Computational repurposing of oncology drugs through off-target drug binding interactions from pharmacological databases

Abstract

Purpose: Systematic repurposing of approved medicines for another indication may accelerate drug development in oncology. We present a strategy combining biomarker testing with drug repurposing to identify new treatments for patients with advanced cancer.

Methods: Tumours were sequenced with the Illumina TruSight Oncology 500 (TSO-500) platform or the FoundationOne CDx panel. Mutations were screened by two medical oncologists and pathogenic mutations were categorised referencing literature. Variants of unknown significance were classified as potentially pathogenic using plausible mechanisms and computational prediction of pathogenicity. Gain of function (GOF) mutations were evaluated through repurposing databases Probe Miner (PM), Broad Institute Drug Repurposing Hub (Broad Institute DRH) and TOPOGRAPH. GOF mutations were repurposing events if identified in PM, not indexed in TOPOGRAPH and excluding mutations with a known Food and Drug Administration (FDA)-approved biomarker. The computational repurposing approach was validated by evaluating its ability to identify FDA-approved biomarkers. The total repurposable genome was identified by evaluating all possible gene-FDA drug-approved combinations in the PM dataset.

Results: The computational repurposing approach was accurate at identifying FDA therapies with known biomarkers (94%). Using next-generation sequencing molecular reports (n = 94), a meaningful percentage of patients (14%) could have an off-label therapeutic identified. The frequency of theoretical drug repurposing events in The Cancer Genome Atlas pan-cancer dataset was 73% of the samples in the cohort.

Conclusion: A computational drug repurposing approach may assist in identifying novel repurposing events in cancer patients with no access to standard therapies. Further validation is needed to confirm a precision oncology approach using drug repurposing.

Keywords

drug repurposing / precision oncology / sequencing

References

[1] Liu R, Rizzo S, Whipple S, et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature. 2021;592(7855):629-633.

[2] Minasian LM, Unger JM. What Keeps Patients out of Clinical Trials? American Society of Clinical Oncology; 2020:125-127.

[3] Chihara D, Lin R, Flowers CR, et al. Early drug development in solid tumours: analysis of National Cancer Institute-sponsored phase 1 trials. Lancet. 2022;400(10351):512-521.

[4] Schwaederle M, Zhao M, Lee JJ, et al. Association of biomarker-based treatment strategies with response rates and progression-free survival in refractory malignant neoplasms: a meta-analysis. JAMA Oncol. 2016;2(11):1452-1459.

[5] Garraway LA, Verweij J, Ballman KV. Precision oncology: an overview. J Clin Oncol. 2013;31(15):1803-1805.

[6] Park JJ, Hsu G, Siden EG, Thorlund K, Mills EJ. An overview of precision oncology basket and umbrella trials for clinicians. Cancer J Clin. 2020;70(2):125-137.

[7] Flaherty KT, Gray RJ, Chen AP, et al. Molecular landscape and actionable alterations in a genomically guided cancer clinical trial: National Cancer Institute molecular analysis for therapy choice (NCI-MATCH). J Clin Oncol. 2020;38(33):3883-3894.

[8] Schipper LJ, Zeverijn LJ, Garnett MJ, Voest EE. Can drug repurposing accelerate precision oncology?Cancer Discov. 2022;12(7):1634-1641.

[9] Antoszczak M, Markowska A, Markowska J, Huczyński A. Old wine in new bottles: drug repurposing in oncology. Eur J Pharmacol. 2020;866:172784.

[10] Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18(1):41-58.

[11] Corsello SM, Nagari RT, Spangler RD, et al. Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. Nat Cancer. 2020;1(2):235-248.

[12] Corsello SM, Bittker JA, Liu Z, et al. The drug repurposing hub: a next-generation drug library and information resource. Nat Med. 2017;23(4):405-408.

