Developing Drugs for Tissue-Agnostic Indications: A Paradigm Shift in Leveraging Cancer Biology for Precision Medicine

Abstract

Targeted therapies have reshaped the landscape of the development of cancer therapeutics. Recent biomarker-driven, tissue-agnostic clinical trials represent a significant paradigm shift in precision cancer medicine. Despite their growth in preclinical and clinical studies, to date only a few biomarker-driven, tissue-agnostic indications have seen approval by the US Food and Drug Administration (FDA). These approvals include pembrolizumab in microsatellite instability-high or mismatch repair deficient solid tumors, as well as both larotrectinib and entrectinib in NTRK fusion-positive tumors. Complex cancer biology, clinical trial design, and identification of resistance mechanisms represent some of the challenges that future tissue-agnostic therapies have to overcome. In this Review, we present a brief history of the development of tissue-agnostic therapies, comparing the similarities in the approval of pembrolizumab, larotrectinib, and entrectinib for tissue-agnostic indications. We also explore the future of tissue-agnostic cancer therapeutics while identifying important challenges for the future that drugs targeting tissue-agnostic indications will face.

Figure 1

Clinical drug development timeline for FDA‐approved tissue‐agnostic therapies. Timeline of clinical development for current FDA‐approved tissue‐agnostic therapies including (a) pembrolizumab, (b) larotrectinib, and (c) entrectinib. FDA, US Food and Drug Administration.

Figure 2

Challenges in the development of tissue‐agnostic drugs. Tissue‐agnostic drug development is complicated by a number of challenges in the preclinical and clinical research space, as well as in clinical practice. Challenges noted here include development of better cancer models, novel clinical trial design, improved bioinformatics training and methods, cost of genomic testing, identifying resistance mechanisms, incorporating old and new multi‐omic data, identifying rare genomic variants, and decoding variants of unknown biological significance.