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

Rabasco Meneghetti, Asier; Zwanenburg, Alex; Linge, Annett; Lohaus, Fabian; Grosser, Marianne; Baretton, Gustavo B; Kalinauskaite, Goda; Tinhofer, Ingeborg; Guberina, Maja; Stuschke, Martin; Balermpas, Panagiotis; von der Grün, Jens; Ganswindt, Ute; Belka, Claus; Peeken, Jan C; Combs, Stephanie E; Böke, Simon; Zips, Daniel; Troost, Esther G C; Krause, Mechthild; Baumann, Michael; Löck, Steffen

doi:10.1038/s41598-022-21159-7

journal article

Titel:: Integrated radiogenomics analyses allow for subtype classification and improved outcome prognosis of patients with locally advanced HNSCC.
Dokumenttyp:: Journal Article
Autor(en):: Rabasco Meneghetti, Asier; Zwanenburg, Alex; Linge, Annett; Lohaus, Fabian; Grosser, Marianne; Baretton, Gustavo B; Kalinauskaite, Goda; Tinhofer, Ingeborg; Guberina, Maja; Stuschke, Martin; Balermpas, Panagiotis; von der Grün, Jens; Ganswindt, Ute; Belka, Claus; Peeken, Jan C; Combs, Stephanie E; Böke, Simon; Zips, Daniel; Troost, Esther G C; Krause, Mechthild; Baumann, Michael; Löck, Steffen
Abstract:: 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.
Zeitschriftentitel:: Sci Rep
Jahr:: 2022
Band / Volume:: 12
Heft / Issue:: 1
Volltext / DOI:: doi:10.1038/s41598-022-21159-7
PubMed:: http://view.ncbi.nlm.nih.gov/pubmed/36202941
Print-ISSN:: 2045-2322
TUM Einrichtung:: Klinik und Poliklinik für RadioOnkologie und Strahlentherapie
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mediaTUM Gesamtbestand Einrichtungen Schools TUM School of Medicine and Health Departments Clinical Medicine Klinik und Poliklinik für RadioOnkologie und Strahlentherapie (Prof. Combs)2022