Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune‐Oncology Therapies: A Tutorial for Clinical Pharmacologists

• Published in: Clinical pharmacology and therapeutics Vol. 107; no. 4; pp. 858 - 870
• Main Authors: Lazarou, Georgia, Chelliah, Vijayalakshmi, Small, Ben G., Walker, Michael, Graaf, Piet H., Kierzek, Andrzej M.
• Format: Journal Article
• Language: English
• Published: United States 01.04.2020
• Subjects: Carcinoma, Non-Small-Cell Lung - immunology; Carcinoma, Non-Small-Cell Lung - therapy; Data Collection - methods; Data Collection - statistics & numerical data; Drug Development - methods; Drug Development - statistics & numerical data; Humans; Immunity, Cellular - drug effects; Immunity, Cellular - immunology; Immunotherapy - methods; Immunotherapy - statistics & numerical data; Lung Neoplasms - immunology; Lung Neoplasms - therapy; Medical Oncology - methods; Medical Oncology - statistics & numerical data; Pharmacology, Clinical - methods; Pharmacology, Clinical - statistics & numerical data
• ISSN: 0009-9236; 1532-6535
• DOI: 10.1002/cpt.1786

Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These “omics” data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno‐oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non‐small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.