Hecht, Markus and Frey, Benjamin and Gaipl, Udo S. and Xie, Tianyu and Eckstein, Markus and Donaubauer, Anna-Jasmina and Klautke, Gunther and Illmer, Thomas and Fleischmann, Maximilian and Laban, Simon and Hautmann, Matthias G. and Tamaskovics, Balint and Brunner, Thomas B. and Becker, Ina and Zhou, Jian-Guo and Hartmann, Arndt and Fietkau, Rainer and Iro, Heinrich and Doellinger, Michael and Gostian, Antoniu-Oreste and Kist, Andreas M. (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: 100953. ISSN 1476-5586,
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | LOCALLY ADVANCED HEAD; CHEMOTHERAPY; CANCER; METAANALYSIS; RECURRENT; Chemotherapy; Immunotherapy; HNSCC; Induction therapy; Immune phenotyping |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Strahlentherapie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 20 Aug 2025 09:05 |
| Last Modified: | 20 Aug 2025 09:05 |
| URI: | https://pred.uni-regensburg.de/id/eprint/65533 |
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