Next Article in Journal
Targeting IL-8 and Its Receptors in Prostate Cancer: Inflammation, Stress Response, and Treatment Resistance
Next Article in Special Issue
Exploratory Study Identifies Matrix Metalloproteinase-14 and -9 as Potential Biomarkers of Regorafenib Efficacy in Metastatic Colorectal Cancer
Previous Article in Journal
Predictive Signatures for Responses to Checkpoint Blockade in Small-Cell Lung Cancer in Second-Line Therapy Do Not Predict Responses in First-Line Patients
Previous Article in Special Issue
Exploring miRNA Profiles in Colon Cancer: A Focus on miR101-3p, miR106a-5p, and miR326
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 16
Issue 16
10.3390/cancers16162796
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Exploring Predictive and Prognostic Biomarkers in Colorectal Cancer: A Comprehensive Review

by
Karam Ashouri
1,†,
Alexandra Wong
1,†,
Pooja Mittal
1,
Lesly Torres-Gonzalez
1,
Jae Ho Lo
1,
Shivani Soni
1,
Sandra Algaze
1,
Taline Khoukaz
1,
Wu Zhang
1,
Yan Yang
1,
Joshua Millstein
2,
Heinz-Josef Lenz
1,*,‡ and
Francesca Battaglin
1,*,‡
1
Division of Medical Oncology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
2
Department of Population and Public Health Sciences, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work as co-first authors.
‡
These authors contributed equally to this work as co-last authors.
Cancers 2024, 16(16), 2796; https://doi.org/10.3390/cancers16162796
Submission received: 28 June 2024 / Revised: 4 August 2024 / Accepted: 7 August 2024 / Published: 8 August 2024
(This article belongs to the Special Issue Predictive Biomarkers for Colorectal Cancer)
Browse Figure
Versions Notes

Abstract

:

Simple Summary

Colorectal cancer is a major health concern globally, and finding ways to improve treatment outcomes is crucial. This review explores the role of biomarkers—biological indicators that can predict how a patient will respond to treatment or indicate the likely course of the disease—in managing colorectal cancer. By examining both well-established and emerging biomarkers, we hope to provide a clearer understanding of how these markers can guide personalized treatment plans. The findings from this research could help doctors make more informed decisions, ultimately improving patient care and outcomes in colorectal cancer.

Abstract

Colorectal cancer (CRC) remains the second leading cause of cancer-related mortality worldwide. While immune checkpoint inhibitors have significantly improved patient outcomes, their effectiveness is mostly limited to tumors with microsatellite instability (MSI-H/dMMR) or an increased tumor mutational burden, which comprise 10% of cases. Advancing personalized medicine in CRC hinges on identifying predictive biomarkers to guide treatment decisions. This comprehensive review examines established tissue markers such as KRAS and HER2, highlighting their roles in resistance to anti-EGFR agents and discussing advances in targeted therapies for these markers. Additionally, this review summarizes encouraging data on promising therapeutic targets and highlights the clinical utility of liquid biopsies. By synthesizing current evidence and identifying knowledge gaps, this review provides clinicians and researchers with a contemporary understanding of the biomarker landscape in CRC. Finally, the review examines future directions and challenges in translating promising biomarkers into clinical practice, with the goal of enhancing personalized medicine approaches for colorectal cancer patients.

1. Introduction

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related deaths worldwide [1]. Approximately 20% of patients have metastatic disease upon presentation, and the 5-year survival rate for these patients remains less than 20%, highlighting the need to expand the therapeutic armamentarium in this setting [2].
Over the last two decades, the treatment landscape for metastatic CRC (mCRC) has significantly evolved. The standard of treatment was 5-fluorouracil (5-FU) combined with leucovorin until it was found that combining 5-FU with oxaliplatin or irinotecan improved median overall survival (OS) to 20 months [3]. The advent of immunotherapy has also led to substantial therapeutic benefits, with a 4-year OS > 70% in mCRC positive for microsatellite instability or mismatch repair deficiency (MSI-H/dMMR) [4]. However, this biomarker applies to 4% of all mCRC cases, leaving most patients reliant on traditional chemotherapy and incremental gains from new biologic agents.
The identification of predictive and prognostic biomarkers with immunohistochemistry (IHC) or next-generation sequencing (NGS) is crucial for advancing personalized medicine in CRC. Predictive biomarkers help determine the likelihood of the response to specific therapies, while prognostic biomarkers provide information on the overall disease outcome, regardless of treatment. Notably, CRC primary tumors and metastatic sites harbor a high concordance (>90%) in their mutational profile for actionable genes [5,6]. Integrating these genetic markers in the four distinct consensus molecular subtypes (CMSs) has enriched our understanding of the clinical and biological characteristics of CRC [7].
Despite the progress in molecular diagnostics, challenges remain in translating these biomarkers into clinical practice. The heterogeneity of CRC, both morphologically and molecularly, complicates the identification of universally applicable biomarkers. The clinical relevance of many emerging biomarkers is still under investigation, necessitating further research and validation in larger patient cohorts. This review aims to provide a comprehensive overview of key established, promising, and potential biomarkers in CRC.

2. Established Biomarkers

2.1. RAS Status

Proteins of the RAS family are GTPases involved in the RAS/RAF/MEK/ERK pathway, playing a crucial role in cell division and proliferation. RAS mutations are prevalent across various cancers, making them one of the most common oncogenes. In CRC, RAS mutations are found in approximately 55% of cases, with KRAS mutations occurring in about 50%, NRAS in 4%, and HRAS in less than 1% [8].
Most KRAS mutations involve glycine substitutions at positions 12 or 13, leading to constitutive activation of the GTPase side chain. The most common KRAS mutations in CRC are KRAS G12D (36%), KRAS G12V (21.8%), and KRAS G13D (18.8%), while KRAS G12C mutations are found in 3–4% of CRC cases (Figure 1) [9,10]. KRAS mutations are more prevalent in left-sided tumors than right-sided tumors and similarly more common in microsatellite stable (MSS) CRC relative to MSI-H/dMMR CRC. In frontline chemotherapy–treated mCRC, the KRAS wildtype status conferred an improved OS relative to mutated tumors (20.9 months vs. 16.9 months, HR = 0.71, 95% CI 0.57, 0.88, HORIZON II, NCT00399035) [11]. The prognostic utility of KRAS mutations extends to both young-onset and late-onset CRC, revealing worse cause-specific survival relative to KRAS wildtype tumors in both age groups [12]. Among specific KRAS mutations, G12C demonstrated the worst prognosis relative to wildtype tumors (4.3 months vs. 23.3 months, p < 0.001) [13].
RAS mutations result in constitutive activation downstream of EGFR blockade, rendering epidermal growth factor receptor (EGFR) inhibitors ineffective. Multiple studies, including the phase 3 CRYSTAL and PRIME trials, have demonstrated that KRAS/NRAS mutations predict a poor response to combination chemotherapy with EGFR inhibitors [14,15]. Consequently, testing for KRAS/NRAS mutations is recommended by the National Comprehensive Cancer Network (NCCN) for all patients with mCRC, because those with these mutations should not receive panitumumab or cetuximab in combination with chemotherapy or as monotherapy [16]. Within 12 months of treatment, most CRCs acquire resistance to anti-EGFR therapies, frequently (~50%) due to secondary KRAS alterations [17,18]. This underscores the importance of checking for these mutations during anti-EGFR therapy.
When selecting biologic agents (bevacizumab, cetuximab, and panitumumab) to pair with frontline chemotherapy in mCRC, several studies highlight the superiority of anti-EGFR therapies (cetuximab and panitumumab) in RAS wildtype/left-sided tumors (Figure 1). The PARADIGM Study demonstrated improved OS with panitumumab versus bevacizumab in combination with chemotherapy for first-line treatment in RAS wildtype patients (37.9 months vs. 34.3 months), with a more pronounced effect in left-sided tumors [19]. Updated results from the RAS wildtype cohort in the FIRE-3 trial indicated improved OS with the FOLFIRI plus cetuximab group compared to the FOLFIRI plus bevacizumab group (33.1 months vs. 25.0 months; HR = 0.70; p = 0.0059) [20]. The cetuximab group also demonstrated a more frequent early tumor shrinkage and a greater median depth of response [20].
Recent advances have led to the development of oral KRAS G12C small molecule inhibitors. Initial studies of sotorasib and adagrasib monotherapy showed modest effects in chemo-refractory mCRC, and combining them with EGFR inhibitors appears to enhance their efficacy and overcome resistance mechanisms [21]. The KRYSTAL-1 trial, a phase I/II study examining adagrasib with or without cetuximab, showed a numerically higher median duration of response and response rates with the addition of cetuximab [22] (NCT03785249). Soon after, adagrasib was awarded accelerated Food and Drug Administration (FDA) approval as a second-line agent in combination with cetuximab for KRAS G12C–mutated CRC. The phase 3 CodeBreak 300 trial evaluates sotorasib in chemo-refractory mCRC at two doses (240 mg and 960 mg) in combination with panitumumab compared to a standard of care (SOC) group (Lonsurf or regorafenib) [23]. Early data demonstrate superior progression free survival (PFS) in both sotorasib groups relative to SOC (960 mg: 5.6 months vs. 240 mg: 3.9 months vs. SOC: 2.2 months) (NCT05198934) [23]. Another KRAS G12C inhibitor, divarasib, is being studied in a Phase 1 trial as monotherapy and in combination with cetuximab (NCT04449874) (Table 1). Additional phase 3 studies will examine adagrasib combined with cetuximab compared to chemotherapy in the second-line setting (NCT04793958) and sotorasib in combination with panitumumab and chemotherapy in treatment-naive patients (NCT06252649). Preclinical data suggest that SHP2-inhibitors augment KRAS G12C inhibitors by improving engagement and downregulating resistance mechanisms, leading to clinical trials evaluating this combination (NCT04330664, NCT04916236, NCT05288205, NCT04699188, NCT04449874) (Table 1) [24]. In preclinical CRC models, KRAS mutations increased the expression of amino acid transporters via the hippo pathway effector YAP1, resulting in mTOR activation and subsequent CRC cell proliferation [25]. Mechanistic studies in lung cancer indicate that the hippo pathway is implicated in KRAS G12C resistance, highlighting its therapeutic potential in augmenting KRAS inhibitors [26]. G12D is the most prevalent KRAS alteration in CRC, with several small molecular inhibitors (MRTX1133 and RMC-9805) undergoing early phase evaluation (NCT05737706, NCT06040541) (Table 1). MRTX1133 (G12D inhibitor) has shown synergism alongside 5-FU in preclinical models, with similar effects in KRAS G12V–mutated cell lines [27]. Alternative strategies targeting specific KRAS mutations utilize T cell receptors (TCRs), which recognize intracellular antigens presented by human leukocyte antigen (HLA) molecules to trigger an immune response [28]. Several clinical trials utilize TCRs in KRAS G12V (NCT06105021, NCT06043713) and G12D (NCT03948763) CRC (Table 1). In addition to the mutation selective inhibitors, clinical trials are evaluating pan-KRAS inhibitors (NCT04975265) (Table 1). RMC-6236 is a pan-RAS inhibitor that targets the full range of RAS alterations (KRAS, HRAS, NRAS) with activity against mutant and wildtype RAS variants and is undergoing Phase 1 studies (NCT05379985) (Table 1) [29].

2.2. BRAF Mutations

The RAF family of serine/threonine kinases are components of the RAS/RAF/MAPK/ERK signaling pathway and contain the following three members: ARAF, BRAF, and CRAF [30]. Under physiological conditions, RAF proteins exist as monomers within the cytosol until they are recruited to the membrane, bind activated RAS, and form active homo- or hetero-dimers [31]. Once activated, RAF dimers go on to activate MEK1/2, subsequently activating the ERK1/2 transcription complex, which regulates cell proliferation, differentiation, survival, senescence, and apoptosis [30,31,32]. The most common BRAF mutation results in valine substitution for glutamic acid at amino acid 600 (BRAFV600E), stimulating the MEK/ERK pathway in tumor cells (Figure 1) [33,34]. Clinical and pathological phenotypes associated with BRAFV600E-mutated CRC include proximal tumor location, aggressive growth, unfavorable metastasizing patterns, EGFR blockade resistance, and reduced overall survival (Figure 1) [35]. Pooled analysis from three mCRC clinical trials (COIN, FOCUS, PICCOLO) noted an increased frequency of MSI-H/dMMR with BRAF mutations compared to the BRAF wildtype (12.6% vs. 3.0%, p < 0.001) [36]. When combining the two frontline trials (COIN, FOCUS), BRAF mutant tumors suffered from worse OS (10.8 months vs. 16.4 months, HR  =  1.49, p  <  0.001) and remained significant when adjusting for confounders [36]. The negative prognostic impact of BRAF mutations has been confirmed in several other studies [37,38,39,40]. Non-V600E BRAF mutations occur less frequently in mCRC (2.2% vs. 7.8%) but offer better prognoses than those with the V600E mutation (p < 0.001) (Figure 1) [41]. Several retrospective studies and meta-analyses identified BRAF V600E as a negative predictor for a response to anti-EGFR therapy [42,43,44]. However, the predictive value of BRAF non-V600E mutations remains unclear (Figure 1) [45,46].
Therapies that target mutated BRAF include the SOC in melanoma, non-small cell lung cancer, and some thyroid malignancies. However, monotherapy with vemurafenib or dabrafenib is ineffective in V600E mCRC due to alternative MAPK activation mechanisms maintaining elevated MEK activity [35,47]. Co-inhibition of BRAF and the alternative activation mechanisms targeting EGFR or MEK have demonstrated successful reduction in MAPK signaling activity and improved patient treatment response [48,49,50]. Compared to chemotherapy control (FOLFIRI or irinotecan + cetuximab) in pretreated BRAFV600E mCRC, both doublet (encorafenib + cetuximab) and triplet therapy (encorafenib + binimetinib + cetuximab) demonstrated superior OS (5.9 months vs. 9.3 months vs. 9.3 months, respectively) and PFS (1.5 months vs. 4.3 months vs. 4.5 months, respectively) in the phase III BEACON trial [51]. Given that adverse events were more frequent with triplet therapy but had similar efficacy, the encorafenib plus cetuximab combination is an approved second-line regimen in BRAFV600E mCRC, with ongoing trials evaluating it in the first-line setting (Figure 1) (phase III BREAKWATER trial NCT04607421).
Notably, preclinical models in microsatellite stable (MSS) CRC demonstrate that combined BRAF/EGFR inhibition induces a transient MSI-H/dMMR phenotype [52]. Encorafenib, cetuximab, and nivolumab combination therapy in MSS/BRAFV600E recently demonstrated encouraging PFS (7.4 months) and OS (15.1 months) with a randomized phase II trial underway [53] (NCT05308446).

