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Ho WM, Chen CY, Chiang TW, Chuang TJ. A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels. Acta Neuropathol Commun 2024; 12:77. [PMID: 38762464 PMCID: PMC11102269 DOI: 10.1186/s40478-024-01790-3] [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: 02/22/2024] [Accepted: 05/05/2024] [Indexed: 05/20/2024] Open
Abstract
Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments.
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Affiliation(s)
- Wei-Min Ho
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
- Ph.D. Program in Translational Medicine, National Taiwan University and Academia Sinica, Taipei, Taiwan
- Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
- School of Medicine, National Tsing Hua University, Hsinchu, Taiwan
| | - Chia-Ying Chen
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Tai-Wei Chiang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Trees-Juen Chuang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan.
- Ph.D. Program in Translational Medicine, National Taiwan University and Academia Sinica, Taipei, Taiwan.
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Joshi SK, Piehowski P, Liu T, Gosline SJC, McDermott JE, Druker BJ, Traer E, Tyner JW, Agarwal A, Tognon CE, Rodland KD. Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine. Annu Rev Pharmacol Toxicol 2024; 64:455-479. [PMID: 37738504 PMCID: PMC10950354 DOI: 10.1146/annurev-pharmtox-022723-113921] [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] [Indexed: 09/24/2023]
Abstract
Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.
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Affiliation(s)
- Sunil K Joshi
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Paul Piehowski
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Tao Liu
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Sara J C Gosline
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Jason E McDermott
- Pacific Northwest National Laboratory, Richland, Washington, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, Oregon, USA
| | - Brian J Druker
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Elie Traer
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Jeffrey W Tyner
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, Oregon, USA
| | - Anupriya Agarwal
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, Oregon, USA
| | - Cristina E Tognon
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Karin D Rodland
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Pacific Northwest National Laboratory, Richland, Washington, USA
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Nair NU, Schäffer AA, Gertz EM, Cheng K, Zerbib J, Sahu AD, Leor G, Shulman ED, Aldape KD, Ben-David U, Ruppin E. Chromosome 7 to the rescue: overcoming chromosome 10 loss in gliomas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576103. [PMID: 38313282 PMCID: PMC10836086 DOI: 10.1101/2024.01.17.576103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
The co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair in human cancers, a phenomenon that has been investigated without resolution since the late 1980s. Expanding beyond previous gene-centric studies, we investigate the co-occurrence in a genome-wide manner taking an evolutionary perspective. First, by mining large tumor aneuploidy data, we predict that the more likely order is 10 loss followed by 7 gain. Second, by analyzing extensive genomic and transcriptomic data from both patients and cell lines, we find that this co-occurrence can be explained by functional rescue interactions that are highly enriched on 7, which can possibly compensate for any detrimental consequences arising from the loss of 10. Finally, by analyzing transcriptomic data from normal, non-cancerous, human brain tissues, we provide a plausible reason why this co-occurrence happens preferentially in cancers originating in certain regions of the brain.
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4
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Demetriou AN, Chow F, Craig DW, Webb MG, Ormond DR, Battiste J, Chakravarti A, Colman H, Villano JL, Schneider BP, Liu JKC, Churchman ML, Zada G. Profiling the molecular and clinical landscape of glioblastoma utilizing the Oncology Research Information Exchange Network brain cancer database. Neurooncol Adv 2024; 6:vdae046. [PMID: 38665799 PMCID: PMC11044707 DOI: 10.1093/noajnl/vdae046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
Background Glioblastoma exhibits aggressive growth and poor outcomes despite treatment, and its marked variability renders therapeutic design and prognostication challenging. The Oncology Research Information Exchange Network (ORIEN) database contains complementary clinical, genomic, and transcriptomic profiling of 206 glioblastoma patients, providing opportunities to identify novel associations between molecular features and clinical outcomes. Methods Survival analyses were performed using the Logrank test, and clinical features were evaluated using Wilcoxon and chi-squared tests with q-values derived via Benjamini-Hochberg correction. Mutational analyses utilized sample-level enrichments from whole exome sequencing data, and statistical tests were performed using the one-sided Fisher Exact test with Benjamini-Hochberg correction. Transcriptomic analyses utilized a student's t-test with Benjamini-Hochberg correction. Expression fold changes were processed with Ingenuity Pathway Analysis to determine pathway-level alterations between groups. Results Key findings include an association of MUC17, SYNE1, and TENM1 mutations with prolonged overall survival (OS); decreased OS associated with higher epithelial growth factor receptor (EGFR) mRNA expression, but not with EGFR amplification or mutation; a 14-transcript signature associated with OS > 2 years; and 2 transcripts associated with OS < 1 year. Conclusions Herein, we report the first clinical, genomic, and transcriptomic analysis of ORIEN glioblastoma cases, incorporating sample reclassification under updated 2021 diagnostic criteria. These findings create multiple avenues for further investigation and reinforce the value of multi-institutional consortia such as ORIEN in deepening our knowledge of intractable diseases such as glioblastoma.
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Affiliation(s)
- Alexandra N Demetriou
- Keck School of Medicine, University of Southern California (USC), Los Angeles, California, USA
| | - Frances Chow
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA
| | - David W Craig
- Department of Integrative Translational Sciences, City of Hope, Duarte, California, USA
| | - Michelle G Webb
- Department of Integrative Translational Sciences, City of Hope, Duarte, California, USA
| | - D Ryan Ormond
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - James Battiste
- Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Arnab Chakravarti
- Department of Radiation Oncology, College of Medicine at The Ohio State University, Columbus, Ohio, USA
| | - Howard Colman
- Huntsman Cancer Institute and Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - John L Villano
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, USA
| | - Bryan P Schneider
- Department of Hematology/Oncology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - James K C Liu
- Department of Neuro-Oncology, Moffitt Cancer Center, Tampa, Florida, USA
| | | | - Gabriel Zada
- Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, California, USA
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Berber I, Erten C, Kazan H. Predator: Predicting the Impact of Cancer Somatic Mutations on Protein-Protein Interactions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3163-3172. [PMID: 37030791 DOI: 10.1109/tcbb.2023.3262119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.
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Schibler AC, Jevtic P, Pegoraro G, Levy DL, Misteli T. Identification of epigenetic modulators as determinants of nuclear size and shape. eLife 2023; 12:e80653. [PMID: 37219077 PMCID: PMC10259489 DOI: 10.7554/elife.80653] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 05/04/2023] [Indexed: 05/24/2023] Open
Abstract
The shape and size of the human cell nucleus is highly variable among cell types and tissues. Changes in nuclear morphology are associated with disease, including cancer, as well as with premature and normal aging. Despite the very fundamental nature of nuclear morphology, the cellular factors that determine nuclear shape and size are not well understood. To identify regulators of nuclear architecture in a systematic and unbiased fashion, we performed a high-throughput imaging-based siRNA screen targeting 867 nuclear proteins including chromatin-associated proteins, epigenetic regulators, and nuclear envelope components. Using multiple morphometric parameters, and eliminating cell cycle effectors, we identified a set of novel determinants of nuclear size and shape. Interestingly, most identified factors altered nuclear morphology without affecting the levels of lamin proteins, which are known prominent regulators of nuclear shape. In contrast, a major group of nuclear shape regulators were modifiers of repressive heterochromatin. Biochemical and molecular analysis uncovered a direct physical interaction of histone H3 with lamin A mediated via combinatorial histone modifications. Furthermore, disease-causing lamin A mutations that result in disruption of nuclear shape inhibited lamin A-histone H3 interactions. Oncogenic histone H3.3 mutants defective for H3K27 methylation resulted in nuclear morphology abnormalities. Altogether, our results represent a systematic exploration of cellular factors involved in determining nuclear morphology and they identify the interaction of lamin A with histone H3 as an important contributor to nuclear morphology in human cells.
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Affiliation(s)
| | - Predrag Jevtic
- Department of Molecular Biology, University of WyomingLaramieUnited States
| | - Gianluca Pegoraro
- High Throughput Imaging Facility (HiTIF), National Cancer Institute, NIHBethesdaUnited States
| | - Daniel L Levy
- Department of Molecular Biology, University of WyomingLaramieUnited States
| | - Tom Misteli
- National Cancer InstituteBethesdaUnited States
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7
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Cheng L, Zhang F, Zhao X, Wang L, Duan W, Guan J, Wang K, Liu Z, Wang X, Wang Z, Wu H, Chen Z, Teng L, Li Y, Xiao F, Fan T, Jian F. Mutational landscape of primary spinal cord astrocytoma. J Pathol 2023. [PMID: 37114614 DOI: 10.1002/path.6084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/13/2023] [Accepted: 03/31/2023] [Indexed: 04/29/2023]
Abstract
Primary spinal cord astrocytoma (SCA) is a rare disease. Knowledge about the molecular profiles of SCAs mostly comes from intracranial glioma; the pattern of genetic alterations of SCAs is not well understood. Herein, we describe genome-sequencing analyses of primary SCAs, aiming to characterize the mutational landscape of primary SCAs. We utilized whole exome sequencing (WES) to analyze somatic nucleotide variants (SNVs) and copy number variants (CNVs) among 51 primary SCAs. Driver genes were searched using four algorithms. GISTIC2 was used to detect significant CNVs. Additionally, recurrently mutated pathways were also summarized. A total of 12 driver genes were identified. Of those, H3F3A (47.1%), TP53 (29.4%), NF1 (19.6%), ATRX (17.6%), and PPM1D (17.6%) were the most frequently mutated genes. Furthermore, three novel driver genes seldom reported in glioma were identified: HNRNPC, SYNE1, and RBM10. Several germline mutations, including three variants (SLC16A8 rs2235573, LMF1 rs3751667, FAM20C rs774848096) that were associated with risk of brain glioma, were frequently observed in SCAs. Moreover, 12q14.1 (13.7%) encompassing the oncogene CDK4 was recurrently amplified and negatively affected patient prognosis. Besides frequently mutated RTK/RAS pathway and PI3K pathway, the cell cycle pathway controlling the phosphorylation of retinoblastoma protein (RB) was mutated in 39.2% of patients. Overall, a considerable degree of the somatic mutation landscape is shared between SCAs and brainstem glioma. Our work provides a key insight into the molecular profiling of primary SCAs, which might represent candidate drug targets and complement the molecular atlas of glioma. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Lei Cheng
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Fan Zhang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, PR China
| | - Xingang Zhao
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, PR China
| | - Leiming Wang
- Department of Pathology, Xuanwu Hospital, Capital Medical University, Beijing, PR China
| | - Wanru Duan
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Jian Guan
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Kai Wang
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Zhenlei Liu
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Xingwen Wang
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Zuowei Wang
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Hao Wu
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Zan Chen
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Lianghong Teng
- Department of Pathology, Xuanwu Hospital, Capital Medical University, Beijing, PR China
| | - Yifei Li
- The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Fei Xiao
- The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Tao Fan
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, PR China
| | - Fengzeng Jian
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
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Wang Z, Dai Z, Zhang H, Liang X, Zhang X, Wen Z, Luo P, Zhang J, Liu Z, Zhang M, Cheng Q. Tumor-secreted lactate contributes to an immunosuppressive microenvironment and affects CD8 T-cell infiltration in glioblastoma. Front Immunol 2023; 14:894853. [PMID: 37122693 PMCID: PMC10130393 DOI: 10.3389/fimmu.2023.894853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 01/05/2023] [Indexed: 05/02/2023] Open
Abstract
Introduction Glioblastoma is a malignant brain tumor with poor prognosis. Lactate is the main product of tumor cells, and its secretion may relate to immunocytes' activation. However, its role in glioblastoma is poorly understood. Methods This work performed bulk RNA-seq analysis and single cell RNA-seq analysis to explore the role of lactate in glioblastoma progression. Over 1400 glioblastoma samples were grouped into different clusters according to their expression and the results were validated with our own data, the xiangya cohort. Immunocytes infiltration analysis, immunogram and the map of immune checkpoint genes' expression were applied to analyze the potential connection between the lactate level with tumor immune microenvironment. Furthermore, machine learning algorithms and cell-cell interaction algorithm were introduced to reveal the connection of tumor cells with immunocytes. By co-culturing CD8 T cells with tumor cells, and performing immunohistochemistry on Xiangya cohort samples further validated results from previous analysis. Discussion In this work, lactate is proved that contributes to glioblastoma immune suppressive microenvironment. High level of lactate in tumor microenvironment can affect CD8 T cells' migration and infiltration ratio in glioblastoma. To step further, potential compounds that targets to samples from different groups were also predicted for future exploration.
