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Volovat, S.;  Augustin, I.;  Zob, D.;  Boboc, D.;  Amurariti, F.;  Volovat, C.;  Stefanescu, C.;  Stolniceanu, C.R.;  Ciocoiu, M.;  Dumitras, E.A.; et al. Personalized Biomarkers in Metastatic Colorectal Cancer. Encyclopedia. Available online: (accessed on 05 December 2023).
Volovat S,  Augustin I,  Zob D,  Boboc D,  Amurariti F,  Volovat C, et al. Personalized Biomarkers in Metastatic Colorectal Cancer. Encyclopedia. Available at: Accessed December 05, 2023.
Volovat, Simona-Ruxandra, Iolanda Augustin, Daniela Zob, Diana Boboc, Florin Amurariti, Constantin Volovat, Cipriana Stefanescu, Cati Raluca Stolniceanu, Manuela Ciocoiu, Eduard Alexandru Dumitras, et al. "Personalized Biomarkers in Metastatic Colorectal Cancer" Encyclopedia, (accessed December 05, 2023).
Volovat, S.,  Augustin, I.,  Zob, D.,  Boboc, D.,  Amurariti, F.,  Volovat, C.,  Stefanescu, C.,  Stolniceanu, C.R.,  Ciocoiu, M.,  Dumitras, E.A.,  Danciu, M.,  Apostol, D.G.C.,  Drug, V.,  Shurbaji, S.A.,  Coca, L.,  Leon, F.,  Iftene, A., & Herghelegiu, P.(2022, November 09). Personalized Biomarkers in Metastatic Colorectal Cancer. In Encyclopedia.
Volovat, Simona-Ruxandra, et al. "Personalized Biomarkers in Metastatic Colorectal Cancer." Encyclopedia. Web. 09 November, 2022.
Personalized Biomarkers in Metastatic Colorectal Cancer

Colorectal cancer is one of the most frequent cancers worldwide, with a high incidence and mortality. Although many treatment options are available for metastatic disease, patient survival is still limited.

metastatic colorectal cancer ncRNA predictive biomarkers prognostic biomarkers

1. Introduction

Colorectal cancer (CRC) is the third most frequent cancer worldwide in both sexes, with mortality rates of 45%, 35% and 47.8% in Europe, the USA and worldwide, respectively [1]. However, CRC is a heterogeneous disease with widely variable clinical outcomes, in terms of both prognosis and drug response. This is the reason for developing effective treatments for patients with CRC, i.e., to prolong survival in metastatic settings. In order to make treatments more efficient, it is very important to identify the prognostic and predictive markers, to allow the efficient targeting of the tumor cells. Epigenetics, defined as alterations in gene expression, play a central role in the pathogenesis of various cancers, including CRC. In fact, there are several markers used to monitor metastatic colon cancer, but studies in recent decades have shown promising possibilities for using epigenetic biomarkers, given the interaction of ncRNA with different gene mutations involved in CRC pathogenesis.

