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Su D, Xiong Y, Wang S, Wei H, Ke J, Li H, Wang T, Zuo Y, Yang L. Structural deep clustering network for stratification of breast cancer patients through integration of somatic mutation profiles. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107808. [PMID: 37716222 DOI: 10.1016/j.cmpb.2023.107808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/15/2023] [Accepted: 09/10/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Breast cancer is among of the most malignant tumor that occurs in women and is one of the leading causes of death from gynecologic malignancy worldwide. The high degree of heterogeneity that characterizes breast cancer makes it challenging to devise effective therapeutic strategies. Accumulating evidence highlights the crucial role of stratifying breast cancer patients into clinically significant subtypes to achieve better prognoses and treatments. The structural deep clustering network is a graph convolutional network-based clustering algorithm that integrates structural information and has achieved state-of-the-art performance in various applications. METHODS In this study, we employed structural deep clustering network to integrate somatic mutation profiles for stratifying 2526 breast cancer patients from the Memorial Sloan Kettering Cancer Center into two clinically differentiable subtypes. RESULTS Breast cancer patients in cluster 1 exhibited better prognosis than breast cancer patients in cluster 2, and the difference between them was statistically significant. The immunogenomic landscape further demonstrated that cluster 1 was associated with remarkable infiltration of the tumor infiltrating lymphocytes. The clustering subtype could be used to evaluate the therapeutic benefit of immunotherapy and chemotherapy in breast cancer patients. Furthermore, our approach effectively classified patients from eight different cancer types, demonstrating its generalizability. CONCLUSIONS Our study represents a step towards a generic methodology for classifying cancer patients using only somatic mutation data and structural deep clustering network approaches. Employing structural deep clustering network to identify breast cancer subtypes is promising and can inform the development of more accurate and personalized therapies.
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Affiliation(s)
- Dongqing Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yuqiang Xiong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Shiyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Haodong Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Jiawei Ke
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Honghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Tao Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China; Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd. Hohhot, 010010, China; Inner Mongolia International Mongolian Hospital, Hohhot 010065, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
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Dudka I, Lundquist K, Wikström P, Bergh A, Gröbner G. Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes. J Transl Med 2023; 21:860. [PMID: 38012666 PMCID: PMC10683247 DOI: 10.1186/s12967-023-04747-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 11/21/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis. METHODS A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity). RESULTS Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B. CONCLUSIONS The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.
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Affiliation(s)
- Ilona Dudka
- Department of Chemistry, Umeå University, Umeå, Sweden
| | | | - Pernilla Wikström
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden.
| | - Anders Bergh
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
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Liu Y, Xie G, Li A, He Z, Hei X. Prediction of Cancer-Related piRNAs Based on Network-Based Stratification Analysis. INT J PATTERN RECOGN 2021. [DOI: 10.1142/s0218001422590029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
PIWI-interacting RNA (PiRNA) was discovered in 2006 and is expected to become a new biomarker for diagnosis and prognosis of various diseases. The purpose of this study is to explore functions of piRNAs and identify cancer subtypes on the basis of the pattern of transcriptome and somatic mutation data. A total of 285 510 SNPs in piRNAs and genes, which might affect piRNA biogenesis or piRNA targets binding were identified. Significant co-expression networks of piRNAs were then constructed separately for 12 major types of cancer. Finally, mutational matrices were mapped to piRNA network, propagated, and clustered for identification of cancer-related piRNAs and cancer subtypes. Findings showed that subtypes of three types of cancer (COAD, STAD and UCEC), which are significantly associated with survival were identified. Analysis of differentially expressed piRNAs in UCEC subtypes showed that piRNA function is closely related to cancer hallmarks “Enabling Replicative Immortality” and contributes to initiation of cancer.
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Affiliation(s)
- Yajun Liu
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, Shaanxi, P. R. China
| | - Guo Xie
- Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, School of Information Technology and Equipment Engineering, Xi’an University of Technology, P. R. China
| | - Aimin Li
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, Shaanxi, P. R. China
| | - Zongzhen He
- Xi’an University of Finance and Economics, Xi’an 710100, Shaanxi, P. R. China
| | - Xinhong Hei
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, Shaanxi, P. R. China
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4
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Wang YA, Sfakianos J, Tewari AK, Cordon-Cardo C, Kyprianou N. Molecular tracing of prostate cancer lethality. Oncogene 2020; 39:7225-7238. [PMID: 33046797 DOI: 10.1038/s41388-020-01496-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/16/2020] [Accepted: 09/28/2020] [Indexed: 01/14/2023]
Abstract
Prostate cancer is diagnosed mostly in men over the age of 50 years, and has favorable 5-year survival rates due to early cancer detection and availability of curative surgical management. However, progression to metastasis and emergence of therapeutic resistance are responsible for the majority of prostate cancer mortalities. Recent advancement in sequencing technologies and computational capabilities have improved the ability to organize and analyze large data, thus enabling the identification of novel biomarkers for survival, metastatic progression and patient prognosis. Large-scale sequencing studies have also uncovered genetic and epigenetic signatures associated with prostate cancer molecular subtypes, supporting the development of personalized targeted-therapies. However, the current state of mainstream prostate cancer management does not take full advantage of the personalized diagnostic and treatment modalities available. This review focuses on interrogating biomarkers of prostate cancer progression, including gene signatures that correspond to the acquisition of tumor lethality and those of predictive and prognostic value in progression to advanced disease, and suggest how we can use our knowledge of biomarkers and molecular subtypes to improve patient treatment and survival outcomes.
