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Unraveling the role of molecular profiling in predicting treatment response in stage III colorectal cancer patients: Insights from the IDEA International Study

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Abstract
Background: This study aimed to investigate the molecular profiles of stage III CRC patients from the international IDEA study. It also sought to correlate these profiles with Toll-like and vitamin D receptor polymorphisms, clinicopathological and epidemiological characteristics, and patient outcomes. Methods: Whole Exome Sequencing and PCR-RFLP on surgical specimens and blood samples, respectively, were performed to identify molecular profiling and the presence of Toll-like and vitamin D polymorphisms. Bioinformatic analysis revealed mutational status. Results: Among the enrolled patients, 63.7% were male, 66.7% had left-sided tumors, and 55.7% received CAPOX as adjuvant chemotherapy. Whole exome sequencing identified 59 mutated genes in 11 different signaling pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) CRC panel. On average, patients had 8 mutated genes (range, 2-21 genes). Mutations in ARAF and MAPK10 emerged as independent prognostic factors for reduced DFS (p=0.027 and p<0.001, respectively), while RAC3 and RHOA genes emerged as independent prognostic factors for reduced OS (p=0.029 and p=0.006, respectively). Right-sided tumors were also identified as independent prognostic factors for reduced DFS (p=0.019) and OS (p=0.043). Additionally, patients with tumors in the transverse colon had mutations in genes related to apoptosis, PIK3-Akt, Wnt, and MAPK signaling pathways. Conclusions: Molecular characterization of tumor cells can enhance our understanding of the disease course. Mutations may serve as promising prognostic biomarkers, offering improved treatment options. Confirming these findings with require larger patient cohorts and international collaborations to establish correlations between molecular profiling, clinicopathological and epidemiological characteristics and clinical outcomes.
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Subject: Medicine and Pharmacology  -   Oncology and Oncogenics

1. Introduction

Colorectal cancer (CRC) is one of the most common malignancies and the second most common cause of death from cancer[1]. In 2020, 1.9 million new cases of CRC and approximately 935,000 deaths were reported[2]. By 2030, the global burden of CRC is predicted to be 60%, with more than 2.2 million new cases and 1.1 million deaths. By 2035, the total number of deaths from rectal and colon cancer is estimated to increase by 60% and 71.5%, respectively[2]. Depending on the stage of the disease at the time of diagnosis, both the treatment and prognosis differ. Patients with stage III CRC have an overall 5-year survival rate of 60%. Adjuvant chemotherapy aims to increase this rate and extend both the overall and disease-free survival[3]. Since 2004, folinic acid, fluorouracil, and oxaliplatin (FOLFOX) or capecitabine and oxaliplatin) for six months has been the standard treatment regimen[3,4]. However, oxaliplatin can lead to adverse effects, particularly peripheral sensory neuropathy[5].
Considering the increased incidence of the disease, toxicity of the treatment, cost, and efforts to reduce the duration of treatment for all the aforementioned reasons[5], the international IDEA study was designed to evaluate the hypothesis of non-inferiority of the 3-month vs. 6-month adjuvant chemotherapy with FOLFOX or CAPOX[6].
Although strong, AJCC/UICC-TNM staging (American Joint Committee on Cancer/Union Internationale Contre le Cancer—extent of primary tumor, regional lymph node involvement, presence of distant metastases) often fails to provide complete prognostic information because the outcome varies even among patients of the same stage[7]. Therefore, there is an urgent need to identify new tools that can contribute to CRC prognosis. The aim of the current study was to investigate the molecular profile of surgical specimens from stage III CRC patients enrolled in the international IDEA study, for whom paraffin-embedded cancer tissue was available. Following genetic profiling, correlations were performed with clinicopathological characteristics, as well as with patient outcomes. Additionally, the patients were tested for vitamin D receptor (VDR) and toll-like receptor (TLR) gene polymorphisms in peripheral blood, as our previous studies have demonstrated the role of such polymorphisms in tumor development and progression[8,9,10].

2. Patients and methods

2.1. Patient enrollment

The Hellenic Oncology Research Group (HORG) enrolled 708 patients with CRC in the international IDEA study (ClinicalTrials.gov Identifier: NCT01308086). Of these, 237 stage III patients with formalin-fixed paraffin-embedded (FFPE) tissues were included in the current study (Supplementary Table S1). All patients were aged > 18 years and received adjuvant chemotherapy with FOLFOX or CAPOX. None of the enrolled subjects had any other documented malignancies.

2.2. Formalin-Fixed Paraffin-embedded (FFPE) tissues

All surgical materials were evaluated by a specialized pathologist at the Department of Pathology of the University General Hospital of Heraklion, Crete, and the most representative and enriched tumor areas were selected for dissection. Healthy tissue was used as the control tissue. The specifications of the FFPE sections used for DNA extraction were as follows: tissue surface area, 25 mm2, section thickness, 10 μm; c. At least 10 sections with >50% cancer cells, d. Cancer cells were collected from areas rich in cancerous tissue and avoiding healthy tissue, adipose tissue, necrotic areas, and lymphocytes that decrease the content of cancer DNA. To facilitate the collection of appropriate cells from the 10 sections, an additional section was stained with hematoxylin-eosin to locate the cancerous areas.

