An international consortium of colorectal cancer researchers undertakes a large-scale data sharing project to achieve a consensus molecular classification of colorectal cancers. Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression–based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor–β activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC—with clear biological interpretability—and the basis for future clinical stratification and subtype-based targeted interventions.

Gene Ontology (GO) is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role. We present a new method for comparing sets of GO terms and for assessing the functional similarity of gene products. The method relies on two semantic similarity measures; simRel and funSim. One measure (simRel) is applied in the comparison of the biological processes found in different groups of organisms. The other measure (funSim) is used to find functionally related gene products within the same or between different genomes. Results indicate that the method, in addition to being in good agreement with established sequence similarity approaches, also provides a means for the identification of functionally related proteins independent of evolutionary relationships. The method is also applied to estimating functional similarity between all proteins in Saccharomyces cerevisiae and to visualizing the molecular function space of yeast in a map of the functional space. A similar approach is used to visualize the functional relationships between protein families. The approach enables the comparison of the underlying molecular biology of different taxonomic groups and provides a new comparative genomics tool identifying functionally related gene products independent of homology. The proposed map of the functional space provides a new global view on the functional relationships between gene products or protein families.

There are no effective therapies for the ~30% of human malignancies with mutant RAS oncogenes. Using a kinome-centered synthetic lethality screen, we find that suppression of the ERBB3 receptor tyrosine kinase sensitizes KRAS mutant lung and colon cancer cells to MEK inhibitors. We show that MEK inhibition results in MYC-dependent transcriptional upregulation of ERBB3, which is responsible for intrinsic drug resistance. Drugs targeting both EGFR and ERBB2, each capable of forming heterodimers with ERBB3, can reverse unresponsiveness to MEK inhibition by decreasing inhibitory phosphorylation of the proapoptotic proteins BAD and BIM. Moreover, ERBB3 protein level is a biomarker of response to combinatorial treatment. These data suggest a combination strategy for treating KRAS mutant colon and lung cancers and a way to identify the tumors that are most likely to benefit from such combinatorial treatment.

In most colorectal cancer (CRC) patients, outcome cannot be predicted because tumors with similar clinicopathological features can have differences in disease progression and treatment response. Therefore, a better understanding of the CRC biology is required to identify those patients who will benefit from chemotherapy and to find a more tailored therapy plan for other patients. Based on unsupervised classification of whole genome data from 188 stages I–IV CRC patients, a molecular classification was developed that consist of at least three major intrinsic subtypes (A‐, B‐ and C‐type). The subtypes were validated in 543 stages II and III patients and were associated with prognosis and benefit from chemotherapy. The heterogeneity of the intrinsic subtypes is largely based on three biological hallmarks of the tumor: epithelial‐to‐mesenchymal transition, deficiency in mismatch repair genes that result in high mutation frequency associated with microsatellite instability and cellular proliferation. A‐type tumors, observed in 22% of the patients, have the best prognosis, have frequent BRAF mutations and a deficient DNA mismatch repair system. C‐type patients (16%) have the worst outcome, a mesenchymal gene expression phenotype and show no benefit from adjuvant chemotherapy treatment. Both A‐type and B‐type tumors have a more proliferative and epithelial phenotype and B‐types benefit from adjuvant chemotherapy. B‐type tumors (62%) show a low overall mutation frequency consistent with the absence of DNA mismatch repair deficiency. Classification based on molecular subtypes made it possible to expand and improve CRC classification beyond standard molecular and immunohistochemical assessment and might help in the future to guide treatment in CRC patients.