Universität zu Köln

Klein, Sebastian ORCID: 0000-0002-2188-9377, Quaas, Alexander, Quantius, Jennifer, Loeser, Heike, Meinel, Jorn, Peifer, Martin, Wagner, Steffen ORCID: 0000-0003-0873-1601, Gattenloehner, Stefan, Wittekindt, Claus, Doeberitz, Magnus von Knebel, Prigge, Elena-Sophie, Langer, Christine, Noh, Ka-Won, Maltseva, Margaret, Reinhardt, Hans Christian, Buettner, Reinhard, Klussmann, Jens Peter ORCID: 0000-0002-8223-7954 and Wuerdemann, Nora (2021). Deep Learning Predicts HPV Association in Oropharyngeal Squamous Cell Carcinomas and Identifies Patients with a Favorable Prognosis Using Regular H&E Stains. Clin. Cancer Res., 27 (4). S. 1131 - 1139. PHILADELPHIA: AMER ASSOC CANCER RESEARCH. ISSN 1557-3265

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Abstract

Purpose: Human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC) is tumorigenic and has been associated with a favorable prognosis compared with OPSCC caused by tobacco, alcohol, and other carcinogens. Meanwhile, machine learning has evolved as a powerful tool to predict molecular and cellular alterations of medical images of various sources. Experimental Design: We generated a deep learning-based HPV prediction score (HPV-ps) on regular hematoxylin and eosin (H&E) stains and assessed its performance to predict HPV association using 273 patients from two different sites (OPSCC; Giessen, n = 163; Cologne, n = 110). Then, the prognostic relevance in a total of 594 patients (Giessen, Cologne, HNSCC TCGA) was evaluated. In addition, we investigated whether four board-certified pathologists could identify HPV association (n = 152) and compared the results to the classifier. Results: Although pathologists were able to diagnose HPV association from H&E-stained slides (AUC = 0.74, median of four observers), the interrater reliability was minimal (Light Kappa = 0.37; P = 0.129), as compared with AUC = 0.8 using the HPV-ps within two independent cohorts (n = 273). The HPV-ps identified individuals with a favorable prognosis in a total of 594 patients from three cohorts (Giessen, OPSCC, HR = 0.55, P < 0.0001; Cologne, OPSCC, HR = 0.44, P = 0.0027; TCGA, non-OPSCC head and neck, HR = 0.69, P = 0.0073). Interestingly, the HPV-ps further stratified patients when combined with p16 status (Giessen, HR = 0.06, P < 0.0001; Cologne, HR = 0.3, P = 0.046). Conclusions: Detection of HPV association in OPSCC using deep learning with help of regular H&E stains may either be used as a single biomarker, or in combination with p16 status, to identify patients with OPSCC with a favorable prognosis, potentially outperforming combined HPV-DNA/p16 status as a biomarker for patient stratification.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Klein, Sebastian UNSPECIFIED orcid.org/0000-0002-2188-9377 UNSPECIFIED Quaas, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Quantius, Jennifer UNSPECIFIED UNSPECIFIED UNSPECIFIED Loeser, Heike UNSPECIFIED UNSPECIFIED UNSPECIFIED Meinel, Jorn UNSPECIFIED UNSPECIFIED UNSPECIFIED Peifer, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED Wagner, Steffen UNSPECIFIED orcid.org/0000-0003-0873-1601 UNSPECIFIED Gattenloehner, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED Wittekindt, Claus UNSPECIFIED UNSPECIFIED UNSPECIFIED Doeberitz, Magnus von Knebel UNSPECIFIED UNSPECIFIED UNSPECIFIED Prigge, Elena-Sophie UNSPECIFIED UNSPECIFIED UNSPECIFIED Langer, Christine UNSPECIFIED UNSPECIFIED UNSPECIFIED Noh, Ka-Won UNSPECIFIED UNSPECIFIED UNSPECIFIED Maltseva, Margaret UNSPECIFIED UNSPECIFIED UNSPECIFIED Reinhardt, Hans Christian UNSPECIFIED UNSPECIFIED UNSPECIFIED Buettner, Reinhard UNSPECIFIED UNSPECIFIED UNSPECIFIED Klussmann, Jens Peter UNSPECIFIED orcid.org/0000-0002-8223-7954 UNSPECIFIED Wuerdemann, Nora UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-595958
DOI:	10.1158/1078-0432.CCR-20-3596
Journal or Publication Title:	Clin. Cancer Res.
Volume:	27
Number:	4
Page Range:	S. 1131 - 1139
Date:	2021
Publisher:	AMER ASSOC CANCER RESEARCH
Place of Publication:	PHILADELPHIA
ISSN:	1557-3265
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language HUMAN-PAPILLOMAVIRUS; CANCER; ENTITY; NECK; HEAD Multiple languages Oncology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/59595