2.3. TLR and VDR genotyping in blood samples

A total of 5 ml of peripheral blood in EDTA was collected from each patient, and DNA was extracted using the QIAamp DNA Blood Mini kit (QIAGEN, Hilden, Germany) according to the manufacturer’s instructions. The DNA concentration was determined using a NanoDrop ND-1000 v3.3 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA).
To determine the genetic variants of TLRs and VDRs, Polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) were used to determine the genetic variants of TLRs and VDRs. For TLR2 196-to-174 Ins/Del genetic variants, PCR was used, while TLR4 (Asp299Gly and Thr399Ile) and TLR9 (T1237C and T1486C) genetic variants were determined using PCR-RFLP. The materials and conditions for each gene target have been previously described[8,9]. Similarly, for genotyping of VDR genetic variants at the TaqI, ApaI, FokI, and BsmI positions, PCR-RFLP was used. The reagents and PCR conditions have been previously described, in detail[8,9,10]. The patients were classified as wild-type, heterozygous, or homozygous for each single nucleotide polymorphism, based on the absence or presence of the restriction site in both alleles.

2.4. Whole Exome Sequencing (WES)

The Illumina DNA Prep with Enrichment kit (Illumina, San Diego, CA 92122) was used for library preparation and enrichment, and sequencing of both tumor and normal tissues was conducted using the NovaSeq 6000 system (Illumina). For each sample, at least 250 ng of high-quality DNA was quantified using a Qubit Fluorometer (Thermo Fisher Scientific, Paisley, UK), was used. Following the WES, two files (. fastq) containing sequencing data were extracted from each tissue (tumor and normal) using forward and reverse reads.

2.5. Bioinformatic analysis

After obtaining the raw data from the WES analysis, a bioinformatics pipeline was used to process the data and generate interpretations. This included alignment to the human genome, variant calling, variant filtering, and annotation of the variants. The processed data were analyzed to identify somatic variants and evaluate their functional significance, particularly those associated with CRC (Figure 1). Initially, the raw sequences were aligned to the human genome (version hg19/GRCh37)[11], and variant calling was conducted using the genome analysis toolkit (GATK) for SNPs and insertion/deletions (INDELs). This generates two variant call format files (VCF) for each patient, one for the tumor and one for the normal tissue, respectively[12]. The somatic variants were isolated by subtracting the germline variants from the tumor, and a custom gene panel composed of 62 genes associated with 11 signaling pathways was utilized based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database on CRC-correlated genes[13] (Table 1 and Table 2). Subsequently, ANNOVAR software was employed to annotate the SNPs and INDELs, providing functional information that can determine the biological significance of each variant and identify CRC-associated variants[14].
To identify clinically significant variants and the signaling pathways involved, a filtering strategy was employed based on the functional position of the variants. Variants located in exons and splice sites are isolated as they are more likely to cause diseases[15]. In addition, the variants located in exons were further filtered based on their functional consequences, excluding variants causing synonymous mutations. This step was considered synonymous mutations that do not affect the amino acid sequence of proteins and are therefore less likely to be clinically relevant. All analyses were run in the Anaconda Powershell Prompt (Anaconda3, Inc.) on Ubuntu 20.04.3 LTS.

2.6. Statistical analysis

After characterizing the patients’ molecular profiles, the clinical, pathological, and epidemiological characteristics were examined to determine their association with patient outcomes. Disease-free (DFS) and overall survival (OS) were calculated from the day of tumor excision until the first documented recurrence or death, respectively. Recurrence was defined as the presence of metastatic disease, local recurrence, or a second primary tumor. The possible associations between baseline characteristics, recurrence, and individual or concurrent mutations were compared using the 2-sided Fisher exact test for categorical variables. The association between risk factors and time-to-event endpoints was evaluated using the log-rank test, and the Kaplan-Meier method was used to generate DFS and OS curves. Univariate and multivariate Cox regression analyses were conducted to evaluate the correlation between the potential prognostic factors and DFS or OS. Statistical significance was defined as p ≤ 0.05, and the statistical tool used was SPSS v. 26.

3. Results

3.1. Patients

The current investigation enrolled 237 patients with stage III CRC, and their characteristics are displayed in Table 3 and Supplementary Table S1. Among these patients, 151 (63.7%) were male, 159 (67.1%) were <70 years old (median:64 years, range:18-84), 158 (66.7%) had tumor localization in the left colon, and 132 (55.7%) patients underwent CAPOX as adjuvant chemotherapy. Of the entire patient population, 116 (48.9%) were administered a 3-month treatment regimen, while 121 (51.1%) were administered a 6-month treatment regimen. Moreover, 84 patients were analyzed for VDR and TLR gene polymorphisms. Regarding VDRs, 10 (11.9%), 7 (8.2%), 5 (6.0%), and 17 (20.2%) patients presented TaqI, ApaI, FokI, and BsmI homozygous phenotypes, respectively. Moreover, regarding TLRs, 48 (57.1%), 38 (46.4%), 38 (46.4%), 37 (44.0%), and 37 (44.0%) patients presented TLR2 196-to-174, TLR4—Asp299Gly, TLR4—Thr399Ile), TLR9—T1237C and TLR9—T1486C homozygous phenotype, respectively.

