Abstract P2-02-02: Dynamics of ctDNA changes during neoadjuvant chemotherapy in triple-negative breast cancer patients
Bibliographic record
Abstract
Abstract Background: Liquid biopsies to monitor response to treatment are a minimally invasive and highly attractive method for clinical application. Detection of ctDNA in plasma is now highly sensitive thanks to the use of novel highly sensitive and specific techniques such as ddPCR. In the present study we set out to analyze the utility of using ctDNA to monitor response to treatment in patients receiving standard neoadjuvant chemotherapy in triple negative breast cancer. Methods: Serial blood was collected from triple negative breast cancer patients participating in the Q-CROC-03 clinical trial (NCT01276899). The trial recruited triple negative breast cancer patients undergoing standard neoadjuvant chemotherapy. Paired biopsies were collected prior and at the end of treatment and serial bloods collected throughout the study. Whole exome sequencing was performed on tissues collected and we identified mutated genes of interest. Cell free DNA (cfDNA) was extracted from 3 ml of plasma and 4-10 variants per patient were analyzed by ddPCR in serial plasma samples collected before and during treatment. Response was measured by evaluating residual cancer burden (RCB), and non-responders were RCBII-III, responders RCB0-I. Results: For the present analysis, we identified 60 variants in tumors from 12 patients (9 RCBII-III and 3 RCB0-I). Except for TP53, none of the genes were shared among the tumors. 20% of the variants were not detected in ctDNA at any time point and we did not find any correlation between cfDNA levels and tumor size or response to treatment. The average variant allele frequency (VAF) of all detected variants at baseline was higher in RCBII-III patients than in RCB0-I patients (7.0 vs 0.7 respectively). Interestingly, variants that were detected either only in the pre-chemo tumor or in the post-chemo tumor were frequently detected throughout neoadjuvant therapy, highlighting the ability of ctDNA to capture tumor heterogeneity. In almost all cases, we observed a dramatic decrease in ctDNA VAF after one cycle of chemotherapy, including 30% to non-detectable levels. By the 5th cycle of chemotherapy 97% of detected variants had decreased (average 95% decrease). This decrease in ctDNA VAF was independent of RCB score. In some RCBII-III cases, ctDNA VAF increased prior to surgery, reflecting residual tumor presence. Conclusion: ctDNA could be detected in plasma of all early TNBC patients undergoing neoadjuvant chemotherapy with the majority of variants detected in plasma collected at baseline prior to chemotherapy. Once treatment started, the abundance of ctDNA markedly decreased in plasma independently of tumor response. The effect of chemotherapy on levels of ctDNA needs further investigation. Citation Format: Cavallone L, Adriana A-M, Aldamry M, Lafleur J, Cathy L, Alirezaie N, Bareke E, Majewski J, Ferrario C, Mihalciou C, Roy J-A, Markus E, Robidoux A, Pelmus M, Aleynikova O, Discepola F, Basik M. Dynamics of ctDNA changes during neoadjuvant chemotherapy in triple-negative breast cancer patients [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-02-02.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".