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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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Konovalov N, Timonin S, Asyutin D, Raevskiy M, Sorokin M, Buzdin A, Kaprovoy S. Transcriptomic Portraits and Molecular Pathway Activation Features of Adult Spinal Intramedullary Astrocytomas. Front Oncol 2022; 12:837570. [PMID: 35387112 PMCID: PMC8978956 DOI: 10.3389/fonc.2022.837570] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 02/21/2022] [Indexed: 11/30/2022] Open
Abstract
In this study, we report 31 spinal intramedullary astrocytoma (SIA) RNA sequencing (RNA-seq) profiles for 25 adult patients with documented clinical annotations. To our knowledge, this is the first clinically annotated RNA-seq dataset of spinal astrocytomas derived from the intradural intramedullary compartment. We compared these tumor profiles with the previous healthy central nervous system (CNS) RNA-seq data for spinal cord and brain and identified SIA-specific gene sets and molecular pathways. Our findings suggest a trend for SIA-upregulated pathways governing interactions with the immune cells and downregulated pathways for the neuronal functioning in the context of normal CNS activity. In two patient tumor biosamples, we identified diagnostic KIAA1549-BRAF fusion oncogenes, and we also found 16 new SIA-associated fusion transcripts. In addition, we bioinformatically simulated activities of targeted cancer drugs in SIA samples and predicted that several tyrosine kinase inhibitory drugs and thalidomide analogs could be potentially effective as second-line treatment agents to aid in the prevention of SIA recurrence and progression.
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Affiliation(s)
| | | | | | - Mikhail Raevskiy
- Omicsway Corp., Walnut, CA, United States
- Moscow Institute of Physics and Technology, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Maxim Sorokin
- Moscow Institute of Physics and Technology, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Anton Buzdin
- Omicsway Corp., Walnut, CA, United States
- Moscow Institute of Physics and Technology, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Oncobox Ltd., Moscow, Russia
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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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Kamashev D, Sorokin M, Kochergina I, Drobyshev A, Vladimirova U, Zolotovskaia M, Vorotnikov I, Shaban N, Raevskiy M, Kuzmin D, Buzdin A. Human blood serum can donor-specifically antagonize effects of EGFR-targeted drugs on squamous carcinoma cell growth. Heliyon 2021; 7:e06394. [PMID: 33748471 PMCID: PMC7966997 DOI: 10.1016/j.heliyon.2021.e06394] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/29/2020] [Accepted: 02/25/2021] [Indexed: 02/09/2023] Open
Abstract
Many patients fail to respond to EGFR-targeted therapeutics, and personalized diagnostics is needed to identify putative responders. We investigated 1630 colorectal and lung squamous carcinomas and 1357 normal lung and colon samples and observed huge variation in EGFR pathway activation in both cancerous and healthy tissues, irrespectively on EGFR gene mutation status. We investigated whether human blood serum can affect squamous carcinoma cell growth and EGFR drug response. We demonstrate that human serum antagonizes the effects of EGFR-targeted drugs erlotinib and cetuximab on A431 squamous carcinoma cells by increasing IC50 by about 2- and 20-fold, respectively. The effects on clonogenicity varied significantly across the individual serum samples in every experiment, with up to 100% differences. EGF concentration could explain many effects of blood serum samples, and EGFR ligands-depleted serum showed lesser effect on drug sensitivity.
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Affiliation(s)
- Dmitry Kamashev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 8-2, Trubetskaya St., Moscow 119992, Russia
| | - Maksim Sorokin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 8-2, Trubetskaya St., Moscow 119992, Russia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
| | - Irina Kochergina
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
| | - Aleksey Drobyshev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 8-2, Trubetskaya St., Moscow 119992, Russia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
| | - Uliana Vladimirova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 8-2, Trubetskaya St., Moscow 119992, Russia
| | - Marianna Zolotovskaia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
| | - Igor Vorotnikov
- Blokhin National Medical Research Center of Oncology of the Ministry of Health of Russia
| | - Nina Shaban
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
| | - Mikhail Raevskiy
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
- OmicsWay Corp., Walnut, CA, USA
| | - Denis Kuzmin
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
| | - Anton Buzdin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 16/10, Miklukho-Maklaya St., Moscow 117997, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, 8-2, Trubetskaya St., Moscow 119992, Russia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region 141700, Russia
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5
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Sorokin M, Borisov N, Kuzmin D, Gudkov A, Zolotovskaia M, Garazha A, Buzdin A. Algorithmic Annotation of Functional Roles for Components of 3,044 Human Molecular Pathways. Front Genet 2021; 12:617059. [PMID: 33633781 PMCID: PMC7900570 DOI: 10.3389/fgene.2021.617059] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 01/20/2021] [Indexed: 12/16/2022] Open
Abstract
Current methods of high-throughput molecular and genomic analyses enabled to reconstruct thousands of human molecular pathways. Knowledge of molecular pathways structure and architecture taken along with the gene expression data can help interrogating the pathway activation levels (PALs) using different bioinformatic algorithms. In turn, the pathway activation profiles can characterize molecular processes, which are differentially regulated and give numeric characteristics of the extent of their activation or inhibition. However, different pathway nodes may have different functions toward overall pathway regulation, and calculation of PAL requires knowledge of molecular function of every node in the pathway in terms of its activator or inhibitory role. Thus, high-throughput annotation of functional roles of pathway nodes is required for the comprehensive analysis of the pathway activation profiles. We proposed an algorithm that identifies functional roles of the pathway components and applied it to annotate 3,044 human molecular pathways extracted from the Biocarta, Reactome, KEGG, Qiagen Pathway Central, NCI, and HumanCYC databases and including 9,022 gene products. The resulting knowledgebase can be applied for the direct calculation of the PALs and establishing large scale profiles of the signaling, metabolic, and DNA repair pathway regulation using high throughput gene expression data. We also provide a bioinformatic tool for PAL data calculations using the current pathway knowledgebase.
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Affiliation(s)
- Maxim Sorokin
- Omicsway Corp., Walnut, CA, United States.,Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Laboratory for Translational Bioinformatics, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Nicolas Borisov
- Omicsway Corp., Walnut, CA, United States.,Laboratory for Translational Bioinformatics, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Denis Kuzmin
- Laboratory for Translational Bioinformatics, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Alexander Gudkov
- Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Marianna Zolotovskaia
- Laboratory for Translational Bioinformatics, Moscow Institute of Physics and Technology, Moscow, Russia
| | | | - Anton Buzdin
- Omicsway Corp., Walnut, CA, United States.,Laboratory for Translational Bioinformatics, Moscow Institute of Physics and Technology, Moscow, Russia.,Laboratory of Systems Biology, 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
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Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments. BMC Med Genomics 2020; 13:111. [PMID: 32948183 PMCID: PMC7499993 DOI: 10.1186/s12920-020-00759-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 07/27/2020] [Indexed: 12/18/2022] Open
Abstract
Background Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics. Methods We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories. Results We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases. Conclusions We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.