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Affiliation(s)
- Yuanshuo Alice Wang
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - John Sfakianos
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ashutosh K Tewari
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Carlos Cordon-Cardo
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Pathology and Laboratory Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Natasha Kyprianou
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. .,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. .,Department of Pathology and Laboratory Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. .,Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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Darrell CM, Montironi R, Paner GP. Potential biomarkers and risk assessment models to enhance the tumor-node-metastasis (TNM) staging classification of urologic cancers. Expert Rev Mol Diagn 2020; 20:921-932. [PMID: 32876523 DOI: 10.1080/14737159.2020.1816827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
INTRODUCTION The anatomic-based TNM classification is considered the benchmark in cancer staging and has been regularly updated since its inception. In the current era of precision medicine, the added intention for future TNM modifications is to heighten its impact in the more 'personalized' level of cancer care. In urologic cancers, this goal may be achieved by incorporating 'non-anatomic' factors into TNM, such as biomarkers (e.g. gene alterations, molecular subtypes, genomic classifiers) and risk assessment models (e.g. nomogram, look-up table), while maintaining the anatomic extent as the foundation of staging. These different prognosticators can be combined and integrated, may serve as substratifiers for T, N, or M categories, and perhaps, incorporated as elements in TNM stage groupings to enhance their prognostic capability in urologic cancers. AREAS COVERED This review highlights candidate biomarkers and risk assessment models that can be explored to potentially improve TNM prognostication of bladder, prostate, kidney, and testicular cancers. EXPERT OPINION Recent advances in molecular analysis have increased the understanding of the genomic, transcriptomic, and epigenetic features for biomarker use in prognostication of urologic cancers, which together with the available risk assessment models, may complement and overcome the limitations of the traditional TNM staging.
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Affiliation(s)
- Caitlin M Darrell
- Departments of Pathology, Section of Urology, University of Chicago , Chicago, IL, USA
| | - Rodolfo Montironi
- School of Medicine, Section of Pathological Anatomy, Polytechnic University of the Marche Region , Ancona, Italy
| | - Gladell P Paner
- Departments of Pathology, Section of Urology, University of Chicago , Chicago, IL, USA.,Departments of Surgery, Section of Urology, University of Chicago , Chicago, IL, USA
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Panebianco V, Pecoraro M, Fiscon G, Paci P, Farina L, Catalano C. Prostate cancer screening research can benefit from network medicine: an emerging awareness. NPJ Syst Biol Appl 2020; 6:13. [PMID: 32382028 PMCID: PMC7206063 DOI: 10.1038/s41540-020-0133-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 04/09/2020] [Indexed: 01/03/2023] Open
Abstract
Up to date, screening for prostate cancer (PCa) remains one of the most appealing but also a very controversial topics in the urological community. PCa is the second most common cancer in men worldwide and it is universally acknowledged as a complex disease, with a multi-factorial etiology. The pathway of PCa diagnosis has changed dramatically in the last few years, with the multiparametric magnetic resonance (mpMRI) playing a starring role with the introduction of the “MRI Pathway”. In this scenario the basic tenet of network medicine (NM) that sees the disease as perturbation of a network of interconnected molecules and pathways, seems to fit perfectly with the challenges that PCa early detection must face to advance towards a more reliable technique. Integration of tests on body fluids, tissue samples, grading/staging classification, physiological parameters, MR multiparametric imaging and molecular profiling technologies must be integrated in a broader vision of “disease” and its complexity with a focus on early signs. PCa screening research can greatly benefit from NM vision since it provides a sound interpretation of data and a common language, facilitating exchange of ideas between clinicians and data analysts for exploring new research pathways in a rational, highly reliable, and reproducible way.
