Criteria-based curation of a therapy-focused compendium to support treatment recommendations in precision oncology

BACKGROUND: While several key resources exist that interpret therapeutic significance of genomic alterations in cancer, many regional real-world issues limit access to drugs. There is a need for a pragmatic, evidence-based, context-adapted tool to guide clinical management based on molecular biomarkers. METHODS: A compendium of approved and experimental therapies with associated biomarkers was built following a survey of drug regulatory databases, existing knowledge bases, and published literature. Each biomarker-disease-therapy triplet was then categorized using a tiering system reflective of key therapeutic considerations: approved and reimbursed standard-of-care therapies with respect to a jurisdiction (Tier 1), evidence of efficacy or approval in another jurisdiction (Tier 2), evidence of antitumour activity (Tier 3), and plausible biological rationale (Tier 4). Two resistance categories were defined: lack of efficacy (Tier R1), and lack of antitumor activity (Tier R2). RESULTS: Following comprehensive literature review and appraisal, we developed a curated knowledge base focused on drugs relevant and accessible in the Australian healthcare system (TOPOGRAPH: Therapy Oriented Precision Oncology Guidelines for Recommending Anticancer Pharmaceuticals). As of November 2020, TOPOGRAPH comprised 2810 biomarker-disease-therapy triplets in 989 expert-appraised entries, including 373 therapies, 199 predictive biomarkers, and 106 cancer types. In the 345 biomarker-linked therapies catalogued, 84 (24%) and 65 (19%) therapies in contexts of different cancer types have Tier 1 and 2 designations respectively, while 271 (79%) therapies were supported by preclinical studies, early clinical trials, retrospective studies, or case series (Tiers 3 and 4). A total of 119 of 373 (33%) therapies associated with biomarkers of resistance were also catalogued. A clinical algorithm was also developed to support therapeutic decision-making using predictive biomarkers. This resource is accessible online at https://topograph.info/. CONCLUSION: TOPOGRAPH is intended to support oncologists with context-appropriate clinical decision-making, optimising selection and accessibility of the most appropriate targeted therapy for any given genomic biomarker. Our approach can be readily adapted to build jurisdiction-specific resources to standardise decision-making in precision oncology.