Retinoblastoma Tumor Suppressor Gene Expression Determines the Response to Sequential Flavopiridol and Doxorubicin Treatment in Small-Cell Lung Carcinoma
Bibliographic record
Abstract
PURPOSE: Small-cell lung cancers (SCLC) are defective in many regulatory mechanisms that control cell cycle progression, i.e., functional retinoblastoma protein (pRb). Flavopiridol inhibits proliferation and induces apoptosis in SCLC cell lines. We hypothesized that the sequence flavopiridol followed by doxorubicin would be synergistic in pRb-deficient SCLC cells. EXPERIMENTAL DESIGN: A H69 pRb-deficient SCLC cell line, H865, with functional pRb and H865 pRb small interfering RNA (siRNA) knockdown cells were used for in vitro and in vivo experiments. The in vivo efficiencies of various sequential combinations were tested using nude/nude athymic mice and human SCLC xenograft models. RESULTS: Flavopiridol then doxorubicin sequential treatment was synergistic in the pRB-negative H69 cell line. By knocking down pRb with specific siRNA, H865 clones with complete pRb knockdown became sensitive to flavopiridol and doxorubicin combinations. pRb-deficient SCLC cell lines were highly sensitive to flavopiridol-induced apoptosis. pRb-positive H865 cells arrested in G0-G1 with flavopiridol exposure, whereas doxorubicin and all flavopiridol/doxorubicin combinations caused a G2-M block. In contrast, pRb-negative SCLC cells did not arrest in G0-G1 with flavopiridol exposure. Flavopiridol treatment alone did not have an in vivo antitumor effect, but sequential flavopiridol followed by doxorubicin treatment provided tumor growth control and a survival advantage in Rb-negative xenograft models, compared with the other sequential treatments. CONCLUSIONS: Flavopiridol and doxorubicin sequential treatment induces potent in vitro and in vivo synergism in pRb-negative SCLC cells and should be clinically tested in tumors lacking functional pRB.
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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) | 0.001 | 0.000 |
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".