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Ruiz-Arenas C, Marín-Goñi I, Wang L, Ochoa I, Pérez-Jurado L, Hernaez M. NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders. Nucleic Acids Res 2024; 52:e44. [PMID: 38597610 PMCID: PMC11109970 DOI: 10.1093/nar/gkae197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/23/2024] [Accepted: 03/12/2024] [Indexed: 04/11/2024] Open
Abstract
Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.
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Affiliation(s)
- Carlos Ruiz-Arenas
- Computational Biology Program, CIMA University of Navarra, idiSNA, Pamplona 31008, Spain
- Department MELIS, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Irene Marín-Goñi
- Computational Biology Program, CIMA University of Navarra, idiSNA, Pamplona 31008, Spain
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Idoia Ochoa
- Department of Electrical and Electronics Engineering, Tecnun, University of Navarra, Donostia, Spain
- Institute for Data Science and Artificial Inteligence (DATAI), University of Navarra, Pamplona 31008, Spain
| | - Luis A Pérez-Jurado
- Department MELIS, Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain
- Genetics Service, Hospital del Mar & Hospital del Mar Research Institute (IMIM), Barcelona, Spain
| | - Mikel Hernaez
- Computational Biology Program, CIMA University of Navarra, idiSNA, Pamplona 31008, Spain
- Institute for Data Science and Artificial Inteligence (DATAI), University of Navarra, Pamplona 31008, Spain
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2
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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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3
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Shaban N, Raevskiy M, Zakharova G, Shipunova V, Deyev S, Suntsova M, Sorokin M, Buzdin A, Kamashev D. Human Blood Serum Counteracts EGFR/HER2-Targeted Drug Lapatinib Impact on Squamous Carcinoma SK-BR-3 Cell Growth and Gene Expression. BIOCHEMISTRY. BIOKHIMIIA 2024; 89:487-506. [PMID: 38648768 DOI: 10.1134/s000629792403009x] [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: 11/10/2023] [Revised: 01/17/2024] [Accepted: 02/20/2024] [Indexed: 04/25/2024]
Abstract
Lapatinib is a targeted therapeutic inhibiting HER2 and EGFR proteins. It is used for the therapy of HER2-positive breast cancer, although not all the patients respond to it. Using human blood serum samples from 14 female donors (separately taken or combined), we found that human blood serum dramatically abolishes the lapatinib-mediated inhibition of growth of the human breast squamous carcinoma SK-BR-3 cell line. This antagonism between lapatinib and human serum was associated with cancelation of the drug induced G1/S cell cycle transition arrest. RNA sequencing revealed 308 differentially expressed genes in the presence of lapatinib. Remarkably, when combined with lapatinib, human blood serum showed the capacity of restoring both the rate of cell growth, and the expression of 96.1% of the genes expression of which were altered by the lapatinib treatment alone. Co-administration of EGF with lapatinib also restores the cell growth and cancels alteration of expression of 95.8% of the genes specific to lapatinib treatment of SK-BR-3 cells. Differential gene expression analysis also showed that in the presence of human serum or EGF, lapatinib was unable to inhibit the Toll-Like Receptor signaling pathway and alter expression of genes linked to the Gene Ontology term of Focal adhesion.
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Affiliation(s)
- Nina Shaban
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia
| | - Mikhail Raevskiy
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | - Galina Zakharova
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | - Victoria Shipunova
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Sergey Deyev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- "Biomarker" Research Laboratory, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, 420008, Russia
| | - Maria Suntsova
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia.
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Maksim Sorokin
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), Brussels, 1200, Belgium
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Dmitri Kamashev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
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4
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Zolotovskaia M, Kovalenko M, Pugacheva P, Tkachev V, Simonov A, Sorokin M, Seryakov A, Garazha A, Gaifullin N, Sekacheva M, Zakharova G, Buzdin AA. Algorithmically Reconstructed Molecular Pathways as the New Generation of Prognostic Molecular Biomarkers in Human Solid Cancers. Proteomes 2023; 11:26. [PMID: 37755705 PMCID: PMC10535530 DOI: 10.3390/proteomes11030026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
Individual gene expression and molecular pathway activation profiles were shown to be effective biomarkers in many cancers. Here, we used the human interactome model to algorithmically build 7470 molecular pathways centered around individual gene products. We assessed their associations with tumor type and survival in comparison with the previous generation of molecular pathway biomarkers (3022 "classical" pathways) and with the RNA transcripts or proteomic profiles of individual genes, for 8141 and 1117 samples, respectively. For all analytes in RNA and proteomic data, respectively, we found a total of 7441 and 7343 potential biomarker associations for gene-centric pathways, 3020 and 2950 for classical pathways, and 24,349 and 6742 for individual genes. Overall, the percentage of RNA biomarkers was statistically significantly higher for both types of pathways than for individual genes (p < 0.05). In turn, both types of pathways showed comparable performance. The percentage of cancer-type-specific biomarkers was comparable between proteomic and transcriptomic levels, but the proportion of survival biomarkers was dramatically lower for proteomic data. Thus, we conclude that pathway activation level is the advanced type of biomarker for RNA and proteomic data, and momentary algorithmic computer building of pathways is a new credible alternative to time-consuming hypothesis-driven manual pathway curation and reconstruction.
