Low junctional adhesion molecule-A expression is associated with an epithelial to mesenchymal transition and poorer outcomes in high-grade serous carcinoma of uterine adnexa.

High-grade serous carcinoma of uterine adnexa (HGSC) is the most frequent histotype of epithelial ovarian cancer and has a poor 5-year survival rate due to late-stage diagnosis and the poor efficacy of standard treatments. Novel biomarkers of cancer outcome are needed to identify new targetable pathways and improve personalized treatments. Cell-surface screening of 26 HGSC cell lines by high-throughput flow cytometry identified junctional adhesion molecule 1 (JAM-A, also known as F11R) as a potential biomarker. Using a multi-labeled immunofluorescent staining coupled with digital image analysis, protein levels of JAM-A were quantified in tissue microarrays from three HGSC patient cohorts: a discovery cohort (n = 101), the Canadian Ovarian Experimental Unified Resource cohort (COEUR, n = 1158), and the Canadian Cancer Trials Group OV16 cohort (n = 267). Low JAM-A level was associated with poorer outcome in the three cohorts by Kaplan-Meier (p = 0.023, p < 0.001, and p = 0.036, respectively) and was an independent marker of shorter survival in the COEUR cohort (HR = 0.517 (0.381-703), p < 0.001). When analyses were restricted to patients treated by taxane-platinum-based chemotherapy, low JAM-A protein expression was associated with poorer responses in the COEUR (p < 0.001) and OV16 cohorts (p = 0.006) by Kaplan-Meier. Decreased JAM-A gene expression was an indicator of poor outcome in gene expression datasets including The Cancer Genome Atlas (n = 606, p = 0.002) and Kaplan-Meier plotter (n = 1816, p = 0.024). Finally, we observed that tumors with decreased JAM-A expression exhibited an enhanced epithelial to mesenchymal transition (EMT) signature. Our results demonstrate that JAM-A expression is a robust prognostic biomarker of HGSC and may be used to discriminate tumors responsive to therapies targeting EMT.

Interrogation of Functional Cell-Surface Markers Identifies CD151 Dependency in High-Grade Serous Ovarian Cancer.

The degree of genetic aberrations characteristic of high-grade serous ovarian cancer (HGSC) makes identification of the molecular features that drive tumor progression difficult. Here, we perform genome-wide RNAi screens and comprehensive expression analysis of cell-surface markers in a panel of HGSC cell lines to identify genes that are critical to their survival. We report that the tetraspanin CD151 contributes to survival of a subset of HGSC cell lines associated with a ZEB transcriptional program and supports the growth of HGSC tumors. Moreover, we show that high CD151 expression is prognostic of poor clinical outcome. This study reveals cell-surface vulnerabilities associated with HGSC, provides a framework for identifying therapeutic targets, and reports a role for CD151 in HGSC.

The RhoGEF GEF-H1 is required for oncogenic RAS signaling via KSR-1.

Cellular transformation by oncogenic RAS engages the MAPK pathway under strict regulation by the scaffold protein KSR-1. Here, we report that the guanine nucleotide exchange factor GEF-H1 plays a critical role in a positive feedback loop for the RAS/MAPK pathway independent of its RhoGEF activity. GEF-H1 acts as an adaptor protein linking the PP2A B' subunits to KSR-1, thereby mediating the dephosphorylation of KSR-1 S392 and activation of MAPK signaling. GEF-H1 is important for the growth and survival of HRAS(V12)-transformed cells and pancreatic tumor xenografts. GEF-H1 expression is induced by oncogenic RAS and is correlated with pancreatic neoplastic progression. Our results, therefore, identify GEF-H1 as an amplifier of MAPK signaling and provide mechanistic insight into the progression of RAS mutant tumors.

Cell surface profiling using high-throughput flow cytometry: a platform for biomarker discovery and analysis of cellular heterogeneity.

Cell surface proteins have a wide range of biological functions, and are often used as lineage-specific markers. Antibodies that recognize cell surface antigens are widely used as research tools, diagnostic markers, and even therapeutic agents. The ability to obtain broad cell surface protein profiles would thus be of great value in a wide range of fields. There are however currently few available methods for high-throughput analysis of large numbers of cell surface proteins. We describe here a high-throughput flow cytometry (HT-FC) platform for rapid analysis of 363 cell surface antigens. Here we demonstrate that HT-FC provides reproducible results, and use the platform to identify cell surface antigens that are influenced by common cell preparation methods. We show that multiple populations within complex samples such as primary tumors can be simultaneously analyzed by co-staining of cells with lineage-specific antibodies, allowing unprecedented depth of analysis of heterogeneous cell populations. Furthermore, standard informatics methods can be used to visualize, cluster and downsample HT-FC data to reveal novel signatures and biomarkers. We show that the cell surface profile provides sufficient molecular information to classify samples from different cancers and tissue types into biologically relevant clusters using unsupervised hierarchical clustering. Finally, we describe the identification of a candidate lineage marker and its subsequent validation. In summary, HT-FC combines the advantages of a high-throughput screen with a detection method that is sensitive, quantitative, highly reproducible, and allows in-depth analysis of heterogeneous samples. The use of commercially available antibodies means that high quality reagents are immediately available for follow-up studies. HT-FC has a wide range of applications, including biomarker discovery, molecular classification of cancers, or identification of novel lineage specific or stem cell markers.

Essential gene profiles in breast, pancreatic, and ovarian cancer cells.

UNLABELLED: Genomic analyses are yielding a host of new information on the multiple genetic abnormalities associated with specific types of cancer. A comprehensive description of cancer-associated genetic abnormalities can improve our ability to classify tumors into clinically relevant subgroups and, on occasion, identify mutant genes that drive the cancer phenotype ("drivers"). More often, though, the functional significance of cancer-associated mutations is difficult to discern. Genome-wide pooled short hairpin RNA (shRNA) screens enable global identification of the genes essential for cancer cell survival and proliferation, providing a "functional genomic" map of human cancer to complement genomic studies. Using a lentiviral shRNA library targeting ~16,000 genes and a newly developed, dynamic scoring approach, we identified essential gene profiles in 72 breast, pancreatic, and ovarian cancer cell lines. Integrating our results with current and future genomic data should facilitate the systematic identification of drivers, unanticipated synthetic lethal relationships, and functional vulnerabilities of these tumor types. SIGNIFICANCE: This study presents a resource of genome-scale, pooled shRNA screens for 72 breast, pancreatic, and ovarian cancer cell lines that will serve as a functional complement to genomics data, facilitate construction of essential gene profiles, help uncover synthetic lethal relationships, and identify uncharacterized genetic vulnerabilities in these tumor types. SIGNIFICANCE: This study presents a resource of genome-scale, pooled shRNA screens for 72 breast, pancreatic, and ovarian cancer cell lines that will serve as a functional complement to genomics data, facilitate construction of essential gene profiles, help uncover synthetic lethal relationships, and identify uncharacterized genetic vulnerabilities in these tumor types.