Greg Finak

Regulation of endocytosis via the oxygen-sensing pathway

Tumor hypoxia is associated with disease progression, resistance to conventional cancer therapies and poor prognosis. Hypoxia, by largely unknown mechanisms, leads to deregulated accumulation of and signaling via receptor tyrosine kinases (RTKs) that are critical for driving oncogenesis. Here, we show that hypoxia or loss of von Hippel–Lindau protein—the principal negative regulator of hypoxia-inducible factor (HIF)—prolongs the activation of epidermal growth factor receptor that is attributable to lengthened receptor half-life and retention in the endocytic pathway. The deceleration in endocytosis is due to the attenuation of Rab5-mediated early endosome fusion via HIF-dependent downregulation of a critical Rab5 effector, rabaptin-5, at the level of transcription. Primary kidney and breast tumors with strong hypoxic signatures show significantly lower expression of rabaptin-5 RNA and protein. These findings reveal a general role of the oxygen-sensing pathway in endocytosis and support a model in which tumor hypoxia or oncogenic activation of HIF prolongs RTK-mediated signaling by delaying endocytosis-mediated deactivation of receptors.

Gene expression signatures of morphologically normal breast tissue identify basal-like tumors

IntroductionThe role of the cellular microenvironment in breast tumorigenesis has become an important research area. However, little is known about gene expression in histologically normal tissue adjacent to breast tumor, if this is influenced by the tumor, and how this compares with non-tumor-bearing breast tissue.MethodsTo address this, we have generated gene expression profiles of morphologically normal epithelial and stromal tissue, isolated using laser capture microdissection, from patients with breast cancer or undergoing breast reduction mammoplasty (n = 44).ResultsBased on this data, we determined that morphologically normal epithelium and stroma exhibited distinct expression profiles, but molecular signatures that distinguished breast reduction tissue from tumor-adjacent normal tissue were absent. Stroma isolated from morphologically normal ducts adjacent to tumor tissue contained two distinct expression profiles that correlated with stromal cellularity, and shared similarities with soft tissue tumors with favorable outcome. Adjacent normal epithelium and stroma from breast cancer patients showed no significant association between expression profiles and standard clinical characteristics, but did cluster ER/PR/HER2-negative breast cancers with basal-like subtype expression profiles with poor prognosis.ConclusionOur data reveal that morphologically normal tissue adjacent to breast carcinomas has not undergone significant gene expression changes when compared to breast reduction tissue, and provide an important gene expression dataset for comparative studies of tumor expression profiles.

Hypoxia promotes ligand-independent EGF receptor signaling via hypoxia-inducible factor–mediated upregulation of caveolin-1

Caveolin-1 (CAV1) is an essential structural constituent of caveolae, specialized lipid raft microdomains on the cell membrane involved in endocytosis and signal transduction, which are inexplicably deregulated and are associated with aggressiveness in numerous cancers. Here we identify CAV1 as a direct transcriptional target of oxygen-labile hypoxia-inducible factor 1 and 2 that accentuates the formation of caveolae, leading to increased dimerization of EGF receptor within the confined surface area of caveolae and its subsequent phosphorylation in the absence of ligand. Hypoxia-inducible factor–dependent up-regulation of CAV1 enhanced the oncogenic potential of tumor cells by increasing the cell proliferative, migratory, and invasive capacities. These results support a concept in which a crisis in oxygen availability or a tumor exhibiting hypoxic signature triggers caveolae formation that bypasses the requirement for ligand engagement to initiate receptor activation and the critical downstream adaptive signaling during a period when ligands required to activate these receptors are limited or are not yet available.

In Silico Ascription of Gene Expression Differences to Tumor and Stromal Cells in a Model to Study Impact on Breast Cancer Outcome

Breast tumors consist of several different tissue components. Despite the heterogeneity, most gene expression analyses have traditionally been performed without prior microdissection of the tissue sample. Thus, the gene expression profiles obtained reflect the mRNA contribution from the various tissue components. We utilized histopathological estimations of area fractions of tumor and stromal tissue components in 198 fresh-frozen breast tumor tissue samples for a cell type-associated gene expression analysis associated with distant metastasis. Sets of differentially expressed gene-probes were identified in tumors from patients who developed distant metastasis compared with those who did not, by weighing the contribution from each tumor with the relative content of stromal and tumor epithelial cells in their individual tumor specimen. The analyses were performed under various assumptions of mRNA transcription level from tumor epithelial cells compared with stromal cells. A set of 30 differentially expressed gene-probes was ascribed solely to carcinoma cells. Furthermore, two sets of 38 and five differentially expressed gene-probes were mostly associated to tumor epithelial and stromal cells, respectively. Finally, a set of 26 differentially expressed gene-probes was identified independently of cell type focus. The differentially expressed genes were validated in independent gene expression data from a set of laser capture microdissected invasive ductal carcinomas. We present a method for identifying and ascribing differentially expressed genes to tumor epithelial and/or stromal cells, by utilizing pathologic information and weighted t-statistics. Although a transcriptional contribution from the stromal cell fraction is detectable in microarray experiments performed on bulk tumor, the gene expression differences between the distant metastasis and no distant metastasis group were mostly ascribed to the tumor epithelial cells of the primary breast tumors. However, the gene PIP5K2A was found significantly elevated in stroma cells in distant metastasis group, compared to stroma in no distant metastasis group. These findings were confirmed in gene expression data from the representative compartments from microdissected breast tissue. The method described was also found to be robust to different histopathological procedures.

Gene-expression profiling of microdissected breast cancer microvasculature identifies distinct tumor vascular subtypes.

