Systems pathology: a bottom-up approach for understanding fibroblast-epithelial interactions in breast cancer

Breast cancer is prevalent both in the United States and worldwide. While both screening tests and targeted therapies are available, there are still challenges in the diagnosis, prognosis, and treatment of breast cancer. We were motivated to develop a new approach for both studying the etiology of heterogeneity in breast cancer phenotypes and predicting the course of disease in tumor biopsies using an emerging chemical imaging technology. It has been described extensively that the tumor microenvironment, or stroma, can promote or suppress cancer phenotypes in many tissues. We hypothesized that interaction with fibroblasts, the predominant cell type found in the breast tumor stroma, is a critical regulatory step in the progression of breast cancer from confined to invasive disease. Hence, we developed a novel three-dimensional co-culture model to investigate this interaction. The development and validation of this model is described in chapter two. We used it first to determine how cancerous molecular signatures can propagate from cancerous to normal epithelium through the activation of fibroblasts. Changes in the architectural morphology of normal mammary acini were used as a metric to determine cancer progression, in addition to gene expression analysis and cell-based assays such as proliferation and migration assays. This system is a robust and easily applied tool for investigating fibroblast-epithelial communication in a physiologically-relevant context. The 3D co-culture system was used to investigate how fibroblasts impact the growth of estrogen receptor-positive (ER+) breast cancer cells and this is described in chapter three. ER+ is the most common subtype of breast cancer (>75%) and while these patients are eligible to receive targeted endocrine therapies, up to 30% of patients will experience a recurrence. Others will fail to respond to front-line endocrine therapies, while more patients will become resistant to endocrine therapies over time. We aimed to understand how fibroblasts play a role in the progression from hormone-dependent to hormone-independent growth. The cell culture data was translated to patient samples using bioinformatics approaches and label-free chemical imaging. Further, we define one aspect of the interaction between breast cancer cells and fibroblasts through identifying secreted proteins that are involved in the stromal-epithelial communication. The 43-protein signature can be used to classify breast cancer patients based on their corresponding gene expression profile, and we found that the signature is significantly upregulated in patients with more invasive disease. In order to continue translating our results from cell culture to patient samples, we describe the application of label-free Fourier transform infrared spectroscopic imaging to monitoring breast cancer cell phenotypes. Chapter four details how biological changes can be spatially resolved in heterogeneous samples while in chapter five an approach to determine estrogen receptor presence and function in cell culture samples and patient biopsies is discussed. We show how FT-IR imaging can be used to define label-free spectroscopic signatures that are consistent between cell culture and patients, and explore how this approach may be used in the future to add additional information to current pathology practice. We have developed a method to both understand the molecular mechanisms involved in how the microenvironment regulates early breast cancer phenotypes and to detect altered cellular phenotypes using label-free FT-IR imaging. We aim to apply this systems pathology approach to the development of novel diagnostic and prognostic signatures for determining the trajectory of cancer progression at very early stages

Muscle biopsy in combination with myositis-specific autoantibodies aids prediction of outcomes in juvenile dermatomyositis

ObjectiveJuvenile dermatomyositis (DM) is a rare and severe autoimmune condition characterized by rash and proximal muscle weakness. While some patients respond to standard treatment, others do not. This study was carried out to investigate whether histopathologic findings and myositis‐specific autoantibodies (MSAs) have prognostic significance in juvenile DM.MethodsMuscle biopsy samples (n = 101) from patients in the UK Juvenile Dermatomyositis Cohort and Biomarker Study were stained, analyzed, and scored for severity of histopathologic features. In addition, autoantibodies were measured in the serum or plasma of patients (n = 90) and longitudinal clinical data were collected (median duration of follow‐up 4.9 years). Long‐term treatment status (on or off medication over time) was modeled using generalized estimating equations.ResultsMuscle biopsy scores differed according to MSA subgroup. When the effects of MSA subgroup were accounted for, increased severity of muscle histopathologic features was predictive of an increased risk of remaining on treatment over time: for the global pathology score (histopathologist's visual analog scale [hVAS] score), 1.48‐fold higher odds (95% confidence interval [95% CI] 1.12–1.96; P = 0.0058), and for the total biopsy score (determined with the standardized score tool), 1.10‐fold higher odds (95% CI 1.01–1.21; P = 0.038). A protective effect was identified in patients with anti–Mi‐2 autoantibodies, in whom the odds of remaining on treatment were 7.06‐fold lower (95% CI 1.41–35.36; P = 0.018) despite muscle biopsy scores indicating more severe disease. In patients with anti–nuclear matrix protein 2 autoantibodies, anti–transcription intermediary factor 1γ autoantibodies, or no detectable autoantibody, increased histopathologic severity alone, without adjustment for the effect of MSA subtype, was predictive of the risk of remaining on treatment: for the hVAS global pathology score, 1.61‐fold higher odds (95% CI 1.16–2.22; P = 0.004), and for the total biopsy score, 1.13‐fold higher odds (95% CI 1.03–1.24; P = 0.013).ConclusionHistopathologic severity, in combination with MSA subtype, is predictive of the risk of remaining on treatment in patients with juvenile DM and may be useful for discussing probable treatment length with parents and patients. Understanding these associations may identify patients at greater risk of severe disease