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Affiliation(s)
- Zeyu Wang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- MRC Centre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, United Kingdom
| | - Ziyu Dai
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hao Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xisong Liang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xun Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhipeng Wen
- Department of Pharmacy, The Affiliated Hospital of Guizhou Medical University, Guizhou Medical University, Guiyang, Guizhou, China
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jian Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Mingyu Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Diagnosis and Therapy Center for Gliomas of Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Diagnosis and Therapy Center for Gliomas of Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, Hunan, China
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Jiang L, Yu H, Guo Y. Modeling the relationship between gene expression and mutational signature. QUANTITATIVE BIOLOGY 2023; 11:31-43. [PMID: 37032811 PMCID: PMC10078980 DOI: 10.15302/j-qb-022-0309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Background Mutational signatures computed from somatic mutations, allow an in-depth understanding of tumorigenesis and may illuminate early prevention strategies. Many studies have shown the regulation effects between somatic mutation and gene expression dysregulation. Methods We hypothesized that there are potential associations between mutational signature and gene expression. We capitalized upon RNA-seq data to model 49 established mutational signatures in 33 cancer types. Both accuracy and area under the curve were used as performance measures in five-fold cross-validation. Results A total of 475 models using unconstrained genes, and 112 models using protein-coding genes were selected for future inference purposes. An independent gene expression dataset on lung cancer smoking status was used for validation which achieved over 80% for both accuracy and area under the curve. Conclusion These results demonstrate that the associations between gene expression and somatic mutations can translate into the associations between gene expression and mutational signatures.
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10
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Genotype-Phenotype Correlations in Human Diseases Caused by Mutations of LINC Complex-Associated Genes: A Systematic Review and Meta-Summary. Cells 2022; 11:cells11244065. [PMID: 36552829 PMCID: PMC9777268 DOI: 10.3390/cells11244065] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Mutations in genes encoding proteins associated with the linker of nucleoskeleton and cytoskeleton (LINC) complex within the nuclear envelope cause different diseases with varying phenotypes including skeletal muscle, cardiac, metabolic, or nervous system pathologies. There is some understanding of the structure of LINC complex-associated proteins and how they interact, but it is unclear how mutations in genes encoding them can cause the same disease, and different diseases with different phenotypes. Here, published mutations in LINC complex-associated proteins were systematically reviewed and analyzed to ascertain whether patterns exist between the genetic sequence variants and clinical phenotypes. This revealed LMNA is the only LINC complex-associated gene in which mutations commonly cause distinct conditions, and there are no clear genotype-phenotype correlations. Clusters of LMNA variants causing striated muscle disease are located in exons 1 and 6, and metabolic disease-associated LMNA variants are frequently found in the tail of lamin A/C. Additionally, exon 6 of the emerin gene, EMD, may be a mutation "hot-spot", and diseases related to SYNE1, encoding nesprin-1, are most often caused by nonsense type mutations. These results provide insight into the diverse roles of LINC-complex proteins in human disease and provide direction for future gene-targeted therapy development.
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11
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Li J, Ek F, Olsson R, Belting M, Bengzon J. Glioblastoma CD105 + cells define a SOX2 - cancer stem cell-like subpopulation in the pre-invasive niche. Acta Neuropathol Commun 2022; 10:126. [PMID: 36038950 PMCID: PMC9426031 DOI: 10.1186/s40478-022-01422-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/04/2022] [Indexed: 11/10/2022] Open
Abstract
Glioblastoma (GBM) is the most common and most aggressive primary brain tumor in adults. Glioma stem like cells (GSC) represent the highest cellular hierarchy in GBM and have a determining role in tumor growth, recurrence and patient prognosis. However, a better definition of GSC subpopulations, especially at the surgical resection margin, is warranted for improved oncological treatment options. The present study interrogated cells expressing CD105 (CD105+) specifically within the tumor front and the pre-invasive niche as a potential GSC subpopulation. GBM primary cell lines were generated from patients (n = 18) and CD105+ cells were isolated and assessed for stem-like characteristics. In vitro, CD105+ cells proliferated and enriched in serum-containing medium but not in serum-free conditions. CD105+ cells were characterized by Nestin+, Vimentin+ and SOX2-, clearly distinguishing them from SOX2+ GCS. GBM CD105+ cells differentiated into osteocytes and adipocytes but not chondrocytes. Exome sequencing revealed that GBM CD105+ cells matched 83% of somatic mutations in the Cancer cell line encyclopedia, indicating a malignant phenotype and in vivo xenotransplantation assays verified their tumorigenic potential. Cytokine assays showed that immunosuppressive and protumorigenic cytokines such as IL6, IL8, CCL2, CXCL-1 were produced by CD105+ cells. Finally, screening for 88 clinical drugs revealed that GBM CD105+ cells are resistant to most chemotherapeutics except Doxorubicin, Idarubicin, Fludarabine and ABT-751. Our study provides a rationale for targeting tumoral CD105+ cells in order to reshape the tumor microenvironment and block GBM progression.
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Affiliation(s)
- Jiaxin Li
- Stem Cell Center, Lund University, Lund, Sweden. .,Division of Neurosurgery, Department of Clinical Sciences, Lund University, Lund, Sweden.
| | - Fredrik Ek
- Chemical Biology and Therapeutics, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Roger Olsson
- Chemical Biology and Therapeutics, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Mattias Belting
- Section of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden.,Department of Hematology, Oncology and Radiophysics, Skane University Hospital, Lund, Sweden.,Science for Life Laboratory, Department of Immunology, Genetics, and Pathology, Uppsala University, Uppsala, Sweden
| | - Johan Bengzon
- Stem Cell Center, Lund University, Lund, Sweden.,Division of Neurosurgery, Department of Clinical Sciences, Lund University, Lund, Sweden.,Department of Neurosurgery, Skane University Hospital, Lund, Sweden
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Pan J, Sheng S, Ye L, Xu X, Ma Y, Feng X, Qiu L, Fan Z, Wang Y, Xia X, Zheng JC. Extracellular vesicles derived from glioblastoma promote proliferation and migration of neural progenitor cells via PI3K-Akt pathway. Cell Commun Signal 2022; 20:7. [PMID: 35022057 PMCID: PMC8756733 DOI: 10.1186/s12964-021-00760-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 06/19/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Glioblastomas are lethal brain tumors under the current combinatorial therapeutic strategy that includes surgery, chemo- and radio-therapies. Extensive changes in the tumor microenvironment is a key reason for resistance to chemo- or radio-therapy and frequent tumor recurrences. Understanding the tumor-nontumor cell interaction in TME is critical for developing new therapy. Glioblastomas are known to recruit normal cells in their environs to sustain growth and encroachment into other regions. Neural progenitor cells (NPCs) have been noted to migrate towards the site of glioblastomas, however, the detailed mechanisms underlying glioblastoma-mediated NPCs' alteration remain unkown. METHODS We collected EVs in the culture medium of three classic glioblastoma cell lines, U87 and A172 (male cell lines), and LN229 (female cell line). U87, A172, and LN229 were co-cultured with their corresponding EVs, respectively. Mouse NPCs (mNPCs) were co-cultured with glioblastoma-derived EVs. The proliferation and migration of tumor cells and mNPCs after EVs treatment were examined. Proteomic analysis and western blotting were utilized to identify the underlying mechanisms of glioblastoma-derived EVs-induced alterations in mNPCs. RESULTS We first show that glioblastoma cell lines U87-, A172-, and LN229-derived EVs were essential for glioblastoma cell prolifeartion and migration. We then demonstrated that glioblastoma-derived EVs dramatically promoted NPC proliferation and migration. Mechanistic studies identify that glioblastoma-derived EVs achieve their functions via activating PI3K-Akt-mTOR pathway in mNPCs. Inhibiting PI3K-Akt pathway reversed the elevated prolfieration and migration of glioblastoma-derived EVs-treated mNPCs. CONCLUSION Our findings demonstrate that EVs play a key role in intercellular communication in tumor microenvironment. Inhibition of the tumorgenic EVs-mediated PI3K-Akt-mTOR pathway activation might be a novel strategy to shed light on glioblastoma therapy. Video Abstract.
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Affiliation(s)
- Jiabin Pan
- Center for Translational Neurodegeneration and Regenerative Therapy, Shanghai Tenth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200072, China
| | - Shiyang Sheng
- Center for Translational Neurodegeneration and Regenerative Therapy, Shanghai Tenth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200072, China
| | - Ling Ye
- Center for Translational Neurodegeneration and Regenerative Therapy, Shanghai Tenth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200072, China
| | - Xiaonan Xu
- Center for Translational Neurodegeneration and Regenerative Therapy, Shanghai Tenth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200072, China
| | - Yizhao Ma
- Center for Translational Neurodegeneration and Regenerative Therapy, Shanghai Tenth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200072, China
| | - Xuanran Feng
- Center for Translational Neurodegeneration and Regenerative Therapy, Shanghai Tenth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200072, China
| | - Lisha Qiu
- Center for Translational Neurodegeneration and Regenerative Therapy, Shanghai Tenth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200072, China
| | - Zhaohuan Fan
- Center for Translational Neurodegeneration and Regenerative Therapy, Shanghai Tenth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200072, China
| | - Yi Wang
- Center for Translational Neurodegeneration and Regenerative Therapy, Shanghai Tenth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200072, China. .,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200434, China.
| | - Xiaohuan Xia
- Center for Translational Neurodegeneration and Regenerative Therapy, Shanghai Tenth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200072, China. .,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200434, China.
| | - Jialin C Zheng
- Center for Translational Neurodegeneration and Regenerative Therapy, Shanghai Tenth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200072, China. .,Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, 200434, China. .,Collaborative Innovation Center for Brain Science, Tongji University, Shanghai, 200092, China.
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Chu YD, Kee KM, Lin WR, Lai MW, Lu SN, Chung WH, Pang ST, Yeh CT. SYNE1 Exonic Variant rs9479297 Contributes to Concurrent Hepatocellular and Transitional Cell Carcinoma Double Primary Cancer. Biomedicines 2021; 9:1819. [PMID: 34944636 PMCID: PMC8698502 DOI: 10.3390/biomedicines9121819] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 11/30/2021] [Accepted: 12/01/2021] [Indexed: 12/25/2022] Open
Abstract
Unexpected high risk of synchronous/metachronous hepatocellular carcinoma (HCC) and transitional cell carcinoma (TCC) co-occurrence has been discovered previously. Here, we searched for genetic variation contributing to the co-occurrence of this double primary cancer (DPC). Using targeted exome sequencing, a panel of variants associated with concurrent DPC was identified. However, only a nonsynonymous variant within the Spectrin Repeat Containing Nuclear Envelope Protein 1 (SYNE1) gene was associated with DPC occurrence (p = 0.002), compared with that in the healthy population. Further independent cohort verification analysis revealed that the SYNE1-rs9479297-TT genotype (versus TC + CC genotypes) was enriched in patients with DPC, compared with that in those with TCC alone (p = 0.039), those with HCC alone (p = 0.006), those with non-HCC/non-TCC (p < 0.001), and healthy population (p < 0.001). SYNE1 mRNA expression reduced in both patients with HCC and TCC, and its lower expression in HCC was associated with shorter recurrence-free (p = 0.0314) and metastasis-free (p = 0.0479) survival. SYNE1-rs9479297 genotypes were correlated with tissue SYNE1 levels and clinical outcomes in HCC patients. Finally, SYNE1 silencing enhanced the cell proliferation and migration of HCC/TCC cells. In conclusion, SYNE1-rs9479297 genotypes were associated with HCC/TCC DPC co-occurrence and correlated with SYNE1 expression, which in turn contributed to HCC/TCC cell proliferation and migration, thereby affecting clinical outcomes.
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Affiliation(s)
- Yu-De Chu
- Liver Research Center, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan; (Y.-D.C.); (W.-R.L.); (M.-W.L.)
| | - Kwong-Ming Kee
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan; (K.-M.K.); (S.-N.L.)
| | - Wey-Ran Lin
- Liver Research Center, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan; (Y.-D.C.); (W.-R.L.); (M.-W.L.)
- Department of Hepatology and Gastroenterology, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Ming-Wei Lai
- Liver Research Center, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan; (Y.-D.C.); (W.-R.L.); (M.-W.L.)
