Michael Hallett

Michael Hallett
Concordia University Montreal · Department of Biology

PhD

About

345
Publications
19,131
Reads
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10,550
Citations
Additional affiliations
May 2017 - present
McGill University
Position
  • Adjunct Member
January 2017 - January 2017
Concordia University Montreal
Position
  • Professor
August 2000 - January 2017
McGill University
Position
  • Professor

Publications

Publications (345)
Article
Coevolution of tumor cells and adjacent stromal elements is a key feature during tumor progression; however, the precise regulatory mechanisms during this process remain unknown. Here, we show stromal p53 loss enhances oncogenic KrasG12D, but not ErbB2, driven tumorigenesis in murine mammary epithelia. Stroma-specific p53 deletion increases both ep...
Article
Full-text available
Background A better understanding underlying radiation (RT) response after breast-conserving surgery (BCS) is needed to mitigate over-treatment of DCIS. The hazard ratio (HR) measures the effect of RT but assumes the effect is constant over time. We examined the hazard function adjusted for adherence to surveillance mammography to examine variation...
Preprint
Full-text available
Precision medicine brings the promise of more precise diagnosis and individualized therapeutic strategies from analyzing a cancer’s genomic signature. Technologies such as high-throughput sequencing enable cheaper data collection at higher speed, but rely on modern data analysis platforms to extract knowledge from these high dimensional datasets. S...
Article
Full-text available
Gap junction transmembrane channels allow the transfer of small molecules between the cytoplasm of adjacent cells. They are formed by proteins named connexins (Cxs) that have long been considered as a tumor suppressor. This widespread view has been challenged by recent studies suggesting that the role of Connexin 43 (Cx43) in cancer is tissue- and...
Article
Full-text available
Although systemic immunity is critical to the process of tumor rejection, cancer research has largely focused on immune cells in the tumor microenvironment. To understand molecular changes in the patient systemic response (SR) to the presence of BC, we profiled RNA in blood and matched tumor from 173 patients. We designed a system (MIxT, Matched In...
Data
Enrichment of clinicopathological and tumor subtypes attributes across subtyping schemes. The table shows statistically significant associations between tumor attributes (columns) and subtypes (rows). For columns representing binary variables (ER, HER2, LN, as well as subtype/cohorts), the table shows the number of patients and the level of signifi...
Data
Top GO terms enriched in SR modules. Legend follows S2 Table. (XLSX)
Data
Gene expression and clinical data processing. (A) Preprocessing of the microarray data was performed identically in each of the five datasets: blood (bl) 1–4 and tumor (t. 1) datasets. Steps that trim samples and probes/genes are presented horizontally and vertically, respectively. In total, we investigated blood and tumor profiles from 173 BC pati...
Data
Associations between the darkgrey tumor module and distinct SR by subtypes. (A) Scatter plot of ranksums of the darkgrey tumor module and the lightyellow SR module in CIT lumA patients. The top corner depicts the background distributions of the correlations coefficients between ranksums of every modules pairs across tissues in CIT lumA patients. (B...
Data
Expression patterns of the green tumor module. (A) Expression heatmap of genes in the green tumor module. Legend follows S3 Fig. Color coding for ER, HER2, pam50, hybrid, cit, claudin-low and lymph follows Fig 1D. In general, a tick for a binary clinical variable refers to a positive value (eg. a red tick in ‘basalL’ refers to patients with basalL...
Data
SR modules associated with clinico-pathological variable. (A) One (saddlebrown) modules in the patient SR is associated with HER2+ BC. (B) Three modules in the patient SR are associated with lumC BC. (C) One module in the patient SR are associated to both lumC and large (>2cm) tumors. (D) Three modules in the patient SR are associated with large (>...
Data
Top 5 gene sets of each signature set enriched in SR modules. Legend follows S3 Table. (XLSX)
Data
Gene co-expression networks in each tissue. (A) Heatmap of the topological overlap between genes expressed in tumors. Each row and column represent a gene, light color indicates low topological overlap and progressively darker red indicates higher topological overlap. Module assignment is displayed along the left and the top of the heatmap. (B) Hea...
Data
Top GO terms enriched in tumor modules. Top 5 GO terms that overlap with each module. “Annotated” indicates the number of genes in the GO term, “Significant” indicates the number of overlapping genes. “Expected” indicates the number of genes that we would expect by chance to be overlapping with the GO term. “classicFisher” presents the p-value from...
Data
Top 5 enrichments among each of the following signature sets. i) c1, c2.cgp, c2.cp, c6, c7 and h gene set collections from MSigDB signatures (v5.1) [66]. ii) peripheral-blood mononuclear cell (PBMC) transcriptional modules (sig.set = i) from [52]. iii) our blood-based gene expression signatures (341- and 50-gene; sig.set = d) for BC [33] iv) immune...
