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IC50 summary of validated drug hits in SCCOHT cell lines.

Purpose: Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare, aggressive ovarian cancer in young women that is universally driven by loss of the SWI/SNF ATPase subunits SMARCA4 and SMARCA2. A great need exists for effective targeted therapies for SCCOHT.Experimental Design: To identify underlying therapeutic vulnerabilities in SCCOHT, we conducted high-throughput siRNA and drug screens. Complementary proteomics approaches profiled kinases inhibited by ponatinib. Ponatinib was tested for efficacy in two patient-derived xenograft (PDX) models and one cell-line xenograft model of SCCOHT.Results: The receptor tyrosine kinase (RTK) family was enriched in siRNA screen hits, with FGFRs and PDGFRs being overlapping hits between drug and siRNA screens. Of multiple potent drug classes in SCCOHT cell lines, RTK inhibitors were only one of two classes with selectivity in SCCOHT relative to three SWI/SNF wild-type ovarian cancer cell lines. We further identified ponatinib as the most effective clinically approved RTK inhibitor. Reexpression of SMARCA4 was shown to confer a 1.7-fold increase in resistance to ponatinib. Subsequent proteomic assessment of ponatinib target modulation in SCCOHT cell models confirmed inhibition of nine known ponatinib target kinases alongside 77 noncanonical ponatinib targets in SCCOHT. Finally, ponatinib delayed tumor doubling time 4-fold in SCCOHT-1 xenografts while reducing final tumor volumes in SCCOHT PDX models by 58.6% and 42.5%.Conclusions: Ponatinib is an effective agent for SMARCA4-mutant SCCOHT in both in vitro and in vivo preclinical models through its inhibition of multiple kinases. Clinical investigation of this FDA-approved oncology drug in SCCOHT is warranted. Clin Cancer Res; 24(8); 1932–43. ©2018 AACR.

Supplemental Figure 1. COV434-pIND20-Brg1 SMARCA4 induction optimization; Supplemental Figure 2. Kinase hits from siRNA screen mapped to kinome dendrogram; Supplemental Figure 3. ClueGo analysis of validated siRNA screen hits.

Supplemental Table 5. SCCOHT-1 ABPP data; Supplemental Table 6. BIN67 ABPP data.

Supplemental Materials and Methods

Genomic analyses of cutaneous melanoma (CM) have yielded biological and therapeutic insights, but understanding of non-ultraviolet (UV)-derived CMs remains limited. Deeper analysis of acral lentiginous melanoma (ALM), a rare sun-shielded melanoma subtype associated with worse survival than CM, is needed to delineate non-UV oncogenic mechanisms. We thus performed comprehensive genomic and transcriptomic analysis of 34 ALM patients. Unlike CM, somatic alterations were dominated by structural variation and absence of UV-derived mutation signatures. Only 38% of patients demonstrated driver BRAF/NRAS/NF1 mutations. In contrast with CM, we observed PAK1 copy gains in 15% of patients, and somatic TERT translocations, copy gains, and missense and promoter mutations, or germline events, in 41% of patients. We further show that in vitro TERT inhibition has cytotoxic effects on primary ALM cells. These findings provide insight into the role of TERT in ALM tumorigenesis and reveal preliminary evidence that TERT inhibition represents a potential therapeutic strategy in ALM.

Radiogenomics or radiomics is an emerging field where tumor genomic data is correlated with radiology image features, thereby potentially providing more biological information about the tumor phenotype. A central challenge is the potential for model over-fitting due to analysis of many thousands of genomic data-points with hundreds of corresponding patient image features. Biological interpretation of the imaging feature correlations is also challenged by overlapping pathways and common gene effects. Our goals were: i) to explore correlations between gene expression and corresponding Magnetic Resonance (MR) Apparent Diffusion Coefficient (ADC) derived imaging features in low grade glioma (LGG); ii) to classify significant gene and imaging correlates by cancer hallmark1. RNA expression data from 32 LGG patients were extracted from The Cancer Genome Atlas (TCGA) and matched with corresponding MR image data from The Cancer Imaging Archive (TCIA). Among 32 patients, 18 were males (56%), and ages ranged from 21 to 74 years (mean age 44). Tumor and normal regions in the MR images were annotated by an expert radiologist using ITK-Snap. The normal reference region was used normalize image intensities in corresponding tumor regions. Tumor texture features were computed on ADC Maps at each voxel location within the disease region (including first and second order statistics, Run Length and co-occurrence matrix derived measures features. The voxel features were finally aggregated within the tumor region using statistical measures of mean, variance, median, kurtosis, and skewness. ADC imaging features (n=310) were correlated with each single gene expression value (11614 genes after MAD>0.4 filtering). Only image features and genes with pairwise correlations higher than 0.68 (0.68 is the 3-standard deviation above average correlation) and FDR (False Discovery Rate) Citation Format: Yunxia Sui, Mirabela Rusu, Dattesh Shanbhag, Uday Patil, Jeffrey Kiefer, Jill Barnholtz-Sloan, Michael Berens, Fiona Ginty, Graf John, Sandeep Gupta, Chinnappa Kodira, Lee Newberg, Sushravya Raghunath, Anup Sood, Sushravya Raghunath. Elucidating cancer hallmark context from glioma MR imaging and RNA expression data [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 883. doi:10.1158/1538-7445.AM2017-883

In this study, cDNA microarrays were used to characterize gene expression changes elicited in prostate cancer cells by plating them on type I collagen. The results clearly reveal changes in the expression of genes associated with cellular signaling, cellular metabolism, gene transcription and gene translation which are indicative of cells that are actively proliferating. Together these results suggest that these changes in the gene expression profiles mediated by type I collagen may influence the proliferative capacity of prostate cancer cells in the bone microenvironment and facilitate development of prostate cancer bone metastases. Additionally, the microarray approach provides an invaluable tool to determine and track changes in gene expression in numerous disease states including prostate cancer. This technology is certain to facilitate discovery of new therapeutic gene targets.