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Rabasco Meneghetti, A.* ; Zwanenburg, A.* ; Linge, A.* ; Lohaus, F.* ; Grosser, M.* ; Kalinauskaite, G.* ; Tinhofer, I.* ; Guberina, M.* ; Stuschke, M.* ; Balermpas, P.* ; Von der Grün, J.* ; Ganswindt, U. ; Belka, C. ; Peeken, J.C. ; Combs, S.E. ; Böke, S.* ; Zips, D.* ; Baumann, M.* ; Löck, S.*

Integrated radiogenomics analyses allow for subtype classification and improved outcome prognosis of patients with locally advanced HNSCC.

Sci. Rep. 12:16755 (2022)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) may benefit from personalised treatment, requiring biomarkers that characterize the tumour and predict treatment response. We integrate pre-treatment CT radiomics and whole-transcriptome data from a multicentre retrospective cohort of 206 patients with locally advanced HNSCC treated with primary radiochemotherapy to classify tumour molecular subtypes based on radiomics, develop surrogate radiomics signatures for gene-based signatures related to different biological tumour characteristics and evaluate the potential of combining radiomics features with full-transcriptome data for the prediction of loco-regional control (LRC). Using end-to-end machine-learning, we developed and validated a model to classify tumours of the atypical subtype (AUC [95% confidence interval] 0.69 [0.53-0.83]) based on CT imaging, observed that CT-based radiomics models have limited value as surrogates for six selected gene signatures (AUC < 0.60), and showed that combining a radiomics signature with a transcriptomics signature consisting of two metagenes representing the hedgehog pathway and E2F transcriptional targets improves the prognostic value for LRC compared to both individual sources (validation C-index [95% confidence interval], combined: 0.63 [0.55-0.73] vs radiomics: 0.60 [0.50-0.71] and transcriptomics: 0.59 [0.49-0.69]). These results underline the potential of multi-omics analyses to generate reliable biomarkers for future application in personalized oncology.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Zeitschrift Scientific Reports
Quellenangaben Band: 12, Heft: 1, Seiten: , Artikelnummer: 16755 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) CCG Personalized Radiotherapy in Head and Neck Cancer (KKG-KRT)
Institute of Radiation Medicine (IRM)
Förderungen Projekt DEAL