Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| European Institute of Oncology | OTHER |
| University Hospital Heidelberg | OTHER |
| Hospital Universitario Virgen Macarena | OTHER |
Not provided
Not provided
Not provided
Not provided
Not provided
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.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients with stage IV or stage III unresectable non-small cell lung cancer (NSCLC) | Patients with stage IV or stage III unresectable non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors as a first-line treatment |
| |
| Patients with stage IV or stage IIIb-d malignant melanoma | Patients with stage IV or stage IIIb-d malignant melanoma treated with immune checkpoint inhibitors as a first-line treatment |
| |
| Patients with stage IV head and neck squamous cell carcinoma (HNSCC) | Patients with stage IV head and neck squamous cell carcinoma (HNSCC) treated with immune checkpoint inhibitors as a first-line treatment |
| |
| Patients with stage IV renal cell carcinoma (RCC) | Patients with stage IV renal cell carcinoma (RCC) treated with immune checkpoint inhibitors as a first-line treatment |
| |
| Patients with stage IV triple-negative breast cancer (TNBC) | Patients with stage IV triple-negative breast cancer (TNBC) treated with immune checkpoint inhibitors as a first-line treatment |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Blood sample collection | Other | Blood sample collection prior treatment initiation |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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 | 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. | Baseline (blood draw) to 6, 12, 18, and 24 months post treatment initiation |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
Not provided
Inclusion Criteria:
• Patients newly diagnosed with advanced-stage/metastatic NSCLC, melanoma, HNSCC, RCC, or TNBC, who are due to receive first-line treatment with a PD-(L)1 inhibitor, either as monotherapy or in combination with other agents, according to current standard-of-care regimens, including (but not limited to) the following approved options: 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.
Absolute neutrophil count > 1500/mm³ Platelets > 100,000/mm³ Hemoglobin > 9 g/dL Creatinine concentration ≤ 1.4 mg/dL, or creatinine clearance > 40 mL/min, Total bilirubin < 1.5 mg/dL ALT + AST levels ≤ 3 times above the upper normal limit
Exclusion Criteria:
Not provided
Not provided
Not provided
At least 600 treatment-naïve patients due to be treated with a first line regimen that includes PD- 1/PD-L1 inhibitors with the following diagnoses are planned to be enrolled in the study over a period of 3 years or as needed: 1. Stage IV or Stage III-unresectable NSCLC 2. Stage IV or Stage IIIb-d malignant melanoma 3. Stage IV HNSCC 4. Stage IV RCC 5. Stage IV TNBC
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Michal Harel VP of Translational Medicine, PhD | Contact | 97248537558 | Michal@oncohost.com | |
| Shani Raveh Shoval VP of Clinical Affairs, PhD | Contact | shani@oncohost.com |
Not provided
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Heidelberg University Hospital | Heidelberg | 69120 | Germany |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37445336 | Background | Alturki NA. Review of the Immune Checkpoint Inhibitors in the Context of Cancer Treatment. J Clin Med. 2023 Jun 27;12(13):4301. doi: 10.3390/jcm12134301. | |
| 34084999 | Background | Man J, Millican J, Mulvey A, Gebski V, Hui R. Response Rate and Survival at Key Timepoints With PD-1 Blockade vs Chemotherapy in PD-L1 Subgroups: Meta-Analysis of Metastatic NSCLC Trials. JNCI Cancer Spectr. 2021 Jan 27;5(3):pkab012. doi: 10.1093/jncics/pkab012. eCollection 2021 Jun. |
Not provided
Not provided
The clinical information and results generated as part of the study may be shared with other researchers and doctors, national and international regulatory agencies as well as in scientific repositories, always ensuring the necessary conditions of quality, security and confidentiality.
