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Shaban N, Raevskiy M, Zakharova G, Shipunova V, Deyev S, Suntsova M, Sorokin M, Buzdin A, Kamashev D. Human Blood Serum Counteracts EGFR/HER2-Targeted Drug Lapatinib Impact on Squamous Carcinoma SK-BR-3 Cell Growth and Gene Expression. Biochemistry (Mosc) 2024; 89:487-506. [PMID: 38648768 DOI: 10.1134/s000629792403009x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/17/2024] [Accepted: 02/20/2024] [Indexed: 04/25/2024]
Abstract
Lapatinib is a targeted therapeutic inhibiting HER2 and EGFR proteins. It is used for the therapy of HER2-positive breast cancer, although not all the patients respond to it. Using human blood serum samples from 14 female donors (separately taken or combined), we found that human blood serum dramatically abolishes the lapatinib-mediated inhibition of growth of the human breast squamous carcinoma SK-BR-3 cell line. This antagonism between lapatinib and human serum was associated with cancelation of the drug induced G1/S cell cycle transition arrest. RNA sequencing revealed 308 differentially expressed genes in the presence of lapatinib. Remarkably, when combined with lapatinib, human blood serum showed the capacity of restoring both the rate of cell growth, and the expression of 96.1% of the genes expression of which were altered by the lapatinib treatment alone. Co-administration of EGF with lapatinib also restores the cell growth and cancels alteration of expression of 95.8% of the genes specific to lapatinib treatment of SK-BR-3 cells. Differential gene expression analysis also showed that in the presence of human serum or EGF, lapatinib was unable to inhibit the Toll-Like Receptor signaling pathway and alter expression of genes linked to the Gene Ontology term of Focal adhesion.
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Affiliation(s)
- Nina Shaban
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia
| | - Mikhail Raevskiy
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | - Galina Zakharova
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | - Victoria Shipunova
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Sergey Deyev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- "Biomarker" Research Laboratory, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, 420008, Russia
| | - Maria Suntsova
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia.
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Maksim Sorokin
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), Brussels, 1200, Belgium
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, 141701, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Dmitri Kamashev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia.
- The National Medical Research Center for Endocrinology, Moscow, 117036, Russia
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
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Zolotovskaia M, Kovalenko M, Pugacheva P, Tkachev V, Simonov A, Sorokin M, Seryakov A, Garazha A, Gaifullin N, Sekacheva M, Zakharova G, Buzdin AA. Algorithmically Reconstructed Molecular Pathways as the New Generation of Prognostic Molecular Biomarkers in Human Solid Cancers. Proteomes 2023; 11:26. [PMID: 37755705 PMCID: PMC10535530 DOI: 10.3390/proteomes11030026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
Individual gene expression and molecular pathway activation profiles were shown to be effective biomarkers in many cancers. Here, we used the human interactome model to algorithmically build 7470 molecular pathways centered around individual gene products. We assessed their associations with tumor type and survival in comparison with the previous generation of molecular pathway biomarkers (3022 "classical" pathways) and with the RNA transcripts or proteomic profiles of individual genes, for 8141 and 1117 samples, respectively. For all analytes in RNA and proteomic data, respectively, we found a total of 7441 and 7343 potential biomarker associations for gene-centric pathways, 3020 and 2950 for classical pathways, and 24,349 and 6742 for individual genes. Overall, the percentage of RNA biomarkers was statistically significantly higher for both types of pathways than for individual genes (p < 0.05). In turn, both types of pathways showed comparable performance. The percentage of cancer-type-specific biomarkers was comparable between proteomic and transcriptomic levels, but the proportion of survival biomarkers was dramatically lower for proteomic data. Thus, we conclude that pathway activation level is the advanced type of biomarker for RNA and proteomic data, and momentary algorithmic computer building of pathways is a new credible alternative to time-consuming hypothesis-driven manual pathway curation and reconstruction.
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Affiliation(s)
- Marianna Zolotovskaia
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- Omicsway Corp., Walnut, CA 91789, USA
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Maks Kovalenko
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
| | - Polina Pugacheva
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
| | | | - Alexander Simonov
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- Omicsway Corp., Walnut, CA 91789, USA
| | - Maxim Sorokin
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
| | | | | | - Nurshat Gaifullin
- Department of Pathology, Faculty of Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Marina Sekacheva
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Galina Zakharova
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Anton A. Buzdin
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119048 Moscow, Russia
- Laboratory of Systems Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
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Kamashev D, Shaban N, Lebedev T, Prassolov V, Suntsova M, Raevskiy M, Gaifullin N, Sekacheva M, Garazha A, Poddubskaya E, Sorokin M, Buzdin A. Human Blood Serum Can Diminish EGFR-Targeted Inhibition of Squamous Carcinoma Cell Growth through Reactivation of MAPK and EGFR Pathways. Cells 2023; 12:2022. [PMID: 37626832 PMCID: PMC10453612 DOI: 10.3390/cells12162022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Sorokin M, Buzdin AA, Guryanova A, Efimov V, Suntsova MV, Zolotovskaia MA, Koroleva EV, Sekacheva MI, Tkachev VS, Garazha A, Kremenchutckaya K, Drobyshev A, Seryakov A, Gudkov A, Alekseenko IV, Rakitina O, Kostina MB, Vladimirova U, Moisseev A, Bulgin D, Radomskaya E, Shestakov V, Baklaushev VP, Prassolov V, Shegay PV, Li X, Poddubskaya EV, Gaifullin N. Large-scale assessment of pros and cons of autopsy-derived or tumor-matched tissues as the norms for gene expression analysis in cancers. Comput Struct Biotechnol J 2023; 21:3964-3986. [PMID: 37635765 PMCID: PMC10448432 DOI: 10.1016/j.csbj.2023.07.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 07/17/2023] [Accepted: 07/30/2023] [Indexed: 08/29/2023] Open
Abstract
Normal tissues are essential for studying disease-specific differential gene expression. However, healthy human controls are typically available only in postmortal/autopsy settings. In cancer research, fragments of pathologically normal tissue adjacent to tumor site are frequently used as the controls. However, it is largely underexplored how cancers can systematically influence gene expression of the neighboring tissues. Here we performed a comprehensive pan-cancer comparison of molecular profiles of solid tumor-adjacent and autopsy-derived "healthy" normal tissues. We found a number of systemic molecular differences related to activation of the immune cells, intracellular transport and autophagy, cellular respiration, telomerase activation, p38 signaling, cytoskeleton remodeling, and reorganization of the extracellular matrix. The tumor-adjacent tissues were deficient in apoptotic signaling and negative regulation of cell growth including G2/M cell cycle transition checkpoint. We also detected an extensive rearrangement of the chemical perception network. Molecular targets of 32 and 37 cancer drugs were over- or underexpressed, respectively, in the tumor-adjacent norms. These processes may be driven by molecular events that are correlated between the paired cancer and adjacent normal tissues, that mostly relate to inflammation and regulation of intracellular molecular pathways such as the p38, MAPK, Notch, and IGF1 signaling. However, using a model of macaque postmortal tissues we showed that for the 30 min - 24-hour time frame at 4ºC, an RNA degradation pattern in lung biosamples resulted in an artifact "differential" expression profile for 1140 genes, although no differences could be detected in liver. Thus, such concerns should be addressed in practice.