3.2. Annotated variants for each position

The KEGG CRC panel detected 85,871 uniquely annotated variants. Of these, 602 were detected in exonic positions, including 25 splice variants. The remaining variants were non-coding, with 81,555 intronic variants being the most common, followed by 2,184 UTR variants, 788 downstream variants, and 742 upstream variants.

3.3. Identification of mutated genes

Mutated genes within the KEGG CRC gene panel for each patient were identified. On average, the patients exhibited eight mutated genes (range, 2–21 genes). The frequencies of mutations in each gene, specifically in exons and splicing sites, are presented in Table 4 and Figure 2.
From this analysis, it was observed that certain groups of patients had a higher mutation frequency in specific genes (Table 5). In brief, males had a significantly higher frequency of mutations in the JUN and MAPK3 genes than females (p=0.05, p=0.05, respectively). In terms of age groups, patients below 70 years of age had a higher frequency of mutations in TGFBR1 than those ≥70 years of age (p=0.012). Similarly, patients between 51-70 years old had more frequent mutations in BAD (p<0.001), RAC (p=0.016), AKT, AKT2, AKT3, APC, APPL1, AXIN1, AXIN2, BIRC5, DCC, GSK3B, KRAS, MAPK1, MAPK8, MAPK9, MAPK10, MLH1, MSH6, PΙK3CA, PIK3R1, PIK3R2, PIK3R5, RAF1, RALGDS, SMAD2, SMAD3, TCF7L2, TGFB1 and TGFB2 genes ( p=0.037). Moreover, mutation rates in ARAF, MAPK10, CASP, TCF7, and TGFΒ3 genes were significantly higher in patients relapsed after adjuvant treatment (p=0.027, p=0.044, p=0.003, and p=0.037, respectively).
Subsequently, it was demonstrated that patients with tumors located in the transverse colon, homozygous for mutated VDR alleles (TaqI, ApaI, FokI, BsmI) and homozygous for mutated TLR9 alleles (T1237C and T1486C), had mutations in genes that are mainly involved in the apoptosis, PIK3-AKT, Wnt and MAPK signaling pathways (Table 6).

3.4. Clinical outcome based on molecular profile and patients’ characteristics

Regarding disease-free survival (DFS), it was demonstrated that patients with ARAF mutations had a significantly shorter DFS (74 months, 95% CI:29.3 – 154.9 months) compared to those with wild-type ARAF mutations (133 months, 95% CI:101 – 138 months, p=0.017) (Figure 3A). Similarly, patients with MAPK10 mutations exhibit a significantly shorter DFS compared to those wild-type (12.5 months, 95% CI:0.0 – 29 months vs 108 months, 95% CI:101 – 114 months; p<0.001) (Figure 3B).
Similarly, it was demonstrated that patients with right-sided tumors experienced a significantly shorter overall survival (OS) compared to patients with left-sided tumors (92.1 months, 95% CI:82.5 – 122 months vs 111.3 months, 95% CI:104 – 135 months; p=0.011) (Figure 4A). Moreover, relapsed patients demonstrated a significantly shorter OS compared to those non-relapsed (81.1 months, 95% CI:70.4 – 118 months vs 104.3 months, 95% CI:93.9 – 130 months; p=0.008) (Figure 4Β). Moreover, patients with AKT1, APC2, ARAF, BAD, MAPK10, RAC3, RHOA, TGFB2 and TGFB3 mutations exhibited a significantly shorter OS compared to wild-type patients (68.9 vs 107.9 months, p=0.03; 89.7 vs 109.4 months, p=0.001; 43.3 vs 109.4 months, p<0.001; 34 vs 107.7 months, p=0.011; 19.3 vs 108.6, p>0.001; 55.6 vs 109, p<0.001; 45.2 vs 108.5 months, p=0.004; 69.5 vs 109.7 months, p=0.032 και 30.5 vs 108.1 months p<0.001, respectively) (Figures 4C-K). Finally, patients with MSH6 gene mutations had a significantly longer OS compared to those wild-type (120.7 months, 95% CI:111.3 – 130.1 months vs 100.9 months, 95% CI:93.7 – 108.1 108.1 months; p=0.008) (Figure 4L).