2.3. HER2

HER2, encoded by ERBB2, is an EGFR tyrosine kinase family member. Its overexpression and/or amplification have been reported in various solid tumors, particularly breast cancer, where HER2-targeted therapies have been highly successful. The NCCN Guidelines now recommend HER2 testing for all mCRC patients, present in 2–6% of cases given therapeutic implications with anti-EGFR and HER2 directed therapy (Figure 1). HER2 is measured by IHC, fluorescence in situ hybridization (FISH), or NGS. HER2 overexpression is indicated by an IHC score of 3+, while an IHC score of 2+ is equivocal and should prompt FISH testing. A FISH ratio ≥2 confirms HER2 amplification [54]. HER2 amplification is more common in left-sided tumors and is more prevalent in RAS/BRAF wildtype tumors [55,56]. Studies have shown that HER2 overexpression may confer resistance to EGFR inhibition (Figure 1) [55,57,58]. On the other hand, a recent analysis from the CALGB/SWOG 80405 trial found that high HER2 expressing mCRC (tumor gene expression above the median) had a longer PFS and OS than tumors with lower HER2 expression [59]. In patients with lower HER2 expression, treatment with cetuximab was associated with a worse PFS and OS compared to bevacizumab. The poor prognostic implication of decreased HER2 expression was confirmed in RAS wildtype mCRC based on the MONSTAR-SCREEN-2 cohort [60]. These findings suggest that HER2 expression is prognostic and may contribute to selecting anti-EGFR versus anti-VEGF treatment in the first-line metastatic setting [59]. However, other studies have reported mixed findings regarding the prognostic role of HER2, and further prospective validation is needed [61].
HER2 is an established therapeutic target in CRC. In the second-line setting for mCRC, RAS/BRAF wildtype patients with HER2 overexpression/amplification who have not received prior HER2 inhibitors can be treated with trastuzumab in combination with pertuzumab, lapatinib, or tucatinib. The HERACLES trial, a phase 2 study of heavily pre-treated patients with the KRAS wildtype, HER2+ mCRC, demonstrated the efficacy of dual HER2 blockade using trastuzumab and lapatinib, with an objective response rate (ORR) of 30% [56]. The MyPathway basket study of trastuzumab and pertuzumab in treatment-refractory CRC reported an ORR of 38% [62]. The combination of trastuzumab and tucatinib was the first HER2-directed treatment approved by the FDA for chemo-refractory mCRC, based on the MOUNTAINEER trial (ORR 38%) [63]. The ongoing phase 3 trial, MOUNTAINEER-03 (NCT05253651), is investigating tucatinib and trastuzumab in combination with chemotherapy in the first-line setting compared to standard care in HER2+ patients. In the DESTINY-CRC01 phase 2 trial of patients with HER2+ mCRC who had received at least two prior lines of therapy, fam-trastuzumab deruxtecan was associated with an ORR of 45% [64]. Notably, fam-trastuzumab deruxtecan is an option for patients who received prior HER2-directed therapy; however, the cohort included only patients with HER2 IHC 3+, and patients must be monitored closely for the potential serious side effect of interstitial lung disease [64]. Future studies will help elucidate ideal sequencing strategies for targeted therapy in this group of patients.

2.4. MSI-H

Microsatellites are short tandem repeated sequences spread throughout DNA. Microsatellite instability is caused by defects in the mismatch repair system, most commonly due to somatic mutations of MLH1, MSH2, MSH6, PMS2, and EPCAM, which lead to defects during DNA replication [65,66]. The prevalence of MSI-H/dMMR decreases with the advancing CRC stage as follows: stage II (20%), then stage III (12%), and stage IV (4%) (Figure 1) [67]. Knowledge of the MSI-H/dMMR status is critical in managing CRC for several reasons. Patients with MSI-H/dMMR tumors must be screened for germline mutations in the mismatch repair genes, which leads to Lynch Syndrome, altering the treatment of patients and relatives [68]. With the introduction of immune checkpoint inhibitors (ICI), MSI-H/dMMR status has offered profound survival benefits and cures for some CRCs [69]. The high neoantigen load of MSI-H/dMMR cancer elicits a strong antitumor immune response, further augmented with ICIs (Figure 1) [70]. Additionally, MSI-H/dMMR CRCs have a favorable tumor microenvironment (TME) with increased tumor-infiltrating lymphocytes and decreased immunosuppressive cells like regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) [71,72]. ICIs further enhance the activity of these preexisting tumor infiltrating lymphocytes (TILs) to target the tumor.
The phase II KEYNOTE-016 trial was the first to demonstrate superior ORR (40%) in MSI-H/dMMR mCRC relative to MSS (0%) with pembrolizumab [73]. Pembrolizumab’s superior antitumor activity was confirmed in the KEYNOTE-164 trial, demonstrating a median PFS and OS of 4.1 and 47.0 months, respectively, in pretreated CRC (chemotherapy arm PFS: 2.3 months, OS: 31.4 months) [69,74]. The 50% increase in OS relative to the SOC and 30% PFS plateau at 5 years, all with fewer adverse events in the experimental arm, were unheard of in previous CRC trials. A similar efficacy was demonstrated with nivolumab (phase II Checkmate 142 trial), awarding both agents FDA approval for second-line use in 2017 [75]. Phase III KEYNOTE-177 demonstrated that first-line pembrolizumab (200 mg q3w for up to 2 years) was superior to standard chemotherapy (mFOLFOX6 or FOLFIRI q2w  ±  bevacizumab or cetuximab) in MSI-H/dMMR mCRCs, confirming a longer median PFS (16.5 months vs. 8.2 months, HR 0.60, p  =  0.0002) [76]. The effect of nivolumab was augmented with the addition of anti-CTLA4 (ipilimumab) therapy (24-month PFS: 74% and OS: 79%) [77]. Phase III Checkmate-8HW confirmed the superior PFS of nivolumab plus ipilimumab compared to chemotherapy (not reached (NR) vs. 5.9 months; HR 0.21, p < 0.0001) after 31.5 months of median follow-up in the first-line setting [4].
Despite the clinical benefit, nearly 30% of MSI-H/dMMR tumors are resistant to ICI [78]. Preclinical data indicate that inhibition of VEGF may promote an immune permissive TME [79,80]. Following encouraging safety and efficacy data, atezolizumab plus bevacizumab plus FOLFOX is being evaluated as a first-line regimen in MSI-H/dMMR mCRC (NCT02997228) [81]. Additional studies combine VEGF inhibitors with pembrolizumab or nivolumab as second-line agents (NCT05035381).

2.5. Tumor Mutational Burden (TMB)

Tumor mutational burden (TMB) is an established biomarker for predicting the efficacy and response to ICIs in multiple cancer types, including CRC, independent of the MSI-H/dMMR status [82]. TMB is an independent biomarker, and studies have shown TMB to be elevated in MSI-H/dMMR tumors. A high TMB (TMB > 37.4 mutations/megabase [Mb]) stratified patients with a better response to ICIs in an MSI-H/dMMR mCRC cohort [82].
TMB can be expressed as the number of acquired/somatic mutations (coding errors, base substitutions, and insertions/deletions) per Mb of the sequenced DNA. NGS and whole exome sequencing (WES) of the tumor genome are used to measure TMB, and examples of diagnostic NGS panels include MSK-IMPACT (Memorial Sloan Kettering) and TSO500 (Illumina) [83,84]. A high TMB could potentially translate into higher loads of neoantigens, stimulating the immune system to recognize and attack tumor cells and thus increasing sensitivity to immunotherapy [85]. Although only a few somatic mutations may give rise to neoantigens, even fewer of these are processed by major histocompatibility complex (MHC) molecules and recognized by T cells as immunogenic. Still, TMB gives a useful estimation of the tumor neoantigen loads [86]. A recent study by Wang et al. (2022) reported a high TMB as an indicator of better prognosis in patients with KRAS-mutated CRC. The study revealed that among CRC patients with KRAS mutations, those with higher TMB (TMB > 10 mutations per Mb) had a longer OS compared to the ones with lower TMB (TMB ≤ 10 mutations per Mb) [84].
The US FDA approved the use of pembrolizumab for the treatment of cancer patients with TMB of >10 mutations per Mb in 2020 (Figure 1) [87]. However, the variation in intratumoral heterogeneity is one of the factors to be considered while characterizing TMB across different tumor types. Cancers such as melanoma, non-small cell lung cancer (NSCLC), CRC, and other squamous carcinomas exhibit a high TMB, while pediatric tumors and leukemias show the lowest levels of TMB [86]. Due to variation in TMB by cancer type, more studies are warranted to develop TMB into a robust predictive marker of ICI efficacy.

2.6. NTRK Fusions

The neurotrophic tyrosine receptor kinase (NTRK) genes include NTRK1, NTRK2, and NTRK3, which encode TRKA, TRKB, and TRKC, respectively. Binding with their respective ligands leads to the transcription of genes involved in neuronal cell differentiation and survival mediated by the MAPK, PI3K, and PKC signaling pathways [88]. The alterations in the NTRK genes have been implicated in the induction of carcinogenesis in both neuronal and non-neuronal cells. These alterations could be in the form of gene mutations, fusions, and deletions. The NTRK fusions have been reported in several cancers in both adult and pediatric populations, with an approximately 80% frequency in rare cancers (including mammary secretory carcinoma and congenital infantile fibrosarcoma). The frequency of NRTK fusion is comparably less (commonly <5%) in common cancers, with the frequency being 0.7–1.5% in CRC (Figure 1) [89].
A recent report by Wang et al. (2022) documented that frequent coalterations in APC and TP53 genes were found with NTRK fusions, while alterations in RAS/BRAF were mutually exclusive. NTRK fusion-positive tumors showed a high TMB, MSI-H/dMMR, and enrichment in POLE/POLD1 mutations compared to molecularly unstratified CRC tumors. Typically, the NTRK fusion proteins contain the kinase domain of NTRK juxtaposed with a different gene. In several cases, this leads to the constitutive activation of downstream signaling pathways such as MAPK and PI3K. NTRK fusions are clinically targetable mutations, and two first-generation NTRK tyrosine kinase inhibitors (TKIs), larotrectinib and entrectinib, have been approved by the FDA for the treatment of both adult and pediatric cancers (Figure 1). Second or next-generation NTRK inhibitors, which could overcome the resistance to first-generation NTRK TKIs, are also in clinical trials, including LOXO-195 and TPX-0005 [88,90].
Another study documented the impact of NTRK fusions on outcomes in patients with locally advanced or metastatic solid cancers. The NTRK fusion-positive cohort (which included 10 different cancer types with the highest number of patients having CRC) showed a hazard ratio of 1.6 (p = 0.05) and a median OS of 10.2 months compared to the NTRK fusion-negative cohort (median OS = 10.4 months). This study suggested that NTRK fusions might be a negative prognostic factor; however, further studies are warranted [91].

3. Promising Biomarkers

3.1. PD-L1

PD-L1 is expressed at low levels on antigen-presenting cells and a variety of nonhematopoietic cells, binding PD-1 on T cells [92]. This interaction inhibits T cell activation and proliferation, suppressing the immune response against tumor cells and allowing them to evade immune surveillance [93]. In theory, higher PD-L1 expression in tumor tissue should confer an improved response to ICI treatment. While PD-L1 is a predictive biomarker in NSCLC, melanoma, and renal cell cancer, its use in CRC is limited [94,95]. PD-L1 expression is induced by IFN-y and TNF-α, secreted by TILs, particularly CD8+ T cells [92]. Therefore, PD-L1 expression by IHC is highly dependent on spatial heterogeneity and sampling. Additionally, the plethora of primary antibodies, staining conditions, and intertumoral heterogeneity add to the technical and biological discrepancy in PD-L1 expression, limiting its use as a biomarker [96]. Analysis of nivolumab plus ipilimumab–treated MSI-H/dMMR CRC showed no difference in ORR utilizing PD-L1 ≥ 1% as a cutoff [97]. Meta-analyses have demonstrated positive tumor cell PD-L1 expression to confer worse OS, but other studies did not find this association or found the opposite effect [98,99,100]. Some studies have highlighted an increased PD-1 expression in immune cells and TILs to confer an improved OS in CRC [100,101]. While the utility of PD-1/PD-L1 as a biomarker requires further research, additional therapies targeting PD-1, such as balstilimab, are being investigated in colorectal cancer (NCT05608044).

3.2. PI3K

PIK3CA is a key driver gene that encodes a subunit of phosphatidylinositol 3-kinase (PI3K), a critical component of the PI3K/AKT/mTOR pathway involved in proliferation, survival, and angiogenesis [102]. PIK3CA mutations activate the PI3K/AKT/mTOR pathway downstream of RAS activation, conferring resistance to anti-EGFR therapy in KRAS-wildtype mCRC, with a more pronounced effect in exon 20 alterations [103,104,105]. The prognostic implication of PIK3CA mutations remains unclear in CRC. Although some studies have reported a worse prognostic effect with PIK3CA mutations in mCRC, others have reported a null association [104,106]. Evaluation of serabelisib (PIK3 inhibitor) in solid tumor patients with PIK3CA mutations is in a phase 1b trial (NCT05300048) (Table 1).

3.3. CXCR4 Axis

CXC chemokine receptor 4 (CXCR4) and its ligand CXCL12 participate in CRC growth, invasion/metastasis, and angiogenesis [107]. Activation of CXCR4 on tumor cells stimulates ICAM-1, enhancing their adhesion to endothelial cells, while CXCR4 on immune cells suppresses the intratumoral immune reaction [108,109,110]. An increased CXCR4 expression has been linked with an increased liver metastasis and a decreased OS (median 10 months vs. 27 months) in mCRC [111,112]. Additionally, CXCR4 predicts worse recurrence-free survival following curative hepatectomy in CRC patients [113]. Preclinical models have implicated CXCR4 in resistance to oxaliplatin (OXA) and 5-FU [114]. Plerixafor, a small molecular antagonist of the CXCR4 receptor, is being evaluated in phase 1 studies for pancreatic and colorectal cancer (NCT02179970) (Table 1).

3.4. CCR5 Axis

The C-C motif chemokine receptor 5 (CCR5)/C-C motif chemokine ligand 5 (CCL5) axis has been implicated in an autocrine and paracrine fashion to stimulate cancer cell proliferation, metastasis, and immune regulation [115]. CCR5 induces vascular endothelial growth factor A (VEGF-A) expression to promote angiogenesis and recruits immunosuppressive tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs) [116]. An increased CCR5 expression showed a favorable treatment interaction toward bevacizumab, while a decreased expression predicted a response to cetuximab in the CALGB/SWOG 80405 cohort [117]. Additionally, single nucleotide polymorphisms (SNPs) in CCL5/CCR5 have been predictive of a response to cetuximab and regorafenib therapy [118,119,120]. CCR5 may have a prognostic effect, as one study noted that an increased CCR5 expression displayed worse disease-specific survival, and the CALGB/SWOG 80405 cohort similarly displayed a decreased OS in cetuximab/FOLFOX-treated mCRC [117,121]. Maraviroc, an FDA-approved HIV medication that inhibits CCR5 expression, showed initial promise in treating refractory mCRC (phase 1 MARACON trial NCT01736813). Given the immunosuppressive role of CCR5 and their expression on immune cells, maraviroc and another CCR5 inhibitor, vicriviroc, are being evaluated in combination with pembrolizumab for MSS mCRC (NCT03274804, NCT03631407) (Table 1).

3.5. TIM3/LAG3

T cell immunoglobulin and mucin domain-containing protein 3 (TIM3) and lymphocyte-activation gene 3 (LAG3) are immune checkpoint receptors expressed on T cells, macrophages, and other immune cells, which suppresses antitumor immune responses [122,123]. Within CRC TME, TIM3 and LAG3 promote immune tolerance by inhibiting T cell activity and favoring M2-like polarization of macrophages [124,125]. In stage I-III CRC, an increased tumor TIM3 and LAG3 expression correlated with a decreased OS, while an increased expression on immune cells had the opposite effect [126]. Murine models revealed that the co-blockade of TIM3 and its ligand (CEACAM1) leads to enhanced antitumor immune responses, improving CRC clearance [127]. Clinical trials are evaluating TSR-022 (anti TIM3 antibody) in solid tumors, including colorectal cancer (phase 1 NCT02817633). Mouse models demonstrated that combining LAG3 and PD1 inhibitors overcame tumor resistance to either agent alone [128]. Similarly, the evaluation of nivolumab with relatlimab (LAG3 monoclonal antibody) in MSS colorectal cancer patients is underway (phase 2 NCT03642067) (Table 1). After a combination of favezelimab (Anti-LAG3) and pembrolizumab demonstrated safety and robust efficacy in the PD-L1 combined positive score (CPS) ≥ 1 MSS mCRC, a phase 3 trial is comparing it to SOC in PD-L1 positive CRC (NCT05064059) (Table 1) [129].

3.6. CEA/CEACAM5

Carcinoembryonic antigen (CEA), a glycoprotein and tumor marker encoded by CEACAM5, is elevated in patients with CRC. Preoperative serum CEA (s-CEA) of > 5 ng/mL confer worse OS and cancer-specific survival [127,128]. In addition to s-CEA, tissue CEA (t-CEA) can be assessed using IHC with expression patterns described as apicoluminal (AL) or diffuse-cytoplasmic (DC). DC pattern t-CEA is associated with a higher rate of recurrence along with a lower OS and disease-free survival (DFS) relative to the AL pattern [129,130,131]. Additionally, increased high-intensity t-CEA is associated with a decreased DFS [131]. The elevated expression of CEACAM5 in colorectal cancer relative to normal tissue highlights its utility as an antibody-drug conjugate (ADC) target. Early data for M9140 (Anti-CEACAM5 ADC with a topoisomerase 1 inhibitor) demonstrated a suitable safety profile and disease control rate (52.5%) in heavily pretreated (≥2 prior lines of therapy) mCRC (NCT05464030) (Table 1) [132]. Cibisatamab (CEACAM5/CD3 T cell–bispecific antibody) is being tested with atezolizumab in mCRC (NCT03866239) (Table 1).

