Defesa de Dissertação de Mestrado: Modeling CAR-T Cell and Tumor Cell Interactions Using Boolean Networks: Investigating Computational Approaches to Enhance Cancer Immunotherapy

Orientadores:
Regina Célia Cerqueira de Almeida - Laboratório Nacional de Computação Científica - LNCC
Luciana Rodrigues Carvalho Barros - Universidade de São Paulo - ICESP/USP
Marcelo Trindade dos Santos - Laboratório Nacional de Computação Científica - LNCC

Banca Examinadora:
Luciana Rodrigues Carvalho Barros - Universidade de São Paulo - ICESP/USP (presidente)
Emanuelle Arantes Paixão - Laboratório Nacional de Computação Científica - LNCC
Vincent Olivier Jean-Marie Noël - Faculdade de Medicina da Universidade de São Paulo - FMUSP

Suplentes:
Marisa Fabiana Nicolas - Laboratório Nacional de Computação Científica - LNCC
Paulo Fernando de Arruda Mancera - Universidade Estadual Paulista - UNESP/Botucatu

Resumo:

Cancer is a complex disease driven by the accumulation of mutations that disrupt cellular homeostasis, leading to uncontrolled proliferation and evasion of regulatory mechanisms. Among emerging treatments, CAR-T cell immunotherapy has shown remarkable success, particularly in hematological malignancies, by genetically engineering T cells to target tumor-associated antigens. However, challenges such as high manufacturing costs, toxic side effects, and incomplete understanding of CAR-T cell signaling dynamics limit its broader application. Computational modeling provides a powerful tool for investigating these challenges, enabling the simulation of CAR-T cell behavior under various conditions without incurring extensive experimental costs. In this study, we explore Boolean network (BN) models as a framework to study CAR-T cell signaling and tumor cell interactions, leveraging their abil ity to capture qualitative regulatory dynamics without requiring precise kinetic parameters. We focus on two BN methodologies—the Stochastic Discrete Dynamic System (SDDS) and the Boolean Kinetic Monte Carlo (BKMC)—applied to a model of CAR-T cell-tumor cell interactions. Our analysis elucidates the technical distinctions between these approaches and proposes optimized methodologies for their implementation. Our analysis highlights the potential of these approaches to elucidate key signaling mechanisms, optimize CAR-T cell design, and predict treatment outcomes. By integrating computational and immunological insights, this work contributes to the advancement of more effective and safer CAR-T cell therapies.