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Affiliation(s)
- Maksim Sorokin
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
- Omicsway Corp., Walnut, CA 91789, USA
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
| | - Anton A. Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
- World-Class Research Center "Digital biodesign and personalized healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium
| | - Anastasia Guryanova
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
| | - Victor Efimov
- World-Class Research Center "Digital biodesign and personalized healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Maria V. Suntsova
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Marianna A. Zolotovskaia
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
- Omicsway Corp., Walnut, CA 91789, USA
| | - Elena V. Koroleva
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
| | - Marina I. Sekacheva
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Victor S. Tkachev
- Omicsway Corp., Walnut, CA 91789, USA
- Oncobox Ltd., Moscow 121205, Russia
| | - Andrew Garazha
- Omicsway Corp., Walnut, CA 91789, USA
- Oncobox Ltd., Moscow 121205, Russia
| | | | - Aleksey Drobyshev
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | | | - Alexander Gudkov
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Irina V. Alekseenko
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
- Institute of Molecular Genetics of National Research Centre "Kurchatov Institute", 2, Kurchatov Square, Moscow 123182, Russian
- FSBI "National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov" Ministry of Healthcare of the Russian Federation, Moscow 117198, Russia
| | - Olga Rakitina
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
| | - Maria B. Kostina
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
| | - Uliana Vladimirova
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
- Oncobox Ltd., Moscow 121205, Russia
| | - Aleksey Moisseev
- I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Dmitry Bulgin
- Research Institute of Medical Primatology, 177 Mira str., Veseloye, Sochi 354376, Russia
| | - Elena Radomskaya
- Research Institute of Medical Primatology, 177 Mira str., Veseloye, Sochi 354376, Russia
| | - Viktor Shestakov
- Research Institute of Medical Primatology, 177 Mira str., Veseloye, Sochi 354376, Russia
| | | | - Vladimir Prassolov
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 32 Vavilova str., Moscow 119991, Russia
| | - Petr V. Shegay
- National Medical Research Radiological Center of the Ministry of Health of the Russian Federation, 249036 Obninsk, Russia
| | - Xinmin Li
- UCLA Technology Center for Genomics & Bioinformatics, Department of Pathology & Laboratory Medicine, 650 Charles E Young Dr., Los Angeles, CA 90095, USA
| | | | - Nurshat Gaifullin
- Department of Physiology and General Pathology, Faculty of Medicine, Lomonosov Moscow State University, Moscow 119991, Russia
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Zolotovskaia MA, Modestov AA, Suntsova MV, Rachkova AA, Koroleva EV, Poddubskaya EV, Sekacheva MI, Tkachev VS, Garazha AV, Glusker AA, Seryakov AP, Vladimirova US, Rumiantsev PO, Moisseev AA, Zharkov DO, Kuzmin DV, Zhao X, Prassolov VS, Shegay PV, Li X, Steinbichler TB, Kim E, Sorokin MI, Wang Y, Buzdin AA. Pan-cancer antagonistic inhibition pattern of ATM-driven G2/M checkpoint pathway vs other DNA repair pathways. DNA Repair (Amst) 2023; 123:103448. [PMID: 36657260 DOI: 10.1016/j.dnarep.2023.103448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 12/22/2022] [Accepted: 01/08/2023] [Indexed: 01/15/2023]
Abstract
DNA repair mechanisms keep genome integrity and limit tumor-associated alterations and heterogeneity, but on the other hand they promote tumor survival after radiation and genotoxic chemotherapies. We screened pathway activation levels of 38 DNA repair pathways in nine human cancer types (gliomas, breast, colorectal, lung, thyroid, cervical, kidney, gastric, and pancreatic cancers). We took RNAseq profiles of the experimental 51 normal and 408 tumor samples, and from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium databases - of 500/407 normal and 5752/646 tumor samples, and also 573 normal and 984 tumor proteomic profiles from Proteomic Data Commons portal. For all the samplings we observed a congruent trend that all cancer types showed inhibition of G2/M arrest checkpoint pathway compared to the normal samples, and relatively low activities of p53-mediated pathways. In contrast, other DNA repair pathways were upregulated in most of the cancer types. The G2/M checkpoint pathway was statistically significantly downregulated compared to the other DNA repair pathways, and this inhibition was strongly impacted by antagonistic regulation of (i) promitotic genes CCNB and CDK1, and (ii) GADD45 genes promoting G2/M arrest. At the DNA level, we found that ATM, TP53, and CDKN1A genes accumulated loss of function mutations, and cyclin B complex genes - transforming mutations. These findings suggest importance of activation for most of DNA repair pathways in cancer progression, with remarkable exceptions of G2/M checkpoint and p53-related pathways which are downregulated and neutrally activated, respectively.
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Pustovalova M, Malakhov P, Guryanova A, Sorokin M, Suntsova M, Buzdin A, Osipov AN, Leonov S. Transcriptome-Based Traits of Radioresistant Sublines of Non-Small Cell Lung Cancer Cells. Int J Mol Sci 2023; 24. [PMID: 36769365 DOI: 10.3390/ijms24033042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/09/2023] Open
Abstract
Radioresistance is a major obstacle for the successful therapy of many cancers, including non-small cell lung cancer (NSCLC). To elucidate the mechanism of radioresistance of NSCLC cells and to identify key molecules conferring radioresistance, the radioresistant subclones of p53 wild-type A549 and p53-deficient H1299 cell cultures were established. The transcriptional changes between parental and radioresistant NSCLC cells were investigated by RNA-seq. In total, expression levels of 36,596 genes were measured. Changes in the activation of intracellular molecular pathways of cells surviving irradiation relative to parental cells were quantified using the Oncobox bioinformatics platform. Following 30 rounds of 2 Gy irradiation, a total of 322 genes were differentially expressed between p53 wild-type radioresistant A549IR and parental A549 cells. For the p53-deficient (H1299) NSCLC cells, the parental and irradiated populations differed in the expression of 1628 genes and 1616 pathways. The expression of genes associated with radioresistance reflects the complex biological processes involved in clinical cancer cell eradication and might serve as a potential biomarker and therapeutic target for NSCLC treatment.
