p53 mutant His175 identified in a newly established fallopian tube carcinoma cell line secreting interleukin 6

AbstractFallopian tube carcinoma is a lethal gynecologic malignancy. Etiologic factors are unknown. No experimental data on molecular alterations exist so far. For an in vitro model, we established the permanent human tubal carcinoma cell line FT-MZ-1. The median doubling time was 14 days with 24.2% in S phase. A point missense mutation of the p53 tumor suppressor gene resulting in the His175 mutant was identified. Aberrant p53 protein accumulated in nucleus and cytoplasm. FT-MZ-1 substantially secreted interleukin 6 (Il-6) coinciding with the inactivation of p53 as a transrepressor on the Il-6 gene promoter

Predictive Biomarkers for Endocrine Therapy:Retrospective Study in Tamoxifen and Exemestane Adjuvant Multinational (TEAM) Trial

Background: Aromatase inhibitors improve disease-free survival compared with tamoxifen in postmenopausal women with hormone receptor–positive breast cancer. The Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial compared exemestane monotherapy with sequential therapy of tamoxifen followed by exemestane. The trial failed to show a statistically significant difference between treatment arms. A robust translational program was established to investigate predictive biomarkers. Methods: A tissue microarray was retrospectively constructed using a subset of patient tissues (n = 4631) from the TEAM trial (n = 9766). Immunohistochemistry was performed for biomarkers, classed into three groups: MAPK pathway, NF-kappa B pathway, and estrogen receptor (ER) phosphorylation. Expression was analyzed for association with relapse-free survival (RFS) at 2.5 and 10 years and treatment regimen using Kaplan-Meier curves and log-rank analysis. All statistical tests were two-sided. Results: In univariate analysis, ER167 (hazard ratio [HR] = 0.71, 95% confidence interval [CI] = 0.59 to 0.85, P < .001), IKKα (HR = 0.74, 95% CI = 0.60 to 0.92, P = .005), Raf-1338 (HR = 0.64, 95% CI = 0.52 to 0.80, P < .001), and p44/42 MAPK202/204 (HR = 0.77, 95% CI = 0.64 to 0.92, P = .004) were statistically significantly associated with improved RFS at 10 years in patients receiving sequential therapy. Associations were strengthened when IKKα, Raf-1338, and ER167 were combined into a cumulative prognostic score (HR = 0.64, 95% CI = 0.52 to 0.77, P <.001). Patients with an all negative IKKα, Raf-1338, and ER167 score favored exemestane monotherapy (odds ratio = 0.56, 95% CI = 0.35 to 0.90). In multivariable analysis, the IKKα, Raf-1338, and ER167 score (P = .001) was an independent prognostic factor for RFS at 10 years in patients receiving sequential therapy. Conclusions: The IKKα, Raf-1338, and ER167 score is an independent predictive biomarker for lower recurrence on sequential therapy. Negative expression may further offer predictive value for exemestane monotherapy

Pathway-based subnetworks enable cross-disease biomarker discovery.

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery

Validation of the IHC4 breast cancer prognostic algorithm using multiple approaches on the multinational TEAM clinical trial

ContextHormone receptors HER2/neu and Ki-67 are markers of residual risk in early breast cancer. An algorithm (IHC4) combining these markers may provide additional information on residual risk of recurrence in patients treated with hormone therapy.ObjectiveTo independently validate the IHC4 algorithm in the multinational Tamoxifen Versus Exemestane Adjuvant Multicenter Trial (TEAM) cohort, originally developed on the trans-ATAC (Arimidex, Tamoxifen, Alone or in Combination Trial) cohort, by comparing 2 methodologies.DesignThe IHC4 biomarker expression was quantified on TEAM cohort samples (n = 2919) by using 2 independent methodologies (conventional 3,3′-diaminobezidine [DAB] immunohistochemistry with image analysis and standardized quantitative immunofluorescence [QIF] by AQUA technology). The IHC4 scores were calculated by using the same previously established coefficients and then compared with recurrence-free and distant recurrence-free survival, using multivariate Cox proportional hazards modeling.ResultsThe QIF model was highly significant for prediction of residual risk (P &amp;lt; .001), with continuous model scores showing a hazard ratio (HR) of 1.012 (95% confidence interval [95% CI]: 1.010–1.014), which was significantly higher than that for the DAB model (HR: 1.008, 95% CI: 1.006–1.009); P &amp;lt; .001). Each model added significant prognostic value in addition to recognized clinical prognostic factors, including nodal status, in multivariate analyses. Quantitative immunofluorescence, however, showed more accuracy with respect to overall residual risk assessment than the DAB model.ConclusionsThe use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used.</jats:sec

Validation of the IHC4 Breast Cancer Prognostic Algorithm Using Multiple Approaches on the Multinational TEAM Clinical Trial

