Download This Paper
Preprints with The Lancet is part of SSRN´s First Look, a place where journals identify content of interest prior to publication. Authors have opted in at submission to The Lancet family of journals to post their preprints on Preprints with The Lancet. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. Preprints available here are not Lancet publications or necessarily under review with a Lancet journal. These preprints are early stage research papers that have not been peer-reviewed. The findings should not be used for clinical or public health decision making and should not be presented to a lay audience without highlighting that they are preliminary and have not been peer-reviewed. For more information on this collaboration, see the comments published in The Lancet about the trial period, and our decision to make this a permanent offering, or visit The Lancet´s FAQ page, and for any feedback please contact preprints@lancet.com.
Machine Learning Based on Blood Test Biomarkers Predicts Fast Progression in Advanced NSCLC Patients Treated with Immunotherapy
20 Pages Posted: 9 Sep 2022
More...Abstract
Background: Fast progression (FP) represents a desperate situation for advanced non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor (ICI) therapy. We aimed to develop a predictive framework based on machine learning (ML) methods to identify FP in advanced NSCLC patients using blood test biomarkers.
Methods: We extracted data of 1546 atezolizumab-treated patients from 4 multicenter clinical trials. In this study, patients from the OAK trial were taken for model training, whereas patients from the other trials were used for independent validations. The FP prediction model was developed using 21 pretreatment blood test variables in seven ML approaches. Prediction performance was evaluated by the receiver operating characteristic (ROC) curve.
Results: The prevalence of FP was 7.6% (118 of 1546) in all atezolizumab-treated patients. The most important variables for the prediction model were: C-Reactive Protein (CRP), Neutrophil count (NEUT), Lactate Dehydrogenase (LDH) and Alanine Transaminase (ALT). The XGBoost method applied on these 4 blood test parameters demonstrated relatively good performance: the AUC obtained from the training cohort (OAK), and validation cohorts 1 (BIRCH) and 2 (merged POPLAR and FIR) were 0.847, 0.640, and 0.754, respectively. In addition, the absolute difference in median survival between the model predicted FP and non-FP groups was significant in both progression-free survival (PFS) and overall survival (OS) (P <0.001).
Conclusion: The application of ML on the 4 blood test biomarkers could potentially predict FP before atezolizumab treatment in advanced NSCLC patients, hence provides evidence for decision-making in immunotherapy for advanced NSCLC patients.
Funding: This research was funded by the National Natural Science Foundation of China (Grant No. 81660512), the National Natural Science Foundation of Guizhou Province (Grant No. ZK2021-YB435), Research Programs of Science and Technology Commission Foundation of Zunyi City (Grant Nos. HZ2019-11, HZ2019-07), Research Programs of Health Commission Foundation of Guizhou Province (Grant Nos. gzwjkj2019-1-073, gzwjkj2019-1-172), Lian Yun Gang Shi Hui Lan Public Foundation (Grant No. HL-HS2020-92).
Declaration of Interest: The authors declare no relevant conflict of interest regarding this manuscript. M.H. reports collaborations with Merck Serono (advisory role, speakers’ bureau, honoraria, travel expenses, research funding); MSD (advisory role, speakers’ bureau, honoraria, travel expenses, research funding); AstraZeneca (research funding); Novartis (research funding); BMS (advisory role, honoraria, speakers’ bureau); Teva (travel expenses). U.S.G. and P.R.F. received support for presentation activities for Dr Sennewald Medizintechnik GmbH, have received support for investigator initiated clinical studies (IITs) from MSD and AstraZeneca and contributed at Advisory Boards Meetings of AstraZeneca and Bristol-Myers Squibb.
Ethical Approval: This series study was approved by the Institutional Review Board of the Second Affiliated Hospital, Zunyi Medical University (No.YXLL(KY-R)-2021-010).
Keywords: Machine Learning, Blood Test Biomarkers, Fast Progression, NSCLC, Immunotherapy, Immune checkpoint inhibitor
Suggested Citation: Suggested Citation