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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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Zolotovskaia MA, Sorokin MI, Petrov IV, Poddubskaya EV, Moiseev AA, Sekacheva MI, Borisov NM, Tkachev VS, Garazha AV, Kaprin AD, Shegay PV, Giese A, Kim E, Roumiantsev SA, Buzdin AA. Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology. Int J Mol Sci 2020; 21:E1580. [PMID: 32111026 PMCID: PMC7084891 DOI: 10.3390/ijms21051580] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 02/17/2020] [Accepted: 02/19/2020] [Indexed: 02/07/2023] Open
Abstract
Inter-patient molecular heterogeneity is the major declared driver of an expanding variety of anticancer drugs and personalizing their prescriptions. Here, we compared interpatient molecular heterogeneities of tumors and repertoires of drugs or their molecular targets currently in use in clinical oncology. We estimated molecular heterogeneity using genomic (whole exome sequencing) and transcriptomic (RNA sequencing) data for 4890 tumors taken from The Cancer Genome Atlas database. For thirteen major cancer types, we compared heterogeneities at the levels of mutations and gene expression with the repertoires of targeted therapeutics and their molecular targets accepted by the current guidelines in oncology. Totally, 85 drugs were investigated, collectively covering 82 individual molecular targets. For the first time, we showed that the repertoires of molecular targets of accepted drugs did not correlate with molecular heterogeneities of different cancer types. On the other hand, we found that the clinical recommendations for the available cancer drugs were strongly congruent with the gene expression but not gene mutation patterns. We detected the best match among the drugs usage recommendations and molecular patterns for the kidney, stomach, bladder, ovarian and endometrial cancers. In contrast, brain tumors, prostate and colorectal cancers showed the lowest match. These findings provide a theoretical basis for reconsidering usage of targeted therapeutics and intensifying drug repurposing efforts.
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Affiliation(s)
- Marianna A. Zolotovskaia
- Oncobox ltd., Moscow, 121205, Russia; (I.V.P.); (A.A.B.)
- Department of Oncology, Hematology and Radiotherapy of Pediatric Faculty, Pirogov Russian National Research Medical University, Moscow, 117997, Russia;
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia;
| | - Maxim I. Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
- Omicsway Corp., Walnut, CA, 91789, USA; (V.S.T.); (A.V.G.)
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Ivan V. Petrov
- Oncobox ltd., Moscow, 121205, Russia; (I.V.P.); (A.A.B.)
| | - Elena V. Poddubskaya
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
| | - Alexey A. Moiseev
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
| | - Marina I. Sekacheva
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
| | - Nicolas M. Borisov
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia;
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
- Omicsway Corp., Walnut, CA, 91789, USA; (V.S.T.); (A.V.G.)
| | | | | | - Andrey D. Kaprin
- National Medical Research Radiological Centre of the Ministry of Health of the Russian Federation, Moscow 125284, Russia;
| | - Peter V. Shegay
- Center for Innovative Radiological and Regenerative Technologies of the Ministry of Health of the Russian Federation, Obninsk 249030, Russia;
| | - Alf Giese
- Orthocentrum Hamburg, Hamburg, Germany; or
| | - Ella Kim
- Johannes Gutenberg University Mainz, Mainz, Germany;
| | - Sergey A. Roumiantsev
- Department of Oncology, Hematology and Radiotherapy of Pediatric Faculty, Pirogov Russian National Research Medical University, Moscow, 117997, Russia;
| | - Anton A. Buzdin
- Oncobox ltd., Moscow, 121205, Russia; (I.V.P.); (A.A.B.)
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia;
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
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Intratumoral Heterogeneity and Longitudinal Changes in Gene Expression Predict Differential Drug Sensitivity in Newly Diagnosed and Recurrent Glioblastoma. Cancers (Basel) 2020; 12:cancers12020520. [PMID: 32102350 PMCID: PMC7072286 DOI: 10.3390/cancers12020520] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 02/21/2020] [Accepted: 02/21/2020] [Indexed: 12/22/2022] Open
Abstract
Background: Inevitable recurrence after radiochemotherapy is the major problem in the treatment of glioblastoma, the most prevalent type of adult brain malignancy. Glioblastomas are notorious for a high degree of intratumor heterogeneity manifest through a diversity of cell types and molecular patterns. The current paradigm of understanding glioblastoma recurrence is that cytotoxic therapy fails to target effectively glioma stem cells. Recent advances indicate that therapy-driven molecular evolution is a fundamental trait associated with glioblastoma recurrence. There is a growing body of evidence indicating that intratumor heterogeneity, longitudinal changes in molecular biomarkers and specific impacts of glioma stem cells need to be taken into consideration in order to increase the accuracy of molecular diagnostics still relying on readouts obtained from a single tumor specimen. Methods: This study integrates a multisampling strategy, longitudinal approach and complementary transcriptomic investigations in order to identify transcriptomic traits of recurrent glioblastoma in whole-tissue specimens of glioblastoma or glioblastoma stem cells. In this study, 128 tissue samples of 44 tumors including 23 first diagnosed, 19 recurrent and 2 secondary recurrent glioblastomas were analyzed along with 27 primary cultures of glioblastoma stem cells by RNA sequencing. A novel algorithm was used to quantify longitudinal changes in pathway activities and model efficacy of anti-cancer drugs based on gene expression data. Results: Our study reveals that intratumor heterogeneity of gene expression patterns is a fundamental characteristic of not only newly diagnosed but also recurrent glioblastomas. Evidence is provided that glioblastoma stem cells recapitulate intratumor heterogeneity, longitudinal transcriptomic changes and drug sensitivity patterns associated with the state of recurrence. Conclusions: Our results provide a transcriptional rationale for the lack of significant therapeutic benefit from temozolomide in patients with recurrent glioblastoma. Our findings imply that the spectrum of potentially effective drugs is likely to differ between newly diagnosed and recurrent glioblastomas and underscore the merits of glioblastoma stem cells as prognostic models for identifying alternative drugs and predicting drug response in recurrent glioblastoma. With the majority of recurrent glioblastomas being inoperable, glioblastoma stem cell models provide the means of compensating for the limited availability of recurrent glioblastoma specimens.
