Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response

Abstract

Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of cancer cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient response nor implicate the mechanisms that underlie immunotherapy resistance. Recent advances in single-cell RNA sequencing technology measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are developed to predict treatment response. Incorporating single-cell data from tumors to parameterize these computational models provides deeper insights into prediction of clinical outcome in individual patients. Here, we integrate whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model. The model comprises a four-compartmental Quantitative System Pharmacology sub-model to represent a whole patient and a spatial agent-based sub-model to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. We demonstrate how this integrated modeling and single-cell analysis framework can be used to relate neoantigen heterogeneity to immunotherapy treatment outcomes. Our results demonstrate feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.

Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer

“…To capture the inter-individual heterogeneity of patients with metastatic TNBC, we estimated the virtual patient distribution according to pre/clinical data on TNBC or other breast cancer subtypes if TNBC data are not available. In addition, multi-omics and digital pathology data have proven useful when generating physiologically reasonable virtual patients, and in future such data can be further collected from TNBC and incorporated into our QSP platform ( Lazarou et al., 2020 ; Zhang et al., 2021a ). Notably, in the tutorial created by ( Lazarou et al., 2020 ), they listed the multi-omics data that can be utilized to predict neoantigen binding affinity with MHC molecules, T cell receptor repertoire diversity, immune cell composition in lymph nodes, and more for QSP model calibration.…”

“…In the past few years, we have developed and expanded a large-scale QSP platform for the analyses of immune checkpoint inhibitors and bispecific T cell engagers in combination with other agents in non-small cell lung cancer ( Jafarnejad et al., 2019 ; Sové et al., 2020 ), colorectal cancer ( Ma et al., 2020a ; 2020b ) and breast cancer ( Wang et al., 2020a , 2021 ). We have also combined the QSP model with a spatial agent-based model of tumor to describe spatial heterogeneity of the tumor microenvironment ( Gong et al., 2021 ; Zhang et al., 2021a ). Here, by integrating a macrophage module into our previously published QSP platform ( Wang et al., 2020a , 2021 ), we are able to investigate the impact of TAMs on the cancer-immune cell interactions and provide a computational framework to predict clinical response to macrophage-targeted agents based on preclinical data.…”

From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers

“…It is of note that this study uses just one mathematical model, with one set of assumptions, to reach our cautionary conclusion regarding the fitting methodology utilized and the resulting biological predictions. And this model is quite a simple one, ignoring many aspects of the immune system, and spatial aspects of immune infiltration (as done in ( 50 ), among many other references). The model used herein was chosen because it has been previously validated against the average of the available experimental data.…”

From fitting the average to fitting the individual: A cautionary tale for mathematical modelers

“…It is of note that this study uses just one mathematical model, with one set of assumptions, to reach our cautionary conclusion regarding the fitting methodology utilized and the resulting biological predictions. And this model is quite a simple one, ignoring many aspects of the immune system, and spatial aspects of immune infiltration (as done in [48], among many other references). The model used herein was chosen because it has been previously validated against the average of the available experimental data.…”