iBBiG: iterative binary bi-clustering of gene sets

Motivation: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods. Results: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set—phenotype association that predicted tumor metastases within tumor subtypes

Stem Cell-Like Gene Expression in Ovarian Cancer Predicts Type II Subtype and Prognosis

Although ovarian cancer is often initially chemotherapy-sensitive, the vast majority of tumors eventually relapse and patients die of increasingly aggressive disease. Cancer stem cells are believed to have properties that allow them to survive therapy and may drive recurrent tumor growth. Cancer stem cells or cancer-initiating cells are a rare cell population and difficult to isolate experimentally. Genes that are expressed by stem cells may characterize a subset of less differentiated tumors and aid in prognostic classification of ovarian cancer. The purpose of this study was the genomic identification and characterization of a subtype of ovarian cancer that has stem cell-like gene expression. Using human and mouse gene signatures of embryonic, adult, or cancer stem cells, we performed an unsupervised bipartition class discovery on expression profiles from 145 serous ovarian tumors to identify a stem-like and more differentiated subgroup. Subtypes were reproducible and were further characterized in four independent, heterogeneous ovarian cancer datasets. We identified a stem-like subtype characterized by a 51-gene signature, which is significantly enriched in tumors with properties of Type II ovarian cancer; high grade, serous tumors, and poor survival. Conversely, the differentiated tumors share properties with Type I, including lower grade and mixed histological subtypes. The stem cell-like signature was prognostic within high-stage serous ovarian cancer, classifying a small subset of high-stage tumors with better prognosis, in the differentiated subtype. In multivariate models that adjusted for common clinical factors (including grade, stage, age), the subtype classification was still a significant predictor of relapse. The prognostic stem-like gene signature yields new insights into prognostic differences in ovarian cancer, provides a genomic context for defining Type I/II subtypes, and potential gene targets which following further validation may be valuable in the clinical management or treatment of ovarian cancer

Angiogenic mRNA and microRNA Gene Expression Signature Predicts a Novel Subtype of Serous Ovarian Cancer

Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding “angiogenesis signature” was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials

Gene Expression Signature of Normal Cell-of-Origin Predicts Ovarian Tumor Outcomes

The potential role of the cell-of-origin in determining the tumor phenotype has been raised, but not adequately examined. We hypothesized that distinct cells-of-origin may play a role in determining ovarian tumor phenotype and outcome. Here we describe a new cell culture medium for in vitro culture of paired normal human ovarian (OV) and fallopian tube (FT) epithelial cells from donors without cancer. While these cells have been cultured individually for short periods of time, to our knowledge this is the first long-term culture of both cell types from the same donors. Through analysis of the gene expression profiles of the cultured OV/FT cells we identified a normal cell-of-origin gene signature that classified primary ovarian cancers into OV-like and FT-like subgroups; this classification correlated with significant differences in clinical outcomes. The identification of a prognostically significant gene expression signature derived solely from normal untransformed cells is consistent with the hypothesis that the normal cell-of-origin may be a source of ovarian tumor heterogeneity and the associated differences in tumor outcome

Therapeutic Implications of GIPC1 Silencing in Cancer

GIPC1 is a cytoplasmic scaffold protein that interacts with numerous receptor signaling complexes, and emerging evidence suggests that it plays a role in tumorigenesis. GIPC1 is highly expressed in a number of human malignancies, including breast, ovarian, gastric, and pancreatic cancers. Suppression of GIPC1 in human pancreatic cancer cells inhibits in vivo tumor growth in immunodeficient mice. To better understand GIPC1 function, we suppressed its expression in human breast and colorectal cancer cell lines and human mammary epithelial cells (HMECs) and assayed both gene expression and cellular phenotype. Suppression of GIPC1 promotes apoptosis in MCF-7, MDA-MD231, SKBR-3, SW480, and SW620 cells and impairs anchorage-independent colony formation of HMECs. These observations indicate GIPC1 plays an essential role in oncogenic transformation, and its expression is necessary for the survival of human breast and colorectal cancer cells. Additionally, a GIPC1 knock-down gene signature was used to interrogate publically available breast and ovarian cancer microarray datasets. This GIPC1 signature statistically correlates with a number of breast and ovarian cancer phenotypes and clinical outcomes, including patient survival. Taken together, these data indicate that GIPC1 inhibition may represent a new target for therapeutic development for the treatment of human cancers

