1
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Raevskiy M, Sorokin M, Emelianova A, Zakharova G, Poddubskaya E, Zolotovskaia M, Buzdin A. Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases. BIOCHEMISTRY. BIOKHIMIIA 2024; 89:737-746. [PMID: 38831509 DOI: 10.1134/s0006297924040126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/13/2024] [Accepted: 03/13/2024] [Indexed: 06/05/2024]
Abstract
Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.
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Affiliation(s)
- Mikhail Raevskiy
- Digital Biodesign and Personalized Healthcare Research Center, Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | - Maxim Sorokin
- Omicsway Corp., Walnut, CA 91789, USA.
- Oncobox Ltd., Moscow, 121205, Russia
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Aleksandra Emelianova
- Digital Biodesign and Personalized Healthcare Research Center, Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | - Galina Zakharova
- Digital Biodesign and Personalized Healthcare Research Center, Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | - Elena Poddubskaya
- Digital Biodesign and Personalized Healthcare Research Center, Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | - Marianna Zolotovskaia
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia.
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Anton Buzdin
- Digital Biodesign and Personalized Healthcare Research Center, Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
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2
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Zolotovskaia MA, Kovalenko MA, Tkachev VS, Simonov AM, Sorokin MI, Kim E, Kuzmin DV, Karademir-Yilmaz B, Buzdin AA. Next-Generation Grade and Survival Expression Biomarkers of Human Gliomas Based on Algorithmically Reconstructed Molecular Pathways. Int J Mol Sci 2022; 23:ijms23137330. [PMID: 35806337 PMCID: PMC9266372 DOI: 10.3390/ijms23137330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/24/2022] [Accepted: 06/25/2022] [Indexed: 02/04/2023] Open
Abstract
In gliomas, expression of certain marker genes is strongly associated with survival and tumor type and often exceeds histological assessments. Using a human interactome model, we algorithmically reconstructed 7494 new-type molecular pathways that are centered each on an individual protein. Each single-gene expression and gene-centric pathway activation was tested as a survival and tumor grade biomarker in gliomas and their diagnostic subgroups (IDH mutant or wild type, IDH mutant with 1p/19q co-deletion, MGMT promoter methylated or unmethylated), including the three major molecular subtypes of glioblastoma (proneural, mesenchymal, classical). We used three datasets from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, which in total include 527 glioblastoma and 1097 low grade glioma profiles. We identified 2724 such gene and 2418 pathway survival biomarkers out of total 17,717 genes and 7494 pathways analyzed. We then assessed tumor grade and molecular subtype biomarkers and with the threshold of AUC > 0.7 identified 1322/982 gene biomarkers and 472/537 pathway biomarkers. This suggests roughly two times greater efficacy of the reconstructed pathway approach compared to gene biomarkers. Thus, we conclude that activation levels of algorithmically reconstructed gene-centric pathways are a potent class of new-generation diagnostic and prognostic biomarkers for gliomas.
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Affiliation(s)
- Marianna A. Zolotovskaia
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
- Correspondence: ; Tel.: +7-9165612175
| | - Max A. Kovalenko
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
| | | | - Alexander M. Simonov
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
- Omicsway Corp., Walnut, CA 91789, USA;
| | - Maxim I. Sorokin
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
- Omicsway Corp., Walnut, CA 91789, USA;
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
| | - Ella Kim
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany;
| | - Denis V. Kuzmin
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
| | - Betul Karademir-Yilmaz
- Department of Biochemistry, School of Medicine/Genetic and Metabolic Diseases Research and Investigation Center (GEMHAM), Marmara University, Istanbul 34854, Turkey;
| | - Anton A. Buzdin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
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3
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Borisov N, Sorokin M, Zolotovskaya M, Borisov C, Buzdin A. Shambhala-2: A Protocol for Uniformly Shaped Harmonization of Gene Expression Profiles of Various Formats. Curr Protoc 2022; 2:e444. [PMID: 35617464 DOI: 10.1002/cpz1.444] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Uniformly shaped harmonization of gene expression profiles is central for the simultaneous comparison of multiple gene expression datasets. It is expected to operate with the gene expression data obtained using various experimental methods and equipment, and to return harmonized profiles in a uniform shape. Such uniformly shaped expression profiles from different initial datasets can be further compared directly. However, current harmonization techniques have strong limitations that prevent their broad use for bioinformatic applications. They can either operate with only up to two datasets/platforms or return data in a dynamic format that will be different for every comparison under analysis. This also does not allow for adding new data to the previously harmonized dataset(s), which complicates the analysis and increases calculation costs. We propose here a new method termed Shambhala-2 that can transform multi-platform expression data into a universal format that is identical for all harmonizations made using this technique. Shambhala-2 is based on sample-by-sample cubic conversion of the initial expression dataset into a preselected shape of the reference definitive dataset. Using 8390 samples of 12 healthy human tissue types and 4086 samples of colorectal, kidney, and lung cancer tissues, we verified Shambhala-2's capacity in restoring tissue-specific expression patterns for seven microarray and three RNA sequencing platforms. Shambhala-2 performed well for all tested combinations of RNAseq and microarray profiles, and retained gene-expression ranks, as evidenced by high correlations between different single- or aggregated gene expression metrics in pre- and post-Shambhalized samples, including preserving cancer-specific gene expression and pathway activation features. © 2022 Wiley Periodicals LLC. Basic Protocol: Shambhala-2 harmonizer Alternate Protocol 1: Linear Shambhala/Shambhala-1 Alternate Protocol 2: Alternative (flexible-format and uniformly shaped) normalization methods Support Protocol 1: Watermelon multisection (WM) Support Protocol 2: Calculation of cancer-to-normal log-fold-change (LFC) and pathway activation level (PAL).
