Mammaglobin,GATA-binding protein 3 (GATA3), and epithelial growth factor receptor (EGFR) expression in different breast cancer subtypes and their clinical significance

Increasing evidence has shown that mammaglobin, GATA-binding protein 3 (GATA3), and epithelial growth factor receptor (EGFR) have unique clinical implications for breast cancer subtyping and classification, as well as for breast cancer targeted therapy. It is particularly important to clarify the correlation between their expression and different molecular breast carcinoma subtypes to better understand the molecular basis of the subtypes and to identify effective therapeutic targets for the disease. This study aimed to evaluate mammaglobin, GATA3, and EGFR expression in different breast cancer subtypes, as well as their clinical significance. Subjects of the study included 228 patients with breast cancer at The First Affiliated Hospital of University of Science and Technology of China. They were divided into triple negative (TN), Luminal A, Luminal B, and HER-2 positive (HER-2.P) breast cancer groups based on molecular classification. Immunohistochemical methods were used to detect mammaglobin, GATA3, and EGFR expression in cases of different molecular subtypes before determining the correlation between protein expression and subtype. Mammaglobin and GATA3 expression levels were found to significantly vary with respect to histopathological grade, lymph node status, and molecular subtype; EGFR expression was significantly correlated with breast cancer histopathological grade and molecular subtype. For breast cancer, the expression levels of mammaglobin and GATA3, as well as mammaglobin and EGFR, were significantly correlated. In addition, there was a significantly negative correlation between the expression levels of GATA3 and EGFR in breast cancer tissue samples, especially in HER-2.P samples. These findings provide a theoretical basis for assessing breast cancer clinical prognosis based on the cancer subtype, and hence, have significant practical value.Key words: breast cancer, mammaglobin, GATA-binding protein 3, epithelial growth factor receptor, molecular subtype     

Multiple environmental determinants of regional species richness and effects of geographic range size

Understanding patterns of species richness at broad geographic extents remains one of the most challenging yet necessary scientific goals of our time. Many hypotheses have been proposed to account for spatial variation in species richness; among them, environmental determinants have played a central role. In this study, we use data on regional bat species richness in the New World to study redundancy and complementarity of three environmental hypotheses: energy, heterogeneity and seasonality. We accomplish this by partitioning variation in species richness among components associated with unique and combined effects of variables from each hypotheses, and by partitioning the overall richness gradient into gradients of species with varying breadths of geographic distribution. These three environmental hypotheses explain most variation in the species richness gradient of all bats, but do not account for all positive spatial autocorrelation at short distances. Although environmental predictors are highly redundant, energy and seasonality explain different and complementary fractions of variation in species richness of all bats. On the other hand, heterogeneity variables contribute little to explain this gradient. However, results change dramatically when richness is estimated for groups of species with different sizes of geographic distribution. First, the amount of variation explained by environment decreases with a decrease in range size; this suggests that richness gradients of small‐ranged species can not be explained as easily as those of broadly distributed species, as has been implied by analyses that do not consider differences in range size among species. Second, the relative contribution of environmental predictors to explained variation also changes with change in range size. Seasonality and energy are good predictors of species with broad distributions, but they loose almost all explanatory power for richness of species with small ranges. In contrast, heterogeneity, which is a relatively poor predictor of richness of species with large ranges, becomes the main predictor of richness gradients of species with restricted distributions. This suggests that range size is a different dimension on which heterogeneity and other environmental characteristics are complementary to each other. Our results suggest that determinants of species richness gradients might be complex, or at least more complex than many studies have previously suggested.     

Class 1 outer membrane protein of Neisseria meningitidis: epitope analysis of the antigenic diversity between strains, implications for subtype definition and molecular epidemiology

The VR1 and VR2 regions of the class 1 protein have been sequenced from a number of meningococcal strains, including non-subtypable strains and strains of apparently identical subtype. The amino acid sequences have been used to construct synthetic peptides for mapping subtype-specific epitopes. The majority of epitopes was found to be located in VR2 at the apex of a predicted surface-exposed loop. A single amino acid change within an epitope, or an amino acid deletion outside an epitope, were both associated with loss of subtype specificity, resulting from a change in the predicted conformation at the apex of the loop structure. Analysis of the sequence information combined with knowledge of defined epitopes also revealed considerable additional information not demonstrated by current subtyping procedures.     

Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer

Ovarian cancer (OC) is associated with high mortality rate. However, the correlation between immune microenvironment and prognosis of OC remains unclear. This study aimed to explore prognostic significance of OC tumour microenvironment. The OC data set was selected from the cancer genome atlas (TCGA), and 307 samples were collected. Hierarchical clustering was performed according to the expression of 756 genes. The immune and matrix scores of all immune subtypes were determined, and Kruskal-Wallis test was used to analyse the differences in the immune and matrix scores between OC samples with different immune subtypes. The model for predicting prognosis was constructed based on the expression of immune-related genes. TIDE platform was applied to predict the effect of immunotherapy on patients with OC of different immune subtypes. The 307 OC samples were classified into three immune subtypes A-C. Patients in subtype B had poorer prognosis and lower survival rate. The infiltration of helper T cells and macrophages in microenvironment indicated significant differences between immune subtypes. Enrichment analyses of immune cell molecular pathways showed that JAK–STAT3 pathway changed significantly in subtype B. Furthermore, predictive response to immunotherapy in subtype B was significantly higher than that in subtype A and C. Immune subtyping can be used as an independent predictor of the prognosis of OC patients, which may be related to the infiltration patterns of immune cells in tumour microenvironment. In addition, patients in immune subtype B have superior response to immunotherapy, suggesting that patients in subtype B are suitable for immunotherapy.     

Malignancy associated changes in epithelial cells of buccal mucosa: a potential cancer detection test

OBJECTIVE: To analyze the presence of malignancy associated changes (MACs) in normal buccal mucosa cells of lung and breast cancer patients and their relationship to tumor subtype, stage and size. STUDY DESIGN: Buccal mucosa smears of 107 lung cancer and 100 breast cancer patients and corresponding healthy subjects were collected, stained by the DNA-specific Feulgen-thionin method and scanned using an automated high-resolution cytometer. Nuclear texture features of a minimum of 500 nuclei per slide were calculated, and statistical classifiers using Gaussian models of class-probability distribution were designed, trained and tested in 3 parts: (1) ability to separate cancer patient samples from controls, (2) cross-validation of classifiers for different cancer types, and (3) correlation of MAC expression with tumor subtype, stage and size. RESULTS: Lung and breast cancer induce MACs in normal buccal mucosa cells. The classifiers based on the selected nuclear features correctly recognized >80% of lung and breast cancer cases. The results indicate that MAC detection is not dependent on the tumor subtype, stage or size. CONCLUSION: The presence of MACs in buccal mucosa cells offers the potential for developing a new noninvasive cancer screening test.     

Novel hepatitis B virus genotype a subtyping assay that distinguishes subtype Aa from Ae and its application in epidemiological studies

The eight genotypes of hepatitis B virus (HBV) have different geographical distributions, virological characteristics, and clinical manifestations. A unique subtype of HBV genotype A (HBV/A) was reported in sub-Saharan Africa, raising the possibility that patients infected with this subtype (HBV/Aa ["a" for African and Asian]) may have different clinical outcomes than other HBV/A isolates (HBV/Ae ["e" for European]). Comparison between 30 HBV/Aa and 30 HBV/Ae isolates indicated that almost all HBV/Ae isolates had G at nucleotide (nt) 1809 and C at nt 1812, whereas HBV/Aa isolates had T1809/T1812. Taking advantage of these two single nucleotide polymorphisms (SNPs), a novel subtype-specific PCR assay in the X/precore/core region was developed. This assay was combined with a restriction fragment length polymorphism assay using BglII in a different region (nt 1984 to 1989), which has a SNP distinguishing HBV/Aa from HBV/Ae, resulting in 100% specificity for the combined assay. Application of the subtyping assay using sera from 109 paid donors in the United States indicated significantly different distributions of HBV/A subtypes among races; African-Americans, Caucasians, and Hispanics had HBV/Ae, whereas Asians had mainly HBV/Aa, suggesting that the HBV/Aa isolates may have been imported by recent immigration from Asia. In conclusion, the specificity and sensitivity of the combined subtyping assay were confirmed, and its usefulness was demonstrated in a practical context.     

