ColoType: a forty gene signature for consensus molecular subtyping of colorectal cancer tumors using whole-genome assay or targeted RNA-sequencing.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
21 07 2020
21 07 2020
Historique:
received:
30
04
2020
accepted:
03
07
2020
entrez:
23
7
2020
pubmed:
23
7
2020
medline:
15
12
2020
Statut:
epublish
Résumé
Colorectal cancer (CRC) tumors can be partitioned into four biologically distinct consensus molecular subtypes (CMS1-4) using gene expression. Evidence is accumulating that tumors in different subtypes are likely to respond differently to treatments. However, to date, there is no clinical diagnostic test for CMS subtyping. In this study, we used novel methodology in a multi-cohort training domain (n = 1,214) to develop the ColoType scores and classifier to predict CMS1-4 based on expression of 40 genes. In three validation cohorts (n = 1,744, in total) representing three distinct gene-expression measurement technologies, ColoType predicted gold-standard CMS subtypes with accuracies 0.90, 0.91, 0.88, respectively. To accommodate for potential intratumoral heterogeneity and tumors of mixed subtypes, ColoType was designed to report continuous scores measuring the prevalence of each of CMS1-4 in a tumor, in addition to specifying the most prevalent subtype. For analysis of clinical specimens, ColoType was also implemented with targeted RNA-sequencing (Illumina AmpliSeq). In a series of formalin-fixed, paraffin-embedded CRC samples (n = 49), ColoType by targeted RNA-sequencing agreed with subtypes predicted by two independent methods with accuracies 0.92, 0.82, respectively. With further validation, ColoType by targeted RNA-sequencing, may enable clinical application of CMS subtyping with widely-available and cost-effective technology.
Identifiants
pubmed: 32694712
doi: 10.1038/s41598-020-69083-y
pii: 10.1038/s41598-020-69083-y
pmc: PMC7374173
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
12123Références
Budinska, E. et al. Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer. J. Pathol. 231, 63–76 (2013).
doi: 10.1002/path.4212
Schlicker, A. et al. Subtypes of primary colorectal tumors correlate with response to targeted treatment in colorectal cell lines. BMC Med. Genom. 5, 66 (2012).
doi: 10.1186/1755-8794-5-66
Roepman, P. et al. Colorectal cancer intrinsic subtypes predict chemotherapy benefit, deficient mismatch repair and epithelial-to-mesenchymal transition. Int. J. Cancer 134, 552–562 (2014).
doi: 10.1002/ijc.28387
Marisa, L. et al. Gene expression classification of colon cancer into molecular subtypes: Characterization, validation, and prognostic value. PLoS Med. 10, e1001453 (2013).
doi: 10.1371/journal.pmed.1001453
de Sousa, E. M. F. et al. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Nat. Med. 19, 614–618 (2013).
doi: 10.1038/nm.3174
Sadanandam, A. et al. A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat. Med. 19, 619–625 (2013).
doi: 10.1038/nm.3175
Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015).
doi: 10.1038/nm.3967
Song, N. et al. Clinical outcome from oxaliplatin treatment in stage II/III colon cancer according to intrinsic subtypes: Secondary analysis of NSABP C-07/NRG oncology randomized clinical trial. JAMA Oncol. 2, 1162–1169 (2016).
doi: 10.1001/jamaoncol.2016.2314
Lenz, H. et al. Impact of consensus molecular subtype on survival in patients with metastatic colorectal cancer: Results from CALGB/SWOG 80405 (Alliance). J. Clin. Oncol. 37, 1876–1885 (2019).
doi: 10.1200/JCO.18.02258
Kim, S. R. et al. Tumour sidedness and intrinsic subtypes in patients with stage II/III colon cancer: Analysis of NSABP C-07 (NRG Oncology). Br. J. Cancer 118, 629–633 (2018).
doi: 10.1038/bjc.2017.448
Sebastian Stintzing, P. W. et al. Consensus molecular subgroups (CMS) of colorectal cancer (CRC) and first-line efficacy of FOLFIRI plus cetuximab or bevacizumab in the FIRE3 (AIO KRK-0306) trial. J. Clin. Oncol. 35(Suppl), abstr 3510 (2017).
doi: 10.1200/JCO.2017.35.15_suppl.3510
Allen, W. L. et al. Transcriptional subtyping and CD8 immunohistochemistry identifies patients with stage II and III colorectal cancer with poor prognosis who benefit from adjuvant chemotherapy. JCO Precsion Oncol. 1–15 (2018).
