GANDALF: Generative AttentioN based Data Augmentation and predictive modeLing Framework for personalized cancer treatment
Aishwarya Jayagopal Yanrong Zhang Robert John Walsh Tuan Zea Tan Anand D Jeyasekharan Vaibhav Rajan
Effective treatment of cancer is a major challenge faced by healthcare providers, due to the highly individualized nature of patient responses to treatment. This is caused by the heterogeneity seen in cancer-causing alterations (mutations) across patient genomes. Limited availability of response data in patients makes it difficult to train personalized treatment recommendation models on mutations from clinical genomic sequencing reports. Prior methods tackle this by utilising larger, labelled pre-clinical laboratory datasets (‘cell lines’), via transfer learning. These methods augment patient data by learning a shared, domain-invariant representation, between the cell line and patient domains, which is then used to train a downstream drug response prediction (DRP) model. This approach augments data in the shared space but fails to model patient-specific characteristics, which have a strong influence on their drug response. We propose a novel generative attention-based data augmentation and predictive modeling framework, GANDALF, to tackle this crucial shortcoming of prior methods. GANDALF not only augments patient genomic data directly, but also accounts for its domain-specific characteristics. GANDALF outperforms state-of-the-art DRP models on publicly available patient datasets and emerges as the front-runner amongst SOTA cancer DRP models.
personalized drug response prediction, cancer, genomic data augmentation, diffusion model, pseudolabelling
A cancer drug response prediction model that addresses the problem of limited labelled data through a novel genomic data augmentation technique.