The consensus molecular subtypes of colorectal cancer.
Justin GuinneyRodrigo DienstmannXin WangAurélien de ReynièsAndreas SchlickerCharlotte SonesonLaetitia MarisaPaul RoepmanGift NyamundandaPaolo AngelinoBrian M BotJeffrey S MorrisIris M SimonSarah GersterEvelyn FesslerFelipe De Sousa E MeloEdoardo MissiagliaHena RamayDavid BarrasKrisztian HomicskoDipen MaruGaniraju C ManyamBradley M BroomValerie BoigeBeatriz Perez-VillamilTed LaderasRamon SalazarJoe W GrayDouglas HanahanJosep TaberneroRené BernardsStephen H FriendPierre Laurent-PuigJan Paul MedemaAnguraj SadanandamLodewyk WesselsMauro DelorenziScott KopetzLouis VermeulenSabine TejparPublished in: Nature medicine (2015)
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.
Keyphrases
- transforming growth factor
- gene expression
- stem cells
- bone marrow
- big data
- clinical practice
- epithelial mesenchymal transition
- machine learning
- cell proliferation
- deep learning
- endothelial cells
- electronic health record
- emergency department
- single molecule
- metabolic syndrome
- cancer therapy
- physical activity
- type diabetes
- signaling pathway
- genetic diversity
- wound healing
- adverse drug
- drug induced
- skeletal muscle
- drug delivery