Jeffrey A. Hooke

Loss of Nuclear Localized and Tyrosine Phosphorylated Stat5 in Breast Cancer Predicts Poor Clinical Outcome and Increased Risk of Antiestrogen Therapy Failure

PURPOSE To investigate nuclear localized and tyrosine phosphorylated Stat5 (Nuc-pYStat5) as a marker of prognosis in node-negative breast cancer and as a predictor of response to antiestrogen therapy. PATIENTS AND METHODS Levels of Nuc-pYStat5 were analyzed in five archival cohorts of breast cancer by traditional diaminobenzidine-chromogen immunostaining and pathologist scoring of whole tissue sections or by immunofluorescence and automated quantitative analysis (AQUA) of tissue microarrays. RESULTS Nuc-pYStat5 was an independent prognostic marker as measured by cancer-specific survival (CSS) in patients with node-negative breast cancer who did not receive systemic adjuvant therapy, when adjusted for common pathology parameters in multivariate analyses both by standard chromogen detection with pathologist scoring of whole tissue sections (cohort I; n = 233) and quantitative immunofluorescence of a tissue microarray (cohort II; n = 291). Two distinct monoclonal antibodies gave concordant results. A progression array (cohort III; n = 180) revealed frequent loss of Nuc-pYStat5 in invasive carcinoma compared to normal breast epithelia or ductal carcinoma in situ, and general loss of Nuc-pYStat5 in lymph node metastases. In cohort IV (n = 221), loss of Nuc-pYStat5 was associated with increased risk of antiestrogen therapy failure as measured by univariate CSS and time to recurrence (TTR). More sensitive AQUA quantification of Nuc-pYStat5 in antiestrogen-treated patients (cohort V; n = 97) identified by multivariate analysis patients with low Nuc-pYStat5 at elevated risk for therapy failure (CSS hazard ratio [HR], 21.55; 95% CI, 5.61 to 82.77; P < .001; TTR HR, 7.30; 95% CI, 2.34 to 22.78; P = .001). CONCLUSION Nuc-pYStat5 is an independent prognostic marker in node-negative breast cancer. If confirmed in prospective studies, Nuc-pYStat5 may become a useful predictive marker of response to adjuvant hormone therapy.

Genomic instability in histologically normal breast tissues: implications for carcinogenesis.

Breast cancer is an important contributor to morbidity and mortality in society, but factors that affect the cause of the disease are poorly defined. Genomic instability drives tumorigenic processes in invasive carcinomas and premalignant breast lesions, and might promote the accumulation of genetic alterations in apparently normal tissues before histological abnormalities are detectable. Evidence suggests that genomic changes in breast parenchyma affect the behaviour of epithelial cells, and ultimately might affect tumour growth and progression. Inherent instability in genes that maintain genomic integrity, as well as exogenous chemicals and environmental pollutants, have been implicated in breast-cancer development. Although molecular mechanisms of tumorigenesis are unclear at present, carcinogenic agents could contribute to fields of genomic instability localised to specific areas of the breast. Understanding the functional importance of genomic instability in early carcinogenesis has important implications for improvement of diagnostic and treatment strategies.

Outer breast quadrants demonstrate increased levels of genomic instability.

AbstractBackground: Theory holds that the upper outer quadrant of the breast develops more malignancies because of increased tissue volume. This study evaluated genomic patterns of loss of heterozygosity (LOH) and allelic imbalance (AI) in non-neoplastic tissues from quadrants of diseased breasts following mastectomy to characterize relationships between genomic instability and the propensity for tumor development. Methods: Tissues from breast quadrants were collected from 21 patients with various stages of breast carcinoma. DNA was isolated from non-neoplastic tissues using standard methods and 26 chromosomal regions commonly deleted in breast cancer were examined to assess genomic instability. Results: Genomic instability was observed in breast quadrants from patients with ductal carcinomas in situ and advanced carcinomas. Levels of instability by quadrant were not predictive of primary tumor location (P = .363), but outer quadrants demonstrated significantly higher levels of genomic instability than did inner quadrants (P = .017). Marker D8S511 on chromosome 8p22– 21.3, one of the most frequently altered chromosomal regions in breast cancer, showed a significantly higher level of instability (P = .039) in outer compared with inner quadrants. Conclusions: Non-neoplastic breast tissues often harbor genetic changes that can be important to understanding the local breast environment within which cancer develops. Greater genomic instability in outer quadrants can partially explain the propensity for breast cancers to develop there, rather than simple volume-related concepts. Patterns of field cancerization in the breast appear to be complex and are not a simple function of distance from a developing tumor.

