Marco A J Iafolla1, Heather Selby2, Kathrin Warner3, Pamela S Ohashi4, Benjamin Haibe-Kains5, Lillian L Siu6. 1. Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada. Electronic address: marco.iafolla@uhn.ca. 2. Bioinformatics Program, Boston University, Boston, MA, 02215, USA. Electronic address: selbyh@bu.edu. 3. Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada; Campbell Family Institute for Breast Cancer Research, Department of Immunology, Suite 7-504, 620 University Ave, Toronto, Ontario, M5G 2C1, Canada. Electronic address: kathrin_warner@yahoo.de. 4. Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada; Campbell Family Institute for Breast Cancer Research, Department of Immunology, Suite 7-504, 620 University Ave, Toronto, Ontario, M5G 2C1, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street Suite 15-701, Toronto, Ontario, M5G 1L7, Canada; Department of Immunology, University of Toronto, 1 King's College Circle, Medical Sciences Building, Room 7207, Ontario, M5S 1A8, Canada. Electronic address: pam.ohashi@uhnresearch.ca. 5. Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada; Department of Medical Biophysics, University of Toronto, 101 College Street Suite 15-701, Toronto, Ontario, M5G 1L7, Canada; Department of Computer Science, University of Toronto, 214 College St, Toronto, Ontario, M5T 3A1, Canada; Ontario Institute of Cancer Research, 661 University Ave, Suite 510, Toronto, Ontario, M5G 0A3, Canada. Electronic address: benjamin.haibe.kains@utoronto.ca. 6. Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada. Electronic address: lillian.siu@uhn.ca.
Abstract
BACKGROUND: Clinical 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. METHODS: IO 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. RESULTS: In 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. CONCLUSION: Our review highlights the great interest in combination IO clinical trials. Our analysis can enrich IO combination therapy selection.
BACKGROUND: Clinical 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. METHODS:IO 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. RESULTS: In 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. CONCLUSION: Our review highlights the great interest in combination IO clinical trials. Our analysis can enrich IO combination therapy selection.
Authors: Sungyong You; Minhyung Kim; Xen Ping Hoi; Yu Cheng Lee; Li Wang; David Spetzler; Jim Abraham; Dan Magee; Prerna Jain; Matthew D Galsky; Keith Syson Chan; Dan Theodorescu Journal: J Natl Cancer Inst Date: 2022-10-06 Impact factor: 11.816