Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data Journal Article
| Authors: | Ramos-Guerra, A. D.; Farina, B.; Rubio Pérez, J.; Vilalta-Lacarra, A.; Zugazagoitia, J.; Peces-Barba, G.; Seijo, L. M.; Paz-Ares, L.; Gil-Bazo, I.; Dómine Gómez, M.; Ledesma-Carbayo, M. J. |
| Article Title: | Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data |
| Abstract: | The identification of non-small cell lung cancer (NSCLC) patients who will benefit from immunotherapy remains a clinical challenge. Monitoring real-world data (RWD) in the first cycles of therapy may provide a more accurate representation of response patterns in a real-world setting. We propose a multivariate Bayesian joint model using generalized linear mixed effects, trained and validated on RWD from 424 advanced NSCLC patients retrospectively collected from three clinical centers. Center1 was used as training (N=212), while Center2 and Center3 were used as independent testing sets (N=137 and N=75, respectively). Peripheral blood data (PBD) were collected at baseline and at three follow-up time points, alongside demographic and epidemiologic features. Six models were trained to predict progression-free survival at 6 months, PFS(6), using different number of longitudinal samples (baseline, two, or four time points) of the neutrophil-to-lymphocyte ratio (NLR) or a multivariate feature selection. Long-term predictions at 12 and 24 months were also evaluated. Prediction accuracy was measured using the area under the receiver operating characteristic curve (AUC). The proposed model significantly improved prediction performance, achieving AUCs of 0.870, 0.804 and 0.827 at 6, 12 and 24 months for Center2, and 0.824, 0.822 and 0.667 for Center3. There was also a significant difference in PFS and overall survival (OS) between predicted response groups, defined by a 6-month PFS cutoff (log-rank test p<0.001). Our study suggests that the integration of multiple biomarkers and monitored PBD in an RWD-based Bayesian joint model framework significantly improves immunotherapy response prediction in advanced NSCLC compared to conventional approaches involving biomarker data at baseline only. © The Author(s) 2025. |
| Keywords: | adult; controlled study; treatment response; aged; aged, 80 and over; middle aged; antibiotic agent; retrospective studies; unclassified drug; major clinical study; overall survival; mortality; advanced cancer; area under the curve; monotherapy; bone metastasis; cancer radiotherapy; comparative study; lymph node metastasis; sensitivity and specificity; cytotoxic t lymphocyte antigen 4 antibody; cancer immunotherapy; progression free survival; multiple cycle treatment; bayes theorem; carcinoma, non-small-cell lung; lung neoplasms; cohort analysis; steroid; retrospective study; tumor marker; prediction; distant metastasis; pneumonia; lung tumor; liver metastasis; lung metastasis; blood; immunology; body mass; albumin; immune response; neutrophil; immunotherapy; proton pump inhibitor; blood brain barrier; colitis; neutrophils; lactate dehydrogenase; multivariate analysis; dermatitis; cancer epidemiology; insulin dependent diabetes mellitus; non insulin dependent diabetes mellitus; kidney metastasis; therapy; drug induced disease; immunosuppressive agent; non-small-cell lung cancer; receiver operating characteristic; sex; inducible t cell costimulator; adrenal metastasis; non small cell lung cancer; central nervous system metastasis; demographics; albumin blood level; procedures; prognostic marker; overall response rate; survival prediction; first-line treatment; immune checkpoint inhibitor; response evaluation criteria in solid tumors; youden index; epidemiological data; treatment protocol; nivolumab; potential difference; very elderly; humans; prognosis; human; male; female; article; pembrolizumab; neutrophil lymphocyte ratio; atezolizumab; biomarkers, tumor; longitudinal analysis; ecog performance status; real-world data; antineoplastic monoclonal antibody; cross validation; t cell dysfunction; population parameters; hepatitis a virus cellular receptor 2 antibody; metastasis site; peripheral blood biomarker; colony stimulating factor 1 receptor antibody; immune related adverse drug reaction; markov chain monte carlo method |
| Journal Title: | Cancer Immunology, Immunotherapy |
| Volume: | 74 |
| Issue: | 4 |
| ISSN: | 03407004 |
| Publisher: | The Author(s) 2025 |
| Date Published: | 2025-04-01 |
| Start Page: | 120 |
| Language: | English |
| DOI: | 10.1007/s00262-025-03966-9 |
| PUBMED: | 39998679 |
| PROVIDER: | scopus |
| PMCID: | PMC11861465 |
| DOI/URL: | |
| Notes: | Article -- Erratum issued, see DOI: 10.1007/s00262-025-04055-7 -- Source: Scopus |
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