Not provided
Not provided
Not provided
Not provided
Not provided
| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Jun 12, 2025 |
Not provided
Not provided
Not provided
Not provided
Blood withdrawal at baseline before treatment initiation
| 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:
| 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. | 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 | Baseline (clinical parameters collected prior to treatment) |
| Intituto Europeo di Oncologia SRL | Milan | 20121 | Italy |
|
| Virgen Macarena University Hospital - Servicio Andaluz de Salud | Seville | 41071 | Spain |
|
| 31092901 | Background | Martins F, Sofiya L, Sykiotis GP, Lamine F, Maillard M, Fraga M, Shabafrouz K, Ribi C, Cairoli A, Guex-Crosier Y, Kuntzer T, Michielin O, Peters S, Coukos G, Spertini F, Thompson JA, Obeid M. Adverse effects of immune-checkpoint inhibitors: epidemiology, management and surveillance. Nat Rev Clin Oncol. 2019 Sep;16(9):563-580. doi: 10.1038/s41571-019-0218-0. |
| 35556235 | Background | Hurwitz JT, Vaffis S, Grizzle AJ, Nielsen S, Dodson A, Parry S. Cost-Effectiveness of PD-L1 Testing in Non-Small Cell Lung Cancer (NSCLC) Using In Vitro Diagnostic (IVD) Versus Laboratory-Developed Test (LDT). Oncol Ther. 2022 Dec;10(2):391-409. doi: 10.1007/s40487-022-00197-1. Epub 2022 May 13. |
| 33777757 | Background | Lei Y, Li X, Huang Q, Zheng X, Liu M. Progress and Challenges of Predictive Biomarkers for Immune Checkpoint Blockade. Front Oncol. 2021 Mar 11;11:617335. doi: 10.3389/fonc.2021.617335. eCollection 2021. |
| 31410780 | Background | Arora S, Velichinskii R, Lesh RW, Ali U, Kubiak M, Bansal P, Borghaei H, Edelman MJ, Boumber Y. Existing and Emerging Biomarkers for Immune Checkpoint Immunotherapy in Solid Tumors. Adv Ther. 2019 Oct;36(10):2638-2678. doi: 10.1007/s12325-019-01051-z. Epub 2019 Aug 13. |
| 32453632 | Background | McKean WB, Moser JC, Rimm D, Hu-Lieskovan S. Biomarkers in Precision Cancer Immunotherapy: Promise and Challenges. Am Soc Clin Oncol Educ Book. 2020 May;40:e275-e291. doi: 10.1200/EDBK_280571. |
| 38181798 | Background | Benguigui M, Cooper TJ, Kalkar P, Schif-Zuck S, Halaban R, Bacchiocchi A, Kamer I, Deo A, Manobla B, Menachem R, Haj-Shomaly J, Vorontsova A, Raviv Z, Buxbaum C, Christopoulos P, Bar J, Lotem M, Sznol M, Ariel A, Shen-Orr SS, Shaked Y. Interferon-stimulated neutrophils as a predictor of immunotherapy response. Cancer Cell. 2024 Feb 12;42(2):253-265.e12. doi: 10.1016/j.ccell.2023.12.005. Epub 2024 Jan 4. |
| Oct 10, 2025 |
| Prot_SAP_000.pdf |
| ID | Term |
|---|---|
| D002289 | Carcinoma, Non-Small-Cell Lung |
| D008545 | Melanoma |
| D064726 | Triple Negative Breast Neoplasms |
| D002292 | Carcinoma, Renal Cell |
| D000077195 | Squamous Cell Carcinoma of Head and Neck |
| ID | Term |
|---|---|
| D002283 | Carcinoma, Bronchogenic |
| D001984 | Bronchial Neoplasms |
| D008175 | Lung Neoplasms |
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D018358 | Neuroendocrine Tumors |
| D017599 | Neuroectodermal Tumors |
| D009373 | Neoplasms, Germ Cell and Embryonal |
| D009370 | Neoplasms by Histologic Type |
| D009380 | Neoplasms, Nerve Tissue |
| D018326 | Nevi and Melanomas |
| D012878 | Skin Neoplasms |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
| D001943 | Breast Neoplasms |
| D001941 | Breast Diseases |
| D000230 | Adenocarcinoma |
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D007680 | Kidney Neoplasms |
| D014571 | Urologic Neoplasms |
| D014565 | Urogenital Neoplasms |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D007674 | Kidney Diseases |
| D014570 | Urologic Diseases |
| D052801 | Male Urogenital Diseases |
| D002294 | Carcinoma, Squamous Cell |
| D006258 | Head and Neck Neoplasms |
Not provided
Not provided