2. Genomics in mCRC

Various genomic alterations have been studied in colorectal cancer, as the development of both predictive and prognostic biomarkers is important in personalized medicine and can be incorporated in treatment decisions.
Mismatch repair deficiency and microsatellite instability (MSI) are frequently associated with Lynch syndrome, in up to 20% of colorectal cancers, and are defined by mutations in mismatch repair (MMR) genes [2], making the cell unable to correct DNA errors. MMR deficiency is characterized by germline or somatic DNA alterations in MMR genes (MLH1, MSH2, MSH6 or PMS2), leading to colorectal cancer. Most frequently, the loss of MLH1 expression leads to sporadic colorectal cancer [3]. MSI status is evaluated in early-stage colorectal cancer and is a predictive biomarker for immunotherapy with pembrolizumab in stage IV disease [4]. Moreover, the MSI status can provide prognostic information, as patients with tumors that are dMMR (MSI-high) show longer survival [5], as well as patients with proximal tumors associated with MSI [6]. In metastatic setting, MSI-H tumors appear to behave more aggressively and have a negative impact on survival [7].
BRAF V600E mutations are downstream targets of the RAS signaling pathway and are altered in 10% of colorectal cancer (CRC) patients; these mutations are also mutually exclusive with the KRAS mutation [8]. Patients with these mutations have lower survival rates when they are associated with MSI-low tumors, and current research approaches include combining BRAF inhibitors with agents that block other signaling pathways. Although BRAF inhibitors are effective treatments in BRAF mutant melanoma, this approach has been ineffective in colorectal cancer. Preclinical studies suggest that could be due to a rapid ERK reactivation [9]. Available data suggest that BRAF mutations are associated with resistance to anti-EGFR therapy [10]. Combinations with MEK inhibitors have demonstrated improved PFS and ORR compared to cetuximab and chemotherapy and could be an option for later lines of treatment [11].
KRAS and NRAS mutations are the most prevalent forms of genomic alteration; they are found in 75% of CRCs and are associated with a worse prognosis and resistance to anti-EGFR therapy [12]. Studies have shown that the presence of KRAS mutations lead to a worse survival when anti-EGFR therapy such as cetuximab or panitumumab are added to the chemotherapy regimen in metastatic setting [13][14].
A new biomarker is represented by KRASG12C mutation, found in 14% of non-small cell lung cancer (NSCLC) and 3% of CRC. Two new molecules, sotorasib and adagrasib were found to decrese the phosphorylation of ERK and promote the tumor regression in mice bearing KRAS G12C-mutant NSCLC tumors [15][16].
In a phase 1 study, sotorasib was evaluated in patients with refractory KRAS G12C-mutated solid tumors (NCT 03600883). In mCRC cohort, the objective response rate (ORR) was 7.1% and the disease control rate (DCR) was 73.8%. The median PFS in this group was 4 months [17]. In the phase 2 CodeBreak 100 (NCT03600883) trial studied sotorasib in patients with metastatic KRASG12C-mutant CRC who had progressed on prior chemotherapy treatment and the ORR was 9.7% and the DCR was 82.3% [18].
The KRYSTAL-1 study (NCT03785249) is a phase 1/2 study investigating adagrasib monotherapy in patients with advanced or metastatic solid tumors harboring a KRAS G12C mutation and previously treated with chemotherapy and/or anti PD-L1 therapy. In the CRC cohort, the disease control rate was 87% and progression-free survival was 5.6 months. One of two patients achieved a partial response (duration of response, 4.2 months) [19].
Some cohorts in the CodeBreak 101 umbrella trial (NCT04185883) combine sotorasib with other approved agents including a PD1/PD-L1 inhibitors, an mTOR inhibitor, MEK inhibitor, a CDK 4/6 inhibitor, a VEGF inhibitor with various chemotherapies. The KRYSTAL-1 umbrella trial is also including similar strategies. However, adding Palbociclib to KRAS G12C inhibitors in preclinical studies, showed significantly more suppression of RAS pathway phosphorylation, cell-division genes, and cell-cycle progression [20].
In combinations with immunotherapy, targeted therapy or KRAS-G12C inhibitors failed to provide significant clinical benefit due to the complexity of the signaling pathway [21].
HER2 alterations occur in 2–6% of metastatic CRCs and confer resistance to treatment with EGFR inhibitors [22]. Efficient treatment options targeting HER2 in other tumors such as gastric cancer or breast cancer supports the role of HER2 as a predictive biomarker. Anti-HER2-targeted therapy has been proven effective in this setting; for example, response rates of up to 38% for the use of trastuzumab, TDM-1 and pan-HER2 inhibitors such as neratinib or lapatinib [23][24] have been documented.
NTRK fusions involve three genes that encode transmembrane receptors. NTRK inhibitors such as entrectinib and larotrectinib have been associated with tumor responses in CRC patients [25].
PI3K mutations have been described in KRAS-wild-type CRC and are responsive to anti-EGFR therapy [26]. Moreover, they are associated with a negative prognosis in BRAF-wild-type tumors [27], especially those showing mutations in exons 9 and 20.
Several genomic alterations have been evaluated as predictive biomarkers for the response to chemotherapy, such as those involving dihydropyrimidine dehydrogenase (DPD), thymidylate synthetase (TS) expression and UDP-glucuronosyltransferase 1A1 (UGT1A1). DPD deficiency has been associated with increased fluoropyrimidine toxicity; thus, it has potential predictive value in clinical settings. It causes a deficit in the metabolism of thymine and uracil, resulting in accumulation in the blood and resulting in increased toxicity. Current guidelines are conflicting in recommending DPD genotyping before fluoropyrimidine-based therapy [28]. Currently, data on its prognostic value are limited [29]. UGT1A1 expression has been associated with increased SN-38, leading to increased toxicity in irinotecan-based chemotherapy. Irinotecan is metabolized into the active form, SN-38, leading to severe treatment hematologic and digestive toxicity [30]. However, UGT1A1 genotyping is not routinely applied in clinical settings [31].
TS and ERCC1 expression levels have been described as potential biomarkers in CRC. ERCC1 is involved in the cellular response to DNA damage, and TS has been shown to be predictive of responses to fluoropyrimidine chemotherapy. Low TS levels are associated with improved response rates and OS in patients treated with a FOLFOX regimen [32].
Molecular profiling using liquid biopsies has been validated in various tumor types in clinical settings and can be used to assess circulating tumor cells (CTCs), circulating tumor DNA (ctDNA) and exosomes released by cancer cells. However, its implementation in clinical practice remains technically challenging. Several studies have shown ctDNA to have both prognostic and predictive value in clinical settings [33]. A reduction in ctDNA levels of at least 80% has been associated with a favorable response rate, and variations in ctDNA after the initial treatment response could predict clinical relapse within several months [34].
The main advantage of the liquid biopsy is that ctDNA captures alterations of many genes, specifically EGFR, ERBB2, PIK3CA or MAP2K1, revealing new potential targets for therapies such as anti-BRAF, anti-EGFR and anti-HER2 agents. In metastatic CRC, ctDNA can represent an important tool to monitor the molecular evolution of CRC over time, during the different courses of treatment. Quantitative and qualitative fluctuation of molecular landscapes, revealed by ctDNA, suggesting a molecular evolution of CRC, which would have been difficult to assess by tissue biopsy were found [35][36][37].
The pulsatile behavior of tumor-specific mutant clones, detected through mutation monitoring over time on ctDNA, provided a scientific rational for the retreatment with anti-EGFR. In CHRONOS trial (NCT03227926), the mCRC patients approaching third or later line of treatment were assessed in ctDNA for RAS, BRAF and EGFR ectodomain status and re-challenged with anti-EGFR therapy (panitumumab) only for the patients with a mutation-negative status. A 30% response rate and a 63% disease control rate was reported, demonstrating that genotyping tumor DNA in the blood of CRC patients can be used to direct therapy and can be included in the management of advanced CRC patients [38][39].
Tumor mutational burden (TMB) in CRC is typically increased in case of microsatellite instability (MSI) or pathogenic mutations occurring in domains of the DNA polymerases POLE and POLD, being correlated with the response to immunotherapy. Recently, Food and Drug Administration (FDA) approved TMB as a companion biomarker for the treatment with pembrolizumab or dostarlimab in mCRC [40].
The golden standard for TMB evaluation is represented by tumor-tissue specimens [41], but the intratumoral heterogeneity represents a limit for TMB evaluation, supporting the role of ctDNA as a monitoring biomarker, being known that TMB can change under treatment with standard cytotoxic agents in CRC [42].
In the ARETHUSA trial (NCT03519412) the metastatic-colorectal patients who failed standard therapies undergo treatment with pembrolizumab, are tested for o6-methylguanine-DNA-methyltransferase (MGMT) expression (IHC), then for MGMT promoter methylation [43].
The microsatellite instability (MSI) also represents a relevant biomarker for immunotherapy sensitivity in CRC, but similarly to TMB, MSI status is subjected to both spatial and temporal heterogeneity, making its monitoring through ctDNA therapeutically valuable [44].