- Division of Pediatric Gastroenterology, Department of Pediatrics, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Sheng-Nan Lu
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan; (K.-M.K.); (S.-N.L.)
| | - Wen-Hung Chung
- Whole-Genome Research Core Laboratory of Human Diseases, Chang Gung Memorial Hospital, Keelung 204, Taiwan;
| | - See-Tong Pang
- Division of Urology, Department of Surgery, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan;
| | - Chau-Ting Yeh
- Liver Research Center, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan; (Y.-D.C.); (W.-R.L.); (M.-W.L.)
- Molecular Medicine Research Center, Chang Gung University, Taoyuan 333, Taiwan
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14
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López-Ginés C, Muñoz-Hidalgo L, San-Miguel T, Megías J, Triviño JC, Calabuig S, Roldán P, Cerdá-Nicolás M, Monleón D. Whole-exome sequencing, EGFR amplification and infiltration patterns in human glioblastoma. Am J Cancer Res 2021; 11:5543-5558. [PMID: 34873478 PMCID: PMC8640814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023] Open
Abstract
Glioblastoma (GBM) is the most common malignant primary brain tumor in adults. This cancer shows rapid, highly infiltrative growth, that invades individually or in small groups the surrounding tissue. The aggressive tumor biology of GBM has devastating consequences with a median survival of 15 months. GBM often has Epidermal Growth Factor Receptor (EGFR) abnormalities. Despite recent advances in the study of GBM tumor biology, it is unclear whether mutations in GBM are related to EGFR amplification and relevant phenotypes like tumor infiltration. This study aimed to perform whole-exome sequencing analysis in 30 human GBM samples for identifying mutational portraits associated with EGFR amplification and infiltrative patterns. Our results show that EGFR-amplified tumors have overall higher mutation rates than EGFR-no-amplified. Six genes out of 2029 candidate genes show mutations associated with EGFR amplification status. Mutations in these genes for GBM are novel, not previously reported in GBM, and with little presence in the TCGA database. GPR179, USP48, and BLK show mutation only in EGFR-amplified cases, and all the affected cases exhibit diffuse infiltrative patterns. On the other hand, mutations in ADGB, EHHADH, and PTPN13, were present only in the EGFR-no-amplified group with a more diverse infiltrative phenotype. Overall, our work identified different mutational portraits of GBM related to well-established features like EGFR amplification and tumor infiltration.
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Affiliation(s)
| | | | | | - Javier Megías
- Departament of Pathology, University of ValenciaValencia, Spain
| | | | - Silvia Calabuig
- Departament of Pathology, University of ValenciaValencia, Spain
| | - Pedro Roldán
- Department of Neurosurgery, University Clinical Hospital ValenciaValencia, Spain
| | | | - Daniel Monleón
- Departament of Pathology, University of ValenciaValencia, Spain
- Health Research Institute INCLIVAValencia, Spain
- CIBERFES_ISCIIIValencia, Spain
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15
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Rose M, Cardon T, Aboulouard S, Hajjaji N, Kobeissy F, Duhamel M, Fournier I, Salzet M. Surfaceome Proteomic of Glioblastoma Revealed Potential Targets for Immunotherapy. Front Immunol 2021; 12:746168. [PMID: 34646273 PMCID: PMC8503648 DOI: 10.3389/fimmu.2021.746168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 09/08/2021] [Indexed: 12/21/2022] Open
Abstract
Glioblastoma (GBM) is the most common and devastating malignant brain tumor in adults. The mortality rate is very high despite different treatments. New therapeutic targets are therefore highly needed. Cell-surface proteins represent attractive targets due to their accessibility, their involvement in essential signaling pathways, and their dysregulated expression in cancer. Moreover, they are potential targets for CAR-based immunotherapy or mRNA vaccine strategies. In this context, we investigated the GBM-associated surfaceome by comparing it to astrocytes cell line surfaceome to identify new specific targets for GBM. For this purpose, biotinylation of cell surface proteins has been carried out in GBM and astrocytes cell lines. Biotinylated proteins were purified on streptavidin beads and analyzed by shotgun proteomics. Cell surface proteins were identified with Cell Surface Proteins Atlas (CSPA) and Gene Ontology enrichment. Among all the surface proteins identified in the different cell lines we have confirmed the expression of 66 of these in patient’s glioblastoma using spatial proteomic guided by MALDI-mass spectrometry. Moreover, 87 surface proteins overexpressed or exclusive in GBM cell lines have been identified. Among these, we found 11 specific potential targets for GBM including 5 mutated proteins such as RELL1, CYBA, EGFR, and MHC I proteins. Matching with drugs and clinical trials databases revealed that 7 proteins were druggable and under evaluation, 3 proteins have no known drug interaction yet and none of them are the mutated form of the identified proteins. Taken together, we discovered potential targets for immune therapy strategies in GBM.
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Affiliation(s)
- Mélanie Rose
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France
| | - Tristan Cardon
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France
| | - Soulaimane Aboulouard
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France
| | - Nawale Hajjaji
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Breast Cancer Unit, Oscar Lambret Center, Lille, France
| | - Firas Kobeissy
- Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Marie Duhamel
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France
| | - Isabelle Fournier
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Institut Universitaire de France, Paris, France
| | - Michel Salzet
- Université Lille, Inserm, CHU Lille, U1192, Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), Lille, France.,Institut Universitaire de France, Paris, France
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16
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Bryant JP, Levy A, Heiss J, Banasavadi-Siddegowda YK. Review of PP2A Tumor Biology and Antitumor Effects of PP2A Inhibitor LB100 in the Nervous System. Cancers (Basel) 2021; 13:cancers13123087. [PMID: 34205611 PMCID: PMC8235527 DOI: 10.3390/cancers13123087] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 12/14/2022] Open
Abstract
Simple Summary Central and peripheral nervous system tumors represent a heterogenous group of neoplasms which often demonstrate resistance to treatment. Given that these tumors are often refractory to conventional therapy, novel pharmaceutical regimens are needed for successfully treating this pathology. One such therapeutic is the serine/threonine phosphatase inhibitor, LB100. LB100 is a water-soluble competitive protein phosphtase inhibitor that has demonstrated antitumor effects in preclinical and clinical trials. In this review, we aim to summarize current evidence demonstrating the efficacy of LB100 as an inhibitor of nervous system tumors. Furthermore, we review the involvement of the well-studied phosphatase, protein phosphatase 2A, in oncogenic cell signaling pathways, neurophysiology, and neurodevelopment. Abstract Protein phosphatase 2A (PP2A) is a ubiquitous serine/threonine phosphatase implicated in a wide variety of regulatory cellular functions. PP2A is abundant in the mammalian nervous system, and dysregulation of its cellular functions is associated with myriad neurodegenerative disorders. Additionally, PP2A has oncologic implications, recently garnering attention and emerging as a therapeutic target because of the antitumor effects of a potent PP2A inhibitor, LB100. LB100 abrogation of PP2A is believed to exert its inhibitory effects on tumor progression through cellular chemo- and radiosensitization to adjuvant agents. An updated and unifying review of PP2A biology and inhibition with LB100 as a therapeutic strategy for targeting cancers of the nervous system is needed, as other reviews have mainly covered broader applications of LB100. In this review, we discuss the role of PP2A in normal cells and tumor cells of the nervous system. Furthermore, we summarize current evidence regarding the therapeutic potential of LB100 for treating solid tumors of the nervous system.
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Affiliation(s)
- Jean-Paul Bryant
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA; (J.-P.B.); (J.H.)
| | - Adam Levy
- Miller School of Medicine, University of Miami, Miami, FL 33136, USA;
| | - John Heiss
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA; (J.-P.B.); (J.H.)
| | - Yeshavanth Kumar Banasavadi-Siddegowda
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA; (J.-P.B.); (J.H.)
- Correspondence: ; Tel.: +1-301-451-0970
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17
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Zhong P, Shu R, Wu H, Liu Z, Shen X, Hu Y. Low KRT15 expression is associated with poor prognosis in patients with breast invasive carcinoma. Exp Ther Med 2021; 21:305. [PMID: 33717248 PMCID: PMC7885068 DOI: 10.3892/etm.2021.9736] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 12/01/2020] [Indexed: 12/13/2022] Open
Abstract
Although keratin 15 (KRT15) has been indicated to be overexpressed in several types of tumor, its role in breast invasive carcinoma (BRCA) has so far remained elusive. The aim of the present study was to explore KRT15 expression in BRCA based on data obtained from The Cancer Genome Atlas and The Genotype-Tissue Expression. KRT15 expression was compared using a Wilcoxon rank-sum test. Functional enrichment analysis was performed to reveal the biological roles and pathways of KRT15. The association between KRT15 expression and immune-cell infiltration was evaluated via single-sample gene set enrichment analysis (ssGSEA). To investigate the relationship between clinicopathological features and KRT15 expression, the prognostic value of KRT15 and other clinical factors was evaluated using Cox regression analysis and Kaplan-Meier (KM) plots. Subgroup prognostic analysis was also performed using forest plots and KM curves. Finally, a tissue microarray was used to assess KRT15 expression in BRCA tissues. KRT15 expression was significantly lower in BRCA tissues compared with that in normal tissues. Functional enrichment analysis suggested that KRT15-related genes were primarily enriched in the transmembrane transporter complex, cornification and ligand-receptor interactions. Increased KRT15 was associated with several tumor-suppressive pathways. ssGSEA revealed that high KRT15 expression was significantly associated with natural killer-cell, B-cell and mast-cell infiltration. Significant associations were observed between low KRT15 expression and advanced stage clinicopathological factors, as well as unfavorable overall survival (OS) and disease-specific survival. Multivariate Cox regression analysis suggested that KRT15 was an independent prognostic factor for OS (P=0.039; hazard ratio, 0.590; 95% CI, 0.358-0.974). Subgroup prognostic analysis demonstrated that low KRT15 was a reliable predictor of poor OS. Immunohistochemistry of a tissue microarray indicated that positive KRT15 expression rates were significantly higher in normal tissues compared with those in the BRCA tissues. In conclusion, low KRT15 expression was significantly associated with poor prognosis in patients with BRCA. Thus, KRT15 may serve an important role in BRCA progression and may be used as a promising prognostic marker for diagnostic and prognostic analyses in patients with BRCA.
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Affiliation(s)
- Pengcheng Zhong
- Laboratory of Herbal Drug Discovery, Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, P.R. China
| | - Rong Shu
- Laboratory of Herbal Drug Discovery, Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, P.R. China
| | - Huiwen Wu
- Laboratory of Herbal Drug Discovery, Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, P.R. China
| | - Zhiwen Liu
- Laboratory of Herbal Drug Discovery, Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, P.R. China
| | - Xiaoling Shen
- Laboratory of Herbal Drug Discovery, Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, P.R. China
| | - Yingjie Hu
- Laboratory of Herbal Drug Discovery, Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510405, P.R. China
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18
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Jiang Q, Jin M. Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression. Front Genet 2021; 12:629946. [PMID: 33719339 PMCID: PMC7952975 DOI: 10.3389/fgene.2021.629946] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 01/21/2021] [Indexed: 01/26/2023] Open
Abstract
Exploring the molecular mechanisms of breast cancer is essential for the early prediction, diagnosis, and treatment of cancer patients. The large scale of data obtained from the high-throughput sequencing technology makes it difficult to identify the driver mutations and a minimal optimal set of genes that are critical to the classification of cancer. In this study, we propose a novel method without any prior information to identify mutated genes associated with breast cancer. For the somatic mutation data, it is processed to a mutated matrix, from which the mutation frequency of each gene can be obtained. By setting a reasonable threshold for the mutation frequency, a mutated gene set is filtered from the mutated matrix. For the gene expression data, it is used to generate the gene expression matrix, while the mutated gene set is mapped onto the matrix to construct a co-expression profile. In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. In the stage of evaluation, we propose a weighted metric to modify the traditional accuracy to solve the sample imbalance problem. We apply the proposed method to The Cancer Genome Atlas breast cancer data and identify a mutated gene set, among which the implicated genes are oncogenes or tumor suppressors previously reported to be associated with carcinogenesis. As a comparison with the integrative network, we also perform the optimal model on the individual gene expression and the gold standard PMA50. The results show that the integrative network outperforms the gene expression and PMA50 in the average of most metrics, which indicate the effectiveness of our proposed method by integrating multiple data sources, and can discover the associated mutated genes in breast cancer.
