Development of Biologically Based Therapies for Basal-like Breast Tumors

There have been many experiments on breast cancer cell lines and tumors with respect to identifying genes/pathways that are involved in cancer initiation, progression and response to therapy; however, only a few actually make suggestions that might affect treatment. The knowledge that breast cancer actually represents several diseases that arise from at least two different epithelial cells has been a major stepping-stone for stratifying patients and identifying more selective and biology-based therapies. Drugs aimed at the estrogen receptor, estrogen production, and HER2 have been very successful in the many patients whose tumors are dependent upon these signaling pathways for growth. Unfortunately for tumors that lack these markers, such as basal-like subtype, there are few treatment options. Until recently, few studies had actually considered if there were subtype-specific differences in response to chemotherapy. This dissertation focuses on the basal-like subtype of cancer and examines responses to chemotherapeutics relative to the luminal subtypes and evaluates the EGFR pathway as a place for potential therapeutic intervention. In response to two chemotherapeutics - doxorubicin and 5-fluorouracil - a general stress response was the dominant profile and this profile varied both in vitro and in vivo between the subtypes. The drug-specific response was more similar in the subtypes. A predictive gene list was identified that could predict both subtype and drug treatment with fairly high accuracy suggesting some degree of subtype-specific mechanism of action. The different responses to doxorubicin and 5-fluorouracil led us to evaluate sensitivity to a larger panel of drugs and cell lines and we determined that the basal-like subtype was more sensitive to carboplatin. While identification of chemotherapy regimens that are beneficial to the basal-like subtype is needed, drugs targeted to specific deregulated pathways in this subtype will be more effective in the long run. My work evaluated the EGFR pathway and determined it is high in 90% of all basal-like tumors, but I also identified high expression of genes downstream of EGFR that can induce EGFR-independent activation of this pathway. My data suggest that inhibition of MEK or PI3K, along with chemotherapeutics, may be an effective regimen for basal-like patients

Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

Research in several fields now requires the analysis of data sets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas (TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such data sets. The decomposition consists of three terms: a low-rank approximation capturing joint variation across data types, low-rank approximations for structured variation individual to each data type, and residual noise. JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data and provides new directions for the visual exploration of joint and individual structures. The proposed method represents an extension of Principal Component Analysis and has clear advantages over popular two-block methods such as Canonical Correlation Analysis and Partial Least Squares. A JIVE analysis of gene expression and miRNA data on Glioblastoma Multiforme tumor samples reveals gene-miRNA associations and provides better characterization of tumor types. Data and software are available at https://genome.unc.edu/jive/Comment: Published in at http://dx.doi.org/10.1214/12-AOAS597 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Harmonizing heterogeneous transcriptomics datasets for machine learning-based analysis to identify spaceflown murine liver-specific changes

NASA has employed high-throughput molecular assays to identify sub-cellular changes impacting human physiology during spaceflight. Machine learning (ML) methods hold the promise to improve our ability to identify important signals within highly dimensional molecular data. However, the inherent limitation of study subject numbers within a spaceflight mission minimizes the utility of ML approaches. To overcome the sample power limitations, data from multiple spaceflight missions must be aggregated while appropriately addressing intra- and inter-study variabilities. Here we describe an approach to log transform, scale and normalize data from six heterogeneous, mouse liver-derived transcriptomics datasets (ntotal = 137) which enabled ML-methods to classify spaceflown vs. ground control animals (AUC ≥ 0.87) while mitigating the variability from mission-of-origin. Concordance was found between liver-specific biological processes identified from harmonized ML-based analysis and study-by-study classical omics analysis. This work demonstrates the feasibility of applying ML methods on integrated, heterogeneous datasets of small sample size

Potential Tumor Suppressor Role for the c-Myb Oncogene in Luminal Breast Cancer

The transcription factor c-Myb has been well characterized as an oncogene in several human tumor types, and its expression in the hematopoietic stem/progenitor cell population is essential for proper hematopoiesis. However, the role of c-Myb in mammopoeisis and breast tumorigenesis is poorly understood, despite its high expression in the majority of breast cancer cases (60-80%).We find that c-Myb high expression in human breast tumors correlates with the luminal/ER+ phenotype and a good prognosis. Stable RNAi knock-down of endogenous c-Myb in the MCF7 luminal breast tumor cell line increased tumorigenesis both in vitro and in vivo, suggesting a possible tumor suppressor role in luminal breast cancer. We created a mammary-derived c-Myb expression signature, comprised of both direct and indirect c-Myb target genes, and found it to be highly correlated with a published mature luminal mammary cell signature and least correlated with a mammary stem/progenitor lineage gene signature.These data describe, for the first time, a possible tumor suppressor role for the c-Myb proto-oncogene in breast cancer that has implications for the understanding of luminal tumorigenesis and for guiding treatment

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

PREDICTION OF TOXICANT-SPECIFIC GENE EXPRESSION SIGNATURES FOLLOWING CHEMOTHERAPEUTIC TREATMENT OF BREAST CELL LINES

Global gene expression profiling has demonstrated that the predominant cellular response to a range of toxicants is a general stress response. This stereotyped environmental stress response commonly includes repression of protein synthesis and cell-cycle–regulated genes and induction of DNA damage and oxidative stress–responsive genes. Our laboratory recently characterized the general stress response of breast cell lines derived from basal-like and luminal epithelium after treatment with doxorubicin (DOX) or 5-fluorouracil (5FU) and showed that each cell type has a distinct response. However, we expected that some of the expression changes induced by DOX and 5FU would be unique to each compound and might reflect the underlying mechanisms of action of these agents. Therefore, we employed supervised analyses (significance analysis of microarrays) to identify genes that showed differential expression between DOX-treated and 5FU-treated cell lines. We then used cross-validation analyses and identified genes that afforded high predictive accuracy in classifying samples into the two treatment classes. To test whether these gene lists had good predictive accuracy in an independent data set, we treated our panel of cell lines with etoposide, a compound mechanistically similar to DOX. We demonstrated that using expression patterns of 100 genes we were able to obtain 100% predictive accuracy in classifying the etoposide samples as being more similar in expression to DOX-treated than to 5FU-treated samples. These analyses also showed that toxicant-specific gene expression patterns, similar to general stress responses, vary according to cell type

Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types

Background: Immune infiltration of the tumor microenvironment has been associated with improved survival for some patients with solid tumors. The precise makeup and prognostic relevance of immune infiltrates across a broad spectrum of tumors remain unclear