Coupled Two-Way Clustering Analysis of Gene Microarray Data

We present a novel coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task: we present an algorithm, based on iterative clustering, which performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research

Ror2-mediated alternative Wnt signaling regulates cell fate and adhesion during mammary tumor progression

Cellular heterogeneity is a common feature in breast cancer, yet an understanding of the coexistence and regulation of various tumor cell subpopulations remains a significant challenge in cancer biology. In the current study, we approached tumor cell heterogeneity from the perspective of Wnt pathway biology to address how different modes of Wnt signaling shape the behaviors of diverse cell populations within a heterogeneous tumor landscape. Using a syngeneic TP53-null mouse model of breast cancer, we identified distinctions in the topology of canonical Wnt β-catenin-dependent signaling activity and non-canonical β-catenin-independent Ror2-mediated Wnt signaling across subtypes and within tumor cell subpopulations in vivo. We further discovered an antagonistic role for Ror2 in regulating canonical Wnt/β-catenin activity in vivo, where lentiviral shRNA depletion of Ror2 expression augmented canonical Wnt/β-catenin signaling activity across multiple basal-like models. Depletion of Ror2 expression yielded distinct phenotypic outcomes and divergent alterations in gene expression programs among different tumors, despite all sharing basal-like features. Notably, we uncovered cell state plasticity and adhesion dynamics regulated by Ror2, which influenced Ras Homology Family Member A (RhoA) and Rho-Associated Coiled-Coil Kinase 1 (ROCK1) activity downstream of Dishevelled-2 (Dvl2). Collectively, these studies illustrate the integration and collaboration of Wnt pathways in basal-like breast cancer, where Ror2 provides a spatiotemporal function to regulate the balance of Wnt signaling and cellular heterogeneity during tumor progression

Bcl2 is an independent prognostic marker of triple negative breast cancer (TNBC) and predicts response to anthracycline combination (ATC) chemotherapy (CT) in adjuvant and neoadjuvant settings

Background: TNBC represents a heterogeneous subgroup of BC with poor prognosis and frequently resistant to CT. Material and methods: The relationship between Bcl2 immunohistochemical protein expression and clinicopathological outcomes was assessed in 736 TNBC-patients: 635 patients had early primary-TNBC (EP-TNBC) and 101 had primary locally advanced (PLA)-TNBC treated with neo-adjuvant- ATC-CT. Results: Negative Bcl2 (Bcl2-) was observed in 70% of EP-TNBC and was significantly associated with high proliferation, high levels of P-Cadherin, E-Cadherin and HER3 (P’s<0.01), while Bcl2+ was significantly associated with high levels of p27, MDM4 and SPAG5 (P<0.01). After controlling for chemotherapy and other prognostic factors, Bcl2- was associated with 2-fold increased risk of death (P=0.006) and recurrence (P=0.0004). Furthermore, the prognosis of EP-TNBC/Bcl2- patients had improved both BC-specific survival (P=0.002) and disease-free survival (P = 0.003), if they received adjuvant-ATC-CT. Moreover, Bcl2- expression was an independent predictor of pathological complete response of primary locally advanced triple negative breast cancer (PLA-TNBC) treated with neoadjuvant-ATC-CT (P=0.008). Conclusion: Adding Bcl2 to the panel of markers used in current clinical practice could provide both prognostic and predictive information in TNBC. TNBC/Bcl2- patients appear to benefit from ATC-CT, whereas Bcl2+ TNBC seems to be resistant to ATC-CT and may benefit from a trial of different type of chemotherapy with/without novel-targeted agents. Key words: anthracyclin chemotherapy, Bcl2, predictive marker, prognostic marker, theraputic targets, triple negative breast cancer

Improved indel detection in DNA and RNA via realignment with ABRA2

Motivation: Genomic variant detection from next-generation sequencing has become established as an extremely important component of research and clinical diagnoses in both cancer and Mendelian disorders. Insertions and deletions (indels) are a common source of variation and can frequently impact functionality, thus making their detection vitally important. While substantial effort has gone into detecting indels from DNA, there is still opportunity for improvement. Further, detection of indels from RNA-Seq data has largely been an afterthought and offers another critical area for variant detection. Results: We present here ABRA2, a redesign of the original ABRA implementation that offers support for realignment of both RNA and DNA short reads. The process results in improved accuracy and scalability including support for human whole genomes. Results demonstrate substantial improvement in indel detection for a variety of data types, including those that were not previously supported by ABRA. Further, ABRA2 results in broad improvements to variant calling accuracy across a wide range of post-processing workflows including whole genomes, targeted exomes and transcriptome sequencing

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics

Genetic determinants of the molecular portraits of epithelial cancers

The ability to characterize and predict tumor phenotypes is crucial to precision medicine. In this study, we present an integrative computational approach using a genome-wide association analysis and an Elastic Net prediction method to analyze the relationship between DNA copy number alterations and an archive of gene expression signatures. Across breast cancers, we are able to quantitatively predict many gene signatures levels within individual tumors with high accuracy based upon DNA copy number features alone, including proliferation status and Estrogen-signaling pathway activity. We can also predict many other key phenotypes, including intrinsic molecular subtypes, estrogen receptor status, and TP53 mutation. This approach is also applied to TCGA Pan-Cancer, which identify repeatedly predictable signatures across tumor types including immune features in lung squamous and basal-like breast cancers. These Elastic Net DNA predictors could also be called from DNA-based gene panels, thus facilitating their use as biomarkers to guide therapeutic decision making

Le décor architectural de la ville de Termez à l’époque kouchane

Cet article s’attache à donner un aperçu de la qualité et à définir l'origine du matériel lapidaire retrouvé à Termez, depuis les fouilles des années 1930 jusqu'aux dernières campagnes de la mission archéologique franco-ouzbèke de Bactriane. Il est question des simples éléments décoratifs de l'architecture des édifices d'époque kouchane. Nous cherchons à montrer qu'en dépit de pertes dues à des destructions très importantes intervenues entre le IIIème et le IVème siècles, le matériel est assez homogène, de bonne facture et peut donner des informations intéressantes sur la sculpture et les pratiques architecturales en Bactriane à cette époque.This article aims to give a glimpse of the quality and the origins of the lapidary material found in Termez, since the excavations of the 1930’s until the last campaigns of the Franco-Uzbek archaeological mission of Bactria. It deals with simple decorative elements of the architecture from the Kushan period. We show that despite huge losses due to destruction during the 3rd and 4th centuries, the material is fairly homogeneous, well sculpted and can provide valuable information about sculptural and architectural practices in Bactria during this period