# Cancer Outcome Prediction

Omics-based cancer outcome prediction usually assigns samples to discrete ancestry groups and fits a separate model within each group, yet accuracy falls continuously with genetic distance from the training set. DA-ICL treats ancestry as a continuous variable, projects genotypes into a low-dimensional space with principal component analysis, builds a context from nearby samples at each ancestry point, and lets a pretrained tabular foundation model predict at inference time, with distribution-aware context enhancement adding distant but distributionally similar samples. Across 18 tasks spanning three TCGA cancer types, three omics modalities and two clinical endpoints, DA-ICL reaches the highest mean AUROC on 10 tasks for African-ancestry samples, with gains that vary by task.

Omics-based cancer outcome prediction usually assigns samples to discrete ancestry groups and fits a separate model within each group, yet accuracy falls continuously with genetic distance from the training set. DA-ICL treats ancestry as a continuous variable, projects genotypes into a low-dimensional space with principal component analysis, builds a context from nearby samples at each ancestry point, and lets a pretrained tabular foundation model predict at inference time, with distribution-aware context enhancement adding distant but distributionally similar samples. Across 18 tasks spanning three TCGA cancer types, three omics modalities and two clinical endpoints, DA-ICL reaches the highest mean AUROC on 10 tasks for African-ancestry samples, with gains that vary by task.