Zhang Wei, Wu Chao, Huang Hanchen, Bleu Paulina, Zambare Wini, Alvarez Janet, Wang Lily, Paty Philip B, Romesser Paul B, Smith J Joshua, Chen X Steven
Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Colorectal Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Transl Oncol. 2025 Feb;52:102238. doi: 10.1016/j.tranon.2024.102238. Epub 2025 Jan 3.
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.
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).
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.
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.
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.
由于分子异质性，结直肠癌（CRC）在化疗反应预测方面面临重大挑战。当前方法往往无法考虑个体肿瘤固有的复杂性和变异性。
我们开发了一种使用匹配的CRC肿瘤和类器官基因表达数据的新方法。我们在三个数据集上应用了共识加权基因共表达网络分析（WGCNA）：CRC肿瘤、匹配的类器官以及具有IC50药物反应值的独立类器官数据集，以识别与化疗反应相关的关键基因模块和枢纽基因，特别是5-氟尿嘧啶（5-FU）。
我们的综合分析确定了与CRC化疗反应相关的重要基因模块和枢纽基因。根据这些发现构建的预测模型在独立数据集上进行测试时，显示出比传统方法更高的准确性。匹配的肿瘤-类器官数据方法被证明在捕获相关生物标志物方面有效，提高了预测可靠性。
本研究通过利用匹配的肿瘤和类器官基因表达数据，为改善CRC化疗反应预测提供了一个强大的框架。我们的方法解决了先前方法的局限性，为CRC个性化治疗规划提供了一个有前景的策略。未来的研究应旨在验证这些发现，并探索整合更全面的药物反应数据。
本研究得到了美国国立癌症研究所（US National Cancer Institute）授予的R37CA248289资助以及西尔维斯特综合癌症中心（Sylvester Comprehensive Cancer Center）的支持，该中心获得了国立癌症研究所授予的P30CA240139奖项。这项工作得到了美国国立卫生研究院（NIH）的以下资助：T32CA009501-31A1和R37CA248289。这项工作还得到了纪念斯隆凯特琳癌症中心（MSK）的P30CA008748资助。