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Tkachev V, Sorokin M, Borisov C, Garazha A, Buzdin A, Borisov N. Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology. Int J Mol Sci 2020; 21:ijms21030713. [PMID: 31979006 PMCID: PMC7037338 DOI: 10.3390/ijms21030713] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 01/16/2020] [Accepted: 01/17/2020] [Indexed: 12/21/2022] Open
Abstract
(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41–235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61–0.88 range to 0.70–0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.
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Affiliation(s)
- Victor Tkachev
- OmicsWayCorp, Walnut, CA 91788, USA; (V.T.); (M.S.); (A.G.)
| | - Maxim Sorokin
- OmicsWayCorp, Walnut, CA 91788, USA; (V.T.); (M.S.); (A.G.)
- Institute for Personailzed Medicine, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Constantin Borisov
- National Research University—Higher School of Economics, 101000 Moscow, Russia;
| | - Andrew Garazha
- OmicsWayCorp, Walnut, CA 91788, USA; (V.T.); (M.S.); (A.G.)
| | - Anton Buzdin
- OmicsWayCorp, Walnut, CA 91788, USA; (V.T.); (M.S.); (A.G.)
- Institute for Personailzed Medicine, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- Moscow Institute of Physics and Technology, 141701 Moscow Oblast, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
| | - Nicolas Borisov
- OmicsWayCorp, Walnut, CA 91788, USA; (V.T.); (M.S.); (A.G.)
- Institute for Personailzed Medicine, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- Moscow Institute of Physics and Technology, 141701 Moscow Oblast, Russia
- Correspondence: ; Tel.: +7-903-218-7261
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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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12
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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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13
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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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14
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Borisov N, Buzdin A. New Paradigm of Machine Learning (ML) in Personalized Oncology: Data Trimming for Squeezing More Biomarkers From Clinical Datasets. Front Oncol 2019; 9:658. [PMID: 31380288 PMCID: PMC6650540 DOI: 10.3389/fonc.2019.00658] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 07/05/2019] [Indexed: 11/13/2022] Open
Affiliation(s)
- Nicolas Borisov
- Department of Personalized Medicine, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Anton Buzdin
- Department of Personalized Medicine, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.,Department of Genomics and Postgenomic Technologies, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.,Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States
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15
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Yang YH, Zhang YX, Gui Y, Liu JB, Sun JJ, Fan H. Analysis of the autophagy gene expression profile of pancreatic cancer based on autophagy-related protein microtubule-associated protein 1A/1B-light chain 3. World J Gastroenterol 2019; 25:2086-2098. [PMID: 31114135 PMCID: PMC6506580 DOI: 10.3748/wjg.v25.i17.2086] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 03/20/2019] [Accepted: 04/10/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Pancreatic cancer is a highly invasive malignant tumor. Expression levels of the autophagy-related protein microtubule-associated protein 1A/1B-light chain 3 (LC3) and perineural invasion (PNI) are closely related to its occurrence and development. Our previous results showed that the high expression of LC3 was positively correlated with PNI in the patients with pancreatic cancer. In this study, we further searched for differential genes involved in autophagy of pancreatic cancer by gene expression profiling and analyzed their biological functions in pancreatic cancer, which provides a theoretical basis for elucidating the pathophysiological mechanism of autophagy in pancreatic cancer and PNI.
AIM To identify differentially expressed genes involved in pancreatic cancer autophagy and explore the pathogenesis at the molecular level.
METHODS Two sets of gene expression profiles of pancreatic cancer/normal tissue (GSE16515 and GSE15471) were collected from the Gene Expression Omnibus. Significance analysis of microarrays algorithm was used to screen differentially expressed genes related to pancreatic cancer. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to analyze the functional enrichment of the differentially expressed genes. Protein interaction data containing only differentially expressed genes was downloaded from String database and screened. Module mining was carried out by Cytoscape software and ClusterOne plug-in. The interaction relationship between the modules was analyzed and the pivot nodes between the functional modules were determined according to the information of the functional modules and the data of reliable protein interaction network.
RESULTS Based on the above two data sets of pancreatic tissue total gene expression, 6098 and 12928 differentially expressed genes were obtained by analysis of genes with higher phenotypic correlation. After extracting the intersection of the two differential gene sets, 4870 genes were determined. GO analysis showed that 14 significant functional items including negative regulation of protein ubiquitination were closely related to autophagy. A total of 986 differentially expressed genes were enriched in these functional items. After eliminating the autophagy related genes of human cancer cells which had been defined, 347 differentially expressed genes were obtained. KEGG pathway analysis showed that the pathways hsa04144 and hsa04020 were related to autophagy. In addition, 65 clustering modules were screened after the protein interaction network was constructed based on String database, and module 32 contains the LC3 gene, which interacts with multiple autophagy-related genes. Moreover, ubiquitin C acts as a pivot node in functional modules to connect multiple modules related to pancreatic cancer and autophagy.
CONCLUSION Three hundred and forty-seven genes associated with autophagy in human pancreatic cancer were concentrated, and a key gene ubiquitin C which is closely related to the occurrence of PNI was determined, suggesting that LC3 may influence the PNI and prognosis of pancreatic cancer through ubiquitin C.
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Affiliation(s)
- Yan-Hui Yang
- Department of Hepatobiliary Surgery, First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang 471000, Henan Province, China
| | - Yu-Xiang Zhang
- Department of Urology Surgery, First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang 471000, Henan Province, China
| | - Yang Gui
- Department of Hepatobiliary Surgery, First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang 471000, Henan Province, China
| | - Jiang-Bo Liu
- Department of General Surgery, First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang 471000, Henan Province, China
| | - Jun-Jun Sun
- Department of Hepatobiliary Surgery, First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang 471000, Henan Province, China
| | - Hua Fan
- First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang 471000, Henan Province, China
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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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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: 100] [Impact Index Per Article: 20.0] [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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18
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Zhavoronkov A, Mamoshina P, Vanhaelen Q, Scheibye-Knudsen M, Moskalev A, Aliper A. Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Res Rev 2019; 49:49-66. [PMID: 30472217 DOI: 10.1016/j.arr.2018.11.003] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Revised: 11/07/2018] [Accepted: 11/21/2018] [Indexed: 12/14/2022]
Abstract
The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tremendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models-extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and reinforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and geroprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker development, target identification, drug discovery and real world evidence pipeline that may help accelerate and improve pharmaceutical research and development practices. Modern AI is therefore expected to contribute to the credibility and prominence of longevity biotechnology in the healthcare and pharmaceutical industry, and to the convergence of countless areas of research.
