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The tumor suppressor gene adenomatous polyposis coli ( APC ) is the initiating mutation in approximately 80% of all colorectal cancers (CRC), underscoring the importance of aberrant regulation of intracellular WNT signaling in CRC development. Recent studies have found that early-onset CRC exhibits an increased proportion of tumors lacking an APC mutation. We set out to identify mechanisms underlying APC mutation-negative ( APC mut– ) CRCs. We analyzed data from The Cancer Genome Atlas to compare clinical phenotypes, somatic mutations, copy number variations, gene fusions, RNA expression, and DNA methylation profiles between APC mut– and APC mutation-positive ( APC mut + ) microsatellite stable CRCs. Transcriptionally, APC mut– CRCs clustered into two approximately equal groups. Cluster One was associated with enhanced mitochondrial activation. Cluster Two was strikingly associated with genetic inactivation or decreased RNA expression of the WNT antagonist RNF43 , increased expression of the WNT agonist RSPO3 , activating mutation of BRAF , or increased methylation and decreased expression of AXIN2 . APC mut– CRCs exhibited evidence of increased immune cell infiltration, with significant correlation between M2 macrophages and RSPO3 . APC mut– CRCs comprise two groups of tumors characterized by enhanced mitochondrial activation or increased sensitivity to extracellular WNT, suggesting that they could be respectively susceptible to inhibition of these pathways.

Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US. Currently, the United States Preventative Services Task Force (USPSTF) recommends anyone ages 50-75 and/or with a family history to be screened for CRC. To improve screening specificity and sensitivity, we have built an artificial neural network (ANN) trained on 12 to 14 categories of personal health data from the National Health Interview Survey (NHIS). Years 1997-2016 of the NHIS contain 583,770 respondents who had never received a diagnosis of any cancer and 1409 who had received a diagnosis of CRC within 4 years of taking the survey. The trained ANN has sensitivity of 0.57 ± 0.03, specificity of 0.89 ± 0.02, positive predictive value of 0.0075 ± 0.0003, negative predictive value of 0.999 ± 0.001, and concordance of 0.80 ± 0.05 per the guidelines of Transparent Reporting of Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) level 2a, comparable to current risk-scoring methods. To demonstrate clinical applicability, both USPSTF guidelines and the trained ANN are used to stratify respondents to the 2017 NHIS into low-, medium- and high-risk categories (TRIPOD levels 4 and 2b, respectively). The number of CRC respondents misclassified as low risk is decreased from 35% by screening guidelines to 5% by ANN (in 60 cases). The number of non-CRC respondents misclassified as high risk is decreased from 53% by screening guidelines to 6% by ANN (in 25,457 cases). Our results demonstrate a robustly-tested method of stratifying CRC risk that is non-invasive, cost-effective, and easy to implement publicly.

Improvement of patient quality of life is the ultimate goal of biomedical research, particularly when dealing with complex, chronic and debilitating conditions such as inflammatory bowel disease (IBD). This is largely dependent on receiving an accurate and rapid diagnose, an effective treatment and in the prediction and prevention of side effects and complications. The low sensitivity and specificity of current markers burden their general use in the clinical practice. New biomarkers with accurate predictive ability are needed to achieve a personalized approach that take the inter-individual differences into consideration. We performed a high throughput approach using microarray gene expression profiling of colon pinch biopsies from IBD patients to identify predictive transcriptional signatures associated with intestinal inflammation, differential diagnosis (Crohn's disease or ulcerative colitis), response to glucocorticoids (resistance and dependence) or prognosis (need for surgery). Class prediction was performed with self-validating Prophet software package. Transcriptional profiling divided patients in two subgroups that associated with degree of inflammation. Class predictors were identified with predictive accuracy ranging from 67 to 100%. The expression accuracy was confirmed by real time-PCR quantification. Functional analysis of the predictor genes showed that they play a role in immune responses to bacteria (PTN, OLFM4 and LILRA2), autophagy and endocytocis processes (ATG16L1, DNAJC6, VPS26B, RABGEF1, ITSN1 and TMEM127) and glucocorticoid receptor degradation (STS and MMD2). We conclude that using analytical algorithms for class prediction discovery can be useful to uncover gene expression profiles and identify classifier genes with potential stratification utility of IBD patients, a major step towards personalized therapy.

