The NeutroFlow study is a multi-center clinical trial designed to develop a computational model that converts flow cytometry results into a prediction of clinical benefit. The study analyzes Ly6Ehi neutrophils in biological samples from patients treated with immune checkpoint inhibitors to evaluate their likelihood of benefiting from treatment. Blood samples are collected prior to treatment and used to support the ongoing development of the algorithm.
The recent introduction of cancer immunotherapy based on immune checkpoint inhibitors (ICIs) has revolutionised the treatment landscape for a broad range of cancer types. However, response to ICIs varies widely between patients, with the majority experiencing resistance to therapy. Moreover, the increasing use of these costly drugs coupled with management of ICI-related toxicities creates a substantial economic burden. Current biomarker tests for determining eligibility for ICIs have limited predictive performance, and many require invasive tumour biopsies. Thus, novel (and preferentially non-invasive) biomarkers for predicting ICI clinical benefit are desperately needed for better guiding clinical decisions. NeutroFlow directly addresses this unmet need. The neutroFlow study is based on a comprehensive academic research describing a flow cytometry assay for measuring a novel predictive biomarker in the blood - Ly6Ehi neutrophil - that accurately predicts therapeutic benefit from ICIs, outperforming the approved PD-L1 biomarker. The objective of the NeutroFlow study is to develop a clinical decision-support tool that includes an antibody panel for detecting Ly6Ehi neutrophils using standard flow cytometry (FC) and a computational model that converts the FC readout into a prediction of clinical benefit. Patients will provide a single blood sample before starting treatment, and clinical data will be collected from their medical records. In the first phase of the trial, blood sample data and clinical information will be used to develop the antibody panel and train the prediction algorithm. In the second phase, the algorithm will be validated by comparing its theoretical predictions with the patients' actual objective response rates.
Study Type
OBSERVATIONAL
Enrollment
600
Blood sample collection prior treatment initiation
Heidelberg University Hospital
Heidelberg, Germany
Intituto Europeo di Oncologia SRL
Milan, Italy
Virgen Macarena University Hospital - Servicio Andaluz de Salud
Seville, Spain
Quantification of Ly6E high (Ly6Ehi) neutrophils in blood using the NeutroFlow flow cytometry assay
Peripheral blood samples will be collected from patients up to 1 month prior to treatment initiation. Ly6Ehi neutrophil populations will be quantified using the NeutroFlow multiparametric flow cytometry assay
Time frame: Baseline (up to 1 month before treatment initiation)
Prediction of clinical benefit rate using baseline Ly6Ehi neutrophils levels
The patients clinical benefit (CB) will be defined according to RECIST 1.1 criteria to one of the following categories: complete response (CR), partial response (PR), or stable disease (SD). Baseline Ly6Ehi neutrophil levels will be used as input in a computational predictive model to estimate the likelihood of CB for each patient. The model will be trained and validated with independent patient cohorts, using cross-validation and ROC AUC metrics to assess predictive performance. Sensitivity, specificity, positive predictive value, and negative predictive value will also be calculated. Predictions will be assessed at multiple timepoints: 6, 12, 18, and 24 months post-initiation of anti-PD-(L)1 therapy. Subgroup analyses will include age, sex, cancer type, disease stage, and treatment line.
Time frame: Baseline (blood draw) to 6, 12, 18, and 24 months post treatment initiation
Clinical benefit rate across individual cancer types
The clinical benefit rate described in Outcome 2 will be evaluated separately for each cancer indication (e.g., NSCLC, melanoma, HNSCC, RCC, TNBC) at several time points (6, 12, 18 and 24 months post treatment initiation). At least 50 patients per indication will be included to ensure adequate statistical power. ROC AUC values and predictive model performance will be computed for each subgroup. Additional exploratory analyses will examine potential modifiers, including tumor burden, prior therapies, and immune-related biomarkers, to evaluate heterogeneity of response
Time frame: Baseline to 6, 12, 18, and 24 months post treatment initiation (per indication)
Clinical benefit rate by PD-(L)1 treatment regimen
The clinical benefit rate will be assessed across various anti-PD-(L)1 regimens, including monotherapy and combination protocols. Treatments are classified according to indication and line of therapy: NSCLC: Monotherapy: Pembrolizumab, Atezolizumab, Cemiplimab. Combination: Pembrolizumab + chemotherapy; Nivolumab + Ipilimumab; Cemiplimab + chemotherapy; Atezolizumab + chemotherapy + Bevacizumab. Melanoma: Monotherapy: Nivolumab, Pembrolizumab. Combination: Nivolumab + Ipilimumab; Nivolumab + Relatlimab. HNSCC: Monotherapy: Pembrolizumab, Cemiplimab. Combination: Pembrolizumab + chemotherapy. RCC: Combination only: Nivolumab + Ipilimumab; Nivolumab + Cabozantinib; Pembrolizumab + Lenvatinib or Axitinib; Avelumab + Axitinib. TNBC: Combination only: Pembrolizumab + chemotherapy. In cases where treatment changes occur during follow-up, two scenarios will be considered: 1. the patient exits the analysis at the time of treatment change; or 2. the new treatment marks a new baseline.
Time frame: Baseline to 6, 12, 18, and 24 months post treatment initiation
Correlation between Ly6Ehi neutrophil levels and PD-L1 status (TPS/CPS)
Evaluation of the association between baseline Ly6Ehi neutrophil levels and PD-L1 expression in tumor tissue, measured as Tumor Proportion Score (TPS) and/or Combined Positive Score (CPS), depending on standard of care and assay availability. PD-L1 will be assessed by IHC using validated assays per standard of care. Subgroup analyses will explore correlations within different cancer types and treatment regimens. These analyses will provide insight into whether Ly6Ehi neutrophils complement or enhance PD-L1-based patient stratification.
Time frame: Baseline (PD-L1 and neutrophils assessed prior to treatment)
Correlation between Ly6Ehi neutrophil levels and other clinical response-associated parameters
Exploratory analyses will assess correlations between baseline Ly6Ehi neutrophil levels and other parameters associated with immune checkpoint inhibitor (ICI) response, including Tumor Mutational Burden (TMB), Microsatellite Instability (MSI), Lactate Dehydrogenase (LDH), C-reactive protein (CRP), and ECOG performance status. Multivariate models and correlation analyses will evaluate whether Ly6Ehi neutrophils provide independent predictive value. Subgroup analyses will include cancer type, treatment regimen, and prior therapy exposure. This outcome will help determine the broader immunological and clinical context of Ly6Ehi neutrophil levels
Time frame: Baseline (clinical parameters collected prior to treatment)
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