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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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Poddubskaya EV, Baranova MP, Allina DO, Smirnov PY, Albert EA, Kirilchev AP, Aleshin AA, Sekacheva MI, Suntsova MV. Personalized prescription of tyrosine kinase inhibitors in unresectable metastatic cholangiocarcinoma. Exp Hematol Oncol 2018; 7:21. [PMID: 30202637 PMCID: PMC6127913 DOI: 10.1186/s40164-018-0113-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 08/28/2018] [Indexed: 02/07/2023] Open
Abstract
Background Cholangiocarcinoma is an aggressive tumor with poor prognosis. Most of the cases are not available for surgery at the stage of the diagnosis and the best clinical practice chemotherapy results in about 12-month median survival. Several tyrosine kinase inhibitors (TKIs) are currently under investigation as an alternative treatment option for cholangiocarcinoma. Thus, the report of personalized selection of effective inhibitor and case outcome are of clinical interest. Case presentation Here we report a case of aggressive metastatic cholangiocarcinoma (MCC) in 72-year-old man, sequentially treated with two targeted chemotherapies. Initially disease quickly progressed during best clinical practice care (gemcitabine in combination with cisplatin or capecitabine), which was accompanied by significant decrease of life quality. Monotherapy with TKI sorafenib was prescribed to the patient, which resulted in stabilization of tumor growth and elimination of pain. The choice of the inhibitor was made based on high-throughput screening of gene expression in the patient’s tumor biopsy, utilized by Oncobox platform to build a personalized rating of potentially effective target therapies. However, time to progression after start of sorafenib administration did not exceed 6 months and the regimen was changed to monotherapy with Pazopanib, another TKI predicted to be effective for this patient according to the same molecular test. It resulted in disease progression according to RECIST with simultaneous elimination of sorafenib side effects such as rash and hand-foot syndrome. After 2 years from the diagnosis of MCC the patient was alive and physically active, which is substantially longer than median survival for standard therapy. Conclusion This case evidences that sequential personalized prescription of different TKIs may show promising efficacy in terms of survival and quality of life in MCC. Electronic supplementary material The online version of this article (10.1186/s40164-018-0113-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Elena V Poddubskaya
- 1I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991 Russia.,Clinical Center Vitamed, 10, Seslavinskaya St., Moscow, 121309 Russia
| | - Madina P Baranova
- Clinical Center Vitamed, 10, Seslavinskaya St., Moscow, 121309 Russia
| | - Daria O Allina
- Pathology Department, Morozov Children's City Hospital, 4th Dobryninsky Lane 1/9, Moscow, 119049 Russia
| | - Philipp Y Smirnov
- 4State Research Center-Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, Moscow, 123098 Russia
| | - Eugene A Albert
- 1I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991 Russia
| | - Alexey P Kirilchev
- 4State Research Center-Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, Moscow, 123098 Russia
| | | | - Marina I Sekacheva
- 1I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991 Russia
| | - Maria V Suntsova
- D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, 117198 Russia
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Zhou Q, Huang L, Gu Y, Lu H, Feng Z. The expression of CCL18 in diffuse large B cell lymphoma and its mechanism research. Cancer Biomark 2018; 21:925-934. [PMID: 29504526 DOI: 10.3233/cbm-171097] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Molecular target therapy has become a hot spot in cancer treatment, finding effective targets for diffuse large B cell lymphoma (DLBCL) is an urgent problem. OBJECTIVE To detect the expression level of C-C motif chemokine ligand 18 (CCL18) in DLBCL and clarify its potential role in the progression of DLBCL. METHODS Gene expression datas of DLBCL were obtained from TCGA and GEO databases. The relationship between CCL18 and clinicopathologic information of DLBCL was assessed using meta-analysis method. Then we conducted bioinformatics analysis to uncover the biological function of CCL18 and its co-expression genes. Immunohistochemistry was applied to detect expression of CCL18 in DLBCL and reactive hyperplasia lymphoid tissues. RESULTS The expression of CCL18 in DLBCL was higher than negative control group. The levels of CCL18 were distinct in different molecular subtypes and ages, and patients with higher level of CCL18 had a shorter overall survival than those with lower level. CCL18 and its co-expression genes were enriched in biological function such as cell proliferation, migration, apoptotic, and correlated with NF-κB, pathway in cancer, PI3K-AKT pathway. CONCLUSIONS CCL18 was up-regulated in DLBCL and related to poor prognosis. CCL18 may act as a valuable target for diagnosis and treatment of DLBCL.
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22
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Tan SK, Jermakowicz A, Mookhtiar AK, Nemeroff CB, Schürer SC, Ayad NG. Drug Repositioning in Glioblastoma: A Pathway Perspective. Front Pharmacol 2018; 9:218. [PMID: 29615902 PMCID: PMC5864870 DOI: 10.3389/fphar.2018.00218] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 02/27/2018] [Indexed: 12/27/2022] Open
Abstract
Glioblastoma multiforme (GBM) is the most malignant primary adult brain tumor. The current standard of care is surgical resection, radiation, and chemotherapy treatment, which extends life in most cases. Unfortunately, tumor recurrence is nearly universal and patients with recurrent glioblastoma typically survive <1 year. Therefore, new therapies and therapeutic combinations need to be developed that can be quickly approved for use in patients. However, in order to gain approval, therapies need to be safe as well as effective. One possible means of attaining rapid approval is repurposing FDA approved compounds for GBM therapy. However, candidate compounds must be able to penetrate the blood-brain barrier (BBB) and therefore a selection process has to be implemented to identify such compounds that can eliminate GBM tumor expansion. We review here psychiatric and non-psychiatric compounds that may be effective in GBM, as well as potential drugs targeting cell death pathways. We also discuss the potential of data-driven computational approaches to identify compounds that induce cell death in GBM cells, enabled by large reference databases such as the Library of Integrated Network Cell Signatures (LINCS). Finally, we argue that identifying pathways dysregulated in GBM in a patient specific manner is essential for effective repurposing in GBM and other gliomas.
