Boris Freydin

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.

HuR status is a powerful marker for prognosis and response to gemcitabine-based chemotherapy for resected pancreatic ductal adenocarcinoma patients.

Background:Pancreatic ductal adenocarcinoma (PDA) is a devastating disease that killed nearly 38,000 people in the United States this past year. Objective:Treatment of PDA typically includes surgery and/or chemotherapy with gemcitabine. No reliable biomarker exists for prognosis or response to chemotherapy. Two previously proposed prognostic markers, cyclooxygenase-2 (COX-2) and vascular endothelial growth factor (VEGF), are regulated by Hu protein antigen R (HuR), an mRNA binding protein that we have previously demonstrated to be a promising predictive marker of gemcitabine response. This study was designed to evaluate the clinical utility of HuR, COX-2, and VEGF as potential prognostic and predictive biomarkers for PDA. Methods:A tissue microarray of 53 PDA specimens from patients who underwent potentially curative pancreatic resection was analyzed. HuR, COX-2, and VEGF status were correlated with clinicopathologic and survival data. We also performed ribonucleoprotein immunoprecipitation assays using an HuR antibody to assess VEGF and COX-2 mRNA binding to HuR in pancreatic cancer cells. Results:Roughly 50% (27/53) of patients had high cytoplasmic HuR expression. These patients had worse pathologic features as assessed by T staging (P = 0.005). Only cytoplasmic HuR status correlated with tumor T staging, whereas VEGF (P = 1.0) and COX-2 (P = 0.39) expression did not correlate with T staging. Additionally, HuR status was an unprecedented positive predictive marker for overall survival in patients treated with gemcitabine, pushing median survival over 45 months in the high cytoplasmic HuR expressing patient population compared with less than 23 months in the low cytoplasmic HuR expressing patient group (P = 0.033 for log-rank test and P = 0.04 in a Cox regression model) for the low versus high cytoplasmic HuR expressing group. We also validated that mRNA transcripts for both VEGF and the gemcitabine metabolizing enzyme, deoxycytidine kinase, are specifically bound by HuR in pancreatic cancer cells. Conclusions:HuR is a useful prognostic biomarker for PDA patients as indicated by its association with higher tumor T stage. Additionally, HuR status is a robust predictor of outcome for patients with resected PDA in the setting of adjuvant gemcitabine therapy. Finally, HuR binds to VEGF mRNA implying that HuR, in part, regulates VEGF expression in PDA. This study supports the notion that HuR status should be used by clinicians for the individualized treatment of PDA in the future.

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.

Stromal CD10 and SPARC expression in ductal carcinoma in situ (DCIS) patients predicts disease recurrence

The current classification of ductal carcinoma in situ (DCIS) is based on nuclear grade, architectural differentiation and the presence of necrosis that does not adequately predict the likelihood of recurrence after breast conserving therapy; therefore, there is a critical need to identify novel predictors of DCIS progression. Ninety seven cases of DCIS were included in the study. CD10 and SPARC expression in tumor stroma was assessed by standard immunoperoxidase method with ani-CD 10 and anti-SPARC antibodies. The staining was scored semi-quantitatively as negative (0; no staining), weak or strong. Statistical analysis was performed using the Fishers exact test. Multivariable analysis was conducted utilizing Exact Logistic Regression software (SAS 9.1 and LogExact). A significant association was observed between the recurrence status and time to recurrence with expression of CD10 (p<0.001) and SPARC (p<0.001). When combining both SPARC and CD10 expression there was a strong correlation with the shortest time to recurrence. Stromal CD10 and SPARC expression are new markers of an increased risk for DCIS recurrence, independent of commonly assessed clinical parameters. Thus, stromal CD10 and SPARC expression levels are promising markers of DCIS recurrence and warrant evaluation in larger prospective studies.

Prolactin suppresses a progestin-induced CK5-positive cell population in luminal breast cancer through inhibition of progestin-driven BCL6 expression

Prolactin controls the development and function of milk-producing breast epithelia but also supports growth and differentiation of breast cancer, especially luminal subtypes. A principal signaling mediator of prolactin, Stat5, promotes cellular differentiation of breast cancer cells in vitro, and loss of active Stat5 in tumors is associated with antiestrogen therapy failure in patients. In luminal breast cancer, progesterone induces a cytokeratin-5 (CK5)-positive basal cell-like population. This population possesses characteristics of tumor stem cells including quiescence, therapy resistance and tumor-initiating capacity. Here we report that prolactin counteracts induction of the CK5-positive population by the synthetic progestin (Pg) R5020 in luminal breast cancer cells both in vitro and in vivo. CK5-positive cells were chemoresistant as determined by fourfold reduced rate of apoptosis following docetaxel exposure. Pg-induction of CK5 was preceded by marked upregulation of BCL6, an oncogene and transcriptional repressor critical for the maintenance of leukemia-initiating cells. Knockdown of BCL6 prevented induction of CK5-positive cell population by Pg. Prolactin suppressed Pg-induced BCL6 through Jak2-Stat5 but not Erk- or Akt-dependent pathways. In premenopausal but not postmenopausal patients with hormone receptor-positive breast cancer, tumor protein levels of CK5 correlated positively with BCL6, and high BCL6 or CK5 protein levels were associated with unfavorable clinical outcome. Suppression of Pg-induction of CK5-positive cells represents a novel prodifferentiation effect of prolactin in breast cancer. The present progress may have direct implications for breast cancer progression and therapy as loss of prolactin receptor-Stat5 signaling occurs frequently and BCL6 inhibitors currently being evaluated for lymphomas may have value for breast cancer.

