WT1 gene expression as a prognostic marker in advanced serous epithelial ovarian carcinoma: an immunohistochemical study

BACKGROUND: WT1 is a tumor suppressor gene responsible for Wilms' tumor. WT1 reactivity is limited to ovarian serous carcinomas. Recent studies have shown that WT1 plays an important role in the progression of disease and indicates a poorer prognosis of human malignancies such as acute myeloid leukemia and breast cancer. The aims of this study were to determine the survival and recurrence-free survival of women with advanced serous epithelial ovarian carcinoma in relation to WT1 gene expression. METHODS: The study accrued women over an 18-year period, from 1987–2004. During the study period, 163 patients were diagnosed with advanced serous epithelial ovarian carcinoma and had undergone complete post-operative chemotherapy, but the final study group comprised 99 patients. The records of these women were reviewed and the paraffin-embedded tissue of these women stained with WT1 immunostaining. Survival analysis was performed using Kaplan-Meier and Cox regression methods. RESULTS: Fifty patients showed WT1 staining and forty-nine did not. Five-year survival of non-staining and staining groups were 39.4% and 10.7% (p < 0.00005); five-year recurrence-free survival of these groups were 29.8% and ≤ 7.5% (p < 0.00005), respectively. For survival the HR of WT1 staining, adjusted for residual tumor and chemotherapy response, was 1.98 (95% CI 1.28–3.79), and for recurrence-free survival the HR was 3.36 (95% CI 1.60–7.03). The HR for recurrence-free survival was not confounded by any other variables. CONCLUSION: This study suggests that expression of WT1 gene may be indicative of an unfavorable prognosis in patients with advanced serous epithelial ovarian carcinoma

NVP-AUY922: a small molecule HSP90 inhibitor with potent antitumor activity in preclinical breast cancer models

INTRODUCTION:Heat shock protein 90 (HSP90) is a key component of a multichaperone complex involved in the post-translational folding of a large number of client proteins, many of which play essential roles in tumorigenesis. HSP90 has emerged in recent years as a promising new target for anticancer therapies.METHODS:The concentrations of the HSP90 inhibitor NVP-AUY922 required to reduce cell numbers by 50% (GI50 values) were established in a panel of breast cancer cell lines and patient-derived human breast tumors. To investigate the properties of the compound in vivo, the pharmacokinetic profile, antitumor effect, and dose regimen were established in a BT-474 breast cancer xenograft model. The effect on HSP90-p23 complexes, client protein degradation, and heat shock response was investigated in cell culture and breast cancer xenografts by immunohistochemistry, Western blot analysis, and immunoprecipitation.RESULTS:We show that the novel small molecule HSP90 inhibitor NVP-AUY922 potently inhibits the proliferation of human breast cancer cell lines with GI50 values in the range of 3 to 126 nM. NVP-AUY922 induced proliferative inhibition concurrent with HSP70 upregulation and client protein depletion � hallmarks of HSP90 inhibition. Intravenous acute administration of NVP-AUY922 to athymic mice (30 mg/kg) bearing subcutaneous BT-474 breast tumors resulted in drug levels in excess of 1,000 times the cellular GI50 value for about 2 days. Significant growth inhibition and good tolerability were observed when the compound was administered once per week. Therapeutic effects were concordant with changes in pharmacodynamic markers, including HSP90-p23 dissociation, decreases in ERBB2 and P-AKT, and increased HSP70 protein levels.CONCLUSION:NVP-AUY922 is a potent small molecule HSP90 inhibitor showing significant activity against breast cancer cells in cellular and in vivo settings. On the basis of its mechanism of action, preclinical activity profile, tolerability, and pharmaceutical properties, the compound recently has entered clinical phase I breast cancer trials

Imaging biomarker roadmap for cancer studies.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.Development of this roadmap received support from Cancer Research UK and the Engineering and Physical Sciences Research Council (grant references A/15267, A/16463, A/16464, A/16465, A/16466 and A/18097), the EORTC Cancer Research Fund, and the Innovative Medicines Initiative Joint Undertaking (grant agreement number 115151), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in kind contribution

"Product ion monitoring" assay for prostate-specific antigen in serum using a linear ion-trap.

While numerous strategies exist for biomarker discovery, the bottleneck to product development and routine use at the clinic is in the verification phase of candidate biomarkers. The aim of this study was to establish a robust and high-throughput product ion monitoring (PIM) assay that is potentially capable of rapidly verifying candidates from discovery phase experiments. Using prostate-specific antigen (PSA), a model biomarker, and a routinely used mass spectrometer for discovery platforms, an ion trap (LTQ, Thermo), the utility of this instrument to perform PIM was explored. The proteotypic doubly charged intact peptide LSEPAELTDAVK ( m/ z 637) fragmenting to m/ z 943 (PAELTDAVK) was monitored. A limit of detection of 10 attomoles with a coefficient of variation (CV) of <20% was obtained for a purified recombinant PSA digest. Immunoextraction of endogenous PSA from serum using a monoclonal antibody on a 96-well microtiter plate, followed by PIM on the LTQ, enabled quantification of PSA down to less than 1 ng/mL with a limit of detection of 0.1 ng/mL and CVs < 20%. Mascot searching and ion ratio confirmation further supported the conclusion that the quantified moiety in serum was the PSA peptide. We conclude that this methodology could be adapted quickly and easily to other candidates, thus providing a much needed technology to bridge the gap between discovery and validation platforms

Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer

Gene expression data is often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. However, pathway enrichment is typically computed with DEGs rather than ‘upstream’ nodes that are potentially causal of ‘downstream’ changes. Here we present graph-based models to predict causal targets using compound-microarray data. We test several approaches to traversing network topology for interactions of varying confidence levels. We found that larger, less-canonical networks outperformed linear canonical interactions. In addition, combining network topology scoring methods with a consensus minimum-rank score beat individual methods and could highly rank compound targets among all network nodes. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To extend our validation, we used integrated datasets from the The Cancer Genome Atlas to define driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including EGFR/PI3K/AKT/MAPK growth pathway and ATR/p53/BRCA DNA damage pathway, as well as unexpected pathways, such as TGF/WNT cytoskeleton remodeling, TNFR/IAP apoptosis, and IL12-induced IFN-gamma production. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer

Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem.

Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology - QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts