Next-Gen Immunotherapy Optimization Using Deep Molecular Interaction Networks

Abstract

Improving lung cancer immunotherapy necessitates better biomarker identification, predictive modeling, and treatment analysis, with challenges in T cell expansion from reagents and contamination. Prognostic evaluations depend on cancer cell arrangement, tumor-infiltrating lymphocytes, and gene alterations. To address this, issue a new technique is developed the Lung Cancer Immunotherapy Optimization Through Integrated Molecular Interaction (LCIOIM) model by utilizing multimodal data, including clinical, genomic, radiographic, and immunological data, from 1, 435 NSCLC patients treated during 2010 to 2025. The Patient Feature Encoder (PFE) employs multi-layer perceptron networks to correlate transcriptome and pathway data with unique signatures. The Immune Evasion vs. Neoantigen Silencing Module uses a tumor dynamics model to assess immunotherapy resistance. The Immunotherapy Sensitivity Calculation (ISC) estimates treatment effectiveness based on Tumor Mutational Burden (TMB), immune checkpoint activity, HLA expression, markers of immunogenic cell death, and mRNA vaccine responsiveness. Mamdani and Sugeno systems enable transparent clinical decision-making. The Image and Data Analysis module assesses tumor cell proliferation and morphology through geometric modeling. The Classification Process employs ensemble methods and SVM classifiers, optimized with MobileNetV3-Small feature vectors. A Hierarchical Multi-Scale Transformer (HMT) learns semantic representations across scales. Additionally, a Semi-Supervised Adversarial Learning Mechanism improves biomarker prediction using labeled and unlabeled data. The Multi-Objective Loss Function Framework integrates focal classification loss, modality-specific learning loss, and cosine alignment loss for effective feature fusion and reliable predictions.