Abstract 3076: Post-transcriptional regulatory mechanisms contributing to the evolution of paclitaxel resistance in breast cancer model cell line, SKBR3
Bibliographic record
Abstract
Abstract Introduction: Breast Cancer (BCa) treatments often include taxanes in the treatment regimens and resistance to these drugs is common in a clinical setting. Several mechanisms were put forth to explain the resistance. These include induction of multidrug (MDR) resistance family of transporters, differential regulation of tubulin isotypes, microRNA (miR) mediated post-transcriptional regulation and autophagy. Aims: (i) to create a panel of breast cancer cell lines from SKBR3 (Her2+ and ER-ve) with varying levels of drug resistance to paclitaxel; (ii) to profile differentially expressed miRs in these panels of cell lines to understand the evolution of drug resistance with common and unique signatures associated with each of the cell lines in the panel and (iii) to assess β-tubulin (TUBB3) expression and its regulation by miRs. Methods: Drug resistant SKBR-3 cells were generated by a stepwise increase in drug concentration to the parental cells until a maximum tolerable dose was reached. Control cells were those without any treatment (wild type, WT) and serial passage of SKBR3 cells for the same numbers of generations as in each of the drug resistant lines. Total RNA extracted from the cell lines was subjected to small RNAome profiling (with focus on miRs) using Next Generation Sequencing (Illumina Genome Analyzer IIx). Partek Genomics Suite 6.6 was used for analyzing sequencing files. Data normalized (RPKM) from triplicate experiments (three independent cell growth experiments) were adjusted for potential batch effects. We report miRs showing statistically significant differences in expression levels (>2-fold and FDR adjusted p<0.05) between WT SKBR3 and drug resistant cell line(s) with concordant profiles from triplicate sequencing experiments. Protein expression levels of β-tubulin (TUBB3) were assessed using Western blots. Results and conclusions: The resistant cell lines expressed TUBB3 (graded up-regulation with the level of resistance) as expected as well as down-regulation of hsa-miR-200c, a known miR directly regulating the expression of TUBB3. The resistant cell lines also showed up-regulation of miRs 221-3p and 99a known to modulate ER and its mediated gene regulatory pathways, consistent with the ER-ve phenotype of SKBR3 cells. Overall, 27 miRs are differentially expressed when all resistant cells are considered (irrespective of the level of resistance conferred). As expected, some of the identified miRs were reported to have a role in Epithelial-Mesenchymal Transition vis-à-vis acquisition of cancer stem cell properties. We also observed unique miRs in cells expressing graded levels of resistance and the data is subject to further analysis for potential hierarchy of programmed expression of miRs facilitating transitioning of cells from sensitive to highly drug resistant phenotype. Citation Format: Sambasivarao Damaraju, Preethi Krishnan, Marc St George, Jack Tuszynski, Carol Cass, IngSwie Goping, Olga Kovalchuk. Post-transcriptional regulatory mechanisms contributing to the evolution of paclitaxel resistance in breast cancer model cell line, SKBR3. [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 3076. doi:10.1158/1538-7445.AM2015-3076
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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 | 0.001 |
| 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) | 0.004 | 0.002 |
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".