PTEN Depletion Increases Radiosensitivity in Response to Ataxia Telangiectasia-Related-3 (ATR) Inhibition in Non-Small Cell Lung Cancer (NSCLC)
Bibliographic record
Abstract
Radiotherapy (RT) treatment is an important strategy for the management of non-small cell lung cancer (NSCLC). Local recurrence amongst patients with late-stage NSCLC remains a challenge. The loss of PTEN has been associated with radio-resistance. This study aimed to examine the efficacy of RT combined with ataxia telangiectasia-mutated Rad3-related (ATR) inhibition using Ceralasertib in phosphatase and tensin homolog (PTEN)-depleted NSCLC cells and to assess early inflammatory responses indicative of radiation pneumonitis (RP) after combined-modality treatment. Small hairpin RNA (shRNA) transfections were used to generate H460 and A549 PTEN-depleted models. Ceralasertib was evaluated as a single agent and in combination with RT in vitro and in vivo. Histological staining was used to assess immune cell infiltration in pneumonitis-prone C3H/NeJ mice. Here, we report that the inhibition of ATR in combination with RT caused a significant reduction in PTEN-depleted NSCLC cells, with delayed DNA repair and reduced cell viability, as shown by an increase in cells in Sub G1. Combination treatment in vivo significantly inhibited H460 PTEN-depleted tumour growth in comparison to H460 non-targeting PTEN-expressing (NT) cell-line-derived xenografts (CDXs). Additionally, there was no significant increase in infiltrating macrophages or neutrophils except at 4 weeks, whereby combination treatment significantly increased macrophage levels relative to RT alone. Overall, our study demonstrates that ceralasertib and RT combined preferentially sensitises PTEN-depleted NSCLC models in vitro and in vivo, with no impact on early inflammatory response indicative of RP. These findings provide a rationale for evaluating ATR inhibition in combination with RT in NSCLC patients with PTEN mutations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".