Honors & Awards
Arnie Charbonneau Cancer Research Postdoctoral Fellowship, University of Calgary (2017)
Howard Research Excellence Award, Charbonneau Cancer Research Institute (2018)
Doctor of Medicine, Tehran University of Medical Sciences (2008)
Doctor of Philosophy, University of Calgary (2016)
- The Immune Landscape of Cancer. Immunity 2019; 51 (2): 411–12
Type 3 inositol 1,4,5-trisphosphate receptor is increased and enhances malignant properties in cholangiocarcinoma.
Hepatology (Baltimore, Md.)
Cholangiocarcinoma (CCA) is the second most common malignancy arising in the liver. It carries a poor prognosis, in part because its pathogenesis is not well understood. The type 3 inositol 1,4,5-trisphosphate receptor (ITPR3) is the principal intracellular Ca2+ release channel in cholangiocytes and its increased expression has been related to the pathogenesis of malignancies in other types of tissues, so we investigated its role in CCA. ITPR3 expression was increased in both hilar and intrahepatic CCA samples as well as in CCA cell lines. Deletion of ITPR3 from CCA cells impaired proliferation and cell migration. A bioinformatic analysis suggested that over-expression of ITPR3 in CCA would have a mitochondrial phenotype, so this also was examined. ITPR3 normally is concentrated in a sub-apical region of endoplasmic reticulum (ER) in cholangiocytes, but both immunogold electron microscopy and super-resolution microscopy showed that ITPR3 in CCA cells also was in regions of ER in close association with mitochondria. Deletion of ITPR3 from these cells impaired mitochondrial Ca2+ signaling and led to cell death. CONCLUSION: ITPR3 expression in cholangiocytes becomes enhanced in cholangiocarcinoma. This contributes to malignant features, including cell proliferation and migration and enhanced mitochondrial Ca2+ signaling. This article is protected by copyright. All rights reserved.
View details for DOI 10.1002/hep.30839
View details for PubMedID 31251815
Gas Chromatography-Mass Spectrometry and Analysis of the Serum Metabolomic Profile Through Extraction and Derivatization of Polar Metabolites.
Methods in molecular biology (Clifton, N.J.)
2019; 1928: 235–49
Metabolite profiling in complex biological matrices such as serum requires high-throughput technologies capable of accurate and reproducible quantitative analysis and detection of slight differences in metabolite concentrations. Gas chromatography-mass spectrometry (GC-MS) is widely used for characterizing the metabolome. This chapter summarizes the necessary preparatory steps required to profile the metabolome using GC-MS. While this chapter focuses on evaluating polar metabolites in serum samples, the methods can be adapted to quantify nonpolar metabolites in other biological matrices.
View details for DOI 10.1007/978-1-4939-9027-6_13
View details for PubMedID 30725459
Host phenotype is associated with reduced survival independent of tumour biology in patients with colorectal liver metastases.
Journal of cachexia, sarcopenia and muscle
2019; 10 (1): 123–30
Most prognostic scoring systems for colorectal liver metastases (CRLMs) account for factors related to tumour biology. Little is known about the effects of the host phenotype to the tumour. Our objective was to delineate the relationship of systemic inflammation and body composition features [i.e. low skeletal muscle mass (sarcopenia) and low visceral adipose tissue (VAT)], two well-described host phenotypes in cancer.Clinical data and pre-operative blood samples were collected from 99 patients who underwent resection of CRLM. Pre-operative computed tomography scans were available for 97 patients; body composition was analysed at the L3 level, stratified for sex and age. Clinicopathological variables, serum C-reactive protein (CRP), and various body composition variables were evaluated. Overall survival was evaluated as a function of these same variables in multivariate Cox regression analysis.Skeletal muscle was significantly correlated with VAT (r = 0.46, P < 0.001). Of patients with sarcopenia, 35 (65%) also had low VAT. C-reactive protein was elevated (≥5 mg/mL) in 42 patients (43.3%). Elevated CRP was more common in patients with sarcopenia (73.8% vs. 51.1%, P = 0.029). The most significant prognostic factors were the coincidence of elevated CRP and adverse body composition features (sarcopenia and/or low VAT; hazard ratio 4.3, 95% confidence interval 1.5-13.0, P = 0.008), as well as Fong clinical prognostic score (hazard ratio 2.9, 95% confidence interval 1.5-5.5, P = 0.002).Body composition in patients with CRLM is not directly linked to the presence of systemic inflammation. However, when systemic inflammation coincides with sarcopenia and/or low VAT, prognosis is adversely affected, independent of the Fong clinical prognostic score.
