PARP Inhibition Enhances Radiotherapy of SMAD4-Deficient Human Head and Neck Squamous Cell Carcinomas in Experimental Models
Bibliographic record
Abstract
Abstract Purpose: SMAD4 loss causes genomic instability and the initiation/progression of head and neck squamous cell carcinoma (HNSCC). Here, we study whether SMAD4 loss sensitizes HNSCCs to olaparib (PARP inhibitor) in combination with radiotherapy (RT). Experimental Design: We analyzed HNSCC The Cancer Genome Atlas data for SMAD4 expression in association with FANC/BRCA family gene expression. Human HNSCC cell lines were screened for sensitivity to olaparib. Isogenic HNSCC cell lines were generated to restore or reduce SMAD4 expression and treated with olaparib, radiation, or the combination. HNSCC pretreatment specimens from a phase I trial investigating olaparib were analyzed. Results: SMAD4 levels correlated with levels of FANC/BRCA genes in HNSCC. HNSCC cell lines with SMAD4 homozygous deletion were sensitive to olaparib. In vivo, olaparib or RT monotherapy reduced tumor volumes in SMAD4-mutant but not SMAD4-positive tumors. Olaparib with RT dual therapy sustained tumor volume reduction in SMAD4-deficient (mutant or knockdown) xenografts, which exhibited increased DNA damage and cell death compared with vehicle-treated tumors. In vitro, olaparib alone or in combination with radiation caused lower clonogenic survival, more DNA damage–associated cell death, and less proliferation in SMAD4-deficient cells than in SMAD4-positive (endogenous SMAD4 or transduced SMAD4) cells. Applicable to clinic, 5 out of 6 SMAD4-negative HNSCCs and 4 out of 8 SMAD4-positive HNSCCs responded to a standard treatment plus olaparib in a phase I clinical trial, and SMAD4 protein levels inversely correlated with DNA damage. Conclusions: SMAD4 levels are causal in determining sensitivity to PARP inhibition in combination with RT in HNSCCs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.001 |
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".