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Affiliation(s)
- Nicolas Borisov
- Omicsway Corp., Walnut, California.,Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia
| | - Maksim Sorokin
- Omicsway Corp., Walnut, California.,Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia.,I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Marianna Zolotovskaya
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia.,Oncobox Ltd., Moscow, Russia
| | | | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.,World-Class Research Center "Digital biodesign and personalized healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium
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4
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Zolotovskaia M, Tkachev V, Sorokin M, Garazha A, Kim E, Kantelhardt SR, Bikar SE, Zottel A, Šamec N, Kuzmin D, Sprang B, Moisseev A, Giese A, Efimov V, Jovčevska I, Buzdin A. Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas. Cancers (Basel) 2021; 13:4117. [PMID: 34439271 PMCID: PMC8394245 DOI: 10.3390/cancers13164117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 01/17/2023] Open
Abstract
Gliomas are the most common malignant brain tumors with high mortality rates. Recently we showed that the FREM2 gene has a role in glioblastoma progression. Here we reconstructed the FREM2 molecular pathway using the human interactome model. We assessed the biomarker capacity of FREM2 expression and its pathway as the overall survival (OS) and progression-free survival (PFS) biomarkers. To this end, we used three literature and one experimental RNA sequencing datasets collectively covering 566 glioblastomas (GBM) and 1097 low-grade gliomas (LGG). The activation level of deduced FREM2 pathway showed strong biomarker characteristics and significantly outperformed the FREM2 expression level itself. For all relevant datasets, it could robustly discriminate GBM and LGG (p < 1.63 × 10-13, AUC > 0.74). High FREM2 pathway activation level was associated with poor OS in LGG (p < 0.001), and low PFS in LGG (p < 0.001) and GBM (p < 0.05). FREM2 pathway activation level was poor prognosis biomarker for OS (p < 0.05) and PFS (p < 0.05) in LGG with IDH mutation, for PFS in LGG with wild type IDH (p < 0.001) and mutant IDH with 1p/19q codeletion(p < 0.05), in GBM with unmethylated MGMT (p < 0.05), and in GBM with wild type IDH (p < 0.05). Thus, we conclude that the activation level of the FREM2 pathway is a potent new-generation diagnostic and prognostic biomarker for multiple molecular subtypes of GBM and LGG.
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Affiliation(s)
- Marianna Zolotovskaia
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Department of Oncology, Hematology and Radiotherapy, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Victor Tkachev
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Maxim Sorokin
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Andrew Garazha
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
| | - Ella Kim
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Sven Rainer Kantelhardt
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Sven-Ernö Bikar
- StarSEQ GmbH, Joh.-Joachim-Becher-Weg 30a, 55128 Mainz, Germany;
| | - Alja Zottel
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Neja Šamec
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Denis Kuzmin
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Bettina Sprang
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Alexey Moisseev
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
| | - Alf Giese
- Orthocentrum Hamburg, Hansastrasse 1, 20149 Hamburg, Germany;
| | - Victor Efimov
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Ivana Jovčevska
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
- European Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, 1200 Brussels, Belgium
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5
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Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:1-53. [PMID: 34340765 DOI: 10.1016/bs.apcsb.2021.02.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.
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Shi XJ, Wei Y, Ji B. Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment. Front Mol Biosci 2020; 7:203. [PMID: 33005629 PMCID: PMC7479200 DOI: 10.3389/fmolb.2020.00203] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 07/27/2020] [Indexed: 12/14/2022] Open
Abstract
Gastric cancer is the fifth most diagnosed cancer in the world, affecting more than a million people and causing nearly 783,000 deaths each year. The prognosis of advanced gastric cancer remains extremely poor despite the use of surgery and adjuvant therapy. Therefore, understanding the mechanism of gastric cancer development, and the discovery of novel diagnostic biomarkers and therapeutics are major goals in gastric cancer research. Here, we review recent progress in application of omics technologies in gastric cancer research, with special focus on the utilization of systems biology approaches to integrate multi-omics data. In addition, the association between gastrointestinal microbiota and gastric cancer are discussed, which may offer insights in exploring the novel microbiota-targeted therapeutics. Finally, the application of data-driven systems biology and machine learning approaches could provide a predictive understanding of gastric cancer, and pave the way to the development of novel biomarkers and rational design of cancer therapeutics.
