Abstract B013: An mDETECT assay for monitoring treatment response in metastatic breast cancer patients
Bibliographic record
Abstract
Abstract Metastatic breast cancer treatment response is currently assessed every 3-6 months and this long delay results in many patients undergoing ineffective and toxic treatments for long periods of time. We have developed the methylation DETEction of Circulating Tumour DNA (mDETECT) assay, a targeted DNA methylation-based Next Generation Sequencing liquid biopsy that detects cancer-specific patterns in all subtypes of breast cancer. The assay targets 60 differentially hypermethylated regions in breast cancer across the genome and assesses over 400 individual CpGs with an average of 8 CpGs per target amplicon. The assay has been assessed on DNA from 73 breast cancer tumour samples which on average had 40 regions hypermethylated per patient with similar rates across all subtypes. We will present results from plasma samples from locally advanced and metastatic breast cancer patients along with plasma from age-matched, healthy female controls. A prospective, multi-centre observational cohort study to monitor metastatic breast cancer patients using the mDETECT liquid biopsy as they undergo treatment is active and recruiting patients. Blood is drawn with every standard-of-care blood draw for up to 3 years. Plasma has been assessed using the mDETECT assay to determine the methylation status of the DNA at each time point. 35 patients have been enrolled to date with ongoing recruitment up to 150 patients. Patients are being monitored with the mDETECT assay for earlier detection of disease progression on a treatment. They will also be monitored through treatment changes to assess a patient’s initial response to a given treatment. Changes in mDETECT levels will be correlated with clinical response to determine if the mDETECT breast cancer assay can detect a response to treatment at an earlier timepoint. This assay could allow for improved monitoring of treatment response allowing clinicians to make informed decisions, improving outcomes and prolonging patient’s lives. Citation Format: Keira Frosst, Brooke Wilson, Katrina Cristall, Catherine Crawford-Brown, Christopher R Mueller. An mDETECT assay for monitoring treatment response in metastatic breast cancer patients [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 B013.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".