Abstract IA018: Liquid biopsy in head and neck cancers: A model for tissue-agnostic MRD detection
Bibliographic record
Abstract
Abstract Head and neck cancer (HNC) is a molecularly heterogenous disease with a variable clinical course. Current treatment decisions rely primarily on pre-treatment prognostic factors without consideration of treatment response. This presentation will introduce liquid biopsy biomarkers that can be repeated in HNC patients for the purpose of dynamic risk stratification, treatment adaptation, and post-treatment surveillance. Novel approaches to measuring circulating tumor DNA (ctDNA) will be described, with particular attention to viral-associated and non-viral- associated subtypes of HNC. Detection of molecular residual disease (MRD) using ctDNA will be discussed. As HNC is often treated by non-surgical means, there is a pressing need for tissue- agnostic methods for ctDNA-based MRD detection. Analysis of tumor-specific genetic and epigenetic aberrations within ctDNA each present distinct opportunities and challenges for tissue-agnostic characterization of ctDNA. Viral ctDNA has emerged as a convenient target for MRD detection in viral-associated HNC, while ctDNA methylome profiling offers a versatile approach for tissue-agnostic MRD detection across HNC subtypes. Future applications of these methods may improve individualized management of HNC patients without requiring access to precious tumor tissue samples. Citation Format: Scott V Bratman. Liquid biopsy in head and neck cancers: A model for tissue-agnostic MRD detection [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr IA018.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".