Integrating Systems Biology and an Ex Vivo Human Tumor Model Elucidates PD-1 Blockade Response Dynamics

Abstract

Ex vivo human tumor models have emerged as promising, yet complex tools to study cancer immunotherapy response dynamics. Here, we present a strategy that integrates empirical data from an ex vivo human system with computational models to interpret the response dynamics of a clinically prescribed PD-1 inhibitor, nivolumab, in head and neck squamous cell carcinoma (HNSCC) biopsies (N = 50). Using biological assays, we show that drug-induced variance stratifies samples by T helper type 1 (Th1)-related pathways. We then built a systems biology network and mathematical framework of local and global sensitivity analyses to simulate and estimate antitumor phenotypes, which implicate a dynamic role for the induction of Th1-related cytokines and T cell proliferation patterns. Together, we describe a multi-disciplinary strategy to analyze and interpret the response dynamics of PD-1 blockade using heterogeneous ex vivo data and in silico simulations, which could provide researchers a powerful toolset to interrogate immune checkpoint inhibitors.

Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

“…We have previously deployed this platform for the quantification of spatial and phenotypic heterogeneity in a variety of heterogeneous cancers, including non-small cell lung cancer ( Craig et al., 2019 ). Glioblastoma tissue (n = 4 patient samples) was cut manually into slices or fragments of various sizes up to 1 mm thick, and treated with rQNestin (0.1–5 PFU per cell, for 24 h) by adding the virus in a 100 L drop of saline to the piece of tissue submerged in 400 L of complete medium and incubated at 37 with 5% CO2 ( Smalley et al., 2020 ). Using a “sequential imaging strategy”, which we described recently ( Smalley et al., 2019 ), serial sections were then fixed and either stained with hematoxylin and eosin (H&E), or immunostained for HSV-1 (DAKO, B0114), GFP (GFP antibody CAT# AM1009a from Abgent, clone 168AT1211), or cleaved caspase-3 (Cell signaling Tech, for clone information see Goldman et al., 2015 ).…”

Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer

“…Mathematical modeling of tumor treatments has become a critical area in mathematical oncology and computational systems biology, with numerous studies highlighting its significance [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Various mathematical methods, including response-diffusion equations, have been applied to predict the dynamic evolution of tumors during combination therapy by quantifying inter-regulatory relationships between tumor cells, immune cells, and cytokines in the tumor microenvironment [29][30][31][32][33][34][35][36][37].…”

“…Robertson-Tessi et al [17,18] proposed a comprehensive mathematical model of tumor-immunity interactions, incorporating a negative feedback loop to account for immunosuppressive mechanisms. Additionally, numerous studies have developed mathematical models to investigate various aspects of tumor-immunity interactions, thus expanding the breadth of research in this field [19][20][21][22][23][24][25][26][27][28]. While attempting to encompass all the cell types and signaling molecules involved in tumor-immunity interactions may be ambitious, it is crucial to strike a balance, as overly simplistic models may fail to capture the complex dynamics observed both experimentally and clinically.…”