Mooi, J. K. et al. The prognostic impact of consensus molecular subtypes (CMS) and its predictive effects for bevacizumab benefit in metastatic colorectal cancer: molecular analysis of the AGITG MAX clinical trial. Ann. Oncol. 29, 2240–2246 (2018).
doi: 10.1093/annonc/mdy410
Sveen, A. et al. Colorectal cancer consensus molecular subtypes translated to preclinical models uncover potentially targetable cancer cell dependencies. Clin. Cancer Res. 24, 794–806 (2018).
doi: 10.1158/1078-0432.CCR-17-1234
Okita, A. et al. Consensus molecular subtypes classification of colorectal cancer as a predictive factor for chemotherapeutic efficacy against metastatic colorectal cancer. Oncotarget 9, 18698–18711 (2018).
doi: 10.18632/oncotarget.24617
Eide, P. W., Bruun, J., Lothe, R. A. & Sveen, A. CMScaller: An R package for consensus molecular subtyping of colorectal cancer pre-clinical models. Sci. Rep. 7, 16618–16628 (2017).
doi: 10.1038/s41598-017-16747-x
Marisa, L. et al. Clinical utility of colon cancer molecular subtypes: Validation of two main colorectal molecular classifications on the PETACC-8 phase III trial cohort. J. Clin. Oncol. 35, 3509 (2017).
doi: 10.1200/JCO.2017.35.15_suppl.3509
Trinh, A. et al. Practical and robust identification of molecular subtypes in colorectal cancer by immunohistochemistry. Clin. Cancer Res. 23, 387–398 (2017).
doi: 10.1158/1078-0432.CCR-16-0680
McCall, M. N., Bolstad, B. M. & Irizarry, R. A. Frozen robust multiarray analysis (fRMA). Biostatistics 11, 242–253 (2010).
doi: 10.1093/biostatistics/kxp059
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
doi: 10.1093/biostatistics/kxj037
Van Cutsem, E. et al. Randomized phase III trial comparing biweekly infusional fluorouracil/leucovorin alone or with irinotecan in the adjuvant treatment of stage iii colon cancer: PETACC-3. J. Clin. Oncol. 27, 3117–3125 (2009).
doi: 10.1200/JCO.2008.21.6663
Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).
Liao, Y., Smyth, G. K. & Shi, W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res. 47, e47 (2019).
doi: 10.1093/nar/gkz114
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
doi: 10.1093/bioinformatics/btt656
Conroy, J. M. et al. Analytical validation of a next-generation sequencing assay to monitor immune responses in solid tumors. J. Mol. Diagn. JMD 20, 95–109 (2018).
doi: 10.1016/j.jmoldx.2017.10.001
Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
doi: 10.1186/gb-2010-11-10-r106
Buechler, S. A., Gökmen Polar, Y. & Badve, S. S. EarlyR: A robust gene expression signature for predicting outcomes of estrogen receptor-positive breast cancer. Clin. Breast Cancer 19, 17–26 (2018).
doi: 10.1016/j.clbc.2018.07.011
Fraley, C. & Raftery, A. E. Model-based clustering, discriminant analysis, and density estimation. J. Am. Stat. Assoc. 97, 611–631 (2002).
doi: 10.1198/016214502760047131
Scrucca, L., Fop, M., Murphy, T., Brendan, & Raftery, A. E. mclust 5: Clustering, classification and density estimation using gaussian finite mixture models. R J. 8, 289 (2016).
doi: 10.32614/RJ-2016-021
McNicholas, P. D. Mixture Model-Based Classification xiii-212 (CRC Press Taylor & Francis Group, Boca Raton, 2017).
Sachs, M. C. plotROC: A tool for plotting ROC curves. J .Stat. Softw. 79, 1–19 (2017).
Kuhn, M. Building predictive models in R using the caret package. J .Stat. Softw. 28, 1–26 (2008).
Le, C. T. A solution for the most basic optimization problem associated with an ROC curve. Stat. Methods Med. Res. 15, 571–584 (2006).
doi: 10.1177/0962280206070637
Ragulan, C. et al. Analytical validation of multiplex biomarker assay to stratify colorectal cancer into molecular subtypes. Sci. Rep. 9, 7665–7712 (2019).
doi: 10.1038/s41598-019-43492-0
Mittempergher, L. et al. MammaPrint and BluePrint molecular diagnostics using targeted RNA next-generation sequencing technology. J. Mol. Diagn. JMD 21, 808–823 (2019).
Sinn, B. V. et al. SETER/PR: A robust 18-gene predictor for sensitivity to endocrine therapy for metastatic breast cancer. NPJ Breast Cancer 5, 1–8 (2019).
doi: 10.1038/s41523-019-0111-0
Dunne, P. D. et al. Challenging the cancer molecular stratification dogma: intratumoral heterogeneity undermines consensus molecular subtypes and potential diagnostic value in colorectal cancer. Clin. Cancer Res. 22, 4095–4104 (2016).
doi: 10.1158/1078-0432.CCR-16-0032
Alderdice, M. et al. Prospective patient stratification into robust cancer-cell intrinsic subtypes from colorectal cancer biopsies. J. Pathol. 245, 19–28 (2018).
doi: 10.1002/path.5051
Trinh, A. et al. Tumour budding is associated with the mesenchymal colon cancer subtype and RAS/RAF mutations: A study of 1320 colorectal cancers with Consensus Molecular Subgroup (CMS) data. Br. J. Cancer 119, 1244–1251 (2018).
doi: 10.1038/s41416-018-0230-7
Ubink, I. et al. A novel diagnostic tool for selecting patients with mesenchymal-type colon cancer reveals intratumor subtype heterogeneity. J. Natl. Cancer Inst. 109, djw303 (2017).
doi: 10.1093/jnci/djw303
Chang, K. et al. Colorectal premalignancy is associated with consensus molecular subtypes 1 and 2. Ann. Oncol. 29, 2061–2067 (2018).
doi: 10.1093/annonc/mdy337