3.5. Univariate and multivariate Cox-regression analysis

Univariate analysis revealed that tumor localization in the right colon, and ARAF and MAPK10 mutations were associated with reduced DFS (Table 7). Multivariate analysis confirmed that tumor localization and ARAF and MAPK10 mutations were independent predictive factors of reduced DFS (HR=2.1; 95% CI:1.1-4.0; p=0.019; HR=3.9 ; 95% CI:1.2-13.1; p=0.027; HR=49; 95% CI:9.8-244.1; p<0.001 (Table 7).
Similarly, right-sided tumors and AKT1, APC2, ARAF, BAD, MAPK10, RAC3, RHOA, TGFB2, and TGFB3 were associated with an increased risk of shorter OS. In contrast, MSH6 mutations were demonstrated to be a good prognostic factor, as they were associated with a reduced risk for shorter OS. Multivariate analysis revealed that right-sided tumors and RAC3 and RHOA gene mutations emerged as independent predictors of reduced OS (HR=2.2; 95% CI:1.0-4.5; p=0.043; HR=3.5; 95% CI:1.1-10.7; p=0.029 and HR=9.5; 95% CI:1.9-47.7; p=0.006 (Table 7).

4. Discussion

Despite the potential benefits of presymptomatic screening and available treatments, CRC continues to be a significant public health concern[16]. Understanding the processes involved in CRC development and progression can help to identify new targets for treatment. Structural and functional changes in the DNA can offer vital insights into patient management[17,18]. As the normal colonic epithelium transforms into cancerous tissue, various mutations occur, leading to adenoma formation[19,20,21,22,23,24,25,26]. Extensive cancer cell proliferation through the RAS-RAF-MEK-ERK signaling pathway drives carcinogenesis, tumor invasion, and metastasis[27]. Moreover, the immune responses to cancer cells differ among patients with mutations[28,29,30,31,32,33,34].
The objective of this study was to analyze genetic changes in surgical samples from patients with stage III CRC. The study included 237 patients, and the WES and KEGG gene panel for CRC revealed 59 mutated genes belonging to 11 distinct signaling pathways. Of these, mutations in APC2, BRAF, MAPK10, MLH1, MSH6, RHOA, TGFβ, and TGFβ2 have been linked to a significant impact on patient survival. APC, TP53, KRAS, and MSH3 were the most commonly observed mutations in this study.
APC encodes an anti-tumor protein that competes with the Wnt signaling pathway and is involved in cell migration, adhesion, and apoptosis. APC mutations are responsible for familial adenomatous polyposis (FAP), an autosomal dominant precancerous disease that typically leads to malignancy. APC mutations are commonly observed in CRC cases[35]. Similarly, APC2 mutations, which are directly associated with APC’s tumor-suppressive function[36], have been linked to worse prognosis in CRC patients[37,38]. This study confirms that APC2 mutations in patients with stage III CRC are associated with lower overall survival but do not represent an independent prognostic factor. TP53 encodes an anti-tumor protein that regulates the expression of target genes, leading to cell cycle arrest, apoptosis, senescence, DNA repair, or metabolic changes. Similar to APC, TP53 are frequently observed in CRC cases[35]. Furthermore, mutations in the APC2 gene, which is directly linked to APC’s tumor-suppressive function[36], are also associated with worse prognosis in CRC patients[37,39]. This study confirms that while APC2 mutations in stage III CRC patients are linked to lower overall survival, they do not represent an independent prognostic factor. Various human cancers, including approximately 60% of CRC, are associated with mutations in the TP53 gene[40,41]. Prior studies have shown that mutations in TP53 resulting in the loss of its transcriptional activity, can lead to uncontrolled cellular proliferation in multiple organs, including the colon[42]. Similarly, KRAS mutations are the primary indicators of gastrointestinal cancers and are found in approximately 40% of patients with CRC (stage II-IV)[43]. They serve as negative prognostic factors for carcinogenesis and anti-EGFR therapy[44] because intracellular signal interruption leads to uncontrolled cellular proliferation and cancer. MSH3 mutations have been mainly linked to endometrial cancer, but there are reports of its relationship with inflammatory processes, such as ulcerative colitis and Crohn’s disease, which considerably increase the likelihood of CRC development[45,46]. MSH3-associated CRC seems to follow the classic APC pathway, as patients with adenomas and CRC carrying APC mutations showed MSH3 deficiency[47], as confirmed in this study. In addition to the common mutations detected in the patient group, mutations in AKT1, ARAF, BAD, MAPK10, RAC3, RHOA, TGFB2, and TGFB3 were associated with worse prognosis in this study. Furthermore, mutations in ARAF and MAPK10 were identified as independent prognostic factors for DFS, whereas mutations in RAC3 and RHOA were identified as independent prognostic factors for decreased OS.
This study sheds light on the association between mutations in genes involved in signaling pathways such as PI3K-Akt, MAPK, apoptosis, and CRC. To the best of our knowledge, this is the first report of its kind in the literature. The reactivation of embryonic self-renewal pathways, such as Hedgehog, Notch, and TGFβ/Stat3, is characteristic of most tumors, including CRC. The Wnt pathway is also essential in most CRC. Targeting embryonic pathways directly is likely to be more effective against stem and differentiated cancer cells [48,49,50]. Tumors that are addicted to increased regulated activity of the embryonic pathway, in combination with high tumor heterogeneity, may be more vulnerable to such therapies [51,52,53]. Patients with VDR polymorphisms had concurrent mutations in genes involved in cell cycle, apoptosis, PI3K-Akt, WNT, MAPK, ErbB, MSI, and RAS. Similarly, TLR9 polymorphisms were associated with mutations in genes involved in apoptotic signaling pathways, PI3K-Akt, and Wnt. Previous studies from our group have demonstrated that higher detection of TLR and VDR polymorphisms in CRC patients, especially advanced-stage patients, highlights the role of these polymorphisms in carcinogenesis, disease progression, and ultimately, patient survival[9,10,54]. Regarding DFS, tumors in the sigmoid or right colon and mutations in the ARAF and/or MAPK10 genes were associated with shorter DFS, a fact that has been confirmed in previous studies[55,56,57]. To our knowledge, this is the first study to highlight the role of ARAF and MAPK10 mutations as independent prognostic factors for decreased DFS.
The observation of statistically lower OS in patients with right colon tumors and gene mutations has been confirmed in the literature. Borakati et al. conducted A retrospective study and found that tumors in the right colon were independent prognostic factors for reduced OS after hepatic metastasectomy, regardless of the higher rates of liver metastases and larger metastases in the left colon[58]. Patients with mutations in genes, such as AKT1, APC2, ARAF, BAD, MAPK10, RAC3, RHOA, TGFB2, and TGFB3 had significantly reduced OS, as reported in other studies that also linked APC2, RHOA, and TGFB mutations to worse prognosis[37,38,59]. Conversely, studies have shown that mutations in MSH6 are associated with a lower risk of developing CRC, and patients with such mutations have a milder clinical presentation[60,61]. In the present study, patients with MSH6 mutations had a significantly longer OS, confirming MSH6 mutations are good prognostic factors. Concurrent mutations (co-mutations) are a significant factor that have been minimally investigated in CRC. Studies in patients with non-small cell lung cancer have shown distinct biological behavior and prognosis in KRAS/LKB1, KRAS/TP53, or KRAS/p16 mutated tumors[62]. Additionally, our group has previously reported the importance of evaluating the loss of LKB1 through immunohistochemistry in early stage CRC, particularly in BRAFV600E mutated tumors[63]. In the present study, several concurrent mutations were detected in patients, but no correlation was found with clinical/pathological characteristics or patient prognosis.