Methylobacterium mesophilicum Infection: Case Report and Literature Review of an Unusual Opportunistic Pathogen

Methylobacterium mesophilicum is a methylotrophic, pink pigmented, gram-negative rod that was initially isolated from environmental sources that is being increasingly reported as a cause of opportunistic infections in immunocompromised hosts. We present the case of an immunocompromised woman who developed a central catheter infection with M. mesophilicum and review the other 29 cases reported in the literature, noting that it is frequently resistant to beta-lactam agents but is generally susceptible to aminoglycosides and quinolones.

Identification of differentially expressed genes in breast tumors from African American compared with Caucasian women.

Breast tumors from African American women have less favorable pathological characteristics and higher mortality rates than those of Caucasian women. Although socioeconomic status may influence prognosis, biological factors are also likely to contribute to tumor behavior.

Differential proteomic analysis of late-stage and recurrent breast cancer from formalin-fixed paraffin-embedded tissues.

The heterogeneity of breast cancer requires the discovery of more incisive molecular tools that better define disease progression and prognosis. Proteomic analysis of homogeneous tumor cell populations derived by laser microdissection from formalin-fixed, paraffin-embedded (FFPE) tissues has proven to be a robust strategy for conducting retrospective cancer biomarker investigations. We describe an MS-based analysis of laser microdissected cancerous epithelial cells derived from twenty-five breast cancer patients at defined clinical disease stages with the goal of identifying protein abundance characteristics indicative of disease progression and recurrence. Comparative analysis of stage 0 and stage III patients revealed 113 proteins that significantly differentiated these groups and included known factors associated with disease pathogenesis, such as CDH1 and CTNNB1, as well as those previously implicated in breast cancer, such as TSP-1. Similar analyses of patients presenting with stage II disease that did or did not exhibit recurrence two years postdiagnosis revealed 42 proteins that significantly differentiated these subgroups and included IRS-1 and PARK7. These data provide evidence supporting the utility of FFPE tissues for functional proteomic analyses and protein biomarker discovery and yielded protein candidates indicative of disease stage and recurrence in breast cancer that warrant further investigation for diagnostic utility and biological relevance.

Low levels of Stat5a protein in breast cancer are associated with tumor progression and unfavorable clinical outcomes

IntroductionSignal transducer and activator of transcripton-5a (Stat5a) and its close homologue, Stat5b, mediate key physiological effects of prolactin and growth hormone in mammary glands. In breast cancer, loss of nuclear localized and tyrosine phosphorylated Stat5a/b is associated with poor prognosis and increased risk of antiestrogen therapy failure. Here we quantify for the first time levels of Stat5a and Stat5b over breast cancer progression, and explore their potential association with clinical outcome.MethodsStat5a and Stat5b protein levels were quantified in situ in breast-cancer progression material. Stat5a and Stat5b transcript levels in breast cancer were correlated with clinical outcome in 936 patients. Stat5a protein was further quantified in four archival cohorts totaling 686 patients with clinical outcome data by using multivariate models.ResultsProtein levels of Stat5a but not Stat5b were reduced in primary breast cancer and lymph node metastases compared with normal epithelia. Low tumor levels of Stat5a but not Stat5b mRNA were associated with poor prognosis. Experimentally, only limited overlap between Stat5a- and Stat5b-modulated genes was found. In two cohorts of therapy-naïve, node-negative breast cancer patients, low nuclear Stat5a protein levels were an independent marker of poor prognosis. Multivariate analysis of two cohorts treated with antiestrogen monotherapy revealed that low nuclear Stat5a levels were associated with a more than fourfold risk of unfavorable outcome.ConclusionsLoss of Stat5a represents a new independent marker of poor prognosis in node-negative breast cancer and may be a predictor of response to antiestrogen therapy if validated in randomized clinical trials.