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Affiliation(s)
- Qin Jiang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Min Jin
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
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19
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Erten C, Houdjedj A, Kazan H. Ranking cancer drivers via betweenness-based outlier detection and random walks. BMC Bioinformatics 2021; 22:62. [PMID: 33568049 PMCID: PMC7877041 DOI: 10.1186/s12859-021-03989-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 01/31/2021] [Indexed: 12/04/2022] Open
Abstract
Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.
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Affiliation(s)
- Cesim Erten
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey
| | - Aissa Houdjedj
- Electrical and Computer Engineering Graduate Program, Antalya Bilim University, Antalya, Turkey
| | - Hilal Kazan
- Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey.
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20
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Menyhárt O, Győrffy B. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput Struct Biotechnol J 2021; 19:949-960. [PMID: 33613862 PMCID: PMC7868685 DOI: 10.1016/j.csbj.2021.01.009] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 01/05/2021] [Accepted: 01/08/2021] [Indexed: 12/17/2022] Open
Abstract
While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.
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Affiliation(s)
- Otília Menyhárt
- Semmelweis University, Department of Bioinformatics and 2 Department of Pediatrics, H-1094 Budapest, Hungary
- Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Magyar tudósok körútja 2., H-1117 Budapest, Hungary
| | - Balázs Győrffy
- Semmelweis University, Department of Bioinformatics and 2 Department of Pediatrics, H-1094 Budapest, Hungary
- Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Magyar tudósok körútja 2., H-1117 Budapest, Hungary
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21
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Qin G, Liu Z, Xie L. Multiple Omics Data Integration. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11508-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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22
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Saurabh R, Nandi S, Sinha N, Shukla M, Sarkar RR. Prediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: An AI‐based approach. Chem Biol Drug Des 2020; 96:1005-1019. [DOI: 10.1111/cbdd.13668] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/24/2020] [Accepted: 02/02/2020] [Indexed: 01/03/2023]
Affiliation(s)
- Rochi Saurabh
- Chemical Engineering and Process Development Division CSIR‐National Chemical Laboratory Pune India
| | - Sutanu Nandi
- Chemical Engineering and Process Development Division CSIR‐National Chemical Laboratory Pune India
- Academy of Scientific & Innovative Research (AcSIR) Ghaziabad India
| | - Noopur Sinha
- Chemical Engineering and Process Development Division CSIR‐National Chemical Laboratory Pune India
- Academy of Scientific & Innovative Research (AcSIR) Ghaziabad India
| | - Mudita Shukla
- Chemical Engineering and Process Development Division CSIR‐National Chemical Laboratory Pune India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development Division CSIR‐National Chemical Laboratory Pune India
- Academy of Scientific & Innovative Research (AcSIR) Ghaziabad India
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23
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Accomando WP, Rao AR, Hogan DJ, Newman AM, Nakao A, Alizadeh AA, Diehn M, Diago OR, Gammon D, Haghighi A, Gruber HE, Jolly DJ, Ostertag D. Molecular and Immunologic Signatures are Related to Clinical Benefit from Treatment with Vocimagene Amiretrorepvec (Toca 511) and 5-Fluorocytosine (Toca FC) in Patients with Glioma. Clin Cancer Res 2020; 26:6176-6186. [PMID: 32816892 DOI: 10.1158/1078-0432.ccr-20-0536] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 04/30/2020] [Accepted: 08/13/2020] [Indexed: 11/16/2022]
Abstract
PURPOSE High-grade gliomas (HGGs) are central nervous system tumors with poor prognoses and limited treatment options. Vocimagene amiretrorepvec (Toca 511) is a retroviral replicating vector encoding cytosine deaminase, which converts extended release 5-fluorocytosine (Toca FC) into the anticancer agent, 5-fluorouracil. According to preclinical studies, this therapy kills cancer cells and immunosuppressive myeloid cells in the tumor microenvironment, leading to T-cell-mediated antitumor immune activity. Therefore, we sought to elucidate this immune-related mechanism of action in humans, and to investigate potential molecular and immunologic indicators of clinical benefit from therapy. PATIENTS AND METHODS In a phase I clinical trial (NCT01470794), patients with recurrent HGG treated with Toca 511 and Toca FC showed improved survival relative to historical controls, and some had durable complete responses to therapy. As a part of this trial, we performed whole-exome DNA sequencing, RNA-sequencing, and multiplex digital ELISA measurements on tumor and blood samples. RESULTS Genetic analyses suggest mutations, copy-number variations, and neoantigens are linked to survival. Quantities of tumor immune infiltrates estimated by transcript abundance may potentially predict clinical outcomes. Peak values of cytokines in peripheral blood samples collected during and after therapy could indicate response. CONCLUSIONS These results support an immune-related mechanism of action for Toca 511 and Toca FC, and suggest that molecular and immunologic signatures are related to clinical benefit from treatment.
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Affiliation(s)
| | | | | | - Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California.,Department of Biomedical Data Science, Stanford University, Stanford, California
| | - Aki Nakao
- CiberMed Inc., Palo Alto, California
| | - Ash A Alizadeh
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California.,Stanford Cancer Institute, Stanford University, Stanford, California.,Division of Oncology, Department of Medicine, Stanford University, Stanford, California.,Division of Hematology, Department of Medicine, Stanford University, Stanford, California
| | - Maximilian Diehn
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California.,Stanford Cancer Institute, Stanford University, Stanford, California.,Department of Radiation Oncology, Stanford University, Stanford, California
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24
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Springer S, Masica DL, Dal Molin M, Douville C, Thoburn CJ, Afsari B, Li L, Cohen JD, Thompson E, Allen PJ, Klimstra DS, Schattner MA, Schmidt CM, Yip-Schneider M, Simpson RE, Fernandez-Del Castillo C, Mino-Kenudson M, Brugge W, Brand RE, Singhi AD, Scarpa A, Lawlor R, Salvia R, Zamboni G, Hong SM, Hwang DW, Jang JY, Kwon W, Swan N, Geoghegan J, Falconi M, Crippa S, Doglioni C, Paulino J, Schulick RD, Edil BH, Park W, Yachida S, Hijioka S, van Hooft J, He J, Weiss MJ, Burkhart R, Makary M, Canto MI, Goggins MG, Ptak J, Dobbyn L, Schaefer J, Sillman N, Popoli M, Klein AP, Tomasetti C, Karchin R, Papadopoulos N, Kinzler KW, Vogelstein B, Wolfgang CL, Hruban RH, Lennon AM. A multimodality test to guide the management of patients with a pancreatic cyst. Sci Transl Med 2020; 11:11/501/eaav4772. [PMID: 31316009 DOI: 10.1126/scitranslmed.aav4772] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 01/07/2019] [Accepted: 06/24/2019] [Indexed: 12/12/2022]
Abstract
Pancreatic cysts are common and often pose a management dilemma, because some cysts are precancerous, whereas others have little risk of developing into invasive cancers. We used supervised machine learning techniques to develop a comprehensive test, CompCyst, to guide the management of patients with pancreatic cysts. The test is based on selected clinical features, imaging characteristics, and cyst fluid genetic and biochemical markers. Using data from 436 patients with pancreatic cysts, we trained CompCyst to classify patients as those who required surgery, those who should be routinely monitored, and those who did not require further surveillance. We then tested CompCyst in an independent cohort of 426 patients, with histopathology used as the gold standard. We found that clinical management informed by the CompCyst test was more accurate than the management dictated by conventional clinical and imaging criteria alone. Application of the CompCyst test would have spared surgery in more than half of the patients who underwent unnecessary resection of their cysts. CompCyst therefore has the potential to reduce the patient morbidity and economic costs associated with current standard-of-care pancreatic cyst management practices.
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Affiliation(s)
- Simeon Springer
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - David L Masica
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Biomedical Engineering, Johns Hopkins Medical Institutions, Johns Hopkins University, Baltimore, MD 21287, USA.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Marco Dal Molin
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Pathology, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Christopher Douville
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Biomedical Engineering, Johns Hopkins Medical Institutions, Johns Hopkins University, Baltimore, MD 21287, USA.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Christopher J Thoburn
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Bahman Afsari
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Oncology, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Lu Li
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Joshua D Cohen
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Biomedical Engineering, Johns Hopkins Medical Institutions, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Elizabeth Thompson
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Pathology, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Peter J Allen
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - David S Klimstra
- Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Mark A Schattner
- Department of Gastroenterology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - C Max Schmidt
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Michele Yip-Schneider
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Rachel E Simpson
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | | | - Mari Mino-Kenudson
- Department of Histopathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - William Brugge
- Department of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Randall E Brand
- Department of Medicine, University of Pittsburgh, Pittsburgh PA 15213, USA
| | - Aatur D Singhi
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Aldo Scarpa
- ARC-Net Research Centre, University and Hospital Trust of Verona, Verona 37134, Italy.,Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona 37134, Italy
| | - Rita Lawlor
- ARC-Net Research Centre, University and Hospital Trust of Verona, Verona 37134, Italy.,Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona 37134, Italy
| | - Roberto Salvia
- General and Pancreatic Surgery, Pancreas Institute, University and Hospital Trust of Verona, Verona 37134, Italy
| | - Giuseppe Zamboni
- Department of Pathology, Ospedale Sacro Cuore-Don Calabria, Negrar 37024, Italy
| | - Seung-Mo Hong
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, South Korea
| | - Dae Wook Hwang
- Hepatobiliary and Pancreas Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, South Korea
| | - Jin-Young Jang
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, South Korea
| | - Wooil Kwon
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, South Korea
| | - Niall Swan
- Department of Histopathology, St. Vincent's University Hospital, Dublin D04 T6F4, Ireland
| | - Justin Geoghegan
- Department of Surgery, St. Vincent's University Hospital, Dublin D04 T6F4, Ireland
| | - Massimo Falconi
- Division of Pancreatic Surgery, Department of Surgery, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Stefano Crippa
- Division of Pancreatic Surgery, Department of Surgery, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Claudio Doglioni
- Department of Pathology, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Jorge Paulino
- Department of Surgery, Centro Hepatobiliopancreático e Transplantação, Hospital Curry Cabral, Lisbon 1050-099, Portugal
| | | | - Barish H Edil
- Department of Surgery, University of Colorado, Aurora, CO 80045, USA
| | - Walter Park
- Department of Medicine, Stanford University Medical Center, Palo Alto, CA 94304, USA
| | - Shinichi Yachida
- Department of Hepatobiliary and Pancreatic Surgery, Pathology and Cancer Genomics, National Cancer Center Hospital and National Cancer Center Research Institute, Tokyo 104-0045, Japan
| | - Susumu Hijioka
- Department of Gastroenterology, Aichi Cancer Center Hospital, Nagoya 464-8681, Japan
| | - Jeanin van Hooft
- Department of Gastroenterology and Hepatology, Amsterdam Medical Center, Amsterdam 1017 ZX, Netherlands
| | - Jin He
- Department of Surgery, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Matthew J Weiss
- Department of Surgery, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Richard Burkhart
- Department of Surgery, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Martin Makary
- Department of Surgery, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Marcia I Canto
- Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Michael G Goggins
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Pathology, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Oncology, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Janine Ptak
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Lisa Dobbyn
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Joy Schaefer
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Natalie Sillman
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Maria Popoli
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Alison P Klein
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Oncology, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Cristian Tomasetti
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA. .,Department of Biostatistics and Bioinformatics, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Rachel Karchin
- Department of Biomedical Engineering, Johns Hopkins Medical Institutions, Johns Hopkins University, Baltimore, MD 21287, USA.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Oncology, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.
| | - Nickolas Papadopoulos
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Kenneth W Kinzler
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Bert Vogelstein
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA. .,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Christopher L Wolfgang
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA. .,Department of Oncology, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Surgery, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Ralph H Hruban
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Pathology, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Oncology, Johns Hopkins University, Baltimore, MD 21287, USA.,Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.
| | - Anne Marie Lennon
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA. .,Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Oncology, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Surgery, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA.,Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA
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25
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The Expressions and Mechanisms of Sarcomeric Proteins in Cancers. DISEASE MARKERS 2020; 2020:8885286. [PMID: 32670437 PMCID: PMC7346232 DOI: 10.1155/2020/8885286] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/07/2020] [Accepted: 06/13/2020] [Indexed: 02/07/2023]
Abstract
The sarcomeric proteins control the movement of cells in diverse species, whereas the deregulation can induce tumours in model organisms and occurs in human carcinomas. Sarcomeric proteins are recognized as oncogene and related to tumor cell metastasis. Recent insights into their expressions and functions have led to new cancer therapeutic opportunities. In this review, we appraise the evidence for the sarcomeric proteins as cancer genes and discuss cancer-relevant biological functions, potential mechanisms by which sarcomeric proteins activity is altered in cancer.
