Integrative kinetics and machine learning modeling for prediction of outcome following immunotherapy in lung cancer - Inria - Institut national de recherche en sciences et technologies du numérique
Conference Papers Year : 2023

Integrative kinetics and machine learning modeling for prediction of outcome following immunotherapy in lung cancer

Abstract

I will present recent results from COMPO (COMPutational pharmacology and clinical Oncology) ai- ming at combining mechanistic modeling and machine learning ("mechanistic learning") to integrate longitudinal, multi-modal and high-dimensional data into predictive models of outcome following im- munotherapy in non-small cell lung cancer (NSCLC). This will be based on two studies. The first leverages clinical trial data to help in drug development by predicting outcome of late-phase trials (e.g., phase 3) from early data (e.g., phase 2). The second is an integrative analysis of multi-modal deep-level biomarkers (multiplex immunohistochemistry, immune-monitoring, vasculo-monitoring, he- matology and biochemistry) collected during the RHU PIONeeR. The results show substantial im- provement of the predictive performances of classical markers (PDL1 expression, AUC = 0.64, tumor mutational burden, AUC = 0.65) using a novel kinetics-machine learning (kML) model (AUC = 0.86, c-index = 0.79, test set). The kML model was also able to predict the positive outcome of the phase 3 of the OAK trial (atezolizumab versus docetaxel) using 30 weeks on-study data (model HR = 0.802 (95% CI : 0.655 - 0.907)) while the observed data at this landmark time point was not conclusive (data HR = 1.04 (95% CI : 0.386 - 2.79)). References : [1] Benzekry et al., Supporting decision making and early prediction of survival for oncology drug development using a pharmacometrics-machine learning based model, PAGE 30 (2022) Abstr 10276 [2] Greillier, L. et al. Comprehensive biomarkers analysis to explain resistances to PD1-L1 ICIs : The precision immuno-oncology for advanced non-small cell lung cancer (PIONeeR) trial. Cancer Res 82, LB120LB120 (2022). [3] Barlesi, F. et al., Benzekry, S. Comprehensive biomarkers (BMs) analysis to predict efficacy of PD1/L1 immune checkpoint inhibitors (ICIs) in combination with chemotherapy : a subgroup analysis of the Precision Immuno-Oncology for advanced Non-Small CEll Lung CancER (PIONeeR) trial. Annals of Oncology, 16 (suppl_1), 2022
No file

Dates and versions

hal-04388499 , version 1 (11-01-2024)

Licence

Identifiers

  • HAL Id : hal-04388499 , version 1

Cite

Sébastien Benzekry. Integrative kinetics and machine learning modeling for prediction of outcome following immunotherapy in lung cancer. Annual congress of the French society of pharmacology and therapeutics (SFPT), Jun 2023, Limoges, France. ⟨hal-04388499⟩
56 View
0 Download

Share

More