TFPI1 Mediates Resistance to Doxorubicin in Breast Cancer Cells by Inducing a Hypoxic-Like Response
Bibliographic record
Abstract
Thrombin and hypoxia are important players in breast cancer progression. Breast cancers often develop drug resistance, but mechanisms linking thrombin and hypoxia to drug resistance remain unresolved. Our studies using Doxorubicin (DOX) resistant MCF7 breast cancer cells reveals a mechanism linking DOX exposure with hypoxic induction of DOX resistance. Global expression changes between parental and DOX resistant MCF7 cells were examined. Westerns, Northerns and immunocytochemistry were used to validate drug resistance and differentially expressed genes. A cluster of genes involved in the anticoagulation pathway, with Tissue Factor Pathway Inhibitor 1 (TFPI1) the top hit, was identified. Plasmids overexpressing TFPI1 were utilized, and 1% O2 was used to test the effects of hypoxia on drug resistance. Lastly, microarray datasets from patients with drug resistant breast tumors were interrogated for TFPI1 expression levels. TFPI1 protein levels were found elevated in 3 additional DOX resistant cells lines, from humans and rats, indicating evolutionarily conservation of the effect. Elevated TFPI1 in DOX resistant cells was active, as thrombin protein levels were coincidentally low. We observed elevated HIF1α protein in DOX resistant cells, and in cells with forced expression of TFPI1, suggesting TFPI1 induces HIF1α. TFPI1 also induced c-MYC, c-SRC, and HDAC2 protein, as well as DOX resistance in parental cells. Growth of cells in 1% O2 induced elevated HIF1α, BCRP and MDR-1 protein, and these cells were resistant to DOX. Our in vitro results were consistent with in vivo patient datasets, as tumors harboring increased BCRP and MDR-1 expression also had increased TFPI1 expression. Our observations are clinically relevant indicating that DOX treatment induces an anticoagulation cascade, leading to inhibition of thrombin and the expression of HIF1α. This in turn activates a pathway leading to drug resistance.
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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) | 0.002 | 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".