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

• Published in: Scientific reports Vol. 12; no. 1; p. 16755
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.10.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1536; 631/67/2321; 692/53/2422; 692/53/2423; Biomarkers; Chemoradiotherapy; Computed tomography; Confidence intervals; E2F protein; Head & neck cancer; Head and neck carcinoma; Humanities and Social Sciences; multidisciplinary; Patients; Radiomics; Science; Science (multidisciplinary); Squamous cell carcinoma; Transcriptomes; Transcriptomics; Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-21159-7

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.