Abstract P6-03-21: Identification of mechanisms driving acquired chemoresistance in preclinical breast cancer models of taxane resistance
Bibliographic record
Abstract
Abstract Background: Taxanes are an established part of the treatment regime for early and metastatic breast cancer. However acquired chemoresistance remains a major factor in therapeutic failure in these patients. Treatment options thereafter are limited. It is key that the underlying molecular mechanisms of the taxane resistance are elucidated to drive the development of novel targeted therapies to treat chemorefractory disease. Methods: In vitro models of taxane resistance in breast cancer were developed through continuous exposure of MDA-MB-231 and MCF7 cell lines to either paclitaxel or docetaxel. Differential gene expression analysis was performed comparing the resistant cells to chemosensitive parent lines using the Nanostring PanCancer Pathway panel. Further, RPPA analysis was utilised to investigate the proteomic signature of the chemoresistant lines and Western blotting performed to confirm key changes. A small molecule kinase screen was performed to identify candidate inhibitors with activity in the taxane resistant models. Results: Nanostring gene expression analysis resulted in the identification of 204 significantly altered mRNA in MDA-MB-231 PACR, 231 mRNA altered in MDA-MB-231 DOCR and 88 mRNA significantly altered in MCF7 PACR (1.5 fold, p-value<0.05). Of these genes, 93 were altered in both the paclitaxel and docetaxel MDA-MB-231 models. Pathway analysis of the differentially expressed genes highlighted involvement of the MAPK and PI3K/Akt pathways in the evolution of chemoresistance. RPPA analysis predicted alterations in the protein expression of a number of select members of these pathways and Western blotting confirmed expression changes in phosphorylation of FAK, Akt and MAPK1. PI3K inhibitors BKM-120 and PIK-75 were identified as part of a small molecule kinase screen and found to inhibit cell growth in both taxane sensitive and resistant cell lines. Table 1: Sensitivity of taxane resistant cell lines models to BKM-120 and PIK-75Cell line modelBKM-120 IC50 (µM)PIK-75 IC50 (µM)MDA-MB-231 Parent3.284 ± 0.5820.033 ± 0.008MDA-MB-231 PACR1.512 ± 0.5240.076 ± 0.018MDA-MB-231 DOCR2.267 ± 0.8160.064 ± 0.01MCF7 Parent0.421 ± 0.0440.022 ± 0.015MCF7 PACR0.289 ± 0.0410.01 ± 0.003 Conclusion: Candidate resistance-associated pathways were identified by differential gene expression analysis and proteomic analysis by RPPA. Western blotting confirmed alterations in the PI3K/Akt and MAPK pathways. PI3K inhibitors were found to have potent activity against the taxane resistant cell line models. Further investigations to confirm their potential as a therapeutic in the treatment of chemoresistant breast cancer is required. Citation Format: Karen J Taylor, Nicola Lyttle, Linda Lao, Charlie Gourley, David A Cameron, John MS Bartlett, Melanie Spears. Identification of mechanisms driving acquired chemoresistance in preclinical breast cancer models of taxane resistance [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 P6-03-21.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".