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26
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Hao S, Takahashi C, Snyder RA, Parikh AA. Stratifying Intraductal Papillary Mucinous Neoplasms by Cyst Fluid Analysis: Present and Future. Int J Mol Sci 2020; 21:ijms21031147. [PMID: 32050465 PMCID: PMC7037360 DOI: 10.3390/ijms21031147] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/04/2020] [Accepted: 02/06/2020] [Indexed: 12/11/2022] Open
Abstract
A significant proportion of patients with intraductal papillary mucinous neoplasms (IPMNs) undergo surgical resection in order to prevent or treat pancreatic cancer at the risk of significant perioperative morbidity. Efforts have been made to stratify the potential risk of malignancy based on the clinical and radiographic features of IPMN to delineate which cysts warrant resection versus observation. An analysis of the cyst fluid obtained by preoperative endoscopic examination appears to be correlative of cyst type and risk, whereas serum markers and radiographic findings have not yet reached a level of sensitivity or specificity that proves they are clinically meaningful. In this review, we investigate the current cyst fluid analysis studies and present those that have shown promise in effectively stratifying high-risk versus low-risk lesions. While new cyst fluid markers continue to be identified, additional efforts in testing panels and marker composites in conjunction with clinical algorithms have also shown promise in distinguishing dysplasia and the risk of malignancy. These should be tested prospectively in order to determine their role in guiding the surveillance of low-risk lesions and to evaluate the new markers detected by proteomics and genetic sequencing.
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Affiliation(s)
- Scarlett Hao
- Department of Surgery, Brody School of Medicine at East Carolina University, Greenville, NC 27834, USA; (S.H.); (C.T.)
| | - Caitlin Takahashi
- Department of Surgery, Brody School of Medicine at East Carolina University, Greenville, NC 27834, USA; (S.H.); (C.T.)
| | - Rebecca A. Snyder
- Division of Surgical Oncology, Department of Surgery, Brody School of Medicine at East Carolina University, Greenville, NC 27834, USA;
| | - Alexander A. Parikh
- Division of Surgical Oncology, Department of Surgery, Brody School of Medicine at East Carolina University, Greenville, NC 27834, USA;
- Correspondence: ; Tel.: +1-252-744-4110
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27
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Faraj Shaglouf LH, Ranjpour M, Wajid S, Jain SK. Elevated expression of cellular SYNE1, MMP10, and GTPase1 and their regulatory role in hepatocellular carcinoma progression. PROTOPLASMA 2020; 257:157-167. [PMID: 31428857 DOI: 10.1007/s00709-019-01423-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 07/19/2019] [Indexed: 06/10/2023]
Abstract
Hepatocellular carcinoma (HCC) is the most common primary liver malignancy resulting in high mortality. HCC progression is associated with abnormal signal transduction that changes cell signaling pathways and ultimately leads to dysregulation of cell functions and uncontrolled cell proliferation. Present study was undertaken with the objective to identify differentially expressed proteins and quantify their transcript expression in the liver of HCC-bearing rats vis-à-vis controls and to decipher the network involving interaction of genes coding for the characterized proteins to an insight into mechanism of HCC tumorigenesis. 2D-Electrophoresis and MALDI-TOF-MS/MS were used to characterize differentially expressed proteins in DEN (diethylnitrosamine)-induced HCC tissue using the protocol reported by us earlier. Real-time PCR was performed to quantify the expression of transcripts for the identified proteins. GENEMANIA, an interacting network of genes coding for selected proteins, was deciphered that provided the functional role of these proteins in HCC progression. Upregulation of proteins SYNE1, MMP10, and MTG1 was observed. The mRNA quantification revealed elevated expression of their transcripts at HCC initiation, progression, and tumor stages. Network analysis showed the involvement of the genes coding for these proteins in dysregulation of signaling pathways during HCC development. The elevated expression of SYNE1, MMP10, and MTG1 suggests the role of these proteins as potential players in HCC progression and tumorigenesis.
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Affiliation(s)
- Laila H Faraj Shaglouf
- Department of Biotechnology, School of Chemical and Life Science, Jamia Hamdard, New Delhi, 110062, India
| | - Maryam Ranjpour
- Department of Biotechnology, School of Chemical and Life Science, Jamia Hamdard, New Delhi, 110062, India
| | - Saima Wajid
- Department of Biotechnology, School of Chemical and Life Science, Jamia Hamdard, New Delhi, 110062, India
| | - Swatantra Kumar Jain
- Department of Biochemistry, Hamdard Institute of Medical Science and Research, Jamia Hamdard, New Delhi, 110062, India.
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28
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Taher MM, Hassan AA, Saeed M, Jastania RA, Nageeti TH, Alkhalidi H, Dairi G, Abduljaleel Z, Athar M, Bouazzaoui A, El-Bjeirami WM, Al-Allaf FA. Next generation DNA sequencing of atypical choroid plexus papilloma of brain: Identification of novel mutations in a female patient by Ion Proton. Oncol Lett 2019; 18:5063-5076. [PMID: 31612017 PMCID: PMC6781611 DOI: 10.3892/ol.2019.10882] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 06/13/2019] [Indexed: 12/16/2022] Open
Abstract
Choroid plexus papilloma (CPP) is a rare benign tumor of the central nervous system that is usually confined to the cerebral ventricles. According to the World Health Organization, CPP corresponds to a grade I atypical CPP (a-CPP); however, it can become more aggressive and reach grade II, which can rarely undergo malignant transformation into a choroid plexus carcinoma (grade III). To the best of our knowledge, identification of these tumors mutations by next generation DNA sequencing (NGS) has not been yet reported. In the present study, NGS analysis of an a-CPP case was performed. Data were analyzed using Advaita Bioinformatics i-VariantGuide and Ion Reporter 5.6 programs. The results from NGS identified 12 novel missense mutations in the following genes: NOTCH1, ATM, STK36, MAGI1, DST, RECQL4, NUMA1, THBS1, MYH11, MALT1, SMARCA4 and CDH20. The PolyPhen score of six variants viz., DST, RECQL4, NUMA1, THBS1, MYHI1 and SMARCA4 were high, which suggested these variants represents pathogenic variants. Two novel insertions that caused frameshift were also found. Furthermore, two novel nonsense mutations and 14 novel intronic variants were identified in this tumor. The novel missense mutation detected in ATM gene was situated in c.5808A>T; p. (Leu1936Phe) in exon 39, and a known ATM mutation was in c.5948A>G; p. (Asn1983Ser). These novel mutations had not been reported in previous database. Subsequently, the quality statistics of these variants, including allele coverage, allele ratio, P-value, Phred quality score, sequencing coverage, PolyPhen score and alleles frequency was performed. For all variants, P-value was highly significant and the Phred quality score was high. In addition, the results from sequencing coverage demonstrated that 97.02% reads were on target and that 97.88% amplicons had at least 500 reads. These findings may serve at determining new strategies to distinguish the types of choroid plexus tumor, and at developing novel targeted therapies. Development of NGS technologies in the Kingdom of Saudi Arabia may be used in molecular pathology laboratories.
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Affiliation(s)
- Mohiuddin M Taher
- Department of Medical Genetics, Faculty of Medicine, Umm-Al-Qura University, Makkah 21955, Saudi Arabia.,Science and Technology Unit, Umm-Al-Qura University, Makkah 21955, Saudi Arabia
| | - Amal Ali Hassan
- Histopathology Division, Al-Noor Specialty Hospital, Makkah 24242, Saudi Arabia.,Faculty of Medicine, Department of Pathology, Al Azhar University, Cairo 11651, Egypt
| | - Muhammad Saeed
- Department of Radiology, Faculty of Medicine, Umm-Al-Qura University, Makkah 21955, Saudi Arabia
| | - Raid A Jastania
- Department of Pathology, Faculty of Medicine, Umm-Al-Qura University, Makkah 21955, Saudi Arabia
| | - Tahani H Nageeti
- Department of Radiation Oncology, King Abdullah Medical City, Makkah 24246, Saudi Arabia
| | - Hisham Alkhalidi
- Department of Pathology, College of Medicine, King Saud University, Riyadh 11461, Saudi Arabia
| | - Ghida Dairi
- Medicine and Medical Sciences Research Center, Deanship of Scientific Research, Umm-Al-Qura University, Makkah 21955, Saudi Arabia
| | - Zainularifeen Abduljaleel
- Department of Medical Genetics, Faculty of Medicine, Umm-Al-Qura University, Makkah 21955, Saudi Arabia.,Science and Technology Unit, Umm-Al-Qura University, Makkah 21955, Saudi Arabia
| | - Mohammad Athar
- Department of Medical Genetics, Faculty of Medicine, Umm-Al-Qura University, Makkah 21955, Saudi Arabia.,Science and Technology Unit, Umm-Al-Qura University, Makkah 21955, Saudi Arabia
| | - Abdellatif Bouazzaoui
- Department of Medical Genetics, Faculty of Medicine, Umm-Al-Qura University, Makkah 21955, Saudi Arabia.,Science and Technology Unit, Umm-Al-Qura University, Makkah 21955, Saudi Arabia
| | - Wafa M El-Bjeirami
- Laboratory Medicine and Molecular Diagnostics Unit, King Abdullah Medical City, Makkah 24246, Saudi Arabia
| | - Faisal A Al-Allaf
- Department of Medical Genetics, Faculty of Medicine, Umm-Al-Qura University, Makkah 21955, Saudi Arabia.,Science and Technology Unit, Umm-Al-Qura University, Makkah 21955, Saudi Arabia
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29
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Gao Y, Li X, Zhi H, Zhang Y, Wang P, Wang Y, Shang S, Fang Y, Shen W, Ning S, Chen SX, Li X. Comprehensive Characterization of Somatic Mutations Impacting lncRNA Expression for Pan-Cancer. MOLECULAR THERAPY-NUCLEIC ACIDS 2019; 18:66-79. [PMID: 31525663 PMCID: PMC6745513 DOI: 10.1016/j.omtn.2019.08.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 07/24/2019] [Accepted: 08/04/2019] [Indexed: 12/26/2022]
Abstract
Somatic mutations have long been recognized as an important feature of cancer. However, analysis of somatic mutations, to date, has focused almost entirely on the protein coding regions of the genome. The potential roles of somatic mutations in human long noncoding RNAs (lncRNAs) are therefore largely unknown, particularly their functional significance across different cancer types. In this study, we characterized some lncRNAs whose expression was affected by somatic mutations (defined as MutLncs) and constructed global MutLnc landscapes across 17 cancer types by systematically integrating multiple levels of data. MutLncs were commonly downregulated and carried low mutation frequencies and non-silent mutations in most cancer types. Co-occurrence analysis in pan-cancer highlighted combined patterns of specific MutLncs, suggesting that a number of MutLncs influence diverse cancer types through combination effects. Several conserved and cancer-specific functions of MutLncs were determined. We further explored the somatic mutations affecting lncRNA expression via mixed and unmixed effects, which led to specific functions in pan-cancer. Survival analysis indicated that MutLncs and co-occurrence pairs can potentially serve as cancer biomarkers. Clarification of the specific roles of MutLncs in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types and developing the appropriate treatments.