The aim of this study was to validate a molecular classification of colorectal cancer (CRC) based on microsatellite instability (MSI), CpG island methylator phenotype (CIMP) status, BRAF, and KRAS and investigate each subtype's response to chemotherapy. This retrospective observational study included a population-based cohort of 878 CRC patients. We classified tumours into five different subtypes based on BRAF and KRAS mutation, CIMP status, and MSI. Patients with advanced stage II (T4N0M0) and stage III tumours received 5-fluoruracil (5-FU)-based chemotherapy or no adjuvant treatment based on clinical criteria. The main outcome was disease-free survival (DFS). Patients with the combination of microsatellite stable (MSS) tumours, BRAF mutation and CIMP positive exhibited the worst prognosis in univariate (log rank P<0.0001) and multivariate analyses (hazard ratio 1.75, 95% CI 1.05-2.93, P = 0.03) after adjusting for age, sex, chemotherapy, and TNM stage. Treatment with 5-FU-based regimens improved prognosis in patients with the combination of MSS tumours, KRAS mutation and CIMP negative (log rank P = 0.003) as well as in patients with MSS tumours plus BRAF and KRAS wild-type and CIMP negative (log-rank P<0.001). After adjusting for age, sex, and TNM stage in the multivariate analysis, only patients with the latter molecular combination had independently improved prognosis after adjuvant chemotherapy (hazard ratio 2.06, 95% CI 1.24-3.44, P = 0.005). We confirmed the prognostic value of stratifying CRC according to molecular subtypes using MSI, CIMP status, and somatic KRAS and BRAF mutation. Patients with traditional chromosomally unstable tumours obtained the best benefit from adjuvant 5-FU-based chemotherapy.

Early-onset colorectal cancer (CRC) represents a clinically distinct form of CRC that is often associated with a poor prognosis. Methylation levels of genomic repeats such as LINE-1 elements have been recognized as independent factors for increased cancer-related mortality. The methylation status of LINE-1 elements in early-onset CRC has not been analyzed previously. We analyzed 343 CRC tissues and 32 normal colonic mucosa samples, including 2 independent cohorts of CRC diagnosed ≤ 50 years old (n=188), a group of sporadic CRC >50 years (MSS n=89; MSI n=46), and a group of Lynch syndrome CRCs (n=20). Tumor mismatch repair protein expression, microsatellite instability status, LINE-1 and MLH1 methylation, somatic BRAF V600E mutation, and germline MUTYH mutations were evaluated. Mean LINE-1 methylation levels (± SD) in the five study groups were early-onset CRC, 56.6% (8.6); sporadic MSI, 67.1% (5.5); sporadic MSS, 65.1% (6.3); Lynch syndrome, 66.3% (4.5) and normal mucosa, 76.5% (1.5). Early-onset CRC had significantly lower LINE-1 methylation than any other group (p<0.0001). Compared to patients with <65% LINE-1 methylation in tumors, those with ≥ 65% LINE-1 methylation had significantly better overall survival (p=0.026, log rank test). LINE-1 hypomethylation constitutes a potentially important feature of early-onset CRC, and suggests a distinct molecular subtype. Further studies are needed to assess the potential of LINE-1 methylation status as a prognostic biomarker for young people with CRC.

