CRISPR Screen Identifies HDAC3 as a Novel Radiosensitizing Target in Small Cell Lung Cancer
Bibliographic record
Abstract
Small cell lung cancer (SCLC) is an aggressive malignancy, with most patients presenting with prognostically poor extensive-stage disease. Limited progress in standard care stresses the urgent need for novel therapies. Radiotherapy offers some survival benefit for selected patients with SCLC but could be enhanced with radiosensitizers. In this study, we identify HDAC3 as a novel radiosensitizing target in SCLC using a CRISPR knockout screen and demonstrate its efficacy and mechanism. SBC5 cells were transduced with a custom EpiDrug single-guide RNA library and treated with ionizing radiation (IR) to identify radiosensitizing genes. HDAC3 emerged as a candidate and was validated through genetic knockdown and pharmacologic inhibition (RGFP966) in multiple SCLC cell lines. Both approaches enhanced radiosensitivity, as shown by cell viability (dose modification factor10 = 1.14-1.69) and clonogenic assays (dose modification factor10 = 1.16-1.41). We assessed changes in chromatin accessibility by assay for transposase-accessible chromatin using sequencing and IR-induced DNA damage and repair using γH2AX foci detection, double-strand break (DSB) repair assays, and immunoblotting of repair proteins. HDAC3-deficient cells exhibited increased chromatin accessibility, greater IR-induced DSBs, and impaired repair capacity, resulting in persistent DNA damage. This repair defect sensitized cells to PARP inhibitors, for which combining RGFP966 with olaparib or talazoparib produced additive to synergistic effects. In SCLC xenograft models, HDAC3 knockdown or RGFP966, combined with IR, achieved significant tumor growth inhibition. Collectively, we identified HDAC3 as a novel radiosensitizing target in SCLC. Its functional loss increased the generation and persistence of IR-induced DNA DSBs, effectively sensitizing SCLC cell lines and xenografts to IR, providing a potential radiosensitization strategy to treat SCLC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 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".