies aim to strengthen the immune system or target the tumor microenvironment. Combinatorial approaches integrating these new therapies with existing treatments could help overcome drug resistance and improve clinical outcomes for CRC patients. In conclusion, managing treatment resistance in CRC requires a thorough understanding of the underlying molecular mechanisms and a personalized therapeutic approach. Advances in next-generation sequencing and liquid biopsies offer new perspectives for early detection of emerging resistance and adaptation of therapeutic strategies to improve the prognosis of CRC patients. Integrating artificial intelligence and machine learning into cancer research shows considerable promise for unraveling and addressing treatment resistance.

## 6. Future translational challenges

As the field advances beyond transcriptomics, the integration of proteomic and metabolomic data offers promising new opportunities to identify resistance-associated biomarkers and therapeutic targets. These complementary layers of biological information, when combined with genomic and clinical datasets, provide a more comprehensive view of tumor behavior and treatment response. AI holds considerable potential to leverage this multidimensional data to uncover subtle resistance mechanisms and support the development of predictive models for personalized therapy. A machine learning model integrating Random Forest and Support Vector Machine (SVM) algorithms applied to gene expression datasets achieved an impressive average prediction accuracy of 93%, with validation and test accuracies exceeding 90% and specificity and sensitivity ranging between 85% and 95%. This model successfully identified 28.6% of CRC patients who could benefit from alternative treatment strategies following FOLFOX or FOLFIRI chemotherapy failure, demonstrating the potential of AI-based transcriptomic profiling to guide personalized therapeutic decisions [220]. In parallel, a deep learning framework combining convolutional neural networks (CNN) and recurrent neural networks (RNN) was prospectively validated in 1028 patients from the VELOUR clinical trial, achieving superior prognostic performance for predicting overall survival through quantitative assessment of tumor morphological dynamics. The model reached a C-index of 0.649 (95% CI: 0.619-0.679) compared to 0.627 using standard size-based criteria ($p = 0.009$), with DL-responders exhibiting a median overall survival of 18.0 months versus 10.4 months for non-responders ($HR = 0.49, p = 1 \times 10^{-6}$). Integration of the deep learning output with traditional metrics further improved the predictive accuracy to a C-index of 0.694, underscoring the clinical potential of AI-based imaging biomarkers in metastatic CRC management [221]. Several large-scale, publicly available resources, including The Cancer Genome Atlas (TCGA) comprehensive molecular characterization of colon and rectal cancer [6], the International Cancer Genome Consortium (ICGC) [222], OncoKB [223], the cBio Cancer Genomics Portal [224], and the Human Tumor Atlas Network's DeepMap [225], have significantly expanded access to integrated multi-omics and clinical datasets that serve as foundations for such AI-driven investigations. However, for AI to be truly effective in this context, a critical prerequisite is the standardization and benchmarking of data generation and reporting practices across laboratories and platforms. Without rigorous and harmonized data quality, AI systems risk being trained on heterogeneous or noisy datasets, increasing the likelihood of false positives and limiting clinical applicability.

Establishing robust data standards will be essential to ensure the reliability and translational value of AI-driven discoveries in precision oncology [226]. Despite significant advances in resistance biology and targeted therapy development for CRC, translational and regulatory challenges persist. Current clinical trials remain limited by insufficient molecular stratification and focus on heavily pretreated populations. The next generation of adaptive clinical trial designs must incorporate real-time liquid biopsy and ctDNA monitoring to dynamically assess tumor evolution and resistance [227,228]. Encouraging results regarding IA and multi-omics integration have shown clinical utility in CRC precision medicine. A Mayo Clinic study utilizing the deep-learning algorithm QuantCRC indeed achieved accurate prediction of recurrence-free survival with identification of patient subsets requiring intensified treatment strategies or capable of avoiding unnecessary chemotherapy [229]. From a regulatory perspective, the integration of multi-omic biomarkers, digital pathology, and AI-assisted diagnostics into clinical decision-making requires harmonized validation frameworks and flexible approval pathways. Current bottlenecks include non-standardized multi-omic data formats, inconsistent interoperability between health systems, and limited regulatory models for combination therapies involving next-generation inhibitors, epigenetic modulators, and ADCs [230,231]. AI-driven analysis of multi-omic data, including genomics, transcriptomics, and spatial proteomics, has been used to classify CRC subtypes, predict treatment response, and uncover novel therapeutic targets [232]. Similarly, machine-learning-based integration of omics profiles has improved predictive accuracy for drug resistance and prognosis across diverse CRC patient cohorts [233]. To maximize clinical utility, however, data standardization and regulatory harmonization are crucial. The proliferation of multi-omic platforms and AI tools has created heterogeneous data ecosystems that often lack interoperability. This inconsistency limits reproducibility and complicates model validation across institutions. Several analyses have highlighted that harmonized data-governance frameworks, standardized electronic health records, and interoperable AI pipelines are indispensable for the safe and equitable deployment of these technologies [234]. In parallel, adaptive clinical trial designs incorporating liquid biopsies and ctDNA are emerging as a strategy to integrate AI-driven predictions into real-time therapeutic decisions. Such trials accelerate early detection of emerging resistance and enable dynamic optimization of combination therapies targeting both genetic and non-genetic mechanisms [235]. Finally, innovations in nanotechnology-based drug delivery, including exosome-inspired nanocarriers and stimuli-responsive nanoparticles, are offering new ways to precisely target DTP and resistant tumor niches while reducing systemic toxicity. When combined with AI-guided modeling of the tumor microenvironment, these systems could enable adaptive, resistance-aware therapy and improved treatment outcomes in CRC [236]. Collectively, the integration of AI, multi-omics, and advanced delivery systems, together with robust data-standardization frameworks, defines the next step in precision oncology. Addressing remaining barriers in data interoperability, ethical governance, and clinical validation will be essential to translate these technological advances into routine clinical practice and patient benefit [226].

