Understanding the role of BHLHE 40 and its regulation by the androgen receptor in prostate cancer

not available for publication Towards single exosome RNA sequencing for phenotyping ovarian cancer Randy Carney, Mac Colquhoun, and Kit Lam Department of Biochemistry and Molecular Medicine, UC Davis The microRNAs (miRNAs) contained in circulating tumor-associated exosomes (TEXs) and related extracellular vesicles (EVs) may improve strategies for ovarian cancer (OvCa) detection and monitoring because (i) they are stable and protected, (ii) their sequences are evolutionarily conserved, and (iii) their expression is tissue and pathology-specific. Ultimately, the functional impact of exosomes on target cells is dictated by combined contribution of their contained proteins and genes. miRNA biosignatures across exosome subpopulations pooled according to multi-parametric tumor-associated peptidicand/or immuno-affinity can be used to improve current methods for diagnosing cancers. To achieve the best resolution of such approach, innovative tools need to be developed that separate vesicles with high resolution and enable molecular characterization at equally high resolution. The technology of flow cytometry sorting for single EVs is currently mature enough and widespread to the degree that it is a viable tool to apply to clinical exosome research. We have begun analyzing the contained miRNAs of sorted EVs to develop biomarker profiles for cancer. Our approach is as follows: First, circulating EVs are isolated from OvCa patient biofluids using ultracentrifugation (UC). Patient samples provide a convenient, well-tested, and clinically relevant model for studying OvCa. Second, exosomes are pooled in groups using fluorescence activated vesicle sorting (FAVS) according to multiplexed fluorescent molecules targeting known cancer-specific surface markers, including novel integrin-binding peptides discovered in our lab to bind to OvCa cells. Third, pooled exosomes are lysed and their miRNA barcoded for high-throughput, indexed small RNAsequencing. This approach provides information about the genetic makeup of exosomes using a scheme adapted from single-cell RNA-seq that minimizes expensive sequencing runs by barcoding FAVS-pooled populations prior to amplification and miRNA profiling. We expect expression signatures of exosomal miRNAs to be more sensitive and specific than current minimallyinvasive circulating RNA or single-cell diagnostic approaches, which may in turn accelerate clinical cancer diagnostic platforms for a wide range of cancers. Figure 1. (a) EVs isolated by UC from human cancer patient plasma were sorted by FAVS into three groups based on PKH67 dye and anti-CD63-AlexaFluor647 binding, plus a fourth group of off-sort beads accounting for background. (b) RNA-seq analysis of the individually barcoded samples revealed major differential miRNA expression between the parent unsorted EVs and various sorted subpopulations. Leveraging big data genomics for the inference of drug targets Nelson Johansen and Gerald Quon UC Davis Genome Center and Department of Molecular and Cellular Biology Big-data genomic studies have recently made possible the identification of critical genetic targets across a wide range of cancer cell lines, while simultaneously enabling the inference of gene targets for over 20,000 compounds and FDA-approved drugs. Such studies have provided an unprecedented opportunity to not only repurpose a wide range of drugs and expand our repertoire of cancer therapies, but also allows us to exploit the synergistic effectiveness of combinations of compounds with differing mechanisms of action. First, we use an approach based on absorbing Markov chains to simultaneously group compounds de novo and use these drug classes to help identify drug-gene targets. Then, utilizing both inferred and annotated genetic targets, we identify synergistic drug combinations through a constrained optimization procedure from which an optimal solution will contain drugs with maximal coverage of cancer vulnerabilities, minimal off target effects and distinct mechanisms of action. As such, the therapies derived from the predicted drug set will decrease the likelihood of resistant cancer cell populations, decrease potential toxicity of the constituent drugs through lower dosage and provide effective treatment across a wide spectrum of cancer specific patients. Fluorescence lifetime imaging for breast cancer evaluation Jennifer E. Phipps, Jakob Unger, Morgan Darrow, Richard J. Bold, Laura Marcu UC Davis Biomedical Engineering Department, UC Davis Health System Department of Pathology and Laboratory Medicine, UC Davis Health System Department of Surgery Nearly 1 in 9 US women will develop breast cancer in their lifetimes. Surgeons still use inaccurate or incomplete methods of evaluating specimen margins, which lead to inappropriately high numbers of reexcisions due to cases of positive margins identified during histologic examination. The objective