Abstract 4663: Human papillomavirus circulating tumor DNA for risk stratification in cervical cancer
Bibliographic record
Abstract
Background: Human papillomavirus (HPV) DNA is detectable in the peripheral blood plasma from patients with locally advanced and metastatic cervical cancer. Levels of HPV circulating tumor DNA (ctDNA) in pretreatment plasma have weak associations with prognosis, but the significance of detecting HPV integration into the host genome or fragmentation features within HPV ctDNA has not been explored. We hypothesized that these molecular features of HPV ctDNA may serve as prognostic biomarkers and reflect HPV biology, independent from total ctDNA abundance. Methods: Plasma cell-free DNA was collected at baseline from 57 patients with locally advanced cervical cancer (stage IB-IVA) and 21 patients with metastatic cervical cancer. Whole viral genome sequencing was performed following hybrid capture. HPV ctDNA levels were expressed in copies/mL plasma. HPV integration sites were detected using SearcHPV. Progression-free survival (PFS) was evaluated using Kaplan-Meier analysis and log-rank tests. The normalized fragment midpoint coverage across the HPV-16 genome was calculated for individual samples and averaged across patient groups. NuPoP, was used to calculate the expected nucleosome occupancy at each base pair of the HPV-16 genome. Peaks corresponding to the observed nucleosome occupancy within our cohorts were called using pracma, and occupancy probabilities were calculated for each peak. Results: Baseline HPV ctDNA levels were detected in 57/57 locally advanced patients (median=283 copies/mL) and 20/21 metastatic patients (median=299 copies/mL). Unique HPV integration sites were detected at varying levels in 21/57 locally advanced patients (median sites:1, range:0-23) and 12/21 metastatic patients (median sites:2, range:0-45) (Wilcoxon p=0.8). No significant differences in PFS were observed in locally advanced or metastatic patients when stratified by median ctDNA levels (p=0.7 and p=0.8 respectively). However, high confidence integration detected at baseline was associated with inferior PFS in locally advanced patients (p=0.01), but not metastatic (p=0.28). HPV ctDNA fragment coverage varied across the HPV-16 genome with the number of observed merged peaks being 32 in the locally advanced cohort (n=36) and 31 in the metastatic cohort (n=14). The median expected nucleosome occupancy probabilities across all peaks were 98.6% and 97.2%, respectively. Conclusion: Detection of viral integration within baseline HPV ctDNA was associated with significantly inferior PFS in locally advanced patients. Peaks in the average HPV-16 ctDNA coverage profiles corresponded to high expected nucleosome occupancy in both the locally advanced and metastatic cohorts, suggesting that ctDNA may reflect chromatin accessibility of the virus. These findings suggest that quantitative and qualitative features of HPV ctDNA from baseline plasma may reflect HPV biology associated with cervical cancer. Citation Format: Emma M. Collier, Lucas Penny, Jinfeng Zou, Zhen Zhao, Yangqiao Zheng, Pamela Soberanis Pina, Michelle McMullen, Eric Y. Stutheit-Zhao, Michael M. Hoffman, Sarah E. Ferguson, Kathy Han, Eric Leung, Stephanie Lheureux, Scott V. Bratman. Human papillomavirus circulating tumor DNA for risk stratification in cervical cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4663.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".