A colorectal cancer classification system that associates cellular phenotype and responses to therapy

By A. Sadanandam, C. Lyssiotis, K. Homicsko, E. Collisson, W. J. Gibb, S. Wullschleger, Liliane C Gonzalez Ostos, William A. Lannon, Carsten Grotzinger, M. Del Rio and 6 more. Nature Medicine, 2013

957 citations recorded by the source corpus.

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor–targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.

Subfields: Oncology, Pathology and Forensic Medicine

View Paper: https://www.semanticscholar.org/paper/9a6b71939a602201df39769e169a6c0d67f3226f

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In Oncology

In Pathology and Forensic Medicine

Hans Clevers

Citations in the corpus

Prospective derivation of a Living Organoid Biobank of colorectal cancer patients (2015, methodology): “Organoid technology may fill the gap between cancer genetics and patient trials, complement cell-line- and xenograft-based drug studies, and allow personalized therapy design.”
Machine learning and feature selection for drug response prediction in precision oncology applications (2018, background): “2016), although there are also a few recent examples aiming at clinical treatment predictions in patient samples (Sadanandam et al. 2013; Geeleher et al. 2014; Majumder et al. 2015; Ding et al. 2016; Noren et al. 2016; Yao et al. 2018; Turki et al. 2018).”
Distal and proximal colon cancers differ in terms of molecular, pathological, and clinical features. (2014, background): “We and others have proposed gene expression-based molecular subgroups [3-6] that share (groups of) molecular characteristics while maintaining significant intragroup heterogeneity.”
Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation (2014, background): “Many researchers have already taken advantage of the large collection of public data, either by considering exclusively already published data sets, or by combining the public data with their own generated data [39,40].”
CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models (2017, background): “The Sadanandam et al . transit-amplifying subtype, largely corresponding to CMS2, has previously been shown to be particularly sensitive to EGFR-inhibitors in vitro 5,27 , and in vivo response has been shown in PDX models with high WNT signaling 18 .”
Stromal gene expression defines poor-prognosis subtypes in colorectal cancer (2015, background): “Whiskers in box plots extend to minimum and maximum values. a All HR > 1 P < 0.01 Stem-like upregulated >2-fold, P < 0.05 Sadanandam et al. GSE14333 313286612 Stem-like ∩ HR All HR > 1 P < 0.01 C4 upregulated >2-fold, P < 0.05 Marisa et al. GSE39582 66898470 C4 ∩ HR De Sousa E Melo et al. GSE33113 A”
Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer (2017, background): “BRAF TKI + MEK TKI + EGFR mAbs Phase II 9/35 (26)(80)”
Towards the introduction of the ‘Immunoscore’ in the classification of malignant tumours (2013, methodology): “Using this approach, six groups of CRCs were recently defined, based on similarities with distinct cell types within the normal colon crypt and the response to classic chemotherapy and targeted therapies [22].”
Advanced Heat Map and Clustering Analysis Using Heatmap3 (2014, background): “Also, cluster analysis can be used to identify novel subtypes [3].”