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Affiliation(s)
- Yue Gao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xin Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Hui Zhi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China; Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136 USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Peng Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yanxia Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shipeng Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Ying Fang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Weitao Shen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shangwei Ning
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
| | - Steven Xi Chen
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136 USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
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30
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Wu S, Wang S, Gao F, Li L, Zheng S, Yung WKA, Koul D. Activation of WEE1 confers resistance to PI3K inhibition in glioblastoma. Neuro Oncol 2019; 20:78-91. [PMID: 29016926 DOI: 10.1093/neuonc/nox128] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background Oncogenic activation of phosphatidylinositol-3 kinase (PI3K) signaling plays a pivotal role in the development of glioblastoma (GBM). However, pharmacological inhibition of PI3K has so far not been therapeutically successful due to adaptive resistance through a rapid rewiring of cancer cell signaling. Here we identified that WEE1 is activated after transient exposure to PI3K inhibition and confers resistance to PI3K inhibition in GBM. Methods Patient-derived glioma-initiating cells and established GBM cells were treated with PI3K inhibitor or WEE1 inhibitor alone or in combination, and cell proliferation was evaluated by CellTiter-Blue assay. Cell apoptosis was analyzed by TUNEL, annexin V staining, and blotting of cleaved caspase-3 and cleaved poly(ADP-ribose) polymerase. Both subcutaneous xenograft and orthotropic xenograft studies were conducted to evaluate the effects of the combination on tumorigenesis; the tumor growth was monitored by bioluminescence imaging, and tumor tissue was analyzed by immunohistochemistry to validate signaling changes. Results PI3K inhibition activates WEE1 kinase, which in turn phosphorylates cell division control protein 2 homolog (Cdc2) at Tyr15 and inhibits Cdc2 activity, leading to G2/M arrest in a p53-independent manner. WEE1 inhibition abrogated the G2/M arrest and propelled cells to prematurely enter into mitosis and consequent cell death through mitotic catastrophe and apoptosis. Additionally, combination treatment significantly suppressed tumor growth in a subcutaneous model but not in an intracranial model due to limited blood-brain barrier penetration. Conclusions Our findings highlight WEE1 as an adaptive resistant gene activated after PI3K inhibition, and inhibition of WEE1 potentiated the effectiveness of PI3K targeted inhibition, suggesting that a combinational inhibition of WEE1 and PI3K might allow successful targeted therapy in GBM.
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Affiliation(s)
- Shaofang Wu
- Brain Tumor Center, Departments of Neuro-Oncology
| | - Shuzhen Wang
- Brain Tumor Center, Departments of Neuro-Oncology
| | - Feng Gao
- Brain Tumor Center, Departments of Neuro-Oncology
| | - Luyuan Li
- Brain Tumor Center, Departments of Neuro-Oncology
| | - Siyuan Zheng
- Brain Tumor Center, Departments of Neuro-Oncology.,Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Dimpy Koul
- Brain Tumor Center, Departments of Neuro-Oncology
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Saito N, Hirai N, Aoki K, Suzuki R, Fujita S, Nakayama H, Hayashi M, Ito K, Sakurai T, Iwabuchi S. The Oncogene Addiction Switch from NOTCH to PI3K Requires Simultaneous Targeting of NOTCH and PI3K Pathway Inhibition in Glioblastoma. Cancers (Basel) 2019; 11:cancers11010121. [PMID: 30669546 PMCID: PMC6356490 DOI: 10.3390/cancers11010121] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 01/11/2019] [Accepted: 01/19/2019] [Indexed: 01/02/2023] Open
Abstract
The NOTCH pathway regulates neural stem cells and glioma initiating cells (GICs). However, blocking NOTCH activity with γ-secretase inhibitors (GSIs) fails to alter the growth of GICs, as GSIs seem to be active in only a fraction of GICs lines with constitutive NOTCH activity. Here we report loss of PTEN function as a critical event leading to resistance to NOTCH inhibition, which causes the transfer of oncogene addiction from the NOTCH pathway to the PI3K pathway. Drug cytotoxicity testing of eight GICs showed a differential growth response to GSI, and the GICs were thus stratified into two groups: sensitive and resistant. In the sensitive group, GICs with loss of PTEN function appeared less sensitive to GSI treatment. Here we show that NOTCH regulates PTEN expression and the activity of the PI3K pathway in GICs, as treatment with GSI attenuated the NOTCH pathway and increased PTEN expression. NOTCH regulates PTEN expression via Hes-1, as knockdown of Notch or Hes1 increased expression of PTEN. This novel observation suggests that both pathways must be simultaneously inhibited in order to improve therapeutic efficacy in human glioblastomas (GBMs).
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Affiliation(s)
- Norihiko Saito
- Department of Neurosurgery, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan.
| | - Nozomi Hirai
- Department of Neurosurgery, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan.
| | - Kazuya Aoki
- Department of Neurosurgery, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan.
| | - Ryo Suzuki
- Department of Neurosurgery, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan.
| | - Satoshi Fujita
- Department of Neurosurgery, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan.
| | - Haruo Nakayama
- Department of Neurosurgery, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan.
| | - Morito Hayashi
- Department of Neurosurgery, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan.
| | - Keisuke Ito
- Department of Neurosurgery, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan.
| | - Takatoshi Sakurai
- Department of Neurosurgery, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan.
| | - Satoshi Iwabuchi
- Department of Neurosurgery, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan.
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Frost HR, Amos CI. A multi-omics approach for identifying important pathways and genes in human cancer. BMC Bioinformatics 2018; 19:479. [PMID: 30541428 PMCID: PMC6292115 DOI: 10.1186/s12859-018-2476-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Accepted: 11/09/2018] [Indexed: 12/15/2022] Open
Abstract
Background Cancer develops when pathways controlling cell survival, cell fate or genome maintenance are disrupted by the somatic alteration of key driver genes. Understanding how pathway disruption is driven by somatic alterations is thus essential for an accurate characterization of cancer biology and identification of therapeutic targets. Unfortunately, current cancer pathway analysis methods fail to fully model the relationship between somatic alterations and pathway activity. Results To address these limitations, we developed a multi-omics method for identifying biologically important pathways and genes in human cancer. Our approach combines single-sample pathway analysis with multi-stage, lasso-penalized regression to find pathways whose gene expression can be explained largely in terms of gene-level somatic alterations in the tumor. Importantly, this method can analyze case-only data sets, does not require information regarding pathway topology and supports personalized pathway analysis using just somatic alteration data for a limited number of cancer-associated genes. The practical effectiveness of this technique is illustrated through an analysis of data from The Cancer Genome Atlas using gene sets from the Molecular Signatures Database. Conclusions Novel insights into the pathophysiology of human cancer can be obtained from statistical models that predict expression-based pathway activity in terms of non-silent somatic mutations and copy number variation. These models enable the identification of biologically important pathways and genes and support personalized pathway analysis in cases where gene expression data is unavailable. Electronic supplementary material The online version of this article (10.1186/s12859-018-2476-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- H Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, 03755, NH, USA.
| | - Christopher I Amos
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, 03755, NH, USA
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Liu Y, He Q, Sun W. Association analysis using somatic mutations. PLoS Genet 2018; 14:e1007746. [PMID: 30388102 PMCID: PMC6235399 DOI: 10.1371/journal.pgen.1007746] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 11/14/2018] [Accepted: 10/07/2018] [Indexed: 11/18/2022] Open
Abstract
Somatic mutations drive the growth of tumor cells and are pivotal biomarkers for many cancer treatments. Genetic association analysis using somatic mutations is an effective approach to study the functional impact of somatic mutations. However, standard regression methods are not appropriate for somatic mutation association studies because somatic mutation calls often have non-ignorable false positive rate and/or false negative rate. While large scale association analysis using somatic mutations becomes feasible recently—thanks for the improvement of sequencing techniques and the reduction of sequencing cost—there is an urgent need for a new statistical method designed for somatic mutation association analysis. We propose such a method with computationally efficient software implementation: Somatic mutation Association test with Measurement Errors (SAME). SAME accounts for somatic mutation calling uncertainty using a likelihood based approach. It can be used to assess the associations between continuous/dichotomous outcomes and individual mutations or gene-level mutations. Through simulation studies across a wide range of realistic scenarios, we show that SAME can significantly improve statistical power than the naive generalized linear model that ignores mutation calling uncertainty. Finally, using the data collected from The Cancer Genome Atlas (TCGA) project, we apply SAME to study the associations between somatic mutations and gene expression in 12 cancer types, as well as the associations between somatic mutations and colon cancer subtype defined by DNA methylation data. SAME recovered some interesting findings that were missed by the generalized linear model. In addition, we demonstrated that mutation-level and gene-level analyses are often more appropriate for oncogene and tumor-suppressor gene, respectively. Cancer is a genetic disease that is driven by the accumulation of somatic mutations. Association studies using somatic mutations is a powerful approach to identify the potential impact of somatic mutations on molecular or clinical features. One challenge for such tasks is the non-ignorable somatic mutation calling errors. We have developed a statistical method to address this challenge and applied our method to study the gene expression traits associated with somatic mutations in 12 cancer types. Our results show that some somatic mutations affect gene expression in several cancer types. In particular, we show that the associations between gene expression traits and TP53 gene level mutation reveal some similarities across a few cancer types.
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Affiliation(s)
- Yang Liu
- Department of Mathematics and Statistics, Wright State University, Dayton, Ohio, United States of America
| | - Qianchan He
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Wei Sun
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
- * E-mail:
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Whole Exome Sequencing Uncovers Germline Variants of Cancer-Related Genes in Sporadic Pheochromocytoma. Int J Genomics 2018; 2018:6582014. [PMID: 30211214 PMCID: PMC6120303 DOI: 10.1155/2018/6582014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 05/08/2018] [Accepted: 05/29/2018] [Indexed: 12/21/2022] Open
Abstract
Background Pheochromocytomas (PCCs) show the highest degree of heritability in human neoplasms. However, despite the wide number of alterations until now reported in PCCs, it is likely that other susceptibility genes remain still unknown, especially for those PCCs not clearly syndromic. Methods Whole exome sequencing of tumor DNA was performed on a set of twelve PCCs clinically defined as sporadic. Results About 50% of PCCs examined had somatic mutations on the known susceptibility VHL, NF1, and RET genes. In addition to these driver events, mutations on SYNE1, ABCC10, and RAD54B genes were also detected. Moreover, extremely rare germline variants were present in half of the sporadic PCC samples analyzed, in particular variants of MAX and SAMD9L were detected in the germline of cases wild-type for mutations in the known susceptibility genes. Conclusions Additional somatic passenger mutations can be associated with known susceptibility VHL, NF1, and RET genes in PCCs, and a wide number of germline variants with still unknown clinical significance can be detected in these patients. Therefore, many efforts should be aimed to better define the pathogenetic role of all these germline variants for discovering novel potential therapeutic targets for this disease still orphan of effective treatments.
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35
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Xi J, Wang M, Li A. Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network. BMC Bioinformatics 2018; 19:214. [PMID: 29871594 PMCID: PMC5989443 DOI: 10.1186/s12859-018-2218-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 05/24/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. RESULTS To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. CONCLUSIONS In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.
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Affiliation(s)
- Jianing Xi
- School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
| | - Minghui Wang
- School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
- Centers for Biomedical Engineering, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
| | - Ao Li
- School of Information Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
- Centers for Biomedical Engineering, University of Science and Technology of China, Huangshan Road, Hefei, 230027 China
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36
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Oliveira DM, Laudanna C, Migliozzi S, Zoppoli P, Santamaria G, Grillone K, Elia L, Mignogna C, Biamonte F, Sacco R, Corcione F, Viglietto G, Malanga D, Rizzuto A. Identification of different mutational profiles in cancers arising in specific colon segments by next generation sequencing. Oncotarget 2018; 9:23960-23974. [PMID: 29844865 PMCID: PMC5963617 DOI: 10.18632/oncotarget.25251] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 04/06/2018] [Indexed: 02/07/2023] Open
Abstract
The objective of this study was to investigate the mutational profiles of cancers arising in different colon segments. To this aim, we have analyzed 37 colon cancer samples by use of the Ion AmpliSeq™ Comprehensive Cancer Panel. Overall, we have found 307 mutated genes, most of which already implicated in the development of colon cancer. Among these, 15 genes were mutated in tumors originating in all six colon segments and were defined "common genes" (i.e. APC, PIK3CA, TP53) whereas 13 genes were preferentially mutated in tumors originating only in specific colon segments and were defined "site-associated genes" (i.e. BLNK, PTPRD). In addition, the presence of mutations in 10 of the 307 identified mutated genes (NBN, SMUG1, ERBB2, PTPRT, EPHB1, ALK, PTPRD, AURKB, KDR and GPR124) were found to be of clinical relevance. Among clinically relevant genes, NBN and SMUG1 were identified as independent prognostic factors that predicted poor survival in colon cancer patients. In conclusion, the findings reported here indicate that tumors arising in different colon segments present differences in the type and/or frequency of genetic variants, with two of them being independent prognostic factors that predict poor survival in colon cancer patients.
