Rational design and identification of immuno-oncology drug combinations

Marco A J Iafolla1, Heather Selby2, Kathrin Warner3

  • 1Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, Ontario, M5G 2M9, Canada.

European Journal of Cancer (Oxford, England : 1990)
|April 10, 2018
PubMed
Abstract

Insights

This study reviewed immuno-oncology (IO) drug combinations in clinical trials and used tumor molecular profiling to guide rational selection of IO therapies. Analysis of gene expression data revealed promising combination strategies for cancer treatment.

Area of Science:

  • Oncology
  • Immunology
  • Genomics

Background:

  • Immuno-oncology (IO) drug combination trials often lack a strong scientific rationale.
  • Current selection of IO drug combinations relies heavily on empiricism or limited preclinical data.

Purpose of the Study:

  • To identify and analyze current clinical trials investigating combination IO drug therapies.
  • To explore the utility of tumor molecular profiling in rationalizing the selection of IO drug combinations.

Main Methods:

  • Compiled a list of IO targets and drugs from scientific literature and databases.
  • Searched clinicaltrials.gov for combination IO drug trials.
  • Analyzed The Cancer Genome Atlas (TCGA) data for IO target gene expression in tumors compared to normal tissues.
  • Utilized statistical methods to identify differentially expressed genes and performed heatmap analysis.

Main Results:

  • Identified 178 IO targets, with 90 having associated therapeutics.
  • Cataloged 410 combination IO trials, predominantly in skin and genitourinary cancers.
  • Found that combinations of cytotoxic T lymphocyte antigen 4 (CTLA4) with programmed cell death protein 1 (PD-1) or programmed cell death ligand 1 (PD-L1) are most frequent.
  • Gene expression analysis revealed promising IO drug combinations through heatmap analysis.

Conclusions:

  • There is significant interest and activity in combination IO clinical trials.
  • This study provides a data-driven approach to enhance the selection of IO combination therapies.

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