Observational Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Oct 24, 2023; 14(10): 409-419
Published online Oct 24, 2023. doi: 10.5306/wjco.v14.i10.409
Classification of patients with metastatic colorectal cancer into consensus molecular subtypes into real-world: A pilot study
Jaime González-Montero, Mauricio Burotto, Guillermo Valenzuela, Debora Mateluna, Florencia Buen-Abad, Jessica Toro, Olga Barajas, Katherine Marcelain
Jaime González-Montero, Mauricio Burotto, Bradford Hill Clinical Research Center, Santiago 8420383, Chile
Jaime González-Montero, Guillermo Valenzuela, Debora Mateluna, Florencia Buen-Abad, Jessica Toro, Olga Barajas, Katherine Marcelain, Basic and Clinical Oncology Department, University of Chile, Santiago 8380453, Chile
Author contributions: González-Montero J led the study, wrote the manuscript, and created the figures and tables; González-Montero J, Burotto M, and Barajas O led the molecular classification of the patients; Valenzuela G, Toro J, and Marcelain K performed molecular biology procedures; Barajas O, Mateluna D, and Buen-Abad F recruited the patients.
Supported by Agencia Nacional de Investigación y Desarrollo de Chile, Fondo Nacional de Investigación en Salud (FONIS), No. SA20I0059.
Institutional review board statement: The study was reviewed and approved by the Medical Oncology Supervisor of Bradford Hill Clinical Research Center.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors declare have not conflict of interest to declare.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jaime González-Montero, MD, PhD, Assistant Professor, Basic and Clinical Oncology Deparment, University of Chile. Bradford Hill Clinical Research Center. Independencia 1027, Casilla 70058, Santiago 8380453, Chile. jagonzalez@ug.uchile.cl
Received: July 25, 2023
Peer-review started: July 25, 2023
First decision: September 13, 2023
Revised: September 24, 2023
Accepted: October 8, 2023
Article in press: October 8, 2023
Published online: October 24, 2023
Processing time: 89 Days and 17 Hours
Abstract
BACKGROUND

Colorectal cancer is a complex disease with high mortality rates. Over time, the treatment of metastatic colorectal cancer (mCRC) has gradually improved due to the development of modern chemotherapy and targeted therapy regimens. However, due to the inherent heterogeneity of this condition, identifying reliable predictive biomarkers for targeted therapies remains challenging. A recent promising classification system—the consensus molecular subtype (CMS) system—offers the potential to categorize mCRC patients based on their unique biological and molecular characteristics. Four distinct CMS categories have been defined: immune (CMS1), canonical (CMS2), metabolic (CMS3), and mesenchymal (CMS4). Nevertheless, there is currently no standardized protocol for accurately classifying patients into CMS categories. To address this challenge, reverse transcription polymerase chain reaction (RT-qPCR) and next-generation genomic sequencing (NGS) techniques may hold promise for precisely classifying mCRC patients into their CMSs.

AIM

To investigate if mCRC patients can be classified into CMS categories using a standardized molecular biology workflow.

METHODS

This observational study was conducted at the University of Chile Clinical Hospital and included patients with unresectable mCRC who were undergoing systemic treatment with chemotherapy and/or targeted therapy. Molecular biology techniques were employed to analyse primary tumour samples from these patients. RT-qPCR was utilized to assess the expression of genes associated with fibrosis (TGF-β and β-catenin) and cell growth pathways (c-MYC). NGS using a 25-gene panel (TumorSec) was performed to identify specific genomic mutations. The patients were then classified into one of the four CMS categories according to the clinical consensus of a Tumour Board. Informed consent was obtained from all the patients prior to their participation in this study. All techniques were conducted at University of Chile.

RESULTS

Twenty-six patients were studied with the techniques and then evaluated by the Tumour Board to determine the specific CMS. Among them, 23% (n = 6), 19% (n = 5), 31% (n = 8), and 19% (n = 5) were classified as CMS1, CMS2, CMS3, and CMS4, respectively. Additionally, 8% of patients (n = 2) could not be classified into any of the four CMS categories. The median overall survival of the total sample was 28 mo, and for CMS1, CMS2, CMS3 and CMS4 it was 11, 20, 30 and 45 mo respectively, with no statistically significant differences between groups.

CONCLUSION

A molecular biology workflow and clinical consensus analysis can be used to accurately classify mCRC patients. This classification process, which divides patients into the four CMS categories, holds significant potential for improving research strategies and targeted therapies tailored to the specific characteristics of mCRC.

Keywords: Metastatic colorectal cancer; Targeted therapy; Consensus molecular subtypes; Personalized medicine

Core Tip: Colorectal cancer is molecularly heterogeneous. Consensus molecular subtype classification sheds light on its biology, potentially guiding targeted therapy selection. However, an optimal consensus molecular subtype classification mechanism remains elusive. This workflow, which combines reverse transcription polymerase chain reaction and next-generation sequencing, introduces a novel approach for molecular patient classification. We aim to use these techniques to improve the precision of tumour subtyping.