Abstract PR09: Detection of minimal residual disease in post-surgical drain fluid can predict locoregional recurrence in HPV-negative head and neck cancer patients
Bibliographic record
Abstract
Abstract Introduction: Locoregional cancer relapse remains a major cause of failure in head and neck squamous cell carcinoma (HNSCC), particularly for HPV-negative patients whose 3-year locoregional failure rate is 32.5%. There is a major unmet need for an accurate diagnostic test that predicts risk of locoregional recurrence prior to adjuvant therapy selection. We present a novel proximal assay for minimal residual disease (MRD) profiled in lymphatic exudate collected via surgical drains (“lymph”). Methods: Lymph, plasma, and peripheral blood were collected from 22 HPV-negative HNSCC patients postoperatively at 24 hours along with resected tumor. Cell-free DNA was extracted from lymph and plasma and sequenced using the TruSeq Oncology 500 panel to a depth of >100 million reads. One plasma sample failed due to inadequate coverage. Two patients were censored due to lack of clinical data, yielding 9 patients with disease recurrence (REC) and 11 with no evidence of disease (NED) with >1 year of follow-up. Somatic mutations were identified from exome sequencing (200x) in tumor with matched blood. Tumor-specific variants were force-called in lymph and plasma using a custom bioinformatic pipeline. Mutation calls were filtered by a base-specific error model to eliminate artifacts. Student’s t-test was used for group comparisons. The Kaplan-Meier (KM) estimator with log-rank test and Cox proportional-hazards model were used for survival analyses. Results: ctDNA allelic fraction was 1.5x higher in lymph than in plasma (lymph = 0.11% ± 0.16%; plasma = 0.076% ± 0.12%. p = 0.018, N = 100 mutations). Significantly more mutations were detected in REC lymph compared to NED (p = 0.009), but not in plasma REC vs. NED (p = 0.16). We classified patients as positive (>1) or negative (£1) for detected mutations in each analyte and performed a KM survival analysis, showing lymph could accurately predict recurrence (sensitivity = 89%, specificity = 82%; p < 0.005) while plasma could not (sensitivity = 67%, specificity = 40%; p = 0.59). The hazard ratio in lymph was 12.52 (95% CI 1.54-101.61). We stratified REC patients by locoregional or locoregional + distant relapse and observed significantly more mutations detected in lymph (p = 0.01) from locoregional relapse while lymph and plasma performed similarly for locoregional + distant relapse (p = 1.0). We compared lymph MRD outcomes to extranodal extension (ENE), a high-risk pathologic feature. Lymph was concordant with ENE in 12/20 patients and identified an additional 6 ENE-negative relapse cases. Conclusion: Postoperative ctDNA analysis of lymph from surgical drains represents a novel MRD approach in HPV-negative HNSCC. Lymph significantly outperforms plasma for prediction of recurrence, particularly in patients with locoregional relapse. Accurate MRD identification in patients with lower risk pathologic features suggests that postoperative lymph MRD testing has the potential to significantly augment traditional pathology and provide more personalized adjuvant treatment decision-making in patients with HPV-negative HNSCC. Citation Format: Aadel A. Chaudhuri, Zhuosheng Gu, Damion Whitfield, Noah Earland, Adam Harmon, Megan Long, Peter Harris, Zhongping Xu, Ricardo Ramirez, Sophie Gerndt, Maciej Pacula, Marra S. Francis, Wendy Winckler, Jose P. Zevallos. Detection of minimal residual disease in post-surgical drain fluid can predict locoregional recurrence in HPV-negative head and neck cancer patients [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PR09.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".