Abstract 3405: Characterizing treatment response in head and neck cancer through viral ctDNA quantification and fragment length
Bibliographic record
Abstract
Abstract Background: Head and neck cancer (HNC) is the sixth most common cancer type worldwide, and despite decreases in the rates of Human Papillomavirus (HPV)-negative HNC, an alarming increase in HPV+ HNC is occurring. Circulating tumor (ct)DNA isolated from liquid biopsy has been shown to have clinical utility as a biomarker in cancer, with ctDNA levels and fragment size used to monitor treatment response. Methods: The aim of this study was to determine the presence of ctDNA in liquid biopsy (plasma and saliva) of HPV+ HNC patients to non-invasively monitor treatment response and disease progression. Blood and saliva samples were collected from 45 HPV+ HNC patients and 14 HPV-negative controls at the McGill University Health Centre before and/or after surgical resection. Samples were analyzed using droplet digital PCR (ddPCR) with primers and probes for HPV16/HPV18, the most common subtypes of HPV in HNC, accounting for 97% of cases in North America. Analysis was performed for all patients with confirmed follow-up scans and/or pathology. ctDNA levels were correlated with disease course as determined through imaging (>6 months post-sampling) and pathological examination. Results: ctDNA was detectable in 21/23 (91%) patients prior to treatment and 8/36 (22%) post treatment. In the two patients who were not positive for ctDNA prior to treatment, tumor tissue was found to be negative for HPV DNA despite being p16-positive by immunohistochemistry. Patients showed shifts in ctDNA fragment length in longitudinal samples following treatment. All post-treatment patients with no detectable ctDNA had no signs of recurrence/residual disease on follow-up imaging. In contrast, all 8 patients who tested positive for ctDNA post-treatment showed signs of recurrence on follow-up imaging. In our cohort, 14 patients had matched pre- and post-treatment samples and all showed major reductions in ctDNA post treatment compared to pre-treatment, with 12/14 patients having a complete loss of detectable ctDNA. Concordance between the presence of ctDNA in saliva and plasma was found in 86% of patients. ctDNA was only detected in one analyte in 6 patients (3 in plasma only and 3 in saliva only). Conclusion: HPV ctDNA was successfully detected in saliva and blood of patients with HPV-positive HNC, with a sensitivity of 92% for ctDNA prior to treatment, and a sensitivity of 100% when adjusted for tumor HPV status as opposed to p16 staining. The inclusion of both plasma and saliva in analysis increases the sensitivity of assays, with the use of both analytes allowing for the detection of ctDNA in patients with low plasma DNA levels and for patients who did not have salivary ctDNA, potentially due to inability to produce sufficient saliva or tumor location. The presence of ctDNA correlated with treatment response, indicating the potential of HPV ctDNA for monitoring tumor progression in HPV-positive HNC patients. Citation Format: Sarah Tadhg Ferrier, Anthony Zeitouni, Nader Sadeghi, Thupten Tsering, Julia V. Burnier. Characterizing treatment response in head and neck cancer through viral ctDNA quantification and fragment length [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3405.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".