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Affiliation(s)
- Xiao-Jing Shi
- Laboratory Animal Center, State Key Laboratory of Esophageal Cancer Prevention and Treatment, Academy of Medical Science, Zhengzhou University, Zhengzhou, China
| | - Yongjun Wei
- School of Pharmaceutical Sciences, Key Laboratory of Advanced Drug Preparation Technologies, Ministry of Education, Zhengzhou University, Zhengzhou, China
| | - Boyang Ji
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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7
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Sorokin M, Ignatev K, Barbara V, Vladimirova U, Muraveva A, Suntsova M, Gaifullin N, Vorotnikov I, Kamashev D, Bondarenko A, Baranova M, Poddubskaya E, Buzdin A. Molecular Pathway Activation Markers Are Associated with Efficacy of Trastuzumab Therapy in Metastatic HER2-Positive Breast Cancer Better than Individual Gene Expression Levels. BIOCHEMISTRY (MOSCOW) 2020; 85:758-772. [DOI: 10.1134/s0006297920070044] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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8
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Sorokin M, Ignatev K, Poddubskaya E, Vladimirova U, Gaifullin N, Lantsov D, Garazha A, Allina D, Suntsova M, Barbara V, Buzdin A. RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens. Biomedicines 2020; 8:E114. [PMID: 32397474 PMCID: PMC7277916 DOI: 10.3390/biomedicines8050114] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/02/2020] [Accepted: 05/07/2020] [Indexed: 12/11/2022] Open
Abstract
RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman's rho 0.65-0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.
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Affiliation(s)
- Maxim Sorokin
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Omicsway Corp., Walnut, CA 91789, USA;
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
| | - Kirill Ignatev
- Karelia Republic Oncological Hospital, 185000 Petrozavodsk, Russia;
| | - Elena Poddubskaya
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Vitamed Oncological Clinical Center, 121309 Moscow, Russia
| | - Uliana Vladimirova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
| | - Nurshat Gaifullin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia;
| | - Dmitriy Lantsov
- Kaluga Regional Oncological Hospital, 248007 Kaluga, Russia;
| | | | - Daria Allina
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
| | - Maria Suntsova
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
| | - Victoria Barbara
- Oncological Dispensary of the Republic of Karelia, 185002 Petrozavodsk, Russia;
| | - Anton Buzdin
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Omicsway Corp., Walnut, CA 91789, USA;
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
- Moscow Institute of Physics and Technology, 141701 Moscow, Russia
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9
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Vinogradov AE, Anatskaya OV. Cell-cycle dependence of transcriptome gene modules: comparison of regression lines. FEBS J 2020; 287:4427-4439. [PMID: 32083797 DOI: 10.1111/febs.15257] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 01/24/2020] [Accepted: 02/20/2020] [Indexed: 12/17/2022]
Abstract
The transcriptome consists of various gene modules that can be mutually dependent, and ignoring these dependencies may lead to misinterpretation. The most important problem is module dependence on cell-cycle activity. Using meta-analysis of over 30 000 single-cell transcriptomes, we show gene module dependencies on cell-cycle signature, which can be consistently observed in various normal and cancer cells. Transcript levels of receptors, plasma membrane, and differentiation-related genes are negatively regressed on cell-cycle signature. Pluripotency, stress response, DNA repair, chromatin remodeling, proteasomal protein degradation, protein network connectivity, and unicellular evolutionary origin are regressed positively. These effects cannot be explained by partial overlap of corresponding gene sets because they remain if the overlapped genes were removed. We propose a visual analysis of gene module-specific regression lines as complement to an uncurated enrichment analysis. The different lines for a same gene module indicate different cell conditions. The approach is tested on several problems (polyploidy, pluripotency, cancer, phylostratigraphy). Intriguingly, we found variation in cell-cycle activity, which is independent of cell progression through the cycle. The upregulation of G2/M checkpoint genes with downregulation of G2/M transition and cytokinesis is revealed in polyploid cells. A temporal increase in cell-cycle activity at transition from pluripotent to more differentiated state is found in human embryonic stem cells. The upregulation of unicellular interactome cluster in human cancers is shown in single cells with control for cell-cycle activity. The greater scatter around regression line in cancer cells suggests greater heterogeneity caused by deviation from a line of normal cells.
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Affiliation(s)
| | - Olga V Anatskaya
- Institute of Cytology, Russian Academy of Sciences, St. Petersburg, Russia
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10
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Mutation Enrichment and Transcriptomic Activation Signatures of 419 Molecular Pathways in Cancer. Cancers (Basel) 2020; 12:cancers12020271. [PMID: 31979117 PMCID: PMC7073226 DOI: 10.3390/cancers12020271] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/16/2020] [Accepted: 01/20/2020] [Indexed: 12/13/2022] Open
Abstract
Carcinogenesis is linked with massive changes in regulation of gene networks. We used high throughput mutation and gene expression data to interrogate involvement of 278 signaling, 72 metabolic, 48 DNA repair and 47 cytoskeleton molecular pathways in cancer. Totally, we analyzed 4910 primary tumor samples with individual cancer RNA sequencing and whole exome sequencing profiles including ~1.3 million DNA mutations and representing thirteen cancer types. Gene expression in cancers was compared with the corresponding 655 normal tissue profiles. For the first time, we calculated mutation enrichment values and activation levels for these pathways. We found that pathway activation profiles were largely congruent among the different cancer types. However, we observed no correlation between mutation enrichment and expression changes both at the gene and at the pathway levels. Overall, positive median cancer-specific activation levels were seen in the DNA repair, versus similar slightly negative values in the other types of pathways. The DNA repair pathways also demonstrated the highest values of mutation enrichment. However, the signaling and cytoskeleton pathways had the biggest proportions of representatives among the outstandingly frequently mutated genes thus suggesting their initiator roles in carcinogenesis and the auxiliary/supporting roles for the other groups of molecular pathways.