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Affiliation(s)
- Duarte Mendes Oliveira
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, Italy
| | - Carmelo Laudanna
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, Italy
| | - Simona Migliozzi
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, Italy
| | - Pietro Zoppoli
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, Italy
| | - Gianluca Santamaria
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, Italy
| | - Katia Grillone
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, Italy
| | - Laura Elia
- Department of Medical and Surgical Sciences, University Magna Graecia, Catanzaro, Italy
| | - Chiara Mignogna
- Department of Health Sciences, University Magna Graecia, Catanzaro, Italy
| | - Flavia Biamonte
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, Italy
| | - Rosario Sacco
- Department of Medical and Surgical Sciences, University Magna Graecia, Catanzaro, Italy
| | | | - Giuseppe Viglietto
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, Italy
| | - Donatella Malanga
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, Italy
| | - Antonia Rizzuto
- Department of Medical and Surgical Sciences, University Magna Graecia, Catanzaro, Italy
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37
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Wang Z, Ng KS, Chen T, Kim TB, Wang F, Shaw K, Scott KL, Meric-Bernstam F, Mills GB, Chen K. Cancer driver mutation prediction through Bayesian integration of multi-omic data. PLoS One 2018; 13:e0196939. [PMID: 29738578 PMCID: PMC5940219 DOI: 10.1371/journal.pone.0196939] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 04/23/2018] [Indexed: 01/23/2023] Open
Abstract
Identification of cancer driver mutations is critical for advancing cancer research and personalized medicine. Due to inter-tumor genetic heterogeneity, many driver mutations occur at low frequencies, which make it challenging to distinguish them from passenger mutations. Here, we show that a novel Bayesian hierarchical modeling approach, named rDriver can achieve enhanced prediction accuracy by identifying mutations that not only have high functional impact scores but also are associated with systemic variation in gene expression levels. In examining 3,080 tumor samples from 8 cancer types in The Cancer Genome Atlas, rDriver predicted 1,389 driver mutations. Compared with existing tools, rDriver identified more low frequency mutations associated with lineage specific functional properties, timing of occurrence and patient survival. Evaluation of rDriver predictions using engineered cell-line models resulted in a positive predictive value of 0.94 in PIK3CA genes. Our study highlights the importance of integrating multi-omic data in predicting cancer driver mutations and provides a statistically rigorous solution for cancer target discovery and development.
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Affiliation(s)
- Zixing Wang
- Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
- Institute for Personalized Cancer Therapy, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Kwok-Shing Ng
- Institute for Personalized Cancer Therapy, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Tenghui Chen
- Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Tae-Beom Kim
- Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Fang Wang
- Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Kenna Shaw
- Institute for Personalized Cancer Therapy, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Kenneth L. Scott
- Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Funda Meric-Bernstam
- Institute for Personalized Cancer Therapy, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
- Department of Investigational Cancer Therapy, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Gordon B. Mills
- Institute for Personalized Cancer Therapy, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
- Department of Systems Biology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
- Institute for Personalized Cancer Therapy, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
- * E-mail:
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38
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Zhang J, Zhang S. The Discovery of Mutated Driver Pathways in Cancer: Models and Algorithms. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:988-998. [PMID: 28113329 DOI: 10.1109/tcbb.2016.2640963] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The pathogenesis of cancer in human is still poorly understood. With the rapid development of high-throughput sequencing technologies, huge volumes of cancer genomics data have been generated. Deciphering that data poses great opportunities and challenges to computational biologists. One of such key challenges is to distinguish driver mutations, genes as well as pathways from passenger ones. Mutual exclusivity of gene mutations (each patient has no more than one mutation in the gene set) has been observed in various cancer types and thus has been used as an important property of a driver gene set or pathway. In this article, we aim to review the recent development of computational models and algorithms for discovering driver pathways or modules in cancer with the focus on mutual exclusivity-based ones.
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39
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Zhang W, Wang SL. An efficient strategy for identifying cancer-related key genes based on graph entropy. Comput Biol Chem 2018; 74:142-148. [PMID: 29609142 DOI: 10.1016/j.compbiolchem.2018.03.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 01/22/2018] [Accepted: 03/20/2018] [Indexed: 02/02/2023]
Abstract
Gene networks are beneficial to identify functional genes that are highly relevant to clinical outcomes. Most of the current methods require information about the interaction of genes or proteins to construct genetic network connection. However, the conclusion of these methods may be bias because of the current incompleteness of human interactome. In this paper, we propose an efficient strategy to use gene expression data and gene mutation data for identifying cancer-related key genes based on graph entropy (iKGGE). Firstly, we construct a gene network using only gene expression data based on the sparse inverse covariance matrix, then, cluster genes use the algorithm of parallel maximal cliques for quickly obtaining a series of subgraphs, and at last, we introduce a novel metric that combine graph entropy and the influence of upstream gene mutations information to measure the impact factors of genes. Testing of the three available cancer datasets shows that our strategy can effectively extract key genes that may play distinct roles in tumorigenesis, and the cancer patient risk groups are well predicted based on key genes.
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Affiliation(s)
- Wei Zhang
- College of Computer Science and Electronics Engineering, Hunan University, Changsha, Hunan, 410082, China.
| | - Shu-Lin Wang
- College of Computer Science and Electronics Engineering, Hunan University, Changsha, Hunan, 410082, China.
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40
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Meng G. Applying Expression Profile Similarity for Discovery of Patient-Specific Functional Mutations. High Throughput 2018; 7:E6. [PMID: 29485617 PMCID: PMC5876532 DOI: 10.3390/ht7010006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Revised: 02/04/2018] [Accepted: 02/14/2018] [Indexed: 02/07/2023] Open
Abstract
The progress of cancer genome sequencing projects yields unprecedented information of mutations for numerous patients. However, the complexity of mutation profiles of cancer patients hinders the further understanding to mechanisms of oncogenesis. One basic question is how to find mutations with functional impacts. In this work, we introduce a computational method to predict functional somatic mutations of each patient by integrating mutation recurrence with expression profile similarity. With this method, the functional mutations are determined by checking the mutation enrichment among a group of patients with similar expression profiles. We applied this method to three cancer types and identified the functional mutations. Comparison of the predictions for three cancer types suggested that most of the functional mutations were cancer-type-specific with one exception to p53. By checking predicted results, we found that our method effectively filtered non-functional mutations resulting from large protein sizes. In addition, this method can also perform functional annotation to each patient to describe their association with signalling pathways or biological processes. In breast cancer, we predicted "cell adhesion" and other terms to be significantly associated with oncogenesis.
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Affiliation(s)
- Guofeng Meng
- BT science Inc., No. 24, Tang'an Road, Shanghai, China.
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41
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Bi-stage hierarchical selection of pathway genes for cancer progression using a swarm based computational approach. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.10.024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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42
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Cieślik M, Chinnaiyan AM. Cancer transcriptome profiling at the juncture of clinical translation. Nat Rev Genet 2017; 19:93-109. [PMID: 29279605 DOI: 10.1038/nrg.2017.96] [Citation(s) in RCA: 156] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.
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Affiliation(s)
- Marcin Cieślik
- Michigan Center for Translational Pathology, University of Michigan.,Department of Pathology, University of Michigan
| | - Arul M Chinnaiyan
- Michigan Center for Translational Pathology, University of Michigan.,Department of Pathology, University of Michigan.,Comprehensive Cancer Center, University of Michigan.,Department of Urology, University of Michigan.,Howard Hughes Medical Institute, University of Michigan, Ann Arbor, Michigan 48109, USA
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43
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Pridham KJ, Varghese RT, Sheng Z. The Role of Class IA Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunits in Glioblastoma. Front Oncol 2017; 7:312. [PMID: 29326882 PMCID: PMC5736525 DOI: 10.3389/fonc.2017.00312] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 12/04/2017] [Indexed: 12/19/2022] Open
Abstract
Phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) plays a critical role in the pathogenesis of cancer including glioblastoma, the most common and aggressive form of brain cancer. Targeting the PI3K pathway to treat glioblastoma has been tested in the clinic with modest effect. In light of the recent finding that PI3K catalytic subunits (PIK3CA/p110α, PIK3CB/p110β, PIK3CD/p110δ, and PIK3CG/p110γ) are not functionally redundant, it is imperative to determine whether these subunits play divergent roles in glioblastoma and whether selectively targeting PI3K catalytic subunits represents a novel and effective strategy to tackle PI3K signaling. This article summarizes recent advances in understanding the role of PI3K catalytic subunits in glioblastoma and discusses the possibility of selective blockade of one PI3K catalytic subunit as a treatment option for glioblastoma.
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Affiliation(s)
- Kevin J Pridham
- Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, VA, United States.,Graduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, VA, United States
| | - Robin T Varghese
- Edward Via College of Osteopathic Medicine, Blacksburg, VA, United States
| | - Zhi Sheng
- Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, VA, United States.,Virginia Tech Carilion School of Medicine, Virginia Tech, Roanoke, VA, United States.,Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, United States.,Faculty of Health Science, Virginia Tech, Blacksburg, VA, United States
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44
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Identification of cancer genes that are independent of dominant proliferation and lineage programs. Proc Natl Acad Sci U S A 2017; 114:E11276-E11284. [PMID: 29229826 PMCID: PMC5748209 DOI: 10.1073/pnas.1714877115] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Large, multidimensional “landscaping” projects have provided datasets that can be mined to identify potential targets for subgroups of tumors. Here, we analyzed genomic and transcriptomic data from human breast tumors to identify genes whose expression is enriched in tumors harboring specific genetic alterations. However, this analysis revealed that two other factors, proliferation rate and tumor lineage, are more dominant factors in shaping tumor transcriptional programs than genetic alterations. This discovery shifted our attention to identifying genes that are independent of the dominant proliferation and lineage programs. A small subset of these genes represents candidate targets for combination cancer therapies because they are druggable, maintained after treatment with chemotherapy, essential for cell line survival, and elevated in drug-resistant stem-like cancer cells. Large, multidimensional cancer datasets provide a resource that can be mined to identify candidate therapeutic targets for specific subgroups of tumors. Here, we analyzed human breast cancer data to identify transcriptional programs associated with tumors bearing specific genetic driver alterations. Using an unbiased approach, we identified thousands of genes whose expression was enriched in tumors with specific genetic alterations. However, expression of the vast majority of these genes was not enriched if associations were analyzed within individual breast tumor molecular subtypes, across multiple tumor types, or after gene expression was normalized to account for differences in proliferation or tumor lineage. Together with linear modeling results, these findings suggest that most transcriptional programs associated with specific genetic alterations in oncogenes and tumor suppressors are highly context-dependent and are predominantly linked to differences in proliferation programs between distinct breast cancer subtypes. We demonstrate that such proliferation-dependent gene expression dominates tumor transcriptional programs relative to matched normal tissues. However, we also identified a relatively small group of cancer-associated genes that are both proliferation- and lineage-independent. A subset of these genes are attractive candidate targets for combination therapy because they are essential in breast cancer cell lines, druggable, enriched in stem-like breast cancer cells, and resistant to chemotherapy-induced down-regulation.
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45
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Eun YG, Lee D, Lee YC, Sohn BH, Kim EH, Yim SY, Kwon KH, Lee JS. Clinical significance of YAP1 activation in head and neck squamous cell carcinoma. Oncotarget 2017; 8:111130-111143. [PMID: 29340043 PMCID: PMC5762311 DOI: 10.18632/oncotarget.22666] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 07/18/2017] [Indexed: 11/25/2022] Open
Abstract
By analyzing the genomic data of head and neck squamous cell cancer (HNSCC), we investigated clinical significance of YAP1 activation. Copy number and mRNA expression of YAP1 were analyzed together to assess clinical relevance of YAP1 activation in HNSCC. The clinical significance of YAP1 activation was further validated in four independent test cohorts. We also assessed the correlation of YAP1 activation with genomic alterations such as copy number alteration, somatic mutation, and miRNA expression. The YAP1-activated (YA) subgroup showed worse prognosis for HNSCC as tested and validated in five cohorts. In a multivariate risk analysis, the YAP1 signature was the most significant predictor of overall survival. The YAP1-inactivated (YI) subgroup was associated with HPV-positive status. In multiplatform analysis, YA tumors had gain of EGFR and SNAI2; loss of tumor-suppressor genes such as CSMD1, CDKN2A, NOTCH1, and SMAD4; and high mutation rates of TP53 and CDKN2A. YI tumors were characterized by gain of PIK3CA, SOX2, and TP63; deletion of 11q23.1; and high mutation rates of NFE2L2, PTEN, SYNE1, and NSD1. YA tumors also showed weaker immune activity as reflected in low IFNG composite scores and YAP1 activity is negatively associated with potential response to treatment of pembrolizumab. In conclusion, activation of YAP1 is associated with worse prognosis of patients with HNSCC and potential resistance to immunotherapy.