Data
Significant univariate gene markers of subtypes in SR (false discovery rate, fdr ≤ 0.2). Blue and red shade correspond to under- and over- expression of the marker in a given subtype vs the others, respectively. Shading is proportional to the level of significance of the gene marker. (TIF)
Data
Gene expression heatmap of the 70 blood markers of lumC tumors. Rows correspond to genes and columns correspond to samples. Gene expression are scaled by row. Patients are linearly ordered based on their ranksum of gene expression. Genes are ordered by their correlation to the observed patient ordering. Genes that are positively and negatively corr...
Data
Background distributions of the correlations coefficients between ranksums of gene expression in modules across tissues within each subtype. The dotted lines represent the lower and higher bounds that were used to call significant associations between modules across tissues. Curves are colored according to the families of subtypes as listed in Fig...
Conference Paper
The significant increase in the rate of data generation by the systems biology community creates a need for interactive exploration tools to explore the resultant datasets. Such tools need to combine advanced statistical analyses, prior knowledge from biological databases, and interactive visualizations with intuitive user interfaces. Each specific...
Article
Triple-negative breast cancer (TNBC) is a molecularly heterogeneous cancer that is difficult to treat. Despite the role it may play in tumor progression and response to therapy, microenvironmental (stromal) heterogeneity in TNBC has not been well characterized. To address this challenge, we investigated the transcriptome of tumor-associated stroma...
Article
Mesodermal cells signal to neighboring epithelial cells to modulate their proliferation in both normal and disease states. We adapted a Caenorhabditis elegans organogenesis model to enable a genome-wide mesodermal-specific RNAi screen and discovered 39 factors in mesodermal cells that suppress the proliferation of adjacent Ras pathway-sensitized ep...
Article
Full-text available
This article [1] has been updated to correct an abbreviation. DART is an abbreviation of 'Denoising Algorithm using Relevance network Topology' and not 'Diversity arrays technology (DART)', as previously stated in the article. The abbreviation has been changed in two places; In the abbreviations section and in the first paragraph of the backgrounds...
Article
Full-text available
Background The ability to reliably identify the state (activated, repressed, or latent) of any molecular process in the tumor of a patient from an individual whole-genome gene expression profile obtained from microarray or RNA sequencing (RNA-seq) promises important clinical utility. Unfortunately, all previous bioinformatics tools are only applica...
Article
s: AACR Special Conference: The Function of Tumor Microenvironment in Cancer Progression; January 7-10, 2016; San Diego, CA Breast cancer heterogeneity is one of the principal obstacles both to predicting outcome and to determining an effective course of treatment for this disease. Although genomic technologies have been used to gain a better unde...
Article
Breast cancer is a heterogeneous disease in terms of presentation, morphology, molecular profile and response to therapy. Gene expression profiling has identified intrinsic molecular subtypes that are associated with clinical markers (ER, PR, HER2) as well as prognosis and survival. However, it is well established that the intrinsic molecular profi...
Article
Full-text available
Breast cancer consists of at least five main molecular “intrinsic” subtypes that are reflected in both pre-invasive and invasive disease. Although previous studies have suggested that many of the molecular features of invasive breast cancer are established early, it is unclear what mechanisms drive progression and whether the mechanisms of progress...
Article
Full-text available
Amplification and overexpression of erbB2/neu proto-oncogene is observed in 20-30% human breast cancer and is inversely correlated with the survival of the patient. Despite this, somatic activating mutations within erbB2 in human breast cancers are rare. However, we have previously reported that a splice isoform of erbB2, containing an in-frame del...
Data
Figure S8: Validation of microarray data suggests an immune infiltration of ErbB2ΔEx16 tumors. (A) Enrichr gene enrichment analysis revealed a strong gene signature corresponding to immune system infiltration in the tumors. (B) Immunohistochemical staining indicated increased Stat3 phosphorylation in ErbB2ΔEx16 tumors, (C) which was significant by...
Data
Figure S1: ErbB2ΔEx16 mRNA expression in normal human tissue. (A) Quantitative PCR, differentiating wild type ErbB2 mRNA from ErbB2ΔEx16, reveals a relative expression level of 2–10% of total ErbB2 transcript across a panel of normal human tissues.
Data
Figure S2: Ectopic expression of ErbB2 isoforms in NMuMG cells. (A) Immunoblot analysis confirms ectopic ErbB2 overexpression in NMuMG cells. (B) When NMuMG cells were injected orthotopically into athymic nude mice, only cells expressing ErbB2ΔEx16 or the oncogenic mutant NeuNT produced tumours in mice, whereas full length ErbB2 (ErbB2wt) expressio...
