Circulating cell-free DNA size distribution as a prediction marker for early progression undergoing immune checkpoint inhibitors
Linh Nguyen Phuong (1,2), Frédéric Fina (3), Laurent Greillier (1,2,4), Caroline Gaudy-Marqueste (2,4,5,6), Jean-Laurent Deville (2,4), Jean-Charles Garcia (7), Sébastien Salas (1,2,4), Sébastien Benzekry (1,2)
(1) COMPutational pharmacology and clinical Oncology Team, Inria Sophia-Antipolis – Mediterrannee, Cancer Research Center of Marseille, Inserm, CNRS, France, (2) Aix-Marseille university, France (3) Id-Solutions oncology, France, (4) Assistance Publique - Hôpitaux de Marseille, France, (5) Multidisciplinary Oncology and Therapeutic innovations Department, Assistance Publique - Hôpitaux de Marseille, France, (6) Dermatology and Skin Cancer Department, France, (7) Adelis, France
Introduction: Despite a significant proportion (20 to 40%)[1] of advanced cancer patients having long-term response to treatment with immune checkpoint inhibitors (ICI), many of them do not respond at all and experience early progression (EP), defined as progression at the first imaging evaluation. Establishing reliable and early predictive biological markers for guiding clinical practice is essential. Analysis of circulating cell-free DNA (cfDNA) fragments size distribution profiles (fragmentome)[2] offer a promising non-invasive method for assessing treatment response independently of a specific molecular target, cancer type, and treatment.
Objectives: The SChISM (Size CfDNA Immunotherapies Signature Monitoring) clinical study focuses on monitoring plasmatic cfDNA size profiles, aiming to early adjust therapy to prevent ICI-related progression or toxicity.
Methods: We performed statistical and survival biomarker analysis to predict EP and progression-free survival (PFS) using pre-treatment data. Using BIABooster analysis technology from ADELIS, plasmatic fragmentome-derived metrics, including concentration, size location of first and second peaks, and specific size ranges (p = 11 variables), were evaluated alongside standard clinical markers such as performance status, age, and pathology. Predictive analysis of EP was performed by splitting between a training (n = 97) and test (n = 42) 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. Empirical longitudinal models were developed to capture both inter- and intra-patient variability in cfDNA metrics.
Results:
Analyses were performed on a cohort of 139 advancer or metastatic cancer patients (melanoma, clear cell kidney cancer, urothelial bladder carcinoma, squamous cell carcinoma of the head and neck or non-small cell lung cancer) treated with monoclonal antibodies in monotherapy or in combination. Three out of the eleven features were associated with EP and/or PFS. The relative quantity of fragments over 1650 base pairs (bp) (OVER_1650) exhibited the highest discriminatory power for EP (median area under ROC curve (AUC) on the test set = 0.7, 95% CI: 0.59-0.80). A lower relative quantity of these long fragments correlated with EP in both the univariate (odd ratio OR = 0.37 (0.22-0.64), p < 0.001) and multivariate (OR = 0.45 (0.25-0.81), p = 0.008) settings. Additionally, all cfDNA metrics except the first peak’s location were better estimated by empirical non-constant models, highlighting significant variability in cfDNA quantities over time.
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, as well as the understanding of the cfDNA release over time, could potentially help in preventing or discontinuing unnecessary treatments for patients who are unlikely to respond to ICI.
References:
[1] TSharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy. Cell. 2017;168(4):707-723. doi:10.1016/j.cell.2017.01.017
[2] Qi T, Pan M, Shi H, Wang L, Bai Y, Ge Q. Cell-Free DNA Fragmentomics: The Novel Promising Biomarker. Int J Mol Sci. 2023;24(2):1503. doi:10.3390/ijms24021503