3.7. c-MET

Mesenchymal–epithelial transition factor (c-MET) is a receptor tyrosine kinase encoded by MET, responsible for cellular proliferation, angiogenesis, and epithelial–mesenchymal transition (EMT) [130]. Several studies, along with a meta-analysis, noted that an increased c-MET expression predicts worse OS and DFS [131,132]. MET amplification correlates with acquired resistance to anti-EGFR therapy in CRC without KRAS mutations [133]. Combining cetuximab with MET inhibitors suppressed MET-induced anti-EGFR resistance in CRC cell lines [134]. Early data on ABBV-400, a c-MET-targeted ADC linked with telisotuzumab (topoisomerase 1 inhibitor), demonstrated a robust safety profile and ORR (61.9%) in CRC (NCT05029882, NCT06107413) (Table 1) [135].

3.8. ARID1A

ARID1A is a subunit of the SWI/SNF complex, which plays a role in regulating DNA repair. A loss of function of the SWI/SNF complex promotes genomic instability and tumor progression. In CRC, ARID1A is a known driver gene, and mutations are associated with right-sided tumors, MSI-H/dMMR, and BRAF mutations [136]. Studies suggest that ARID1A mutations may be implicated in both intrinsic and acquired resistance to cetuximab [137]. Patients enrolled in CALGB/SWOG 80405, a randomized phase III study evaluating the efficacy of chemotherapy plus cetuximab vs. chemotherapy plus bevacizumab in first-line CRC, who had ARID1A mutations at baseline, had poorer clinical outcomes with cetuximab compared to bevacizumab. These findings were supported in patient-derived xenograft models of extended RAS/BRAF wildtype tumors. In post-treatment cell-free DNA (cfDNA) analysis of these patients, KRAS and ARID1A mutations were enriched in those who were treated with cetuximab but not bevacizumab. Additional CRC genomic analysis suggests that ARID1A and EGFR-pathway mutations are mutually exclusive, consistent with findings in lung cancer. Further exploration is warranted to delineate if ARID1A mutations identify a group of patients who benefit from MAPK inhibition, in addition to being a predictive biomarker of a poor response to cetuximab treatment. Phase 2 trials are evaluating tislelizumab (PD-1 Antibody) plus fruquintinib (VEGFR 1/2/3 Inhibitor) for ARID1A-mutated MSS mCRC (NCT05690035) (Table 1).

3.9. PLK1

Polo-like Kinase 1 (PLK1) is a serine/threonine protein kinase family that plays a role in cell cycle progression by regulating progression to mitosis at the G2/M checkpoint [138]. It also plays a role in DNA damage response and cell death pathways. PLK1 is overexpressed in many tumor types and associated with poor prognosis, making it a promising therapeutic target. Higher levels of PLK-1/phosphorylated-PLK1 were found in relapsed/mCRC tissues compared to matched primary CRC tissues, suggesting that it is a marker of poor prognosis and resistance to oxaliplatin-based chemotherapy [139]. Inhibition of PLK1 in CRC cell lines demonstrated that PLK1 inhibitors may attenuate oxaliplatin resistance [139]. A Phase 1b/2 trial studied onvansertib, an oral highly selective PLK1 inhibitor, in combination with FOLFIRI and bevacizumab as a second-line treatment for KRAS-mutated mCRC and found that the combination was well-tolerated with durable responses [140]. A Phase 2 trial is underway to assess its utility combined with chemotherapy in the first-line setting for KRAS- or RAS-mutated mCRC (NCT06106308) (Table 1).

4. The Role of Liquid Biopsy/ctDNA

Liquid biopsies (circulating tumor DNA [ctDNA]) are valuable tools in the management of CRC. When a tumor cell dies, or through active secretion, ctDNA is released in body fluids, most commonly blood [141]. This allows for the detection of mutations, chromosomal rearrangements, methylation patterns, and different fragment lengths [142]. Liquid biopsies can be performed serially during a patient’s disease course and used to interpret tumor biology and assess tumor response to therapy [143]. Although tissue biopsy is the gold standard for molecular profiling, it has several limitations, including insufficient tissue sampling and a longer turnaround time. A prospective study from Japan noted ctDNA to have a lower sequencing failure rate (0.1% vs. 10.6%) and a shorter screening time (11 vs. 33 days, sample acquisition + test duration), ultimately leading to a higher clinical trial enrollment (9.5% vs. 4.1%) [144]. Tumor heterogeneity and evolution have necessitated resampling with liquid biopsy mutations that confer resistance to treatments such as RAS mutations in those receiving anti-EGFR therapy [145]. The concordance of tissue and liquid biopsy is acceptable, ranging from 77–96%, with NGS-based methods [145].
ctDNA has shown significant promise in detecting minimal residual disease (MRD) and monitoring for early recurrence in CRC. A prospective study of postsurgical stage III CRC highlighted that MRD positivity following adjuvant chemotherapy is significantly associated with an increased risk of recurrence (HR, 7.5; 95% CI, 3.5–16.1; p < 0.001) [146]. These findings were replicated in several studies, even demonstrating prognostic value with ctDNA positivity having worse OS [147,148]. The prognostic utility of ctDNA has been leveraged to select CRC patients likely to benefit from adjuvant chemotherapy (ACT), reducing unnecessary toxicities. Stage II/III CRC patients were prospectively randomized to SOC or a ctDNA-guided protocol for ACT and demonstrated noninferior DFS and OS [149]. When evaluating metastatic gastrointestinal cancers, a four week decrease following systemic therapy initiation predicted the clinical benefit and PFS [149]. Several trials are utilizing dynamic changes in ctDNA to guide treatment decisions in mCRC (NCT04786600, NCT03844620, NCT05062317). Liquid biopsy lacks sensitivity in detecting fusions and copy number losses while having a higher TMB relative to tissue biopsy [150]. The utility of ctDNA has broadened from its initial use in the advanced/metastatic setting for molecular profiling and detection of acquired resistance mechanisms to the identification of MRD and early detection.

5. Future Directions

NGS has altered the paradigm of genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. Third-generation sequencing has allowed for longer read lengths, real-time sequencing, minimal sample preparation, and direct detection of epigenetic modifications, providing a comprehensive understanding of genomic structure [151]. Using NGS in ctDNA offers a minimally invasive approach to monitor disease progression and treatment response. Studying the dynamics of these biomarkers over time and across different treatment regimens can provide valuable insights into the tumor landscape and inform personalized treatment strategies [152].
CRC is known to exhibit a high intratumor heterogeneity, with different subpopulations of tumor cells harboring distinct genetic and transcriptomic profiles. Single-cell RNA sequencing (scRNA-seq) evaluates the heterogeneity at the single-cell level, enabling the identification of rare cell subpopulations that may play crucial roles in disease progression, metastasis, and treatment resistance [153]. Additionally, scRNA-seq can delineate the complex interactions between tumor cells and immune cells, potentially identifying biomarkers related to immunosuppression or immune evasion.
Multiomics integrates genomic, transcriptomic, proteomic, and epigenomic data along with clinical metadata through artificial intelligence (AI) and bioinformatics tools. By combining data from various omic levels, researchers can gain a more comprehensive understanding of the underlying mechanisms, regulatory pathways, and functional relationships with CRC. Utilizing feature selection and extraction techniques such as principal component analysis, researchers can reduce the dimensionality of multiomics data while preserving the variance and information [154]. Machine learning (ML) models have demonstrated a remarkable accuracy in predicting responses to first-line chemotherapy based on multiomics data [155].
With advances in molecular testing and modeling, we highlight emerging evidence on novel pathways and biomarkers that may double as therapeutic targets. HLAs are divided into two classes, HLA class I and class II, which present peptides to CD8+ T cells and CD4+ T cells, respectively. Reduced expression of HLA class I genes has been associated with immune evasion in immunotherapy-naive MSI-H/dMMR CRC, suggesting a possible explanation for primary resistance to ICI [156,157]. HLA class I and II SNPs predict outcomes in first-line treatment in mCRC, with differential effects based on biologic agent and chemotherapy backbone [158,159]. While not prognostic, MCL-1, an anti-apoptotic protein from the BCL-2 family, induces chemoresistance and plays a crucial role in CRC by maintaining intestinal homeostasis [160,161,162]. Given that MCL-1 and BCL-xL are co-expressed in late-stage CRC, this chemoresistance can be countered by coadministration of A-1331852 (BCL-xL inhibitor) and A-1210477 (MCL-1 inhibitor) [162]. A high MCL-1 expression also correlates with an increased M1 and M2 macrophage infiltration in CRC tumors, affecting cancer cell proliferation and EMT [163,164].
The circadian rhythm, regulated by the core clock genes (BMAL1 and CLOCK), is another pathway closely linked with CRC proliferation and metastasis [165,166]. BMAL1 suppresses a hippo-dependent, self-renewal pathway in the intestinal epithelium such that the loss of BMAL1 in knockout mouse models results in the upregulation of stem cell signaling and increased CRC growth [167]. BMAL1 drives the transcription of VEGFA conferring resistance to bevacizumab treatment, therefore combining bevacizumab with a CRY2 stabilizer (SHP1705) to inhibit BMAL1 activity led to a decreased tumor volume and an improved OS in mouse models [168]. Like BMAL1, dihydropyrimidine dehydrogenase (DPD) is another protein involved in therapeutic resistance. A high tumoral DPD expression is associated with 5-FU resistance in CRC; consequently, combining 5-FU with DPD inhibitors (S1PR2 inhibitors or SLR080811) improves efficacy [169,170,171]. DPD expression positively correlates with CD4+ T cells, CD8+ T cells, and macrophages in the TME, in addition to the expression of immune checkpoint markers (PD-L1 and PD-1) [172]. This suggests that DPD not only has a predictive value for 5-FU resistance but also holds potential for immunotherapy-treated CRC.
Both neurotransmitters and ferroptosis play a role in modulating the immune response and are growing areas of CRC research. Neurotransmitters in gastrointestinal tumors facilitate interactions between cancer and immune cells, promoting oncogenic processes [173,174,175,176]. The expression of neurotransmitter receptors on immune cells affects tumor immune responses and may influence immune-targeting treatments [174]. Blocking receptors like muscarinic receptor 3 (M3R) and monoamine oxidase A (MAOA) have shown decreased tumor growth and altered immune responses in CRC models [177,178]. These findings suggest that neurotransmitters have potential as predictive biomarkers and may pave the way for novel therapies targeting neurotransmitters to overcome therapy resistance [179]. Ferroptosis, an iron-dependent form of cell death, is regulated by genes involved in iron metabolism and has been implicated in CRC tumor biology [180,181,182]. In CRC, the altered expression of ferroptosis-related genes predicts OS and the immune response [183,184,185,186,187]. Ferroptosis is being explored in combination therapy with ICIs as a promising treatment strategy to increase sensitivity and overcome resistance [188,189]. Glutamine metabolism regulates ferroptosis by contributing to the synthesis of glutathione (GSH) and through the process of glutaminolysis [190]. Oncogenic KRAS reprograms cellular metabolism, limiting these processes and thereby making KRAS-mutant tumors more susceptible to ferroptosis induction as a potential clinical strategy [191].
As the era of targeted therapies continues to evolve, the development of companion diagnostics and predictive biomarkers becomes increasingly crucial. Identifying biomarkers that can accurately predict the response to targeted agents will maximize therapeutic efficacy while minimizing unnecessary toxicities and healthcare costs.

6. Conclusions

The predictive capacity of MSI-H, RAS, and BRAF has necessitated a molecular-targeted approach in CRC. The MSI status confers substantial improvements in clinical outcomes and decreased toxicities through ICI, offering patients long-term survival or near curative results [69]. BRAF V600E had a negative prognostic value but is now predictive of the response to combined targeted BRAF/EGFR inhibition, doubling survival over standard treatments [51]. While the prevalence of these alterations constitutes the minority of CRCs, they underscore the necessity of identifying predictive biomarkers.

Author Contributions

All authors have made substantial contributions to the review. Conceptualization: K.A., A.W., H.-J.L. and F.B.; writing, review, and/or revision of the manuscript: all authors; study supervision/guarantor: K.A., A.W., H.-J.L. and F.B. All authors have read and agreed to the published version of the manuscript.

Funding

This study was partly supported by the National Cancer Institute (grant number P30CA014089) [to H.-J.L.], the Gloria Borges WunderGlo Foundation, Dhont Family Foundation, Victoria and Philip Wilson Research Fund, San Pedro Peninsula Cancer Guild, Ming Hsieh Research award, and Daniel Butler Research Fund. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Conflicts of Interest

H.-J.L. reports receiving honoraria from consultant/advisory board membership for Merck Serono, Bayer, and Genentech. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

5-FU5-fluorouracil
ADCantibody-drug conjugate
AIartificial intelligence
ALapicoluminal
BRAFV600EBRAF mutation with valine substitution for glutamic acid at amino acid 600
c-METmesenchymal–epithelial transition factor
CCR5C-C motif chemokine receptor 5
CEAcarcinoembryonic antigen
cfDNAcell-free DNA
CMSconsensus molecular subtype
CRCcolorectal cancer
ctDNAcirculating tumor DNA
CXCR4CXC chemokine receptor 4
DCdiffuse-cytoplasmic
DFSdisease-free survival
DPDdihydropyrimidine dehydrogenase
EGFRepidermal growth factor receptor
EMTepithelia–mesenchymal transition
FDAFood and Drug Administration
FISHfluorescence in situ hybridization
GSHglutathione
HLAhuman leukocyte antigen
ICIimmune checkpoint inhibitor
IHCimmunohistochemistry
LAG3lymphocyte-activation gene 3
M3Rmuscarinic receptor 3
MAOAmonoamine oxidase A
Mbmegabase
mCRCmetastatic colorectal cancer
MHCmajor histocompatibility complex
MLmachine learning
MRDminimal residual disease
MSI-H/dMMRmicrosatellite instability or mismatch repair deficiency
MSSmicrosatellite stable
NCCNNational Comprehensive Cancer Network
NGSnext-generation sequencing
NRnot reached
NSCLCnon-small cell lung cancer
OSoverall survival
PFSprogression-free survival
PI3Kphosphatidylinositol 3-kinase
PLK1polo-like Kinase 1
s-CEAserum carcinoembryonic antigen
scRNA-seqsingle-cell RNA sequencing
SNPsingle nucleotide polymorphism
SOCstandard of care
t-CEAtissue carcinoembryonic antigen
TAMstumor-associated macrophages
TCRT cell receptor
TILstumor infiltrating lymphocytes
TIM3T cell immunoglobulin and mucin domain-containing protein 3
TKItyrosine kinase inhibitor
TMBtumor mutational burden
TMEtumor microenvironment
Tregregulatory T cell
VEGF-Avascular endothelial growth factor A
WESwhole exome sequencing

