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Abstract PR09: Detection of minimal residual disease in post-surgical drain fluid can predict locoregional recurrence in HPV-negative head and neck cancer patients

2023· article· en· W4386784013 on OpenAlexaboutno aff
Aadel A. Chaudhuri, Zhuosheng Gu, Damion Whitfield, Noah Earland, Adam Harmon, Megan Long, Peter K. Harris, Zhongping Xu, Ricardo J. Ramirez, Sophie P. Gerndt, Maciej Pacula, Marra S. Francis, Wendy Winckler, José P. Zevallos

Bibliographic record

VenueClinical Cancer Research · 2023
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLymphHead and neck squamous-cell carcinomaInternal medicineHead and neck cancerOncologyCancerMinimal residual diseaseAdjuvant therapyPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.163
GPT teacher head0.495
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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