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Zakharova G, Efimov V, Raevskiy M, Rumiantsev P, Gudkov A, Belogurova-Ovchinnikova O, Sorokin M, Buzdin A. Reclassification of TCGA Diffuse Glioma Profiles Linked to Transcriptomic, Epigenetic, Genomic and Clinical Data, According to the 2021 WHO CNS Tumor Classification. Int J Mol Sci 2022; 24:ijms24010157. [PMID: 36613601 PMCID: PMC9820617 DOI: 10.3390/ijms24010157] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 11/25/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
In 2021, the fifth edition of the WHO classification of tumors of the central nervous system (WHO CNS5) was published. Molecular features of tumors were directly incorporated into the diagnostic decision tree, thus affecting both the typing and staging of the tumor. It has changed the traditional approach, based solely on histopathological classification. The Cancer Genome Atlas project (TCGA) is one of the main sources of molecular information about gliomas, including clinically annotated transcriptomic and genomic profiles. Although TCGA itself has played a pivotal role in developing the WHO CNS5 classification, its proprietary databases still retain outdated diagnoses which frequently appear incorrect and misleading according to the WHO CNS5 standards. We aimed to define the up-to-date annotations for gliomas from TCGA's database that other scientists can use in their research. Based on WHO CNS5 guidelines, we developed an algorithm for the reclassification of TCGA glioma samples by molecular features. We updated tumor type and diagnosis for 828 out of a total of 1122 TCGA glioma cases, after which available transcriptomic and methylation data showed clustering features more consistent with the updated grouping. We also observed better stratification by overall survival for the updated diagnoses, yet WHO grade 3 IDH-mutant oligodendrogliomas and astrocytomas are still indistinguishable. We also detected altered performance in the previous diagnostic transcriptomic molecular biomarkers (expression of SPRY1, CRNDE and FREM2 genes and FREM2 molecular pathway) and prognostic gene signature (FN1, ITGA5, OSMR, and NGFR) after reclassification. Thus, we conclude that further efforts are needed to reconsider glioma molecular biomarkers.
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Affiliation(s)
- Galina Zakharova
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Victor Efimov
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Mikhail Raevskiy
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Pavel Rumiantsev
- Multidisciplinary Medical Center, Group of Clinics, 194044 Saint-Petersburg, Russia
| | - Alexander Gudkov
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | | | - Maksim Sorokin
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119048 Moscow, Russia
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
| | - Anton Buzdin
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119048 Moscow, Russia
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
- Correspondence:
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Sorokin M, Zolotovskaia M, Nikitin D, Suntsova M, Poddubskaya E, Glusker A, Garazha A, Moisseev A, Li X, Sekacheva M, Naskhletashvili D, Seryakov A, Wang Y, Buzdin A. Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data. BMC Cancer 2022; 22:1113. [PMCID: PMC9623986 DOI: 10.1186/s12885-022-10177-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 09/26/2022] [Indexed: 12/24/2022] Open
Abstract
Background: Overall survival of advanced colorectal cancer (CRC) patients remains poor, and gene expression analysis could potentially complement detection of clinically relevant mutations to personalize CRC treatments. Methods: We performed RNA sequencing of formalin-fixed, paraffin-embedded (FFPE) cancer tissue samples of 23 CRC patients and interpreted the data obtained using bioinformatic method Oncobox for expression-based rating of targeted therapeutics. Oncobox ranks cancer drugs according to the efficiency score calculated using target genes expression and molecular pathway activation data. The patients had primary and metastatic CRC with metastases in liver, peritoneum, brain, adrenal gland, lymph nodes and ovary. Two patients had mutations in NRAS, seven others had mutated KRAS gene. Patients were treated by aflibercept, bevacizumab, bortezomib, cabozantinib, cetuximab, crizotinib, denosumab, panitumumab and regorafenib as monotherapy or in combination with chemotherapy, and information on the success of totally 39 lines of therapy was collected. Results: Oncobox drug efficiency score was effective biomarker that could predict treatment outcomes in the experimental cohort (AUC 0.77 for all lines of therapy and 0.91 for the first line after tumor sampling). Separately for bevacizumab, it was effective in the experimental cohort (AUC 0.87) and in 3 independent literature CRC datasets, n = 107 (AUC 0.84–0.94). It also predicted progression-free survival in univariate (Hazard ratio 0.14) and multivariate (Hazard ratio 0.066) analyses. Difference in AUC scores evidences importance of using recent biosamples for the prediction quality. Conclusion: Our results suggest that RNA sequencing analysis of tumor FFPE materials may be helpful for personalizing prescriptions of targeted therapeutics in CRC. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10177-3.
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Affiliation(s)
- Maxim Sorokin
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia ,grid.18763.3b0000000092721542Moscow Institute of Physics and Technology, 141701 Moscow Region, Russia ,OmicsWay Corp, 91789 Walnut, CA USA
| | - Marianna Zolotovskaia
- grid.18763.3b0000000092721542Moscow Institute of Physics and Technology, 141701 Moscow Region, Russia
| | - Daniil Nikitin
- OmicsWay Corp, 91789 Walnut, CA USA ,grid.418853.30000 0004 0440 1573Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
| | - Maria Suntsova
- grid.448878.f0000 0001 2288 8774World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Elena Poddubskaya
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia ,Clinical Center Vitamed, 121309 Moscow, Russia
| | - Alexander Glusker
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | | | - Alexey Moisseev
- grid.448878.f0000 0001 2288 8774I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Xinmin Li
- grid.19006.3e0000 0000 9632 6718Department of Pathology and Laboratory Medicine, University of California, 90095 Los Angeles, CA USA
| | - Marina Sekacheva
- grid.448878.f0000 0001 2288 8774World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - David Naskhletashvili
- grid.466904.90000 0000 9092 133XN.N. Blokhin Russian Cancer Research Center, 115478 Moscow, Russia
| | | | - Ye Wang
- grid.410645.20000 0001 0455 0905Core Laboratory, The Affiliated Qingdao Central Hospital of Qingdao University, Qingdao, China
| | - Anton Buzdin
- grid.18763.3b0000000092721542Moscow Institute of Physics and Technology, 141701 Moscow Region, Russia ,grid.418853.30000 0004 0440 1573Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia ,grid.448878.f0000 0001 2288 8774World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
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9
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Lebedev T, Buzdin A, Khabusheva E, Spirin P, Suntsova M, Sorokin M, Popenko V, Rubtsov P, Prassolov V. Subtype of Neuroblastoma Cells with High KIT Expression Are Dependent on KIT and Its Knockdown Induces Compensatory Activation of Pro-Survival Signaling. Int J Mol Sci 2022; 23:7724. [PMID: 35887076 PMCID: PMC9324519 DOI: 10.3390/ijms23147724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/06/2022] [Accepted: 07/08/2022] [Indexed: 12/04/2022] Open
Abstract
Neuroblastoma (NB) is a pediatric cancer with high clinical and molecular heterogeneity, and patients with high-risk tumors have limited treatment options. Receptor tyrosine kinase KIT has been identified as a potential marker of high-risk NB and a promising target for NB treatment. We investigated 19,145 tumor RNA expression and molecular pathway activation profiles for 20 cancer types and detected relatively high levels of KIT expression in NB. Increased KIT expression was associated with activation of cell survival pathways, downregulated apoptosis induction, and cell cycle checkpoint control pathways. KIT knockdown with shRNA encoded by lentiviral vectors in SH-SY5Y cells led to reduced cell proliferation and apoptosis induction up to 50%. Our data suggest that apoptosis induction was caused by mitotic catastrophe, and there was a 2-fold decrease in percentage of G2-M cell cycle phase after KIT knockdown. We found that KIT knockdown in NB cells leads to strong upregulation of other pro-survival growth factor signaling cascades such as EPO, NGF, IL-6, and IGF-1 pathways. NGF, IGF-1 and EPO were able to increase cell proliferation in KIT-depleted cells in an ERK1/2-dependent manner. Overall, we show that KIT is a promising therapeutic target in NB, although such therapy efficiency could be impeded by growth factor signaling activation.
