Background High-grade serous carcinoma (HGSC) is the most frequent and lethal type of ovarian cancer. It has been proposed that tubal secretory cells are the origin of ovarian HGSC in women with familial BRCA1/2 mutations. However, the molecular changes underlying malignant transformation remain unknown. Method We performed single-cell RNA and T cell receptor sequencing of tubal fimbriated ends from 3 BRCA1 germline mutation carriers (BRCA1 carriers) and 3 normal controls with no high-risk history (non-BRCA1 carriers). Results Exploring the transcriptomes of 19,008 cells, predominantly from BRCA1+ samples, we identified 5 major cell populations in the fallopian tubal mucosae. The secretory cells of BRCA1+ samples had differentially expressed genes involved in tumor growth and regulation, chemokine signaling, and antigen presentation compared to the wild-type BRCA1 controls. There are several novel findings in this study. First, a subset of the fallopian tubal secretory cells from one BRCA1 carrier exhibited an epithelial-to-mesenchymal transition (EMT) phenotype, which was also present in the mucosal fibroblasts. Second, we identified a previously unreported phenotypic split of the EMT secretory cells with distinct evolutionary endpoints. Third, we observed increased clonal expansion among the CD8+ T cell population from BRCA1+ carriers. Among those clonally expanded CD8+ T cells, PD-1 was significantly increased in tubal mucosae of BRCA1+ patients compared with that of normal controls, indicating that T cell exhaustion may occur before the development of any premalignant or malignant lesions. Conclusion These results indicate that EMT and immune evasion in normal-looking tubal mucosae may represent early events leading to the development of HGSC in women with BRCA1 germline mutation. Our findings provide a probable molecular mechanism explaining why some, but not all, women with BRCA1 germline mutation present with early development and rapid dissemination of HGSC.

Highlights The majority of respondents evaluate lymph nodes via sentinel lymph node mapping for grade 1–2 endometrial cancer. Only 50% of respondents perform intraoperative sentinel lymph node mapping for grade 3 endometrial cancer. 90% of respondents give chemotherapy-based adjuvant treatment for advanced endometrial cancer. 75% of respondents combine radiation therapy with chemotherapy in stage III endometrial cancer.

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Metabolomics is the global analysis of metabolites within various tissues and biological fluids. The metabolome more closely represents the phenotype than the genome or proteome as metabolite fluxes are a product of both genetic and environmental influences. Thus, metabolomics may provide insight into the nutritional, metabolic, and general health status during different stages of the life cycle and during pathological conditions. The workflow is as follows: formulate hypothesis ‐ design study ‐ collect samples ‐ prepare and analyze samples to identify compounds ‐ analyze data ‐ evaluate hypothesis and generate hypotheses ‐ apply to patient care. Although experts in each area of the workflow are needed, at least one team member must provide continuity from formulation of hypotheses to patient care. We are testing the hypotheses that the plasma carnitinome will change in piglets between days 2 through 8 of life, in young healthy adults before and after being placed on a trial ketogenic diet, and in patients with epilepsy when treated with ketogenic therapy. The long term goal is to more effectively and efficiently translate research concerning changes in the carnitinome and metabolome into practical solutions for patients with various pathological conditions. This continuity and integration of knowledge from basic science to the clinic will accelerate research from discovery to improved patient care.

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#5063 Background: Prognostication for breast cancer patients is improving, in part through the use of genomic predictors. However, most predictors only utilize one data type that is gene expression data, and thus prognostication that simultaneously uses multiple data types may result in even more accurate predictors.
 Methods: We recently developed a unique risk of relapse predictor based upon a Cox proportional hazards model that contained gene expression (5 intrinsic subtypes) and clinical variables (tumor size and node status)(Parker ASCO 2008 abstract #11008). This model was accurate in predicting 7 year relapse probabilities and out performed the genomic or clinical variables only models. Building upon this combined genomic plus clinical parameters approach, we reasoned that including other expression modules indicative of pathway activation and cell type signatures might further improve our models. Therefore we build a collection of >200 expression modules using 1) unsupervised hierarchical and bi-clustering analysis 600 human tumors, 2) similar cluster analyses of 250 mouse mammary tumors, 3) >50 published breast tumor genomic profiles, and 4) signaling pathway activation modules derived from our own work. Each signature, or module, was then used with LASSO variable selection to determine if prognostic Cox Proportional Hazards models could be built using a local therapy and tamoxifen only treated patient set.
 Results: These analyses show that highly significant prognostic models could be built for 1) all patients, 2) ER-positive patients, and 3) Luminal patients. Less predictive, yet significant models were created Basal-like patients, and no successful models could be developed for ER-negative patients, or for the expression-defined HER2-enriched subtype. For most significant predictors, the Cox model contained human-derived modules, mouse-derived modules, and pathology variables; for example the predictor for ER-positive patients contained human modules for proliferation, hypoxia, an embryonic stem cell derived signature, three mouse modules with one containing multiple metalloproteinases, tumor size and node status. In addition, the actual modules selected for the different patient subsets varied such that the modules selected for the Basal-like patients did not overlap any module selected for all patients, or for the ER-positive patient subset.
 Conclusions: These analyses further reinforce the idea that breast cancer is a heterogeneous disease and suggest that prognostic models for each important patient subset should be developed and used. Models that contain treatment variables, in addition to prognostic terms, are also being developed and will be presented. In summary, these analyses argue that the best prognostic algorithms for breast cancer patients contain biological variables coming for humans, from model systems, and from classic pathology. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 5063.