F-044DETECTION OF TUMOUR-SPECIFIC MUTATIONS IN PLASMA DEOXYRIBONUCLEIC ACID: A POTENTIAL OESOPHAGEAL ADENOCARCINOMA BIOMARKER
Bibliographic record
Abstract
Objectives: There has recently been renewed interest in the use of “liquid biopsy” and detection of cell-free tumour DNA as a potential biomarker for cancer diagnosis, prognosis, treatment-monitoring and therapeutic selection. We have developed a novel, barcoded next-generation sequencing approach called SimSen-Seq to facilitate ultra-sensitive detection of circulating tumour DNA and we are applying it to samples from patients with oesophageal adenocarcinoma (EAC). The purpose of this study is to determine how detection and quantification of circulating tumour DNA (ctDNA) changes with disease burden in patients with EAC and to thus evaluate its potential as a biomarker in this patient population. Methods: Blood samples were obtained from patients with Stage I-IV EAC. Longitudinal blood samples were collected from a subset of patients undergoing a combination of neoadjuvant therapy, surgery and adjuvant chemotherapy. Imaging studies and pathology reports were reviewed to determine disease course. Tumour samples were obtained and tumour DNA was sequenced using a targeted EAC panel to identify mutations. SimSen-Seq assays were developed for each patient and used to generate sequencing libraries from cell-free DNA isolated from plasma. Mutations in plasma were identified, quantified and associations with disease stage and response to therapy were explored. Results: Plasma from 30 patients has been analysed; 5 stage I, 6 stage II, 14 stage III, and 5 stage IV. Mutations have been detected in 15 plasma samples (1/5 stage I, 3/6 stage II, 7/14 stage III, 4/5 stage IV). The fraction of ctDNA was found to increase with tumour stage. Longitudinal plasma samples have been analysed from 2 patients and the fraction of ctDNA shows correlation with disease burden and response to therapy. Conclusions: ctDNA can be detected in plasma of EAC patients and correlates with disease burden. ctDNA should be explored further as a possible biomarker in EAC. Disclosure: No significant relationships.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".