Data
Figure S9: ErbB2ΔEx16-driven tumors transcriptionally resemble basal and claudin-low tumors. When subjecting ErbB2 (WildTypeHer2) and ErbB2ΔEx16 (Delta16) tumors to cross-species comparisons, the gene expression profile of ErbB2 tumors most resemble Her2/ErbB2-amplified human breast cancer, whereas ErbB2ΔEx16 tumors align more closely with basal or...
Data
Figure S4: Organ survey of ErbB2ΔEx16 mRNA levels in ErbB2ΔEx16 transgenic mice. ErbB2ΔEx16 transcripts in major organs of ErbB2ΔEx16 transgenic animals without (− Dox) or with (+Dox) a 5-day doxycycline induction are measured with RT-qPCR, which demonstrates drastic elevations of ErbB2ΔEx16 mRNA level in mammary and salivary tissues in the presenc...
Data
Figure S6: Doxycycline withdrawal of ErbB2ΔEx16-driven tumors. (A) Upon withdrawal of doxycycline, tumors rapidly regress and disappear. After a variable latency, approximately 60% of mice display recurrent tumors in the absence of doxycycline. (B) In contrast to the primary lesions, the recurrent tumors lack ErbB2 and E-cadherin expression. (C) Re...
Data
Figure S7: RPPA analysis of transgenic tumors indicates ErbB2ΔEx16 activates a distinct set of pathways from full length ErbB2 tumors. Unsupervised hierarchical clustering of independent tumors separates tumors by genotype, indicating inherent similarities within genotype, and differences between groups.
Data
Figure S3: Characterization of a doxycycline-inducible ErbB2ΔEx16 transgenic mouse. (A) Mice from two separate founder lines were interbred with the MMTV/rtTA (MTB) transgenic strain. Female bigenic mice were treated with 2mg/ml doxycycline for 5 days, and mammary glands subjected to wholemount analysis. After 5 days of induction, EGFP fluorescence...
Data
Figure S5: (A) Mouse mammary tumors driven by full length ErbB2 stain positive for the epithelial cytokeratin CK8. By contrast, tumors driven by ErbB2ΔEx16 also express myoepithelial cytokeratins CK5 and CK14, in addition to CK8. (B) ErbB2ΔEx16-driven tumors contain cells positive for CK6 (green) and CK14 (red), as well as double-positive cells (ye...
Article
ShcA is an important mediator of ErbB2 and TGFβ-induced breast cancer cell migration, invasion and metastasis. We show that in the context of reduced ShcA levels, the BMP antagonist, Chordin-like 1 (Chrdl1), is upregulated in numerous breast cancer cells following TGFβ stimulation. BMPs have emerged as important modulators of breast cancer aggressi...
Article
Rab Coupling Protein (FIP1C), an effector of the Rab11 GTPases, including Rab25, is amplified and overexpressed in 10-25% of primary breast cancers and correlates with poor clinical outcome. Rab25 is also frequently silenced in triple negative breast cancer, suggesting its ability to function as either an oncogene or a tumor suppressor, depending o...
Article
Full-text available
A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and envi...
Data
Genetic interaction classes and associated missing data. Percentage of interacting gene pairs missing co-expression, phenotype or protein-protein interaction data are indicated. (TIF)
Data
Genetic interactions found in C2 GIs. Refer to S5 Fig for the nodes and edges descriptions. (TIF)
Data
Representation of the 22 classes of phenotypes identified in C. elegans. Phenotype IDs retrieved from WormBase (release WS220-bugfix) are represented by nodes and their hierarchical relationships represented by edges. Groups of phenotypes corresponding to the 22 most general phenotypes, and their first neighbors in the network were identified by di...
Data
GIs-all with selected GI classes. (See Methods.) (XLSX)
Data
Cluster selection within GIs-all. The x-axis shows the number of clusters with approximately unbiased P-value (higher values indicate greater significance, see Methods) greater than or equal to the threshold indicated in the parentheses. The y-axis shows area under the curve (AUC) values following cross-validation analysis of each cluster-based mod...
Data
Examples of genetic interactions from C1 to C6 classes. Nodes represent genes connected by genetic interactions from the six classes of GIs. Are also indicated, protein-protein interaction dense subnetworks (PDS) and genes involved in C1 to C6 GIs and also involved in signalling pathways controlling vulval development (S3 Table). (TIF)
Data
Genetic interactions found in C4 GIs. Refer to S5 Fig for the nodes and edges descriptions. (TIF)
Data
Enrichment of within- and between-PDS relationship within pathways is not significantly influenced by the topology of PDS. (a) Size distribution of 106 protein-protein interaction dense subnetworks (PDS). (b) Log-Ratio scores for within-PDS and between-PDS relationships occurring within-pathways. Different PDS networks were built by varying the num...
