Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.
Sudhir B MallaRyan M ByrneMaxime W LafargeShania M CorryNatalie C FisherPetros K TsantoulisMegan L MillsRachel A RidgwayTamsin R M LannaganArafath K NajumudeenKathryn L GilroyRaheleh AmirkhahSarah L MaguireEoghan J MulhollandHayley L Belnoue-DavisElena GrassiMarco VivianiEmily RoganKeara L RedmondSvetlana SakhnevychAoife J McCooeyCourtney BullEmily HoeyNicoleta SineviciHolly HallBaharak AhmaderaghiEnric DomingoAndrew BlakeSusan D RichmanClaudio IsellaCrispin MillerAndrea BertottiLivio TrusolinoMaurice B LoughreyEmma M KerrSabine TejparTimothy S MaughanMark LawlerAndrew D CampbellSimon J LeedhamViktor Hendrik KoelzerOwen James SansomPhilip David DunnePublished in: Nature genetics (2024)
Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5 + stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1 + stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.
Keyphrases
- genome wide
- copy number
- single cell
- locally advanced
- stem cells
- genome wide identification
- induced apoptosis
- electronic health record
- rna seq
- squamous cell carcinoma
- gene expression
- big data
- bone marrow
- high intensity
- rectal cancer
- clinical trial
- neoadjuvant chemotherapy
- single molecule
- cell cycle arrest
- health information
- machine learning
- oxidative stress
- endoplasmic reticulum stress
- signaling pathway