Purpose: HPV-associated carcinomas (HPV+ cancers) account for 5% of all cancers. Circulating tumor HPV DNA (ctHPVDNA) assays for HPV+ cancer surveillance have limited prognostic utility at the time of cancer diagnosis. While HPV integration into the host genome is a proven tissue-based biomarker predicting poor clinical outcomes, existing clinically utilized ctHPVDNA assays cannot classify the viral physical state. Methods: We previously developed HPV-DeepSeek, a multi-feature HPV whole-genome sequencing liquid biopsy with 99% diagnostic accuracy at the time of HPV+ oropharynx cancer diagnosis. We test the diagnostic accuracy of HPV-DeepSeek in a cohort of 235 HPV+ cancers across nine anatomic sites and employ a novel blood-based computational classifier to infer HPV genome physical state from plasma, termed HPV-SIGNAL, to assess its prognostic potential. Results: HPV-DeepSeek demonstrated a sensitivity and specificity of 99%. In 181 eligible samples, HPV-SIGNAL identified four viral physical states: episomal-only (N = 69), episomal-rearranged (N = 48), integrated-mixed (N = 55), and integrated-clonal (N = 9), which were confirmed and further elucidated via three orthogonal tissue and blood approaches. Patients harboring integrated viral states in the blood exhibited significantly worse progression-free survival (HR 3.28, 95% CI 1.63-6.58, p = 0.00084) and overall survival (HR 2.98, 95% CI 1.16-7.64, p = 0.023) compared to patients with episomal states. Conclusion: HPV whole-genome sequencing liquid biopsy has high diagnostic accuracy across HPV+ cancer types and can be used to identify and classify HPV physical state from blood. Patients with integrated viral states detected in the blood demonstrated worse progression-free and overall survival, suggesting blood-based HPV physical state classification could be used as a prognostic tool at the time of cancer diagnosis.

DLF: In the past 36 months, research, salary or in-kind funding from Calico, Predicine, BostonGene, NeoGenomics, and Haystack (Quest). Consulting fees from Merck, Noetic, Chrysalis Biomedical Advisors, NeoGenomics, Arcadia, Focus, GT Molecular, Pacbio, and Olympus. ASF: Research funding from Haystack (Quest).

Funding for this work came from NIH/NIDCR R03DE030550 (Daniel L. Faden).

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