Gene Expression of Solute Carrier Organic Anion Transporters in Multidrug Resistant Breast Cancer Cells
Bibliographic record
Abstract
Introduction Breast cancer is the second leading cause of death due to cancer in women, and often becomes multidrug resistant (MDR) to adjuvant drug therapies due to the overexpression of ATP‐binding cassette (ABC) drug efflux transporters or through the decreased expression of drug uptake transporters, which can lead to treatment failure. MDR breast cancer cells can have complex drug translocation processes due to attempts to reduce intracellular anti‐cancer drug concentrations. It is critical to investigate possible drug targets for MDR cancers that can lead to increased drug accumulation. Solute carrier organic anion (SLCO) transporters are a family of transmembrane proteins that can uptake amphiphilic organic compounds into cells. Preliminary evidence suggests that SLCO transporters may be responsible for the targeted uptake of an emerging class of anticancer molecules called jadomycins, topoisomerase II and aurora B kinase‐targeting compounds which retain their cytotoxic potency in ABC‐transporter overexpressing MDR breast cancer cells. Objective The objective of this study is to evaluate the gene expression of SLCO transporters in a panel of breast cancer cell subtypes. Methods The mRNA expression 11 SLCO transporters was quantified using quantitative polymerase chain reaction (qPCR) in drug sensitive MCF7 (MCF7‐CON) and taxol (MCF7‐TXL), etoposide (MCF7‐ETP), and mitoxantrone (MCF7‐MITX) resistant MCF7 breast cancer cell lines which overexpress ABCB1, ABCC1, and ABCG2 drug efflux transporters, respectively. Comparisons were also made between BT474, SKBR3, and MDA‐MB‐231 breast cancer cell lines with different hormone receptor profiles as well as non‐cancerous MCF‐10A breast epithelial cells. Results Generally, SLCO4A1 and 3A1 are significantly higher than the remaining transporters in drug resistant MCF7 cells, whereas this is not seen in the drug sensitive MCF7‐CON cells. There was significantly higher expression of: SLCO3A1 and 4A1 compared to the remaining transporters in MCF7‐MITX cells; SLCO3A1 , 4A1 , 4C1 compared to the remaining transporters in MCF7‐TXL cells; SLCO4A1 compared to the remaining transporters in MCF7‐ETP and SKBR3 cells; and SLCO3A1 and 4C1 versus all other transporters in MCF7‐ETP cells. There was significantly higher expression of SLCO1C1 versus SLCO3A1 , 5A1 , 1B3 , 2B1 , 1B1 , 2A1 , and 6A1 in MCF7‐CON cells. When differentiating between cancerous cells and non‐transformed MCF‐10A cells there were no significant differences between SLCO3A1 and any transporters found in breast epithelial MCF‐10A cells, however there was significantly higher expression of SLCO4A1 versus SLCO5A1 , 1B3 , 2B1 , 1B1 , and 4C1 . Furthermore, there were no significant differences when comparing the transporters in BT474 and MDA‐MB‐231 cell lines. Conclusion SLCOs are differently expressed in breast cancer cell lines. SLCO4A1 and 3A1 were the most commonly expressed at significantly higher levels versus the other transporter genes in the cell lines used. They also were most highly expressed in the MDR MCF7 breast cancer cell lines and could serve as a conserved transport mechanism for intracellular delivery of anti‐cancer drugs, thereby suggesting they may be responsible for the cellular uptake of jadomycins. To explore this hypothesis, our future work will evaluate the jadomycin accumulation and cytotoxicity in SLCO3A1 and 4A1 knockdown cell lines. Support or Funding Information Leah Bennett is a trainee in the Cancer Research Training Program of the Beatrice Hunter Cancer Research Institute, with funds provided by a CIBC Graduate Scholarship in Medical Research and the QEll Foundation. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 | 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 | 0.000 |
| 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".