Nihar Mauskar

Patient-derived xenografts (PDX) implant human tumors into immunocompromised mice to test drugs, but screening every tumor-drug combination is infeasible in time and cost. GrAFT-PDX is a Bayesian model that jointly models tumor growth trajectories and mouse death risk, using a low-rank tensor decomposition to share information across PDX lines, treatments and time, so it can impute untested experiments and predict death times. On the large PDXe screen it assigns higher log probability to death times than Royston-Parmar and other survival baselines, matches strong baselines on tumor response, and preserves known gene-drug associations without genomic input.

Patient-derived xenografts (PDX) implant human tumors into immunocompromised mice to test drugs, but screening every tumor-drug combination is infeasible in time and cost. GrAFT-PDX is a Bayesian model that jointly models tumor growth trajectories and mouse death risk, using a low-rank tensor decomposition to share information across PDX lines, treatments and time, so it can impute untested experiments and predict death times. On the large PDXe screen it assigns higher log probability to death times than Royston-Parmar and other survival baselines, matches strong baselines on tumor response, and preserves known gene-drug associations without genomic input.