5. Conclusions

In conclusion, molecular characterization of cancer cells can enhance our understanding of the biological progression of this disease[64,65]. The findings of this study suggest that mutations are promising prognostic biomarkers. As personalized medicine has become the primary mode of therapy, knowledge of the precise mutation status of patients with CRC can lead to better therapeutic choices. However, further research is necessary with a larger patient cohort and international collaborations to confirm the correlation between patients’ molecular profiles, clinicopathological and epidemiological characteristics, and outcomes. Such research is expected to contribute to more precise clinical decision-making, personalized and improved care, and reduced toxicity of treatment, costs to patients, and burden on health systems.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org, Table S1: Raw patient data.

Author Contributions

Conceptualization, I.M. and J.S.; methodology, I.M., E.P., K.V., M.S. and M.T.; software, I.M., E.P., P.T. and I.I.; validation, I.M., P.T. and M.T.; formal analysis, I.M.; data curation, I.M, D.M., J.T., N.G., M.T. and J.S..; writing—original draft preparation, I.M.; writing—review and editing, I.M., P.T., I.L. and J.S.; supervision, I.M., P.T. and J.S.; funding acquisition, I.M. and J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was partially funded by the Hellenic Society of Medical Oncology (HeSMO) and the Gastrointestinal Cancer Study Group (GIC-SG).

Institutional Review Board Statement

This study was approved by the Ethics Committee/Institutional Review Board of the University Hospital of Heraklion (Number 7302/19-8-2009). All procedures were performed in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All patients signed a written informed consent form for participation.

Data Availability Statement

All relevant data are within the paper and its Supporting Information files. Supplementary Table S1: Raw patient data