The ghost of unbalanced species distribution data in geographical model predictions

Unbalanced samples are considered a drawback in predictive modelling of species' potential habitats, and a prevalence of 0.5 has been extensively recommended. We argue that unbalanced species distribution data are not such a problem from a statistical point of view, and that good models can be obtained provided that the right predictors and cut-off to convert probabilities into presence/absence are chosen. The effects of unbalanced prevalence should not be confused with those of low-quality data affected by false absences, low sample size, or unrepresentativeness of the environmental and spatial gradient. Finally, we point out the necessity of greater research effort aimed at improving both the quality of training data sets, and the processes of validating and testing of models.     

Non-Sentinel Lymph Node Metastasis Prediction in Breast Cancer with Metastatic Sentinel Lymph Node: Impact of Molecular Subtypes Classification

Introduction

To decipher the interaction between the molecular subtype classification and the probability of a non-sentinel node metastasis in breast cancer patients with a metastatic sentinel lymph-node, we applied two validated predictors (Tenon Score and MSKCC Nomogram) on two large independent datasets.

Materials and Methods

Our datasets consisted of 656 and 574 early-stage breast cancer patients with a metastatic sentinel lymph-node biopsy treated at first by surgery. We applied both predictors on the whole dataset and on each molecular immune-phenotype subgroups. The performances of the two predictors were analyzed in terms of discrimination and calibration. Probability of non-sentinel lymph node metastasis was detailed for each molecular subtype.

Results

Similar results were obtained with both predictors. We showed that the performance in terms of discrimination was as expected in ER Positive HER2 negative subgroup in both datasets (MSKCC AUC Dataset 1 = 0.73 [0.69–0.78], MSKCC AUC Dataset 2 = 0.71 (0.65–0.76), Tenon Score AUC Dataset 1 = 0.7 (0.65–0.75), Tenon Score AUC Dataset 2 = 0.72 (0.66–0.76)). Probability of non-sentinel node metastatic involvement was slightly under-estimated. Contradictory results were obtained in other subgroups (ER negative HER2 negative, HER2 positive subgroups) in both datasets probably due to a small sample size issue. We showed that merging the two datasets shifted the performance close to the ER positive HER2 negative subgroup.

Discussion

We showed that validated predictors like the Tenon Score or the MSKCC nomogram built on heterogeneous population of breast cancer performed equally on the different subgroups analyzed. Our present study re-enforce the idea that performing subgroup analysis of such predictors within less than 200 samples subgroup is at major risk of misleading conclusions.     

Unique features of breast cancer in Asian women—Breast cancer in Taiwan as an example

Breast carcinoma is one of the most common cancers in women and is known to arise from a multifactorial process, the effect of reproductive risk factors strongly supporting a hormonal role in its etiology. Breast cancer in Asia is characterized by a lower incidence than in Western populations, but is still the leading type of cancer in Asian women, and a significant increasing tread indicates that it is an issue of particular public health importance. Asian breast cancer is characterized by early tumor onset, showing a relatively younger median age at diagnosis. Recently, scientists began to explore the tumorigenic mechanisms underlying breast cancer formation at the molecular level. Both a candidate-gene approach and genome-wide association studies have yielded crucial insights into breast cancer susceptibility genes initiating breast tumorigenesis. As expected, ethnic/racial variation in the genotypic frequency of these genes results in differences in breast cancer incidence in different populations. Furthermore, the question of how important these genes are in Asian breast cancer remains to be explored.It has been demonstrated that gene expression profiles and gene sets are prognostic and predictive for patients with breast cancer. Originally, due to its early onset, it was speculated that Asian breast cancer would have a higher frequency of the basal-like subtype of breast cancer, a molecular subtype characterized by poor differentiation, resulting in a relatively poor progression; however, recent findings do not support this speculation. The frequency of the luminal-A subtype of breast cancer, characterized by estrogen receptor expression, is similar to that in breast cancer in Caucasian, supporting the usefulness of hormone-based therapy in Asian breast cancer.     

Performance Characteristics of the BluePrint® Breast Cancer Diagnostic Test

The analytical performance of a multi-gene diagnostic signature depends on many parameters, including precision, repeatability, reproducibility and intra-tumor heterogeneity. Here we study the analytical performance of the BluePrint 80-gene breast cancer molecular subtyping test through determination of these performance characteristics. BluePrint measures the expression of 80 genes that assess functional pathways which determine the intrinsic breast cancer molecular subtypes (i.e. Luminal-type, HER2-type, Basal-type). Knowing a tumor's dominant functional pathway can help allocate effective treatment to appropriate patients.Here we show that BluePrint is a highly precise and highly reproducible test with correlations above 98% based on the generated index and subtype concordance above 99%. Therefore, BluePrint can be used as a robust and reliable tool to identify breast cancer molecular subtypes.     

Improving Hidden Markov Models for classification of human immunodeficiency virus-1 subtypes through linear classifier learning

Profile Hidden Markov Models (pHMMs) are widely used to model nucleotide or protein sequence families. In many applications, a sequence family classified into several subfamilies is given and each subfamily is modeled separately by one pHMM. A major drawback of this approach is the difficulty of coping with subfamilies composed of very few sequences.Correct subtyping of human immunodeficiency virus-1 (HIV-1) sequences is one of the most crucial bioinformatic tasks affected by this problem of small subfamilies, i.e., HIV-1 subtypes with a small number of known sequences. To deal with small samples for particular subfamilies of HIV-1, we employ a machine learning approach. More precisely, we make use of an existing HMM architecture and its associated inference engine, while replacing the unsupervised estimation of emission probabilities by a supervised method. For that purpose, we use regularized linear discriminant learning together with a balancing scheme to account for the widely varying sample size. After training the multiclass linear discriminants, the corresponding weights are transformed to valid probabilities using a softmax function.We apply this modified algorithm to classify HIV-1 sequence data (in the form of partial-length HIV-1 sequences and semi-artificial recombinants) and show that the performance of pHMMs can be significantly improved by the proposed technique.     

Testing Heterozygote Excess and Deficiency

Currently used tests of Hardy-Weinberg proportions do not take into account the nature of the alternative hypothesis, which is generally a heterozygote deficiency. Different exact tests, appropriate for small sample size and large number of alleles, are proposed in this perspective, and their properties are evaluated by power comparisons. Some tests are found to be close to optimal for the detection of inbreeding or heterozygote excess, one of which is a score test closely related to Robertson and Hill's estimator of the inbreeding coefficient. This test is also easily applied to multiple samples. Such tests are not always the most appropriate if alternative hypotheses differ from those considered here.     

Unraveling the Hidden Heterogeneities of Breast Cancer Based on Functional miRNA Cluster

It has become increasingly clear that the current taxonomy of clinical phenotypes is mixed with molecular heterogeneity, which potentially affects the treatment effect for involved patients. Defining the hidden molecular-distinct diseases using modern large-scale genomic approaches is therefore useful for refining clinical practice and improving intervention strategies. Given that microRNA expression profiling has provided a powerful way to dissect hidden genetic heterogeneity for complex diseases, the aim of the study was to develop a bioinformatics approach that identifies microRNA features leading to the hidden subtyping of complex clinical phenotypes. The basic strategy of the proposed method was to identify optimal miRNA clusters by iteratively partitioning the sample and feature space using the two-ways super-paramagnetic clustering technique. We evaluated the obtained optimal miRNA cluster by determining the consistency of co-expression and the chromosome location among the within-cluster microRNAs, and concluded that the optimal miRNA cluster could lead to a natural partition of disease samples. We applied the proposed method to a publicly available microarray dataset of breast cancer patients that have notoriously heterogeneous phenotypes. We obtained a feature subset of 13 microRNAs that could classify the 71 breast cancer patients into five subtypes with significantly different five-year overall survival rates (45%, 82.4%, 70.6%, 100% and 60% respectively; p = 0.008). By building a multivariate Cox proportional-hazards prediction model for the feature subset, we identified has-miR-146b as one of the most significant predictor (p = 0.045; hazard ratios = 0.39). The proposed algorithm is a promising computational strategy for dissecting hidden genetic heterogeneity for complex diseases, and will be of value for improving cancer diagnosis and treatment.