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Affiliation(s)
- Young-Gyu Eun
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Dongjin Lee
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Hallym University, Seoul, Republic of Korea
| | - Young Chan Lee
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Bo Hwa Sohn
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Eui Hyun Kim
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Neurosurgery, Severance Hospital, Brain Tumor Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sun Young Yim
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Division of Gastroenterology and Hepatology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
| | - Kee Hwan Kwon
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Hallym University, Seoul, Republic of Korea
| | - Ju-Seog Lee
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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46
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Delavan B, Roberts R, Huang R, Bao W, Tong W, Liu Z. Computational drug repositioning for rare diseases in the era of precision medicine. Drug Discov Today 2017; 23:382-394. [PMID: 29055182 DOI: 10.1016/j.drudis.2017.10.009] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/19/2017] [Accepted: 10/11/2017] [Indexed: 12/12/2022]
Abstract
There are tremendous unmet needs in drug development for rare diseases. Computational drug repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational drug repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating drug repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational drug repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease drug development and outline the remaining challenges.
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Affiliation(s)
- Brian Delavan
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; University of Arkansas at Little Rock, Little Rock, AR 72204, USA
| | - Ruth Roberts
- ApconiX, BioHub at Alderley Park, Alderley Edge SK10 4TG, UK; University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health Rockville, MD 20850, USA
| | | | - Weida Tong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Zhichao Liu
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
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47
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Preclinical therapeutic efficacy of a novel blood-brain barrier-penetrant dual PI3K/mTOR inhibitor with preferential response in PI3K/PTEN mutant glioma. Oncotarget 2017; 8:21741-21753. [PMID: 28423515 PMCID: PMC5400620 DOI: 10.18632/oncotarget.15566] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Accepted: 01/23/2017] [Indexed: 01/14/2023] Open
Abstract
Glioblastoma (GBM) is an ideal candidate disease for signal transduction targeted therapy because the majority of these tumors harbor genetic alterations that result in aberrant activation of growth factor signaling pathways. Loss of heterozygosity of chromosome 10, mutations in the tumor suppressor gene PTEN, and PI3K mutations are molecular hallmarks of GBM and indicate poor prognostic outcomes in many cancers. Consequently, inhibiting the PI3K pathway may provide therapeutic benefit in these cancers. PI3K inhibitors generally block proliferation rather than induce apoptosis. To restore the sensitivity of GBM to apoptosis induction, targeted agents have been combined with conventional therapy. However, the molecular heterogeneity and infiltrative nature of GBM make it resistant to traditional single agent therapy. Our objectives were to test a dual PI3K/mTOR inhibitor that may cross the blood–brain barrier (BBB) and provide the rationale for using this inhibitor in combination regimens to chemotherapy-induced synergism in GBM. Here we report the preclinical potential of a novel, orally bioavailable PI3K/mTOR dual inhibitor, DS7423 (hereafter DS), in in-vitro and in-vivo studies. DS was tested in mice, and DS plasma and brain concentrations were determined. DS crossed the BBB and led to potent suppression of PI3K pathway biomarkers in the brain. The physiologically relevant concentration of DS was tested in 9 glioma cell lines and 22 glioma-initiating cell (GIC) lines. DS inhibited the growth of glioma tumor cell lines and GICs at mean 50% inhibitory concentration values of less than 250 nmol/L. We found that PI3K mutations and PTEN alterations were associated with cellular response to DS treatment; with preferential inhibition of cell growth in PI3KCA-mutant and PTEN altered cell lines. DS showed efficacy and survival benefit in the U87 and GSC11 orthotopic models of GBM. Furthermore, administration of DS enhanced the antitumor efficacy of temozolomide against GBM in U87 glioma models, which shows that PI3K/mTOR inhibitors may enhance alkylating agent-mediated cytotoxicity, providing a novel regimen for the treatment of GBM. Our present findings establish that DS can specifically be used in patients who have PI3K pathway activation and/or loss of PTEN function. Further studies are warranted to determine the potential of DS for glioma treatment.
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48
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Liu B, Wu C, Shen X, Pan W. A NOVEL AND EFFICIENT ALGORITHM FOR DE NOVO DISCOVERY OF MUTATED DRIVER PATHWAYS IN CANCER. Ann Appl Stat 2017; 11:1481-1512. [PMID: 29479394 DOI: 10.1214/17-aoas1042] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Next-generation sequencing studies on cancer somatic mutations have discovered that driver mutations tend to appear in most tumor samples, but they barely overlap in any single tumor sample, presumably because a single driver mutation can perturb the whole pathway. Based on the corresponding new concepts of coverage and mutual exclusivity, new methods can be designed for de novo discovery of mutated driver pathways in cancer. Since the computational problem is a combinatorial optimization with an objective function involving a discontinuous indicator function in high dimension, many existing optimization algorithms, such as a brute force enumeration, gradient descent and Newton's methods, are practically infeasible or directly inapplicable. We develop a new algorithm based on a novel formulation of the problem as non-convex programming and non-convex regularization. The method is computationally more efficient, effective and scalable than existing Monte Carlo searching and several other algorithms, which have been applied to The Cancer Genome Atlas (TCGA) project. We also extend the new method for integrative analysis of both mutation and gene expression data. We demonstrate the promising performance of the new methods with applications to three cancer datasets to discover de novo mutated driver pathways.
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Affiliation(s)
- Binghui Liu
- Northeast Normal University.,University of Minnesota
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49
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Zhou W, Zhao Z, Wang R, Han Y, Wang C, Yang F, Han Y, Liang H, Qi L, Wang C, Guo Z, Gu Y. Identification of driver copy number alterations in diverse cancer types and application in drug repositioning. Mol Oncol 2017; 11:1459-1474. [PMID: 28719033 PMCID: PMC5623819 DOI: 10.1002/1878-0261.12112] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 06/01/2017] [Accepted: 07/06/2017] [Indexed: 01/03/2023] Open
Abstract
Results from numerous studies suggest an important role for somatic copy number alterations (SCNAs) in cancer progression. Our work aimed to identify the drivers (oncogenes or tumor suppressor genes) that reside in recurrently aberrant genomic regions, including a large number of genes or non-coding genes, which remain a challenge for decoding the SCNAs involved in carcinogenesis. Here, we propose a new approach to comprehensively identify drivers, using 8740 cancer samples involving 18 cancer types from The Cancer Genome Atlas (TCGA). On average, 84 drivers were revealed for each cancer type, including protein-coding genes, long non-coding RNAs (lncRNA) and microRNAs (miRNAs). We demonstrated that the drivers showed significant attributes of cancer genes, and significantly overlapped with known cancer genes, including MYC, CCND1 and ERBB2 in breast cancer, and the lncRNA PVT1 in multiple cancer types. Pan-cancer analyses of drivers revealed specificity and commonality across cancer types, and the non-coding drivers showed a higher cancer-type specificity than that of coding drivers. Some cancer types from different tissue origins were found to converge to a high similarity because of the significant overlap of drivers, such as head and neck squamous cell carcinoma (HNSC) and lung squamous cell carcinoma (LUSC). The lncRNA SOX2-OT, a common driver of HNSC and LUSC, showed significant expression correlation with the oncogene SOX2. In addition, because some drivers are common in multiple cancer types and have been targeted by known drugs, we found that some drugs could be successfully repositioned, as validated by the datasets of drug response assays in cell lines. Our work reported a new method to comprehensively identify drivers in SCNAs across diverse cancer types, providing a feasible strategy for cancer drug repositioning as well as novel findings regarding cancer-associated non-coding RNA discovery.
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Affiliation(s)
- Wenbin Zhou
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Zhangxiang Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, China.,Training Center for Student Innovation and Entrepreneurship Education, Harbin Medical University, China
| | - Ruiping Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Yue Han
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Chengyu Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, China.,Training Center for Student Innovation and Entrepreneurship Education, Harbin Medical University, China
| | - Fan Yang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, China.,Training Center for Student Innovation and Entrepreneurship Education, Harbin Medical University, China
| | - Ya Han
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, China.,Training Center for Student Innovation and Entrepreneurship Education, Harbin Medical University, China
| | - Haihai Liang
- Department of Pharmacology, Harbin Medical University, China
| | - Lishuang Qi
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Chenguang Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, China
| | - Zheng Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, China.,Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Yunyan Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, China.,Training Center for Student Innovation and Entrepreneurship Education, Harbin Medical University, China
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50
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Abdul SN, Ab Mutalib NS, Sean KS, Syafruddin SE, Ishak M, Sagap I, Mazlan L, Rose IM, Abu N, Mokhtar NM, Jamal R. Molecular Characterization of Somatic Alterations in Dukes' B and C Colorectal Cancers by Targeted Sequencing. Front Pharmacol 2017; 8:465. [PMID: 28769798 PMCID: PMC5513919 DOI: 10.3389/fphar.2017.00465] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 06/30/2017] [Indexed: 12/12/2022] Open
Abstract
Despite global progress in research, improved screening and refined treatment strategies, colorectal cancer (CRC) remains as the third most common malignancy. As each type of cancer is different and exhibits unique alteration patterns, identifying and characterizing gene alterations in CRC that may serve as biomarkers might help to improve diagnosis, prognosis and predict potential response to therapy. With the emergence of next generation sequencing technologies (NGS), it is now possible to extensively and rapidly identify the gene profile of individual tumors. In this study, we aimed to identify actionable somatic alterations in Dukes’ B and C in CRC via NGS. Targeted sequencing of 409 cancer-related genes using the Ion AmpliseqTM Comprehensive Cancer Panel was performed on genomic DNA obtained from paired fresh frozen tissues, cancer and normal, of Dukes’ B (n = 10) and Dukes’ C (n = 9) CRC. The sequencing results were analyzed using Torrent Suite, annotated using ANNOVAR and validated using Sanger sequencing. A total of 141 somatic non-synonymous sequence variations were identified in 86 genes. Among these, 64 variants (45%) were predicted to be deleterious, 38 variants (27%) possibly deleterious while the other 39 variants (28%) have low or neutral protein impact. Seventeen genes have alterations with frequencies of ≥10% in the patient cohort and with 14 overlapped genes in both Dukes’ B and C. The adenomatous polyposis coli gene (APC) was the most frequently altered gene in both groups (n = 6 in Dukes’ B and C). In addition, TP53 was more frequently altered in Dukes’ C (n = 7) compared to Dukes’ B (n = 4). Ten variants in APC, namely p.R283∗, p.N778fs, p.R805∗, p.Y935fs, p.E941fs, p.E1057∗, p.I1401fs, p.Q1378∗, p.E1379∗, and p.A1485fs were predicted to be driver variants. APC remains as the most frequently altered gene in the intermediate stages of CRC. Wnt signaling pathway is the major affected pathway followed by P53, RAS, TGF-β, and PI3K signaling. We reported the alteration profiles in each of the patient which has the potential to affect the clinical decision. We believe that this study will add further to the understanding of CRC molecular landscape.
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Affiliation(s)
- Shafina-Nadiawati Abdul
- UKM Medical Molecular Biology InstituteUniversiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | | | | | - Saiful E Syafruddin
- UKM Medical Molecular Biology InstituteUniversiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Muhiddin Ishak
- UKM Medical Molecular Biology InstituteUniversiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Ismail Sagap
- Department of Surgery, Faculty of Medicine, Universiti Kebangsaan MalaysiaKuala Lumpur, Malaysia
| | - Luqman Mazlan
- Department of Surgery, Faculty of Medicine, Universiti Kebangsaan MalaysiaKuala Lumpur, Malaysia
| | - Isa M Rose
- Department of Pathology, Faculty of Medicine, Universiti Kebangsaan MalaysiaKuala Lumpur, Malaysia
| | - Nadiah Abu
- UKM Medical Molecular Biology InstituteUniversiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Norfilza M Mokhtar
- Department of Physiology, Faculty of Medicine, Universiti Kebangsaan MalaysiaKuala Lumpur, Malaysia
| | - Rahman Jamal
- UKM Medical Molecular Biology InstituteUniversiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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