Molecular Classification of Colorectal Cancers and Clinical Application

So Yeon Jeon; Won Kyu Kim; Hoguen Kim

doi:10.4166/kjg.2016.68.6.297

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Jeon, Kim, and Kim: Molecular Classification of Colorectal Cancers and Clinical Application

Review Article

Korean J Gastroenterol 2016;68(6):297-302.

Published online: 4 October 2016

DOI: https://doi.org/10.4166/kjg.2016.68.6.297

Molecular Classification of Colorectal Cancers and Clinical Application

So Yeon Jeon^1,², Won Kyu Kim¹, Hoguen Kim¹

¹Department of Pathology and Brain Korea 21 PLUS Projects for Medical Science, Yonsei University College of Medicine, Seoul, Korea

²Ajou University Graduate School of Medicine, Seoul, Korea

Correspondence to: Hoguen Kim, Department of Pathology, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea. Tel: +82-2-2228-1761, Fax: +82-2-363-5263, E-mail: hkyonsei@yuhs.ac

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The molecular genetics of colorectal cancers (CRCs) is among the best understood of common human cancers. It is difficult to predict the prognosis and/or to predict chemoresponding in CRC patients. At present, prognosis is based predominantly on the tumor stage and pathological examination of the disease. Molecular classification of CRCs, based on genomics and transcriptomics, proposed that CRCs can be classified into at least three-to-six subtypes, depending on the gene expression pattern, and groups of marker genes representing to each subtype have also been reported. Gene expression-based subtyping is now widely accepted as a relevant source of disease stratification. We reviewed the previous studies on CRC subtyping, international consortium dedicated to large-scale data sharing and analytics recently established four consensus molecular subtypes with distinguishing features. Predictive markers identified in these studies are under investigation and large-scale clinical evaluations of molecular markers are currently in progress.

Keywords: Colonic neoplasms, Molecular typing, Molecular medicine

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Table 1.

Prediction of Prognosis according to the Previously Reported Molecular Classification of Colorectal Cancers

Research article	Number of samples			Tissue type	Subtype	Prognostic
Budinska	Stage	I	−	425 fresh frozen 688 FFPE	Surface crypt
et al.¹⁶		II	320		Lower crypt	Better RFS, SAR and OS
		III	793		CIMP	Better RFS, worse SAR and OS
		IV	−		Mesenchymal	Worse RFS and OS
					Mixed
Marisa	Stage	I	27	All fresh frozen	CIN Immune down
et al.¹⁷		II	198		dMMR
		III	164		KRAS mutated
		IV	54		Cancer stem cell	Worse RFS
						(enriched Oncotype Dx high risk)
					CIN WNT up
					CIN normal	Worse RFS
Roepman	Stage	I	24	All fresh frozen	A type	Better DMFS
et al.¹⁸		II	100		B type	Worse DMFS
		III	56		C type	Worse DMFS and OS
		IV	8
De Sousa E	Stage	I	−	All fresh frozen	CCS1
Melo et al.¹⁹		II	90		CCS2
		III	−		CCS3	Worse DFS
		IV	−			(enriched oncotype Dx high risk)
Sadanandam	Stage	I	48	All fresh frozen	Inflammatory	Intermediate DFS
et al.²⁰		II	236		Enterocyte	Intermediate DFS
		III	119		Transit-amplifying	Better DFS
		IV	23		Goblet-like	Better DFS
		NA	19		Stem-like	Worse DFS
Schlicker	Stage	I	−	All fresh frozen	1.1	Worse DFS
et al.²¹		II	26		1.2
		III	7		1.3
		IV	1		2.1	Better DFS
		NA	28		2.2

CIMP, CpG island methylator phenotype; RFS, relapse-free survival; SAR, survival after relapse; OS, overall survival; CIN, chromosomal instability; dMMR, DNA mismatch repair; WNT, wingless-type MMTV integration site; Oncotype Dx, a commercial diagnostic test; CCS, colon cancer subtype.

Table 2.

Genetic and Clinical Features of Consensus Molecular Subtypes (CMSs) in Colorectal Cancers

Subtype	Dominant feature	Prevalence	Genome instability	Mutation	Pathway and microenvironment	Prognostic
CMS1	MSI immune	14%	MSI high	BRAF	Immune activation	Worse SAR
			CIMP high
			Hypermutation
CMS2	Canonical	37%	SCNA high		WNT and MYC activation
CMS3	Metabolic	13%	Mixed MSI	KRAS	Metabolic dysregulation
			CIMP low
			SCNA low
CMS4	Mesenchymal	23%	SCNA high		Stromal invasion	Worse RFS and OS
					TGF- activation angiogenesis

MSI, microsatellite instable; CIMP, CpG island methylator phenotype; SAR, survival after relapse; SCNA, somatic copy number alteration

WNT, wingless-type MMTV integration site; MYC, v-myc avian myelocytomatosis viral oncogene; TGF-, transforming growth factor ; RFS, relapse-free survival; OS, overall survival.

Generated from the previous study.²²

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