DW4TR: A Data Warehouse for Translational Research

The linkage between the clinical and laboratory research domains is a key issue in translational research. Integration of clinicopathologic data alone is a major task given the number of data elements involved. For a translational research environment, it is critical to make these data usable at the point-of-need. Individual systems have been developed to meet the needs of particular projects though the need for a generalizable system has been recognized. Increased use of Electronic Medical Record data in translational research will demand generalizing the system for integrating clinical data to support the study of a broad range of human diseases. To ultimately satisfy these needs, we have developed a system to support multiple translational research projects. This system, the Data Warehouse for Translational Research (DW4TR), is based on a light-weight, patient-centric modularly-structured clinical data model and a specimen-centric molecular data model. The temporal relationships of the data are also part of the model. The data are accessed through an interface composed of an Aggregated Biomedical-Information Browser (ABB) and an Individual Subject Information Viewer (ISIV) which target general users. The system was developed to support a breast cancer translational research program and has been extended to support a gynecological disease program. Further extensions of the DW4TR are underway. We believe that the DW4TR will play an important role in translational research across multiple disease types.

Does Breast Tumor Heterogeneity Necessitate Further Immunohistochemical Staining on Surgical Specimens

BACKGROUND Prognostic and predictive tumor markers in breast cancer are most commonly performed on core needle biopsies (CNB) of the primary tumor. Because treatment recommendations are influenced by these markers, it is imperative to verify strong concordance between tumor markers on CNB specimens and the corresponding surgical specimens (SS). STUDY DESIGN A prospective study was performed on 165 women (205 samples) with breast cancer diagnosed from January 2009 to July 2011. Tumor type, grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2), and Ki67 expression by immunohistochemical (IHC) testing were retrospectively analyzed in the CNB and SS. Contingency tables and agreement modeling were performed. RESULTS There was substantial agreement between the CNB and SS for PR% and HER2; moderate agreement for tumor type, grade, and ER%; and fair agreement for Ki67%. In 8% of patients (n = 13), tumor heterogeneity was seen. In heterogeneous tumors the overall concordance between the CNB and SS was worse, especially for HER2. Six of these patients had areas of tumor that were positive for HER2, which were not detected in their CNBs. Nine patients had multiple distinct molecular subtypes within their tumor(s). CONCLUSIONS The heterogeneous distribution of antigens in breast cancer tumors raises concern that the CNB may not adequately represent the true biologic profile in all patients. There is strong concordance for tumor type, ER, and PR between CNB and SS (although a quantitative decline was noted from CNB to SS); however, HER2 activity does not appear to be adequately detected on CNB in patients with heterogeneous tumors. These data suggest that IHC testing on the CNB alone may not be adequate to tailor targeted therapy in all patients.

A Bayesian derived network of breast pathology co-occurrence

In this paper, we present the validation and verification of a machine-learning based Bayesian network of breast pathology co-occurrence. The present/not present occurrences of 29 common breast pathologies from 1631 pathology reports were used to build the network. All pathology reports were developed by a single pathologist. The resulting network has 25 diagnosis nodes interconnected by 40 arcs. Each arc represents a predicted co-occurrence or null co-occurrence. Model verification involved assessing the robustness of the original network structure after random exclusion of 25%, 50%, and 75% of the pathology report dataset. The structure of the network appears stable as random removal of 75% of the records in the original dataset leaves 81% of the original network intact. Model validation was primarily assessed by review of the breast pathology literature for each arc in the network. Almost all network identified co-occurrences (95%) have been published in the breast pathology literature or were verified by expert opinion. In conclusion, the Bayesian network of breast pathology co-occurrence presented here is both robust with respect to incomplete data and validated by consistency with the breast pathology literature and by expert opinion. Further, the ability to utilize a specific pathology observation to predict multiple co-current pathologies enables exploration of pathology co-occurrence patterns in an intuitive manner that may have broader application in both the breast pathologist clinical community and the breast cancer research community.