Integrating Immunotherapy With Targeted Therapy: Evaluating Efficacy in NSCLC Treatment Through Computational Analysis of Gene Regulatory Networks

Pranabesh Bhattacharjee, Aditya Lahiri, Tanmay Sindhwani, Norman Peter Reeves, Aniruddha Datta

Abstract: Non-Small Cell Lung Cancer (NSCLC) remains a leading cause of cancer-related deaths, often characterized by complex mutational landscapes and resistance to monotherapies. To address this, we developed a novel Boolean network (BN) model that integrates oncogenic and immunotherapy signaling pathways to evaluate the efficacy of FDA-approved drug combinations in NSCLC. This computational framework simulates the behavior of key cancer-related pathways under single and multiple mutation scenarios, offering a system-level understanding of drug response. Our model incorporates sixteen targeted inhibitors and simulates their effects on proliferation-promoting and apoptosis-regulating genes. Drug efficacy was quantitatively assessed using a normalized mean size difference (NMSD) metric. Unlike prior models that examine targeted therapy or immunotherapy in isolation, our integrated approach enables systematic evaluation of synergistic effects between these modalities. Key results show that the inclusion of immunotherapy—particularly PD-L1 inhibitors such as Durvalumab—significantly improves therapeutic outcomes, especially in networks with multiple co-occurring mutations. The most effective four-drug combination identified (Durvalumab + Lumakras + Ribociclib + Capivasertib) targets immune evasion, KRAS signaling, cell cycle regulation, and AKT activation, reducing tumor-promoting signals by 89.2% compared to the untreated state. This study provides a theoretical and mechanistic basis for combining immune checkpoint blockade with targeted therapies in NSCLC and demonstrates the utility of BN modeling in optimizing personalized, mutation-specific treatment strategies.