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11
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Borisov N, Sorokin M, Garazha A, Buzdin A. Quantitation of Molecular Pathway Activation Using RNA Sequencing Data. Methods Mol Biol 2020; 2063:189-206. [PMID: 31667772 DOI: 10.1007/978-1-0716-0138-9_15] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Intracellular molecular pathways (IMPs) control all major events in the living cell. IMPs are considered hotspots in biomedical sciences and thousands of IMPs have been discovered for humans and model organisms. Knowledge of IMPs activation is essential for understanding biological functions and differences between the biological objects at the molecular level. Here we describe the Oncobox system for accurate quantitative scoring activities of up to several thousand molecular pathways based on high throughput molecular data. Although initially designed for gene expression and mainly RNA sequencing data, Oncobox is now also applicable for quantitative proteomics, microRNA and transcription factor binding sites mapping data. The Oncobox system includes modules of gene expression data harmonization, aggregation and comparison and a recursive algorithm for automatic annotation of molecular pathways. The universal rationale of Oncobox enables scoring of signaling, metabolic, cytoskeleton, immunity, DNA repair, and other pathways in a multitude of biological objects. The Oncobox system can be helpful to all those working in the fields of genetics, biochemistry, interactomics, and big data analytics in molecular biomedicine.
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Affiliation(s)
- Nicolas Borisov
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Omicsway Corp., Walnut, CA, USA
| | - Maxim Sorokin
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Omicsway Corp., Walnut, CA, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | - Anton Buzdin
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
- Omicsway Corp., Walnut, CA, USA.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
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12
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Tkachev V, Sorokin M, Garazha A, Borisov N, Buzdin A. Oncobox Method for Scoring Efficiencies of Anticancer Drugs Based on Gene Expression Data. Methods Mol Biol 2020; 2063:235-255. [PMID: 31667774 DOI: 10.1007/978-1-0716-0138-9_17] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
We describe here the Oncobox method for scoring efficiencies of anticancer target drugs (ATDs) using high throughput gene expression data. The method rationale, design, and validation are given along with the examples of its practical applications in biomedicine. The method is based on the analysis of intracellular molecular pathways activation and measuring expressions of molecular target genes for every ATD under consideration. Using Oncobox method requires collection of normal (control) expression profiles and annotated databases of molecular pathways and drug target genes. Both microarray and RNA sequencing profiles are acceptable, although the latter type of data prevails in the most recent applications of this technique.
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Affiliation(s)
| | - Maxim Sorokin
- Omicsway Corp., Walnut, CA, USA
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | | | - Nicolas Borisov
- Omicsway Corp., Walnut, CA, USA
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Anton Buzdin
- Omicsway Corp., Walnut, CA, USA.
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
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13
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Igolkina AA, Zinkevich A, Karandasheva KO, Popov AA, Selifanova MV, Nikolaeva D, Tkachev V, Penzar D, Nikitin DM, Buzdin A. H3K4me3, H3K9ac, H3K27ac, H3K27me3 and H3K9me3 Histone Tags Suggest Distinct Regulatory Evolution of Open and Condensed Chromatin Landmarks. Cells 2019; 8:E1034. [PMID: 31491936 PMCID: PMC6770625 DOI: 10.3390/cells8091034] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 08/28/2019] [Accepted: 09/03/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Transposons are selfish genetic elements that self-reproduce in host DNA. They were active during evolutionary history and now occupy almost half of mammalian genomes. Close insertions of transposons reshaped structure and regulation of many genes considerably. Co-evolution of transposons and host DNA frequently results in the formation of new regulatory regions. Previously we published a concept that the proportion of functional features held by transposons positively correlates with the rate of regulatory evolution of the respective genes. METHODS We ranked human genes and molecular pathways according to their regulatory evolution rates based on high throughput genome-wide data on five histone modifications (H3K4me3, H3K9ac, H3K27ac, H3K27me3, H3K9me3) linked with transposons for five human cell lines. RESULTS Based on the total of approximately 1.5 million histone tags, we ranked regulatory evolution rates for 25075 human genes and 3121 molecular pathways and identified groups of molecular processes that showed signs of either fast or slow regulatory evolution. However, histone tags showed different regulatory patterns and formed two distinct clusters: promoter/active chromatin tags (H3K4me3, H3K9ac, H3K27ac) vs. heterochromatin tags (H3K27me3, H3K9me3). CONCLUSION In humans, transposon-linked histone marks evolved in a coordinated way depending on their functional roles.
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Affiliation(s)
- Anna A Igolkina
- Mathematical Biology & Bioinformatics Laboratory, Institute of Applied Mathematics and Mechanics, Peter the Great St.Petersburg Polytechnic University, Polytechnicheskaya 29, St. Petersburg 195251, Russia.
- Laboratory of Microbiological Monitoring and Bioremediation of Soil, All-Russia Research Institute for Agricultural Microbiology, Podbel'skogo, 3, St. Petersburg 196608, Russia.
| | - Arsenii Zinkevich
- Lomonosov Moscow State University, Vorobiovy Gory 1, Moscow 119991, Russia
| | | | - Aleksey A Popov
- Lomonosov Moscow State University, Vorobiovy Gory 1, Moscow 119991, Russia
| | - Maria V Selifanova
- Lomonosov Moscow State University, Vorobiovy Gory 1, Moscow 119991, Russia
| | - Daria Nikolaeva
- Lomonosov Moscow State University, Vorobiovy Gory 1, Moscow 119991, Russia
| | | | - Dmitry Penzar
- Lomonosov Moscow State University, Vorobiovy Gory 1, Moscow 119991, Russia
- Vavilov Institute of General Genetics Russian Academy of Sciences, Gubkina 3, Moscow 119991, Russia
| | - Daniil M Nikitin
- Omicsway Corp., Walnut, CA 91789, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
| | - Anton Buzdin
- Omicsway Corp., Walnut, CA 91789, USA.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia.