Transkriptionelle Charakterisierung von aggressiven Lymphomen

Lymphoma is the fifth most frequent cancer in North America and Western Europe. This thesis is concerned with transcriptional profiling of diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL) using supervised and semi-supervised machine learning methodology. It investigates two aspects of lymphoma classification in detail. (I) Diagnosis of Burkitt lymphoma: The distinction of BL and DLBCL based on traditional diagnostic criteria is often imprecise. Expert pathologist disagree frequently. Nevertheless, an accurate diagnostic distinction is mandatory for treatment decision. (II) Functional Stratification: Traditional molecular biological inference is based on hypothesis-driven intervention (e.g. via mutagenesis or over-expression of genes) in cellular systems to gain insight into molecular mechanisms. However, human cancer cells in their natural environment are not accessible to interventional assays. Thus, clinical microarray studies predominantly provide purely observational data. The contributions of the present work are: (1) The introduction of the semi-supervised learning problem of core group extension. Starting from a small set of unambiguously diagnosed tumors, the problem is to find additional cases similar to the core group from an unlabeled pool of tumors without diagnosis. (2) The development of an Expectation-Maximization (EM) based Algorithm to core group extension. (3) The generation of a linear signature allowing a quantitative and reproducible diagnostic distinction of BL and DLBCL implementing the core group extension strategy. (4) The development of a semi-supervised learning method allowing stratification of tumors from clinical microarray studies based on data from hypothesis-driven interventional cell line assays. (5) The generation of a novel functional stratification of DLBCL.Lymphome sind die fünfthäufigste Krebserkrankung in westlichen Staaten (Europa und Nordamerika). In dieser Arbeit geht es um die molekulare Charakterisierung des diffus großzelligen B-Zell Lymphoms (DLBCL) und des Burkitt Lymphoms (BL) mit Hilfe von Transkriptionsprofilen und überwachten und halbüberwachten maschinellen Lernverfahren. Zwei wesentliche Probleme der Lymphomklassifikation werden mit Hilfe von Transkriptionsprofilen untersucht. (I) Diagnostik des Burkitt Lymphoms: Die diagnostische Unterscheidung von BL und DLBCL ist oft nicht präzise. Das heißt, verschiedene Pathologen kommen hier oft zu verschiedenen Ergebnissen. Eine zuverlässige Unterscheidung der beiden Lymphomtypen ist unerlässlich für die Auswahl der Therapie. (II) Funktionale Stratifikation: Traditionelle molekularbiologische Untersuchungen beruhen darauf, dass man experimentell gezielt in biologische Prozesse eingreift (z.B. durch Mutagenese oder Überexperession), um diese besser verstehen zu können. Das Problem bei der Untersuchung von Krebs im Menschen ist, dass man den individuellen Tumor in seiner natürlichen Umgebung nicht experimentell untersuchen kann. Eine klinische Microarraystudie liefert lediglich Beobachtungsdaten. Beiträge dieser Arbeit sind: (1) Die Einführung des halbüberwachten Lernproblems der Kerngruppenerweiterung. Dabei werden ausgehend von einer sicher diagnostizierten Kerngruppe von Tumoren weitere Fälle gesucht, die die gleichen Eigenschaften haben, von denen man aber die Diagnose nicht kennt. (2) Die Entwicklung eines Expectation-Maximization (EM) basierten Algorithmus zur zur Kerngruppenerweiterung. (3) Die Generierung einer linearen Signatur zur quantitativen und reproduzierbaren diagnostischen Unterscheidung von BL und DLBCL mit Hilfe der Kerngruppenerweiterung. (4) Entwicklung einer halbüberwachten Lernmethode, die es erlaubt Tumore in klinischen Genexpressionsstudien aufgrund der Daten aus hypothesengetriebenen Interventionsexperimenten in Zelllinien zu stratifizieren. (5) Die Generierung einer neuen funktionalen Stratifikation von DLBCL

Detecting common gene expression patterns in multiple cancer outcome entities

Most oncological microarray studies focus on molecular distinctions in different cancer entities. Recently, researchers started using microarrays for investigating molecular commonalities of multiple cancer types. This poses novel bioinformatics challenges. In this paper we describe a method that detects common molecular mechanisms in different cancer entities. The method extends previously described concepts by introducing Meta-Analysis Pattern Matches. In an analysis of four prognostic cancer studies, involving breast cancer, leukemia, and mesothelioma, we are able to identify 42 genes that show consistent up- or down-regulation in patients with a poor disease outcome. These genes complement the set of previously published candidates for universal prognostic markers in cancer

Role of Calcineurin in Stress Resistance, Morphogenesis, and Virulence of a Candida albicans Wild-Type Strain

By generating a calcineurin mutant of the Candida albicans wild-type strain SC5314 with the help of a new recyclable dominant selection marker, we confirmed that calcineurin mediates tolerance to a variety of stress conditions but is not required for the ability of C. albicans to switch to filamentous growth in response to hypha-inducing environmental signals. While calcineurin was essential for virulence of C. albicans in a mouse model of disseminated candidiasis, deletion of CMP1 did not significantly affect virulence during vaginal or pulmonary infection, demonstrating that the requirement for calcineurin for a successful infection depends on the host niche