Greene, Casey S.

Abstract Purpose: Gene expression–based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. Experimental Design: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. Results: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations. Conclusions: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications. See related commentary by McMullen et al., p. 5271

Abstract The lysine deacetylase inhibitor suberoylanilide acid (SAHA) exhibits promising but limited activity in the treatment of acute myeloid leukemia (AML). To identify potential targets for rational combination therapies that increase the efficacy of SAHA in AML, we performed a functional RNA-interference (RNAi) drug modifier screen to identify genes that, when inhibited, potentiate or antagonize the in vitro anti-leukemic activity of SAHA. A total of 901 kinase, phosphatase and closely associated signaling genes were silenced in TF-1, HEL and THP-1 cells, with four different siRNA sequences per gene, both alone and in combination with SAHA treatment. Screen hit lists for each cell line were over-laid on an integrated functional relationship network. A community detection algorithm was then applied to this sub-network and siRNA sensitive modules were identified. Each module represents a highly connected set of genes in the integrated network. To identify pathways represented by each module, enrichment was evaluated using the National Cancer Institute (NCI) Protein Interaction Database (PID) pathways. Both cell line-specific and universal sensitizing targets, grouped into a small number of pathways, emerged from these screens. Seven pharmacologic inhibitors interfering with these pathways have been assessed in a panel of four or five AML cell lines, including SET-2, TF-1, HEL, THP-1, and OCI-AML3. Of the seven tested inhibitors targeting SAHA sensitizing networks, one combination exhibiting universal SAHA sensitization in all AML cell lines tested was further assessed in drug dose response assays using ex vivo cultures of bone marrow mononuclear cell populations derived from patients with various myeloid malignancies. The combination demonstrated “one-sided” SAHA EC50 fold-enhancement, as well as strong synergy by “two-sided” CalcuSyn combination index determination in all ex vivo specimens examined to date (N=6), including polycythemia vera (PV), chronic myelomonocytic leukemia (CMML), myelodysplastic syndrome/myeloproliferative neoplasm (MDS/MPN) overlap syndrome, MDS transformed to AML, and de novo AML samples. Thus, this first reported large-scale SAHA RNAi modifier screen in AML has identified a specific pathway/target whose inhibition broadly synergizes with HDAC inhibition in myeloid malignancies. A novel compound putatively targeting this respective pathway/target is currently in early clinical development and has shown limited single-agent activity. With the full dataset presented, we will propose a novel rational combination to enhance the activity of HDAC inhibition in AML and other myeloid malignancies that could be translated into design of a clinical trial. Disclosures: No relevant conflicts of interest to declare.

Abstract Four gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these early studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and nonnegative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by the inclusion of serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.

Abstract The lysine deacetylase inhibitor suberoylanilide acid (SAHA) has shown promising but limited activity in the treatment of acute myeloid leukemia (AML). To identify potential targets for rational combination therapies that increase the efficacy of SAHA in AML, we used a functional RNA-interference (RNAi) screening approach to identify genes, that when inhibited, potentiate the in vitro anti-leukemic activity of SAHA. A total of 901 kinase, phosphatase and associated signaling genes were silenced, with four different siRNA sequences per gene, in combination with SAHA treatment. Log2 values of the ratio [(siRNA + SAHA)/(siRNA alone)] were calculated, with median and standard deviation determined on a per-plate basis. Hits were defined as ≥ 2 standard deviations from the log2 ratio median. Hit lists for each cell line were over-laid on an integrated functional relationship network. We applied a community detection algorithm to this sub-network and identified siRNA sensitive modules. Each module represents a highly connected set of genes in the integrated network. To identify the pathways represented by each module, we evaluated enrichment using the National Cancer Institute (NCI) Protein Interaction Database (PID) pathways. Several novel sensitizing targets, grouped into a small number of pathways, emerged from these screens. Some hits exhibit little to no anti-leukemic activity when silenced alone, indicative of synthetic lethal interaction with SAHA treatment. Initial validation experiments with siRNA and novel small molecule inhibitors confirm RNAi screen results and pharmacological sensitization is observed. The first reported large-scale HDAC inhibitor RNAi screen in leukemias has identified a novel rational combination that can be translated into design of a clinical trial. Citation Format: James M. Bogenberger, James E. Rudd, Donald Chow, Michelle Kassner, Holly Yin, Casey S. Greene, Raoul Tibes. Identification of HDAC inhibitor potentiating targets in acute myeloid leukemia cells by large-scale RNA-interference. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr A28.