3. Transcriptomics in mCRC: Immunoscore

The classification of colorectal cancer plays an essential role in establishing the prognosis and the choice of therapeutic management for the patient. The TNM classification is the system most commonly used to determine the progression of CRC, but a more in-depth approach is needed to establish the prognosis and therapeutic strategy.
In 2015, the International Consortium of CRC Subtypes proposed a unified transcriptomic classification that allowed the identification of four biologically distinct consensus molecular subtypes (CMS), which subsequently allowed the classification of CRC into four subtypes with distinct molecular and biological characteristics: CMS1 (immune to microsatellite instability), CMS2 (canonical), CMS3 (metabolic) and CMS4 (mesenchymal) [45].

4. Epigenomics in mCRC

It is well established that a significant part of the pathogenesis of cancer, including colorectal cancer, can be explained by epigenetic modifications, such as DNA methylation and histone modifications, and epigenetic regulators, including ncRNAs.

5. Artificial Intelligence Methods Used in mCRC

Since 2010, the use of AI in medical disease diagnosis and treatment has grown over the years [46][47]. AI techniques have been used with success in many contexts, including colon polyps, adenomas, colon cancer, ulcerative colitis and intestinal motor diseases. Although the application of AI to the diagnosis and treatment of CRC still lacks systematic research, the continuous development of AI applications in the medical field is an indication that AI will eventually be used for the diagnosis and therapy of CRC.
A classification of AI applications for the identification of new prediction/prognosis biomarkers in mCRC is related to machine learning (ML) models that can be described according to the basic features: (1) support vector machines (SVMs) that are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis, and; (2) the artificial neural networks (ANN) usually simply called neural networks (NNs) or neural nets, including convolutional neural network (CNN, or ConvNet), that can be defined as regularized versions of multilayer perceptrons.
ML is divided into supervised and unsupervised based on whether the training data is labeled or not [48].


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