View details for DOI 10.1002/jcsm.12358
View details for PubMedID 30378742
View details for PubMedCentralID PMC6438330
A strategy for early detection of response to chemotherapy drugs based on treatment-related changes in the metabolome.
2019; 14 (4): e0213942
We describe a biomarker-based approach to delivering chemotherapy that entails monitoring treatment changes in the circulating metabolome that reflect efficacy. In-vitro, multiple tumor cell lines were exposed to numerous chemotherapeutics. Supernatants were collected at baseline and 72 hours post treatment. MTT assays were used to quantify growth inhibition. Clinical samples were derived from a phase II clinical trial of second-line axitinib in patients with advanced hepatocellular carcinoma. Sera were collected at baseline and 2-4 weeks after treatment initiation. Response to therapy was estimated by CT scan at 8 weeks. Samples were analyzed by gas chromatography-mass spectrometry to identify metabolomic changes associated with response. In vitro, we found drug-specific and generalizable patterns of change in the extracellular metabolome accompany growth inhibition. A cell death signature was also identified. This approach was also applied to clinical samples. While the in vitro signatures were detectable in vivo, a more robust signal was identified clinically that appeared within 4 weeks of administering drug that distinguished individuals with a treatment response. These changes were extinguished as tumor growth resumed. Serial monitoring of the metabolome during chemotherapy is a means to follow treatment efficacy and emergence of resistance, informing the oncologist whether to modify treatment.
View details for DOI 10.1371/journal.pone.0213942
View details for PubMedID 30939138
View details for PubMedCentralID PMC6445409
The Immune Landscape of Cancer
2018; 48 (4): 812-+
We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.
View details for PubMedID 29628290
Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas
2018; 33 (4): 721-+
We analyzed 921 adenocarcinomas of the esophagus, stomach, colon, and rectum to examine shared and distinguishing molecular characteristics of gastrointestinal tract adenocarcinomas (GIACs). Hypermutated tumors were distinct regardless of cancer type and comprised those enriched for insertions/deletions, representing microsatellite instability cases with epigenetic silencing of MLH1 in the context of CpG island methylator phenotype, plus tumors with elevated single-nucleotide variants associated with mutations in POLE. Tumors with chromosomal instability were diverse, with gastroesophageal adenocarcinomas harboring fragmented genomes associated with genomic doubling and distinct mutational signatures. We identified a group of tumors in the colon and rectum lacking hypermutation and aneuploidy termed genome stable and enriched in DNA hypermethylation and mutations in KRAS, SOX9, and PCBP1.
View details for PubMedID 29622466
Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers.
2018; 23 (1): 255–69.e4
Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1-master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility.
View details for PubMedID 29617665
A Framework for Development of Useful Metabolomic Biomarkers and Their Effective Knowledge Translation.
2018; 8 (4)
Despite the significant advantages of metabolomic biomarkers, no diagnostic tests based on metabolomics have been introduced to clinical use. There are many reasons for this, centered around substantial obstacles in developing clinically useful metabolomic biomarkers. Most significant is the need for interdisciplinary teams with expertise in metabolomics, analysis of complex clinical and metabolomic data, and clinical care. Importantly, the clinical need must precede biomarker discovery, and the experimental design for discovery and validation must reflect the purpose of the biomarker. Standard operating procedures for procuring and handling samples must be developed from the beginning, to ensure experimental integrity. Assay design is another challenge, as there is not much precedent informing this. Another obstacle is that it is not yet clear how to protect any intellectual property related to metabolomic biomarkers. Viewing a metabolomic biomarker as a natural phenomenon would inhibit patent protection and potentially stifle commercial interest. However, demonstrating that a metabolomic biomarker is actually a derivative of a natural phenomenon that requires innovation would enhance investment in this field. Finally, effective knowledge translation strategies must be implemented, which will require engagement with end users (clinicians and lab physicians), patient advocate groups, policy makers, and payer organizations. Addressing each of these issues comprises the framework for introducing a metabolomic biomarker to practice.
