Abstract 144: Dasatinib enhances the effects of paclitaxel on chemotherapy-resistant breast cancer through targeting breast cancer stem cells
Bibliographic record
Abstract
Abstract Many patients with triple negative breast cancer (TNBC) exhibit poor prognosis and are at high risk of tumor relapse and metastasis due to the resistance to conventional chemotherapy. These tumor recurrences have been attributed to the presence of breast cancer stem cells (BCSCs), a subpopulation of breast cancer cells that possesses stem-like properties and displays resistance to chemotherapy. Therefore, targeting BCSCs is a priority to overcoming chemotherapy failure in TNBCs. We generated chemotherapy-resistant TNBC cells through cyclic treatments with paclitaxel (pac). The pac-resistant cells displayed increased self-renewal potential compared to the parental TNBC cells, higher percentage BCSCs and increased phosphorylation of Src kinase at Tyr416. After performing a kinase library screening on BCSCs in chemotherapy-resistant cells, we identified dasatinib, a Src kinase family inhibitor, potently reduced BCSC self-renewal and two BCSC populations (ALDH+ and CD24lowCD44high) as well as phosphor-Src. We also demonstrated dasatinib to block pac-induced BCSC enrichment and Src activation in the parental TNBC cells. Interestingly, we found that dasatinib induced an epithelial differentiation of pac-resistant cells, resulting in their enhanced sensitivity to paclitaxel. The combination treatment of dasatinib and paclitaxel not only decreased the proportion of BCSCs and their self-renewal capacity but also synergistically reduced cell viability of pac-resistant cells. In vivo studies further demonstrated the effectiveness of dasatinib/paclitaxel combination treatment in inhibiting breast tumor growth. All these data suggest that dasatinib is a promising anti-BCSC agent and can be used in combination with paclitaxel to overcome chemotherapy resistance in TNBCs. Citation Format: Jun Tian, Chieh Lo, Fatmah Al Raffa, Meiou Dai, Jean-Jacques Lebrun. Dasatinib enhances the effects of paclitaxel on chemotherapy-resistant breast cancer through targeting breast cancer stem cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 144.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".