Abstract
BACKGROUND Patients with p16+ oropharyngeal squamous cell carcinoma (OPSCC) are potentially cured with definitive treatment. However, there are currently no reliable biomarkers of treatment failure for p16+ OPSCC. Pathologist-based visual assessment of tumor cell multinucleation (MN) has been shown to be independently prognostic of disease-free survival (DFS) in p16+ OPSCC. However, its quantification is time intensive, subjective, and at risk of interobserver variability.METHODS We present a deep-learning–based metric, the multinucleation index (MuNI), for prognostication in p16+ OPSCC. This approach quantifies tumor MN from digitally scanned H&E-stained slides. Representative H&E-stained whole-slide images from 1094 patients with previously untreated p16+ OPSCC were acquired from 6 institutions for optimization and validation of the MuNI.RESULTS The MuNI was prognostic for DFS, overall survival (OS), or distant metastasis–free survival (DMFS) in p16+ OPSCC, with HRs of 1.78 (95% CI: 1.37–2.30), 1.94 (1.44–2.60), and 1.88 (1.43–2.47), respectively, independent of age, smoking status, treatment type, or tumor and lymph node (T/N) categories in multivariable analyses. The MuNI was also prognostic for DFS, OS, and DMFS in patients with stage I and stage III OPSCC, separately.CONCLUSION MuNI holds promise as a low-cost, tissue-nondestructive, H&E stain–based digital biomarker test for counseling, treatment, and surveillance of patients with p16+ OPSCC. These data support further confirmation of the MuNI in prospective trials.FUNDING National Cancer Institute (NCI), NIH; National Institute for Biomedical Imaging and Bioengineering, NIH; National Center for Research Resources, NIH; VA Merit Review Award from the US Department of VA Biomedical Laboratory Research and Development Service; US Department of Defense (DOD) Breast Cancer Research Program Breakthrough Level 1 Award; DOD Prostate Cancer Idea Development Award; DOD Lung Cancer Investigator-Initiated Translational Research Award; DOD Peer-Reviewed Cancer Research Program; Ohio Third Frontier Technology Validation Fund; Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering; Clinical and Translational Science Award (CTSA) program, Case Western Reserve University; NCI Cancer Center Support Grant, NIH; Career Development Award from the US Department of VA Clinical Sciences Research and Development Program; Dan L. Duncan Comprehensive Cancer Center Support Grant, NIH; and Computational Genomic Epidemiology of Cancer Program, Case Comprehensive Cancer Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, the US Department of VA, the DOD, or the US Government.
Authors
Can F. Koyuncu, Cheng Lu, Kaustav Bera, Zelin Zhang, Jun Xu, Paula Toro, German Corredor, Deborah Chute, Pingfu Fu, Wade L. Thorstad, Farhoud Faraji, Justin A. Bishop, Mitra Mehrad, Patricia D. Castro, Andrew G. Sikora, Lester D.R. Thompson, R.D. Chernock, Krystle A. Lang Kuhs, Jingqin Luo, Vlad Sandulache, David J. Adelstein, Shlomo Koyfman, James S. Lewis Jr., Anant Madabhushi
Figure 4
KM DFS curves.
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ISSN: 0021-9738 (print), 1558-8238 (online)