Context.-Hormone receptors HER2/neu and Ki-67 are markers of residual risk in early breast cancer. An algorithm (IHC4) combining these markers may provide additional information on residual risk of recurrence in patients treated with hormone therapy. Objective.-To independently validate the IHC4 algorithm in the multinational Tamoxifen Versus Exemestane Adjuvant Multicenter Trial (TEAM) cohort, originally developed on the trans-ATAC (Arimidex, Tamoxifen, Alone or in Combination Trial) cohort, by comparing 2 methodologies. Design.-The IHC4 biomarker expression was quantified on TEAM cohort samples (n = 2919) by using 2 independent methodologies (conventional 3,3’-diaminobezidine [DAB] immunohistochemistry with image analysis and standardized quantitative immunofluorescence [QIF] by AQUA technology). The IHC4 scores were calculated by using the same previously established coefficients and then compared with recurrence-free and distant recurrence-free survival, using multivariate Cox proportional hazards modeling. Results.-The QIF model was highly significant for prediction of residual risk (P &lt; .001), with continuous model scores showing a hazard ratio (HR) of 1.012 (95% confidence interval [95% CI]: 1.010-1.014), which was significantly higher than that for the DAB model (HR: 1.008, 95% CI: 1.006-1.009); P &lt; .001). Each model added significant prognostic value in addition to recognized clinical prognostic factors, including nodal status, in multivariate analyses. Quantitative immunofluorescence, however, showed more accuracy with respect to overall residual risk assessment than the DAB model. Conclusions.-The use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used

Molecular stratification of early breast cancer identifies drug targets to drive stratified medicine.

Many women with hormone receptor-positive early breast cancer can be managed effectively with endocrine therapies alone. However, additional systemic chemotherapy treatment is necessary for others. The clinical challenges in managing high-risk women are to identify existing and novel druggable targets, and to identify those who would benefit from these therapies. Therefore, we performed mRNA abundance analysis using the Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial pathology cohort to identify a signature of residual risk following endocrine therapy and pathways that are potentially druggable. A panel of genes compiled from academic and commercial multiparametric tests as well as genes of importance to breast cancer pathogenesis was used to profile 3825 patients. A signature of 95 genes, including nodal status, was validated to stratify endocrine-treated patients into high-risk and low-risk groups based on distant relapse-free survival (DRFS; Hazard Ratio = 5.05, 95% CI 3.53-7.22, p = 7.51 × 10-19). This risk signature was also found to perform better than current multiparametric tests. When the 95-gene prognostic signature was applied to all patients in the validation cohort, including patients who received adjuvant chemotherapy, the signature remained prognostic (HR = 4.76, 95% CI 3.61-6.28, p = 2.53× 10-28). Functional gene interaction analyses identified six significant modules representing pathways involved in cell cycle control, mitosis and receptor tyrosine signaling; containing a number of genes with existing targeted therapies for use in breast or other malignancies. Thus the identification of high-risk patients using this prognostic signature has the potential to also prioritize patients for treatment with these targeted therapies

Mutational Analysis of PI3K/AKT Signaling Pathway in Tamoxifen Exemestane Adjuvant Multinational Pathology Study

Purpose Deregulation of key PI3K/AKT pathway genes may contribute to endocrine resistance in breast cancer (BC). PIK3CA is the most frequently mutated gene in luminal BC (similar to 35%); however, the effect of mutations in helical versus kinase domains remains controversial. We hypothesize that improved outcomes occur in patients with estrogen receptor-positive (ER positive) BC receiving endocrine therapy and possessing PIK3CA mutations. Materials and Methods DNA was extracted from 4,540 formalin-fixed paraffin-embedded BC samples from the Exemestane Versus Tamoxifen-Exemestane pathology study. Mutational analyses were performed for 25 mutations (PIK3CAx10, AKT1x1, KRASx5, HRASx3, NRASx2 and BRAFx4). Results PIK3CA mutations were frequent (39.8%), whereas RAS/RAF mutations were rare (&lt;1%). In univariable analyses PIK3CA mutations were associated with significantly improved 5-year distant relapse-free survival (DRFS; HR, 0.76; 95% CI, 0.63 to 0.91; P = .003). However, a multivariable analysis correcting for known clinical and biologic prognostic factors failed to demonstrate that PIK3CA mutation status is an independent prognostic marker for DRFS (HR, 0.92; 95% CI, 0.75 to 1.12; P = .4012). PIK3CA mutations were more frequent in low-risk luminal BCs (eg, grade 1 node v 3, node-negative v-positive), confounding the relationship between mutations and outcome. Conclusion PIK3CA mutations are present in approximately 40% of luminal BCs but are not an independent predictor of outcome in the context of endocrine therapy, whereas RAS/RAF mutations are rare in luminal BC. A complex relationship between low-risk cancers and PIK3CA mutations was identified. Although the PI3K/AKT pathway remains a viable therapeutic target as the result of a high mutation frequency, PIK3CA mutations do not seem to affect residual risk following treatment with endocrine therapy. (C) 2014 by American Society of Clinical Oncolog