Acknowledgement

The authors would like to thank NATERA Inc. for providing whole exome sequencing analysis and sharing the raw data for further bioinformatic analysis.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Overview of the next generation sequencing (NGS) analysis pipeline.
Figure 1. Overview of the next generation sequencing (NGS) analysis pipeline.
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Figure 2. Frequency of mutations in each gene located in exons and splicing sites.
Figure 2. Frequency of mutations in each gene located in exons and splicing sites.
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Figure 3. Kaplan Meier curve for disease-free survival (DFS) according to (A) ARAF and (B) MAPK10 mutations.
Figure 3. Kaplan Meier curve for disease-free survival (DFS) according to (A) ARAF and (B) MAPK10 mutations.
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Figure 4. Kaplan Meier curve for overall survival (OS) according to (A) tumor sidedness, (B) relapse status, (C–L) AKT1, APC2, ARAF, BAD, MAPK10, RAC3, RHOA, TGFB2, TGFB3 and MSH6 mutations.
Figure 4. Kaplan Meier curve for overall survival (OS) according to (A) tumor sidedness, (B) relapse status, (C–L) AKT1, APC2, ARAF, BAD, MAPK10, RAC3, RHOA, TGFB2, TGFB3 and MSH6 mutations.
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Table 1. Gene panel for colorectal cancer (CRC) based on Kyoto Encyclopedia of Genes and Genomes (KEGG).
Table 1. Gene panel for colorectal cancer (CRC) based on Kyoto Encyclopedia of Genes and Genomes (KEGG).
Gene Chromosome Location based on GRCh37.p13 assembly Source[https://www.ensembl.org/index.html]
Start End
AKT1 14 105235686 105262085 Ensembl:ENSG00000142208
AKT2 19 40736224 40791252 Ensembl:ENSG00000105221
AKT3 1 243651535 244014381 Ensembl:ENSG00000117020
APC 5 112043195 112181936 Ensembl:ENSG00000134982
APC2 19 1450120 1473243 Ensembl:ENSG00000115266
APPL1 3 57261757 57307499 Ensembl:ENSG00000157500
ARAF X 47420604 47431307 Ensembl:ENSG00000078061
AXIN1 16 337440 402723 Ensembl:ENSG00000103126
AXIN2 17 63524681 63557766 Ensembl:ENSG00000168646
BAD 11 64037300 64052176 Ensembl:ENSG00000002330
BAX 19 49458132 49465055 Ensembl:ENSG00000087088
BCL2 18 60790579 60987002 Ensembl:ENSG00000171791
BIRC5 17 76210334 76221716 Ensembl:ENSG00000089685
BRAF 7 140413128 140624729 Ensembl:ENSG00000157764
CASP3 4 185548850 185570601 Ensembl:ENSG00000164305
CASP9 1 15817896 15851285 Ensembl:ENSG00000132906
CCND1 11 69455924 69469242 Ensembl:ENSG00000110092
CTNNB1 3 41240996 41281934 Ensembl:ENSG00000168036
CYCS 7 25158275 25164879 Ensembl:ENSG00000172115
DCC 18 49866567 51062269 Ensembl:ENSG00000187323
FOS 14 75745531 75748933 Ensembl:ENSG00000170345
GSK3B 3 119540168 119813294 Ensembl:ENSG00000082701
JUN 1 59246463 59249719 Ensembl:ENSG00000177606
KRAS 12 25358180 25403863 Ensembl:ENSG00000133703
LEF1 4 108968704 109090088 Ensembl:ENSG00000138795
MAP2K1 15 66679250 66783882 Ensembl:ENSG00000169032
MAPK1 22 22113946 22221970 Ensembl:ENSG00000100030
MAPK10 4 86931558 87374348 Ensembl:ENSG00000109339
MAPK3 16 30125426 30134541 Ensembl:ENSG00000102882
MAPK8 10 49514720 49647403 Ensembl:ENSG00000107643
MAPK9 5 179660143 179719083 Ensembl:ENSG00000050748
MLH1 3 3703500 37092337 Ensembl:ENSG00000076242
MSH2 2 47630206 47710367 Ensembl:ENSG00000095002
MSH3 5 79950471 80172634 Ensembl:ENSG00000113318
MSH6 2 48010284 48034092 Ensembl:ENSG00000116062
MYC 8 128747680 128755197 Ensembl:ENSG00000136997
PIK3CA 3 178866145 178957881 Ensembl:ENSG00000121879
PIK3CB 3 138371540 138553770 Ensembl:ENSG00000051382