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10
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Zolotovskaia MA, Kovalenko MA, Tkachev VS, Simonov AM, Sorokin MI, Kim E, Kuzmin DV, Karademir-Yilmaz B, Buzdin AA. Next-Generation Grade and Survival Expression Biomarkers of Human Gliomas Based on Algorithmically Reconstructed Molecular Pathways. Int J Mol Sci 2022; 23:ijms23137330. [PMID: 35806337 PMCID: PMC9266372 DOI: 10.3390/ijms23137330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/24/2022] [Accepted: 06/25/2022] [Indexed: 02/04/2023] Open
Abstract
In gliomas, expression of certain marker genes is strongly associated with survival and tumor type and often exceeds histological assessments. Using a human interactome model, we algorithmically reconstructed 7494 new-type molecular pathways that are centered each on an individual protein. Each single-gene expression and gene-centric pathway activation was tested as a survival and tumor grade biomarker in gliomas and their diagnostic subgroups (IDH mutant or wild type, IDH mutant with 1p/19q co-deletion, MGMT promoter methylated or unmethylated), including the three major molecular subtypes of glioblastoma (proneural, mesenchymal, classical). We used three datasets from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, which in total include 527 glioblastoma and 1097 low grade glioma profiles. We identified 2724 such gene and 2418 pathway survival biomarkers out of total 17,717 genes and 7494 pathways analyzed. We then assessed tumor grade and molecular subtype biomarkers and with the threshold of AUC > 0.7 identified 1322/982 gene biomarkers and 472/537 pathway biomarkers. This suggests roughly two times greater efficacy of the reconstructed pathway approach compared to gene biomarkers. Thus, we conclude that activation levels of algorithmically reconstructed gene-centric pathways are a potent class of new-generation diagnostic and prognostic biomarkers for gliomas.
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Affiliation(s)
- Marianna A. Zolotovskaia
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
- Correspondence: ; Tel.: +7-9165612175
| | - Max A. Kovalenko
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
| | | | - Alexander M. Simonov
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
- Omicsway Corp., Walnut, CA 91789, USA;
| | - Maxim I. Sorokin
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
- Omicsway Corp., Walnut, CA 91789, USA;
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
| | - Ella Kim
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany;
| | - Denis V. Kuzmin
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (M.A.K.); (A.M.S.); (M.I.S.); (D.V.K.)
| | - Betul Karademir-Yilmaz
- Department of Biochemistry, School of Medicine/Genetic and Metabolic Diseases Research and Investigation Center (GEMHAM), Marmara University, Istanbul 34854, Turkey;
| | - Anton A. Buzdin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
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11
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Borisov N, Sorokin M, Zolotovskaya M, Borisov C, Buzdin A. Shambhala-2: A Protocol for Uniformly Shaped Harmonization of Gene Expression Profiles of Various Formats. Curr Protoc 2022; 2:e444. [PMID: 35617464 DOI: 10.1002/cpz1.444] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Uniformly shaped harmonization of gene expression profiles is central for the simultaneous comparison of multiple gene expression datasets. It is expected to operate with the gene expression data obtained using various experimental methods and equipment, and to return harmonized profiles in a uniform shape. Such uniformly shaped expression profiles from different initial datasets can be further compared directly. However, current harmonization techniques have strong limitations that prevent their broad use for bioinformatic applications. They can either operate with only up to two datasets/platforms or return data in a dynamic format that will be different for every comparison under analysis. This also does not allow for adding new data to the previously harmonized dataset(s), which complicates the analysis and increases calculation costs. We propose here a new method termed Shambhala-2 that can transform multi-platform expression data into a universal format that is identical for all harmonizations made using this technique. Shambhala-2 is based on sample-by-sample cubic conversion of the initial expression dataset into a preselected shape of the reference definitive dataset. Using 8390 samples of 12 healthy human tissue types and 4086 samples of colorectal, kidney, and lung cancer tissues, we verified Shambhala-2's capacity in restoring tissue-specific expression patterns for seven microarray and three RNA sequencing platforms. Shambhala-2 performed well for all tested combinations of RNAseq and microarray profiles, and retained gene-expression ranks, as evidenced by high correlations between different single- or aggregated gene expression metrics in pre- and post-Shambhalized samples, including preserving cancer-specific gene expression and pathway activation features. © 2022 Wiley Periodicals LLC. Basic Protocol: Shambhala-2 harmonizer Alternate Protocol 1: Linear Shambhala/Shambhala-1 Alternate Protocol 2: Alternative (flexible-format and uniformly shaped) normalization methods Support Protocol 1: Watermelon multisection (WM) Support Protocol 2: Calculation of cancer-to-normal log-fold-change (LFC) and pathway activation level (PAL).
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Affiliation(s)
- Nicolas Borisov
- Omicsway Corp., Walnut, California.,Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia
| | - Maksim Sorokin
- Omicsway Corp., Walnut, California.,Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia.,I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Marianna Zolotovskaya
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia.,Oncobox Ltd., Moscow, Russia
| | | | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.,World-Class Research Center "Digital biodesign and personalized healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium
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12
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Zolotovskaia MA, Tkachev VS, Guryanova AA, Simonov AM, Raevskiy MM, Efimov VV, Wang Y, Sekacheva MI, Garazha AV, Borisov NM, Kuzmin DV, Sorokin MI, Buzdin AA. OncoboxPD: human 51 672 molecular pathways database with tools for activity calculating and visualization. Comput Struct Biotechnol J 2022. [PMID: 35615022 PMCID: PMC9120235 DOI: 10.1016/j.csbj.2022.05.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/05/2022] [Accepted: 05/05/2022] [Indexed: 12/29/2022] Open
Abstract
OncoboxPD (Oncobox pathway databank) available at https://open.oncobox.com is the collection of 51 672 uniformly processed human molecular pathways. Superposition of all pathways formed interactome graph of protein–protein interactions and metabolic reactions containing 361 654 interactions and 64 095 molecular participants. Pathways are uniformly classified by biological processes, and each pathway node is algorithmically functionally annotated by specific activator/repressor role. This enables online calculation of statistically supported pathway activation levels (PALs) with the built-in bioinformatic tool using custom RNA/protein expression profiles. Each pathway can be visualized as static or dynamic graph, where vertices are molecules participating in a pathway and edges are interactions or reactions between them. Differentially expressed nodes in a pathway can be visualized in two-color mode with user-defined color scale. For every comparison, OncoboxPD also generates a graph summarizing top up- and downregulated pathways.