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Affiliation(s)
- Sze Kiat Tan
- Department of Psychiatry and Behavioral Sciences, Center for Therapeutic Innovation, Miami Project to Cure Paralysis, Sylvester Comprehensive Cancer Center, University of Miami Brain Tumor Initiative, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Anna Jermakowicz
- Department of Psychiatry and Behavioral Sciences, Center for Therapeutic Innovation, Miami Project to Cure Paralysis, Sylvester Comprehensive Cancer Center, University of Miami Brain Tumor Initiative, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Adnan K Mookhtiar
- Department of Psychiatry and Behavioral Sciences, Center for Therapeutic Innovation, Miami Project to Cure Paralysis, Sylvester Comprehensive Cancer Center, University of Miami Brain Tumor Initiative, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Charles B Nemeroff
- Department of Psychiatry and Behavioral Sciences and Center on Aging, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Stephan C Schürer
- Department of Molecular Pharmacology, Center for Computational Sciences, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Nagi G Ayad
- Department of Psychiatry and Behavioral Sciences, Center for Therapeutic Innovation, Miami Project to Cure Paralysis, Sylvester Comprehensive Cancer Center, University of Miami Brain Tumor Initiative, University of Miami Miller School of Medicine, Miami, FL, United States
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23
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Fertig EJ, Ozawa H, Thakar M, Howard JD, Kagohara LT, Krigsfeld G, Ranaweera RS, Hughes RM, Perez J, Jones S, Favorov AV, Carey J, Stein-O'Brien G, Gaykalova DA, Ochs MF, Chung CH. CoGAPS matrix factorization algorithm identifies transcriptional changes in AP-2alpha target genes in feedback from therapeutic inhibition of the EGFR network. Oncotarget 2018; 7:73845-73864. [PMID: 27650546 PMCID: PMC5342018 DOI: 10.18632/oncotarget.12075] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 09/02/2016] [Indexed: 01/03/2023] Open
Abstract
Patients with oncogene driven tumors are treated with targeted therapeutics including EGFR inhibitors. Genomic data from The Cancer Genome Atlas (TCGA) demonstrates molecular alterations to EGFR, MAPK, and PI3K pathways in previously untreated tumors. Therefore, this study uses bioinformatics algorithms to delineate interactions resulting from EGFR inhibitor use in cancer cells with these genetic alterations. We modify the HaCaT keratinocyte cell line model to simulate cancer cells with constitutive activation of EGFR, HRAS, and PI3K in a controlled genetic background. We then measure gene expression after treating modified HaCaT cells with gefitinib, afatinib, and cetuximab. The CoGAPS algorithm distinguishes a gene expression signature associated with the anticipated silencing of the EGFR network. It also infers a feedback signature with EGFR gene expression itself increasing in cells that are responsive to EGFR inhibitors. This feedback signature has increased expression of several growth factor receptors regulated by the AP-2 family of transcription factors. The gene expression signatures for AP-2alpha are further correlated with sensitivity to cetuximab treatment in HNSCC cell lines and changes in EGFR expression in HNSCC tumors with low CDKN2A gene expression. In addition, the AP-2alpha gene expression signatures are also associated with inhibition of MEK, PI3K, and mTOR pathways in the Library of Integrated Network-Based Cellular Signatures (LINCS) data. These results suggest that AP-2 transcription factors are activated as feedback from EGFR network inhibition and may mediate EGFR inhibitor resistance.
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Affiliation(s)
- Elana J Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
| | - Hiroyuki Ozawa
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.,Department of Otorhinolaryngology-Head and Neck Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Manjusha Thakar
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
| | - Jason D Howard
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
| | - Luciane T Kagohara
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
| | - Gabriel Krigsfeld
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
| | - Ruchira S Ranaweera
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.,Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Robert M Hughes
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
| | - Jimena Perez
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
| | - Siân Jones
- Personal Genome Diagnostics, Baltimore, MD, USA
| | - Alexander V Favorov
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.,Vavilov Institute of General Genetics, Moscow, Russia.,Research Institute for Genetics and Selection of Industrial Microorganisms, Moscow, Russia
| | - Jacob Carey
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Genevieve Stein-O'Brien
- Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.,Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Daria A Gaykalova
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael F Ochs
- Department of Mathematics and Statistics, The College of New Jersey, Ewing Township, NJ, USA
| | - Christine H Chung
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.,Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL, USA
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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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26
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Pathway-focused PCR array profiling of CAL-27 cell with over-expressed ZNF750. Oncotarget 2017; 9:566-575. [PMID: 29416636 PMCID: PMC5787490 DOI: 10.18632/oncotarget.23075] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 11/14/2017] [Indexed: 02/01/2023] Open
Abstract
Zinc-finger protein 750 (ZNF750) is the potential anti-cancer gene in oral squamous cell carcinoma (OSCC). The present study was to investigate the expression changes of ZNF750 in OSCC tissue and to reveal the induction of altered mRNA expression profiles caused by over-expressed ZNF750 in CAL-27 cell. The expression level of ZNF750 in tissue specimens from OSCC patients was detected by immunohistochemistry. Gene expression profiling was performed using Human Signal Transduction PathwayFinder RT2 Profiler™ PCR Array. The expression changes of 84 key genes representing 10 signal transduction pathways in human following over-expressed ZNF750 in CAL-27 cell was examined. The expression of ZNF750 protein was reduced in OSCC tissues. The R2 PCR Array analysis revealed that 39 of the 84 examined genes that changed at least a two-fold between control and ZNF750 groups. These genes related to oxidative stress, WNT, JAK/STAT, TGFβ, NF-kappaB (NFκB), p53, Notch, Hedgehog, PPAR and Hypoxia signaling. ZNF750 could inhibit the candidate genes ATF4, SQSTM1, HMOX1, CCND1, TNF-alpha, TNFSF10 and FOSL1 but activate CDKN1A and EMP1. Our studies suggest that ZNF750 can regulate signaling pathways that related to proliferation, cell cycle, inflammation and oxidative stress in CAL-27 cell.
