Abstract P6-01-17: A single-center prospective cohort study to evaluate circulating tumor cells as a monitoring tool in women with breast cancer treated with neoadjuvant chemotherapy
Bibliographic record
Abstract
Abstract Background The presence of circulating tumor cells (CTCs) among women before and/or after completion of neoadjuvant chemotherapy (NAC) for breast cancer may be associated with an increased risk of recurrence, but limited data is available. Objectives To use the Epic Sciences platform to detect and enumerate CTCs in blood samples from women with a new diagnosis of non-metastatic breast cancer of any subtype both i) prior to commencing NAC, and ii) after completion of NAC and surgery. Methods Inclusion criteria included women of any age with non-metastatic breast cancer of any subtype who have not yet commenced NAC. Those diagnosed with prior invasive cancer at any site (apart from non-melanoma skin cancer diagnosed more than five years prior to enrollment) were excluded. Blood samples were obtained to measure CTCs prior to NAC and after NAC and surgery, respectively. CTC identification was based on immunofluorescence analysis using Epic Sciences platform as previously described (Ueno et al 2017). The presence of CTCs was correlated with clinical/pathological data and treatment response, which were abstracted from patients’ medical records. The association between the presence of CTCs and clinical/pathologic characteristics was tested 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. An ad-hoc preliminary analysis was conducted among the first 34 of 50 participants. Results 41 patients (out of an intended 50) have been recruited to-date. 34 participants have a pre- and/or post-treatment CTC measurement available, but 1 was excluded because it was identified to have metastatic disease shortly after enrollment. Among 33 evaluable patients without metastatic disease, 6 (19%) had triple negative breast cancer (TNBC), 13 (39%) had HER2+ and 13 (39%) had hormone receptor (HR)+/HER2- breast cancer. Most (94%) received anthracycline and taxane-based NAC. The median age of breast cancer diagnosis was 50 (29-75). A total of 53 samples were tested for CTC enumeration (5 mL per sample) including 33 pre-treatment and 20 post-treatment samples. CTCs were detected in 32 samples (n=32/53, 60%), including 24 pre-NAC (n=24/33, 73%) with a median of 0.9 CTCs per mL (0.2-19) and 8 post-NAC and surgery (n= 8/20, 40%) with a median of 0.6 CTCs per mL (0.3-3.3). Among the 24 patients (73%) who had detectable CTCs pre-NAC, 10 had HR+/HER2- (41.7%), 9 had HER2+ (37.5%) and 4 (16.7%) had triple negative disease. 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. Among the 8 patients (40%) for whom CTCs were detectable post NAC and surgery, 4 (50%) had HR+/HER2-, 2 had HER2+ (25%) and 2 (25%) had triple negative disease. 3 of these patients had numerically higher CTC levels after completion of NAC and surgery compared to pre-NAC levels, 2 of whom had HR+/HER2- breast cancer and one of whom had TNBC. A total of 7 of 33 patients 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 3 in 4 women with non-metastatic breast cancer who undergo NAC have detectable CTCs pre-treatment using the Epic Sciences Platform. Of 20 patients with matched pre-/post-treatment results, a high proportion (40%) have persistently detectable CTCs. Hence, CTCs may represent an additional measure of minimal residual disease for patients undergoing NAC for breast cancer. Citation Format: Rania chehade, Arushi Jain, Veronika Moravan, Giuseppe Di Caro, Amanda Anderson, Megan M. Slade, 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 [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 P6-01-17.
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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 | 0.001 |
| 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) | 0.003 | 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".