Reusability report: Evaluating reproducibility and reusability of a fine-tuned model to predict drug response in cancer patient samples

This Reusability Report revisits a recently developed machine learning method for precision oncology, called 'transfer of cell line response prediction' (TCRP). Emily So et al. confirm the reproducibility of the previously reported results in drug-response prediction and also test the reusability of the method on new case studies with clinical relevance. Machine learning and artificial intelligence methods are increasingly being used in personalized medicine, including precision oncology. Ma et al. (Nature Cancer 2021) have developed a new method called '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 has been presented as a successful modelling approach in multiple case studies. Given the importance of this approach for assisting clinicians in their treatment decision processes, we sought to independently reproduce 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 reproducibility results, while not reaching the same level of superiority as those of the original authors, were able to confirm the superiority of TCRP in the original clinical context. Our reusability results indicate that, in the majority of novel clinical contexts, TCRP remains the superior method for predicting response for both preclinical and clinical settings. Our results thus support the superiority of TCRP over established statistical and machine learning approaches in preclinical and clinical settings. We also developed new resources to increase the reusability of the TCRP model for future improvements and validation studies.