In silico simulation of a clinical trial with anti-CTLA-4 and anti-PD-L1 immunotherapies in metastatic breast cancer using a systems pharmacology model. Issue 5 (22nd May 2019)

• Record Type: Journal Article
• Authors: Wang, Hanwen; Milberg, Oleg; Bartelink, Imke H.; Vicini, Paolo; Wang, Bing; Narwal, Rajesh; Roskos, Lorin; Santa-Maria, Cesar A.; Popel, Aleksander S.
• Is Part Of: Royal Society open science. Volume 6:Issue 5(2019)
• Publication Date: 2019-05-22
• Subjects: immuno-oncology -- immune checkpoint inhibitor -- computational model -- systems biology -- computational biology; Science -- Periodicals; 500
• DOI: 10.1098/rsos.190366
• Languages: English
• ISSNs: 2054-5703

Abstract

Abstract : The low response rate of immune checkpoint blockade in breast cancer has highlighted the need for predictive biomarkers to identify responders. While a number of clinical trials are ongoing, testing all possible combinations is not feasible. In this study, a quantitative systems pharmacology model is built to integrate immune–cancer cell interactions in patients with breast cancer, including central, peripheral, tumour-draining lymph node (TDLN) and tumour compartments. The model can describe the immune suppression and evasion in both TDLN and the tumour microenvironment due to checkpoint expression, and mimic the tumour response to checkpoint blockade therapy. We investigate the relationship between the tumour response to checkpoint blockade therapy and composite tumour burden, PD-L1 expression and antigen intensity, including their individual and combined effects on the immune system, using model-based simulations. The proposed model demonstrates the potential to make predictions of tumour response of individual patients given sufficient clinical measurements, and provides a platform that can be further adapted to other types of immunotherapy and their combination with molecular-targeted therapies. The patient predictions demonstrate how this systems pharmacology model can be used to individualize immunotherapy treatments. When appropriately validated, these approaches may contribute to optimization of breast cancer treatment.