Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Translational Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1936523324003644 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6f998a336cf54d6285e3a30589ebe956 |
| institution | Kabale University |
| issn | 1936-5233 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj-art-6f998a336cf54d6285e3a30589ebe9562025-01-22T05:41:25ZengElsevierTranslational Oncology1936-52332025-02-0152102238Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selectionWei Zhang0Chao Wu1Hanchen Huang2Paulina Bleu3Wini Zambare4Janet Alvarez5Lily Wang6Philip B. Paty7Paul B. Romesser8J. Joshua Smith9X. Steven Chen10Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USAColorectal Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USADepartment of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USAColorectal Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USAColorectal Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USAColorectal Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USADepartment of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA; John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL 33136, USAColorectal Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USADepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USAColorectal Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Corresponding author.Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S1936523324003644Colorectal cancerChemotherapy responseGene expressionOrganoidPredictive modelCancer biomarker |
| spellingShingle | 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 Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection Translational Oncology Colorectal cancer Chemotherapy response Gene expression Organoid Predictive model Cancer biomarker |
| title | Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection |
| title_full | Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection |
| title_fullStr | Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection |
| title_full_unstemmed | Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection |
| title_short | Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection |
| title_sort | enhancing chemotherapy response prediction via matched colorectal tumor organoid gene expression analysis and network based biomarker selection |
| topic | Colorectal cancer Chemotherapy response Gene expression Organoid Predictive model Cancer biomarker |
| url | http://www.sciencedirect.com/science/article/pii/S1936523324003644 |
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