References

  1. Bray, F.; Laversanne, M.; Sung, H.; Ferlay, J.; Siegel, R.L.; Soerjomataram, I.; Jemal, A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2024, 74, 229–263. [Google Scholar] [CrossRef] [PubMed]
  2. Biller, L.H.; Schrag, D. Diagnosis and Treatment of Metastatic Colorectal Cancer: A Review. JAMA 2021, 325, 669–685. [Google Scholar] [CrossRef]
  3. Grothey, A.; Sargent, D.; Goldberg, R.M.; Schmoll, H.J. Survival of patients with advanced colorectal cancer improves with the availability of fluorouracil-leucovorin, irinotecan, and oxaliplatin in the course of treatment. J. Clin. Oncol. 2004, 22, 1209–1214. [Google Scholar] [CrossRef] [PubMed]
  4. Lenz, H.-J.; Lonardi, S.; Elez, E.; Cutsem, E.V.; Jensen, L.H.; Bennouna, J.; Mendez, G.; Schenker, M.; Fouchardiere, C.D.L.; Limon, M.L.; et al. Nivolumab (NIVO) plus ipilimumab (IPI) vs chemotherapy (chemo) as first-line (1L) treatment for microsatellite instability-high/mismatch repair-deficient (MSI-H/dMMR) metastatic colorectal cancer (mCRC): Expanded efficacy analysis from CheckMate 8HW. J. Clin. Oncol. 2024, 42, 3503. [Google Scholar] [CrossRef]
  5. Lee, S.E.; Park, H.Y.; Hwang, D.Y.; Han, H.S. High Concordance of Genomic Profiles between Primary and Metastatic Colorectal Cancer. Int. J. Mol. Sci. 2021, 22, 5561. [Google Scholar] [CrossRef] [PubMed]
  6. Brannon, A.R.; Vakiani, E.; Sylvester, B.E.; Scott, S.N.; McDermott, G.; Shah, R.H.; Kania, K.; Viale, A.; Oschwald, D.M.; Vacic, V.; et al. Comparative sequencing analysis reveals high genomic concordance between matched primary and metastatic colorectal cancer lesions. Genome Biol. 2014, 15, 454. [Google Scholar] [CrossRef] [PubMed]
  7. Guinney, J.; Dienstmann, R.; Wang, X.; de Reyniès, A.; Schlicker, A.; Soneson, C.; Marisa, L.; Roepman, P.; Nyamundanda, G.; Angelino, P.; et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 2015, 21, 1350–1356. [Google Scholar] [CrossRef]
  8. Prior, I.A.; Hood, F.E.; Hartley, J.L. The Frequency of Ras Mutations in Cancer. Cancer Res. 2020, 80, 2969–2974. [Google Scholar] [CrossRef]
  9. Neumann, J.; Zeindl-Eberhart, E.; Kirchner, T.; Jung, A. Frequency and type of KRAS mutations in routine diagnostic analysis of metastatic colorectal cancer. Pathol. Res. Pr. 2009, 205, 858–862. [Google Scholar] [CrossRef]
  10. Zhu, C.; Guan, X.; Zhang, X.; Luan, X.; Song, Z.; Cheng, X.; Zhang, W.; Qin, J.J. Targeting KRAS mutant cancers: From druggable therapy to drug resistance. Mol. Cancer 2022, 21, 159. [Google Scholar] [CrossRef]
  11. Smith, J.C.; Brooks, L.; Hoff, P.M.; McWalter, G.; Dearden, S.; Morgan, S.R.; Wilson, D.; Robertson, J.D.; Jürgensmeier, J.M. KRAS mutations are associated with inferior clinical outcome in patients with metastatic colorectal cancer, but are not predictive for benefit with cediranib. Eur. J. Cancer 2013, 49, 2424–2432. [Google Scholar] [CrossRef]
  12. Aljehani, M.A.; Bien, J.; Lee, J.S.H.; Fisher, G.A.; Lin, A.Y. KRAS Sequence Variation as Prognostic Marker in Patients With Young- vs Late-Onset Colorectal Cancer. JAMA Netw. Open 2023, 6, e2345801. [Google Scholar] [CrossRef] [PubMed]
  13. Ottaiano, A.; Sabbatino, F.; Perri, F.; Cascella, M.; Sirica, R.; Patrone, R.; Capuozzo, M.; Savarese, G.; Ianniello, M.; Petrillo, N.; et al. p.G12C Mutation in Metastatic Colorectal Cancer: Prognostic Implications and Advancements in Targeted Therapies. Cancers 2023, 15, 3579. [Google Scholar] [CrossRef] [PubMed]
  14. Van Cutsem, E.; Köhne, C.H.; Láng, I.; Folprecht, G.; Nowacki, M.P.; Cascinu, S.; Shchepotin, I.; Maurel, J.; Cunningham, D.; Tejpar, S.; et al. Cetuximab plus irinotecan, fluorouracil, and leucovorin as first-line treatment for metastatic colorectal cancer: Updated analysis of overall survival according to tumor KRAS and BRAF mutation status. J. Clin. Oncol. 2011, 29, 2011–2019. [Google Scholar] [CrossRef] [PubMed]
  15. Douillard, J.Y.; Oliner, K.S.; Siena, S.; Tabernero, J.; Burkes, R.; Barugel, M.; Humblet, Y.; Bodoky, G.; Cunningham, D.; Jassem, J.; et al. Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer. N. Engl. J. Med. 2013, 369, 1023–1034. [Google Scholar] [CrossRef] [PubMed]
  16. National Comprehensive Cancer Network. Colon Cancer (Version 3.2024). Available online: https://www.nccn.org/professionals/physician_gls/pdf/colon.pdf (accessed on 16 June 2024).
  17. Diaz, L.A.; Williams, R.T.; Wu, J.; Kinde, I.; Hecht, J.R.; Berlin, J.; Allen, B.; Bozic, I.; Reiter, J.G.; Nowak, M.A.; et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 2012, 486, 537–540. [Google Scholar] [CrossRef] [PubMed]
  18. Misale, S.; Yaeger, R.; Hobor, S.; Scala, E.; Janakiraman, M.; Liska, D.; Valtorta, E.; Schiavo, R.; Buscarino, M.; Siravegna, G.; et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 2012, 486, 532–536. [Google Scholar] [CrossRef]
  19. Watanabe, J.; Muro, K.; Shitara, K.; Yamazaki, K.; Shiozawa, M.; Ohori, H.; Takashima, A.; Yokota, M.; Makiyama, A.; Akazawa, N.; et al. Panitumumab vs Bevacizumab Added to Standard First-line Chemotherapy and Overall Survival Among Patients With RAS Wild-type, Left-Sided Metastatic Colorectal Cancer: A Randomized Clinical Trial. JAMA 2023, 329, 1271–1282. [Google Scholar] [CrossRef]
  20. Stintzing, S.; Modest, D.P.; Rossius, L.; Lerch, M.M.; von Weikersthal, L.F.; Decker, T.; Kiani, A.; Vehling-Kaiser, U.; Al-Batran, S.E.; Heintges, T.; et al. FOLFIRI plus cetuximab versus FOLFIRI plus bevacizumab for metastatic colorectal cancer (FIRE-3): A post-hoc analysis of tumour dynamics in the final RAS wild-type subgroup of this randomised open-label phase 3 trial. Lancet Oncol. 2016, 17, 1426–1434. [Google Scholar] [CrossRef]
  21. Xue, J.Y.; Zhao, Y.; Aronowitz, J.; Mai, T.T.; Vides, A.; Qeriqi, B.; Kim, D.; Li, C.; de Stanchina, E.; Mazutis, L.; et al. Rapid non-uniform adaptation to conformation-specific KRAS(G12C) inhibition. Nature 2020, 577, 421–425. [Google Scholar] [CrossRef]
  22. Yaeger, R.; Weiss, J.; Pelster, M.S.; Spira, A.I.; Barve, M.; Ou, S.I.; Leal, T.A.; Bekaii-Saab, T.S.; Paweletz, C.P.; Heavey, G.A.; et al. Adagrasib with or without Cetuximab in Colorectal Cancer with Mutated. N. Engl. J. Med. 2023, 388, 44–54. [Google Scholar] [CrossRef] [PubMed]
  23. Fakih, M.G.; Salvatore, L.; Esaki, T.; Modest, D.P.; Lopez-Bravo, D.P.; Taieb, J.; Karamouzis, M.V.; Ruiz-Garcia, E.; Kim, T.W.; Kuboki, Y.; et al. Sotorasib plus Panitumumab in Refractory Colorectal Cancer with Mutated. N. Engl. J. Med. 2023, 389, 2125–2139. [Google Scholar] [CrossRef] [PubMed]
  24. Fedele, C.; Li, S.; Teng, K.W.; Foster, C.J.R.; Peng, D.; Ran, H.; Mita, P.; Geer, M.J.; Hattori, T.; Koide, A.; et al. SHP2 inhibition diminishes KRASG12C cycling and promotes tumor microenvironment remodeling. J. Exp. Med. 2021, 218, e20201414. [Google Scholar] [CrossRef] [PubMed]
  25. Kandasamy, P.; Zlobec, I.; Nydegger, D.T.; Pujol-Giménez, J.; Bhardwaj, R.; Shirasawa, S.; Tsunoda, T.; Hediger, M.A. Oncogenic KRAS mutations enhance amino acid uptake by colorectal cancer cells via the hippo signaling effector YAP1. Mol. Oncol. 2021, 15, 2782–2800. [Google Scholar] [CrossRef] [PubMed]
  26. Mukhopadhyay, S.; Huang, H.Y.; Lin, Z.; Ranieri, M.; Li, S.; Sahu, S.; Liu, Y.; Ban, Y.; Guidry, K.; Hu, H.; et al. Genome-Wide CRISPR Screens Identify Multiple Synthetic Lethal Targets That Enhance KRASG12C Inhibitor Efficacy. Cancer Res. 2023, 83, 4095–4111. [Google Scholar] [CrossRef] [PubMed]
  27. Tajiknia, V.; El-Deiry, W.S.; Schwermann, M.; Huntington, K.; Zhou, L.; Srinivasan, P. Combination of 5-FU plus KRAS G12D inhibitor MRTX1133 against human colorectal and pancreatic cancer cells and the affects on inhibition of pERK and immune-stimulatory cytokine patterns in in KRAS G12D and KRAS G12V tumor cells. J. Clin. Oncol. 2023, 41, e16301. [Google Scholar] [CrossRef]
  28. Lu, D.; Chen, Y.; Jiang, M.; Wang, J.; Li, Y.; Ma, K.; Sun, W.; Zheng, X.; Qi, J.; Jin, W.; et al. KRAS G12V neoantigen specific T cell receptor for adoptive T cell therapy against tumors. Nat. Commun. 2023, 14, 6389. [Google Scholar] [CrossRef] [PubMed]
  29. Singhal, A.; Li, B.T.; O’Reilly, E.M. Targeting KRAS in cancer. Nat. Med. 2024, 30, 969–983. [Google Scholar] [CrossRef] [PubMed]
  30. Lavoie, H.; Therrien, M. Regulation of RAF protein kinases in ERK signalling. Nat. Rev. Mol. Cell Biol. 2015, 16, 281–298. [Google Scholar] [CrossRef]
  31. Cope, N.; Candelora, C.; Wong, K.; Kumar, S.; Nan, H.; Grasso, M.; Novak, B.; Li, Y.; Marmorstein, R.; Wang, Z. Mechanism of BRAF Activation through Biochemical Characterization of the Recombinant Full-Length Protein. Chembiochem 2018, 19, 1988–1997. [Google Scholar] [CrossRef]
  32. Horbinski, C. To BRAF or not to BRAF: Is that even a question anymore? J. Neuropathol. Exp. Neurol. 2013, 72, 2–7. [Google Scholar] [CrossRef] [PubMed]
  33. Cantwell-Dorris, E.R.; O’Leary, J.J.; Sheils, O.M. BRAFV600E: Implications for carcinogenesis and molecular therapy. Mol. Cancer Ther. 2011, 10, 385–394. [Google Scholar] [CrossRef] [PubMed]
  34. Davies, H.; Bignell, G.R.; Cox, C.; Stephens, P.; Edkins, S.; Clegg, S.; Teague, J.; Woffendin, H.; Garnett, M.J.; Bottomley, W.; et al. Mutations of the BRAF gene in human cancer. Nature 2002, 417, 949–954. [Google Scholar] [CrossRef] [PubMed]
  35. Potocki, P.M.; Wojcik, P.; Chmura, L.; Goc, B.; Fedewicz, M.; Bielanska, Z.; Swadzba, J.; Konopka, K.; Kwinta, L.; Wysocki, P.J. Clinical Characterization of Targetable Mutations (BRAF V600E and KRAS G12C) in Advanced Colorectal Cancer-A Nation-Wide Study. Int. J. Mol. Sci. 2023, 24, 9073. [Google Scholar] [CrossRef] [PubMed]
  36. Seligmann, J.F.; Fisher, D.; Smith, C.G.; Richman, S.D.; Elliott, F.; Brown, S.; Adams, R.; Maughan, T.; Quirke, P.; Cheadle, J.; et al. Investigating the poor outcomes of BRAF-mutant advanced colorectal cancer: Analysis from 2530 patients in randomised clinical trials. Ann. Oncol. 2017, 28, 562–568. [Google Scholar] [CrossRef] [PubMed]
  37. Tol, J.; Nagtegaal, I.D.; Punt, C.J. BRAF mutation in metastatic colorectal cancer. N. Engl. J. Med. 2009, 361, 98–99. [Google Scholar] [CrossRef] [PubMed]
  38. Price, T.J.; Hardingham, J.E.; Lee, C.K.; Weickhardt, A.; Townsend, A.R.; Wrin, J.W.; Chua, A.; Shivasami, A.; Cummins, M.M.; Murone, C.; et al. Impact of KRAS and BRAF Gene Mutation Status on Outcomes From the Phase III AGITG MAX Trial of Capecitabine Alone or in Combination With Bevacizumab and Mitomycin in Advanced Colorectal Cancer. J. Clin. Oncol. 2011, 29, 2675–2682. [Google Scholar] [CrossRef] [PubMed]
  39. Sanz-Garcia, E.; Argiles, G.; Elez, E.; Tabernero, J. BRAF mutant colorectal cancer: Prognosis, treatment, and new perspectives. Ann. Oncol. 2017, 28, 2648–2657. [Google Scholar] [CrossRef] [PubMed]
  40. Souglakos, J.; Philips, J.; Wang, R.; Marwah, S.; Silver, M.; Tzardi, M.; Silver, J.; Ogino, S.; Hooshmand, S.; Kwak, E.; et al. Prognostic and predictive value of common mutations for treatment response and survival in patients with metastatic colorectal cancer. Br. J. Cancer 2009, 101, 465–472. [Google Scholar] [CrossRef]
  41. Jones, J.C.; Renfro, L.A.; Al-Shamsi, H.O.; Schrock, A.B.; Rankin, A.; Zhang, B.Y.; Kasi, P.M.; Voss, J.S.; Leal, A.D.; Sun, J.; et al. (Non-V600) BRAF Mutations Define a Clinically Distinct Molecular Subtype of Metastatic Colorectal Cancer. J. Clin. Oncol. 2017, 35, 2624–2630. [Google Scholar] [CrossRef]
  42. Seymour, M.T.; Brown, S.R.; Middleton, G.; Maughan, T.; Richman, S.; Gwyther, S.; Lowe, C.; Seligmann, J.F.; Wadsley, J.; Maisey, N.; et al. Panitumumab and irinotecan versus irinotecan alone for patients with KRAS wild-type, fluorouracil-resistant advanced colorectal cancer (PICCOLO): A prospectively stratified randomised trial. Lancet Oncol. 2013, 14, 749–759. [Google Scholar] [CrossRef] [PubMed]
  43. Pietrantonio, F.; Petrelli, F.; Coinu, A.; Di Bartolomeo, M.; Borgonovo, K.; Maggi, C.; Cabiddu, M.; Iacovelli, R.; Bossi, I.; Lonati, V.; et al. Predictive role of BRAF mutations in patients with advanced colorectal cancer receiving cetuximab and panitumumab: A meta-analysis. Eur. J. Cancer 2015, 51, 587–594. [Google Scholar] [CrossRef] [PubMed]
  44. Rowland, A.; Dias, M.M.; Wiese, M.D.; Kichenadasse, G.; McKinnon, R.A.; Karapetis, C.S.; Sorich, M.J. Meta-analysis of BRAF mutation as a predictive biomarker of benefit from anti-EGFR monoclonal antibody therapy for RAS wild-type metastatic colorectal cancer. Br. J. Cancer 2015, 112, 1888–1894. [Google Scholar] [CrossRef] [PubMed]
  45. Shinozaki, E.; Yoshino, T.; Yamazaki, K.; Muro, K.; Yamaguchi, K.; Nishina, T.; Yuki, S.; Shitara, K.; Bando, H.; Mimaki, S.; et al. Clinical significance of BRAF non-V600E mutations on the therapeutic effects of anti-EGFR monoclonal antibody treatment in patients with pretreated metastatic colorectal cancer: The Biomarker Research for anti-EGFR monoclonal Antibodies by Comprehensive Cancer genomics (BREAC) study. Br. J. Cancer 2017, 117, 1450–1458. [Google Scholar] [CrossRef] [PubMed]
  46. Yaeger, R.; Kotani, D.; Mondaca, S.; Parikh, A.R.; Bando, H.; Van Seventer, E.E.; Taniguchi, H.; Zhao, H.; Thant, C.N.; de Stanchina, E.; et al. Response to Anti-EGFR Therapy in Patients with BRAF non-V600-Mutant Metastatic Colorectal Cancer. Clin. Cancer Res. 2019, 25, 7089–7097. [Google Scholar] [CrossRef] [PubMed]
  47. Hanrahan, A.J.; Solit, D.B. BRAF Mutations: The Discovery of Allele- and Lineage-Specific Differences. Cancer Res. 2022, 82, 12–14. [Google Scholar] [CrossRef] [PubMed]
  48. Yao, Z.; Torres, N.M.; Tao, A.; Gao, Y.; Luo, L.; Li, Q.; de Stanchina, E.; Abdel-Wahab, O.; Solit, D.B.; Poulikakos, P.I.; et al. BRAF Mutants Evade ERK-Dependent Feedback by Different Mechanisms that Determine Their Sensitivity to Pharmacologic Inhibition. Cancer Cell 2015, 28, 370–383. [Google Scholar] [CrossRef] [PubMed]
  49. Middleton, G.; Yang, Y.; Campbell, C.D.; Andre, T.; Atreya, C.E.; Schellens, J.H.M.; Yoshino, T.; Bendell, J.C.; Hollebecque, A.; McRee, A.J.; et al. BRAF-Mutant Transcriptional Subtypes Predict Outcome of Combined BRAF, MEK, and EGFR Blockade with Dabrafenib, Trametinib, and Panitumumab in Patients with Colorectal Cancer. Clin. Cancer Res. 2020, 26, 2466–2476. [Google Scholar] [CrossRef]
  50. Kopetz, S.; Grothey, A.; Yaeger, R.; Van Cutsem, E.; Desai, J.; Yoshino, T.; Wasan, H.; Ciardiello, F.; Loupakis, F.; Hong, Y.S.; et al. Encorafenib, Binimetinib, and Cetuximab in BRAF V600E-Mutated Colorectal Cancer. N. Engl. J. Med. 2019, 381, 1632–1643. [Google Scholar] [CrossRef]
  51. Tabernero, J.; Grothey, A.; Van Cutsem, E.; Yaeger, R.; Wasan, H.; Yoshino, T.; Desai, J.; Ciardiello, F.; Loupakis, F.; Hong, Y.S.; et al. Encorafenib Plus Cetuximab as a New Standard of Care for Previously Treated. J. Clin. Oncol. 2021, 39, 273–284. [Google Scholar] [CrossRef]