Methods for visual mining of genomic and proteomic data atlases

<p>Abstract</p> <p>Background</p> <p>As the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.</p> <p>Results</p> <p>This paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.</p> <p>Conclusions</p> <p>The mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.</p

Discovery and functional prioritization of Parkinson's disease candidate genes from large-scale whole exome sequencing.

BACKGROUND: Whole-exome sequencing (WES) has been successful in identifying genes that cause familial Parkinson's disease (PD). However, until now this approach has not been deployed to study large cohorts of unrelated participants. To discover rare PD susceptibility variants, we performed WES in 1148 unrelated cases and 503 control participants. Candidate genes were subsequently validated for functions relevant to PD based on parallel RNA-interference (RNAi) screens in human cell culture and Drosophila and C. elegans models. RESULTS: Assuming autosomal recessive inheritance, we identify 27 genes that have homozygous or compound heterozygous loss-of-function variants in PD cases. Definitive replication and confirmation of these findings were hindered by potential heterogeneity and by the rarity of the implicated alleles. We therefore looked for potential genetic interactions with established PD mechanisms. Following RNAi-mediated knockdown, 15 of the genes modulated mitochondrial dynamics in human neuronal cultures and four candidates enhanced α-synuclein-induced neurodegeneration in Drosophila. Based on complementary analyses in independent human datasets, five functionally validated genes-GPATCH2L, UHRF1BP1L, PTPRH, ARSB, and VPS13C-also showed evidence consistent with genetic replication. CONCLUSIONS: By integrating human genetic and functional evidence, we identify several PD susceptibility gene candidates for further investigation. Our approach highlights a powerful experimental strategy with broad applicability for future studies of disorders with complex genetic etiologies

Use of fourier transform- infrared spectroscopic imaging as a tool for understanding early molecular events driving breast cancer progression

The presence of an activated stroma surrounding a primary breast tumor is a well-documented phenomenon. Fibroblasts are the cell type responsible for maintaining the stroma, and cancer-associated fibroblasts, characterized by the expression of α-smooth muscle actin (α-SMA) protein, are highly contractile compared with those found in normal tissue. Although the fibroblast to myofibroblast phenotypic transition is drastic and involves changes in cell morphology as well as increased expression of proteins, the best marker for myofibroblasts used in both the laboratory and the clinic is α-SMA. Due to the role of the fibroblast, in particular, and the stroma, in general, in cancer progression, it is important to study this characteristic cellular transformation in vitro. We report a direct comparison between α-SMA expression in primary normal human dermal fibroblasts and molecular spectra obtained through Fourier Transform infrared (FT-IR) spectroscopic imaging. Fibroblasts were stimulated using the growth factor TGFβ1, co-culture with MCF-7 tumorigenic breast epithelial cells, and co-culture with MCF10A normal breast epithelial cells in trans-well co-culture and also three-dimensional cell culture. α-SMA expression was determined using immunofluorescence and the samples were surveyed in a holistic and label-free approach using chemical imaging in two modes: transflection and attenuated total reflectance (ATR) FT-IR. This correlation is also compared with expression of α-SMA and spectra obtained from normal and tumorigenic human breast tissue biopsies

Integration of molecular profiling and chemical imaging to elucidate fibroblast-microenvironment impact on cancer cell phenotype and endocrine resistance in breast cancer.

