Machine learning algorithms develop a tumor-educated platelets-related gene signature to predict colorectal cancer prognosis and therapy response.
A blood test using immune cells predicts colorectal cancer outcomes better than standard staging, potentially guiding treatment decisions.
Using tumor-educated platelets (TEPs) as a liquid biopsy substrate, this study applied multiple ML algorithms to derive a robust gene expression signature for colorectal cancer prognosis and treatment response prediction. The TEP-based signature demonstrated superior performance to standard staging across multiple validation cohorts, offering a minimally invasive prognostic tool that could guide adjuvant treatment decisions in stage II-III CRC patients.
What the study was
- Study design
- Machine learning prognostic study
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- iScience
Why it surfaced
Tumor-educated platelets as liquid biopsy is an emerging field; ML consensus approach adds methodological rigor; iScience (Cell Press) is a solid venue; CRC is high-incidence with major unmet need for treatment response prediction.
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