Abstract PO-042: Next generation liquid biopsy for diagnosis and prognostication in HPV+OPSCC
Notice bibliographique
Résumé
Abstract Introduction: Circulating tumor (ct)DNA approaches are revolutionizing cancer diagnostic and monitoring approaches. In HPV-associated oropharyngeal cancer (HPV+OPSCC), droplet digital PCR(ddPCR)-based detection of ctHPVDNA has gained significant attention but only demonstrates ~90% sensitivity at time of diagnosis and critically, performs poorly in the minimal residual disease setting, and is unable to provide additional prognostic information. For example, our group recently demonstrated the strong prognostic impact of single nucleotide polymorphisms (SNPs) in the HPV genome, which cannot be annotated by existing ddPCR approaches. To overcome these current limitations, we developed, optimized, validated, and applied a fully custom HPV whole genome hybrid-capture-based NGS assay termed HPVseek. Here, we evaluate the performance and capabilities of multiple blood-based tests, both as standalone assays and taken together in a machine learning approach, for HPV+OPSCC in a large patient cohort. Methods: Plasma samples were obtained from 150 HPV+OPSCC patients at time of presentation as part of a prospective observational study, along with 150 controls. From these, cell-free DNA was extracted and run on HPVseek, as well as on our existing multiplexed ddPCR assay. Plasma samples also underwent multiplexed HPV antibody profiling. Paired tumor samples from 25 patients additionally had gold-standard HPV whole genome sequencing performed for a head-to-head comparison of viral variant calling from plasma and tissue. HPV genotyping, lineage assignment, variant calling, fragmentomics, and antibody titers were performed for all samples by custom bioinformatic pipelines. A machine learning approach incorporating multiple features was applied to composite datasets to identify the most informative features for detection. Results: Sensitivity and specificity of HPVseek for detecting HPV+OPSCC were 99% and 100%, compared to 92% and 99% with ddPCR and 90% and 99% with E6 serology. HPV genotyping from blood was concordant with tissue in all positive samples, with 91% of patients having HPV16 (78% A1 sublineage), and the remaining patients with HPV 18, 33, 35, 45, 56, 66, 67, and 69, including patients with multiple genotypes detected in the blood. 100% genome coverage from blood was obtained from 91% of positive cases. 91% of HPV16 positive cases had complete coverage of prognostic HPV16 SNP regions, with 18 patients harboring at least one known high-risk SNPs. ctHPVDNA fragmentomics demonstrated a median fragment size of 141 bp, 26 bases shorter than that of non-cancer controls. Comparison of HPV variant calling from a gold-standard tissue assay and HPVseek demonstrated 95% concordance between high allele frequency variants. Conclusions: Here, we present the largest single-institution analysis of ctHPVDNA, utilizing a fully custom HPV whole genome NGS-based liquid biopsy, with matched HPV ddPCR, serology, and tissue WGS, demonstrating the powerful capabilities of a fully non-invasive test to both diagnose and prognosticate HPV+OPSCC at time of presentation. Citation Format: Brian Y. Zhao, Shun Hirayama, Saskia Naegele, Vasileios Efthymiou, Julia Mendel, Michael Lawrence, Viktor Adalsteinsson, Anthony J. Iafrate, Dipon Das, Daniel L. Faden. Next generation liquid biopsy for diagnosis and prognostication in HPV+OPSCC [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 PO-042.
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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».