TGFβ1 Inhibition Increases the Radiosensitivity of Breast Cancer Cells<i>In Vitro</i>and Promotes Tumor Control by Radiation<i>In Vivo</i>
Bibliographic record
Abstract
PURPOSE: To determine whether inhibition of TGFβ signaling prior to irradiation sensitizes human and murine cancer cells in vitro and in vivo. EXPERIMENTAL DESIGN: TGFβ-mediated growth and Smad phosphorylation of MCF7, Hs578T, MDA-MB-231, and T47D human breast cancer cell lines were examined and correlated with clonogenic survival following graded radiation doses with and without pretreatment with LY364947, a small molecule inhibitor of the TGFβ type I receptor kinase. The DNA damage response was assessed in irradiated MDA-MB-231 cells pretreated with LY364947 in vitro and LY2109761, a pharmacokinetically stable inhibitor of TGFβ signaling, in vivo. The in vitro response of a syngeneic murine tumor, 4T1, was tested using a TGFβ neutralizing antibody, 1D11, with single or fractionated radiation doses in vivo. RESULTS: Human breast cancer cell lines pretreated with TGFβ small molecule inhibitor were radiosensitized, irrespective of sensitivity to TGFβ growth inhibition. Consistent with increased clonogenic cell death, radiation-induced phosphorylation of H2AX and p53 was significantly reduced in MDA-MB-231 triple-negative breast cancer cells when pretreated in vitro or in vivo with a TGFβ type I receptor kinase inhibitor. Moreover, TGFβ neutralizing antibodies increased radiation sensitivity, blocked γH2AX foci formation, and significantly increased tumor growth delay in 4T1 murine mammary tumors in response to single and fractionated radiation exposures. CONCLUSION: These results show that TGFβ inhibition prior to radiation attenuated DNA damage responses, increased clonogenic cell death, and promoted tumor growth delay, and thus may be an effective adjunct in cancer radiotherapy.
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) | 0.002 | 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".