Abstract 709: Resistance to paclitaxel in triple negative breast cancer cells is associated with ABCB1 gene rearrangement
Bibliographic record
Abstract
Abstract Breast cancer represents a heterogeneous group of tumors that exhibit a wide spectrum of clinical, pathologic, and molecular features. Of these tumors, triple-negative breast cancer (TNBCs), shows one of the most aggressive clinical behaviors with distinctive metastatic patterns and very poor prognosis. Due to the “triple negative” status of receptor expression, TNBC patients do not benefit from hormonal therapies or treatments targeted against HER2. Therefore, chemotherapy and, in particular, taxane-based therapy (e.g. paclitaxel) remains the treatment of choice for patients with TNBC. Paclitaxel (PTX) is among the most effective anti-cancer agents developed in the past decades, which is widely used in the treatment of patients with locally advanced and metastatic breast cancer. TNBCs are initially highly responsive to PTX however; the majorities of TNBC patients acquire resistance and develop progressive disease. Therefore, development of resistance to chemotherapeutic agents is the major obstacle to the successful treatment of TNBC patients. In vitro studies on cell lines with acquired resistance provide models for characterization of the biological mechanisms of resistance. We have developed five resistant TNBC cell lines by exposure of cells to increasing concentrations of PTX. We used an integrative analysis of array CGH and gene expression data to gain insights into the interplay of functional changes of the genome in TNBC resistant cell lines. Integrative analysis of aCGH and gene expression revealed deregulated genes whose expression was correlated with their copy number alterations. The most highly up-regulated gene in all 5 resistant cell lines was ABCB1. This up-regulation was accompanied by gene amplification in three resistant cell lines: BT20R, SUM149R and MDA-468 but not in 2 other lines, MDA-MB-231 and MDA-MB-436. The overexpression of ABCB1 at protein level was confirmed only in the three resistant cell lines with the gene amplification, suggesting that changes in ABCB1 copy number may result in gene activation and acquisition of PTX resistance. Array CGH analysis of 7q21.12 in BT20R, SUM149R and MDA-468R revealed similar, but not identical, breakpoints in ABCB1. Using 5′RACE, we identified novel inter-chromosomal translocations of ABCB1 in 2 drug resistance cell lines containing ABCB1 breakpoints. These genomic rearrangements occurred in the 5′ un-translated region (UTR) of ABCB1. The deletion of the 5′UTR of ABCB1 in these resistant cell lines may serve to overcome a translation initiation block, which would then favor the expression of ABCB1. Thus the detection of ABCB1 genomic rearrangements may be a candidate biomarker to predict resistance to paclitaxel in triple negative breast cancers. To our knowledge, we are the first to report ABCB1 amplification and ABCB1 gene fusions as a potential mechanism of drug resistance in TNBC cell lines. Citation Format: Elaheh Ahmadzadeh, Ewa Przybytkowski, Regina Kiu, Adriana Aguilar-Mahecha, Mark Basik. Resistance to paclitaxel in triple negative breast cancer cells is associated with ABCB1 gene rearrangement. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 709. doi:10.1158/1538-7445.AM2015-709
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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) | 0.000 | 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 | 0.001 |
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".