Abstract PO3-14-10: A single-center prospective cohort study to evaluate circulating tumor cells as a monitoring tool in women with breast cancer treated with neoadjuvant chemotherapy: final results of baseline data
Bibliographic record
Abstract
Abstract Background: The presence of liquid biomarkers such as circulating tumor cells (CTCs) among women undergoing neoadjuvant chemotherapy (NAC) for breast cancer may be associated with treatment response and/or an increased risk of recurrence, but limited data is available. Objectives: To detect and enumerate CTCs in blood samples from women with a new diagnosis of non-metastatic breast cancer of any subtype both i) at baseline (prior to commencing NAC), and ii) after completion of NAC and surgery using the Epic Sciences platform. Methods: Women with non-metastatic breast cancer of any subtype who have not yet commenced NAC were included, irrespective of age. Those with a prior history of another invasive cancer (apart from non-melanoma skin cancer identified 5+ years prior to enrollment) were excluded. Blood samples were obtained to measure CTCs prior to commencing NAC, and after completion of NAC and surgery (at least 4 weeks post-operatively). As previously described, CTC identification was based on immunofluorescence analysis using the Epic Sciences platform (Ueno et al 2017). Clinical/pathological data and clinical outcomes were abstracted from patients’ medical records. Associations between CTC detection and clinical/pathologic characteristics were evaluated using Fisher’s exact test for categorical variables and t-test or Wilcoxon rank sum tests for numerical variables. All analyses were performed using the R software package. Results: 50 participants who met eligibility criteria were included; a baseline blood sample was evaluable for CTC detection and enumeration for 47 patients. The median age at breast cancer diagnosis was 52 (29-75). Among them, 18 (38.3%) had HER2+ breast cancer, 17 (36.1%) had hormone receptor (HR)+/HER2- breast cancer and 12 (25.5%) had triple negative breast cancer (TNBC). The majority of patients (91%) received anthracycline and taxane-based NAC. A total of 68 samples were tested for CTC enumeration (5 mL equivalent per sample) including 47 pre-treatment and 21 post-treatment samples. CTCs were detected in 37 (79%) of patients for whom a baseline (pre-NAC) sample was available with a mean of 4.2 (SD 17.0) CTCs/mL and a range of 0 - 115.4 CTCs/mL. Detection of CTCs at baseline was highest among patients with TNBC (n=10/12, 83%), followed by those with HER2+ (n=14/18, 78%) and HR+/HER2- (n=13/17, 76%) breast cancer. CTCs were detected in 43% (n=9/21) of post-treatment samples, with a mean of 1.2 (SD 3.6) CTCs/mL and range of 0 – 16.4 CTCs/mL. Among the 20 patients for whom matched pre- and post-treatment CTC results were available, 16 (80%) had detectable CTCs pre-treatment and 8 (40%) had detectable levels post-NAC and surgery; of the 8 patients with post-treatment CTCs, 4 (50%) had HR+/HER2- breast cancer, 2 had HER2+ (25%) and 2 (25%) had TNBC. Three patients had numerically higher CTC levels after completion of NAC and surgery compared to baseline levels, 2 of whom had HR+/HER2- breast cancer and one of whom had TNBC. To-date, only 8 patients (19% of 43 who have undergone surgery) have achieved a pathological complete response (PCR) to NAC, among whom 3 had matched pre- and post-treatment CTC results available. None of these 3 patients had detectable CTCs post treatment. Conclusions: Approximately 4 in 5 women with non-metastatic breast cancer who undergo NAC have detectable CTCs at baseline (pre-treatment) using the Epic Sciences Platform. Given that CTCs remain detectable in a high proportion (40%) of patients after NAC and surgery, evaluation of CTCs as a potential measure of minimal residual disease warrants further evaluation in this patient population. Citation Format: Rania Chehade, Arushi Jain, Veronika Moravan, Giuseppe Di Caro, Megan Slade, Nadine Hartmann, Rick Wenstrup, Ana Elisa Lohmann, William Tran, Katarzyna Jerzak. A single-center prospective cohort study to evaluate circulating tumor cells as a monitoring tool in women with breast cancer treated with neoadjuvant chemotherapy: final results of baseline data [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 PO3-14-10.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".