Abstract PO4-15-12: Personalized circulating tumor DNA monitoring to predict response to neoadjuvant therapy in patients with early-stage breast cancer
Bibliographic record
Abstract
Abstract Introduction Longitudinal ctDNA testing offers a minimally invasive approach for monitoring treatment response in patients with breast cancer (BC). In this study, we evaluated whether serial ctDNA testing during neoadjuvant therapy (NAT) can provide an early indication of treatment response or resistance and disease progression. Methods In this real world study, longitudinal blood samples collected from 159 patients with stage I-III BC were analyzed using a personalized, tumor-informed ctDNA assay (SignateraTM bespoke mPCR-NGS assay). Of the 159 patients, 157 received standard of care NAT; the remaining 2 patients were treated with endocrine therapy only in the neoadjuvant setting. Blood samples were collected at the time of diagnosis from 71 patients (baseline) and longitudinally during treatment from 159 patients (median time between time points: 1.5 months). ctDNA status and dynamics were correlated with clinicopathological features and surgical outcomes available at the time of data cut off. Results At baseline, ctDNA was detected in 72% (51/71) of patients. Baseline ctDNA detection rates varied based on histologic subtypes: 82% (28/34) in patients with triple negative breast cancer, 88% (15/17) in patients with HER2+ disease and 40% (8/20) in patients with ER+HER2- BC. ctDNA was detectable at baseline in 72% (26/36) of patients with clinical T1-2 disease and 92% (11/12) of patients with T3-4 disease. ctDNA was detected in 64% (16/25) of patients with clinical N0, whereas among patients with at least one positive lymph node, ctDNA was detected in 92% (22/24). At baseline, 65% (31/48) of patients with low/intermediate-grade disease were ctDNA-positive, while 81% (25/31) of patients with high-grade disease were ctDNA-positive. Similarly, 83% (24/29) of patients with high levels (>20%) of Ki67 expression had detectable ctDNA prior to treatment initiation, while only 22% (2/9) of those with low level (< 20%) of Ki67 expression had ctDNA detected. Surgical outcomes were available for 22 patients of whom 16 had achieved pathologic complete response (pCR). While 3/16 pts were ctDNA negative at baseline, all of the remaining cases except one patient with baseline ctDNA detection and serial measurements (11/12) became ctDNA negative by the end of two months of treatment. Despite achieving pCR, one patient with inflammatory ER+HER2+ BC remained ctDNA positive before and after the surgery, which informed a change in the adjuvant treatment strategy. Among those with residual disease (pT1c-T3), 28% (7/25) remained ctDNA positive after two months of NAT. Association between ctDNA dynamics and surgical outcomes of the remaining 137 patients will be presented at the time of the conference. Conclusions This real world study demonstrates the prevalence of ctDNA detection and dynamics in patients with eBC treated with NAT. ctDNA monitoring during NAT can facilitate real-time assessment of treatment response and serve as an early indicator of subsequent pCR to NAT. Citation Format: Mridula George, Trishala Meghal, Coral Omene, Ekaterina Kalashnikova, Janie Fielder, Nisha Ohri, Shicha Kumar, Valeria Burkovskaya, Preena Patel, Ashley Young, Melanie Racenstein, Tinamarie Bauman, Wassim Mchayleh, Deborah Toppmeyer, Shridar Ganesan, Barry Rosen, Angel Rodriguez, Minetta Liu. Personalized circulating tumor DNA monitoring to predict response to neoadjuvant therapy in patients with early-stage breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-15-12.
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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 | 0.000 | 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".