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27
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Aliper A, Belikov AV, Garazha A, Jellen L, Artemov A, Suntsova M, Ivanova A, Venkova L, Borisov N, Buzdin A, Mamoshina P, Putin E, Swick AG, Moskalev A, Zhavoronkov A. In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state. Aging (Albany NY) 2017; 8:2127-2152. [PMID: 27677171 PMCID: PMC5076455 DOI: 10.18632/aging.101047] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2016] [Accepted: 09/10/2016] [Indexed: 12/19/2022]
Abstract
Populations in developed nations throughout the world are rapidly aging, and the search for geroprotectors, or anti-aging interventions, has never been more important. Yet while hundreds of geroprotectors have extended lifespan in animal models, none have yet been approved for widespread use in humans. GeroScope is a computational tool that can aid prediction of novel geroprotectors from existing human gene expression data. GeroScope maps expression differences between samples from young and old subjects to aging-related signaling pathways, then profiles pathway activation strength (PAS) for each condition. Known substances are then screened and ranked for those most likely to target differential pathways and mimic the young signalome. Here we used GeroScope and shortlisted ten substances, all of which have lifespan-extending effects in animal models, and tested 6 of them for geroprotective effects in senescent human fibroblast cultures. PD-98059, a highly selective MEK1 inhibitor, showed both life-prolonging and rejuvenating effects. Natural compounds like N-acetyl-L-cysteine, Myricetin and Epigallocatechin gallate also improved several senescence-associated properties and were further investigated with pathway analysis. This work not only highlights several potential geroprotectors for further study, but also serves as a proof-of-concept for GeroScope, Oncofinder and other PAS-based methods in streamlining drug prediction, repurposing and personalized medicine.
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Affiliation(s)
- Alexander Aliper
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
| | - Aleksey V Belikov
- Moscow Institute of Physics and Technology, Dolgoprudny, 141700, Russia
| | - Andrew Garazha
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA.,Moscow Institute of Physics and Technology, Dolgoprudny, 141700, Russia.,Center for Biogerontology and Regenerative Medicine, Moscow, 121099, Russia
| | - Leslie Jellen
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA.,Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Artem Artemov
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
| | - Maria Suntsova
- D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology, and Immunology, Moscow, 117997, Russia
| | - Alena Ivanova
- D. Rogachev Federal Research and Clinical Center for Pediatric Hematology, Oncology, and Immunology, Moscow, 117997, Russia
| | - Larisa Venkova
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA.,Pathway Pharmaceuticals, Ltd, Hong Kong, Hong Kong
| | - Nicolas Borisov
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA.,Pathway Pharmaceuticals, Ltd, Hong Kong, Hong Kong
| | - Anton Buzdin
- Pathway Pharmaceuticals, Ltd, Hong Kong, Hong Kong
| | - Polina Mamoshina
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
| | - Evgeny Putin
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
| | | | - Alexey Moskalev
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA.,Moscow Institute of Physics and Technology, Dolgoprudny, 141700, Russia.,Laboratory of Molecular Radiobiology and Gerontology, Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences, Syktyvkar, 167982, Russia.,School of Systems Biology, George Mason University (GMU), Fairfax, VA 22030, USA.,Engelhardt Institute of Molecular Biology of Russian Academy of Sciences, Moscow, 119991, Russia
| | - Alex Zhavoronkov
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA.,The Biogerontology Research Foundation, Oxford, UK
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28
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Aliper A, Jellen L, Cortese F, Artemov A, Karpinsky-Semper D, Moskalev A, Swick AG, Zhavoronkov A. Towards natural mimetics of metformin and rapamycin. Aging (Albany NY) 2017; 9:2245-2268. [PMID: 29165314 PMCID: PMC5723685 DOI: 10.18632/aging.101319] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 11/02/2017] [Indexed: 12/14/2022]
Abstract
Aging is now at the forefront of major challenges faced globally, creating an immediate need for safe, widescale interventions to reduce the burden of chronic disease and extend human healthspan. Metformin and rapamycin are two FDA-approved mTOR inhibitors proposed for this purpose, exhibiting significant anti-cancer and anti-aging properties beyond their current clinical applications. However, each faces issues with approval for off-label, prophylactic use due to adverse effects. Here, we initiate an effort to identify nutraceuticals-safer, naturally-occurring compounds-that mimic the anti-aging effects of metformin and rapamycin without adverse effects. We applied several bioinformatic approaches and deep learning methods to the Library of Integrated Network-based Cellular Signatures (LINCS) dataset to map the gene- and pathway-level signatures of metformin and rapamycin and screen for matches among over 800 natural compounds. We then predicted the safety of each compound with an ensemble of deep neural network classifiers. The analysis revealed many novel candidate metformin and rapamycin mimetics, including allantoin and ginsenoside (metformin), epigallocatechin gallate and isoliquiritigenin (rapamycin), and withaferin A (both). Four relatively unexplored compounds also scored well with rapamycin. This work revealed promising candidates for future experimental validation while demonstrating the applications of powerful screening methods for this and similar endeavors.
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Affiliation(s)
- Alexander Aliper
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
| | - Leslie Jellen
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
| | - Franco Cortese
- Biogerontology Research Foundation, Research Department, Oxford, United Kingdom
- Department of Biomedical and Molecular Science, Queen's University School of Medicine, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Artem Artemov
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
| | | | - Alexey Moskalev
- Laboratory of Molecular Radiobiology and Gerontology, Institute of Biology of Komi Science Center of Ural Branch of Russian Academy of Sciences, Syktyvkar, 167982, Russia
| | | | - Alex Zhavoronkov
- Insilico Medicine, Inc, Research Department, Baltimore, MD 21218, USA
- Biogerontology Research Foundation, Research Department, Oxford, United Kingdom
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29
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Mamoshina P, Ojomoko L, Yanovich Y, Ostrovski A, Botezatu A, Prikhodko P, Izumchenko E, Aliper A, Romantsov K, Zhebrak A, Ogu IO, Zhavoronkov A. Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget 2017; 9:5665-5690. [PMID: 29464026 PMCID: PMC5814166 DOI: 10.18632/oncotarget.22345] [Citation(s) in RCA: 238] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 11/02/2017] [Indexed: 12/19/2022] Open
Abstract
The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.
