News and Awards

Our integral genomic signature study for precision oncology published in Nature Communications

May 20, 2022 /

Low-cost multi-omics sequencing is expected to become clinical routine and transform precision oncology. Viable computational methods that can facilitate tailored intervention while tolerating sequencing biases are in high demand. In this study, we develop an integral genomic signature —iGenSig— approach to predict drug responses using multi-omics data from tumour samples, and validate this approach using genomic datasets from six clinical studies and clinical trials. iGenSig will provide a computational framework to empower tailored cancer therapy based on multi-omics data.

Schematic showing the principle and key features of iGenSig modeling: i) the iGenSig approach intentionally retains and creates redundant genomic features, a concept like the use of redundant steel rods to reinforce the pillars of a building. ii) iGenSig modeling utilizes the average correlation intensities of significant genomic features detected in specific samples to diminish the effect of false positive detection resulting from sequencing errors and prevent overweighing. iii) iGenSig modeling extract the second genomic information from unlabeled genomic datasets for large cohorts of human cancers, in addition to the labeled genomic datasets of drug sensitivity, which will substantially improve its cross-dataset applicability, particularly on clinical trial datasets. iv) iGenSig modeling is a white box approach, thus will be more interpretable and controllable than machine learning or deep learning approaches.