BackgroundMismatch repair (MMR) deficiency is the hallmark of tumours from Lynch syndrome (LS), sporadic MLH1 hypermethylated and Lynch-like syndrome (LLS), but there is a lack of understanding of the variability in their mutational profiles based on clinical phenotypes. The aim of this study was to perform a molecular characterisation to identify novel features that can impact tumour behaviour and clinical management.MethodsWe tested 105 MMR-deficient colorectal cancer tumours (25 LS, 35 LLS and 45 sporadic) for global exome microsatellite instability, cancer mutational signatures, mutational spectrum and neoepitope load.ResultsFifty-three percent of tumours showed high contribution of MMR-deficient mutational signatures, high level of global exome microsatellite instability, loss of MLH1/PMS2 protein expression and included sporadic tumours. Thirty-one percent of tumours showed weaker features of MMR deficiency, 62% lost MSH2/MSH6 expression and included 60% of LS and 44% of LLS tumours. Remarkably, 9% of all tumours lacked global exome microsatellite instability. Lastly, HLA-B07:02 could be triggering the neoantigen presentation in tumours that show the strongest contribution of MMR-deficient tumours.ConclusionsNext-generation sequencing approaches allow for a granular molecular characterisation of MMR-deficient tumours, which can be essential to properly diagnose and treat patients with these tumours in the setting of personalised medicine.

Colorectal cancer (CRC) is a leading cause of cancer death, and the incidence and mortality rates among young adults are rising. Although a subset of CRC cases presents with a family history, suggesting a hereditary component, the specific genetic underpinnings remain incompletely understood, particularly in early-onset CRC (EOCRC). This study aimed to discover novel risk genes for EOCRC using exome sequencing and gene-based rare variant burden testing. Our cohort consisted of 212 European-ancestry cases (174 diagnosed with CRC and 38 with significant polyps) from the South Australian Young Onset Colorectal Polyp and Cancer Study (SAYO) and 31,699 unaffected controls from the Simons Foundation Powering Autism Research for Knowledge (SPARK) cohort. After filtering for ancestry, relatedness, variant quality, and population allele frequency, we performed gene-set and individual-gene burden tests using predicted deleterious missense and loss-of-function variants. Statistical significance was assessed using permutation-corrected binomial testing. An independent validation was conducted in the UK Biobank. Loss-of-function variants in known CRC tumor suppressor genes were significantly enriched in SAYO cases. Gene-level analyses identified as a novel EOCRC susceptibility candidate ( value = 1.0 × 10 ), with supporting enrichment of deleterious missense and loss-of-function variants in distal colon cancer cases from the UK Biobank. Additional genes ( , , , , , and ) demonstrated borderline significance, implicating pathways related to kinetochore assembly, autophagy regulation, and immune signaling. Both predicted gain-of-function and loss-of-function variants contributed to the EOCRC risk, supporting heterogeneous mechanisms of CRC pathogenesis. This study identified novel candidate risk genes for EOCRC, underscoring the role of rare variants and expanding our understanding of the genetic architecture of CRC. Future studies should include functional validation and replication studies on other ancestries to confirm and extend these results.

Aberrant hypermethylation of cancer-related genes has emerged as a promising strategy for the development of diagnostic, prognostic and predictive biomarkers in human cancer, including colorectal cancer (CRC). The aim of this study was to perform a systematic and comprehensive analysis of a panel of CRC-specific genes as potential diagnostic, prognostic and predictive biomarkers in a large, population-based CRC cohort. Methylation status of the SEPT9, TWIST1, IGFBP3, GAS7, ALX4 and miR137 genes was studied by quantitative bisulfite pyrosequencing in a population-based cohort of 425 CRC patients. Methylation levels of all genes analyzed were significantly higher in tumor tissues compared to normal mucosa (p<0.0001); however, cancer-associated hypermethylation was most frequently observed for miR137 (86.7%) and IGFBP3 (83%) in CRC patients. Methylation analysis using the combination of these two genes demonstrated greatest accuracy for the identification of colonic tumors (sensitivity 95.5%; specificity 90.5%). Low levels of IGFBP3 promoter methylation emerged as an independent risk factor for predicting poor disease free survival in stage II and III CRC patients (HR = 0.49, 95% CI: 0.28-0.85, p = 0.01). Our results also suggest that stage II & III CRC patients with high levels of IGFBP3 methylation do not benefit from adjuvant 5FU-based chemotherapy. By analyzing a large, population-based CRC cohort, we demonstrate the potential clinical significance of miR137 and IGFBP3 hypermethylation as promising diagnostic biomarkers in CRC. Our data also revealed that IGFBP3 hypermethylation may serve as an independent prognostic and predictive biomarker in stage II and III CRC patients.