Prolactin-Stat5 signaling in breast cancer is potently disrupted by acidosis within the tumor microenvironment

IntroductionEmerging evidence in estrogen receptor-positive breast cancer supports the notion that prolactin-Stat5 signaling promotes survival and maintenance of differentiated luminal cells, and loss of nuclear tyrosine phosphorylated Stat5 (Nuc-pYStat5) in clinical breast cancer is associated with increased risk of antiestrogen therapy failure. However, the molecular mechanisms underlying loss of Nuc-pYStat5 in breast cancer remain poorly defined.MethodsWe investigated whether moderate extracellular acidosis of pH 6.5 to 6.9 frequently observed in breast cancer inhibits prolactin-Stat5 signaling, using in vitro and in vivo experimental approaches combined with quantitative immunofluorescence protein analyses to interrogate archival breast cancer specimens.ResultsModerate acidosis at pH 6.8 potently disrupted signaling by receptors for prolactin but not epidermal growth factor, oncostatin M, IGF1, FGF or growth hormone. In breast cancer specimens there was mutually exclusive expression of Nuc-pYStat5 and GLUT1, a glucose transporter upregulated in glycolysis-dependent carcinoma cells and an indirect marker of lactacidosis. Mutually exclusive expression of GLUT1 and Nuc-pYStat5 occurred globally or regionally within tumors, consistent with global or regional acidosis. All prolactin-induced signals and transcripts were suppressed by acidosis, and the acidosis effect was rapid and immediately reversible, supporting a mechanism of acidosis disruption of prolactin binding to receptor. T47D breast cancer xenotransplants in mice displayed variable acidosis (pH 6.5 to 6.9) and tumor regions with elevated GLUT1 displayed resistance to exogenous prolactin despite unaltered levels of prolactin receptors and Stat5.ConclusionsModerate extracellular acidosis effectively blocks prolactin signaling in breast cancer. We propose that acidosis-induced prolactin resistance represents a previously unrecognized mechanism by which breast cancer cells may escape homeostatic control.

Validation of tumor protein marker quantification by two independent automated immunofluorescence image analysis platforms

Protein marker levels in formalin-fixed, paraffin-embedded tissue sections traditionally have been assayed by chromogenic immunohistochemistry and evaluated visually by pathologists. Pathologist scoring of chromogen staining intensity is subjective and generates low-resolution ordinal or nominal data rather than continuous data. Emerging digital pathology platforms now allow quantification of chromogen or fluorescence signals by computer-assisted image analysis, providing continuous immunohistochemistry values. Fluorescence immunohistochemistry offers greater dynamic signal range than chromogen immunohistochemistry, and combined with image analysis holds the promise of enhanced sensitivity and analytic resolution, and consequently more robust quantification. However, commercial fluorescence scanners and image analysis software differ in features and capabilities, and claims of objective quantitative immunohistochemistry are difficult to validate as pathologist scoring is subjective and there is no accepted gold standard. Here we provide the first side-by-side validation of two technologically distinct commercial fluorescence immunohistochemistry analysis platforms. We document highly consistent results by (1) concordance analysis of fluorescence immunohistochemistry values and (2) agreement in outcome predictions both for objective, data-driven cutpoint dichotomization with Kaplan–Meier analyses or employment of continuous marker values to compute receiver-operating curves. The two platforms examined rely on distinct fluorescence immunohistochemistry imaging hardware, microscopy vs line scanning, and functionally distinct image analysis software. Fluorescence immunohistochemistry values for nuclear-localized and tyrosine-phosphorylated Stat5a/b computed by each platform on a cohort of 323 breast cancer cases revealed high concordance after linear calibration, a finding confirmed on an independent 382 case cohort, with concordance correlation coefficients >0.98. Data-driven optimal cutpoints for outcome prediction by either platform were reciprocally applicable to the data derived by the alternate platform, identifying patients with low Nuc-pYStat5 at ~3.5-fold increased risk of disease progression. Our analyses identified two highly concordant fluorescence immunohistochemistry platforms that may serve as benchmarks for testing of other platforms, and low interoperator variability supports the implementation of objective tumor marker quantification in pathology laboratories.

