Abstract PR09: Detection of minimal residual disease in post-surgical drain fluid can predict locoregional recurrence in HPV-negative head and neck cancer patients
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».