Elsevier

Seminars in Cancer Biology

Volume 55, April 2019, Pages 37-52
Seminars in Cancer Biology

Review
Molecular subtyping of colorectal cancer: Recent progress, new challenges and emerging opportunities

https://doi.org/10.1016/j.semcancer.2018.05.002Get rights and content

Abstract

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Similar to many other malignancies, CRC is a heterogeneous disease, making it a clinical challenge for optimization of treatment modalities in reducing the morbidity and mortality associated with this disease. A more precise understanding of the biological properties that distinguish patients with colorectal tumors, especially in terms of their clinical features, is a key requirement towards a more robust, targeted-drug design, and implementation of individualized therapies. In the recent decades, extensive studies have reported distinct CRC subtypes, with a mutation-centered view of tumor heterogeneity. However, more recently, the paradigm has shifted towards transcriptome-based classifications, represented by six independent CRC taxonomies. In 2015, the colorectal cancer subtyping consortium reported the identification of four consensus molecular subtypes (CMSs), providing thus far the most robust classification system for CRC. In this review, we summarize the historical timeline of CRC classification approaches; discuss their salient features and potential limitations that may require further refinement in near future. In other words, in spite of the recent encouraging progress, several major challenges prevent translation of molecular knowledge gleaned from CMSs into the clinic. Herein, we summarize some of these potential challenges and discuss exciting new opportunities currently emerging in related fields. We believe, close collaborations between basic researchers, bioinformaticians and clinicians are imperative for addressing these challenges, and eventually paving the path for CRC subtyping into routine clinical practice as we usher into the era of personalized medicine.

Introduction

Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality worldwide, with an estimated 50,260 deaths recorded in the United States alone in 2017 [1]. Even with the recent advances in novel therapies, the 5-year overall survival from this malignancy remains ∼50%, highlighting the need to develop early detection, prognostic and predictive biomarkers that can be utilized in routine clinical practice for reducing the morbidity and mortality associated with this disease [2]. It is now widely recognized that cancers are heterogeneous groups of diseases with distinct molecular properties, resulting in diverse clinical outcomes. In the recent decades, several cancer initiatives and consortia have generated large-scale multi-omic data profiles, enabling unsupervised classifications of various types of cancers followed by comprehensive characterizations. In this context, previous ‘mutation-centered’ categorization of cancers has now gradually shifted towards a more ‘transcriptome-based’ molecular subtyping. Several of such transcriptome-based classification systems have demonstrated superior associations with clinical outcomes, making them more attractive for their eventual translation into the clinic. For instance, in breast cancer patients, intrinsic molecular subtyping based on immunohistochemical staining for ER, PR and HER2 markers, is now widely accepted in routine clinical practice for stratifying these patients for appropriate treatments.

For CRC, six independent classification systems have been reported recently [[3], [4], [5], [6], [7], [8]]. Due to inherent differences in the discovery cohort patients, gene expression profiling platforms, bioinformatic analyses, and data interpretation, each subtyping system have identified different numbers of CRC subtypes, which are seemingly discrepant. In order to elucidate potential overlaps and generate a more unified taxonomy, in 2014, a CRC Subtyping Consortium (CRCSC) was established, which involved six independent research groups and Sage Bionetworks as an additional evaluation team. Using a network-based meta-analysis of the six subtyping systems, four unique consensus molecular subtypes (CMSs) were identified, followed by comprehensive multi-omic and clinical characterization [9]. In spite of the tremendous early enthusiasm for this large community effort, the clinical translation of this CMS system has been challenging due to several potential limitations. Meanwhile, new opportunities are emerging to address these challenges, including recent advances in single-cell sequencing and big data sciences such as deep learning, new biological insights into intratumor heterogeneity and tumor microenvironment, as well as a growing number of clinical trials interrogating novel targeted therapies, combination therapies and immunotherapies.

In this article, we summarize genetic and epigenetic characteristics used for conventional CRC classification, transcriptome-based molecular subtyping, and the consensus molecular subtypes recently identified by the CRCSC. We will compare and discuss their contributions to the biological understanding of CRC heterogeneity and clinical associations, as well as their limitations. Furthermore, we highlight and discuss six major challenges preventing effective translation of CMSs into routine clinical practice, and possible solutions, alternative strategies and new opportunities. We stress that multidisciplinary collaborations are key to addressing these challenges, which is a prerequisite for more widespread embracement and implementation of molecular subtyping into the clinical management of patients with CRC.

Section snippets

The three major pathways of colorectal cancer and clinical implications

Clinically, colorectal cancer is still categorized based on the histopathological features such as tumor size, grade and disease stage. Although these classification methods are in use for decades, identifying the true high-risk population post-surgery still remains a major clinical concern. Furthermore, they provide limited understanding of the underlying tumor biology, and are in many cases inadequate for decision-making about appropriate treatment regimens in CRC patients. Increasingly, it

Gene-expression based CRC classifications

Emerging evidence in the last decade has made it clear that intratumor heterogeneity is best captured at the transcriptomic level, as it provides a more comprehensive molecular landscape of the disease process. Not surprisingly, the paradigm of cancer subtyping has gradually shifted from mutation-centered to transcriptome-based approaches, and from supervised to unsupervised classifications. Different from histopathological subtyping and classical mutation-oriented stratification,

The consensus molecular subtypes of colorectal cancer

The six CRC subtyping systems differ in terms of the methodological approaches used for specific analysis, but follow the same single-omic classification workflow (Fig. 1a). The strategy often started with a training/discovery cohort of primary tissue samples subjected for whole-genome gene expression profiling followed by normalization, as well as non-biological batch effect detection and correction in cases where multiple data sets were merged together. For the identification of specific

New challenges and emerging opportunities

Despite the massive community effort and encouraging progress of consensus molecular subtyping of CRC, it has been somewhat unfortunate that its true translational potential in CRC patients remains to be harnessed. As it stands currently, there is still a large gap between these exciting molecular subtypes and what role, if any, these findings may play in the clinical management of patients with CRC. To accomplish the eventual goal of personalized management of CRC patients, we propose a

Conflict of interest

Authors declare no conflicts of interest.

Acknowledgments

The present work was supported by a start-up grant for new faculty (7200455) and a VPRT grant (9610337) from the City University of Hong Kong, a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 21101115), as well as a grant from The Science Technology and Innovation Committee of Shenzhen Municipality (JCYJ20170307091256048) awarded to Xin Wang, and the CA72851, CA181572, CA184792, CA187956 and CA202797 grants from the National

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