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Affiliation(s)
- Polina Mamoshina
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Lucy Ojomoko
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | | | | | | | | | - Eugene Izumchenko
- Department of Otolaryngology-Head & Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alexander Aliper
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | - Konstantin Romantsov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | - Alexander Zhebrak
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA
| | - Iraneus Obioma Ogu
- Africa Blockchain Artificial Intelligence for Healthcare Initiative, Insilico Medicine, Inc, Abuja, Nigeria
| | - Alex Zhavoronkov
- Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.,The Biogerontology Research Foundation, London, United Kingdom
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30
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Integrative computational analysis of transcriptional and epigenetic alterations implicates DTX1 as a putative tumor suppressor gene in HNSCC. Oncotarget 2017; 8:15349-15363. [PMID: 28146432 PMCID: PMC5362490 DOI: 10.18632/oncotarget.14856] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 01/16/2017] [Indexed: 01/23/2023] Open
Abstract
Over a half million new cases of Head and Neck Squamous Cell Carcinoma (HNSCC) are diagnosed annually worldwide, however, 5 year overall survival is only 50% for HNSCC patients. Recently, high throughput technologies have accelerated the genome-wide characterization of HNSCC. However, comprehensive pipelines with statistical algorithms that account for HNSCC biology and perform independent confirmatory and functional validation of candidates are needed to identify the most biologically relevant genes. We applied outlier statistics to high throughput gene expression data, and identified 76 top-scoring candidates with significant differential expression in tumors compared to normal tissues. We identified 15 epigenetically regulated candidates by focusing on a subset of the genes with a negative correlation between gene expression and promoter methylation. Differential expression and methylation of 3 selected candidates (BANK1, BIN2, and DTX1) were confirmed in an independent HNSCC cohorts from Johns Hopkins and TCGA (The Cancer Genome Atlas). We further performed functional evaluation of NOTCH regulator, DTX1, which was downregulated by promoter hypermethylation in tumors, and demonstrated that decreased expression of DTX1 in HNSCC tumors maybe associated with NOTCH pathway activation and increased migration potential.
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31
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Borisov N, Suntsova M, Sorokin M, Garazha A, Kovalchuk O, Aliper A, Ilnitskaya E, Lezhnina K, Korzinkin M, Tkachev V, Saenko V, Saenko Y, Sokov DG, Gaifullin NM, Kashintsev K, Shirokorad V, Shabalina I, Zhavoronkov A, Mishra B, Cantor CR, Buzdin A. Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data. Cell Cycle 2017; 16:1810-1823. [PMID: 28825872 DOI: 10.1080/15384101.2017.1361068] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly seen at the level of individual genes. We created a mathematical model of cumulative suppression of data variation that predicts the ideal parameters and the optimal size of a molecular pathway. We compared the abilities to aggregate experimental molecular data for the 5 alternative methods, also evaluated by their capacity to retain meaningful features of biologic samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses.
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Affiliation(s)
- Nicolas Borisov
- a Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre "Kurchatov Institute" , Moscow , Russia.,b Department of R&D, First Oncology Research and Advisory Center , Moscow , Russia
| | - Maria Suntsova
- c Department of R&D, Center for Biogerontology and Regenerative Medicine , Moscow , Russia.,d Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology , Moscow , Russia
| | - Maxim Sorokin
- a Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre "Kurchatov Institute" , Moscow , Russia.,e Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Moscow , Russia
| | - Andrew Garazha
- c Department of R&D, Center for Biogerontology and Regenerative Medicine , Moscow , Russia.,f Department of R&D, OmicsWay Corporation , Walnut , CA , USA
| | - Olga Kovalchuk
- g Department of Biological Sciences , University of Lethbridge , Lethbridge , AB , Canada
| | - Alexander Aliper
- d Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology , Moscow , Russia
| | - Elena Ilnitskaya
- c Department of R&D, Center for Biogerontology and Regenerative Medicine , Moscow , Russia
| | - Ksenia Lezhnina
- b Department of R&D, First Oncology Research and Advisory Center , Moscow , Russia
| | - Mikhail Korzinkin
- c Department of R&D, Center for Biogerontology and Regenerative Medicine , Moscow , Russia
| | - Victor Tkachev
- f Department of R&D, OmicsWay Corporation , Walnut , CA , USA
| | - Vyacheslav Saenko
- h Technological Research Institute S.P. Kapitsa , Ulyanovsk State University , Ulyanovsk , Russia
| | - Yury Saenko
- h Technological Research Institute S.P. Kapitsa , Ulyanovsk State University , Ulyanovsk , Russia
| | - Dmitry G Sokov
- i Chemotherapy Department, Moscow 1st Oncological Hospital , Moscow , Russia
| | - Nurshat M Gaifullin
- j Faculty of Fundamental Medicine , Lomonosov Moscow State University , Moscow , Russia.,k Department of Oncology, Russian Medical Postgraduate Academy , Moscow , Russia
| | - Kirill Kashintsev
- l Chemotherapy Department, Moscow Oncological Hospital 62 , Stepanovskoye , Russia
| | - Valery Shirokorad
- l Chemotherapy Department, Moscow Oncological Hospital 62 , Stepanovskoye , Russia
| | - Irina Shabalina
- m Faculty of Mathematics and Information Technologies , Petrozavodsk State University , Petrozavodsk , Russia
| | - Alex Zhavoronkov
- d Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology , Moscow , Russia
| | | | - Charles R Cantor
- o Department of Biomedical Engineering , Boston University , Boston , MA , USA
| | - Anton Buzdin
- a Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre "Kurchatov Institute" , Moscow , Russia.,b Department of R&D, First Oncology Research and Advisory Center , Moscow , Russia.,e Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Moscow , Russia.,f Department of R&D, OmicsWay Corporation , Walnut , CA , USA
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32
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Exosomal microRNAs in giant panda (Ailuropoda melanoleuca) breast milk: potential maternal regulators for the development of newborn cubs. Sci Rep 2017; 7:3507. [PMID: 28615713 PMCID: PMC5471263 DOI: 10.1038/s41598-017-03707-8] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2017] [Accepted: 05/03/2017] [Indexed: 11/09/2022] Open
Abstract
The physiological role of miRNAs is widely understood to include fine-tuning the post-transcriptional regulation of a wide array of biological processes. Extensive studies have indicated that exosomal miRNAs in the bodily fluids of various organisms can be transferred between living cells for the delivery of gene silencing signals. Here, we illustrated the expression characteristics of exosomal miRNAs in giant panda breast milk during distinct lactation periods and highlighted the enrichment of immune- and development-related endogenous miRNAs in colostral and mature giant panda milk. These miRNAs are stable, even under certain harsh conditions, via the protection of extracellular vesicles. These findings indicate that breast milk may facilitate the dietary intake of maternal miRNAs by infants for the regulation of postnatal development. We also detected exogenous plant miRNAs from the primary food source of the giant panda (bamboo) in the exosomes of giant panda breast milk that were associated with regulatory roles in basic metabolism and neuron development. This result suggested that dietary plant miRNAs are absorbed by host cells and subsequently secreted into bodily fluids as potential cross-kingdom regulators. In conclusion, exosomal miRNAs in giant panda breast milk may be crucial maternal regulators for the development of intrinsic 'slink' newborn cubs.