Modeling qRT-PCR dynamics with application to cancer biomarker quantification

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is widely used for molecular diagnostics and evaluating prognosis in cancer. The utility of mRNA expression biomarkers relies heavily on the accuracy and precision of quantification, which is still challenging for low abundance transcripts. The critical step for quantification is accurate estimation of efficiency needed for computing a relative qRT-PCR expression. We propose a new approach to estimating qRT-PCR efficiency based on modeling dynamics of polymerase chain reaction amplification. In contrast, only models for fluorescence intensity as a function of polymerase chain reaction cycle have been used so far for quantification. The dynamics of qRT-PCR efficiency is modeled using an ordinary differential equation model, and the fitted ordinary differential equation model is used to obtain effective polymerase chain reaction efficiency estimates needed for efficiency-adjusted quantification. The proposed new qRT-PCR efficiency estimates were used to quantify GUCY2C (Guanylate Cyclase 2C) mRNA expression in the blood of colorectal cancer patients. Time to recurrence and GUCY2C expression ratios were analyzed in a joint model for survival and longitudinal outcomes. The joint model with GUCY2C quantified using the proposed polymerase chain reaction efficiency estimates provided clinically meaningful results for association between time to recurrence and longitudinal trends in GUCY2C expression.

Estimation of nonlinear differential equation model for glucose–insulin dynamics in type I diabetic patients using generalized smoothing

In this work we develop an ordinary differential equations (ODE)model of physiological regulation of glycemia in type 1 diabetes mel-litus (T1DM) patients in response to meals and intravenous insulininfusion. Unlike for the majority of existing mathematical modelsof glucose–insulin dynamics, parameters in our model are estimablefrom a relatively small number of noisy observations of plasma glu-cose and insulin concentrations. For estimation, we adopt the general-ized smoothing estimation of nonlinear dynamic systems of Ramsayet al. [J. R. Stat. Soc. Ser. B Stat. Methodol. 69 (2007) 741–796].In this framework, the ODE solution is approximated with a penal-ized spline, where the ODE model is incorporated in the penalty.We propose to optimize the generalized smoothing by using penaltyweights that minimize the covariance penalties criterion (Efron [J.Amer. Statist. Assoc. 99 (2004) 619–642]). The covariance penaltiescriterion provides an estimate of the prediction error for nonlinear es-timation rules resultingfrom nonlinear and/or nonhomogeneous ODEmodels, such as our model of glucose–insulin dynamics. We also pro-pose to select the optimal number and location of knots for B-splinebases used to represent the ODE solution. The results of the smallsimulation study demonstrate advantages of optimized generalizedsmoothing in terms of smaller estimation errors for ODE parametersand smaller prediction errors for solutions of differential equations.Using the proposed approach to analyze the glucose and insulin con-centration data in T1DM patients, we obtained good approximationof global glucose–insulin dynamics and physiologically meaningful pa-rameter estimates.Received October 2012; revised August 2013.

Abstract P1-08-20: Increased risk of hormone therapy failure in breast cancers expressing low phospho-Stat5: Validation of quantitative immunofluorescence assay parameters

Previous analyses of three breast cancer cohorts revealed that loss of phospho-Stat5 in breast cancer is associated with significantly elevated risk of hormone therapy failure (1, 2). Nuclear localized tyrosine phosphorylated Stat5 (Nuc-pYStat5) may therefore have clinical value as a predictive marker. Analysis of two of the three previously reported anti-estrogen treated patient cohorts used pathologist scoring of diaminobenzidine (DAB) chromogen-stained Stat5. However the third cohort, analyzed by quantitative immunofluorescence analysis (QIF) on the Genoptix/HistoRx AQUA platform, revealed a greater hazard ratio than the cohorts analyzed by pathologist DAB-scoring. To extend and validate these observations, we applied the Nuc-pYStat5 cutpoint derived in our previous study (2) to an independent cohort of anti-estrogen-treated breast cancer patients using two distinct QIF software platforms, AQUA and Definiens Tissue Studio. Tissue Studio relies on supervised machine learning and multiparametric features of a high-resolution whole slide image to identify cancer cell regions, while AQUA software relies on costaining of a tumor marker to identify cancer cell regions. The two QIF platforms produced highly concordant Nuc-pYStat5 levels (R2 linear = 0.96, P References: 1) Yamashita et al. Stat5 expression predicts response to endocrine therapy and improves survival in estrogen receptor-positive breast cancer. Endocr Relat Cancer. 2006;13:885-93. 2) Peck et al. Loss of nuclear localized and tyrosine phosphorylated Stat5 in breast cancer predicts poor clinical outcome and increased risk of antiestrogen therapy failure. J Clin Oncol. 2011;29:2448-58. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-08-20.