[13] Administration USFaD. Table of Pharmacogenomic Biomarkers in Drug Labeling. 2022. Accessed 26 March 2022. Available from:

[14] Antolin AA, Tym JE, Komianou A, Collins I, Workman P, Al-Lazikani B. Objective, quantitative, data-driven assessment of chemical probes. Cell Chem Biol. 2018;25(2):194-205. e5

[15] Mitsopoulos C, Di Micco P, Fernandez EV, et al. canSAR: update to the cancer translational research and drug discovery knowledgebase. Nucleic Acids Res. 2021;49(D1):D1074-D1082.

[16] Diaz LA, Marabelle A, Delord J-P, et al. Pembrolizumab therapy for microsatellite instability high (MSI-H) colorectal cancer (CRC) and non-CRC. J Clin Oncol. 2017;35:3071. 15_suppl

[17] Tate JG, Bamford S, Jubb HC, et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 2018;47(D1):D941-D947.

[18] Shihab HA, Gough J, Cooper DN, et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat. 2013;34(1):57-65.

[19] Lin FP, Thavaneswaran S, Grady JP, et al. Criteria-based curation of a therapy-focused compendium to support treatment recommendations in precision oncology. NPJ Precis Oncol. 2021;5(1):1-10.

[20] Leichsenring J, Horak P, Kreutzfeldt S, et al. Variant classification in precision oncology. Int J Cancer. 2019;145(11):2996-3010.

[21] Picco G, Chen ED, Alonso LG, et al. Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR-Cas9 screening. Nat Commun. 2019;10(1):1-12.

[22] Iorio F, Knijnenburg TA, Vis DJ, et al. A landscape of pharmacogenomic interactions in cancer. Cell. 2016;166(3):740-754.

[23] Ghandi M, Huang FW, Jané-Valbuena J, et al. Next-generation characterization of the cancer cell line encyclopedia. Nature. 2019;569(7757):503-508.

[24] Dempster JM, Rossen J, Kazachkova M, et al. Extracting biological insights from the project Achilles genome-scale CRISPR screens in cancer cell lines. bioRxiv. 2019:720243. bioRxiv.

[25] Meyers RM, Bryan JG, McFarland JM, et al. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat Genet. 2017;49(12):1779-1784.

[26] Dempster JM, Boyle I, Vazquez F, et al. Chronos: a CRISPR cell population dynamics model. BioRxiv. 2021:432728.

[27] Pacini C, Dempster JM, Boyle I, et al. Integrated cross-study datasets of genetic dependencies in cancer. Nat Commun. 2021;12(1):1-14.

[28] Corsello SM, Nagari RT, Spangler RD, et al. Non-oncology drugs are a source of previously unappreciated anti-cancer activity. BioRxiv. 2019:730119.

[29] Ghandi M, Huang FW, Jané-Valbuena J, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019;569(7757):503-508.

[30] Yang W, Soares J, Greninger P, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2012;41(D1):D955-D961.

[31] Campbell PJ, Getz G, Korbel JO, et al. Pan-cancer analysis of whole genomes. Nature. 2020;578(7793):82-93.

[32] Cerami E, Gao J, Dogrusoz U, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401-404.

[33] Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1.

[34] Davis MI, Hunt JP, Herrgard S, et al. Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol. 2011;29(11):1046-1051.

[35] Musumeci F, Radi M, Brullo C, Schenone S. Vascular endothelial growth factor (VEGF) receptors: drugs and new inhibitors. J Med Chem. 2012;55(24):10797-10822.

[36] Bergeron P, Koehler MF, Blackwood EM, et al. Design and development of a series of potent and selective type II inhibitors of CDK8. ACS Med Chem Lett. 2016;7(6):595-600.

[37] Murciano-Goroff YR, Drilon A, Stadler ZK. The NCI-MATCH: a national, collaborative precision oncology trial for diverse tumor histologies. Cancer Cell. 2021;39(1):22-24.

[38] Kaelin WG. Common pitfalls in preclinical cancer target validation. Nat Rev Cancer. 2017;17(7):441-450.

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2024 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.