HRD-MILN: Accurately estimate tumor homologous recombination deficiency status from targeted panel sequencing data.

Homologous recombination deficiency (HRD) is a critical feature guiding drug and treatment selection, mainly for ovarian and breast cancers. As it cannot be directly observed, HRD status is estimated on a small set of genomic instability features from sequencing data. The existing methods often perform poorly when handling targeted panel sequencing data; however, the targeted panel is the most popular sequencing strategy in clinical practices. Thus, we proposed HRD-MILN to overcome the computational challenges from targeted panel sequencing. HRD-MILN incorporated a multi-instance learning framework to discover as many loss of heterozygosity (LOH) associated with HRD status to cluster as possible. Then the HRD score is obtained based on the association between the LOHs and the cluster in the sample to be estimated, and finally, the HRD status is estimated based on the score. In comparison experiments on targeted panel sequencing data, the Precision of HRD-MILN could achieve 87%, significantly improved from 63% reported by the existing methods, where the highest margin of improvement reached 14%. It also presented advantages on whole exome sequencing data. Based on our best knowledge, HRD-MILN is the first practical tool for estimating HRD status from targeted panel sequencing data and could benefit clinical applications.