  52. Gandini, A.; Puglisi, S.; Pirrone, C.; Martelli, V.; Catalano, F.; Nardin, S.; Seeber, A.; Puccini, A.; Sciallero, S. The role of immunotherapy in microsatellites stable metastatic colorectal cancer: State of the art and future perspectives. Front. Oncol. 2023, 13, 1161048. [Google Scholar] [CrossRef] [PubMed]
  53. Morris, V.K.; Parseghian, C.M.; Escano, M.; Johnson, B.; Raghav, K.P.S.; Dasari, A.; Huey, R.; Overman, M.J.; Willis, J.; Lee, M.S.; et al. Phase I/II trial of encorafenib, cetuximab, and nivolumab in patients with microsatellite stable, BRAFV600E metastatic colorectal cancer. J. Clin. Oncol. 2022, 40, 12. [Google Scholar] [CrossRef]
  54. Valtorta, E.; Martino, C.; Sartore-Bianchi, A.; Penaullt-Llorca, F.; Viale, G.; Risio, M.; Rugge, M.; Grigioni, W.; Bencardino, K.; Lonardi, S.; et al. Assessment of a HER2 scoring system for colorectal cancer: Results from a validation study. Mod. Pathol. 2015, 28, 1481–1491. [Google Scholar] [CrossRef] [PubMed]
  55. Sartore-Bianchi, A.; Amatu, A.; Porcu, L.; Ghezzi, S.; Lonardi, S.; Leone, F.; Bergamo, F.; Fenocchio, E.; Martinelli, E.; Borelli, B.; et al. HER2 Positivity Predicts Unresponsiveness to EGFR-Targeted Treatment in Metastatic Colorectal Cancer. Oncologist 2019, 24, 1395–1402. [Google Scholar] [CrossRef] [PubMed]
  56. Sartore-Bianchi, A.; Trusolino, L.; Martino, C.; Bencardino, K.; Lonardi, S.; Bergamo, F.; Zagonel, V.; Leone, F.; Depetris, I.; Martinelli, E.; et al. Dual-targeted therapy with trastuzumab and lapatinib in treatment-refractory, KRAS codon 12/13 wild-type, HER2-positive metastatic colorectal cancer (HERACLES): A proof-of-concept, multicentre, open-label, phase 2 trial. Lancet Oncol. 2016, 17, 738–746. [Google Scholar] [CrossRef] [PubMed]
  57. Raghav, K.; Loree, J.M.; Morris, J.S.; Overman, M.J.; Yu, R.; Meric-Bernstam, F.; Menter, D.; Korphaisarn, K.; Kee, B.; Muranyi, A.; et al. Validation of HER2 Amplification as a Predictive Biomarker for Anti–Epidermal Growth Factor Receptor Antibody Therapy in Metastatic Colorectal Cancer. JCO Precis. Oncol. 2019, 3, 1–13. [Google Scholar] [CrossRef] [PubMed]
  58. Martin, V.; Landi, L.; Molinari, F.; Fountzilas, G.; Geva, R.; Riva, A.; Saletti, P.; De Dosso, S.; Spitale, A.; Tejpar, S.; et al. HER2 gene copy number status may influence clinical efficacy to anti-EGFR monoclonal antibodies in metastatic colorectal cancer patients. Br. J. Cancer 2013, 108, 668–675. [Google Scholar] [CrossRef]
  59. Battaglin, F.; Ou, F.S.; Qu, X.; Hochster, H.S.; Niedzwiecki, D.; Goldberg, R.M.; Mayer, R.J.; Ashouri, K.; Zemla, T.J.; Blanke, C.D.; et al. Gene Expression Levels Are Predictive and Prognostic in Patients With Metastatic Colorectal Cancer Enrolled in CALGB/SWOG 80405. J. Clin. Oncol. 2024, 42, 1890–1902. [Google Scholar] [CrossRef] [PubMed]
  60. Hashimoto, T.; Nakamura, Y.; Yang, Y.; Battaglin, F.; Innocenti, F.; Venook, A.P.; Makiyama, A.; Boku, S.; Machida, N.; Takahashi, N.; et al. Prognostic impact of HER family expressions for metastatic colorectal cancer (mCRC): SCRUM-Japan MONSTAR-SCREEN-2 and CALGB/SWOG 80405 trial (Alliance) international collaboration. J. Clin. Oncol. 2024, 42, 3548. [Google Scholar] [CrossRef]
  61. Wu, S.W.; Ma, C.C.; Li, W.H. Does overexpression of HER-2 correlate with clinicopathological characteristics and prognosis in colorectal cancer? Evidence from a meta-analysis. Diagn. Pathol. 2015, 10, 144. [Google Scholar] [CrossRef]
  62. Hainsworth, J.D.; Meric-Bernstam, F.; Swanton, C.; Hurwitz, H.; Spigel, D.R.; Sweeney, C.; Burris, H.; Bose, R.; Yoo, B.; Stein, A.; et al. Targeted Therapy for Advanced Solid Tumors on the Basis of Molecular Profiles: Results From MyPathway, an Open-Label, Phase IIa Multiple Basket Study. J. Clin. Oncol. 2018, 36, 536–542. [Google Scholar] [CrossRef] [PubMed]
  63. Strickler, J.H.; Cercek, A.; Siena, S.; André, T.; Ng, K.; Van Cutsem, E.; Wu, C.; Paulson, A.S.; Hubbard, J.M.; Coveler, A.L.; et al. Tucatinib plus trastuzumab for chemotherapy-refractory, HER2-positive, RAS wild-type unresectable or metastatic colorectal cancer (MOUNTAINEER): A multicentre, open-label, phase 2 study. Lancet Oncol. 2023, 24, 496–508. [Google Scholar] [CrossRef] [PubMed]
  64. Yoshino, T.; Di Bartolomeo, M.; Raghav, K.; Masuishi, T.; Loupakis, F.; Kawakami, H.; Yamaguchi, K.; Nishina, T.; Wainberg, Z.; Elez, E.; et al. Final results of DESTINY-CRC01 investigating trastuzumab deruxtecan in patients with HER2-expressing metastatic colorectal cancer. Nat. Commun. 2023, 14, 3332. [Google Scholar] [CrossRef]
  65. Bonneville, R.; Krook, M.A.; Kautto, E.A.; Miya, J.; Wing, M.R.; Chen, H.Z.; Reeser, J.W.; Yu, L.; Roychowdhury, S. Landscape of Microsatellite Instability Across 39 Cancer Types. JCO Precis. Oncol. 2017, 1, 1–15. [Google Scholar] [CrossRef] [PubMed]
  66. Ligtenberg, M.J.; Kuiper, R.P.; Geurts van Kessel, A.; Hoogerbrugge, N. EPCAM deletion carriers constitute a unique subgroup of Lynch syndrome patients. Fam. Cancer 2013, 12, 169–174. [Google Scholar] [CrossRef] [PubMed]
  67. Vilar, E.; Gruber, S.B. Microsatellite instability in colorectal cancer-the stable evidence. Nat. Rev. Clin. Oncol. 2010, 7, 153–162. [Google Scholar] [CrossRef] [PubMed]
  68. Peltomäki, P.; Nyström, M.; Mecklin, J.P.; Seppälä, T.T. Lynch Syndrome Genetics and Clinical Implications. Gastroenterology 2023, 164, 783–799. [Google Scholar] [CrossRef]
  69. Le, D.T.; Diaz, L.A.; Kim, T.W.; Van Cutsem, E.; Geva, R.; Jäger, D.; Hara, H.; Burge, M.; O’Neil, B.H.; Kavan, P.; et al. Pembrolizumab for previously treated, microsatellite instability-high/mismatch repair-deficient advanced colorectal cancer: Final analysis of KEYNOTE-164. Eur. J. Cancer 2023, 186, 185–195. [Google Scholar] [CrossRef] [PubMed]
  70. Chen, C.; Liu, S.; Qu, R.; Li, B. Recurrent Neoantigens in Colorectal Cancer as Potential Immunotherapy Targets. Biomed. Res. Int. 2020, 2020, 2861240. [Google Scholar] [CrossRef]
  71. Grasso, C.S.; Giannakis, M.; Wells, D.K.; Hamada, T.; Mu, X.J.; Quist, M.; Nowak, J.A.; Nishihara, R.; Qian, Z.R.; Inamura, K.; et al. Genetic Mechanisms of Immune Evasion in Colorectal Cancer. Cancer Discov. 2018, 8, 730–749. [Google Scholar] [CrossRef]
  72. Ros, J.; Baraibar, I.; Saoudi, N.; Rodriguez, M.; Salvà, F.; Tabernero, J.; Élez, E. Immunotherapy for Colorectal Cancer with High Microsatellite Instability: The Ongoing Search for Biomarkers. Cancers 2023, 15, 4245. [Google Scholar] [CrossRef]
  73. Le, D.T.; Uram, J.N.; Wang, H.; Bartlett, B.R.; Kemberling, H.; Eyring, A.D.; Skora, A.D.; Luber, B.S.; Azad, N.S.; Laheru, D.; et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N. Engl. J. Med. 2015, 372, 2509–2520. [Google Scholar] [CrossRef]
  74. Le, D.T.; Kim, T.W.; Van Cutsem, E.; Geva, R.; Jäger, D.; Hara, H.; Burge, M.; O’Neil, B.; Kavan, P.; Yoshino, T.; et al. Phase II Open-Label Study of Pembrolizumab in Treatment-Refractory, Microsatellite Instability-High/Mismatch Repair-Deficient Metastatic Colorectal Cancer: KEYNOTE-164. J. Clin. Oncol. 2020, 38, 11–19. [Google Scholar] [CrossRef]
  75. Overman, M.J.; McDermott, R.; Leach, J.L.; Lonardi, S.; Lenz, H.J.; Morse, M.A.; Desai, J.; Hill, A.; Axelson, M.; Moss, R.A.; et al. Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (CheckMate 142): An open-label, multicentre, phase 2 study. Lancet Oncol. 2017, 18, 1182–1191. [Google Scholar] [CrossRef] [PubMed]
  76. André, T.; Shiu, K.K.; Kim, T.W.; Jensen, B.V.; Jensen, L.H.; Punt, C.; Smith, D.; Garcia-Carbonero, R.; Benavides, M.; Gibbs, P.; et al. Pembrolizumab in Microsatellite-Instability-High Advanced Colorectal Cancer. N. Engl. J. Med. 2020, 383, 2207–2218. [Google Scholar] [CrossRef] [PubMed]
  77. Lenz, H.J.; Van Cutsem, E.; Luisa Limon, M.; Wong, K.Y.M.; Hendlisz, A.; Aglietta, M.; García-Alfonso, P.; Neyns, B.; Luppi, G.; Cardin, D.B.; et al. First-Line Nivolumab Plus Low-Dose Ipilimumab for Microsatellite Instability-High/Mismatch Repair-Deficient Metastatic Colorectal Cancer: The Phase II CheckMate 142 Study. J. Clin. Oncol. 2022, 40, 161–170. [Google Scholar] [CrossRef] [PubMed]
  78. Heregger, R.; Huemer, F.; Steiner, M.; Gonzalez-Martinez, A.; Greil, R.; Weiss, L. Unraveling Resistance to Immunotherapy in MSI-High Colorectal Cancer. Cancers 2023, 15, 5090. [Google Scholar] [CrossRef]
  79. Kato, Y.; Tabata, K.; Kimura, T.; Yachie-Kinoshita, A.; Ozawa, Y.; Yamada, K.; Ito, J.; Tachino, S.; Hori, Y.; Matsuki, M.; et al. Lenvatinib plus anti-PD-1 antibody combination treatment activates CD8+ T cells through reduction of tumor-associated macrophage and activation of the interferon pathway. PLoS ONE 2019, 14, e0212513. [Google Scholar] [CrossRef]
  80. Doleschel, D.; Hoff, S.; Koletnik, S.; Rix, A.; Zopf, D.; Kiessling, F.; Lederle, W. Regorafenib enhances anti-PD1 immunotherapy efficacy in murine colorectal cancers and their combination prevents tumor regrowth. J. Exp. Clin. Cancer Res. 2021, 40, 288. [Google Scholar] [CrossRef]
  81. Bendell, J.C.; Powderly, J.D.; Lieu, C.H.; Eckhardt, S.G.; Hurwitz, H.; Hochster, H.S.; Murphy, J.E.; Funke, R.P.; Rossi, C.; Wallin, J.; et al. Safety and efficacy of MPDL3280A (anti-PDL1) in combination with bevacizumab (bev) and/or FOLFOX in patients (pts) with metastatic colorectal cancer (mCRC). J. Clin. Oncol. 2015, 33, 704. [Google Scholar] [CrossRef]
  82. Schrock, A.B.; Ouyang, C.; Sandhu, J.; Sokol, E.; Jin, D.; Ross, J.S.; Miller, V.A.; Lim, D.; Amanam, I.; Chao, J.; et al. Tumor mutational burden is predictive of response to immune checkpoint inhibitors in MSI-high metastatic colorectal cancer. Ann. Oncol. 2019, 30, 1096–1103. [Google Scholar] [CrossRef]
  83. Doig, K.D.; Fellowes, A.; Scott, P.; Fox, S.B. Tumour mutational burden: An overview for pathologists. Pathology 2022, 54, 249–253. [Google Scholar] [CrossRef] [PubMed]
  84. Wang, J.; Song, J.; Liu, Z.; Zhang, T.; Liu, Y. High tumor mutation burden indicates better prognosis in colorectal cancer patients with KRAS mutations. Front. Oncol. 2022, 12, 1015308. [Google Scholar] [CrossRef] [PubMed]
  85. Xiao, J.; Li, W.; Huang, Y.; Huang, M.; Li, S.; Zhai, X.; Zhao, J.; Gao, C.; Xie, W.; Qin, H.; et al. A next-generation sequencing-based strategy combining microsatellite instability and tumor mutation burden for comprehensive molecular diagnosis of advanced colorectal cancer. BMC Cancer 2021, 21, 282. [Google Scholar] [CrossRef] [PubMed]
  86. Chan, T.A.; Yarchoan, M.; Jaffee, E.; Swanton, C.; Quezada, S.A.; Stenzinger, A.; Peters, S. Development of tumor mutation burden as an immunotherapy biomarker: Utility for the oncology clinic. Ann. Oncol. 2019, 30, 44–56. [Google Scholar] [CrossRef] [PubMed]
  87. Zheng, M. Tumor mutation burden for predicting immune checkpoint blockade response: The more, the better. J. Immunother. Cancer 2022, 10, e003087. [Google Scholar] [CrossRef] [PubMed]
  88. Cocco, E.; Scaltriti, M.; Drilon, A. NTRK fusion-positive cancers and TRK inhibitor therapy. Nat. Rev. Clin. Oncol. 2018, 15, 731–747. [Google Scholar] [CrossRef] [PubMed]
  89. Manea, C.A.; Badiu, D.C.; Ploscaru, I.C.; Zgura, A.; Bacinschi, X.; Smarandache, C.G.; Serban, D.; Popescu, C.G.; Grigorean, V.T.; Botnarciuc, V. A review of NTRK fusions in cancer. Ann. Med. Surg. 2022, 79, 103893. [Google Scholar] [CrossRef] [PubMed]
  90. Wang, H.; Li, Z.W.; Ou, Q.; Wu, X.; Nagasaka, M.; Shao, Y.; Ou, S.I.; Yang, Y. NTRK fusion positive colorectal cancer is a unique subset of CRC with high TMB and microsatellite instability. Cancer Med. 2022, 11, 2541–2549. [Google Scholar] [CrossRef] [PubMed]
  91. Hibar, D.P.; Demetri, G.D.; Peters, S.; Davies, J.; Humblet, O.; Maund, S.L.; Perez, L. Real-world survival outcomes in patients with locally advanced or metastatic NTRK fusion-positive solid tumors receiving standard-of-care therapies other than targeted TRK inhibitors. PLoS ONE 2022, 17, e0270571. [Google Scholar] [CrossRef]
  92. Boussiotis, V.A. Molecular and Biochemical Aspects of the PD-1 Checkpoint Pathway. N. Engl. J. Med. 2016, 375, 1767–1778. [Google Scholar] [CrossRef] [PubMed]
  93. Han, Y.; Liu, D.; Li, L. PD-1/PD-L1 pathway: Current researches in cancer. Am. J. Cancer Res. 2020, 10, 727–742. [Google Scholar] [PubMed]
  94. Wang, X.; Teng, F.; Kong, L.; Yu, J. PD-L1 expression in human cancers and its association with clinical outcomes. Onco Targets Ther. 2016, 9, 5023–5039. [Google Scholar] [CrossRef] [PubMed]
  95. Aguilar, E.J.; Ricciuti, B.; Gainor, J.F.; Kehl, K.L.; Kravets, S.; Dahlberg, S.; Nishino, M.; Sholl, L.M.; Adeni, A.; Subegdjo, S.; et al. Outcomes to first-line pembrolizumab in patients with non-small-cell lung cancer and very high PD-L1 expression. Ann. Oncol. 2019, 30, 1653–1659. [Google Scholar] [CrossRef] [PubMed]
  96. Xin, H.; Zhou, C.; Wang, G.; Liu, Y.; Zhang, J.; Li, B.; Su, M.; Li, Z. Heterogeneity of PD-L1 expression and CD8 lymphocyte infiltration in metastatic colorectal cancer and their prognostic significance. Heliyon 2023, 9, e13048. [Google Scholar] [CrossRef] [PubMed]
  97. André, T.; Overman, M.; Lonardi, S.; Aglietta, M.; McDermott, R.; Wong, K.Y.M.; Morse, M.; Hendlisz, A.; Moss, R.A.; Ledeine, J.M.; et al. Analysis of tumor PD-L1 expression and biomarkers in relation to clinical activity in patients (pts) with deficient DNA mismatch repair (dMMR)/high microsatellite instability (MSI-H) metastatic colorectal cancer (mCRC) treated with nivolumab (NIVO) + ipilimumab (IPI): CheckMate 142. Ann. Oncol. 2017, 28, v163. [Google Scholar] [CrossRef]
  98. Wang, S.; Yuan, B.; Wang, Y.; Li, M.; Liu, X.; Cao, J.; Li, C.; Hu, J. Clinicopathological and prognostic significance of PD-L1 expression in colorectal cancer: A meta-analysis. Int. J. Color. Dis. 2021, 36, 117–130. [Google Scholar] [CrossRef]
  99. Yang, L.; Xue, R.; Pan, C. Prognostic and clinicopathological value of PD-L1 in colorectal cancer: A systematic review and meta-analysis. Onco Targets Ther. 2019, 12, 3671–3682. [Google Scholar] [CrossRef]
  100. Li, Y.; Liang, L.; Dai, W.; Cai, G.; Xu, Y.; Li, X.; Li, Q.; Cai, S. Prognostic impact of programed cell death-1 (PD-1) and PD-ligand 1 (PD-L1) expression in cancer cells and tumor infiltrating lymphocytes in colorectal cancer. Mol. Cancer 2016, 15, 55. [Google Scholar] [CrossRef]
  101. Lee, K.S.; Kwak, Y.; Ahn, S.; Shin, E.; Oh, H.K.; Kim, D.W.; Kang, S.B.; Choe, G.; Kim, W.H.; Lee, H.S. Prognostic implication of CD274 (PD-L1) protein expression in tumor-infiltrating immune cells for microsatellite unstable and stable colorectal cancer. Cancer Immunol. Immunother. 2017, 66, 927–939. [Google Scholar] [CrossRef]
  102. Peng, Y.; Wang, Y.; Zhou, C.; Mei, W.; Zeng, C. PI3K/Akt/mTOR Pathway and Its Role in Cancer Therapeutics: Are We Making Headway? Front. Oncol. 2022, 12, 819128. [Google Scholar] [CrossRef] [PubMed]
  103. Mao, C.; Yang, Z.Y.; Hu, X.F.; Chen, Q.; Tang, J.L. PIK3CA exon 20 mutations as a potential biomarker for resistance to anti-EGFR monoclonal antibodies in KRAS wild-type metastatic colorectal cancer: A systematic review and meta-analysis. Ann. Oncol. 2012, 23, 1518–1525. [Google Scholar] [CrossRef] [PubMed]
