Abstract P5-05-01: Personalized Cancer Monitoring (PCM): a novel ctDNA tool to detect molecular residual disease in patients with early-stage breast cancer
Bibliographic record
Abstract
Abstract Introduction: Identification of Molecular Residual Disease (MRD) in patients with breast cancer with circulating tumor DNA (ctDNA) presents a strategy to identify patients at high risk of relapse. Approaches that detect ctDNA at lower concentrations are required to increase sensitivity and improve on the lead time between ctDNA detection and clinical relapse. Here we present results using novel highly sensitive tumor-informed sequencing assays for ctDNA detection of MRD based on detection of multiple patient specific mutations in ctDNA. Methods: 62 stage II-III breast cancer patients (23 hormone receptor positive HER2 negative (HR+HER2-), 20 HER2+, 15 triple negative breast cancer (TNBC) and 4 unknown receptor status) enrolled in the ChemoNEAR sample collection study were included. All patients received neoadjuvant chemotherapy, followed up by surgery, with samples taken at diagnosis, and post-surgery every 3 months for the first two years, followed by every 6 months for up to five years. Tumor DNA from FFPE samples and germline was Whole Exome Sequenced to identify patient specific mutations and design anchored-multiplex PCR (AMP™) Personalized Cancer Monitoring (PCMTM) assays to track mutations in plasma. Cell free DNA was extracted from 613 plasma samples (median volume 4ml, range 0.5-4.5ml) and sequenced with PCMTM assays, with 37-177 variants (median 52) per panel, to a depth of 100,000x per locus. A proprietary algorithm was used to identify ctDNA. Results: At a median follow-up of 52.7 months post-surgery (range 15.3-96.4 months), ctDNA was detected in 25.8% (16/62) of patients, with detected ctDNA levels ranging from allele frequency (AF) of 0.01%, to 32.5%) (median 0.24% AF). Detection of ctDNA was associated with a high risk of future relapse (HR 65.4, 95% CI 14.5-293.7), with a median lead-time from ctDNA detection to clinical relapse of 13.7 months (range 3.9-58.9). MRD was identified in 76.9% (10/13) of patients who relapsed. ctDNA was detected prior to relapse in both patients with brain only relapse, but with a reduced lead time over clinical relapse (5.73 and 3.90 months), which was previously not achievable with digital PCR MRD-detection assays. Of patients with assessable baseline samples, 81% (39/48) had ctDNA detected. No patients with undetected ctDNA, or detectable ctDNA with AF< 0.1%, relapsed during follow-up, whereas ctDNA was detected at baseline in all 10 patients who relapsed during follow-up (p=0.1). Conclusions: PCMTM detected breast cancer relapse with a long lead-time over clinical relapse, and strong association with relapse free survival, an advancement over previously published data with digital PCR MRD detection. Prospective, interventional trials are now required to assess whether treatment on the basis of MRD detection improves outcome, including the TRAK ER Trial (NCT04985266). Citation Format: Isaac Garcia-Murillas, Giselle Walsh-Crestani, Edward Phillips, Rosalind Cutts, Sarah Hrebien, Kathryn Dunne, Kally Sidhu, Robert Daber, Amber C. Carter, Lorena De La Peña, Stephen Johnston, Alistair Ring, Simon Russell, Abigail Evans, Anthony Skene, Duncan Wheatley, Ian Smith, Nicholas Turner. Personalized Cancer Monitoring (PCM): a novel ctDNA tool to detect molecular residual disease in patients with early-stage breast cancer [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 P5-05-01.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".