Rational design and identification of immuno-oncology drug combinations

Publication date: May 2018
Source:European Journal of Cancer, Volume 95
Author(s): Marco A.J. Iafolla, Heather Selby, Kathrin Warner, Pamela S. Ohashi, Benjamin Haibe-Kains, Lillian L. Siu
BackgroundClinical trials investigating immuno-oncology (IO) drug combinations are largely based on empiricism or limited non-clinical evaluations. This study identified the current combination IO drug clinical trials and investigated how tumour molecular profiling can help rationalise IO drug combinations.MethodsIO targets were identified via PubMed search and expert opinion. IO drugs were compiled by searching the National Cancer Institute Drug Dictionary and pharmaceutical pipelines, August 2016. Combination IO trials were obtained by searching doublet IO drug combinations in www.clinicaltrials.gov from September to November 2016. IO target gene expressions were extracted from The Cancer Genome Atlas (TCGA) data set and compared with normal tissues from the Genotype-Tissue Expression database. Differentially expressed genes for each cancer were determined using the Wilcoxon rank-sum test, and p-values were corrected for multiple testing.ResultsIn total, 178 IO targets were identified; 90 targets have either regulatory approved or investigational therapeutics. In total, 410 combination trials involving ≥2 IO drugs were identified: skin (n = 102) and genitourinary (n = 41) malignancies have the largest number of combination IO trials; 109 trials involved >2 disease sites. Summative patient accrual estimates among all trials are 71,345. Trials combining cytotoxic T lymphocyte antigen 4 (CTLA4) with programmed cell death protein 1 (n = 79) and CTLA4 with programmed cell death ligand 1 (n = 44) are the most common. Gene expression data from TCGA were mined to extract the 178 IO targets in 9089 tumours originating from 19 cancer types. IO target expression–clustered heatmap analysis identified several promising drug combinations.ConclusionOur review highlights the great interest in combination IO clinical trials. Our analysis can enrich IO combination therapy selection.



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