CC BY-NC-ND 4.0 · Laryngorhinootologie 2021; 100(S 02): S121
DOI: 10.1055/s-0041-1727959
Abstracts
Head-Neck-Oncology: Molecular Tumorboard

Establishment of a risk model for perineural invasion by integrative analysis of multi-omics data from head and neck cancer

C Weusthof
1   Universitätsklinikum Heidelberg, Hals-, Nasen- und Ohrenklinik, Heidelberg
,
K Metzger
2   Universitätsklinikum Heidelberg, Mund-, Kiefer- und Gesichtschirurgie, Heidelberg
,
S Burkart
1   Universitätsklinikum Heidelberg, Hals-, Nasen- und Ohrenklinik, Heidelberg
,
B Feng
1   Universitätsklinikum Heidelberg, Hals-, Nasen- und Ohrenklinik, Heidelberg
,
K Khorani
1   Universitätsklinikum Heidelberg, Hals-, Nasen- und Ohrenklinik, Heidelberg
,
S Bode
1   Universitätsklinikum Heidelberg, Hals-, Nasen- und Ohrenklinik, Heidelberg
,
P Plinkert
1   Universitätsklinikum Heidelberg, Hals-, Nasen- und Ohrenklinik, Heidelberg
,
K Zaoui
1   Universitätsklinikum Heidelberg, Hals-, Nasen- und Ohrenklinik, Heidelberg
,
J Hess
1   Universitätsklinikum Heidelberg, Hals-, Nasen- und Ohrenklinik, Heidelberg
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    Content Perineural invasion (PNI) is a prevalent pathological finding in head and neck squamous cell carcinoma (HNSCC) and of clinical relevance as a risk factor for unfavorable prognostic factor for survival. Although the research and especially the understanding, how PNI develops through nerve-cancer cell crosstalk has improved over the last years, a molecular gene signature, which is associated with PNI and predicts unfavorable survival between risk groups, has not yet been identified. Gene expression and clinical data of The Cancer Genome Atlas (TCGA) was used as a training cohort to reveal differentially expressed genes (DEGs) that are associated with the pathological characteristic of PNI. The machine learning algorithm LASSO Cox regression was performed to establish a risk model regarding survival; analysis of multi-omics data was conducted to detect differences between risk groups in mutational landscape. We identified a gene signature that is able to differentiate between pathologically characterized PNI- and PNI+ patients. Survival analysis revealed a highly significant difference between risk groups classified by the risk model. The gene signature was also confirmed in independent validation cohorts of HNSCC and even in SCCs of other tumor entities. Our results show a gene signature with the ability to classify patients with HNSCC into risk groups. Clinical relevance was proven as our model worked as a predictor for unfavorable survival, at least equivalent to the classification of pathological PNI-status. It is also applicable for patients where pathological examination is impossible due to primary radiotherapy. Thus, this gene signature, stratifying patients in risk groups, could play an important role in risk assessment and therapeutic stratification in the future.

    Poster-PDF A-1699.pdf


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    Conflict of interest

    Der Erstautor gibt keinen Interessenskonflikt an.

    Address for correspondence

    Weusthof Christopher
    Universitätsklinikum Heidelberg, Hals-, Nasen- und Ohrenklinik
    Heidelberg

    Publication History

    Article published online:
    13 May 2021

    © 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

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