Reusability Report: Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients

So, E.; Yu, F.; Wang, B.; Haibe-Kains, B.

doi:10.1101/2023.07.06.547938

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bioRxiv · 10.1101/2023.07.06.547938

Reusability Report: Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients

So, E.·Yu, F.·Wang, B.·Haibe-Kains, B.

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Abstract

Machine learning (ML) and artificial intelligence (AI) methods are increasingly used in personalized medicine, including precision oncology. Ma et al. (Nature Cancer 2021) developed a new method c alled "Transfer of Cell Line Response Prediction" (TCRP) to train predictors of drug response in cancer cell lines and optimize their performance in higher complex cancer model systems via few-shot learning. TCRP was presented as a successful modeling approach in multiple case studies. Given the importance of this approach to assist clinicians in their treatment decision process, we sought to reproduce independently the authors findings and improve the reusability of TCRP in new case studies, including validation in clinical trial datasets, a high bar for drug response prediction. Our results support the superiority of TCRP over established statistical and machine learning approaches in preclinical and clinical settings. We developed new resources to increase the reusability of the TCRP model for future improvements and validation studies.

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So, E., Yu, F., Wang, B., Haibe-Kains, B.. 2023-07-07. Reusability Report: Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. https://doi.org/10.1101/2023.07.06.547938

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