PARP inhibitor radiosensitization enhances anti-PD-L1 immunotherapy through stabilizing chemokine mRNA in small cell lung cancer
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Bibliographic record
Abstract
Immunotherapy (IO) is an effective treatment for various cancers; however, the benefits are modest for small cell lung cancer (SCLC). The poor response of SCLC to anti-PD-1/PD-L1 IO is due in part to the lack of cytotoxic T cells because of limited chemokine expression from SCLC tumors. Immunogenic radiosensitizers that enhance chemokine expression may be a promising strategy forward. Here, we show that the PARP inhibitors (PARPi), including olaparib, talazoparib and veliparib, in combination with radiotherapy (RT) enhance the immune activation and anti-tumor efficacy in SCLC cell lines, patient-derived xenograft (PDX) and syngeneic mouse models. The effect is further enhanced by continued delivery of adjuvant PARPi. The combination treatment (PARPi with RT) activates the cGAS-STING pathway and increases the mRNA levels of the T cell chemo-attractants CCL5 and CXCL10. In addition to upregulation of transcription, the combination treatment increases chemokine CXCL10 protein levels via stabilization of CXCL10 mRNA in an EIF4E2-dependent manner. The incorporation of anti-PD-L1 IO into the PARPi with RT combination therapy further improves the anti-tumor efficacy by increasing T cell infiltration and function. This study thus provides a proof of principle for the combination of PARP inhibitors, RT and anti-PD-L1 IO as a treatment strategy for SCLC. ‘Small cell lung cancers do not respond well to immune checkpoint blockade therapy, due to the poor recruitment of CD8 + T cells to the tumours. Here authors show that via combining radiotherapy, PARP inhibitors and anti-PD-L1 treatments, T cell infiltration and function could be improved via mechanisms that increase the chemo-attractants CCL5 and CXCL10.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it