## CRediT authorship contribution statement

**Tanguy Fenouil:** Writing – review & editing. **Marie-Alexandra Albaret:** Writing – review & editing. **Oliver Piercey:** Writing – review & editing. **Ting Wu:** Writing – review & editing. **Jean-Jacques Diaz:** Writing – review & editing. **Frédéric Hollande:** Writing – review & editing. **Lars Petter Jordheim:** Writing – review & editing. **Mounira Chalabi-Dchar:** Writing – review & editing. **Kamel Chettab:** Writing – review & editing. **Charles Dumontet:** Writing – review & editing. **Solène Favier:** Writing – review & editing, Writing – original draft, Conceptualization. **Wei Xiong:** Writing – review & editing. **Mertani Hichem Claude:** Writing – review & editing, Writing – original draft,
Validation, Supervision, Formal analysis, Data curation, Conceptualization.

Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used Open AI in order to improve language and readability, with caution. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Funding

This research was funded by a grant from the European network COST Translacore (COST Action CA21154), the LYriCAN+ (INCADGOS-INSERM-ITMO cancer #18003), the LabEx DEVweCAN (ANR-10-LABX #0061), the PLASTIFLU project (INCa PLBIO #2024-132) and the TRANSLATOL project (Fondation ARC # ARCPGA12021020003052_3561) to Jean-Jacques Diaz. Wei Xiong was supported by a scholarship from the LabEx DevWeCan program, Lyon, France. Ting Wu is a recipient of a PhD scholarship from the China Scholarship Council (No. 202008510141) “

Declaration of Competing Interest

The authors declare that they have no competing financial interests that could influence the work reported in this paper.

Acknowledgment

The authors apologize to all researchers whose valuable work could not be cited due to space limitations

All figures included in this article were created with BioRender

Data availability

No data was used for the research described in the article.

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## Glossary

**DTP (Drug-Tolerant Persister):** A transient subpopulation of cancer cells that survive initial drug exposure through reversible, non-genetic adaptations—such as epigenetic reprogramming and metabolic shifts—thereby evading cytotoxicity and enabling tumor recurrence.

**CRC (Colorectal Cancer):** A malignancy arising from the epithelial lining of the colon or rectum, characterized by a multistep accumulation of genetic and epigenetic alterations in oncogenes (e.g. KRAS, BRAF) and tumor suppressors (e.g. APC, TP53) leading to a hyperproliferative state.

**Resistance:** The capacity of cancer cells to withstand the effects of therapy, either pre-existing (intrinsic) or acquired, via mechanisms including secondary mutations, pathway reactivation, phenotypic plasticity (e.g. EMT), and microenvironmental influences.

**Immunotherapy:** Treatment modality that harnesses the host immune system, through immune checkpoint inhibitors, adoptive cell transfer, vaccines, or cytokine therapies, to recognize and eliminate tumor cells by restoring or enhancing anti-tumor immunity.

**Targeted Therapy:** A therapeutic approach employing agents (small molecules or monoclonal antibodies) designed to specifically inhibit oncogenic drivers or signaling pathways (e.g. EGFR, VEGF, BRAF, KRAS) critical for tumor cell proliferation and survival.

**Clinical Trials:** Structured, prospective studies in human subjects to evaluate the safety, efficacy, dosing, and pharmacodynamics of new treatments, ranging from phase I safety assessments to phase III randomized comparisons against standard of care.