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13
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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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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14
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Raevskiy M, Sorokin M, Zakharova G, Tkachev V, Borisov N, Kuzmin D, Kremenchutckaya K, Gudkov A, Kamashev D, Buzdin A. Better Agreement of Human Transcriptomic and Proteomic Cancer Expression Data at the Molecular Pathway Activation Level. Int J Mol Sci 2022; 23:ijms23052611. [PMID: 35269755 PMCID: PMC8910457 DOI: 10.3390/ijms23052611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/19/2022] [Accepted: 02/23/2022] [Indexed: 12/10/2022] Open
Abstract
Previously, we have shown that the aggregation of RNA-level gene expression profiles into quantitative molecular pathway activation metrics results in lesser batch effects and better agreement between different experimental platforms. Here, we investigate whether pathway level of data analysis provides any advantage when comparing transcriptomic and proteomic data. We compare the paired proteomic and transcriptomic gene expression and pathway activation profiles obtained for the same human cancer biosamples in The Cancer Genome Atlas (TCGA) and the NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects, for a total of 755 samples of glioblastoma, breast, liver, lung, ovarian, pancreatic, and uterine cancers. In a CPTAC assay, expression levels of 15,112 protein-coding genes were profiled using the Thermo QE series of mass spectrometers. In TCGA, RNA expression levels of the same genes were obtained using the Illumina HiSeq 4000 engine for the same biosamples. At the gene level, absolute gene expression values are compared, whereas pathway-grade comparisons are made between the pathway activation levels (PALs) calculated using average sample-normalized transcriptomic and proteomic profiles. We observed remarkably different average correlations between the primary RNA- and protein expression data for different cancer types: Spearman Rho between 0.017 (p = 1.7 × 10−13) and 0.27 (p < 2.2 × 10−16). However, at the pathway level we detected overall statistically significantly higher correlations: averaged Rho between 0.022 (p < 2.2 × 10−16) and 0.56 (p < 2.2 × 10−16). Thus, we conclude that data analysis at the PAL-level yields results of a greater similarity when comparing high-throughput RNA and protein expression profiles.
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Affiliation(s)
- Mikhail Raevskiy
- I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia;
- OmicsWay Corp., Walnut, CA 91789, USA; (M.S.); (G.Z.); (A.G.); (D.K.)
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
| | - Maxim Sorokin
- OmicsWay Corp., Walnut, CA 91789, USA; (M.S.); (G.Z.); (A.G.); (D.K.)
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
| | - Galina Zakharova
- OmicsWay Corp., Walnut, CA 91789, USA; (M.S.); (G.Z.); (A.G.); (D.K.)
| | | | - Nicolas Borisov
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (N.B.); (D.K.); (K.K.)
| | - Denis Kuzmin
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (N.B.); (D.K.); (K.K.)
| | | | - Alexander Gudkov
- OmicsWay Corp., Walnut, CA 91789, USA; (M.S.); (G.Z.); (A.G.); (D.K.)
| | - Dmitry Kamashev
- OmicsWay Corp., Walnut, CA 91789, USA; (M.S.); (G.Z.); (A.G.); (D.K.)
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
| | - Anton Buzdin
- OmicsWay Corp., Walnut, CA 91789, USA; (M.S.); (G.Z.); (A.G.); (D.K.)
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
- Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia; (N.B.); (D.K.); (K.K.)
- Correspondence: ; Tel./Fax: +1-626-7657785
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15
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Sorokin M, Rabushko E, Efimov V, Poddubskaya E, Sekacheva M, Simonov A, Nikitin D, Drobyshev A, Suntsova M, Buzdin A. Experimental and Meta-Analytic Validation of RNA Sequencing Signatures for Predicting Status of Microsatellite Instability. Front Mol Biosci 2021; 8:737821. [PMID: 34888350 PMCID: PMC8650122 DOI: 10.3389/fmolb.2021.737821] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 10/19/2021] [Indexed: 01/16/2023] Open
Abstract
Microsatellite instability (MSI) is an important diagnostic and prognostic cancer biomarker. In colorectal, cervical, ovarian, and gastric cancers, it can guide the prescription of chemotherapy and immunotherapy. In laboratory diagnostics of susceptible tumors, MSI is routinely detected by the size of marker polymerase chain reaction products encompassing frequent microsatellite expansion regions. Alternatively, MSI status is screened indirectly by immunohistochemical interrogation of microsatellite binding proteins. RNA sequencing (RNAseq) profiling is an emerging source of data for a wide spectrum of cancer biomarkers. Recently, three RNAseq-based gene signatures were deduced for establishing MSI status in tumor samples. They had 25, 15, and 14 gene products with only one common gene. However, they were developed and tested on the incomplete literature of The Cancer Genome Atlas (TCGA) sampling and never validated experimentally on independent RNAseq samples. In this study, we, for the first time, systematically validated these three RNAseq MSI signatures on the literature colorectal cancer (CRC) (n = 619), endometrial carcinoma (n = 533), gastric cancer (n = 380), uterine carcinosarcoma (n = 55), and esophageal cancer (n = 83) samples and on the set of experimental CRC RNAseq samples (n = 23) for tumors with known MSI status. We found that all three signatures performed well with area under the curve (AUC) ranges of 0.94–1 for the experimental CRCs and 0.94–1 for the TCGA CRC, esophageal cancer, and uterine carcinosarcoma samples. However, for the TCGA endometrial carcinoma and gastric cancer samples, only two signatures were effective with AUC 0.91–0.97, whereas the third signature showed a significantly lower AUC of 0.69–0.88. Software for calculating these MSI signatures using RNAseq data is included.