Elevated Microsatellite Alterations at Selected Tetranucleotide repeats (EMAST) is a genetic signature found in up to 60% of colorectal cancers (CRCs) that is caused by somatic dysfunction of the DNA mismatch repair (MMR) protein hMSH3. We have previously shown in vitro that recognition of 5-fluorouracil (5-FU) within DNA and subsequent cytotoxicity was most effective when both hMutSα (hMSH2-hMSH6 heterodimer) and hMutSβ (hMSH2-hMSH3 heterodimer) MMR complexes were present, compared to hMutSα > hMutSβ alone. We tested if patients with EMAST CRCs (hMutSβ defective) had diminished response to adjuvant 5-FU chemotherapy, paralleling in vitro findings. We analyzed 230 patients with stage II/III sporadic colorectal cancers for which we had 5-FU treatment and survival data. Archival DNA was analyzed for EMAST (>2 of 5 markers mutated among UT5037, D8S321, D9S242, D20S82, D20S85 tetranucleotide loci). Kaplan-Meier survival curves were generated and multivariate analysis was used to determine contribution to risk. We identified 102 (44%) EMAST cancers. Ninety-four patients (41%) received adjuvant 5-FU chemotherapy, and median follow-up for all patients was 51 months. Patients with EMAST CRCs demonstrated improved survival with adjuvant 5FU to the same extent as patients with non-EMAST CRCs (P<0.05). We observed no difference in survival between patients with stage II/III EMAST and non-EMAST cancers (P = 0.36). There is improved survival for stage II/III CRC patients after adjuvant 5-FU-based chemotherapy regardless of EMAST status. The loss of contribution of hMSH3 for 5-FU cytotoxicity may not adversely affect patient outcome, contrasting patients whose tumors completely lack DNA MMR function (MSI-H).

The prediction of microsatellite instability (MSI) using deep learning (DL) techniques could have significant benefits, including reducing cost and increasing MSI testing of colorectal cancer (CRC) patients. Nonetheless, batch effects or systematic biases are not well characterized in digital histology models and lead to overoptimistic estimates of model performance. Methods to not only palliate but to directly abrogate biases are needed. We present a multiple bias rejecting DL system based on adversarial networks for the prediction of MSI in CRC from tissue microarrays (TMAs), trained and validated in 1788 patients from EPICOLON and HGUA. The system consists of an end-to-end image preprocessing module that tile samples at multiple magnifications and a tissue classification module linked to the bias-rejecting MSI predictor. We detected three biases associated with the learned representations of a baseline model: the project of origin of samples, the patient’s spot and the TMA glass where each spot was placed. The system was trained to directly avoid learning the batch effects of those variables. The learned features from the bias-ablated model achieved maximum discriminative power with respect to the task and minimal statistical mean dependence with the biases. The impact of different magnifications, types of tissues and the model performance at tile vs patient level is analyzed. The AUC at tile level, and including all three selected tissues (tumor epithelium, mucin and lymphocytic regions) and 4 magnifications, was 0.87 ± 0.03 and increased to 0.9 ± 0.03 at patient level. To the best of our knowledge, this is the first work that incorporates a multiple bias ablation technique at the DL architecture in digital pathology, and the first using TMAs for the MSI prediction task.