Abstract 427: Genome-wide CRISPR knockout screen identifies novel determinant of PARP inhibitor resistance in small-cell lung cancer
Bibliographic record
Abstract
Abstract Introduction: Small cell lung cancer (SCLC) is an aggressive tumor with poor prognosis. The standard-of-care treatment (chemotherapy +/- radiation) has seen little advancement over three decades, apart from the addition of immunotherapy for extensive-stage patients. PARP inhibitors (PARPi) have emerged as a promising therapeutic option in SCLC; however, their clinical application remains challenging due to variable patient responses. The biological determinants of PARPi response in SCLC are not fully understood. This study aims to identify novel biological factors that predict PARPi response in SCLC. Methods: A genome-wide CRISPR-Cas9 knockout (KO) screen was conducted in the SBC-5 SCLC cell line using the Toronto Knockout v1 (TKOv1) library with PARPi olaparib (5 µM) as the selection pressure. Cells were collected on days 25 and 35 for genomic sequencing to determine sgRNA enrichment. Isogenic FBXO11 and LacZ (negative control) KO cell lines were generated, and cell viability was assessed after 7-day PARPi (olaparib, talazoparib) treatment. Immunofluorescence staining of γH2AX foci was performed in isogenic FBXO11 KO SBC-5 cells after 24hr talazoparib (100 nM) treatment. Proximity-dependent biotin labeling combined with LC-MS protein identification (BioID) was performed in FlpIn HEK293 cells expressing BirA*-FBXO11 fusion protein. KEGG pathway analysis of putative interactors was performed to examine enrichment of relevant biological pathways. Protein-protein interaction was assessed using proximity ligation assays (PLA). LC-MS-based global proteomic profiling was conducted in isogenic FBXO11 KO SBC-5 cells (+/- olaparib), and gene ontology (GO) analysis was performed to identify differentially regulated pathways. Results: The genome-wide CRISPR-Cas9 KO screen revealed that FBXO11 loss provides a survival advantage under stringent PARPi selection. FBXO11 KO resulted in a 2- to 20-fold increase in the IC50 of olaparib and talazoparib in three SCLC cell lines (NCI-H196, SBC-5, NCI-H69). FBXO11 KO cells exhibited fewer γH2AX foci after PARPi treatment compared to LacZ KO (p < 0.0001). BioID of FBXO11 identified putative protein interactors from a pre-mRNA splicing complex (XAB2, ISY1, AQR). PLA showed a significantly reduced interaction between XAB2 and AQR in FBXO11 KO cells (p < 0.0001), suggesting that FBXO11 affects pre-mRNA splicing complex formation. GO analysis of global proteomics showed an upregulation of pathways involved in apoptosis, mRNA splicing, and spliceosomal complex assembly after olaparib treatment exclusively in LacZ KO cells, but not FBXO11 KO cells, which may explain the differential response to PARPi. Conclusion: We identified a novel gene, FBXO11, whose loss results in PARPi resistance in SCLC cell lines. BioID of FBXO11 and global proteomic profiling suggests that pre-mRNA splicing may play a role in mediating drug resistance. Citation Format: Jiaqi Xiong, Mansi Aparnathi, Tony Yu, Lifang Song, Jonathan St-Germain, Brian Raught, Troy Ketela, Sami Ul Haq, Vivek Philip, Benjamin Lok. Genome-wide CRISPR knockout screen identifies novel determinant of PARP inhibitor resistance in small-cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 427.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".