Ability to Acquire Drug Resistance Arises Early during the Tumorigenesis Process
Bibliographic record
Abstract
Resistance to chemotherapy is one of the principal causes of cancer mortality and is generally considered a late event in tumor progression. Although cellular models of drug resistance have been useful in identifying the molecules responsible for conferring drug resistance, most of these cellular models are derived from cell lines isolated from patients at a late stage in cancer progression. To ask at which stage in the tumorigenic progression does the cell gain the ability to acquire drug resistance, we generated a series of pre-tumorigenic and tumorigenic cells from human embryonic skin fibroblasts by introducing, sequentially, the catalytic subunit of telomerase, SV40 large T and small T oncoproteins, and an oncogenic form of ras. We show that the ability to acquire multidrug resistance (MDR) can arise before the malignant transformation stage. The minimal set of changes necessary to obtain pre-tumorigenic drug-resistant cells is expression of telomerase and inactivation of p53 and pRb. Thus, the pathways inactivated during tumorigenesis also confer the ability to acquire drug resistance. Microarray and functional studies of drug-resistant pre-tumorigenic cells indicate that the drug efflux pump P-glycoprotein is responsible for the MDR phenotype in this pre-tumorigenic cell model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".