Immune Checkpoint Inhibitors in Advanced Non-Small Cell Lung Cancer: A Real-World Three Year Cohort: PD-L1-Stratified Outcomes, Adverse Event Profiles and Predictors of Durable Response

Dr. Rishi Rana, Anjali Yadav, Dr. Nidhi Tyagi

Non-Small Cell Lung Cancer; NSCLC; Immunotherapy; Pembrolizumab; Nivolumab; PD-L1; Immune-Related Adverse Events; Tumour Mutational Burden.

Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have transformed advanced non-small cell lung cancer (NSCLC) management, but real-world outcomes data from South Asian populations remain relatively limited. We prospectively followed 284 patients with advanced NSCLC initiated on immunotherapy at a tertiary cancer centre over 36 months, stratified by PD-L1 expression. Pembrolizumab monotherapy accounted for 174 patients (61.3%), pembrolizumab-chemotherapy combination for 92 (32.4%), and nivolumab or other agents for 18 (6.3%). Three-year overall survival was 50% in patients with PD-L1 >= 50%, 35% in those with PD-L1 1-49%, and 18% in PD-L1 negative patients. Objective response rate (complete or partial response) was 52% in PD-L1 ≥50% patients on monotherapy compared with 32% in PD-L1 <1% patients. Immune-related adverse events (any grade) occurred in 196 patients (69.0%), with thyroid dysfunction (38.6%) and cutaneous reactions (32.4%) the most common. Grade >=3 irAEs occurred in 18.0%. Strongest independent predictors of durable response (PFS ≥12 months) included PD-L1 expression, ECOG performance status, combination therapy, tumour mutational burden ≥10, and early irAE development. The findings support continued use of immunotherapy across PD-L1 strata with structured irAE surveillance.

[1] Agarwal, A., Kumar, D., & S, P. M. (2026). Optimizing clinical effectiveness of enhanced recovery after surgery (ERAS): Multidisciplinary pathways, patient-centered outcomes, and data-driven performance analytics. International Innovations & Scholarly Trends Journal, 2(2).
[2] Aneeshkumar A.S., C JothiVenkateswaran (2012), Estimating the surveillance of liver disorder using classification algorithms, International Journal of Computer Applications, 57(6), pp. 39-42.
[3] Bhatnagar, M., Kumar, N., & Shivam. (2026). Quality improvement frameworks in modern surgical practice: Evidence-based models, implementation science, and outcome-oriented performance evaluation. International Journal of Scientific Research and Engineering Development, 9(2).
[4] Catherine, S., Gupta, N., Gopi, E., & Swadhi, R. (2025). Enhancing patient engagement and outcomes through digital transformation: Machine learning in medical marketing. In Impact of digital transformation on business growth and performance (pp. 285–312). IGI Global.
[5] Deepa, R., Swadhi, R., Udayavani, V., Lakshmi, R., & Rafiq, S. (2026). Motion-controlled wearables for physiological monitoring and predictive diagnostics. In R. Vettriselvan & N. Suresh (Eds.), Intelligent motion control for human-centered systems (pp. 1–28). IGI Global.
[6] Gautam, M., Samyal, M., & Chaudhary, S. (2026). Preoperative risk stratification and surgical outcome prediction: Integrating clinical scoring systems, data-driven models, and patient-centered optimization. International Innovations & Scholarly Trends Journal, 2(3).
[7] Jha, S. C., Kumar, P., & Neha. (2026). Artificial intelligence-assisted decision support in internal medicine: Enhancing clinical judgment, precision care, and health system performance. International Journal of Scientific Development and Research, 11(2).
[8] Kumar, P., Gautam, S., & Maitiy, S. (2026). Diagnostic utility of biomarkers in early disease stratification: Clinical applications, predictive value, and emerging innovations. Journal of Emerging Technologies and Innovative Research, 13(2).
[9] Kumar, R., Sharma, K., & Gupta, S. K. (2026). Multimorbidity patterns and therapeutic complexity in adult medical practice: Implications for polypharmacy and patient-centred care. International Journal of Creative Research Thoughts, 14(2).
[10] Rasi, R. A., & Ashifa, K. M. (2019). Role of community-based programmes for active ageing: Elders self-help group in Kerala. Indian Journal of Public Health Research & Development, 10(12).
[11] Sahu, R. L., Sharma, K., & Gupta, S. K. (2026). Biological and mechanical determinants of fracture healing: An integrated mechano-biological, systemic, and translational framework. International Journal of Recent Development in Engineering and Technology, 15(3).
[12] A. S. Aneeshkumar (2018). Substantiation Study for Disease Prediction, Journal of Emerging Technologies and Innovative Research (JETIR), 5(7), pp. 249-254.
[13] Selvi, K., Anbarasan, P., Madhumita, G., Janaki, L., & Devi, K. K. (2026). Governance, security, and ethical considerations in AI-driven motion control systems. In Methodologies and applications of intelligent motion control systems (pp. 217–242). IGI Global.
[14] Subramani, M., Chillagattu, V., Gayathri, K., Rastogi, V., & Ranganathan, S. (2026). Digital twin integration for predictive and real-time motion control in infrastructure engineering. In Methodologies and applications of intelligent motion control systems (pp. 189–216). IGI Global.
[15] Swadhi, R., Gayathri, K., Suresh, N. V., Catherine, S., & Velmurugan, P. R. (2025). Leveraging machine learning for enhanced patient engagement and outcomes: Revolutionizing healthcare marketing. In Impact of digital transformation on business growth and performance (pp. 313–340). IGI Global.
[16] Vettriselvan, R., Ramya, R., Selvalakshmi, V., Jyothi, P., & Velmurugan, P. R. (2026). Empowering patients through knowledge: Educational strategies in rehabilitation. In Holistic approaches to health recovery (pp. 263–290). IGI Global.
[17] Vettriselvan, R., Velmurugan, P. R., Varshney, K. R., EP, J., & Deepika, R. (2025). Health impacts of smartphone and internet addictions across age groups: Physical and mental health across generations. In Impacts of digital technologies across generations (pp. 187–210). IGI Global.
[18] Aneeshkumar A. S, (2016). Relevance of Data Mining for Presumption of Anarchy, International Journal of Linguistics and computational Applications (IJLCA), 3(4), pp. 72-74.
[19] Vijayalakshmi, M., Subramani, A. K., Vettriselvan, R., Velmurugan, P. R., & Hasine, J. (2025). Strategic collaborations in medical innovation and AI-driven globalization: Advancing healthcare startups. In Navigating strategic partnerships for sustainable startup growth (pp. 85–110). IGI Global.
[20] Vinodh, N., Subramani, A. K., & Vettriselvan, R. (2026). Transforming the future of management and medical education: AI-driven innovations in curriculum design. In AI education strategies for future-proofing curriculum design (pp. 459–476). IGI Global.
[21] Yatish, Khatoon, N., & Kumar, A. (2026). Advancing preventive strategies for chronic disease management: Clinical, behavioral, and population-level perspectives. International Journal of Novel Trends and Innovation, 4(2).