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Affiliation(s)
- Marianna Zolotovskaia
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- Omicsway Corp., Walnut, CA 91789, USA
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Maks Kovalenko
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
| | - Polina Pugacheva
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
| | | | - Alexander Simonov
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- Omicsway Corp., Walnut, CA 91789, USA
| | - Maxim Sorokin
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
| | | | | | - Nurshat Gaifullin
- Department of Pathology, Faculty of Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Marina Sekacheva
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Galina Zakharova
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Anton A. Buzdin
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119048 Moscow, Russia
- Laboratory of Systems Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
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Kamashev D, Shaban N, Lebedev T, Prassolov V, Suntsova M, Raevskiy M, Gaifullin N, Sekacheva M, Garazha A, Poddubskaya E, Sorokin M, Buzdin A. Human Blood Serum Can Diminish EGFR-Targeted Inhibition of Squamous Carcinoma Cell Growth through Reactivation of MAPK and EGFR Pathways. Cells 2023; 12:2022. [PMID: 37626832 PMCID: PMC10453612 DOI: 10.3390/cells12162022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/03/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
Regardless of the presence or absence of specific diagnostic mutations, many cancer patients fail to respond to EGFR-targeted therapeutics, and a personalized approach is needed to identify putative (non)responders. We found previously that human peripheral blood and EGF can modulate the activities of EGFR-specific drugs on inhibiting clonogenity in model EGFR-positive A431 squamous carcinoma cells. Here, we report that human serum can dramatically abolish the cell growth rate inhibition by EGFR-specific drugs cetuximab and erlotinib. We show that this phenomenon is linked with derepression of drug-induced G1S cell cycle transition arrest. Furthermore, A431 cell growth inhibition by cetuximab, erlotinib, and EGF correlates with a decreased activity of ERK1/2 proteins. In turn, the EGF- and human serum-mediated rescue of drug-treated A431 cells restores ERK1/2 activity in functional tests. RNA sequencing revealed 1271 and 1566 differentially expressed genes (DEGs) in the presence of cetuximab and erlotinib, respectively. Erlotinib- and cetuximab-specific DEGs significantly overlapped. Interestingly, the expression of 100% and 75% of these DEGs restores to the no-drug level when EGF or a mixed human serum sample, respectively, is added along with cetuximab. In the case of erlotinib, EGF and human serum restore the expression of 39% and 83% of DEGs, respectively. We further assessed differential molecular pathway activation levels and propose that EGF/human serum-mediated A431 resistance to EGFR drugs can be largely explained by reactivation of the MAPK signaling cascade.
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Affiliation(s)
- Dmitri Kamashev
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia;
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia; (N.S.); (A.B.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia;
| | - Nina Shaban
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia; (N.S.); (A.B.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia;
| | - Timofey Lebedev
- Engelhardt Institute of Molecular Biology, Moscow 119991, Russia; (T.L.); (V.P.)
| | - Vladimir Prassolov
- Engelhardt Institute of Molecular Biology, Moscow 119991, Russia; (T.L.); (V.P.)
| | - Maria Suntsova
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia;
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (M.R.); (E.P.)
| | - Mikhail Raevskiy
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (M.R.); (E.P.)
| | - Nurshat Gaifullin
- Department of Pathology, Faculty of Medicine, Lomonosov Moscow State University, Moscow 119992, Russia;
| | - Marina Sekacheva
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (M.R.); (E.P.)
| | - Andrew Garazha
- Oncobox Ltd., Moscow 121205, Russia;
- Omicsway Corp., Walnut, CA 91789, USA
| | - Elena Poddubskaya
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (M.R.); (E.P.)
| | - Maksim Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia;
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia;
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
| | - Anton Buzdin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia; (N.S.); (A.B.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia;
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow 119991, Russia; (M.R.); (E.P.)
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
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6
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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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