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Affiliation(s)
- Maksim Sorokin
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,OmicsWay Corp., Walnut, CA, United States
| | - Elizaveta Rabushko
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
| | - Victor Efimov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,Oncobox Ltd., Moscow, Russia
| | - Elena Poddubskaya
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Marina Sekacheva
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Alexander Simonov
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,Oncobox Ltd., Moscow, Russia
| | - Daniil Nikitin
- Oncobox Ltd., Moscow, Russia.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Aleksey Drobyshev
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Maria Suntsova
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,OmicsWay Corp., Walnut, CA, United States.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
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16
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Lebedev T, Vagapova E, Spirin P, Rubtsov P, Astashkova O, Mikheeva A, Sorokin M, Vladimirova U, Suntsova M, Konovalov D, Roumiantsev A, Stocking C, Buzdin A, Prassolov V. Growth factor signaling predicts therapy resistance mechanisms and defines neuroblastoma subtypes. Oncogene 2021; 40:6258-6272. [PMID: 34556815 PMCID: PMC8566230 DOI: 10.1038/s41388-021-02018-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 08/25/2021] [Accepted: 09/10/2021] [Indexed: 02/08/2023]
Abstract
Neuroblastoma (NB) has a low frequency of recurrent mutations compared to other cancers, which hinders the development of targeted therapies and novel risk stratification strategies. Multikinase inhibitors have shown potential in treating high-risk NB, but their efficacy is likely impaired by the cancer cells' ability to adapt to these drugs through the employment of alternative signaling pathways. Based on the expression of 48 growth factor-related genes in 1189 NB tumors, we have developed a model for NB patient survival prediction. This model discriminates between stage 4 NB tumors with favorable outcomes (>80% overall survival) and very poor outcomes (<10%) independently from MYCN-amplification status. Using signaling pathway analysis and gene set enrichment methods in 60 NB patients with known therapy response, we identified signaling pathways, including EPO, NGF, and HGF, upregulated in patients with no or partial response. In a therapeutic setting, we showed that among six selected growth factors, EPO, and NGF showed the most pronounced protective effects in vitro against several promising anti-NB multikinase inhibitors: imatinib, dasatinib, crizotinib, cabozantinib, and axitinib. Mechanistically kinase inhibitors potentiated NB cells to stronger ERK activation by EPO and NGF. The protective action of these growth factors strongly correlated with ERK activation and was ERK-dependent. ERK inhibitors combined with anticancer drugs, especially with dasatinib, showed a synergistic effect on NB cell death. Consideration of growth factor signaling activity benefits NB outcome prediction and tailoring therapy regimens to treat NB.
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Affiliation(s)
- Timofey Lebedev
- Department of Cancer Cell Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia.
| | - Elmira Vagapova
- Department of Cancer Cell Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - Pavel Spirin
- Department of Cancer Cell Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - Petr Rubtsov
- Department of Cancer Cell Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - Olga Astashkova
- Department of Cancer Cell Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region, Russia
| | - Alesya Mikheeva
- Department of Cancer Cell Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
- Moscow Institute of Physics and Technology (National Research University), Moscow Region, Russia
| | - Maxim Sorokin
- Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, USA
- Institute of Personalized Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Uliana Vladimirova
- Institute of Personalized Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Maria Suntsova
- Institute of Personalized Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Dmitry Konovalov
- D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Alexander Roumiantsev
- D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Carol Stocking
- Research Department Cell and Gene Therapy, Department of Stem Cell Transplantation, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Heinrich-Pette-Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Anton Buzdin
- Moscow Institute of Physics and Technology (National Research University), Moscow Region, Russia
- Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
- Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, USA
- Institute of Personalized Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Vladimir Prassolov
- Department of Cancer Cell Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
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17
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Zolotovskaia M, Tkachev V, Sorokin M, Garazha A, Kim E, Kantelhardt SR, Bikar SE, Zottel A, Šamec N, Kuzmin D, Sprang B, Moisseev A, Giese A, Efimov V, Jovčevska I, Buzdin A. Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas. Cancers (Basel) 2021; 13:4117. [PMID: 34439271 PMCID: PMC8394245 DOI: 10.3390/cancers13164117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 01/17/2023] Open
Abstract
Gliomas are the most common malignant brain tumors with high mortality rates. Recently we showed that the FREM2 gene has a role in glioblastoma progression. Here we reconstructed the FREM2 molecular pathway using the human interactome model. We assessed the biomarker capacity of FREM2 expression and its pathway as the overall survival (OS) and progression-free survival (PFS) biomarkers. To this end, we used three literature and one experimental RNA sequencing datasets collectively covering 566 glioblastomas (GBM) and 1097 low-grade gliomas (LGG). The activation level of deduced FREM2 pathway showed strong biomarker characteristics and significantly outperformed the FREM2 expression level itself. For all relevant datasets, it could robustly discriminate GBM and LGG (p < 1.63 × 10-13, AUC > 0.74). High FREM2 pathway activation level was associated with poor OS in LGG (p < 0.001), and low PFS in LGG (p < 0.001) and GBM (p < 0.05). FREM2 pathway activation level was poor prognosis biomarker for OS (p < 0.05) and PFS (p < 0.05) in LGG with IDH mutation, for PFS in LGG with wild type IDH (p < 0.001) and mutant IDH with 1p/19q codeletion(p < 0.05), in GBM with unmethylated MGMT (p < 0.05), and in GBM with wild type IDH (p < 0.05). Thus, we conclude that the activation level of the FREM2 pathway is a potent new-generation diagnostic and prognostic biomarker for multiple molecular subtypes of GBM and LGG.
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Affiliation(s)
- Marianna Zolotovskaia
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Department of Oncology, Hematology and Radiotherapy, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Victor Tkachev
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Maxim Sorokin
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
| | - Andrew Garazha
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
| | - Ella Kim
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Sven Rainer Kantelhardt
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Sven-Ernö Bikar
- StarSEQ GmbH, Joh.-Joachim-Becher-Weg 30a, 55128 Mainz, Germany;
| | - Alja Zottel
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Neja Šamec
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Denis Kuzmin
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Bettina Sprang
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Langenbeckstrasse 1, 55124 Mainz, Germany; (E.K.); (S.R.K.); (B.S.)
| | - Alexey Moisseev
- Omicsway Corp., Walnut, CA 91789, USA; (M.S.); (A.G.); (A.M.)
| | - Alf Giese
- Orthocentrum Hamburg, Hansastrasse 1, 20149 Hamburg, Germany;
| | - Victor Efimov
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
| | - Ivana Jovčevska
- Medical Center for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov Trg 2, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.); (I.J.)
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia; (V.T.); (D.K.); (V.E.); (A.B.)