  104. Lu, X.; Li, Y.; Zhang, X.; Shi, J.; Feng, H.; Yu, Z.; Gao, Y. Prognostic and predictive biomarkers for anti-EGFR monoclonal antibody therapy in RAS wild-type metastatic colorectal cancer: A systematic review and meta-analysis. BMC Cancer 2023, 23, 1117. [Google Scholar] [CrossRef] [PubMed]
  105. Castellano, E.; Downward, J. RAS Interaction with PI3K: More Than Just Another Effector Pathway. Genes. Cancer 2011, 2, 261–274. [Google Scholar] [CrossRef] [PubMed]
  106. Tan, E.S.; Fan, W.; Knepper, T.C.; Schell, M.J.; Sahin, I.H.; Fleming, J.B.; Xie, H. Prognostic and Predictive Value of PIK3CA Mutations in Metastatic Colorectal Cancer. Target. Oncol. 2022, 17, 483–492. [Google Scholar] [CrossRef]
  107. Goïta, A.A.; Guenot, D. Colorectal Cancer: The Contribution of CXCL12 and Its Receptors CXCR4 and CXCR7. Cancers 2022, 14, 1810. [Google Scholar] [CrossRef] [PubMed]
  108. Ottaiano, A.; di Palma, A.; Napolitano, M.; Pisano, C.; Pignata, S.; Tatangelo, F.; Botti, G.; Acquaviva, A.M.; Castello, G.; Ascierto, P.A.; et al. Inhibitory effects of anti-CXCR4 antibodies on human colon cancer cells. Cancer Immunol. Immunother. 2005, 54, 781–791. [Google Scholar] [CrossRef] [PubMed]
  109. Zeelenberg, I.S.; Ruuls-Van Stalle, L.; Roos, E. The chemokine receptor CXCR4 is required for outgrowth of colon carcinoma micrometastases. Cancer Res. 2003, 63, 3833–3839. [Google Scholar]
  110. Biasci, D.; Smoragiewicz, M.; Connell, C.M.; Wang, Z.; Gao, Y.; Thaventhiran, J.E.D.; Basu, B.; Magiera, L.; Johnson, T.I.; Bax, L.; et al. CXCR4 inhibition in human pancreatic and colorectal cancers induces an integrated immune response. Proc. Natl. Acad. Sci. USA 2020, 117, 28960–28970. [Google Scholar] [CrossRef]
  111. Kim, J.; Mori, T.; Chen, S.L.; Amersi, F.F.; Martinez, S.R.; Kuo, C.; Turner, R.R.; Ye, X.; Bilchik, A.J.; Morton, D.L.; et al. Chemokine receptor CXCR4 expression in patients with melanoma and colorectal cancer liver metastases and the association with disease outcome. Ann. Surg. 2006, 244, 113–120. [Google Scholar] [CrossRef]
  112. Kim, J.; Takeuchi, H.; Lam, S.T.; Turner, R.R.; Wang, H.J.; Kuo, C.; Foshag, L.; Bilchik, A.J.; Hoon, D.S. Chemokine receptor CXCR4 expression in colorectal cancer patients increases the risk for recurrence and for poor survival. J. Clin. Oncol. 2005, 23, 2744–2753. [Google Scholar] [CrossRef] [PubMed]
  113. Yopp, A.C.; Shia, J.; Butte, J.M.; Allen, P.J.; Fong, Y.; Jarnagin, W.R.; DeMatteo, R.P.; D’Angelica, M.I. CXCR4 expression predicts patient outcome and recurrence patterns after hepatic resection for colorectal liver metastases. Ann. Surg. Oncol. 2012, 19 (Suppl. S3), S339–S346. [Google Scholar] [CrossRef] [PubMed]
  114. Huang, W.S.; Hsieh, M.C.; Huang, C.Y.; Kuo, Y.H.; Tung, S.Y.; Shen, C.H.; Hsieh, Y.Y.; Teng, C.C.; Lee, K.F.; Chen, T.C.; et al. The Association of CXC Receptor 4 Mediated Signaling Pathway with Oxaliplatin-Resistant Human Colorectal Cancer Cells. PLoS ONE 2016, 11, e0159927. [Google Scholar] [CrossRef] [PubMed]
  115. Aldinucci, D.; Borghese, C.; Casagrande, N. The CCL5/CCR5 Axis in Cancer Progression. Cancers 2020, 12, 1765. [Google Scholar] [CrossRef] [PubMed]
  116. Schlecker, E.; Stojanovic, A.; Eisen, C.; Quack, C.; Falk, C.S.; Umansky, V.; Cerwenka, A. Tumor-infiltrating monocytic myeloid-derived suppressor cells mediate CCR5-dependent recruitment of regulatory T cells favoring tumor growth. J. Immunol. 2012, 189, 5602–5611. [Google Scholar] [CrossRef] [PubMed]
  117. Battaglin, F.; Baca, Y.; Millstein, J.; Yang, Y.; Xiu, J.; Arai, H.; Wang, J.; Ou, F.S.; Innocenti, F.; Mumenthaler, S.M.; et al. CCR5 and CCL5 gene expression in colorectal cancer: Comprehensive profiling and clinical value. J. Immunother. Cancer 2024, 12, e007939. [Google Scholar] [CrossRef] [PubMed]
  118. Suenaga, M.; Cao, S.; Zhang, W.; Yang, D.; Ning, Y.; Okazaki, S.; Berger, M.D.; Miyamoto, Y.; Schirripa, M.; Soni, S.; et al. Genetic variants in CCL5 and CCR5 genes and serum VEGF-A levels predict efficacy of bevacizumab in metastatic colorectal cancer patients. Int. J. Cancer 2019, 144, 2567–2577. [Google Scholar] [CrossRef]
  119. Suenaga, M.; Schirripa, M.; Cao, S.; Zhang, W.; Yang, D.; Ning, Y.; Cremolini, C.; Antoniotti, C.; Borelli, B.; Mashima, T.; et al. Gene Polymorphisms in the CCL5/CCR5 Pathway as a Genetic Biomarker for Outcome and Hand-Foot Skin Reaction in Metastatic Colorectal Cancer Patients Treated With Regorafenib. Clin. Color. Cancer 2018, 17, e395–e414. [Google Scholar] [CrossRef] [PubMed]
  120. Suenaga, M.; Zhang, W.U.; Mashima, T.; Schirripa, M.; Cao, S.; Okazaki, S.; Berger, M.D.; Miyamoto, Y.; Barzi, A.; Yamaguchi, T.; et al. Potential Molecular Cross Talk Among CCR5 Pathway Predicts Regorafenib Responsiveness in Metastatic Colorectal Cancer Patients. Cancer Genom. Proteom. 2021, 18, 317–324. [Google Scholar] [CrossRef]
  121. Suarez-Carmona, M.; Chaorentong, P.; Kather, J.N.; Rothenheber, R.; Ahmed, A.; Berthel, A.; Heinzelmann, A.; Moraleda, R.; Valous, N.A.; Kosaloglu, Z.; et al. CCR5 status and metastatic progression in colorectal cancer. Oncoimmunology 2019, 8, e1626193. [Google Scholar] [CrossRef]
  122. Nygaard, V.; Ree, A.H.; Dagenborg, V.J.; Børresen-Dale, A.L.; Edwin, B.; Fretland, Å.A.; Grzyb, K.; Haugen, M.H.; Mælandsmo, G.M.; Flatmark, K. A PRRX1 Signature Identifies TIM-3 and VISTA as Potential Immune Checkpoint Targets in a Subgroup of Microsatellite Stable Colorectal Cancer Liver Metastases. Cancer Res. Commun. 2023, 3, 235–244. [Google Scholar] [CrossRef]
  123. Sauer, N.; Szlasa, W.; Jonderko, L.; Oślizło, M.; Kunachowicz, D.; Kulbacka, J.; Karłowicz-Bodalska, K. LAG-3 as a Potent Target for Novel Anticancer Therapies of a Wide Range of Tumors. Int. J. Mol. Sci. 2022, 23, 9958. [Google Scholar] [CrossRef]
  124. Katagata, M.; Okayama, H.; Nakajima, S.; Saito, K.; Sato, T.; Sakuma, M.; Fukai, S.; Endo, E.; Sakamoto, W.; Saito, M.; et al. TIM-3 Expression and M2 Polarization of Macrophages in the TGFβ-Activated Tumor Microenvironment in Colorectal Cancer. Cancers 2023, 15, 4943. [Google Scholar] [CrossRef]
  125. Ma, Q.; Liu, J.; Wu, G.; Teng, M.; Wang, S.; Cui, M.; Li, Y. Co-expression of LAG3 and TIM3 identifies a potent Treg population that suppresses macrophage functions in colorectal cancer patients. Clin. Exp. Pharmacol. Physiol. 2018, 45, 1002–1009. [Google Scholar] [CrossRef] [PubMed]
  126. Kuai, W.; Xu, X.; Yan, J.; Zhao, W.; Li, Y.; Wang, B.; Yuan, N.; Li, Z.; Jia, Y. Prognostic Impact of PD-1 and Tim-3 Expression in Tumor Tissue in Stage I-III Colorectal Cancer. Biomed. Res. Int. 2020, 2020, 5294043. [Google Scholar] [CrossRef]
  127. Huang, Y.H.; Zhu, C.; Kondo, Y.; Anderson, A.C.; Gandhi, A.; Russell, A.; Dougan, S.K.; Petersen, B.S.; Melum, E.; Pertel, T.; et al. CEACAM1 regulates TIM-3-mediated tolerance and exhaustion. Nature 2015, 517, 386–390. [Google Scholar] [CrossRef] [PubMed]
  128. Woo, S.R.; Turnis, M.E.; Goldberg, M.V.; Bankoti, J.; Selby, M.; Nirschl, C.J.; Bettini, M.L.; Gravano, D.M.; Vogel, P.; Liu, C.L.; et al. Immune inhibitory molecules LAG-3 and PD-1 synergistically regulate T-cell function to promote tumoral immune escape. Cancer Res. 2012, 72, 917–927. [Google Scholar] [CrossRef]
  129. Garralda, E.; Sukari, A.; Lakhani, N.J.; Patnaik, A.; Lou, Y.; Im, S.A.; Golan, T.; Geva, R.; Wermke, M.; de Miguel, M.; et al. A first-in-human study of the anti-LAG-3 antibody favezelimab plus pembrolizumab in previously treated, advanced microsatellite stable colorectal cancer. ESMO Open 2022, 7, 100639. [Google Scholar] [CrossRef] [PubMed]
  130. Wang, S.; Ma, H.; Yan, Y.; Chen, Y.; Fu, S.; Wang, J.; Wang, Y.; Chen, H.; Liu, J. cMET promotes metastasis and epithelial-mesenchymal transition in colorectal carcinoma by repressing RKIP. J. Cell Physiol. 2021, 236, 3963–3978. [Google Scholar] [CrossRef]
  131. Lee, S.J.; Lee, J.; Park, S.H.; Park, J.O.; Lim, H.Y.; Kang, W.K.; Park, Y.S.; Kim, S.T. c-MET Overexpression in Colorectal Cancer: A Poor Prognostic Factor for Survival. Clin. Color. Cancer 2018, 17, 165–169. [Google Scholar] [CrossRef]
  132. Gao, H.; Guan, M.; Sun, Z.; Bai, C. High c-Met expression is a negative prognostic marker for colorectal cancer: A meta-analysis. Tumour Biol. 2015, 36, 515–520. [Google Scholar] [CrossRef] [PubMed]
  133. Bardelli, A.; Corso, S.; Bertotti, A.; Hobor, S.; Valtorta, E.; Siravegna, G.; Sartore-Bianchi, A.; Scala, E.; Cassingena, A.; Zecchin, D.; et al. Amplification of the MET receptor drives resistance to anti-EGFR therapies in colorectal cancer. Cancer Discov. 2013, 3, 658–673. [Google Scholar] [CrossRef] [PubMed]
  134. Song, N.; Liu, S.; Zhang, J.; Liu, J.; Xu, L.; Liu, Y.; Qu, X. Cetuximab-induced MET activation acts as a novel resistance mechanism in colon cancer cells. Int. J. Mol. Sci. 2014, 15, 5838–5851. [Google Scholar] [CrossRef]
  135. Sharma, M.; Kuboki, Y.; Camidge, D.R.; Perets, R.; Sommerhalder, D.; Yamamoto, N.; Bar, J.; Parikh, A.; Li, R.; Thiele, G.M.; et al. Dose escalation results from a first-in-human study of ABBV-400, a novel c-Met–targeting antibody-drug conjugate, in advanced solid tumors. J. Clin. Oncol. 2023, 41, 3015. [Google Scholar] [CrossRef]
  136. Tokunaga, R.; Xiu, J.; Goldberg, R.M.; Philip, P.A.; Seeber, A.; Battaglin, F.; Arai, H.; Lo, J.H.; Naseem, M.; Puccini, A.; et al. The impact of ARID1A mutation on molecular characteristics in colorectal cancer. Eur. J. Cancer 2020, 140, 119–129. [Google Scholar] [CrossRef]
  137. Johnson, R.M.; Qu, X.; Lin, C.F.; Huw, L.Y.; Venkatanarayan, A.; Sokol, E.; Ou, F.S.; Ihuegbu, N.; Zill, O.A.; Kabbarah, O.; et al. ARID1A mutations confer intrinsic and acquired resistance to cetuximab treatment in colorectal cancer. Nat. Commun. 2022, 13, 5478. [Google Scholar] [CrossRef]
  138. Chiappa, M.; Petrella, S.; Damia, G.; Broggini, M.; Guffanti, F.; Ricci, F. Present and Future Perspective on PLK1 Inhibition in Cancer Treatment. Front. Oncol. 2022, 12, 903016. [Google Scholar] [CrossRef]
  139. Yu, Z.; Deng, P.; Chen, Y.; Liu, S.; Chen, J.; Yang, Z.; Fan, X.; Wang, P.; Cai, Z.; Wang, Y.; et al. Inhibition of the PLK1-Coupled Cell Cycle Machinery Overcomes Resistance to Oxaliplatin in Colorectal Cancer. Adv. Sci. 2021, 8, e2100759. [Google Scholar] [CrossRef] [PubMed]
  140. Ahn, D.H.; Barzi, A.; Ridinger, M.; Samuëlsz, E.; Subramanian, R.A.; Croucher, P.J.P.; Smeal, T.; Kabbinavar, F.F.; Lenz, H.J. Onvansertib in Combination with FOLFIRI and Bevacizumab in Second-Line Treatment of KRAS-Mutant Metastatic Colorectal Cancer: A Phase Ib Clinical Study. Clin. Cancer Res. 2024, 30, 2039–2047. [Google Scholar] [CrossRef]
  141. Diaz, L.A.; Bardelli, A. Liquid biopsies: Genotyping circulating tumor DNA. J. Clin. Oncol. 2014, 32, 579–586. [Google Scholar] [CrossRef]
  142. Keller, L.; Belloum, Y.; Wikman, H.; Pantel, K. Clinical relevance of blood-based ctDNA analysis: Mutation detection and beyond. Br. J. Cancer 2021, 124, 345–358. [Google Scholar] [CrossRef]
  143. Siravegna, G.; Marsoni, S.; Siena, S.; Bardelli, A. Integrating liquid biopsies into the management of cancer. Nat. Rev. Clin. Oncol. 2017, 14, 531–548. [Google Scholar] [CrossRef]
  144. Nakamura, Y.; Taniguchi, H.; Ikeda, M.; Bando, H.; Kato, K.; Morizane, C.; Esaki, T.; Komatsu, Y.; Kawamoto, Y.; Takahashi, N.; et al. Clinical utility of circulating tumor DNA sequencing in advanced gastrointestinal cancer: SCRUM-Japan GI-SCREEN and GOZILA studies. Nat. Med. 2020, 26, 1859–1864. [Google Scholar] [CrossRef] [PubMed]
  145. Kastrisiou, M.; Zarkavelis, G.; Pentheroudakis, G.; Magklara, A. Clinical Application of Next-Generation Sequencing as A Liquid Biopsy Technique in Advanced Colorectal Cancer: A Trick or A Treat? Cancers 2019, 11, 1573. [Google Scholar] [CrossRef] [PubMed]
  146. Tie, J.; Cohen, J.D.; Wang, Y.; Christie, M.; Simons, K.; Lee, M.; Wong, R.; Kosmider, S.; Ananda, S.; McKendrick, J.; et al. Circulating Tumor DNA Analyses as Markers of Recurrence Risk and Benefit of Adjuvant Therapy for Stage III Colon Cancer. JAMA Oncol. 2019, 5, 1710–1717. [Google Scholar] [CrossRef] [PubMed]
  147. Taieb, J.; Taly, V.; Henriques, J.; Bourreau, C.; Mineur, L.; Bennouna, J.; Desrame, J.; Louvet, C.; Lepere, C.; Mabro, M.; et al. Prognostic Value and Relation with Adjuvant Treatment Duration of ctDNA in Stage III Colon Cancer: A Post Hoc Analysis of the PRODIGE-GERCOR IDEA-France Trial. Clin. Cancer Res. 2021, 27, 5638–5646. [Google Scholar] [CrossRef]
  148. Henriksen, T.V.; Tarazona, N.; Frydendahl, A.; Reinert, T.; Gimeno-Valiente, F.; Carbonell-Asins, J.A.; Sharma, S.; Renner, D.; Hafez, D.; Roda, D.; et al. Circulating Tumor DNA in Stage III Colorectal Cancer, beyond Minimal Residual Disease Detection, toward Assessment of Adjuvant Therapy Efficacy and Clinical Behavior of Recurrences. Clin. Cancer Res. 2022, 28, 507–517. [Google Scholar] [CrossRef]
  149. Slater, S.; Bryant, A.; Chen, H.C.; Begum, R.; Rana, I.; Aresu, M.; Peckitt, C.; Zhitkov, O.; Lazaro-Alcausi, R.; Borja, V.; et al. ctDNA guided adjuvant chemotherapy versus standard of care adjuvant chemotherapy after curative surgery in patients with high risk stage II or stage III colorectal cancer: A multi-centre, prospective, randomised control trial (TRACC Part C). BMC Cancer 2023, 23, 257. [Google Scholar] [CrossRef]
  150. Parikh, A.R.; Leshchiner, I.; Elagina, L.; Goyal, L.; Levovitz, C.; Siravegna, G.; Livitz, D.; Rhrissorrakrai, K.; Martin, E.E.; Van Seventer, E.E.; et al. Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers. Nat. Med. 2019, 25, 1415–1421. [Google Scholar] [CrossRef]
  151. Satam, H.; Joshi, K.; Mangrolia, U.; Waghoo, S.; Zaidi, G.; Rawool, S.; Thakare, R.P.; Banday, S.; Mishra, A.K.; Das, G.; et al. Next-Generation Sequencing Technology: Current Trends and Advancements. Biology 2023, 12, 997. [Google Scholar] [CrossRef]
  152. Malla, M.; Loree, J.M.; Kasi, P.M.; Parikh, A.R. Using Circulating Tumor DNA in Colorectal Cancer: Current and Evolving Practices. J. Clin. Oncol. 2022, 40, 2846–2857. [Google Scholar] [CrossRef] [PubMed]
  153. Hong, M.; Tao, S.; Zhang, L.; Diao, L.T.; Huang, X.; Huang, S.; Xie, S.J.; Xiao, Z.D.; Zhang, H. RNA sequencing: New technologies and applications in cancer research. J. Hematol. Oncol. 2020, 13, 166. [Google Scholar] [CrossRef]
  154. Ma, Y.; Li, J.; Zhao, X.; Ji, C.; Hu, W.; Qu, F.; Sun, Y.; Zhang, X. Multi-omics cluster defines the subtypes of CRC with distinct prognosis and tumor microenvironment. Eur. J. Med. Res. 2024, 29, 207. [Google Scholar] [CrossRef]
  155. Russo, V.; Lallo, E.; Munnia, A.; Spedicato, M.; Messerini, L.; D’Aurizio, R.; Ceroni, E.G.; Brunelli, G.; Galvano, A.; Russo, A.; et al. Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis. Cancers 2022, 14, 4012. [Google Scholar] [CrossRef]
  156. Na, H.Y.; Park, Y.; Nam, S.K.; Lee, K.S.; Oh, H.K.; Kim, D.W.; Kang, S.B.; Kim, W.H.; Lee, H.S. Expression of human leukocyte antigen class I and β2-microglobulin in colorectal cancer and its prognostic impact. Cancer Sci. 2021, 112, 91–100. [Google Scholar] [CrossRef] [PubMed]
