Browsing by Author "Nevins, Joseph R"

The disease process giving rise to cancer involves the consecutive accumulation of genetic or genomic alterations impacting the normal regulation of cellular functions. In cases of hereditary cancers, this process may be stepwise, with a shared initiating lesion leading to common subsequent alterations. However, in many non-hereditary forms of cancer the initiating and subsequent alterations giving rise to the tumor can vary substantially from individual to individual, and multiple molecularly distinct subsets of the disease can exist within histopathologically similar tumors. This molecular heterogeneity between patients hinders the ability to identify which alterations are responsible for tumor development and subsequent maintenance, and confounds the ability to effectively treat patients as response to a particular therapeutic intervention may be highly dependent on the molecular composition of the disease.

To further our understanding of the molecular alterations associated with tumorigenesis, we analyzed aggressive brain cancer, glioblastoma (GBM), samples for which multiple types of genome-wide information was available. We utilized a series of in vitro or clinically derived gene expression signatures by comparing gene expression of samples based on whether a particular cellular signaling pathway was known to be active or inactive. Using these signatures for cellular signaling deregulation, we examined the association between various genomic alterations and the relative activity of each pathway, identifying alterations that were enriched within patients that harbored similar profiles of pathway activation. These analyses lead to the identification of numerous previously uncharacterized alterations in GBM, including the identification of a ubiquitin-like gene, UBL3, that was associated not only with pathway signaling, but was also associated with poor patient outcome, as well as response of GBM xenograft models to treatment with standard of care therapeutic agents.

Further, given that the challenges involved in analyzing clinical samples include development methods for timely analysis of genomic data, we have described a framework to utilize these genomic signatures in a prospective setting by incorporating a non-overlapping reference dataset of similar tumor samples. This methodology allows the examination of pathway signaling, as captured by the signature, to be run in real-time when only a single patient sample is analyzed, and has a high degree of fidelity to the results generated from retrospective analysis across multiple tumor types. Together these studies have provided a novel framework for identification of significant genomic alterations that impact pathway signaling, as well as moving providing the mechanisms to analyze genomic signatures in a robust manner that accounts for the challenges associated with the prospective clinical setting.

My thesis research has centered on the concept of the peripheral blood cell (PBC) as an indicator of disease and environmental exposure. The PBC is not only easily accessible and constantly replenished, but it provides a snapshot of an individual's health. Doctors have long utilized PBCs as indicators of health based on count, morphology or the expression of particular cell surface markers. Using these methods, PBCs can serve as indicators of infection, inflammation or certain types of hematological malignancies. Now PBCs can be characterized as a function of their gene expression profiles in response to disease and toxicant exposure. Advances in cDNA microarray technology have made it possible to analyze global gene expression in small volumes of whole blood, or even in a sorted population of blood cells. The resulting gene expression data can serve as a molecular phenotype, or signature, of disease or toxicant exposure. These signatures serve a twofold purpose. First, they act as biological markers (biomarkers) that can indicate the presence of disease or aid monitoring the response to treatment. Second, they provide insight into the underlying biological mechanisms that are at work, by revealing genes, networks and pathways that are affected by the disease or toxin. This paradigm has been applied in a number of contexts, including infection, inflammation, leukemia, lymphoma, neurological disorders, cardiovascular disease, environmental exposures and solid tumors.

In the work presented here, we describe signatures of lead (Pb) exposure and breast cancer based on peripheral blood gene expression. Our objective in generating a blood-based signature of lead exposure was to develop a potential predictor of past and present exposure. This is particularly relevant because of continued widespread lead exposure through both environmental and occupational sources. Pb causes significant toxicities in a number of different organ systems including the hematological, endocrine, neurological and renal systems. Pb is considered a potential carcinogen due to evidence that it causes cancer in animal models and contributes to an elevated cancer risk in humans. Pb is thought to contribute to cancer risk indirectly through a variety of mechanisms, such as inhibition of DNA synthesis and repair, oxidative damage, interaction with DNA-binding proteins and tumor suppressor proteins, causing chromosomal aberrations and alterations to gene transcription. In addition, it has been shown to exacerbate the effects of other mutagens. Recent work also indicates that even low-level Pb exposure (defined here as levels below the threshold of detection of many common tests or below the level set by the CDC as an "elevated blood lead level" in children, or 10µg/dL) can impact health, especially in children, who are more susceptible to these negative health consequences.

