Digital immune twins and ai-integrated multi-omic biomarkers: Redefining personalized immunotherapy in non-small cell lung cancer
نویسندگان
1 Faculty of Allied Medical Sciences, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan
2 Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India
3 Department of General Medicine, IMS and SUM Hospital, Siksha ‘O’ Anusandhan, Bhubaneswar, Odisha-751003, India
4 University institute of Pharma Sciences, Chandigarh University, Mohali, Punjab, India
5 Department of Pharmaceutics and Pharmaceutical Technology, School of Pharmacy, University of Jordan, Amman, 11942, Jordan
6 Department of Informatics, Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 949 01 Nitra, Slovakia
7 Kandó Kálmán Faculty of Electrical Engineering, Óbuda University 1034 Budapest, Bécsi út 94-96, Hungary
8 Department of Biotechnology, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
9 School of Applied and Life Sciences, Division of Research and Innovation, Uttaranchal University, Dehradun, Uttarakhand, India
doi
10.22038/ijbms.2026.92560.19984چکیده
Non-small cell lung cancer (NSCLC) remains one of the leading causes of global cancer mortality despite advances in immunotherapy. While immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 axis have transformed clinical outcomes for selected patients, response rates remain highly variable due to tumor heterogeneity, immune escape mechanisms, and evolving biomarker complexity. The need for dynamic, integrative biomarkers that better predict treatment response and guide personalized therapy is increasingly critical. This narrative review synthesizes recent advances (2023–2025) in genomic, transcriptomic, proteomic, metabolomic, and liquid-biopsy-based biomarkers relevant to NSCLC immunotherapy. Key databases, including PubMed, Scopus, and Web of Science, were screened, with emphasis on emerging artificial intelligence (AI) and digital twin–based frameworks supporting precision immuno-oncology. Across studies, single biomarkers such as PD-L1 or tumor mutational burden (TMB) demonstrate limited standalone predictive value. Multi-omic signatures incorporating circulating tumor DNA (ctDNA) fragmentomics, exosomal PD-L1, T-cell receptor (TCR) repertoire diversity, DDR alterations, metabolic checkpoint activity, and spatial immune profiling demonstrate improved accuracy and clinical relevance (clinical and preclinical evidence). AI-based multimodal models and digital immune twins further enhance predictive capacity by mapping resistance trajectories and simulating individualized therapeutic responses (computational/model-based evidence).The transition from static biomarkers toward integrated multi-omic and AI-driven decision frameworks represents a paradigm shift in NSCLC immunotherapy. These emerging platforms support a future of adaptive, anticipatory, and personalized treatment strategies with strong translational potential.
