Relative contribution of clinicopathological variables, genomic markers, transcriptomic subtyping and microenvironment features for outcome prediction in stage II/III colorectal cancer.

BACKGROUND: It remains unknown to what extent consensus molecular subtype (CMS) groups and immune-stromal infiltration patterns improve our ability to predict outcomes over tumor-node-metastasis (TNM) staging and microsatellite instability (MSI) status in early-stage colorectal cancer (CRC). PATIENTS AND METHODS: We carried out a comprehensive retrospective biomarker analysis of prognostic markers in adjuvant chemotherapy-untreated (N = 1656) and treated (N = 980), stage II (N = 1799) and III (N = 837) CRCs. We defined CMS scores and estimated CD8+ cytotoxic lymphocytes (CytoLym) and cancer-associated fibroblasts (CAF) infiltration scores from bulk tumor tissue transcriptomes (CMSclassifier and MCPcounter R packages); constructed a stratified multivariable Cox model for disease-free survival (DFS); and calculated the relative proportion of explained variation by each marker (clinicopathological [ClinPath], genomics [Gen: MSI, BRAF and KRAS mutations], CMS scores [CMS] and microenvironment cells [MicroCells: CytoLym+CAF]). RESULTS: In multivariable models, only ClinPath and MicroCells remained significant prognostic factors, with both CytoLym and CAF infiltration scores improving survival prediction beyond other markers. The explained variation for DFS models of ClinPath, MicroCells, Gen markers and CMS4 scores was 77%, 14%, 5.3% and 3.7%, respectively, in stage II; and 55.9%, 35.1%, 4.1% and 0.9%, respectively, in stage III. Patients whose tumors were CytoLym high/CAF low had better DFS than other strata [HR=0.71 (0.6-0.9); P = 0.004]. Microsatellite stable tumors had the strongest signal for improved outcomes with CytoLym high scores (interaction P = 0.04) and the poor prognosis linked to high CAF scores was limited to stage III disease (interaction P = 0.04). CONCLUSIONS: Our results confirm that tumor microenvironment infiltration patterns represent potent determinants of the risk for distant dissemination in early-stage CRC. Multivariable models suggest that the prognostic value of MSI and CMS groups is largely explained by CytoLym and CAF infiltration patterns.

Colorectal premalignancy is associated with consensus molecular subtypes 1 and 2.

Background: Gene expression-based profiling of colorectal cancer (CRC) can be used to identify four molecularly homogeneous consensus molecular subtype (CMS) groups with unique biologic features. However, its applicability to colorectal premalignant lesions remains unknown. Patients and methods: We assembled the largest transcriptomic premalignancy dataset by integrating different public and proprietary cohorts of adenomatous and serrated polyps from sporadic (N = 311) and hereditary (N = 78) patient populations and carried out a comprehensive analysis of carcinogenesis pathways using the CMS random forest (RF) classifier. Results: Overall, transcriptomic subtyping of sporadic and hereditary polyps revealed CMS2 and CMS1 subgroups as the predominant molecular subtypes in premalignancy. Pathway enrichment analysis showed that adenomatous polyps from sporadic or hereditary cases (including Lynch syndrome) displayed a CMS2-like phenotype with WNT and MYC activation, whereas hyperplastic and serrated polyps with CMS1-like phenotype harbored prominent immune activation. Rare adenomas with CMS4-like phenotype showed significant enrichment for stromal signatures along with transforming growth factor-ß activation. There was a strong association of CMS1-like polyps with serrated pathology, right-sided anatomic location and BRAF mutations. Conclusions: Based on our observations made in premalignancy, we propose a model of pathway activation associated with CMS classification in colorectal carcinogenesis. Specifically, while adenomatous polyps are largely CMS2, most hyperplastic and serrated polyps are CMS1 and may transition into other CMS groups during evolution into carcinomas. Our findings shed light on the transcriptional landscape of premalignant colonic polyps and may help guide the development of future biomarkers or preventive treatments for CRC.

Prediction of overall survival in stage II and III colon cancer beyond TNM system: a retrospective, pooled biomarker study.

Background: TNM staging alone does not accurately predict outcome in colon cancer (CC) patients who may be eligible for adjuvant chemotherapy. It is unknown to what extent the molecular markers microsatellite instability (MSI) and mutations in BRAF or KRAS improve prognostic estimation in multivariable models that include detailed clinicopathological annotation. Patients and methods: After imputation of missing at random data, a subset of patients accrued in phase 3 trials with adjuvant chemotherapy (n = 3016)-N0147 (NCT00079274) and PETACC3 (NCT00026273)-was aggregated to construct multivariable Cox models for 5-year overall survival that were subsequently validated internally in the remaining clinical trial samples (n = 1499), and also externally in different population cohorts of chemotherapy-treated (n = 949) or -untreated (n = 1080) CC patients, and an additional series without treatment annotation (n = 782). Results: TNM staging, MSI and BRAFV600E mutation status remained independent prognostic factors in multivariable models across clinical trials cohorts and observational studies. Concordance indices increased from 0.61-0.68 in the TNM alone model to 0.63-0.71 in models with added molecular markers, 0.65-0.73 with clinicopathological features and 0.66-0.74 with all covariates. In validation cohorts with complete annotation, the integrated time-dependent AUC rose from 0.64 for the TNM alone model to 0.67 for models that included clinicopathological features, with or without molecular markers. In patient cohorts that received adjuvant chemotherapy, the relative proportion of variance explained (R2) by TNM, clinicopathological features and molecular markers was on an average 65%, 25% and 10%, respectively. Conclusions: Incorporation of MSI, BRAFV600E and KRAS mutation status to overall survival models with TNM staging improves the ability to precisely prognosticate in stage II and III CC patients, but only modestly increases prediction accuracy in multivariable models that include clinicopathological features, particularly in chemotherapy-treated patients.

Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology.

BACKGROUND: Comparative genomic hybridization (CGH) arrays are increasingly used in personalized medicine programs to identify gene copy number aberrations (CNAs) that may be used to guide clinical decisions made during molecular tumor boards. However, analytical processes such as the centralization step may profoundly affect CGH array results and therefore may adversely affect outcomes in the precision medicine context. PATIENTS AND METHODS: The effect of three different centralization methods: median, maximum peak, alternative peak, were evaluated on three datasets: (i) the NCI60 cell lines panel, (ii) the Cancer Cell Line Encyclopedia (CCLE) panel, and (iii) the patients enrolled in prospective molecular screening trials (SAFIR-01 n = 283, MOSCATO-01 n = 309), and compared with karyotyping, drug sensitivity, and patient-drug matching, respectively. RESULTS: Using the NCI60 cell lines panel, the profiles generated by the alternative peak method were significantly closer to the cell karyotypes than those generated by the other centralization strategies (P < 0.05). Using the CCLE dataset, selected genes (ERBB2, EGFR) were better or equally correlated to the IC50 of their companion drug (lapatinib, erlotinib), when applying the alternative centralization. Finally, focusing on 24 actionable genes, we observed as many as 7.1% (SAFIR-01) and 6.8% (MOSCATO-01) of patients originally not oriented to a specific treatment, but who could have been proposed a treatment based on the alternative peak centralization method. CONCLUSION: The centralization method substantially affects the call detection of CGH profiles and may thus impact precision medicine approaches. Among the three methods described, the alternative peak method addresses limitations associated with existing approaches.