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Affiliation(s)
- Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I of Rome, Rome, Italy.
| | - Martina Pecoraro
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I of Rome, Rome, Italy
| | - Giulia Fiscon
- Institute for System Analysis and Computer Science (IASI), National Research Council, Rome, Italy
| | - Paola Paci
- Institute for System Analysis and Computer Science (IASI), National Research Council, Rome, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Carlo Catalano
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I of Rome, Rome, Italy
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Fleck JL, Pavel AB, Cassandras CG. A pan-cancer analysis of progression mechanisms and drug sensitivity in cancer cell lines. Mol Omics 2019; 15:399-405. [PMID: 31570905 DOI: 10.1039/c9mo00119k] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Biomarker discovery involves identifying genetic abnormalities within a tumor. However, one of the main challenges in defining such therapeutic targets is accounting for the molecular heterogeneity of cancer. By integrating somatic mutation and gene expression data from hundreds of heterogeneous cell lines from the Cancer Cell Line Encyclopedia (CCLE), we identify sequences of genetic events that may help explain common patterns of oncogenesis across 22 tumor types, and evaluate the general effect of late-stage mutations on drug sensitivity and resistance mechanisms. Through gene enrichment analysis, we find several cancer-specific and immune pathways that are significantly enriched in each of our three proposed phases of cancer progression. By further analyzing the drug activity area associated with compounds that target the BRAF oncogene, a known predictor of drug sensitivity for several compounds used in cancer treatment, we verify that the acquisition of new driver mutations interferes with the targeted drug mechanism, meaning that cells without late-stage mutations generally respond better to drugs.
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Affiliation(s)
- Julia L Fleck
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rua Marques de Sao Vicente, 225, Rio de Janeiro, Brazil.
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8
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Pseudogene Associated Recurrent Gene Fusion in Prostate Cancer. Neoplasia 2019; 21:989-1002. [PMID: 31446281 PMCID: PMC6713813 DOI: 10.1016/j.neo.2019.07.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 07/22/2019] [Accepted: 07/23/2019] [Indexed: 02/08/2023] Open
Abstract
We present the functional characterization of a pseudogene associated recurrent gene fusion in prostate cancer. The fusion gene KLK4-KLKP1 is formed by the fusion of the protein coding gene KLK4 with the noncoding pseudogene KLKP1. Screening of a cohort of 659 patients (380 Caucasian American; 250 African American, and 29 patients from other races) revealed that the KLK4-KLKP1 is expressed in about 32% of prostate cancer patients. Correlative analysis with other ETS gene fusions and SPINK1 revealed a concomitant expression pattern of KLK4-KLKP1 with ERG and a mutually exclusive expression pattern with SPINK1, ETV1, ETV4, and ETV5. Development of an antibody specific to KLK4-KLKP1 fusion protein confirmed the expression of the full-length KLK4-KLKP1 protein in prostate tissues. The in vitro and in vivo functional assays to study the oncogenic properties of KLK4-KLKP1 confirmed its role in cell proliferation, cell invasion, intravasation, and tumor formation. Presence of strong ERG and AR binding sites located at the fusion junction in KLK4-KLKP1 suggests that the fusion gene is regulated by ERG and AR. Correlative analysis of clinical data showed an association of KLK4-KLKP1 with lower preoperative PSA values and in young men (<50 years) with prostate cancer. Screening of patient urine samples showed that KLK4-KLKP1 can be detected noninvasively in urine. Taken together, we present KLK4-KLKP1 as a class of pseudogene associated fusion transcript in cancer with potential applications as a biomarker for routine screening of prostate cancer.
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Tan A, Huang H, Zhang P, Li S. Network-based cancer precision medicine: A new emerging paradigm. Cancer Lett 2019; 458:39-45. [DOI: 10.1016/j.canlet.2019.05.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/29/2019] [Accepted: 05/15/2019] [Indexed: 12/20/2022]
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10
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Ozturk K, Dow M, Carlin DE, Bejar R, Carter H. The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine. J Mol Biol 2018; 430:2875-2899. [PMID: 29908887 PMCID: PMC6097914 DOI: 10.1016/j.jmb.2018.06.016] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/30/2018] [Accepted: 06/06/2018] [Indexed: 12/19/2022]
Abstract
Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.
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Affiliation(s)
- Kivilcim Ozturk
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Michelle Dow
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Daniel E Carlin
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Rafael Bejar
- Moores Cancer Center, Division of Hematology and Oncology, University of California San Diego, La Jolla, CA 92093, USA
| | - Hannah Carter
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center and Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA; CIFAR, MaRS Centre, West Tower, 661 University Ave., Suite 505, Toronto, ON M5G 1M1, Canada.
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