of this work was to determine whether fluorescence lifetime imaging (FLIm) could be used to detect cancer in breast specimens from mastectomy and lumpectomy procedures. Breast cancer patients (N=14) were recruited for this study when they were scheduled to undergo lumpectomy or mastectomy procedures at the UCDHS. All breast cancer patients who could provide informed consent were eligible to participate in the study. Tissue specimens were imaged within 1 hour of resection, prior to fixation. Histologic sections were co-registered with FLIm data. A classification algorithm was implemented to automate the process of determining breast tissue type (cancerous, fibrous or adipose) based on the FLIm data. FLIm measurements were able to distinguish between regions of cancerous, fibrous and adipose tissue with accuracies greater than 97%. The FLIm images were acquired and displayed simultaneously and in real time. Data processing for the classification could also occur in real time, allowing on-line classification during the acquisition of the FLIm measurement. The strong ability of FLIm to discriminate between cancerous and normal breast tissue leads to the conclusion that this technique would be a useful method to assess breast cancer margins intraoperatively or immediately following tumor resection. The ability to identify positive margins in real-time could drastically reduce the numbers of patients requiring re-excision. Breast dose in mammography is about 30% lower when realistic heterogeneous glandular distributions are considered Andrew M. Hernandez, J. Anthony Seibert, and John M. Boone Department of Radiology, UC Davis Medical Center, University of California Davis, Sacramento, USA Purpose: Current dosimetry methods in mammography assume that the breast is comprised of a homogeneous mixture of glandular and adipose tissues. Three-dimensional (3D) dedicated breast CT (bCT) data sets were used previously to assess the complex anatomical structure within the breast, characterizing the statistical distribution of glandular tissue in the breast. The purpose of this work was to investigate the effect of bCT-derived heterogeneous glandular distributions on dosimetry in mammography. Methods: bCT-derived breast diameters, volumes, and 3D fibroglandular distributions were used to design realistic compressed breast models comprised of heterogeneous distributions of glandular tissue. The bCTderived glandular distributions were fit to biGaussian functions and used as probability density maps to assign the density distributions within compressed breast models. The MCNPX 2.6.0 Monte Carlo code was used to estimate monoenergetic normalized mean glandular dose “DgN(E)” values in mammography geometry. The DgN(E) values were then weighted by typical mammography x-ray spectra to determine polyenergetic DgN (pDgN) coefficients for heterogeneous (pDgNhetero) and homogeneous (pDgNhomo) cases. The dependence of estimated pDgN values on phantom size, volumetric glandular fraction (VGF), x-ray technique factors, and location of the heterogeneous glandular distributions was investigated. Results: The pDgNhetero coefficients were on average 35.3% (SD, 4.1) and 24.2% (SD, 3.0) lower than the pDgNhomo coefficients for the Mo–Mo and W–Rh x-ray spectra, respectively, across all phantom sizes and VGFs when the glandular distributions were centered within the breast phantom in the coronal plane. At constant breast size, increasing VGF from 7.3% to 19.1% lead to a reduction in pDgNhetero relative to pDgNhomo of 23.6%–27.4% for a W–Rh spectrum. Displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width in the superior and inferior directions, resulted in a 37.3% and a −26.6% change in the pDgNhetero coefficient, respectively, relative to the centered distribution for the Mo–Mo spectrum. Lateral displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width, resulted in a 1.5% change in the pDgNhetero coefficient relative to the centered distribution for the W–Rh spectrum. Conclusions: Introducing bCT-derived heterogeneous glandular distributions into mammography phantom design resulted in decreased glandular dose relative to the widely used homogeneous assumption. A homogeneous distribution overestimates the amount of glandular tissue near the entrant surface of the breast, where dose deposition is exponentially higher. While these findings are based on clinically measured distributions of glandular tissue using a large cohort of women, future work is required to improve the classification of glandular distributions based on breast size and overall glandular fraction. Personalized dosimetry for liver cancer radioembolization using fluid dynamics Emilie Roncali1, Ekaterina Mikhaylova1, Yuki Tsuzuki2, Ralph C. Aldredge2, Simon R. Cherry1 1 Department of Biomedical Engineering, University of California Davis 2 Department of Mechanical and Aerospace Engineering, University of California Davis Objectives: To improve individualized radioembolization