Investigating the Mechanisms and Kinetics of Circulating Tumour DNA Release to Improve its Clinical Utility
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease with distinct subtypes, molecular features and clinical behaviours. Significant advances to better define this heterogeneity have revealed new targets and attempted to stratify patients into clinically meaningful groups. Human papillomavirus (HPV)-positive HNSCC has been recognized as a distinct etiology with significantly better treatment response and overall survival compared to HPV-negative HNSCC. However, despite the improved prognosis of these patients, current therapies result in significant toxicities, and subtype-specific treatments are still lacking. Thus, there remains an urgent need for additional biomarkers of therapeutic sensitivity to better risk stratify these patients and enable personalized treatment regimens. Circulating tumour DNA (ctDNA) has sparked tremendous interest in the last decade as a non-invasive method to monitor dynamic changes in treatment response. However, the mechanisms that dictate ctDNA release kinetics have not been thoroughly investigated, hampering the interpretation of such ctDNA kinetic patterns. To address this gap, I first evaluated the biological underpinnings that dictate ctDNA release using preclinical models of HNSCC. To evaluate the generalizability of my findings, I simultaneously evaluated ctDNA release from other cell types including mesenchymal cells and lung adenocarcinoma. Through detailed mechanistic studies, I uncovered a complex interplay between apoptosis, necrosis and senescence in determining ctDNA release kinetics, where treatment type and timing from treatment exposure were key factors influencing release. I next investigated the longitudinal kinetics of ctDNA release in 70 HNSCC patients treated with definitive radiotherapy or chemoradiotherapy. Three patterns of ctDNA kinetics were observed, with HNSCC patients exhibiting frequent on-treatment spikes, with a strong dependence on treatment type. Lastly, utilizing the knowledge gained in regard to the underlying biology and longitudinal profiles of ctDNA release, I identified a cohort of 235 HPV-positive oropharyngeal carcinoma patients and discussed the next steps in evaluating the relationship between ctDNA kinetic changes and clinical response. These studies provide novel insights into the biological mechanisms and dynamic changes of treatment-induced ctDNA release. Overall, these findings highlight the potential clinical utility of dynamic ctDNA monitoring to facilitate biomarker-driven adaptive therapy, with the intent to maximize disease control and minimize current treatment related-morbidities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".