PIK3CD 1 9711789 9789172 Ensembl:ENSG00000171608
PIK3CG 7 106505727 106549425 Ensembl:ENSG00000105851
PIK3R1 5 67511584 67597649 Ensembl:ENSG00000145675
PIK3R2 19 18263973 18281342 Ensembl:ENSG00000105647
PIK3R3 1 46505812 46640573 Ensembl:ENSG00000117461
PIK3R5 17 8782233 8869024 Ensembl:ENSG00000141506
RAC1 7 6414158 6443598 Ensembl:ENSG00000136238
RAC2 22 37621310 37640309 Ensembl:ENSG00000128340
RAC3 17 79989554 79992080 Ensembl:ENSG00000169750
RAF1 3 12625100 12705616 Ensembl:ENSG00000132155
RALGDS 9 135973109 136024597 Ensembl:ENSG00000160271
RHOA 3 49396578 49449409 Ensembl:ENSG00000067560
SMAD2 18 45335328 45457243 Ensembl:ENSG00000175387
SMAD3 15 67357940 67487507 Ensembl:ENSG00000166949
SMAD4 18 48556583 48611412 Ensembl:ENSG00000141646
TCF7 5 133450372 133483901 Ensembl:ENSG00000081059
TCF7L1 2 85360515 85537510 Ensembl:ENSG00000152284
TCF7L2 10 114710006 114927437 Ensembl:ENSG00000148737
TGFB1 19 41836228 41859827 Ensembl:ENSG00000105329
TGFB2 1 218518678 218617961 Ensembl:ENSG00000092969
TGFB3 14 76424440 76449354 Ensembl:ENSG00000119699
TGFBR1 9 101867395 101916474 Ensembl:ENSG00000106799
TGFBR2 3 30647994 30735634 Ensembl:ENSG00000163513
TP53 17 7571739 7590808 Ensembl:ENSG00000141510
Table 2. Signaling pathways and the associated genes for colorectal cancer (CRC) based on Kyoto Encyclopedia of Genes and Genomes (KEGG).
Table 2. Signaling pathways and the associated genes for colorectal cancer (CRC) based on Kyoto Encyclopedia of Genes and Genomes (KEGG).
Pathway KEGG CRC genes References [https://www.genome.jp]
Cell cycle CCND1, GSK3B, MYC, SMAD2, SMAD3, SMAD4, TGFB1, TGFB2, TGFB3, TP53 https://www.genome.jp/pathway/hsa04110
p53 signaling pathway BAX, BCL2, CASP3, CASP9, CCND1, CYCS, TP53 https://www.genome.jp/pathway/hsa04115
Apoptosis AKT1, AKT2, AKT3, BAD, BAX, BCL2, BIRC5, CASP3, CASP9, CYCS, FOS, JUN, KRAS, MAP2K1, MAPK1, MAPK10, MAPK3, MAPK8, MAPK9, PIK3CA, PIK3CB, PIK3CD, PIK3R1, PIK3R2, PIK3R3, RAF1, TP53, DCC, APPL1 https://www.genome.jp/pathway/hsa04210
mTOR signaling pathway BRAF, GSK3B, KRAS, MAP2K1, MAPK1, PIK3CA, PIK3CB, PIK3CD, PIK3R1, PIK3R2, PIK3R3, RAF1, RHOA https://www.genome.jp/pathway/hsa04150
PI3K-Akt signaling pathway AKT1, AKT2, AKT3, BAD, BCL2, CASP9, GSK3B, KRAS, MAP2K1, MAPK1, MAPK3, MYC, PIK3CA, PIK3CB, PIK3CD, PIK3CG, PIK3R1, PIK3R2, PIK3R3, PIK3R5, RAC1, RAF1, TP53 https://www.genome.jp/pathway/hsa04151
Wnt signaling pathway APC, APC2, AXIN1, AXIN2, CCND1,CTNNB1, GSK3B, JUN, LEF1, MAPK10, MAPK8, MAPK9, MYC, RAC1, RAC2, RAC3, RHOA, SMAD3, SMAD4, TCF7, TCF7L1, TCF7L2, TP53 https://www.genome.jp/pathway/hsa04310
TGF-beta signaling pathway MAPK1, MAPK3, MYC, RHOA, SMAD2, SMAD3, SMAD4, TGFB1, TGFB2, TGFB3, TGFBR1, TGFBR2, https://www.genome.jp/pathway/hsa04350
MAPK signaling pathway AKT1, AKT2, AKT3, ARAF, BRAF, CASP3, FOS, JUN, KRAS, LEF1, MAP2K1, MAPK1, MAPK10, MAPK3, MAPK8, MAPK9, MYC, RAC1, RAC2, RAC3, RAF1, TGFB1, TGFB2, TGFB3, TGFBR1, TGFBR2, TP53 https://www.genome.jp/pathway/hsa04010
ErbB signaling pathway AKT1, AKT2, AKT3, ARAF, BAD, BRAF, GSK3B, JUN, KRAS, MAP2K1, MAPK1, MAPK10, MAPK3, MAPK8, MAPK9, MYC, PIK3CA, PIK3CB, PIK3CD, PIK3R1, PIK3R2, PIK3R3, RAF https://www.genome.jp/pathway/hsa04012
MSI pathway APC, AXIN1, AXIN2, BAD, BAX, BCL2, GSK3B, MLH1, MSH2, MSH3, MSH6, TGFBR2 https://www.genome.jp/dbget-bin/www_bget?path:map05210
Ras signaling pathway AKT1, AKT2, AKT3, BAD, KRAS, MAP2K1, MAPK1, MAPK10, MAPK3, MAPK8, MAPK9, PIK3CA, PIK3CB, PIK3CD, PIK3R1, PIK3R2, PIK3R3, RAC1, RAC2, RAC3, RAF1, RALGDS, RHOA https://www.genome.jp/pathway/hsa04014