View details for DOI 10.3390/metabo8040059
View details for PubMedID 30274369
View details for PubMedCentralID PMC6316283
A quantitative multimodal metabolomic assay for colorectal cancer.
2018; 18 (1): 26
Early diagnosis of colorectal cancer (CRC) simplifies treatment and improves treatment outcomes. We previously described a diagnostic metabolomic biomarker derived from semi-quantitative gas chromatography-mass spectrometry. Our objective was to determine whether a quantitative assay of additional metabolomic features, including parts of the lipidome could enhance diagnostic power; and whether there was an advantage to deriving a combined diagnostic signature with a broader metabolomic representation.The well-characterized Biocrates P150 kit was used to quantify 163 metabolites in patients with CRC (N = 62), adenoma (N = 31), and age- and gender-matched disease-free controls (N = 81). Metabolites included in the analysis included phosphatidylcholines, sphingomyelins, acylcarnitines, and amino acids. Using a training set of 32 CRC and 21 disease-free controls, a multivariate metabolomic orthogonal partial least squares (OPLS) classifier was developed. An independent set of 28 CRC and 20 matched healthy controls was used for validation. Features characterizing 31 colorectal adenomas from their healthy matched controls were also explored, and a multivariate OPLS classifier for colorectal adenoma could be proposed.The metabolomic profile that distinguished CRC from controls consisted of 48 metabolites (R2Y = 0.83, Q2Y = 0.75, CV-ANOVA p-value < 0.00001). In this quantitative assay, the coefficient of variance for each metabolite was <10%, and this dramatically enhanced the separation of these groups. Independent validation resulted in AUROC of 0.98 (95% CI, 0.93-1.00) and sensitivity and specificity of 93% and 95%. Similarly, we were able to distinguish adenoma from controls (R2Y = 0.30, Q2Y = 0.20, CV-ANOVA p-value = 0.01; internal AUROC = 0.82 (95% CI, 0.72-0.93)). When combined with the previously generated GC-MS signatures for CRC and adenoma, the candidate biomarker performance improved slightly.The diagnostic power for metabolomic tests for colorectal neoplasia can be improved by utilizing a multimodal approach and combining metabolites from diverse chemical classes. In addition, quantification of metabolites enhances separation of disease-specific metabolomic profiles. Our future efforts will be focused on developing a quantitative assay for the metabolites comprising the optimal diagnostic biomarker.
View details for DOI 10.1186/s12885-017-3923-z
View details for PubMedID 29301511
View details for PubMedCentralID PMC5755335
- Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles. Cell reports 2017; 19 (13): 2878–80
A validated metabolomic signature for colorectal cancer: exploration of the clinical value of metabolomics
BRITISH JOURNAL OF CANCER
2016; 115 (7): 848-857
Timely diagnosis and classification of colorectal cancer (CRC) are hindered by unsatisfactory clinical assays. Our aim was to construct a blood-based biomarker series using a single assay, suitable for CRC detection, prognostication and staging.Serum metabolomic profiles of adenoma (N=31), various stages of CRC (N=320) and healthy matched controls (N=254) were analysed by gas chromatography-mass spectrometry (GC-MS). A diagnostic model for CRC was derived by orthogonal partial least squares-discriminant analysis (OPLS-DA) on a training set, and then validated on an independent data set. Metabolomic models suitable for identifying adenoma, poor prognosis stage II CRC and discriminating various stages were generated.A diagnostic signature for CRC with remarkable multivariate performance (R(2)Y=0.46, Q(2)Y=0.39) was constructed, and then validated (sensitivity 85%; specificity 86%). Area under the receiver-operating characteristic curve was 0.91 (95% CI, 0.87-0.96). Adenomas were also detectable (R(2)Y=0.35, Q(2)Y=0.26, internal AUROC=0.81, 95% CI, 0.70-0.92). Also of particular interest, we identified models that stratified stage II by prognosis, and classified cases by stage.Using a single assay system, a suite of CRC biomarkers based on circulating metabolites enables early detection, prognostication and preliminary staging information. External population-based studies are required to evaluate the repeatability of our findings and to assess the clinical benefits of these biomarkers.
View details for DOI 10.1038/bjc.2016.243
View details for Web of Science ID 000384576100012
View details for PubMedID 27560555
View details for PubMedCentralID PMC5046202