Abstract P6-01-16: Circulating Tumour DNA (ctDNA) Detection and Dynamics in Patients with Early Breast Cancer (EBC): Results of the Neoadjuvant TRACER cohort
Bibliographic record
Abstract
Abstract Background: ctDNA dynamics are associated with treatment response, and ctDNA detection following treatment is associated with disease recurrence in early breast cancer (EBC). Highly sensitive assays may permit effective risk stratification and guide interventional strategies. RaDaR, a bespoke assay using deep sequencing of tumour-specific variants, has been shown to detect recurrence in the postoperative setting. We quantified ctDNA using RaDaR in serial samples from a large prospective cohort of EBC patients who received standard neoadjuvant chemo- (+/- HER2- targeted) therapy. Methods: Patients with EBC of all receptor subtypes receiving neoadjuvant therapy were enrolled in the TRACER cohort from 2015. Plasma samples (Streck) were collected at baseline, during treatment, perioperatively, and during follow-up. RaDaR was performed on all available timepoints for patients with tissue available for exome sequencing (assay requirement). Clinical and pathologic characteristics, treatment, and recurrence outcomes were collected. Results: 145 patients were recruited as of April 2021, and over 700 plasma samples were collected through December 2021 (patient characteristics, Table 1). 115 (79%) tissue samples were retrieved for assay design. Data are presented for the initial 43 patients and 265 samples analyzed, including 82 post-surgical time points. Median time since diagnosis was 3.5 years (range, 1.5-5.0). Exome sequencing and assay generation were successful in all patients (n=43), yielding assays targeting a median of 48 (range 22-50) variants. 88% (38/43 patients) had ctDNA detected at baseline, with median variant allele frequency (eVAF) in positive patients of 0.15% (range, 0.0019-4.9%). ctDNA levels fell rapidly with treatment: 19/37 (51.3%; median eVAF in positive patients: 0.0098%, range 0.001-0.156%) had ctDNA detected prior to cycle 2, and 4/32 (12.5%) had ctDNA detected at cycle 4 or 5 (mid treatment; median eVAF in positive patients: 0.001%, range, 0.0007-0.011%). In the perioperative period, 17/18 (94%) of patients with available pre-operative specimens and 27/28 (96%) of patients with available post-operative samples had clearance of ctDNA. In adjuvant follow up, ctDNA was detected in 4 of the 43 analyzed patients, with ctDNA clearance observed following planned switch of endocrine therapy in one. Clinical follow-up continues, and analysis of the remaining samples (72 patients and over 425 samples) is underway. Conclusion: RaDaR demonstrated high sensitivity for ctDNA prior to treatment in patients receiving neoadjuvant therapy for EBC, permitted dynamic monitoring of treatment effect, and identified patients with persistent ctDNA after curative-intent therapy, as well as ctDNA emergence prior to clinical recurrence. Full results for the TRACER cohort and analysis of clinical covariates will be presented at the meeting. ClinicalTrials.gov NCT03702309. Table 1. Patient Baseline Characteristics Citation Format: Mitchell J. Elliott, Zachary Veitch, Philippe Bedard, Eitan Amir, Aaron Dou, Jesus Fuentes Antras, Michelle Nadler, Nicholas Meti, Nancy Gregorio, Elizabeth Shah, Helen Chow, Nathan Campbell, Samantha Terrell, Charlene Knape, Karen Howarth, Lillian Siu, Hal Berman, David W. Cescon. Circulating Tumour DNA (ctDNA) Detection and Dynamics in Patients with Early Breast Cancer (EBC): Results of the Neoadjuvant TRACER cohort [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-16.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".