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

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.

Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models

“…In several studies [1,71,173,174], the authors analyzed the tumor-immune interaction in BC with respect to the use of various drugs. Specifically, the three main mechanisms involved in tumor-immune interaction: (1) elimination of tumor by the immune system, (2) equilibrium status (stable, dormant) attained by the tumor under the action of the immune system, and (3) escape of the tumor from immune action leading to uncontrolled growth, were studied in detail using mathematical models [1,175,176]. Apart from depicting tumor-immune interactions, mathematical models were used to determine the growth of tumor in different stages of primary BC (T1a, T1b, T1c, T2, T3) in patients with no metastasis (M0) and no lymph node involvement (N0) [177].…”

“…The mathematical models discussed in [39,54,145,182], in relation to immune checkpoint pathways are general and not for BC in particular. In 2019, the first in silico trial with the use of immune checkpoint inhibitors (anti-CDLA-4, anti-PD-L1) in patients with metastatic BC has been reported [175]. In this paper, mathematical models are used to explain immune suppression and evasion in tumor-draining lymph node and tumor microenvironment.…”

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

“…Although modeling of the interaction between the immune system and tumors is a longstanding topic in mathematical biology, [7][8][9] the new interest triggered by the success of IO has led to intensive development of large-scale QSP platform models to support development of combination therapies. 10,11 However, determination of the parameters of these models and their further calibration for specific compounds, cancers, and patient populations has been an enduring challenge, frequently addressed by the allometric scaling of models developed first for syngeneic mouse tumors. However, the translatability of such preclinical models and their utility for clinical pharmacologists remains controversial.…”

Combining microenvironment normalization strategies to improve cancer immunotherapy

“…We hypothesize that a mathematical model that incorporates the known mechanisms of immune response as well as vascular and stroma normalization can explain the experimental observations reported in the literature and provide guidelines for its optimal use and for future experiments. Systems-level mathematical models for the prediction of anti-CTLA-4 and anti-PD-1/anti-PD-L1 response have been developed previously (43)(44)(45); however, they do not account for spatial variation in the TME. Here, based on our previous study (41) and the work of others (46,47), we developed a continuum mathematical modeling framework to account for interactions among different types of cancer cells, immune cells, tumor blood vessels, oxygen supply, and drug delivery.…”