We hypothesized that we could detect subtle and lasting changes in the PBC transcriptome that correlated to Pb exposure. We used a mouse model of per os Pb exposure to generate signatures corresponding to two different doses of Pb. One dose reflected a high-level exposure and the other a low-level exposure. We also analyzed the gene expression changes following removal of the Pb source. We were able to generate robust, dose-specific signatures of Pb exposure. This supports the growing body of evidence that even low levels of Pb exposure can have biological effects, and that there is likely no safe level of exposure. We also utilized a collection of pathway signatures to identify those pathways that were activated or repressed in response to Pb exposure compared to controls. We observed an increase in interferon-gamma pathway activity in response to low-level Pb exposure and an increase in E2F1 pathway activity in response to high-level Pb exposure. These results support previous findings that low-level Pb exposure can increase interferon-gamma production, whereas high-level Pb has been shown to increase DNA synthesis. The Pb signatures we report here were not predictive of a past lead exposure. These results suggest that the effect of Pb exposure on PBC gene expression is transient, perhaps due to the rapid turnover of blood cells and the absorption of Pb by the bones. We have proposed further studies to identify cells in the bone marrow that may serve as indicators of past Pb exposure based previous reports on the lasting effects of genotoxic stress on this tissue.

We also describe a predictor of human breast cancer based on peripheral blood gene expression. The objective of this study was to identify and characterize PBC gene expression patterns associated with the presence of a breast tumor. This work has the potential to make a significant impact on breast cancer screening and diagnosis. Despite the success of mammography in reducing mortality from breast cancer, many cancers go undetected due to factors such as breast density, age of the woman, or type of cancer. A blood-based breast tumor predictor would potentially offer an easy and noninvasive means of detecting primary breast cancer as well as monitoring patients for recurrences or metastases. In addition, the concept of using a blood-based biomarker for cancer detection would have positive implications for other types of cancer. For instance, patients with ovarian cancer are typically diagnosed at a late stage because of the absence of definitive symptoms and the lack of effective screenings methods.

We were able to successfully identify robust predictors of both mouse mammary tumors and human breast tumors based on PBC gene expression. The human breast tumor predictor exhibits a high level of sensitivity and specificity in distinguishing breast cancer patients and controls in an independent validation cohort. However, the true novelty in this study is that it integrates a factor modeling approach and a transgenic mouse model of breast cancer to identify biologically meaningful gene expression changes in the mouse PBC transcriptome. These genes were then used as the starting point for developing a human breast cancer predictor. This establishes an experimental system in which we can address questions that are inherently difficult to answer in human studies, such as whether this predictor is useful in detecting breast tumors early or in monitoring patients for recurrence or metastasis. In fact, our work suggests that tumor-associated gene expression changes in the PBCs can be detected in asymptomatic mice. Our results support those of previous studies, which identified blood gene expression profiles that are associated with a variety of solid tumors, including breast cancer. However, the sensitivity and specificity of our predictor are higher than that of the previously reported breast cancer signature. This may suggest that our strategy of using a mouse model to first identify informative genes allowed us to focus on those genes most relevant to the presence of a breast tumor and overcome the influence of the high degree of variation in blood gene expression in our human population. In order to be clinically useful, the predictor we report here would need to be tested in additional, large validation sets to establish its utility in an early detection setting and its specificity in distinguishing breast cancer from other cancer types as well as other potentially confounding conditions such as infection and inflammation. We describe some preliminary experiments in the mouse model intended to address these important questions.

Cancer is a disease state that arises as a result of multiple alterations in signaling pathways that are critical for making key cell fate decisions in normal cells. Understanding how these pathways operate under normal circumstances, therefore, is crucial for comprehensive understanding of tumorigenic process. With Myc and E2F pathways being central components for controlling cell proliferation, an important property that defines a cancer cell, as well as expanding roles for microRNAs(miRNA) in control of gene expression, we asked if we may better understand the underlying regulatory (transcription factor, microRNA) structure that contribute to Myc and E2F pathway activities. Through integrative analysis of mRNA and miRNA expression profile, we observe a distinct regulatory pattern in which, in the case of Myc pathway, Myc-induced miRNAs were contributing to the repression of negative regulators of cell cycle, including PTEN, while in case of E2F pathway, E2F-induced miRs were forming an incoherent Feed-Forward Loop(iFFL) with a number of E2F-induced genes including cyclin E. We further demonstrate through functional studies, as well as through single cell imaging of gene expression dynamics that miRNAs, depending on the context of either Myc or E2F pathway, play distinct roles in ensuring that cell fate decisions relevant to these pathways are properly executed.