Abstract P6-10-05: Mutations in the RNA Splicing Factor SF3B1 drive endocrine therapy resistance and confer a targetable replication stress response defect through PARP inhibition
Bibliographic record
Abstract
Abstract Background: Heterozygous hotspot mutations in the RNA splicing factor SF3B1, occur in 3% of unselected breast cancers and are associated with oestrogen receptor (ER+) breast cancer (BC) where they are enriched in metastatic disease and are associated with a poor clinical outcome. SF3B1 mutations drive distinct signatures of alternative splicing through cryptic 3’ splice site selection leading to global transcriptomic and proteomic changes. The functional consequences of the mis-splicing events and resultant genetic vulnerabilities are poorly understood and precision medicine approaches that exploit these characteristics are not clinically available (Table 1). Methods: To understand the role of SF3B1 mutations in ER+ BC, we generated a series of SF3B1 mutant (SF3B1MUT) isogenic cell lines which were characterised using RNA-sequencing and high content mass-spectrometry proteomic profiling. SF3B1 interactome analysis was also performed using immunoprecipitation of SF3B1 followed by mass-spectrometry. The molecular consequences of aberrant splicing were investigated using a targeted screening approach of 280 genes predicted to be alternatively spliced in SF3B1MUT BC, while high-throughput drug screens were used to identify novel therapeutic options for patients with SF3B1MUT breast cancer using isogenic cells. Hits were validated in vitro and in vivo using cell line and patient derived xenografts. Results: Transcriptomic and proteomic profiling of SF3B1MUT cells identified global alternative 3’ splice site selection and subsequent proteomic changes induced by the mutations. Investigation of the SF3B1K700E interactome identified an enrichment of SF3B1K700E binding with ER, aberrant splicing of ER target genes, global rewiring of ER chromatin binding and resistance to endocrine therapy. Silencing of the aberrantly spliced candidate genes PPIH, TRIM37, HIGD1A, BRD9, and PHKG2 significantly enhanced the growth of the SF3B1 mutant cells, suggestive of a dose dependent tumour suppressive effect. Through synthetic-lethal drug screens we found that SF3B1MUT cells are selectively sensitive to PARP inhibitors. SF3B1MUT cells display a defective response to PARPi induced replication stress. Mechanistically, this occurs via defective ATR signalling in SF3B1MUT cells, which upon PARPi exposure leads to increased replication origin firing and loss of pChk1 (S317) induction. The resultant replication stress leads to failure to resolve DNA replication intermediates via the endonuclease MUS81 and cell cycle stalling at the G2/M checkpoint. These defects can be further targeted by ATM, CDK7 or FACT inhibition, when used in combination with PARPi treatment. This SF3B1MUT selective PARPi sensitivity is preserved across multiple cell lines and patient derived tumour models. In vivo, PARPi produce profound anti-tumour effects in multiple SF3B1MUT cancer models and eliminate distant metastases. Conclusions: Our integrative analysis reveals mechanistic insight into the role of SF3B1 mutations in endocrine therapy response in ER+ breast cancers, where altered SF3B1 induces ER-transcriptional re-programming. We further identified a robust synthetic-lethal relationship of mutant SF3B1 with PARP inhibition that is caused by a defective response to PARPi induced replication stress. Furthermore, we identified several potential selective combination strategies together with PARPi that are selective for SF3B1MUT cells. Together, these data provide the pre-clinical and mechanistic rationale for assessing already-approved PARPi in a biomarker-defined subset of advanced ER+ BC. Table 1. Identified potential therapies for SF3B1 mutant cancers from this study and the literature Citation Format: Phil Bland, Harry Saville, Abigail Read, Patty Wai, Gareth Muirhead, Lucinda Curnow, Jadwiga Nieminuszczy, Nivedita Ravindran, Marie John, Somaieh Hedayat, Holly Barker, James Wright, Lu Yu, Ioanna Mavrommati, Barrie Peck, Mark Allen, Patrycja Gazinska, Helen Pemberton, Aditi Gulati, Sarah Nash, Farzana Noor, Naomi Guppy, Ioannis Roxanis, Samantha Barlow, Helen Kalirai, Sarah Coupland, Ronan Broderick, Samar Alsafadi, Alexandre Houy, Marc-Henri Stern, Stephen Pettit, Jyoti Choudhary, Syed Haider, Wojciech Niedzwiedz, Christopher Lord, Rachael Natrajan. Mutations in the RNA Splicing Factor SF3B1 drive endocrine therapy resistance and confer a targetable replication stress response defect through PARP inhibition. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-10-05.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".