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Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.

Authors :
Malla SB
Byrne RM
Lafarge MW
Corry SM
Fisher NC
Tsantoulis PK
Mills ML
Ridgway RA
Lannagan TRM
Najumudeen AK
Gilroy KL
Amirkhah R
Maguire SL
Mulholland EJ
Belnoue-Davis HL
Grassi E
Viviani M
Rogan E
Redmond KL
Sakhnevych S
McCooey AJ
Bull C
Hoey E
Sinevici N
Hall H
Ahmaderaghi B
Domingo E
Blake A
Richman SD
Isella C
Miller C
Bertotti A
Trusolino L
Loughrey MB
Kerr EM
Tejpar S
Maughan TS
Lawler M
Campbell AD
Leedham SJ
Koelzer VH
Sansom OJ
Dunne PD
Source :
Nature genetics [Nat Genet] 2024 Mar; Vol. 56 (3), pp. 458-472. Date of Electronic Publication: 2024 Feb 13.
Publication Year :
2024

Abstract

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 <superscript>+</superscript> stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1 <superscript>+</superscript> 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.<br /> (© 2024. The Author(s).)

Details

Language :
English
ISSN :
1546-1718
Volume :
56
Issue :
3
Database :
MEDLINE
Journal :
Nature genetics
Publication Type :
Academic Journal
Accession number :
38351382
Full Text :
https://doi.org/10.1038/s41588-024-01654-5