Peter Horak

TOPAS converts phosphoproteomic data into patient-specific reports for molecular tumor boards, focusing on clinically relevant signaling and therapeutic targets.

This study uses pan-cancer phosphoproteomics to identify therapeutic targets and improve precision oncology by analyzing 1,998 tumor samples and developing new scores for kinase activity and immune response.

The paper develops a hierarchical classification framework combining epigenetic and genetic data with machine learning to improve diagnosis and risk stratification in hematologic malignancies.

The paper identifies genomic and transcriptomic features of telomere maintenance mechanisms in complex karyotype sarcomas, linking ALT status to therapeutic vulnerabilities.

This study develops an ex vivo drug sensitivity testing platform for rare solid tumors and finds that high in vitro sensitivity correlates with clinical benefit and progression-free survival.

The paper introduces Knowledge Connector, a decision support system that integrates multiomics data with clinical information to aid in precision oncology decision-making.

The paper describes the evolution of the CIViC knowledgebase to meet the needs of the cancer variant interpretation community by expanding its functionality and data types.