Clinical outcomes and prognostic biomarkers among pregnant, post-partum and nulliparous women with breast cancer: a prospective cohort study.

PURPOSE: To compare clinical-pathologic characteristics and outcomes of pregnancy-associated, post-partum (PP) and nulliparous (NP) breast cancer (BC) patients and explore mediators of the poor prognosis associated with post-partum BC. METHODS: A prospective database of 233 women ≤ 40 years of age diagnosed with BC between February 2008 and January 2015 was analysed. Clinical-pathologic characteristics and outcomes among pregnant, PP and NP patients were compared using chi-square or Kruskal-Wallis tests. The Kaplan-Meier method was used to estimate disease-free survival (DFS), distant DFS and overall survival (OS). Survival curves were compared using the log-rank test. Univariable Cox proportional hazards regression models were used to evaluate factors that were potentially prognostic for the clinical outcomes of interest; a multivariable Cox model was constructed using a forward stepwise selection process. Androgen receptor (AR), GATA3, PDL1 status and the presence/absence of tumour-infiltrating lymphocytes (TILs) were assessed when possible. Pre-treatment neutrophil and lymphocyte counts were abstracted retrospectively. Statistical significance was defined as a p value ≤ 0.05. RESULTS: Women ≤ 2 years PP had a numerically higher incidence of lymph node-positive and high-grade disease and were significantly more likely to have estrogen receptor-negative BC compared to NP controls. With a median follow-up of 7.2 years, increasingly poor outcomes were observed among NP (longest OS), > 2 years PP, ≤ 2 years PP and pregnant (shortest OS) patients, but these differences were not statistically significant. The ≤ 2 years PP group had significantly lower AR expression, a strong trend toward higher PDL1 expression and a higher expression of stromal TILs compared to NP women. CONCLUSIONS: PPBC patients had numerically lower DFS and OS compared to NP controls. Higher PDL1 and stromal TILs in PPBC suggest that adjuvant immunotherapy may be effective in the post-partum BC subgroup.

Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma.

BACKGROUND: The outcome of clear cell renal cell carcinoma (ccRCC) is still unpredictable. Even with new targeted therapies, the average progression-free survival is dismal. Markers for early detection and progression could improve disease outcome. METHODS: To identify efficient and hitherto unrecognized pathogenic factors of the disease, we performed a uniquely comprehensive pathway analysis and built a gene interaction network based on large publicly available data sets assembled from 28 publications, comprising a 3-prong approach with high-throughput mRNA, microRNA, and protein expression profiles of 593 ccRCC and 389 normal kidney samples. We validated our results on 2 different data sets of 882 ccRCC and 152 normal tissues. Functional analyses were done by proliferation, migration, and invasion assays following siRNA (small interfering RNA) knockdown. RESULTS: After integration of multilevel data, we identified aryl-hydrocarbon receptor (AHR), grainyhead-like-2 (GRHL2), and KIAA0101 as new pathogenic factors. GRHL2 expression was associated with higher chances for disease relapse and retained prognostic utility after controlling for grade and stage [hazard ratio (HR), 3.47, P = 0.012]. Patients with KIAA0101-positive expression suffered worse disease-free survival (HR, 3.64, P < 0.001), and in multivariate analysis KIAA0101 retained its independent prognostic significance. Survival analysis showed that GRHL2- and KIAA0101-positive patients had significantly lower disease-free survival (P = 0.002 and P < 0.001). We also found that KIAA0101 silencing decreased kidney cancer cell migration and invasion in vitro. CONCLUSIONS: Using an integrative system biology approach, we identified 3 novel factors as potential biomarkers (AHR, GRHL2 and KIAA0101) involved in ccRCC pathogenesis and not linked to kidney cancer before.

Immunohistochemical Characterization of a Large Cohort of Triple Negative Breast Cancer.

Introduction. Triple negative breast carcinomas are characterized by a lack of hormone receptor and HER2 expression and inconsistent expression of breast-specific immunohistochemical markers. The expression of many site-specific markers in these tumors is largely unknown. The objective of the study was to examine the expression of widely used immunohistochemical markers on a large cohort of triple negative breast cancer. Methods. Sections from tissue microarrays were stained with 47 markers using routine protocols. Most markers were scored using a modified Allred method. ATRX, BAP1, SMAD4, e-cadherin, and beta-catenin were scored as retained or lost. Mammaglobin was considered positive if there was at least moderate intensity staining in any tumor cells. P16 was scored as overexpressed or not overexpressed; p53 was scored as wildtype, overexpressed, null, or cytoplasmic. Results. The cohort consisted of 639 tumors including 601 primary and 32 metastases. Overall, 96% expressed GATA3, mammaglobin, and/or SOX10 while 97% of no special type tumors expressed this panel. Carcinoma of apocrine differentiation demonstrated an AR positive, SOX10 negative, K5 negative/focal immunophenotype. PAX8 (SP348), WT1, Napsin A, and TTF1 (8G7G3/1) were never or rarely expressed while CA9, CDX2, NKX3.1, SATB2 (SATBA410), synaptophysin, and vimentin were variably expressed. Conclusions. Almost all TNBC express at least 1 of the 3 IHC markers: GATA3, mammaglobin, and/or SOX10. Carcinoma of apocrine differentiation is characterized by an AR positive, SOX10 negative, K5 negative or focal immunophenotype. Cautious interpretation of so-called site-specific markers, with knowledge of antibody clones, is required in excluding the diagnosis of triple negative breast cancer.