- Laboratory of Clinical Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow 117997, Russia
- European Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, 1200 Brussels, Belgium
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18
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Adamyan L, Elagin V, Vechorko V, Stepanian A, Dashko A, Doroshenko D, Aznaurova Y, Sorokin M, Suntsova M, Garazha A, Buzdin A. COVID-19 - associated inhibition of energy accumulation pathways in human semen samples. ACTA ACUST UNITED AC 2021; 2:355-364. [PMID: 34377996 PMCID: PMC8339600 DOI: 10.1016/j.xfss.2021.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/21/2021] [Accepted: 07/27/2021] [Indexed: 11/25/2022]
Abstract
Objective To investigate transcriptional alterations in human semen samples associated with COVID-19 infection. Design Retrospective observational cohort study. Setting City hospital. Patient(s) Ten patients who had recovered from mild COVID-19 infection. Eight of these patients had different sperm abnormalities that were diagnosed before infection. The control group consisted of 5 healthy donors without known abnormalities and no history of COVID-19 infection. Intervention(s) We used RNA sequencing to determine gene expression profiles in all studied biosamples. Original standard bioinformatic instruments were used to analyze activation of intracellular molecular pathways. Main Outcome Measure(s) Routine semen analysis, gene expression levels, and molecular pathway activation levels in semen samples. Result(s) We found statistically significant inhibition of genes associated with energy production pathways in the mitochondria, including genes involved in the electron transfer chain and genes involved in toll-like receptor signaling. All protein-coding genes encoded by the mitochondrial genome were significantly down-regulated in semen samples collected from patients after recovery from COVID-19. Conclusion(s) Our results may provide a molecular basis for the previously observed phenomenon of decreased sperm motility associated with COVID-19 infection. Moreover, the data will be beneficial for the optimization of preconception care for men who have recently recovered from COVID-19 infection.
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Affiliation(s)
- Leila Adamyan
- A.I. Evdokimov Moscow State University of Medicine and Dentistry, 20b1 Delegatskaya St., Moscow, 127473, Russian Federation
| | - Vladimir Elagin
- A.I. Evdokimov Moscow State University of Medicine and Dentistry, 20b1 Delegatskaya St., Moscow, 127473, Russian Federation.,O.M. Filatov City clinical hospital №15, 23 Veshnjakovskaja St., Moscow, 111539, Russian Federation
| | - Valeriy Vechorko
- O.M. Filatov City clinical hospital №15, 23 Veshnjakovskaja St., Moscow, 111539, Russian Federation
| | - Assia Stepanian
- Academia of Women's Health and Endoscopic Surgery, 755 Mount Vernon Hwy, Atlanta, GA, 30328, USA
| | - Anton Dashko
- O.M. Filatov City clinical hospital №15, 23 Veshnjakovskaja St., Moscow, 111539, Russian Federation
| | - Dmitriy Doroshenko
- O.M. Filatov City clinical hospital №15, 23 Veshnjakovskaja St., Moscow, 111539, Russian Federation
| | - Yana Aznaurova
- A.I. Evdokimov Moscow State University of Medicine and Dentistry, 20b1 Delegatskaya St., Moscow, 127473, Russian Federation
| | - Maxim Sorokin
- Moscow Institute of Physics and Technology (National Research University), 9 Institutskij pereulok, Dolgoprudnyj city, Moscow region, 141700, Russian Federation.,OmicsWay Corp., 340 S Lemon Ave, Walnut, CA, 91789, USA.,World-Class Research Center "Digital biodesign and personalized healthcare", Sechenov First Moscow State Medical University, 2-4 Bolshaya Pirogovskaya St., Moscow, 119435, Russian Federation
| | - Maria Suntsova
- World-Class Research Center "Digital biodesign and personalized healthcare", Sechenov First Moscow State Medical University, 2-4 Bolshaya Pirogovskaya St., Moscow, 119435, Russian Federation
| | | | - Anton Buzdin
- Moscow Institute of Physics and Technology (National Research University), 9 Institutskij pereulok, Dolgoprudnyj city, Moscow region, 141700, Russian Federation.,OmicsWay Corp., 340 S Lemon Ave, Walnut, CA, 91789, USA.,World-Class Research Center "Digital biodesign and personalized healthcare", Sechenov First Moscow State Medical University, 2-4 Bolshaya Pirogovskaya St., Moscow, 119435, Russian Federation
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19
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Samii A, Sorokin M, Kar S, Makovskaia L, Garazha A, Hartmann C, Moisseev A, Kim E, Giese A, Buzdin A. Case of multifocal glioblastoma with four fusion transcripts of ALK, FGFR2, NTRK2, and NTRK3 genes stresses the need for tumor tissue multisampling for transcriptomic analysis. Cold Spring Harb Mol Case Stud 2021; 7:mcs.a006100. [PMID: 34341009 PMCID: PMC8327882 DOI: 10.1101/mcs.a006100] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/14/2021] [Indexed: 02/07/2023] Open
Abstract
Glioblastoma multiforme (GBM) is the most malignant brain tumor with patient mortality rate close to 100%, 5-yr survival rate of ∼5%, and a median survival of 14 mo. GBMs have notorious histomorphologic and molecular heterogeneities thus giving hope for development of future personalized therapies. We describe here a case of a 48-yr-old male patient with three-nodular GBM. To address the question of intratumoral molecular heterogeneity, a comparative analysis of gene expression was performed by using multiple samples collected from different tumor sites with the aid of intraoperative magnetic resonance imaging (MRI). Sixteen GBM biosamples from parietal, temporal, and temporo-polar localizations were collected from primary, recurrent, and second recurrent tumors and were obtained and investigated by RNA sequencing. Our investigations revealed that biosamples derived from different tumor sites differ in their gene expression profiles with classical or mesenchymal signatures associated with clinically distinct molecular subtypes of GBM found within the same tumor. The results also showed significant differences in the expression of genes specific for targeted therapeutics. Our investigations have enabled the identification of four novel fusion transcripts—KIF5C-NTRK3, AC016907.2-ALK, CNTNAP3-NTRK2, and ZNF135-FGFR2—each present in only one sample. We found no differences between untreated and recurrent stages in the expression levels of genes involved in fusion transcripts, suggesting the lack of association between fusion transcript and treatment response. In contrast, longitudinal changes in the expression of VEGF and MGMT genes were concordant with the tumor response to bevacizumab and temozolomide. Our study underscores the importance of integrating a multisampling approach and RNA sequencing and demonstrates the predictive merit of an integrated approach for differentiating genomic aberrations associated with untreated or post-treatment recurrent GBMs.