  157. Kawazu, M.; Ueno, T.; Saeki, K.; Sax, N.; Togashi, Y.; Kanaseki, T.; Chida, K.; Kishigami, F.; Sato, K.; Kojima, S.; et al. HLA Class I Analysis Provides Insight Into the Genetic and Epigenetic Background of Immune Evasion in Colorectal Cancer With High Microsatellite Instability. Gastroenterology 2022, 162, 799–812. [Google Scholar] [CrossRef]
  158. Mittal, P.; Battaglin, F.; Yang, Y.; Millstein, J.; Stintzing, S.; Parikh, A.R.; Soni, S.; Lo, J.H.; Torres-Gonzalez, L.; Algaze, S.; et al. Single nucleotide polymorphisms (SNPs) in MHC class I and II genes to predict outcome in patients (pts) with metastatic colorectal cancer (mCRC): Data from FIRE-3, MAVERICC, and TRIBE trials. J. Clin. Oncol. 2023, 41, 3578. [Google Scholar] [CrossRef]
  159. Gambella, A.; Scabini, S.; Zoppoli, G.; De Silvestri, A.; Pigozzi, S.; Paudice, M.; Campora, M.; Fiocca, R.; Grillo, F.; Mastracci, L. HLA-G as a prognostic marker in stage II/III colorectal cancer: Not quite there yet. Histochem. Cell Biol. 2022, 158, 535–543. [Google Scholar] [CrossRef] [PubMed]
  160. Mittal, P.; Singh, S.; Sinha, R.; Shrivastava, A.; Singh, A.; Singh, I.K. Myeloid cell leukemia 1 (MCL-1): Structural characteristics and application in cancer therapy. Int. J. Biol. Macromol. 2021, 187, 999–1018. [Google Scholar] [CrossRef]
  161. Healy, M.E.; Boege, Y.; Hodder, M.C.; Böhm, F.; Malehmir, M.; Scherr, A.L.; Jetzer, J.; Chan, L.K.; Parrotta, R.; Jacobs, K.; et al. MCL1 Is Required for Maintenance of Intestinal Homeostasis and Prevention of Carcinogenesis in Mice. Gastroenterology 2020, 159, 183–199. [Google Scholar] [CrossRef]
  162. Fu, D.; Pfannenstiel, L.; Demelash, A.; Phoon, Y.P.; Mayell, C.; Cabrera, C.; Liu, C.; Zhao, J.; Dermawan, J.; Patil, D.; et al. MCL1 nuclear translocation induces chemoresistance in colorectal carcinoma. Cell Death Dis. 2022, 13, 63. [Google Scholar] [CrossRef] [PubMed]
  163. Mittal, P.; Battaglin, F.; Baca, Y.; Xiu, J.; Farrell, A.P.; Soni, S.; Lo, J.H.; Torres-Gonzalez, L.; Algaze, S.; Jayachandran, P.; et al. Characterization of MCL-1 in patients with colorectal cancer (CRC): Expression, molecular profiles, and outcomes. J. Clin. Oncol. 2023, 41, 3085. [Google Scholar] [CrossRef]
  164. Lee, Y.S.; Song, S.J.; Hong, H.K.; Oh, B.Y.; Lee, W.Y.; Cho, Y.B. The FBW7-MCL-1 axis is key in M1 and M2 macrophage-related colon cancer cell progression: Validating the immunotherapeutic value of targeting PI3Kγ. Exp. Mol. Med. 2020, 52, 815–831. [Google Scholar] [CrossRef] [PubMed]
  165. Li, H.X. The role of circadian clock genes in tumors. Onco Targets Ther. 2019, 12, 3645–3660. [Google Scholar] [CrossRef] [PubMed]
  166. Lévi, F.; Dugué, P.A.; Innominato, P.; Karaboué, A.; Dispersyn, G.; Parganiha, A.; Giacchetti, S.; Moreau, T.; Focan, C.; Waterhouse, J.; et al. Wrist actimetry circadian rhythm as a robust predictor of colorectal cancer patients survival. Chronobiol. Int. 2014, 31, 891–900. [Google Scholar] [CrossRef]
  167. Stokes, K.; Nunes, M.; Trombley, C.; Flôres, D.E.F.L.; Wu, G.; Taleb, Z.; Alkhateeb, A.; Banskota, S.; Harris, C.; Love, O.P.; et al. The Circadian Clock Gene, Bmal1, Regulates Intestinal Stem Cell Signaling and Represses Tumor Initiation. Cell Mol. Gastroenterol. Hepatol. 2021, 12, 1847–1872.e1840. [Google Scholar] [CrossRef]
  168. Lo, J.H.; Soni, S.; Smbatyan, G.; Torres-Gonzalez, L.; Mittal, P.; Yang, Y.; Battaglin, F.; Chan, P.; Pan, Y.; Algaze, S.; et al. Abstract 7221: A compound targeting the circadian clock protein CRY2 enhances therapeutic efficacy of bevacizumab in a colorectal cancer (CRC) xenograft model. Cancer Res. 2024, 84, 7221. [Google Scholar] [CrossRef]
  169. Zhang, Y.H.; Shi, W.N.; Wu, S.H.; Miao, R.R.; Sun, S.Y.; Luo, D.D.; Wan, S.B.; Guo, Z.K.; Wang, W.Y.; Yu, X.F.; et al. SphK2 confers 5-fluorouracil resistance to colorectal cancer via upregulating H3K56ac-mediated DPD expression. Oncogene 2020, 39, 5214–5227. [Google Scholar] [CrossRef]
  170. Zhang, Y.H.; Luo, D.D.; Wan, S.B.; Qu, X.J. S1PR2 inhibitors potently reverse 5-FU resistance by downregulating DPD expression in colorectal cancer. Pharmacol. Res. 2020, 155, 104717. [Google Scholar] [CrossRef]
  171. Malier, M.; Gharzeddine, K.; Laverriere, M.H.; Marsili, S.; Thomas, F.; Decaens, T.; Roth, G.; Millet, A. Hypoxia Drives Dihydropyrimidine Dehydrogenase Expression in Macrophages and Confers Chemoresistance in Colorectal Cancer. Cancer Res. 2021, 81, 5963–5976. [Google Scholar] [CrossRef]
  172. Cui, Z.; He, S.; Wen, F.; Lu, L.; Xu, L.; Wu, H.; Wu, S. Dihydropyrimidine Dehydrogenase (DPD) as a Bridge between the Immune Microenvironment of Colon Cancers and 5-FU Resistance. Front. Biosci. Landmark Ed. 2023, 28, 80. [Google Scholar] [CrossRef] [PubMed]
  173. Zahalka, A.H.; Frenette, P.S. Nerves in cancer. Nat. Rev. Cancer 2020, 20, 143–157. [Google Scholar] [CrossRef] [PubMed]
  174. Jiang, S.H.; Zhang, X.X.; Hu, L.P.; Wang, X.; Li, Q.; Zhang, X.L.; Li, J.; Gu, J.R.; Zhang, Z.G. Systemic Regulation of Cancer Development by Neuro-Endocrine-Immune Signaling Network at Multiple Levels. Front. Cell Dev. Biol. 2020, 8, 586757. [Google Scholar] [CrossRef] [PubMed]
  175. Wan, C.; Yan, X.; Hu, B.; Zhang, X. Emerging Roles of the Nervous System in Gastrointestinal Cancer Development. Cancers 2022, 14, 3722. [Google Scholar] [CrossRef] [PubMed]
  176. Battaglin, F.; Jayachandran, P.; Strelez, C.; Lenz, A.; Algaze, S.; Soni, S.; Lo, J.H.; Yang, Y.; Millstein, J.; Zhang, W.; et al. Neurotransmitter signaling: A new frontier in colorectal cancer biology and treatment. Oncogene 2022, 41, 4769–4778. [Google Scholar] [CrossRef]
  177. Kuol, N.; Davidson, M.; Karakkat, J.; Filippone, R.T.; Veale, M.; Luwor, R.; Fraser, S.; Apostolopoulos, V.; Nurgali, K. Blocking Muscarinic Receptor 3 Attenuates Tumor Growth and Decreases Immunosuppressive and Cholinergic Markers in an Orthotopic Mouse Model of Colorectal Cancer. Int. J. Mol. Sci. 2022, 24, 596. [Google Scholar] [CrossRef] [PubMed]
  178. Ma, Y.; Chen, H.; Li, H.; Zhao, Z.; An, Q.; Shi, C. Targeting monoamine oxidase A: A strategy for inhibiting tumor growth with both immune checkpoint inhibitors and immune modulators. Cancer Immunol. Immunother. 2024, 73, 48. [Google Scholar] [CrossRef] [PubMed]
  179. Leung, E.L.; Huang, J.; Zhang, J.; Zhang, J.; Wang, M.; Zhu, Y.; Meng, Z.; Yu, H.; Neher, E.; Ma, L.; et al. Novel Anticancer Strategy by Targeting the Gut Microbial Neurotransmitter Signaling to Overcome Immunotherapy Resistance. Antioxid. Redox Signal 2023, 38, 298–315. [Google Scholar] [CrossRef]
  180. Dixon, S.J.; Lemberg, K.M.; Lamprecht, M.R.; Skouta, R.; Zaitsev, E.M.; Gleason, C.E.; Patel, D.N.; Bauer, A.J.; Cantley, A.M.; Yang, W.S.; et al. Ferroptosis: An iron-dependent form of nonapoptotic cell death. Cell 2012, 149, 1060–1072. [Google Scholar] [CrossRef]
  181. Plays, M.; Muller, S.; Rodriguez, R. Chemistry and biology of ferritin. Metallomics 2021, 13, mfab021. [Google Scholar] [CrossRef] [PubMed]
  182. Zhou, B.; Liu, J.; Kang, R.; Klionsky, D.J.; Kroemer, G.; Tang, D. Ferroptosis is a type of autophagy-dependent cell death. Semin. Cancer Biol. 2020, 66, 89–100. [Google Scholar] [CrossRef] [PubMed]
  183. Liu, Y.; Guo, F.; Guo, W.; Wang, Y.; Song, W.; Fu, T. Ferroptosis-related genes are potential prognostic molecular markers for patients with colorectal cancer. Clin. Exp. Med. 2021, 21, 467–477. [Google Scholar] [CrossRef] [PubMed]
  184. Chen, Y.; Li, H. Prognostic and Predictive Models for Left- and Right- Colorectal Cancer Patients: A Bioinformatics Analysis Based on Ferroptosis-Related Genes. Front. Oncol. 2022, 12, 833834. [Google Scholar] [CrossRef] [PubMed]
  185. Yan, L.; Chen, X.; Bian, Z.; Gu, C.; Ji, H.; Chen, L.; Xu, H.; Tang, Q. A ferroptosis associated gene signature for predicting prognosis and immune responses in patients with colorectal carcinoma. Front. Genet. 2022, 13, 971364. [Google Scholar] [CrossRef]
  186. Jiang, Z.; Lim, S.O.; Yan, M.; Hsu, J.L.; Yao, J.; Wei, Y.; Chang, S.S.; Yamaguchi, H.; Lee, H.H.; Ke, B.; et al. TYRO3 induces anti-PD-1/PD-L1 therapy resistance by limiting innate immunity and tumoral ferroptosis. J. Clin. Investig. 2021, 131, e139434. [Google Scholar] [CrossRef] [PubMed]
  187. Peng, B.; Peng, J.; Kang, F.; Zhang, W.; Peng, E.; He, Q. Ferroptosis-Related Gene MT1G as a Novel Biomarker Correlated With Prognosis and Immune Infiltration in Colorectal Cancer. Front. Cell Dev. Biol. 2022, 10, 881447. [Google Scholar] [CrossRef]
  188. Conche, C.; Finkelmeier, F.; Pesic, M.; Nicolas, A.M.; Bottger, T.W.; Kennel, K.B.; Denk, D.; Ceteci, F.; Mohs, K.; Engel, E.; et al. Combining ferroptosis induction with MDSC blockade renders primary tumours and metastases in liver sensitive to immune checkpoint blockade. Gut 2023, 72, 1774–1782. [Google Scholar] [CrossRef] [PubMed]
  189. Lin, X.; Chen, H.; Deng, T.; Cai, B.; Xia, Y.; Xie, L.; Wang, H.; Huang, C. Improved Immune Response for Colorectal Cancer Therapy Triggered by Multifunctional Nanocomposites with Self-Amplifying Antitumor Ferroptosis. ACS Appl. Mater. Interfaces 2024, 16, 13481–13495. [Google Scholar] [CrossRef]
  190. Gao, M.; Monian, P.; Quadri, N.; Ramasamy, R.; Jiang, X. Glutaminolysis and Transferrin Regulate Ferroptosis. Mol. Cell 2015, 59, 298–308. [Google Scholar] [CrossRef] [PubMed]
  191. Mukhopadhyay, S.; Vander Heiden, M.G.; McCormick, F. The Metabolic Landscape of RAS-Driven Cancers from biology to therapy. Nat. Cancer 2021, 2, 271–283. [Google Scholar] [CrossRef]
Cancers 16 02796 g001
Figure 1. Current predictive biomarkers for metastatic colorectal cancer (CRC). EGFRi = EGFR inhibition; VEGFi = VEGF inhibition; G12Ci = KRAS G12C inhibition; BRAFi = BRAF inhibition; ICI = Immune checkpoint inhibition; HER2i = HER2 inhibition; CRC = colorectal cancer. (Created in Biorender.com).
Figure 1. Current predictive biomarkers for metastatic colorectal cancer (CRC). EGFRi = EGFR inhibition; VEGFi = VEGF inhibition; G12Ci = KRAS G12C inhibition; BRAFi = BRAF inhibition; ICI = Immune checkpoint inhibition; HER2i = HER2 inhibition; CRC = colorectal cancer. (Created in Biorender.com).
Cancers 16 02796 g001
Table 1. Clinical trials evaluating therapeutic potential in KRAS mutations and promising biomarkers.
Table 1. Clinical trials evaluating therapeutic potential in KRAS mutations and promising biomarkers.
GeneMolecular CriteriaSettingTherapy of InterestTrial Number/PhaseStatus
KRASKRAS G12C mutationmCRCSotorasib (G12C inhibitor) + panitumumabNCT05198934 (Phase 3)Active
mCRCAdagrasib (G12C inhibitor) + cetuximabNCT03785249 (Phase 1/2)Active
mCRCAdagrasib (G12C inhibitor) + cetuximabNCT04793958 (Phase 3)Active
mCRCSotorasib (G12C inhibitor) + panitumumab + FOLFIRINCT06252649 (Phase 3)Not yet recruiting
mCRCDivarasib (G12C inhibitor) + cetuximabNCT04449874 (Phase 1)Active
Advanced solid tumorsGlecirasib (G12C inhibitor) + JAB-3312 (SHP2 inhibitor)NCT05288205 (Phase 1/2)Active
Advanced solid tumorsOpnurasib (G12C inhibitor)NCT04699188 (Phase 1/2)Active
Advanced solid tumorsDivarasib (G12C inhibitor)NCT04449874 (Phase 1)Active
KRAS mutated (except G13 mutations)mCRCVociprotafib
(SHP2 inhibitor) + Temuterkib (ERK ½ inhibitor)		 NCT04916236 (Phase 1) Active
KRAS G12D mutationmCRCAdagrasib (G12C inhibitor) + Batoprotafib (SHP2 inhibitor)NCT04330664 (Phase 1/2)Active
Advanced solid tumorsMRTX1133 (G12D inhibitor)NCT05737706 (Phase 1/2)Active
Advanced solid tumorsRMC-9805 (G12D inhibitor)NCT06040541 (Phase 1)Active
KRAS G12V mutationAdvanced solid tumors AFNT-211 (G12V TCR) NCT06105021 (Phase 1/2)Active
Advanced solid tumors FHA11 (G12V TCR) NCT06043713 (Phase 1)Active
KRAS G12D, G12V, G13D or G12CNon-MSI-H/dMMR mCRC mRNA-5671/V941 NCT03948763 (Phase 1)Completed (2022)
RAS mutationAdvanced solid tumors RMC-6236 (RASmulti inhibitor) NCT05379985 (Phase 1)Active
CEACAM5 Advanced solid tumors M9140 (CEACAM5 ADC) NCT05464030 (Phase 1)Active
High CEACAM5 Expression Previously treated MSS mCRC Obinutuzumab (CD20 mAb) + Cibisatamab (CEA-CD3 bispecific Ab) + atezolizumab (PDL1 mAb) NCT03866239 (Phase 1)Completed (2024)
c-MET Advanced solid tumorsABBV-400 (MET Ab) + Bevacizumab NCT05029882 (Phase 1) Active
Previously treated unresectable mCRC ABBV-400 (MET Ab) + 5FU + Folinic acid + Bevacizumab NCT06107413 (Phase 2) Active
PIK3CAPIK3CA mutationAdvanced solid cancers including CRCSerabelisib (PI3Kα inhibitor)NCT05300048 (Phase 1b)Active
PIK3CA mutationmCRCAlpelisib (PI3Kα inhibitor) + capecitabineNCT04753203 (Phase 1b/2)Active
CXCR4 Advanced pancreatic, ovarian, and CRCPlerixafor (CXCR4 inhibitor)NCT02179970 (Phase 1)Completed 2018
CCR5 mCRCMaraviroc (CCR5 inhibitor)NCT01736813 (phase 1)Completed 2014
Advanced solid cancers including CRCOB-002 (CCR5 antagonist)NCT05940844 (Phase 1)Not yet recruiting
MSSmCRCVicriviroc (CCR5 inhibitor) + pembrolizumabNCT03631407 (Phase 2)Completed 2021
Advanced pancreatic and CRCIpilimumab + Maraviroc (CCR5 inhibitor) + nivolumabNCT04721301 (Phase 1)Completed 2023
MSSmCRCMaraviroc (CCR5 inhibitor) + pembrolizumabNCT03274804 (Phase 1)Completed 2020
TIM3None for inclusion but evaluating TIM3 expression levels Advanced solid cancers including CRCTSR-022 (anti-TIM3) + multiple ICI/chemotherapy armsNCT02817633 (Phase 1)Active
LAG3 Advanced solid cancers including CRCTSR-033 (LAG3 antibody) + dostarlimabNCT03250832 (Phase 1)Completed 2023
MSI-HLocalized and locally advanced CRCFianlimab (LAG3 inhibitor) + CemiplimabNCT06205836 (Phase 1)Active
MSS, cohort A CPM ≥ 15%, cohort B CPM < 15%.Metastatic or locally advanced CRCRelatlimab (LAG3 antibody) + NivolumabNCT03642067 (Phase 2)Active
Advanced solid cancers including CRCXmAb®22841 (CTLA-4 × LAG3 bispecific antibody) + PembrolizumabNCT03849469 (Phase 1)Completed 2023
MSS CPS ≥ 1mCRCFavezelimab (LAG3 antibody) + PembrolizumabNCT05064059 (Phase 3)Active
ARID1AMSS and ARID1A mutationmCRCTislelizumab (PD-1 Antibody) + Fruquintinib (VEGFR 1/2/3 Inhibitor)NCT05690035 (Phase 2)Active
PLK1KRAS mutated and BRAFV600E negativemCRCOnvansertib (PLK1 inhibitor) + FOLFIRI + BevacizumabNCT03829410 (Phase 1b/2)Completed 2024
KRAS mutated and BRAFV600E negativemCRCOnvansertib (PLK1 inhibitor) + Bevacizumab + FOLFIRI or FOLFOXNCT06106308 (Phase 2)Active
KRAS or NRAS Mutation mCRCOnvansertib (PLK1 inhibitor) + Bevacizumab + FOLFIRINCT05593328 (Phase 2)Active
CRC = colorectal cancer; mCRC = metastatic colorectal cancer; MSS = microsatellite stable; CPM = composite PD-L1/Mucin; CPS = combined positive score.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ashouri, K.; Wong, A.; Mittal, P.; Torres-Gonzalez, L.; Lo, J.H.; Soni, S.; Algaze, S.; Khoukaz, T.; Zhang, W.; Yang, Y.; et al. Exploring Predictive and Prognostic Biomarkers in Colorectal Cancer: A Comprehensive Review. Cancers 2024, 16, 2796. https://doi.org/10.3390/cancers16162796

