arXiv subjects

Laurent Greillier

Publications and source records attributed to Laurent Greillier.

3 recordsLinked to original sources

arXiv2026

Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy

Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets. Standard survival models often struggle with these gaps, leading to biased results or patient exclusion. We introduce Multimodality Stacking with Blockwise missing values (MSB), a late-fusion framework for survival analysis that independently models modality-specific features before aggregating predictions via a cross-validated stacking meta-learner. MSB was validated on the PIONeeR study (n=443 patients, 378 biomarkers across eight heterogeneous sources) to predict progression-free survival in advanced non-small cell lung cancer patients receiving immunotherapy. MSB yielded higher predictive performance (C-index) than baseline algorithms. Improvements varied by baseline strength: linear models showed a 15.9% increase (p<0.001 for the Wilcoxon signed-rank test), random survival forests gained 5.4% (p=0.002), and gradient boosting methods improved by 2.1% (p=0.030). Beyond discrimination, MSB reduced the generalization gap (train-test difference in 5 folds cross-validation repeated 3 times: 0.055 vs 0.380 for linear models). Permutation importance analysis identified routine laboratory markers, clinical features, and PD-L1 expression as primary predictive drivers. Missing block indicators showed negligible importance, suggesting the model learned from biomarker values rather than data availability patterns. MSB provides a statistically validated framework for multimodal survival prediction with blockwise missingness. By enabling systematic biomarker evaluation without requiring complete data, MSB offers a practical tool for predictive modeling in biomedical research, pending external validation. Implementation is available at https://github.com/MohamedBoussena/MSB under Inria license.

stat.AP↗

arXiv2026

ROOFS: RObust biOmarker Feature Selection

Feature selection (FS) is essential for biomarker discovery and clinical predictive modeling. Over the past decades, methodological literature on FS has become rich and mature, offering a wide spectrum of algorithmic approaches. However, much of this methodological progress has not fully translated into applied biomedical research. Moreover, challenges inherent in biomedical data, such as high-dimensional feature space, low sample size, multicollinearity, and missing values, make FS non-trivial. To help bridge this gap between methodological development and practical application, we propose ROOFS (RObust biOmarker Feature Selection), a Python package available at https://gitlab.inria.fr/compo/roofs, designed to help researchers in the choice of FS method adapted to their problem. ROOFS benchmarks multiple FS methods on the user's data and generates reports summarizing a comprehensive set of evaluation metrics, including downstream predictive performance estimated using optimism correction, stability, robustness of individual features, and true positive and false positive rates assessed on semi-synthetic data with a simulated outcome. We demonstrate the utility of ROOFS on data from the PIONeeR clinical trial, aimed at identifying predictors of resistance to anti-PD-(L)1 immunotherapy in lung cancer. Of the 34 FS methods gathered in ROOFS, we evaluated 23 in combination with 11 classifiers (253 models) and identified a filter based on the union of Benjamini-Hochberg false discovery rate-adjusted p-values from t-test and logistic regression as the optimal approach, outperforming other methods including widely used LASSO. We conclude that comprehensive benchmarking with ROOFS has the potential to improve the reproducibility of FS discoveries and increase the translational value of clinical models.

stat.ML↗

arXiv2025

The SChISM study: Cell-free DNA size profiles as predictors of progression in advanced carcinoma treated with immune-checkpoint inhibitors

Background: Many advanced cancer patients experience progression under immune-checkpoint inhibitors (ICIs). Circulating cell-free DNA (cfDNA) size profiles offer a promising noninvasive multi-cancer approach to monitor and predict immunotherapy response. Methods: In the SChISM (Size CfDNA Immunotherapy Signature Monitoring) study (NCT05083494), pre-treatment plasmatic cfDNA size profiles from 126 ICI-treated advanced carcinomas were quantified using the BIABooster device. Fragmentomederived variables (concentration, peaks' position, and fragment size ranges) at baseline were analyzed for associations with early progression (EP, progression at first imaging) and progression-free survival (PFS), using logistic and Cox regression models. Bootstrap analysis validated robustness. Additional analyses were performed in homogeneous subpopulations: first-line lung cancer patients (n = 60) and head-andneck patients treated with Nivolumab (n = 25). Results: Higher cfDNA concentration and high quantities of short fragments (111-240 base pairs (bp)) were associated with poor response, unlike long fragments (> 300 bp). The proportion of fragments longer than 1650 bp demonstrated highest discriminatory power (AUC = 0.73, C-index = 0.69). It was significantly associated with non-EP (odds ratio = 0.39 [95% CI: 0.25-0.62]) and longer PFS (hazard ratio: 0.54 [95% CI: 0.42-0.68]). These associations remained significant when adjusted for confounders (age, sex, Eastern Cooperative Oncology Group performance status, tumor type, and neutrophil-to-lymphocyte ratio) and across both subpopulations. Bootstrap analysis confirmed robustness with mean accuracy of 70.1 $\pm$ 4.17% and positive predictive value of 55.6 $\pm$ 7.37%, in test sets. Conclusion: cfDNA size profiles significantly predicted ICI response and anticipate relapse, outperforming the routinely used marker programmed death-ligand 1 immunohistochemistry and reflecting enhanced immune system activation. Trial registration: (NCT05083494), date of registration: 2021-10-19.

q-bio.QM↗