Neutrophil Biomarker Test for Predicting Clinical Benefit From Immunotherapy Based on Flow Cytometry Analysis

Official title

NEUTROFLOW: Development and Validation of a Pan-cancer Neutrophil Biomarker Test for Predicting Clinical Benefit From Immunotherapy Based on Flow Cytometry Analysis of Blood Samples

Overview

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.

Detailed description

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.

Interventions

• Other Blood sample collection
  Blood sample collection prior treatment initiation

Primary outcome measures

• Quantification of Ly6E high (Ly6Ehi) neutrophils in blood using the NeutroFlow flow cytometry assay [Time frame: Baseline (up to 1 month before treatment initiation)]
• Prediction of clinical benefit rate using baseline Ly6Ehi neutrophils levels [Time frame: Baseline (blood draw) to 6, 12, 18, and 24 months post treatment initiation]

Secondary outcome measures (4)

• Clinical benefit rate across individual cancer types [Time frame: Baseline to 6, 12, 18, and 24 months post treatment initiation (per indication)]
• Clinical benefit rate by PD-(L)1 treatment regimen [Time frame: Baseline to 6, 12, 18, and 24 months post treatment initiation]
• Correlation between Ly6Ehi neutrophil levels and PD-L1 status (TPS/CPS) [Time frame: Baseline (PD-L1 and neutrophils assessed prior to treatment)]
• Correlation between Ly6Ehi neutrophil levels and other clinical response-associated parameters [Time frame: Baseline (clinical parameters collected prior to treatment)]

Eligibility criteria

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.

• Male or female aged at least 18 years
• ECOG PS: 0/1-2
• Normal hematologic, renal and liver function:

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

• Any concurrent and/or other active malignancy that has required systemic treatment within 2 years of the first dose of treatment.
• For NSCLC: presence of activating EGFR, ALK, ROS1, RET, NTRK alterations linked to an approved first-line targeted drug.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

Germany · 1 center

• Heidelberg University Hospital — Heidelberg

Italy · 1 center

• Intituto Europeo di Oncologia SRL — Milan

Spain · 1 center

• Virgen Macarena University Hospital - Servicio Andaluz de Salud — Seville

Publications

• 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. PMID 37445336
• 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. PMID 34084999
• 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. PMID 31092901
• 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. PMID 35556235
• 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. PMID 33777757
• 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. PMID 31410780
• 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. PMID 32453632
• 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 PMID 38181798

Identifiers

NCT: NCT07246759 · NEUTROFLOW

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