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Meneghetti, Asier Rabasco; Zwanenburg, Alex; Linge, Annett; Lohaus, Fabian; Grosser, Marianne; Baretton, Gustavo B.; Kalinauskaite, Goda; Tinhofer, Ingeborg; Guberina, Maja; Stuschke, Martin; Balermpas, Panagiotis; Gruen, Jens von der; Ganswindt, Ute; Belka, Claus; Peeken, Jan C.; Combs, Stephanie E.; Böke, Simon; Zips, Daniel; Troost, Esther G. C.; Krause, Mechthild; Baumann, Michael and Löck, Steffen (2022): Integrated radiogenomics analyses allow for subtype classification and improved outcome prognosis of patients with locally advanced HNSCC. In: Scientific Reports, Vol. 12, No. 1, 16755

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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.

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