III: Medium: Knowledge-Guided Meta Learning for Multi-Omics Survival Analysis

University Of Virginia Main Campus, Charlottesville VA

Investigators

Abstract

Cancers are among the world's most deadly malignant diseases. Survival analysis, which predicts the time to an event (i.e., death in the case of cancer), is of crucial importance for cancer biology research and disease treatment. Stratifying patients according to disease stage or severity using molecular information from the tumor is critical for tailoring treatments for each patient. Identifying molecular features that drive the prediction of poor survival outcomes could provide insights into mechanistic studies of cancer and lead to discoveries of new therapeutic targets. Recent advances in high-throughput genomics technologies have led to a massive amount of high-dimensional omics data being available which makes it possible to apply machine learning approaches to build predictive models that predict tumor stage or severity as well as identifying the key molecular factors driving tumorigenesis and metastasis which could lead to new therapeutic targets. While deep learning has been applied to cancer survival analysis, it does not work very well on biological data with a relatively small number of samples with high-dimensional features in each cohort (i.e., the well-known big p, small n problem). The Cancer Genome Atlas (TCGA) is such an example, which characterizes multi-level high-dimensional clinical and molecular profiles spanning many cancer types. To make machine learning effectively work on a small amount of training data such as each cancer type in TCGA, we propose a set of advanced machine learning approaches to tackle the well-known big p, small n problem for cancer survival analysis. This project develops a new knowledge-guided meta-learning framework which innovatively integrates biological knowledge with meta-learning for multi-omics survival analysis. This framework tackles a series of technical challenges posed by the scenario of many domains (e.g., cancer) but small numbers of samples in each domain and utilizes the principle of few-shot learning to implement a fast adaptation process to new cancer samples by training of a set of different cancers. The proposed knowledge-guided embedding-learning framework formulates a new fundamental structure that enables the integration of biological knowledge into the construction of a latent patient representation, which exploits the rich information present in human-curated knowledge bases such as gene ontology and regulatory networks that can be shared across different cancers. The proposed meta-learning for survival analysis approach enables the prediction of survival probability for patients from unseen cancers. To further enhance meta-learning for survival predictions, a meta-learning framework called Context-Aware Learning with Meta-Knowledge is developed, which explicitly incorporates curated domain knowledge into the meta-learning framework, and is aware of the prediction context, including the prediction difficulty and heterogeneity of the input patient samples. The proposed paradigm of knowledge-guided meta-learning is significantly more powerful than traditional transfer learning in adapting to new tasks or domains. The project will lead to a novel, advanced approach to analyzing a variety of cancer datasets with each cancer having a small sample size. Thus, this project has significant potential to advance the theory and practice of few-shot learning, with strong social implications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.