Unlocking the Power of Multi-institutional Data: Integrating and Harmonizing Genomic Data Across Institutions
This work addresses the problem of leveraging multi-institutional genomic data for precision oncology, which is incremental as it builds on existing databases like GENIE BPC to improve data integration methods.
The authors tackled the challenge of integrating and harmonizing genomic data from multiple institutions, which suffers from variations in gene panels and sequencing techniques, by introducing the Bridge model. The model's extracted latent features consistently excelled in predicting patient survival across six cancer types in the GENIE BPC data.
Cancer is a complex disease driven by genomic alterations, and tumor sequencing is becoming a mainstay of clinical care for cancer patients. The emergence of multi-institution sequencing data presents a powerful resource for learning real-world evidence to enhance precision oncology. GENIE BPC, led by the American Association for Cancer Research, establishes a unique database linking genomic data with clinical information for patients treated at multiple cancer centers. However, leveraging such multi-institutional sequencing data presents significant challenges. Variations in gene panels result in loss of information when the analysis is conducted on common gene sets. Additionally, differences in sequencing techniques and patient heterogeneity across institutions add complexity. High data dimensionality, sparse gene mutation patterns, and weak signals at the individual gene level further complicate matters. Motivated by these real-world challenges, we introduce the Bridge model. It uses a quantile-matched latent variable approach to derive integrated features to preserve information beyond common genes and maximize the utilization of all available data while leveraging information sharing to enhance both learning efficiency and the model's capacity to generalize. By extracting harmonized and noise-reduced lower-dimensional latent variables, the true mutation pattern unique to each individual is captured. We assess the model's performance and parameter estimation through extensive simulation studies. The extracted latent features from the Bridge model consistently excel in predicting patient survival across six cancer types in GENIE BPC data.