The SChISM study: Circulating cell-free DNA size profiles as predictors of progression in advanced carcinoma treated with immune-checkpoint inhibitors
Abstract
Background
Circulating cell-free DNA (cfDNA) offers a promising noninvasive way to predict resistance to immune-checkpoint inhibitors (ICI), for which robust biomarkers are still lacking.
Methods
The SChISM (Size CfDNA Immunotherapy Signature Monitoring) proof-of-concept study (NCT05083494) collected baseline plasmatic cfDNA size profiles from 126 ICI-treated advanced carcinomas, quantified using the innovative, patented and standardized BIABooster device (Adelis). Fragmentome-derived variables and standard clinical variables (including neutrophils-to-lymphocyte ratio, NLR) were analyzed for univariable associations with early progression (EP, progression at first imaging) and progression-free survival (PFS). Multivariable analysis was carried through both unsupervised and supervised learning. Twenty-six variable selection methods combined with 11 models were benchmarked to derive a multivariable predictive model relying on a minimal subset of variables.
Results
Higher cfDNA concentration and high quantities of short fragments (111-240 base pairs (bp)) were associated with poor response and reduced PFS, unlike long fragments (> 300 bp). The proportion of fragments longer than 1650 bp exhibited the strongest association, with non-EP odds ratio = 0.39 [95% CI: 0.25-0.62] and PFS hazard ratio = 0.54 [95% CI: 0.42-0.68]. Unsupervised learning identified four patient clusters significantly associated with EP (p=0.004, Pearson’s Chi-squared test) and PFS (p=0.001, log-rank test). The multivariable machine learning analysis identified a subset of nine variables that shown greater performances in a logistic regression model:AUCsignature=88.5±3.3% EP positive predictive value PPVsignature=69.4±7.49% compared to single marker AUCR[>1650]=73.6±3.70% PPVR[>1650]=55.8±7.46% AUCNLR=68.9±5.02% PPVNLR=52.6±8.75%.
Conclusion
cfDNA size profiles significantly associated with progression and PFS during ICI, outperforming the routinely used markers.
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BibTeX
@misc{phuong2026schism,
title = {{The SChISM study: Circulating cell-free DNA size profiles as predictors of progression in advanced carcinoma treated with immune-checkpoint inhibitors}},
author = {Linh Nguyen Phuong and Frederic Fina and Laurent Greillier and Anastasiia Bakhmach and Pascale Tomasini and Jean-Laurent Deville and Romain Zakrasjek and Lucie Della-Negra and Audrey Boutonnet and Frédéric Ginot and Jean-Charles Garcia and Sébastien Benzekry and Sébastien Salas},
howpublished = {HAL},
year = {2026},
month = oct,
eprint = {2509.04939},
archivePrefix = {arXiv},
url = {https://inria.hal.science/hal-05238567v4},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - The SChISM study: Circulating cell-free DNA size profiles as predictors of progression in advanced carcinoma treated with immune-checkpoint inhibitors
AU - Linh Nguyen Phuong
AU - Frederic Fina
AU - Laurent Greillier
AU - Anastasiia Bakhmach
AU - Pascale Tomasini
AU - Jean-Laurent Deville
AU - Romain Zakrasjek
AU - Lucie Della-Negra
AU - Audrey Boutonnet
AU - Frédéric Ginot
AU - Jean-Charles Garcia
AU - Sébastien Benzekry
AU - Sébastien Salas
PY - 2026
DA - 2026/10/12
PB - HAL
UR - https://inria.hal.science/hal-05238567v4
AB - Background Circulating cell-free DNA (cfDNA) offers a promising noninvasive way to predict resistance to immune-checkpoint inhibitors (ICI), for which robust biomarkers are still lacking. Methods The SChISM (Size CfDNA Immunotherapy Signature Monitoring) proof-of-concept study (NCT05083494) collected baseline plasmatic cfDNA size profiles from 126 ICI-treated advanced carcinomas, quantified using the innovative, patented and standardized BIABooster device (Adelis). Fragmentome-derived variables and standard clinical variables (including neutrophils-to-lymphocyte ratio, NLR) were analyzed for univariable associations with early progression (EP, progression at first imaging) and progression-free survival (PFS). Multivariable analysis was carried through both unsupervised and supervised learning. Twenty-six variable selection methods combined with 11 models were benchmarked to derive a multivariable predictive model relying on a minimal subset of variables. Results Higher cfDNA concentration and high quantities of short fragments (111-240 base pairs (bp)) were associated with poor response and reduced PFS, unlike long fragments (> 300 bp). The proportion of fragments longer than 1650 bp exhibited the strongest association, with non-EP odds ratio = 0.39 [95% CI: 0.25-0.62] and PFS hazard ratio = 0.54 [95% CI: 0.42-0.68]. Unsupervised learning identified four patient clusters significantly associated with EP (p=0.004, Pearson’s Chi-squared test) and PFS (p=0.001, log-rank test). The multivariable machine learning analysis identified a subset of nine variables that shown greater performances in a logistic regression model:AUC_signature=88.5±3.3% EP positive predictive value PPV_signature=69.4±7.49% compared to single marker AUC_R_[>1650]=73.6±3.70% PPV_R_[>1650]=55.8±7.46% AUC_NLR=68.9±5.02% PPV_NLR=52.6±8.75%. Conclusion cfDNA size profiles significantly associated with progression and PFS during ICI, outperforming the routinely used markers.
ER -