PARP inhibitor radiosensitization enhances anti-PD-L1 immunotherapy through stabilizing chemokine mRNA in small cell lung cancer
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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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".