Data
GI groups and GI classes enrichments for different biological characteristics. Enrichment in GI classes and GI groups of Genetic interaction-Hubs (GI-Hubs), redundant genes, protein-protein interaction Hubs (PPI-Hubs), Highly pleiotropic genes (High-PI) and essential genes. -Log of P-values from Fisher’s exact test are indicated. GI groups are asso...
Data
C. elegans signaling pathways curated from the literature. (See Methods.) (XLSX)
Data
GI groups. Thresholds used to identify GI groups associated to a positive or negative value for attributes as well as the size of each group. (TXT)
Data
GI classes tend to form GDS in a biased manner. (a) Hierarchical clustering of monochromatic indices of GI classes. Each row represents a GDS extracted from GIs-all using the MINE tool. GDS sizes are indicated by the blue shaded legend. (b) Enrichments of GIs classes and pair combinations of classes in GDS when compared to GIs-all. Only the statist...
Data
Genetic interactions found in C3 GIs. Refer to S5 Fig for the nodes and edges descriptions. (TIF)
Data
Protein-protein interactions degree distribution for the multi-species Caenorhabditis elegans interactome. PPI-Hubs are identified as the top 20% most connected proteins in the PPI network indicated by a dashed line. (TIF)
Data
C7 to C9 classes are enriched in between-pathways interactions. Log-Ratio scores for within-pathway (red bars) and between-pathway (blue bars) relationships observed between genes interacting through unselected GI classes (C7-C10) or present in GIs-all. A positive Log-Ratio score means that the frequency of within- or between-pathway GIs occurring...
Data
Interaction between gene within or across pleiotropic ranges. Log odds ratios of GI classes enriched in interactions between genes displaying the same range of PI higher or equal to a given threshold (τ) (upper panel); or lower or equal to τ (middle panel). GI classes enriched in interactions in which one partner (gene A) displays a PI ≥ τ and the...
Data
Summary of the distinctive characteristics identified for modules and connectors. (TIF)
Data
Gene Ontology terms enriched amongst genes in GI classes. (XLSX)
Data
Genes involved in the same pathway and either involved in the same PDS or in different PDS. (XLSX)
Data
Log odds ratio and hypergeometric test P-value relative to S17 Fig. (XLSX)
Data
Hierarchical clustering of GI classes based on their genes frequencies. Gene frequencies are clustered using Binary (left dendrogram) and Canberra (right dendrogram) distance metrics (see Methods). (TIF)
Data
Genetic interactions found in C1 GIs. Refer to S5 Fig for the nodes and edges descriptions. (TIF)
Data
Genetic interactions found in C5 GIs. Refer to S5 Fig for the nodes and edges descriptions. (TIF)
Data
Genetic interactions found in C6 GIs. Refer to S5 Fig for the nodes and edges descriptions. (TIF)
Data
Distribution of GI degree and pleiotropic index (PI). (a) Degree distribution for GI degrees in GIs-all. GI-Hubs, corresponding to the 20% GIs with the highest GI degrees, are located on the right side of the dashed line (GI degree ≥ 16). (b) PI distribution for all genes with PI > 0 in the C. elegans genome and for all interacting genes in GIs-all...
Data
Log-Ratios for GI groups and GI classes occurring within or between PDS or pathways. Log-Ratios profiles for GI classes and GI groups associated to a positive (+) or a negative (-) values for indicated attributes. Blue boxes indicate enrichment of biological characteristics for C4 and C5 GI classes and CI(+) GI group. (TIF)
Article
Immunosurveillance constitutes the first step of cancer immunoediting in which developing malignant lesions are eliminated by anti-tumorigenic immune cells. However, the mechanisms by which neoplastic cells induce an immunosuppressive state to evade the immune response are still unclear. The transcription factor Stat3 has been implicated in breast...
Article
Full-text available
Cancer-associated fibroblasts (CAFs) play an important role in breast cancer pathogenesis by paracrine regulation of breast cancer cell biology. Several in vitro and mouse models have characterized the role of cell contact and cytokine molecules mediating this relationship, although few reports have used human CAFs from breast tumors. Primary breas...
Article
A father’s lifetime experiences can be transmitted to his offspring to affect health and development. However, the mechanisms underlying paternal epigenetic transmission are unclear. Unlike in somatic cells, there are few nucleosomes in sperm, and their function in epigenetic inheritance is unknown. We generated transgenic mice in which overexpress...
Article
It is now accepted that changes in the normal cells that constitute the tumor microenvironment (TME) play important roles in determining cancer progression and ultimate outcome. We and others have established that an immune signature, and more recently that the ability of CD8+ T cells to infiltrate the tumor bed, are correlated with good outcome in...