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33
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Mathematical and Computational Modeling in Complex Biological Systems. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5958321. [PMID: 28386558 PMCID: PMC5366773 DOI: 10.1155/2017/5958321] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Revised: 12/20/2016] [Accepted: 01/16/2017] [Indexed: 12/22/2022]
Abstract
The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.
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Buzdin AA, Prassolov V, Zhavoronkov AA, Borisov NM. Bioinformatics Meets Biomedicine: OncoFinder, a Quantitative Approach for Interrogating Molecular Pathways Using Gene Expression Data. Methods Mol Biol 2017; 1613:53-83. [PMID: 28849558 DOI: 10.1007/978-1-4939-7027-8_4] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
We propose a biomathematical approach termed OncoFinder (OF) that enables performing both quantitative and qualitative analyses of the intracellular molecular pathway activation. OF utilizes an algorithm that distinguishes the activator/repressor role of every gene product in a pathway. This method is applicable for the analysis of any physiological, stress, malignancy, and other conditions at the molecular level. OF showed a strong potential to neutralize background-caused differences between experimental gene expression data obtained using NGS, microarray and modern proteomics techniques. Importantly, in most cases, pathway activation signatures were better markers of cancer progression compared to the individual gene products. OF also enables correlating pathway activation with the success of anticancer therapy for individual patients. We further expanded this approach to analyze impact of micro RNAs (miRs) on the regulation of cellular interactome. Many alternative sources provide information about miRs and their targets. However, instruments elucidating higher level impact of the established total miR profiles are still largely missing. A variant of OncoFinder termed MiRImpact enables linking miR expression data with its estimated outcome on the regulation of molecular processes, such as signaling, metabolic, cytoskeleton, and DNA repair pathways. MiRImpact was used to establish cancer-specific and cytomegaloviral infection-linked interactomic signatures for hundreds of molecular pathways. Interestingly, the impact of miRs appeared orthogonal to pathway regulation at the mRNA level, which stresses the importance of combining all available levels of gene regulation to build a more objective molecular model of cell.
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Affiliation(s)
- Anton A Buzdin
- Pathway Pharmaceuticals, Wan Chai, Hong Kong SAR.
- Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre "Kurchatov Institute", Bldg 140, Suite 415, 1, Akademika Kurchatova sq., Moscow, 123182, Russia.
- Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- Laboratory of Bioinformatics, D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia.
| | - Vladimir Prassolov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilova street 32, Mosow, 119991, Russia
| | - Alex A Zhavoronkov
- Pathway Pharmaceuticals, Wan Chai, Hong Kong SAR
- Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Nikolay M Borisov
- Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre "Kurchatov Institute", Bldg 140, Suite 415, 1, Akademika Kurchatova sq., Moscow, 123182, Russia
- Department of Personalized Medicine, First Oncology Research and Advisory Center, Moscow, Russia
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Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A. Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. Mol Pharm 2016; 13:2524-30. [PMID: 27200455 DOI: 10.1021/acs.molpharmaceut.6b00248] [Citation(s) in RCA: 266] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.
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Affiliation(s)
- Alexander Aliper
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States
| | - Sergey Plis
- Datalytic Solutions , 1101 Yale Boulevard NE, Albuquerque, New Mexico 87106, United States.,The Mind Research Network , Albuquerque, New Mexico 87106, United States
| | - Artem Artemov
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States
| | - Alvaro Ulloa
- The Mind Research Network , Albuquerque, New Mexico 87106, United States
| | - Polina Mamoshina
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States
| | - Alex Zhavoronkov
- Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States.,The Biogerontology Research Foundation , Oxford, U.K
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Makarev E, Izumchenko E, Aihara F, Wysocki PT, Zhu Q, Buzdin A, Sidransky D, Zhavoronkov A, Atala A. Common pathway signature in lung and liver fibrosis. Cell Cycle 2016; 15:1667-73. [PMID: 27267766 PMCID: PMC4957589 DOI: 10.1080/15384101.2016.1152435] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Fibrosis, a progressive accumulation of extracellular matrix components, encompasses a wide spectrum of distinct organs, and accounts for an increasing burden of morbidity and mortality worldwide. Despite the tremendous clinical impact, the mechanisms governing the fibrotic process are not yet understood, and to date, no clinically reliable therapies for fibrosis have been discovered. Here we applied Regeneration Intelligence, a new bioinformatics software suite for qualitative analysis of intracellular signaling pathway activation using transcriptomic data, to assess a network of molecular signaling in lung and liver fibrosis. In both tissues, our analysis detected major conserved signaling pathways strongly associated with fibrosis, suggesting that some of the pathways identified by our algorithm but not yet wet-lab validated as fibrogenesis related, may be attractive targets for future research. While the majority of significantly disrupted pathways were specific to histologically distinct organs, several pathways have been concurrently activated or downregulated among the hepatic and pulmonary fibrosis samples, providing new evidence of evolutionary conserved pathways that may be relevant as possible therapeutic targets. While future confirmatory studies are warranted to validate these observations, our platform proposes a promising new approach for detecting fibrosis-promoting pathways and tailoring the right therapy to prevent fibrogenesis.
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Affiliation(s)
- Eugene Makarev
- a Atlas Regeneration, Inc. , Winston-Salem , NC , USA.,b Insilico Medicine, Inc., ETC, Johns Hopkins University , Baltimore , MD , USA
| | - Evgeny Izumchenko
- c Department of Otolaryngology-Head & Neck Surgery , Johns Hopkins University School of Medicine , Baltimore , MD , USA
| | - Fumiaki Aihara
- d Advanced Academic Programs, Johns Hopkins University , Baltimore , MD , USA
| | - Piotr T Wysocki
- c Department of Otolaryngology-Head & Neck Surgery , Johns Hopkins University School of Medicine , Baltimore , MD , USA
| | - Qingsong Zhu
- b Insilico Medicine, Inc., ETC, Johns Hopkins University , Baltimore , MD , USA
| | - Anton Buzdin
- e The Biogerontology Research Foundation , London , UK
| | - David Sidransky
- c Department of Otolaryngology-Head & Neck Surgery , Johns Hopkins University School of Medicine , Baltimore , MD , USA
| | - Alex Zhavoronkov
- b Insilico Medicine, Inc., ETC, Johns Hopkins University , Baltimore , MD , USA.,f Wake Forest Institute for Regenerative Medicine, Wake Forest School of Medicine , Winston-Salem , NC , USA
| | - Anthony Atala
- a Atlas Regeneration, Inc. , Winston-Salem , NC , USA.,g Pathway Pharmaceuticals, Ltd , Hong Kong , Hong Kong
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