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.
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14
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Buzdin A, Sorokin M, Garazha A, Glusker A, Aleshin A, Poddubskaya E, Sekacheva M, Kim E, Gaifullin N, Giese A, Seryakov A, Rumiantsev P, Moshkovskii S, Moiseev A. RNA sequencing for research and diagnostics in clinical oncology. Semin Cancer Biol 2019; 60:311-323. [PMID: 31412295 DOI: 10.1016/j.semcancer.2019.07.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 07/16/2019] [Indexed: 12/26/2022]
Abstract
Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.
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Affiliation(s)
- Anton Buzdin
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Omicsway Corp., Walnut, CA, USA; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
| | - Maxim Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Omicsway Corp., Walnut, CA, USA; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | | | - Alex Aleshin
- Stanford University School of Medicine, Stanford, 94305, CA, USA
| | - Elena Poddubskaya
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Vitamed Oncological Clinics, Moscow, Russia
| | - Marina Sekacheva
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Ella Kim
- Johannes Gutenberg University Mainz, Mainz, Germany
| | - Nurshat Gaifullin
- Lomonosov Moscow State University, Faculty of Medicine, Moscow, Russia
| | | | | | | | - Sergey Moshkovskii
- Institute of Biomedical Chemistry, Moscow, 119121, Russia; Pirogov Russian National Research Medical University (RNRMU), Moscow, 117997, Russia
| | - Alexey Moiseev
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
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15
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Retroelement-Linked Transcription Factor Binding Patterns Point to Quickly Developing Molecular Pathways in Human Evolution. Cells 2019; 8:cells8020130. [PMID: 30736359 PMCID: PMC6406739 DOI: 10.3390/cells8020130] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 01/29/2019] [Accepted: 01/30/2019] [Indexed: 12/12/2022] Open
Abstract
Background: Retroelements (REs) are transposable elements occupying ~40% of the human genome that can regulate genes by providing transcription factor binding sites (TFBS). RE-linked TFBS profile can serve as a marker of gene transcriptional regulation evolution. This approach allows for interrogating the regulatory evolution of organisms with RE-rich genomes. We aimed to characterize the evolution of transcriptional regulation for human genes and molecular pathways using RE-linked TFBS accumulation as a metric. Methods: We characterized human genes and molecular pathways either enriched or deficient in RE-linked TFBS regulation. We used ENCODE database with mapped TFBS for 563 transcription factors in 13 human cell lines. For 24,389 genes and 3124 molecular pathways, we calculated the score of RE-linked TFBS regulation reflecting the regulatory evolution rate at the level of individual genes and molecular pathways. Results: The major groups enriched by RE regulation deal with gene regulation by microRNAs, olfaction, color vision, fertilization, cellular immune response, and amino acids and fatty acids metabolism and detoxication. The deficient groups were involved in translation, RNA transcription and processing, chromatin organization, and molecular signaling. Conclusion: We identified genes and molecular processes that have characteristics of especially high or low evolutionary rates at the level of RE-linked TFBS regulation in human lineage.
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16
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Zolotovskaia MA, Sorokin MI, Emelianova AA, Borisov NM, Kuzmin DV, Borger P, Garazha AV, Buzdin AA. Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs. Front Pharmacol 2019; 10:1. [PMID: 30728774 PMCID: PMC6351482 DOI: 10.3389/fphar.2019.00001] [Citation(s) in RCA: 98] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 01/03/2019] [Indexed: 12/20/2022] Open
Abstract
Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets.
