Integrative multi-omics of matched primary and liver metastatic colorectal cancer organoids identifies putative molecular targets
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Updated: Jan 12, 2026
Portal Vein Injection of Colorectal Cancer Organoids to Study the Liver Metastasis Stroma
Published on: September 3, 2021
Chae-Young Kim1, Soon-Chan Kim2, Rumi Shin3
1Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.
Abstract:
Hepatic metastasis from colorectal cancer (CRC) mainly accounts for the dismal prognosis of advanced CRC. Metastasized tumor cells exhibit strong resistance to commonly used systemic chemotherapy, which causes the high mortality of colorectal liver metastasis (CLM) patients. Although previous studies using biological resources have suggested multiple metastatic factors, molecular integration into chemotherapeutic responses is seldom studied. In this study, we established five pairs of CLM organoids, which were subjected to a multi-omics approach including genetic, transcriptional, exosomal miRNA and pharmacological analyses. These multi-omics layers were interlinked through machine learning analysis to construct a drug response prediction model. Our approach not only recapitulated druggable molecular targets that were specifically dysregulated in LM organoids but also potentially revealed a statistical connection to systemic therapy regimens.
Insights
This study uses multi-omics and machine learning on colorectal liver metastasis (CLM) organoids to predict drug responses. Findings reveal new therapeutic targets and connections to systemic therapies for advanced colorectal cancer (CRC).
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Hepatic metastasis is a major cause of poor prognosis in advanced colorectal cancer (CRC).
- Colorectal liver metastasis (CLM) cells show resistance to chemotherapy, leading to high patient mortality.
- Molecular factors driving metastasis and their link to chemotherapy response in CLM are not fully understood.
Purpose of the Study:
- To integrate multi-omics data from CLM organoids to understand drug resistance.
- To develop a predictive model for chemotherapeutic responses in CLM.
- To identify novel druggable molecular targets in CLM.
Main Methods:
- Established five pairs of CLM organoids.
- Performed multi-omics analyses: genetic, transcriptional, exosomal miRNA, and pharmacological profiling.
- Utilized machine learning to integrate multi-omics data and build a drug response prediction model.
Main Results:
- Identified dysregulated molecular targets specific to CLM organoids.
- Successfully constructed a machine learning model for predicting drug response.
- Revealed potential statistical connections between molecular profiles and systemic therapy outcomes.
Conclusions:
- Multi-omics analysis of CLM organoids provides insights into chemoresistance mechanisms.
- The developed model can aid in predicting therapeutic responses for CLM patients.
- This approach highlights potential new targets for colorectal liver metastasis treatment.
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