Abstract 1238: Targeting the platelet derived growth factor receptor (PDGFR) with the receptor tyrosine kinase inhibitor ponatinib in small cell carcinoma of the ovary, hypercalcemic type
Bibliographic record
Abstract
Abstract Subunits of the SWI/SNF chromatin-remodeling complex are tumor suppressors that are inactivated in ~20% of all cancers, yet few targeted treatments have shown selective activity in SWI/SNF-mutant cancers. Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare, aggressive ovarian cancer in young women that is universally driven by SWI/SNF dysregulation. Given that two-year survival following standard high-dose chemotherapy and radiation in SCCOHT is less than 35%, a great need exists for effective targeted therapies to improve outcomes for these women. We previously demonstrated that SCCOHT tumors are driven by inactivating mutations in SMARCA4, one of two mutually exclusive SWI/SNF ATPases. In addition, we have shown that SCCOHT lacks expression of the alternative SWI/SNF ATPase, SMARCA2. We have now found through integrated genomic and functional analyses in SCCOHT tumors and cell lines that SMARCA4 loss correlates with increased expression of receptor tyrosine kinases (RTKs) including the platelet derived growth factor receptors (PDGFRs). Through integration of high-throughput RNA interference and drug screens in SCCOHT cells we have identified sensitivity to RTK knockdown and RTK inhibitors including the FDA-approved oncology drug, ponatinib. These data corroborate prior studies showing RTK dependence in rhabdoid tumors, rare cancers that are also driven by mutations in the SWI/SNF complex. Of the known ponatinib targets, PDGFR-alpha and FGFR1 were highly expressed in SCCOHT tumors, as confirmed in RNA-Seq data (four tumors) and a SCCOHT tissue microarray (TMA; ten tumors). Furthermore, PDGFR-alpha and -beta phosphorylation and downstream signaling are inhibited by ponatinib in SCCOHT cells, suggesting that these tumors are sensitive to ponatinib due to dependence on signaling through these RTKs. Finally, given ponatinib’s potency in vitro and the proposed mechanism of action, we tested this agent in xenograft models of SCCOHT. In addition to confirming efficacy in a SCCOHT cell line xenograft model, superior efficacy was demonstrated in two patient-derived xenograft (PDX) models of SCCOHT with ponatinib. Thus, ponatinib effectively targets SWI/SNF-mutant SCCOHT tumors through inhibition of PDGFR signaling and may have clinical utility for the treatment of these cancers. Citation Format: Jessica Diane Lang, William Hendricks, Pilar Ramos, Holly Yin, Chris Sereduk, Jeffrey Kiefer, Yemin Wang, Anthony N. Karnezis, Bernard Weissman, David Huntsman, Jeffrey Trent. Targeting the platelet derived growth factor receptor (PDGFR) with the receptor tyrosine kinase inhibitor ponatinib in small cell carcinoma of the ovary, hypercalcemic type [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1238. doi:10.1158/1538-7445.AM2017-1238
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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 | 0.001 |
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".