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Affiliation(s)
- Marianna A Zolotovskaia
- Oncobox Ltd., Moscow, Russia.,Department of Oncology, Hematology and Radiotherapy of Pediatric Faculty, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Maxim I Sorokin
- The Laboratory of Clinical Bioinformatics, IM Sechenov First Moscow State Medical University, Moscow, Russia.,Omicsway Corp., Walnut, CA, United States.,Science-Educational Center Department, M. M. Shemyakin and Yu. A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Anna A Emelianova
- Science-Educational Center Department, M. M. Shemyakin and Yu. A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Nikolay M Borisov
- The Laboratory of Clinical Bioinformatics, IM Sechenov First Moscow State Medical University, Moscow, Russia.,Omicsway Corp., Walnut, CA, United States
| | - Denis V Kuzmin
- Science-Educational Center Department, M. M. Shemyakin and Yu. A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Pieter Borger
- Laboratory of the Swiss Hepato-Pancreato-Biliary, Department of Surgery, Transplantation Center, University Hospital Zurich, Zurich, Switzerland
| | | | - Anton A Buzdin
- Oncobox Ltd., Moscow, Russia.,The Laboratory of Clinical Bioinformatics, IM Sechenov First Moscow State Medical University, Moscow, Russia.,Science-Educational Center Department, M. M. Shemyakin and Yu. A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
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17
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Zolotovskaia MA, Sorokin MI, Roumiantsev SA, Borisov NM, Buzdin AA. Pathway Instability Is an Effective New Mutation-Based Type of Cancer Biomarkers. Front Oncol 2019; 8:658. [PMID: 30662873 PMCID: PMC6328788 DOI: 10.3389/fonc.2018.00658] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 12/12/2018] [Indexed: 01/20/2023] Open
Abstract
DNA mutations play a crucial role in cancer development and progression. Mutation profiles vary dramatically in different cancer types and between individual tumors. Mutations of several individual genes are known as reliable cancer biomarkers, although the number of such genes is tiny and does not enable differential diagnostics for most of the cancers. We report here a technique enabling dramatically increased efficiency of cancer biomarkers development using DNA mutations data. It includes a quantitative metric termed Pathway instability (PI) based on mutations enrichment of intracellular molecular pathways. This method was tested on 5,956 tumor mutation profiles of 15 cancer types from The Cancer Genome Atlas (TCGA) project. Totally, we screened 2,316,670 mutations in 19,872 genes and 1,748 molecular pathways. Our results demonstrated considerable advantage of pathway-based mutation biomarkers over individual gene mutation profiles, as reflected by more than two orders of magnitude greater numbers by high-quality [ROC area-under-curve (AUC)>0.75] biomarkers. For example, the number of such high-quality mutational biomarkers distinguishing between different cancer types was only six for the individual gene mutations, and already 660 for the pathway-based biomarkers. These results evidence that PI value can be used as a new generation of complex cancer biomarkers significantly outperforming the existing gene mutation biomarkers.
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Affiliation(s)
- Marianna A Zolotovskaia
- Department of Oncology, Hematology and Radiotherapy of Pediatric Faculty, Pirogov Russian National Research Medical University, Moscow, Russia.,Oncobox Ltd., Moscow, Russia
| | - Maxim I Sorokin
- The Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Omicsway Corp., Walnut, CA, United States
| | - Sergey A Roumiantsev
- Department of Oncology, Hematology and Radiotherapy of Pediatric Faculty, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Nikolay M Borisov
- Oncobox Ltd., Moscow, Russia.,The Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Anton A Buzdin
- The Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Omicsway Corp., Walnut, CA, United States.,The Laboratory of Systems Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
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18
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Emelianova AA, Kuzmin DV, Panteleev PV, Sorokin M, Buzdin AA, Ovchinnikova TV. Anticancer Activity of the Goat Antimicrobial Peptide ChMAP-28. Front Pharmacol 2018; 9:1501. [PMID: 30622471 PMCID: PMC6308165 DOI: 10.3389/fphar.2018.01501] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 12/07/2018] [Indexed: 12/14/2022] Open
Abstract
Cytotoxic effect of the antimicrobial peptide ChMAP-28 from leucocytes of the goat Capra hircus was examined against five cancer and two normal human cell lines. ChMAP-28 has the amino acid sequence GRFKRFRKKLKRLWHKVGPFVGPILHY and is homologous to other α-helical mammalian antimicrobial peptides. ChMAP-28 shows considerably higher cytotoxicity against cultured tumor cells than toward normal cells at concentrations of <10 μM. Our findings suggest that ChMAP-28 can initiate necrotic death of cancer cells. Its cytotoxic effect is accomplished due to disruption of the plasma membrane integrity and is not abrogated by the addition of the caspase inhibitor Z-VAD-FMK. ChMAP-28 causes permeabilization of cytoplasmic membrane of human leukemia cells HL-60 already after 15 min of incubation. Here, we show that ChMAP-28 has one of the highest antitumor activity in vitro among all known antimicrobial peptides. We speculate that the observed specificity of ChMAP-28 cytotoxic effect against tumor cells is due to its relatively low hydrophobicity and high cationicity. In the meantime, this peptide has low hemolytic activity, which generates a potential for its use as a therapeutic agent.