MicroRNA signature helps distinguish early from late biochemical failure in prostate cancer.

PURPOSE: Prostate-specific antigen testing has led to overtreatment of prostate cancer (PCa). Only a small subset of PCa patients will have an aggressive disease that requires intensive therapy, and there is currently no biomarker to predict disease aggressiveness at the time of surgery. MicroRNAs (miRNAs) are reported to be involved in PCa pathogenesis. METHODS: This study involved 105 participants. For the discovery phase, prostatectomy samples were dichotomized to high-risk (n = 27, biochemical failure <36 months after prostatectomy) and low-risk groups (n = 14, ≥ 36 months without biochemical failure). Expression of 754 mature miRNAs was compared between the 2 groups. Linear regression models were built to accurately predict biochemical failure risk. miRNA mimics were transfected into PCa model cell lines to test effects on proliferation and to deduce responding signaling pathways. RESULTS: We identified 25 differentially expressed miRNAs between the biochemical failure risk groups. Based on the expression of 2-3 miRNAs, 3 logistic regression models were developed, each with a high positive predictive value. Candidate miRNAs and the best-performing model were also verified on an independent PCa set. miRNA-152, featured in the models, was further investigated by using cell line models and was shown to affect cell proliferation. Predicted interaction between miR-152 and (mRNA)ERBB3 (erythroblastic leukemia viral oncogene homolog 3) was experimentally validated in vitro. CONCLUSIONS: miRNAs can help to predict biochemical failure risk at the time of prostatectomy.

Integrated Phenotypic/Genotypic Analysis of Papillary Renal Cell Carcinoma Subtypes: Identification of Prognostic Markers, Cancer-related Pathways, and Implications for Therapy.

BACKGROUND: Two histologic subtypes are recognized for papillary renal cell carcinoma (PRCC). Studies have shown that the subtypes differ in characteristic genetic alterations and clinical behavior. Clinically, the subtypes are managed similarly. OBJECTIVES: To analyze the biological differences between the two PRCC histological subtypes, in order to further guide their clinical management. DESIGN, SETTING, AND PARTICIPANTS: PRCC cohort consisting of 317 patients from the Cancer Genome Atlas database and our institution. Patients were stratified according to histologic criteria as type 1, type 2, or not otherwise specified (NOS). Gene and miRNA expression data for the cohort were examined via unsupervised and supervised clustering. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Significant molecular signatures for each subtype were used to unravel the implicated molecular pathways via bioinformatics analysis. Survival was compared between the subtypes. Newly discovered biomarkers were used to further stratify survival of patients in the NOS category. RESULTS AND LIMITATIONS: Tumor genotyping revealed two distinct PRCC subtypes. The top molecular pathways enriched in PRCC1 were WNT, Hedgehog, and Notch signaling (p=0.001-0.01); highlighting an embryonic developmental theme to the pathogenesis of this subtype. PRCC2 showed enrichment in the mTOR, VEGF (p=7.49E-09) and HIF (p=7.63E-05) signaling pathways. Overall survival and disease-free survival significantly differed between the types. ABCC2 expression was identified as a significant prognostic biomarker for the NOS group in univariate (log rank p<0.0001; hazard ratio [HR] >11.63) and multivariate analysis (p=0.003; HR >2.12). ABCC2 expression and its effect on survival should be further validated at the protein level. CONCLUSIONS: The classical PRCC types 1 and 2 have two distinct genotypes. We unraveled pathways that indicate that the two types could potentially respond differently to current therapies. We also identified biomarkers that stratify tumors within the PRCC NOS category into prognostic subgroups. Our findings highlight the need for molecular markers to accurately subtype PRCC and guide clinical management. PATIENT SUMMARY: The two types of papillary renal cancer are treated similarly. We show that the two types have a different genetic makeup, and hence they should be considered two different tumors. There is a different biology underlying each tumor type that can potentially affect the way they respond to treatment. We uncovered genes that can be tested for to guide therapy in some problematic cases for which it hard to define the tumor type.