Abstract P4-01-12: The association of circulating tumor cells with tumor response in breast cancer patients undergoing neoadjuvant chemotherapy
Bibliographic record
Abstract
Abstract Introduction Despite its limitations, Cellsearch® remains the only validated technique to detect circulating tumors cells (CTCs) in the peripheral blood of cancer patients. A new technique (CytoFind) allows trapping CTCs with varied numbers of antigen-targeted magnetic particles to be trapped in different compartments of a fluidic chip based on their level of expression and velocity, allowing the detection and count of CTC subpopulations (profiling-based technique). Additionally, this technique is able to use different capture agents for binning and profiling beside the EpCAM expression. We used magnetic nanoparticles tagged with anti-Her2 in a new microfluidic device to capture CTCs (Her2 technique). We aimed to prospectively assess the performance of this new technology compared to Cellsearch® and evaluate the association of CTCs with tumor response in breast cancer patients on neoadjuvant chemotherapy. Methods Breast cancer patients who underwent neoadjuvant chemotherapy followed by surgery were eligible. CTCs were enumerated before the start of neoadjuvant chemotherapy. The concordance of CTC detection between techniques was assessed with the intraclass correlation coefficient (ICC). The association between CTCs and baseline characteristics as well as tumor response was evaluated with a Fisher exact test. Tumor response was assessed as pathologic complete response (pCR) and with the residual cancer burden index (RCB). Results Thirty-five patients were successfully assessed for CTCs using at least one technology. Fifty four percent and 23% of the patients had a HER2 positive and triple negative disease respectively. Thirty eight percent and 53% of the patients had a pCR and a good tumor response rate (RCB 0 or 1) respectively. The mean CTC count detected was 0.63 (SD 1.8), 0.71 (SD 1.51) and 1.22 (SD 1.62) with Cellsearch®, profiling-based and Her2 technologies respectively. An association was seen between detected CTCs and tumor size as well as progesterone receptor status (p<0.05) but not with the rest of the baseline characteristics: age, estrogen receptor, Her2, or nodal status, tumor grade and presence of lymphovascular invasion (p>0.05). No association was found between CTC counts and tumor response (pCR or RCB). The profiling-based and Her2 technologies were moderately concordant (ICC=0.5) but a low concordance with Cellsearch® was observed (ICC=0.07). Conclusions A new profiling-based technology detected CTCs in breast cancer patients and may overcome the Cellsearch® limitations and provide more information on CTCs subpopulations. Yet, further calibration and validation are needed before using this technology in future research. While no association between CTCs and tumor response was observed, a longer follow up is needed to assess the impact on survival. Citation Format: Jacques Raphael, Reza Mohamadi, Alex Kiss, Maureen Trudeau, Sunil Verma, Alison Allan, Shana Kelley, Joelle Helou, Fang-I Lu, Siqi Zhu, Kathleen I Pritchard. The association of circulating tumor cells with tumor response in breast cancer patients undergoing neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-01-12.
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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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".