Table 3. Patients characteristics.
Table 3. Patients characteristics.
Characteristics Number of patients (Ν=237) %
Median age (range) 64 (18–84)
<70 159 67.1
≥70 78 32.9
Gender
Males 151 63.7
Females 86 36.3
Tumor location
Cecum 38 16.0
Ascending 42 17.7
Transverse 22 9.3
Descending 24 10.1
Sigmoid 111 46.8
Sidedness
Left 158 66.7
Right 79 33.3
Performance status
0-1 236 99.6
>2 1 .4
Regimen
Folfox 105 44.3
Capox 132 55.7
Treatment duration
3 months 116 48.9
6 months 121 51.1
Table 4. Frequency of mutated patients in each gene.
Table 4. Frequency of mutated patients in each gene.
Gene Mutant patients
AKT1 6
AKT2 3
AKT3 3
APC 181
APC2 38
APPL1 19
ARAF 8
AXIN1 11
AXIN2 61
BAD 2
BAX 14
BCL2 2
BIRC5 70
BRAF 15
CASP3 0
CASP9 62
CCND1 3
CTNNB1 7
CYCS 1
DCC 62
FOS 2
GSK3B 2
JUN 5
KRAS 80
LEF1 3
MAP2K1 2
MAPK1 4
MAPK10 3
MAPK3 0
MAPK8 4
MAPK9 3
MLH1 55
MSH2 6
MSH3 110
MSH6 51
MYC 11
PIK3CA 62
PIK3CB 8
PIK3CD 11
PIK3CG 48
PIK3R1 23
PIK3R2 70
PIK3R3 0
PIK3R5 9
RAC1 0
RAC2 0
RAC3 11
RAF1 1
RALGDS 42
RHOA 6
SMAD2 3
SMAD3 14
SMAD4 25
TCF7 35
TCF7L1 61
TCF7L2 39
TGFB1 74
TGFB2 11
TGFB3 2
TGFBR1 16
TGFBR2 25
TP53 148
Table 5. Frequency of mutations according to patients’ characteristics.
Table 5. Frequency of mutations according to patients’ characteristics.
Characteristics Gene No % (p value)
Gender
    Male vs Female JUN 43.4% vs 25.0% (0.05)
MAPK3 57.5% vs 31.1% (0.05)
Sideness
    Left vs Right MLH1 80% vs 20% (0.007)
MSH6 84.3% vs 15.7% (0.001)
TCF7L1 77% vs 33% (0.019)
Location
    Colon vs Sigmoid DCC 90.3% vs 9.7% (0.04)
KRAS 88.8% vs 11.2% (0.048)
TGFBR2 96% vs 4% (0.043)
Age
    <70 vs ≥70 TGFBR1 57.5% vs 33% (0.012)
    51-70 vs ≥70 vs <50 BAD 56.1% vs 30.7% vs 12.7% (<0.001)
    51-70 vs ≥70 vs <50 RAC 56.1% vs 30.7% vs 12.7% (0.016)
    51-70 vs ≥70 vs <50 AKT, AKT2, AKT3, APC, APPL1, AXIN1, AXIN2, BIRC5, DCC, GSK3B, KRAS, MAPK1, MAPK8, MAPK9, MAPK10, MLH1, MSH6, PIK3CA, PIK3R1, PIK3R2, PIK3R3, PIK3R5, RAF1, RALGD5, SMAD2, SMAD3, TCF7L2, TGFB1, TGFB2 56.1% vs 30.7% vs 12.7% (0.037)
Relapse post Adj Chemotherapy
Yes vs No ARAF, MAPK10 71.7% vs 20.5% (0.027)
CASP3 75.5% vs 22.6% (0.044)
TCF7 75% vs 21.2% (0.003)
TGFΒ3 74.5% vs 22.2% (0.037)
Table 6. Correlation of mutated signaling pathways and patients characteristics.
Table 6. Correlation of mutated signaling pathways and patients characteristics.
Cell cycle Apoptosis PI3K-Akt Wnt MAPK ErbB MSI RAS
Transverse Colon X X X X
TaqI Homozygous X X X X X X X
ApaI Homozygous X X X X
FokI Homozygous X X X
BsmI Homozygous X X X
TLR9 -T1237C Homozygous X X X
TLR9–T1486C Homozygous X X X
Table 7. Univariate and multivariate Cox regression analysis.
Table 7. Univariate and multivariate Cox regression analysis.
Univariate Multivariate
DFS OS DFS OS
Feature HR (95%CI) p-value HR (95%CI) p-value HR (95%CI) p-value HR (95%CI) p-value
Sidedness (Right vs Left) 1.9 (1.2–3.2) 0.012 2.1 (1.0–4.0) 0.043 2.1 (1.1-4.0) 0.019 2.2 (1.0-4.5) 0.043
AKT1 (Mutant vs Wild type) 2.9 (1.1-8.2) 0.039
APC2 (Mutant vs Wild type) 2.5 (1.4-4.5) 0.002
ARAF (Mutant vs Wild type) 3.8 (1.2-12.1) 0.027 5.0 (2.0-12.7) 0.001 3.9 (1.2-13.1) 0.027
BAD (Mutant vs Wild type) 8.6 (1.1-64.4) 0.036
MAPK10 (Mutant vs Wild type) 43.0 (9.1-203.7) <0.001 15.1 (4.6-50.3) <0.001 49.0 (9.8-244.1) <0.001
MSH6 (Wild type vs Mutant) 2.3 (1.0-5.1) 0.041
RAC3 (Mutant vs Wild type) 4.7 (1.8-11.9) 0.001 3.5 (1.1-10.7) 0.029
RHOA (Mutant vs Wild type) 4.8 (1.5-15.6) 0.009 9.5 (1.9-47.7) 0.006
TGFB2 (Mutant vs Wild type) 2.6 (1.1-6.7) 0.040
TGFB3 (Mutant vs Wild type) 9.0 (2.1-37.8) 0.003
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