AMA Style

Ashouri K, Wong A, Mittal P, Torres-Gonzalez L, Lo JH, Soni S, Algaze S, Khoukaz T, Zhang W, Yang Y, et al. Exploring Predictive and Prognostic Biomarkers in Colorectal Cancer: A Comprehensive Review. Cancers. 2024; 16(16):2796. https://doi.org/10.3390/cancers16162796

Chicago/Turabian Style

Ashouri, Karam, Alexandra Wong, Pooja Mittal, Lesly Torres-Gonzalez, Jae Ho Lo, Shivani Soni, Sandra Algaze, Taline Khoukaz, Wu Zhang, Yan Yang, and et al. 2024. "Exploring Predictive and Prognostic Biomarkers in Colorectal Cancer: A Comprehensive Review" Cancers 16, no. 16: 2796. https://doi.org/10.3390/cancers16162796

APA Style

Ashouri, K., Wong, A., Mittal, P., Torres-Gonzalez, L., Lo, J. H., Soni, S., Algaze, S., Khoukaz, T., Zhang, W., Yang, Y., Millstein, J., Lenz, H.-J., & Battaglin, F. (2024). Exploring Predictive and Prognostic Biomarkers in Colorectal Cancer: A Comprehensive Review. Cancers, 16(16), 2796. https://doi.org/10.3390/cancers16162796

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop