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

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

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

The Iterative Signature Algorithm for the analysis of large scale gene expression data

We present a new approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, that searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of Singular Value Decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in-silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast S. cerevisiae.Comment: Latex, 36 pages, 8 figure

BlackOPs: Increasing confidence in variant detection through mappability filtering

Identifying variants using high-throughput sequen-cing data is currently a challenge because true biological variants can be indistinguishable from technical artifacts. One source of technical arti-fact results from incorrectly aligning experimen-tally observed sequences to their true genomic origin (‘mismapping’) and inferring differences in mismapped sequences to be true variants. We de-veloped BlackOPs, an open-source tool that simu-lates experimental RNA-seq and DNA whole exome sequences derived from the reference genome, aligns these sequences by custom parameters, detects variants and outputs a blacklist of positions and alleles caused by mismapping. Blacklist

SigFuge: Single gene clustering of RNA-seq reveals differential isoform usage among cancer samples

High-throughput sequencing technologies, including RNA-seq, have made it possible to move beyond gene expression analysis to study transcriptional events including alternative splicing and gene fusions. Furthermore, recent studies in cancer have suggested the importance of identifying transcriptionally altered loci as biomarkers for improved prognosis and therapy. While many statistical methods have been proposed for identifying novel transcriptional events with RNA-seq, nearly all rely on contrasting known classes of samples, such as tumor and normal. Few tools exist for the unsupervised discovery of such events without class labels. In this paper, we present SigFuge for identifying genomic loci exhibiting differential transcription patterns across many RNA-seq samples. SigFuge combines clustering with hypothesis testing to identify genes exhibiting alternative splicing, or differences in isoform expression. We apply SigFuge to RNA-seq cohorts of 177 lung and 279 head and neck squamous cell carcinoma samples from the Cancer Genome Atlas, and identify several cases of differential isoform usage including CDKN2A, a tumor suppressor gene known to be inactivated in a majority of lung squamous cell tumors. By not restricting attention to known sample stratifications, SigFuge offers a novel approach to unsupervised screening of genetic loci across RNA-seq cohorts. SigFuge is available as an R package through Bioconductor

Impaired metabolism in donor kidney grafts after steroid pretreatment

We recently showed in a randomized control trial that steroid pretreatment of the deceased organ donor suppressed inflammation in the transplant organ but did not reduce the rate or duration of delayed graft function (DGF). This study sought to elucidate such of those factors that caused DGF in the steroid-treated subjects. Genome-wide gene expression profiles were used from 20 steroid-pretreated donor-organs and were analyzed on the level of regulatory protein protein interaction networks. Significance analysis of microarrays (SAM) yielded 63 significantly down-regulated sequences associated with DGF that could be functionally categorized according to Protein ANalysis THrough Evolutionary Relationships ontologies into two main biologic processes: transport (P < 0.001) and metabolism (P < 0.001). The identified genes suggest hypoxia as the cause of DGF, which cannot be counterbalanced by steroid treatment. Our data showed that molecular pathways affected by ischemia such as transport and metabolism are associated with DGF. Potential interventional targeted therapy based on these findings includes peroxisome proliferator-activated receptor agonists or caspase inhibitors

Distribution of p63, a novel myoepithelial marker, in fine-needle aspiration biopsies of the breast: an analysis of 82 samples

BACKGROUND. The presence of myoepithelial cells (MECs) in fine-needle aspiration biopsies (FNAB) of the breast constitute an important criterion used to diagnose benign breast lesions. However, MECs sometimes have a distorted cytomorphology, and most of the previously evaluated myoepithelial markers do not have satisfactory sensitivity and specificity. p63, a recently characterized p53 homolog, is a nuclear transcription factor that is expressed in basal cells of multilayered epithelia and myoepithelial cells of the breast. The authors analyzed the immunocytochemical distribution of p63 in a series of 82 breast FNABs (30 benign lesions and 52 malignant breast lesions). METHODS. Eighty-two archival, Papanicolaou-stained smears of breast lesions were retrieved from the files of the authors’ institutions. Immunocytochemistry was performed according to the streptavidin-biotin-peroxidase complex technique using the antibody 4A4 (against all p63 isoforms). Two pathologists evaluated the distribution of p63 positive cells. Only nuclear reactivity was considered specific. RESULTS. In benign lesions, p63 decorated the nuclei of MECs in all samples. p63 also stained naked nuclei in fibroadenomas. In malignant lesions, p63 was positive in MECs overlying malignant cell clusters in all 8 samples of ductal carcinoma in situ (DCIS), in 9 of 16 samples of pure invasive carcinomas (IC), and in 16 of 20 samples that contained both DCIS and IC. In 18 samples (36%), a variable population of p63 positive, malignant cells was observed. p63 failed to decorate stromal, neural, adipocytic, and smooth muscle cells in all samples. CONCLUSIONS. p63 is a reliable nuclear marker of MECs in breast aspirates. Regardless of the fact that variable proportions of p63 positive, malignant cells were observed in 36% of breast carcinoma aspirates, p63 may be a useful adjunct antibody to confirm the presence of MECs in FNABs of benign breast lesions.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/5386/2001

Evaluation of the current knowledge limitations in breast cancer research: a gap analysis

BACKGROUND A gap analysis was conducted to determine which areas of breast cancer research, if targeted by researchers and funding bodies, could produce the greatest impact on patients. METHODS Fifty-six Breast Cancer Campaign grant holders and prominent UK breast cancer researchers participated in a gap analysis of current breast cancer research. Before, during and following the meeting, groups in seven key research areas participated in cycles of presentation, literature review and discussion. Summary papers were prepared by each group and collated into this position paper highlighting the research gaps, with recommendations for action. RESULTS Gaps were identified in all seven themes. General barriers to progress were lack of financial and practical resources, and poor collaboration between disciplines. Critical gaps in each theme included: (1) genetics (knowledge of genetic changes, their effects and interactions); (2) initiation of breast cancer (how developmental signalling pathways cause ductal elongation and branching at the cellular level and influence stem cell dynamics, and how their disruption initiates tumour formation); (3) progression of breast cancer (deciphering the intracellular and extracellular regulators of early progression, tumour growth, angiogenesis and metastasis); (4) therapies and targets (understanding who develops advanced disease); (5) disease markers (incorporating intelligent trial design into all studies to ensure new treatments are tested in patient groups stratified using biomarkers); (6) prevention (strategies to prevent oestrogen-receptor negative tumours and the long-term effects of chemoprevention for oestrogen-receptor positive tumours); (7) psychosocial aspects of cancer (the use of appropriate psychosocial interventions, and the personal impact of all stages of the disease among patients from a range of ethnic and demographic backgrounds). CONCLUSION Through recommendations to address these gaps with future research, the long-term benefits to patients will include: better estimation of risk in families with breast cancer and strategies to reduce risk; better prediction of drug response and patient prognosis; improved tailoring of treatments to patient subgroups and development of new therapeutic approaches; earlier initiation of treatment; more effective use of resources for screening populations; and an enhanced experience for people with or at risk of breast cancer and their families. The challenge to funding bodies and researchers in all disciplines is to focus on these gaps and to drive advances in knowledge into improvements in patient care