Machine Learning-assisted immunophenotyping of peripheral blood identifies innate immune cells as best predictor of response to induction chemo-immunotherapy in head and neck squamous cell carcinoma – knowledge obtained from the CheckRad-CD8 trial

Hecht M, Frey B, Gaipl U, Tianyu X, Eckstein M, Donaubauer AJ, Klautke G, Illmer T, Fleischmann M, Laban S, Hautmann MG, Tamaskovics B, Brunner TB, Becker I, Zhou J, Hartmann A, Fietkau R, Iro H, Döllinger M, Gostian AO, Kist A (2024)

Publication Language: English

Publication Type: Journal article

Publication year: 2024

Journal

Neoplasia Neoplasia

Book Volume: 49

Article Number: 100953

DOI: 10.1016/j.neo.2023.100953

Abstract

Purpose: Individual prediction of treatment response is crucial for personalized treatment in multimodal approaches against head-and-neck squamous cell carcinoma (HNSCC). So far, no reliable predictive parameters for treatment schemes containing immunotherapy have been identified. This study aims to predict treatment response to induction chemo-immunotherapy based on the peripheral blood immune status in patients with locally advanced HNSCC. Methods: The peripheral blood immune phenotype was assessed in whole blood samples in patients treated in the phase II CheckRad-CD8 trial as part of the pre-planned translational research program. Blood samples were analyzed by multicolor flow cytometry before (T1) and after (T2) induction chemo-immunotherapy with cisplatin/docetaxel/durvalumab/tremelimumab. Machine Learning techniques were used to predict pathological complete response (pCR) after induction therapy. Results: The tested classifier methods (LDA, SVM, LR, RF, DT, and XGBoost) allowed a distinct prediction of pCR. Highest accuracy was achieved with a low number of features represented as principal components. Immune parameters obtained from the absolute difference (lT2-T1l) allowed the best prediction of pCR. In general, less than 30 parameters and at most 10 principal components were needed for highly accurate predictions. Across several datasets, cells of the innate immune system such as polymorphonuclear cells, monocytes, and plasmacytoid dendritic cells are most prominent. Conclusions: Our analyses imply that alterations of the innate immune cell distribution in the peripheral blood following induction chemo-immuno-therapy is highly predictive for pCR in HNSCC.

Authors with CRIS profile

Benjamin Frey Department of Radiation Oncology Udo Gaipl Department of Radiation Oncology Markus Eckstein Institute of Pathology Anna-Jasmina Donaubauer Department of Radiation Oncology Ina Becker Department of Radiation Oncology Jianguo Zhou Department of Radiation Oncology Arndt Hartmann Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie Rainer Fietkau Lehrstuhl für Strahlentherapie Heinrich Iro Lehrstuhl für Hals-, Nasen- und Ohrenheilkunde Michael Döllinger Professur für Computational Medicine (DFG-Forschungsprofessur) Antoniu-Oreste Gostian Department of Otorhinolaryngology – Head and Neck Surgery Andreas Kist Juniorprofessur für Artificial Intelligence in Communication Disorders

Involved external institutions

Universitätsklinikum Düsseldorf DE Germany (DE) Medizinische Universität Graz AT Austria (AT) Klinikum Chemnitz DE Germany (DE) Universitätsklinikum Frankfurt am Main (KGU) DE Germany (DE) Universitätsklinikum Ulm DE Germany (DE) Universitätsklinikum Regensburg DE Germany (DE) Universitätsklinikum des Saarlandes (UKS) DE Germany (DE)

How to cite

APA:

Hecht, M., Frey, B., Gaipl, U., Tianyu, X., Eckstein, M., Donaubauer, A.-J.,... Kist, A. (2024). Machine Learning-assisted immunophenotyping of peripheral blood identifies innate immune cells as best predictor of response to induction chemo-immunotherapy in head and neck squamous cell carcinoma – knowledge obtained from the CheckRad-CD8 trial. Neoplasia, 49. https://doi.org/10.1016/j.neo.2023.100953

MLA:

Hecht, Markus, et al. "Machine Learning-assisted immunophenotyping of peripheral blood identifies innate immune cells as best predictor of response to induction chemo-immunotherapy in head and neck squamous cell carcinoma – knowledge obtained from the CheckRad-CD8 trial." Neoplasia 49 (2024).

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