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Affiliation(s)
- Anna A Emelianova
- M. M. Shemyakin and Yu. A. Ovchinnikov Institute of Bioorganic Chemistry, The Russian Academy of Sciences, Moscow, Russia
| | - Denis V Kuzmin
- M. M. Shemyakin and Yu. A. Ovchinnikov Institute of Bioorganic Chemistry, The Russian Academy of Sciences, Moscow, Russia
| | - Pavel V Panteleev
- M. M. Shemyakin and Yu. A. Ovchinnikov Institute of Bioorganic Chemistry, The Russian Academy of Sciences, Moscow, Russia
| | - Maxim Sorokin
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States
| | - Anton A Buzdin
- M. M. Shemyakin and Yu. A. Ovchinnikov Institute of Bioorganic Chemistry, The Russian Academy of Sciences, Moscow, Russia.,I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Tatiana V Ovchinnikova
- M. M. Shemyakin and Yu. A. Ovchinnikov Institute of Bioorganic Chemistry, The Russian Academy of Sciences, Moscow, Russia.,I.M. Sechenov First Moscow State Medical University, Moscow, Russia
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19
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Molecular pathway activation – New type of biomarkers for tumor morphology and personalized selection of target drugs. Semin Cancer Biol 2018; 53:110-124. [DOI: 10.1016/j.semcancer.2018.06.003] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 06/19/2018] [Accepted: 06/19/2018] [Indexed: 02/06/2023]
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20
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Oncobox Bioinformatical Platform for Selecting Potentially Effective Combinations of Target Cancer Drugs Using High-Throughput Gene Expression Data. Cancers (Basel) 2018; 10:cancers10100365. [PMID: 30274248 PMCID: PMC6209915 DOI: 10.3390/cancers10100365] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 09/24/2018] [Accepted: 09/25/2018] [Indexed: 12/22/2022] Open
Abstract
Sequential courses of anticancer target therapy lead to selection of drug-resistant cells, which results in continuous decrease of clinical response. Here we present a new approach for predicting effective combinations of target drugs, which act in a synergistic manner. Synergistic combinations of drugs may prevent or postpone acquired resistance, thus increasing treatment efficiency. We cultured human ovarian carcinoma SKOV-3 and neuroblastoma NGP-127 cancer cell lines in the presence of Tyrosine Kinase Inhibitors (Pazopanib, Sorafenib, and Sunitinib) and Rapalogues (Temsirolimus and Everolimus) for four months and obtained cell lines demonstrating increased drug resistance. We investigated gene expression profiles of intact and resistant cells by microarrays and analyzed alterations in 378 cancer-related signaling pathways using the bioinformatical platform Oncobox. This revealed numerous pathways linked with development of drug resistant phenotypes. Our approach is based on targeting proteins involved in as many as possible signaling pathways upregulated in resistant cells. We tested 13 combinations of drugs and/or selective inhibitors predicted by Oncobox and 10 random combinations. Synergy scores for Oncobox predictions were significantly higher than for randomly selected drug combinations. Thus, the proposed approach significantly outperforms random selection of drugs and can be adopted to enhance discovery of new synergistic combinations of anticancer target drugs.
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21
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Nikitin D, Penzar D, Garazha A, Sorokin M, Tkachev V, Borisov N, Poltorak A, Prassolov V, Buzdin AA. Profiling of Human Molecular Pathways Affected by Retrotransposons at the Level of Regulation by Transcription Factor Proteins. Front Immunol 2018; 9:30. [PMID: 29441061 PMCID: PMC5797644 DOI: 10.3389/fimmu.2018.00030] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 01/04/2018] [Indexed: 12/22/2022] Open
Abstract
Endogenous retroviruses and retrotransposons also termed retroelements (REs) are mobile genetic elements that were active until recently in human genome evolution. REs regulate gene expression by actively reshaping chromatin structure or by directly providing transcription factor binding sites (TFBSs). We aimed to identify molecular processes most deeply impacted by the REs in human cells at the level of TFBS regulation. By using ENCODE data, we identified ~2 million TFBS overlapping with putatively regulation-competent human REs located in 5-kb gene promoter neighborhood (~17% of all TFBS in promoter neighborhoods; ~9% of all RE-linked TFBS). Most of REs hosting TFBS were highly diverged repeats, and for the evolutionary young (0–8% diverged) elements we identified only ~7% of all RE-linked TFBS. The gene-specific distributions of RE-linked TFBS generally correlated with the distributions for all TFBS. However, several groups of molecular processes were highly enriched in the RE-linked TFBS regulation. They were strongly connected with the immunity and response to pathogens, with the negative regulation of gene transcription, ubiquitination, and protein degradation, extracellular matrix organization, regulation of STAT signaling, fatty acids metabolism, regulation of GTPase activity, protein targeting to Golgi, regulation of cell division and differentiation, development and functioning of perception organs and reproductive system. By contrast, the processes most weakly affected by the REs were linked with the conservative aspects of embryo development. We also identified differences in the regulation features by the younger and older fractions of the REs. The regulation by the older fraction of the REs was linked mainly with the immunity, cell adhesion, cAMP, IGF1R, Notch, Wnt, and integrin signaling, neuronal development, chondroitin sulfate and heparin metabolism, and endocytosis. The younger REs regulate other aspects of immunity, cell cycle progression and apoptosis, PDGF, TGF beta, EGFR, and p38 signaling, transcriptional repression, structure of nuclear lumen, catabolism of phospholipids, and heterocyclic molecules, insulin and AMPK signaling, retrograde Golgi-ER transport, and estrogen signaling. The immunity-linked pathways were highly represented in both categories, but their functional roles were different and did not overlap. Our results point to the most quickly evolving molecular pathways in the recent and ancient evolution of human genome.