The tumor microenvironment is known to play a key role in altering the properties and behavior of nearby cancer cells. Its influence on resistance to endocrine therapy and cancer relapse, however, is poorly understood. Here we investigate the interaction of mammary fibroblasts and estrogen receptor-positive breast cancer cells in three-dimensional culture models in order to characterize gene expression, cellular changes, and the secreted protein factors involved in the cellular cross-talk. We show that fibroblasts, which are the predominant cell type found in the stroma adjacent to the cancer cells in a tumor, induce an epithelial-to-mesenchymal transition in the cancer cells, leading to hormone-independent growth, a more invasive phenotype, and resistance to endocrine therapy. Here, we applied a label-free chemical imaging modality, Fourier transform infrared (FT-IR) spectroscopic imaging, to identify cells that had transitioned to hormone-independent growth. Both the molecular and chemical profiles identified here were translated from cell culture to patient samples: a secreted protein signature was used to stratify patient populations based on gene expression and FT-IR was used to characterize breast tumor patient biopsies. Our findings underscore the role of mammary fibroblasts in promoting aggressiveness and endocrine therapy resistance in ER-positive breast cancers and highlight the utility of FT-IR for the further characterization of breast cancer samples

Addressing anemia among women in India—an informed intervention using Facebook Ad Manager

BackgroundWomen in India continue to have high rates of anemia, especially in critical periods of adolescence and pregnancy. Anemia persists despite decades of government programs aimed at combatting anemia, including through the provision of iron folic acid tablets (IFA). With growing access to mobile phones and social media, even among women, mobile interventions hold potential for influencing knowledge and behaviors. Social media consumption, including on Facebook, is high in India. However, little research has explored if women can be reached with health messages using social media, if data can be collected through this mechanism, and if interventions using social media impact knowledge and behaviors surrounding important health issues such as anemia. The aims of this study were to test an advertising campaign on Facebook about anemia and IFA to women, collect data through social media, and measure its impact.MethodsA baseline survey and two follow-up surveys were sent to women via Facebook in 2019 in two states of India: Madhya Pradesh and Uttar Pradesh. Between each round of data collection, a set of advertisements based on gaps in knowledge and practices identified in the baseline on anemia and IFA were delivered to more than 3 million women on Facebook. Using this data, we analyze current knowledge and behaviors around anemia and IFA and look at differences by socio-demographics including age, marital status, and pregnancy. Using logistic regression models, we then look for evidence of a significant change in knowledge and behaviors between rounds of data collection.ResultsWe find that while general knowledge about anemia was high, especially related to its importance in pregnancy, misperceptions persist, such as that it makes babies large and delivery difficult. Only about 15% of the population was currently taking IFA, although nearly 50% noted that they had at some point. Younger women had lower levels of knowledge and practices. Findings about the impact of the Ad campaigns were mixed, with some indicators changing in positive and some in negative directions and no clear trend between surveys. Two indicators that were of high focus in the Ad campaigns changed in the desired direction after both ad campaigns (iron can make labor/delivery difficult and iron supplements are only for women who have anemia).ConclusionsOverall, this study shows that a social media campaign about anemia has the potential to reach a large number of women in India, including young women, who are hard to reach if they are not in school or currently pregnant and seeing a physician. This study shows that it is possible to collect data from women through social media and measure the impact of an intervention. More research is needed to know if social media is an effective approach to actually change attitudes and behaviors related to anemia, or other important health concerns

Three-dimensional co-culture models and analytical approaches in this study.

<p>Schematic of the several three-dimensional co-culture models we utilized to study the interactions between MCF-7 breast cancer cells and primary mammary fibroblasts. The MCF-7 were grown as spheroids in a Matrigel overlay culture. Fibroblasts were incorporated either in a direct-contact mixed culture (MCF-7^M) or in a separate collagen layer in a sandwich culture (MCF-7^S). To study the effects of paracrine signaling, conditioned medium (CM) studies were done in which CM was taken from the mixed culture and used to treat MCF-7 or normal mammary epithelial cells (HMEC) grown alone. The CM was also profiled using protein arrays to obtain the secreted protein interaction signature (iSig). We used gene expression and phenotypic assays to study response to hormone and the expression of markers of EMT. This molecular profiling approach was correlated to label-free FT-IR spectroscopic imaging and also gene expression from patient samples.</p

Secreted proteins that were increased 5-fold or higher in co-culture conditioned medium (CM) compared to CM from naïve MCF-7.

<p>Secreted proteins that were increased 5-fold or higher in co-culture conditioned medium (CM) compared to CM from naïve MCF-7.</p