Abstract
e14550 Background: Immune-checkpoint inhibition (ICI) has significantly advanced cancer treatment, but many patients experience early progression (EP). Establishing reliable and early predictive biomarkers for guiding clinical practice is essential. Analysis of genome-wide cfDNA fragmentation profiles (fragmentome) is a promising non-invasive method for assessing treatment response independently of a specific molecular target, cancer type, and treatment. SChISM (Size CfDNA Immunotherapies Signature Monitoring) is a clinical study that monitors plasmatic cfDNA concentration and quantitative characteristics of the fragmentome in advanced pan-cancer patients, treated with ICI (n = 139). The aim is to predict EP, defined as progression at the first imaging evaluation, using pre-treatment data. Methods: Using BIABooster analysis technology from ADELIS, we assessed the predictive performances of plasmatic fragmentome-derived metrics: concentration, size distribution of first, second peaks and specific size ranges (total p=11 variables). Classical statistical analyses were conducted including hypothesis testing and modeling for association with EP (logistic regression) or progression-free-survival (PFS, Cox proportional hazard regression) in both the univariate and multivariate settings. Predictive analysis of EP was also performed. The dataset was split between a training (n=99) and test (n=40) set, using stratified sampling. Optimal thresholds were determined on the training dataset through receiver-operator characteristics (ROC) curve analysis, and confidence intervals determined using bootstrap resampling. Classification metrics were assessed in both the training and testing set. The entire process was bootstrapped 100 times to assess the robustness of the results. Results: Four out of the eleven features were associated with EP or PFS. Quantity of long fragments over 1650 bp showed the best discriminatory power (median AUC = 0.7, 95% CI: 0.59-0.80) of EP. Long fragments were significantly different between the EP groups (t-test p-val < 0.001) and PFS groups (log-rank p-val < 0.001). Patients with higher quantity of fragments over 1650 bp were likely not to progress before the first evaluation in both the univariate (odd ratio OR = 0.37 (0.22-0.64), p-val < 0.001) and multivariate (OR = 0.45 (0.25-0.81), p-val = 0.008) settings. Accuracy was 0.71 (0.67-0.73), positive predictive value was 0.59 (0.53-0.63) and negative predictive value was 0.78 (0.75-0.79). Conclusions: These findings highlight the association of high-molecular-weight fragments with the early progressors ICI-treated. Thus, the improvement of the knowledge in the long fragments’ biology could potentially help in preventing or discontinuing unnecessary treatments for patients who are unlikely to respond to ICI.
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