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Affiliation(s)
- Daniil Nikitin
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.,D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Dmitry Penzar
- The Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Andrew Garazha
- D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.,OmicsWay Corp., Walnut, CA, United States
| | - Maxim Sorokin
- OmicsWay Corp., Walnut, CA, United States.,National Research Centre Kurchatov Institute, Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow, Russia.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | - Nicolas Borisov
- OmicsWay Corp., Walnut, CA, United States.,National Research Centre Kurchatov Institute, Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow, Russia
| | - Alexander Poltorak
- Program in Immunology, Sackler Graduate School, Tufts University, Boston, MA, United States
| | - Vladimir Prassolov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - Anton A Buzdin
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.,D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.,OmicsWay Corp., Walnut, CA, United States.,National Research Centre Kurchatov Institute, Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow, Russia
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22
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Kovalchuk A, Ilnytskyy Y, Woycicki R, Rodriguez-Juarez R, Metz GAS, Kovalchuk O. Adverse effects of paternal chemotherapy exposure on the progeny brain: intergenerational chemobrain. Oncotarget 2018. [PMID: 29515791 PMCID: PMC5839372 DOI: 10.18632/oncotarget.24311] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Recent advances in cancer treatments have led to significant increases in cure rates. Most cancer patients are treated with various cytotoxic chemotherapy regimens. These treatment modalities are mutagenic and genotoxic and cause a wide array of late-occurring health problems, and even exert a deleterious influence on future offspring. The adverse effects from exposed parents on offspring are referred to as transgenerational effects, and currently little is known about chemotherapy-induced transgenerational effects. Furthermore, transgenerational effects have not been studied in the brains of progeny of exposed parents. In this study, we analyzed the existence and molecular nature of transgenerational effects in the brains of progeny of animals exposed to three common chemotherapy agents: cyclophosphamide (CPP), procarbazine (PCB) and mitomycin C (MMC). For the first time, our results show that paternal exposure to chemotherapy drugs causes transgenerational changes in the brain of unexposed progeny. Although no DNA damage was observed in terms of γH2AX levels, some alterations were found in levels of PCNA, protein involved in DNA repair, replication and profileration. Furthermore, there were changes in proliferation and apoptosis proteins BCL2 and AKT1, the proteins associated with DNA methylation, DNMT1 and MeCP2. Some altered expression trends were noted in proteins involved in myelin biogenesis, MBP and MYT1L. Moreover, global transcriptome profiling revealed changes in over 200 genes in the whole brains of progeny of animals exposed to CPP, and the changes in the levels of FOXP2 and ELK1proteins were confirmed by western blot analysis. These findings suggest that paternal chemotherapy significantly affects offspring brain development and may affect brain functioning. This research provides a key roadmap for future investigations of the novel phenomenon of transgenerational effects of chemotherapy in the brain of progeny of exposed parents.
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Affiliation(s)
- Anna Kovalchuk
- Canadian Center for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB, T1K3M4, Canada.,Department of Biology, University of Lethbridge, Lethbridge, AB, T1K 3M4, Canada
| | - Yaroslav Ilnytskyy
- Department of Biology, University of Lethbridge, Lethbridge, AB, T1K 3M4, Canada
| | - Rafal Woycicki
- Department of Biology, University of Lethbridge, Lethbridge, AB, T1K 3M4, Canada
| | | | - Gerlinde A S Metz
- Canadian Center for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB, T1K3M4, Canada.,Alberta Epigenetics Network, Calgary, AB, T2L 2A6, Canada
| | - Olga Kovalchuk
- Department of Biology, University of Lethbridge, Lethbridge, AB, T1K 3M4, Canada.,Alberta Epigenetics Network, Calgary, AB, T2L 2A6, Canada
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Borisov N, Tkachev V, Suntsova M, Kovalchuk O, Zhavoronkov A, Muchnik I, Buzdin A. A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency. Cell Cycle 2018; 17:486-491. [PMID: 29251172 DOI: 10.1080/15384101.2017.1417706] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficiency of preceding cases, i.e. the gene expression data on patients combined with the confirmed outcome of known treatment methods. At the same time, there exist thousands of various cell lines that were treated with hundreds of anti-cancer drugs in order to check the ability of these drugs to stop the cell proliferation, and all these cell line cultures were profiled in terms of their gene expression. Here we present a new approach in machine learning, which can predict clinical efficiency of anti-cancer drugs for individual patients by transferring features obtained from the expression-based data from cell lines. The method was validated on three datasets for cancer-like diseases (chronic myeloid leukemia, as well as lung adenocarcinoma and renal carcinoma) treated with targeted drugs - kinase inhibitors, such as imatinib or sorafenib.
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Affiliation(s)
- Nicolas Borisov
- a National Research Centre "Kurchatov Institute" , Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow , Russia.,b Department of R&D , First Oncology Research and Advisory Center, Moscow , Russia
| | - Victor Tkachev
- b Department of R&D , First Oncology Research and Advisory Center, Moscow , Russia.,c Department of R&D , OmicsWay Corporation, Walnut , CA , USA
| | - Maria Suntsova
- b Department of R&D , First Oncology Research and Advisory Center, Moscow , Russia.,d Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Moscow , Russia.,e Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology , Oncology and Immunology, Moscow , 117198 , Russia
| | - Olga Kovalchuk
- f Department of Biological Sciences , University of Lethbridge , Lethbridge , AB , Canada.,g Canada Cancer and Aging Research Laboratories , Lethbridge , AB , Canada
| | - Alex Zhavoronkov
- h Insilico Medicine, Inc, ETC, Johns Hopkins University , Baltimore , MD , USA
| | - Ilya Muchnik
- i Rutgers University , Hill Center, Busch Campus, Piscataway , NJ , USA
| | - Anton Buzdin
- a National Research Centre "Kurchatov Institute" , Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, Moscow , Russia.,b Department of R&D , First Oncology Research and Advisory Center, Moscow , Russia.,c Department of R&D , OmicsWay Corporation, Walnut , CA , USA.,d Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Moscow , Russia.,e Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology , Oncology and Immunology, Moscow , 117198 , Russia
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Prediction of Drug Efficiency by Transferring Gene Expression Data from Cell Lines to Cancer Patients. BRAVERMAN READINGS IN MACHINE LEARNING. KEY IDEAS FROM INCEPTION TO CURRENT STATE 2018. [DOI: 10.1007/978-3-319-99492-5_9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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