Development, Maintenance, and Reversal of Multiple Drug Resistance: At the Crossroads of TFPI1, ABC Transporters, and HIF1
Bibliographic record
Abstract
Early detection and improved therapies for many cancers are enhancing survival rates. Although many cytotoxic therapies are approved for aggressive or metastatic cancer; response rates are low and acquisition of de novo resistance is virtually universal. For decades; chemotherapeutic treatments for cancer have included anthracyclines such as Doxorubicin (DOX); and its use in aggressive tumors appears to remain a viable option; but drug resistance arises against DOX; as for all other classes of compounds. Our recent work suggests the anticoagulant protein Tissue Factor Pathway Inhibitor 1α (TFPI1α) plays a role in driving the development of multiple drug resistance (MDR); but not maintenance; of the MDR state. Other factors; such as the ABC transporter drug efflux pumps MDR-1/P-gp (ABCB1) and BCRP (ABCG2); are required for MDR maintenance; as well as development. The patient population struggling with therapeutic resistance specifically requires novel treatment options to resensitize these tumor cells to therapy. In this review we discuss the development, maintenance, and reversal of MDR as three distinct phases of cancer biology. Possible means to exploit these stages to reverse MDR will be explored. Early molecular detection of MDRcancers before clinical failure has the potential to offer new approaches to fightingMDRcancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".