IntroductionAngiogenesis represents a potential therapeutic target in breast cancer. However, responses to targeted antiangiogenic therapies have been reported to vary among patients. This suggests that the tumor vasculature may be heterogeneous and that an appropriate choice of treatment would require an understanding of these differences.MethodsTo investigate whether and how the breast tumor vasculature varies between individuals, we isolated tumor-associated and matched normal vasculature from 17 breast carcinomas by laser-capture microdissection, and generated gene-expression profiles. Because microvessel density has previously been associated with disease course, tumors with low (n = 9) or high (n = 8) microvessel density were selected for analysis to maximize heterogeneity for this feature.ResultsWe identified differences between tumor and normal vasculature, and we describe two subtypes present within tumor vasculature. These subtypes exhibit distinct gene-expression signatures that reflect features including hallmarks of vessel maturity. Potential therapeutic targets (MET, ITGAV, and PDGFRβ) are differentially expressed between subtypes. Taking these subtypes into account has allowed us to derive a vascular signature associated with disease outcome.ConclusionsOur results further support a role for tumor microvasculature in determining disease progression. Overall, this study provides a deeper molecular understanding of the heterogeneity existing within the breast tumor vasculature and opens new avenues toward the improved design and targeting of antiangiogenic therapies.

BIAS: Bioinformatics Integrated Application Software

MOTIVATION We introduce a development platform especially tailored to Bioinformatics research and software development. BIAS (Bioinformatics Integrated Application Software) provides the tools necessary for carrying out integrative Bioinformatics research requiring multiple datasets and analysis tools. It follows an object-relational strategy for providing persistent objects, allows third-party tools to be easily incorporated within the system and supports standards and data-exchange protocols common to Bioinformatics. AVAILABILITY BIAS is an OpenSource project and is freely available to all interested users at http://www.mcb.mcgill.ca/~bias/. This website also contains a paper containing a more detailed description of BIAS and a sample implementation of a Bayesian network approach for the simultaneous prediction of gene regulation events and of mRNA expression from combinations of gene regulation events. CONTACT hallett@mcb.mcgill.ca.

Abstract P4-04-01: A new breast cancer classification scheme based on novel classes of tumor stroma

A major challenge in cancer treatment is the heterogeneous nature of the disease. This is particularly evident in breast cancer where gene expression profiling of whole tumours has identified multiple intrinsic subtypes of breast cancer. These subtypes are associated with differential prognoses and are correlated with previously identified clinical biomarkers (i.e., ER and HER2 status) used to stratify patients for targeted therapy. Despite recent studies demonstrating that elements within the tumour microenvironment can affect breast cancer progression and outcome, and that information contained within this compartment carries significant prognostic information for patient stratification above and beyond the information supplied by the intrinsic subtypes and existing therapeutic biomarkers, a limited understanding of stromal heterogeneity across the population has hindered the development of effective prognostic tools and targeted therapies directed against these processes. Here we perform expression profiling of the microdissected tumour-associated stromal components of 49 human breast tumours, and demonstrate that stromal heterogeneity can be captured by categorization into six classes which bear distinct molecular phenotypes. These stromal classes exhibit distinct biological functions and carry prognostic information independent of existing tumor-intrinsic biomarkers and molecular breast cancer subtypes. Specific combinations of stromal class and tumour subtype are significantly over- and under-represented; furthermore, simultaneous stratification of tumors in external datasets by both tumour subtype and stroma class identifies good- and poor-outcome cohorts within four of the five molecular breast cancer subtypes. The stroma classes identified here form the basis for an improved breast cancer classification scheme which takes the contribution of the microenvironment into account. Citation Format: Crista Thompson, Nicholas Bertos, Tina Gruosso, Greg Finak, Robert Lesurf, Sadiq M Saleh, Hong Zhao, Margarita Souleimanova, Sarkis Meterissian, Atilla Omeroglu, Michael T Hallett, Morag Park. A new breast cancer classification scheme based on novel classes of tumor stroma [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-04-01.

Abstract ES8-2: Breast Cancer Stroma: A Predictor of Clinical Outcome and Tumour Heterogeneity

Breast cancer heterogeneity is one of the principal obstacles both to predicting outcome and to determining an effective course of treatment for this disease. Individual cases demonstrate heterogeneity at multiple levels, including those parameters assessed by studies of gene expression, chromosomal aberrations, and classical and immuno-pathology. Although genomic technologies have been used to gain a better understanding of the impact of gene expression heterogeneity on breast cancer outcome by identifying gene expression signatures associated with clinical outcome, histopathological breast cancer subtypes, and a variety of cancer–related pathways and processes, relatively little is known about the effects of heterogeneity in the tumor microenvironment. We have addressed changes in stroma by analyzing changes in gene expression in stromal tissue associated with breast tumors when compared to normal breast tissue. We have integrated gene expression data from laser capture microdissected breast tumor stroma with matched normal stroma. Using this approach we have identified that the microenvironment of a breast tumor can be classified into one of six distinct molecular phenotypes exhibiting distinct biological functions and carrying prognostic information independent of existing therapeutic biomarkers and tumor subtypes. A trained predictor of 23 genes was developed and contains new information to stratify breast cancer subtypes. This is independent of clinical parameters and published predictors of outcome and identifies patients with poor outcome in multiple breast cancer expression data generated using using whole tissue. The stromal predictor selects poor outcome patients from multiple clinical subtypes of breast cancer and contains genes representing distinct biological features, including differential immune response, angiogenic response, as well as a hypoxic response. Elements of this signature are present in murine models of breast cancer. These results highlight the complex relationship between the tumor and its microenvironment, and underline the role that the stroma plays in tumor progression. These results demonstrate an important role for the tumor microenvironment in defining breast cancer heterogeneity, with a consequent impact upon clinical outcome. Novel therapies could be targeted at the processes that define the stroma classes, suggesting new avenues for the development of individualized treatment regimens. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr ES8-2.