Targeting pan-essential genes in cancer: Challenges and opportunities

Abstract

Despite remarkable successes in the clinic, cancer targeted therapy development remains challenging and the failure rate is disappointingly high. This problem is partly due to the misapplication of the targeted therapy paradigm to therapeutics targeting pan-essential genes, which can result in therapeutics whereby efficacy is attenuated by dose-limiting toxicity. Here we summarize the key features of successful chemotherapy and targeted therapy agents, and use case studies to outline recurrent challenges to drug development efforts targeting pan-essential genes. Finally, we suggest strategies to avoid previous pitfalls for ongoing and future development of pan-essential therapeutics.

Keywords: drug development; pan-essential genes; target identification; target validation; targeted therapies.

Figure 1.. Therapeutic index of cancer therapeutic agents

Therapeutic index (TI) is the ratio of the dose or exposure of a drug required to elicit the desired therapeutic effect (green arrow) compared with the dose or exposure at which toxicity becomes limiting (orange arrow). High-TI drugs (many successful targeted therapy drugs) are efficacious at well-tolerated doses; narrow-TI drugs (chemotherapies, some pan-essential gene inhibitors) often have high-efficacy doses slightly below doses leading to dose-limiting toxicities; the effective doses of inverted-TI drugs (some pan-essential gene inhibitors) are lower than doses that lead to severe toxicities, and these drugs often fail in clinical development.

Figure 2.. Genome-wide CRISPR knockout and compound profiling in cancer cell lines revealed the pan-essential nature of many cancer drug targets

(A–D) Density plots representing the CERES score distribution of cancer cell lines after the indicated gene knockout. A CERES score of −1 represents the median effect of knocking out essential genes, and a CERES score of 0 represents no growth disadvantage. CRISPR (Avana) Public 20Q2 dataset from Broad Institute was used in this analysis. (E–H) Scatterplots representing the IC₅₀ distributions of the indicated drugs and their molecular targets in ~800 cancer cell lines. GDSC2 dataset from Sanger Institute was used in this analysis.

Figure 3.. Biomarker correlations with BRD inhibitor, BRAF inhibitor, and ABL inhibitor drug sensitivities

The AUC values of two BET inhibitors, OTX015 (A) and I-BET-762 (B), are plotted against the CERES score of BRD4 knockouts in the same cell line. The AUC values of the BRAF inhibitor dabrafenib (C) and ABL inhibitor nilotinib (D) are plotted against the CERES score of BRAF knockout in the same cell line. Each dot represents a cancer cell line and red colors represent MYC amplification (A and B), BRAF hotspot mutation (C), or BCR-ABL fusion (D) in the cell line. Genomic and CRISPR data were obtained from Broad Institute Depmap portal. BET inhibitor AUC data were obtained from Genomics of Drug Sensitivity in Cancer Portal.

Figure 4.. Clinical development trajectories of a selection of pan-essential and highly selective targeted therapy drug candidates

Clinical trial information for three pan-essential drug candidates discussed in case studies (A) and three successful targeted therapy agents (B) were obtained from clinicaltrials.gov. Clinical trials of corresponding drug candidates are included for this analysis if they have the status suspended, terminated, completed, or withdrawn, or have published trial results.