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

Author:

Lin Frank P.ORCID,Thavaneswaran SubotheniORCID,Grady John P.ORCID,Ballinger MandyORCID,Kansara MayaORCID,Oakes Samantha R.ORCID,Desai JayeshORCID,Lee Chee KhoonORCID,Simes JohnORCID,Thomas David M.ORCID

Abstract

AbstractBACKGROUNDWhile 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.METHODSA 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).RESULTSFollowing 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/.CONCLUSIONTOPOGRAPH 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.

Publisher

Cold Spring Harbor Laboratory

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