Enhancing Chemotherapy Response Prediction Via Matched Colorectal Tumor-organoid Gene Expression Analysis and Network-based Biomarker Selection

Overview

Journal Transl Oncol

Specialty Oncology

Date 2025 Jan 4

PMID 39754813

Authors

Wei Zhang

Chao Wu

Hanchen Huang

Paulina Bleu

Wini Zambare

Janet Alvarez

Lily Wang

Philip B Paty

Paul B Romesser

J Joshua Smith

X Steven Chen

Affiliations

Soon will be listed here.

Abstract

Background: Colorectal cancer (CRC) presents significant challenges in chemotherapy response prediction due to its molecular heterogeneity. Current methods often fail to account for the complexity and variability inherent in individual tumors.

Methods: We developed a novel approach using matched CRC tumor and organoid gene expression data. We applied Consensus Weighted Gene Co-expression Network Analysis (WGCNA) across three datasets: CRC tumors, matched organoids, and an independent organoid dataset with IC50 drug response values, to identify key gene modules and hub genes linked to chemotherapy response, particularly 5-fluorouracil (5-FU).

Findings: Our integrative analysis identified significant gene modules and hub genes associated with CRC chemotherapy response. The predictive model built from these findings demonstrated superior accuracy over traditional methods when tested on independent datasets. The matched tumor-organoid data approach proved effective in capturing relevant biomarkers, enhancing prediction reliability.

Interpretation: This study provides a robust framework for improving CRC chemotherapy response predictions by leveraging matched tumor and organoid gene expression data. Our approach addresses the limitations of previous methods, offering a promising strategy for personalized treatment planning in CRC. Future research should aim to validate these findings and explore the integration of more comprehensive drug response data.

Funding: This research was supported by US National Cancer Institute grant R37CA248289, and Sylvester Comprehensive Cancer Center. which receives funding from the National Cancer Institute award P30CA240139. This work was supported by National Institutes of Health (NIH) under the following grants: T32CA009501-31A1 and R37CA248289. This work was also supported by the MSK P30CA008748 grant.

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