Biology subjects

Sweis, R. F.

Publications and source records attributed to Sweis, R. F..

4 recordsLinked to original sources

bioRxiv2026

Enhancing Predictive Accuracy in Immunotherapy Models through Data Integration and Parameter Identifiability

Immune checkpoint inhibitors (ICIs), a class of immunotherapy, offer promising benefits but face challenges such as low response rates to be used as a broadly effective treatment for all patients. In this study, we use a set of ordinary different equation (ODE) models and bladder cancer in vivo data as a case study to outline a biologically informed, data-driven framework for formulating, calibrating and validating immunotherapy models, and thus ensuring their predictive reliability. We consider multiple treatment scenarios and distinct immune cell-mediated killing mechanisms for tumor cells of different antigenicity. By integrating sensitivity analysis and identifiability analysis with targeted experimental design, we demonstrate how mathematical models can move beyond qualitative insight to quantitative prediction. We generate virtual cohorts to show that insufficient data integration leads to systematically overestimated therapeutic benefits of ICIs. We also explore dosing schedules that enhance survival or reduce dosage without compromising survival.

cell biology↗

bioRxiv2024

Agent-Based Modeling of Virtual Tumors Reveals the Critical Influence of Microenvironmental Complexity on Immunotherapy Efficacy

Since the introduction of the first immune checkpoint inhibitor (ICI), immunotherapy has changed the landscape of molecular therapeutics for cancers. However, ICIs do not work equally well on all cancers and for all patients. There has been a growing interest in using mathematical and computational models to optimize clinical responses. Ordinary differential equations (ODEs) have been widely used for mechanistic modeling in immuno-oncology and immunotherapy because they allow rapid simulations of temporal changes in the cellular and molecular populations involved. Nonetheless, ODEs cannot describe the spatial structure in the tumor microenvironment or quantify the influence of spatially-dependent characteristics of tumor-immune dynamics. For these reasons, agent-based models (ABMs) have gained popularity because they can model more detailed phenotypic and spatial heterogeneity that better reflect the complexity seen in vivo. In the context of anti-PD-1 ICIs, we compare treatment outcomes simulated from an ODE model and an ABM to show the importance of including spatial components in computational models of cancer immunotherapy. We consider tumor cells of high and low antigenicity and two distinct cytotoxic T lymphocyte (CTL) killing mechanisms. The preferred mechanism differs based on the antigenicity of tumor cells. Our ABM reveals varied phenotypic shifts within the tumor and spatial organization of tumor and CTLs, despite similarities in key immune parameters, initial conditions of simulation, and early temporal trajectories of the cell populations.

cancer biology↗

bioRxiv2023

Mathematical Model Predicts Tumor Control Patterns Induced by Fast and Slow CTL Killing Mechanisms

Immunotherapy has dramatically transformed the cancer treatment landscape largely due to the efficacy of immune checkpoint inhibitors (ICIs). Although ICIs have shown promising results for many patients, the low response rates in many cancers highlight the ongoing challenges in cancer treatment. Cytotoxic T lymphocytes (CTLs) execute their cell-killing function via two distinct mechanisms: a fast-acting, perforin-mediated process and a slower, Fas ligand (FasL)-driven path-way. Evidence also suggests that the preferred killing mechanism of CTLs depends on the anti-genicity of tumor cells. To determine the critical factors affecting responses to ICIs, we construct an ordinary differential equation model describing in vivo tumor-immune dynamics in the presence of active or blocked PD-1/PD-L1 immune checkpoint. Specifically, we identify important aspects of the tumor-immune landscape that affect tumor size and composition in the short and long term. By generating a virtual cohort with differential tumor and immune attributes, we also simulate the therapeutic outcomes of immune checkpoint blockade in a heterogenous population. In this way, we identify key tumor and immune characteristics that are associated with tumor elimination, dor-mancy, and escape. Our analysis sheds light on which fraction of a population potentially responds well to ICIs and ways to enhance therapeutic outcomes with combination therapy.

cancer biology↗

bioRxiv2020

Germline genetic contribution to the immune landscape of cancer

The role of germline genetics in shaping the tumor immune landscape is largely unknown. Using genotypes from >9,000 individuals in The Cancer Genome Atlas, we investigated the association of common and rare variants with 139 well-defined immune traits. Our analysis of common variants identified 10 immune traits with significant heritability estimates, and an additional 23 with suggestive heritability, including estimates of T-cell subset abundance and interferon signaling. We performed genome-wide association on the 33 heritable traits and identified 23 genome-wide significant loci associated with at least one immune trait, including SNPs in the IFIH1 locus previously associated with several autoimmune diseases. We also found significant associations between immune traits and pathogenic or likely-pathogenic rare variants in BRCA1 and in genes functionally linked to telomere stabilization, and Wnt/Beta-catenin signaling. We conclude that germline genetic variants significantly impact the composition and functional orientation of the tumor immune microenvironment.

immunology↗