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Affiliation(s)
- Amir Samii
- International Neuroscience Institute, Hannover, 30625 Germany
| | - Maxim Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991 Russia.,Omicsway Corp., Walnut, California 91789, USA.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997 Russia
| | - Souvik Kar
- International Neuroscience Institute, Hannover, 30625 Germany
| | - Luidmila Makovskaia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 117997 Russia
| | | | - Christian Hartmann
- Department of Neuropathology, Institute of Pathology at Hannover Medical School, Hannover, 30625 Germany
| | - Aleksey Moisseev
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991 Russia
| | - Ella Kim
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, 55124 Mainz, 55124 Germany
| | - Alf Giese
- Orthocentrum Hamburg, Hamburg, 20149 Germany
| | - Anton Buzdin
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991 Russia.,Omicsway Corp., Walnut, California 91789, USA.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997 Russia.,Moscow Institute of Physics and Technology (National Research University), Moscow, 141701 Russia
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20
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Sorokin M, Raevskiy M, Zottel A, Šamec N, Skoblar Vidmar M, Matjašič A, Zupan A, Mlakar J, Suntsova M, Kuzmin DV, Buzdin A, Jovčevska I. Large-Scale Transcriptomics-Driven Approach Revealed Overexpression of CRNDE as a Poor Survival Prognosis Biomarker in Glioblastoma. Cancers (Basel) 2021; 13:3419. [PMID: 34298634 PMCID: PMC8303503 DOI: 10.3390/cancers13143419] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 07/03/2021] [Accepted: 07/05/2021] [Indexed: 12/16/2022] Open
Abstract
Glioblastoma is the most common and malignant brain malignancy worldwide, with a 10-year survival of only 0.7%. Aggressive multimodal treatment is not enough to increase life expectancy and provide good quality of life for glioblastoma patients. In addition, despite decades of research, there are no established biomarkers for early disease diagnosis and monitoring of patient response to treatment. High throughput sequencing technologies allow for the identification of unique molecules from large clinically annotated datasets. Thus, the aim of our study was to identify significant molecular changes between short- and long-term glioblastoma survivors by transcriptome RNA sequencing profiling, followed by differential pathway-activation-level analysis. We used data from the publicly available repositories The Cancer Genome Atlas (TCGA; number of annotated cases = 135) and Chinese Glioma Genome Atlas (CGGA; number of annotated cases = 218), and experimental clinically annotated glioblastoma tissue samples from the Institute of Pathology, Faculty of Medicine in Ljubljana corresponding to 2-58 months overall survival (n = 16). We found one differential gene for long noncoding RNA CRNDE whose overexpression showed correlation to poor patient OS. Moreover, we identified overlapping sets of congruently regulated differential genes involved in cell growth, division, and migration, structure and dynamics of extracellular matrix, DNA methylation, and regulation through noncoding RNAs. Gene ontology analysis can provide additional information about the function of protein- and nonprotein-coding genes of interest and the processes in which they are involved. In the future, this can shape the design of more targeted therapeutic approaches.
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Affiliation(s)
- Maxim Sorokin
- European Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, 1000 Brussels, Belgium;
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia;
- Moscow Institute of Physics and Technology, National Research University, 141700 Moscow, Russia; (M.R.); (D.V.K.)
| | - Mikhail Raevskiy
- Moscow Institute of Physics and Technology, National Research University, 141700 Moscow, Russia; (M.R.); (D.V.K.)
| | - Alja Zottel
- Medical Centre for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.)
| | - Neja Šamec
- Medical Centre for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.)
| | | | - Alenka Matjašič
- Institute of Pathology, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia; (A.M.); (A.Z.); (J.M.)
| | - Andrej Zupan
- Institute of Pathology, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia; (A.M.); (A.Z.); (J.M.)
| | - Jernej Mlakar
- Institute of Pathology, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia; (A.M.); (A.Z.); (J.M.)
| | - Maria Suntsova
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia;
| | - Denis V. Kuzmin
- Moscow Institute of Physics and Technology, National Research University, 141700 Moscow, Russia; (M.R.); (D.V.K.)
| | - Anton Buzdin
- European Organization for Research and Treatment of Cancer (EORTC), Biostatistics and Bioinformatics Subgroup, 1000 Brussels, Belgium;
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia;
- Moscow Institute of Physics and Technology, National Research University, 141700 Moscow, Russia; (M.R.); (D.V.K.)
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- OmicsWay Corp., Walnut, CA 91789, USA
| | - Ivana Jovčevska
- Medical Centre for Molecular Biology, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia; (A.Z.); (N.Š.)
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21
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Wang Y, Tong Z, Zhang W, Zhang W, Buzdin A, Mu X, Yan Q, Zhao X, Chang HH, Duhon M, Zhou X, Zhao G, Chen H, Li X. FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients. Front Oncol 2021; 11:683419. [PMID: 34164344 PMCID: PMC8216110 DOI: 10.3389/fonc.2021.683419] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 05/17/2021] [Indexed: 12/14/2022] Open
Abstract
A patient's response to immune checkpoint inhibitors (ICIs) is a complex quantitative trait, and determined by multiple intrinsic and extrinsic factors. Three currently FDA-approved predictive biomarkers (progra1mmed cell death ligand-1 (PD-L1); microsatellite instability (MSI); tumor mutational burden (TMB)) are routinely used for patient selection for ICI response in clinical practice. Although clinical utility of these biomarkers has been demonstrated in ample clinical trials, many variables involved in using these biomarkers have poised serious challenges in daily practice. Furthermore, the predicted responders by these three biomarkers only have a small percentage of overlap, suggesting that each biomarker captures different contributing factors to ICI response. Optimized use of currently FDA-approved biomarkers and development of a new generation of predictive biomarkers are urgently needed. In this review, we will first discuss three widely used FDA-approved predictive biomarkers and their optimal use. Secondly, we will review four novel gene signature biomarkers: T-cell inflamed gene expression profile (GEP), T-cell dysfunction and exclusion gene signature (TIDE), melanocytic plasticity signature (MPS) and B-cell focused gene signature. The GEP and TIDE have shown better predictive performance than PD-L1, and PD-L1 or TMB, respectively. The MPS is superior to PD-L1, TMB, and TIDE. The B-cell focused gene signature represents a previously unexplored predictive biomarker to ICI response. Thirdly, we will highlight two combined predictive biomarkers: TMB+GEP and MPS+TIDE. These integrated biomarkers showed improved predictive outcomes compared to a single predictor. Finally, we will present a potential nucleic acid biomarker signature, allowing DNA and RNA biomarkers to be analyzed in one assay. This comprehensive signature could represent a future direction of developing robust predictive biomarkers, particularly for the cold tumors, for ICI response.
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Affiliation(s)
- Ye Wang
- Clinical Laboratory, Qingdao Central Hospital, The Second Affiliated Hospital of Medical College of Qingdao University, Qingdao, China
| | - Zhuang Tong
- Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Wenhua Zhang
- Clinical Laboratory, Qingdao Central Hospital, The Second Affiliated Hospital of Medical College of Qingdao University, Qingdao, China
| | - Weizhen Zhang
- Department of Biology, University of California – Santa Cruz, Santa Cruz, CA, United States
| | - Anton Buzdin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, Moscow, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Xiaofeng Mu
- Clinical Laboratory, Qingdao Central Hospital, The Second Affiliated Hospital of Medical College of Qingdao University, Qingdao, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Qing Yan
- Clinical Laboratory, Qingdao Central Hospital, The Second Affiliated Hospital of Medical College of Qingdao University, Qingdao, China
| | - Xiaowen Zhao
- Clinical Laboratory, Qingdao Central Hospital, The Second Affiliated Hospital of Medical College of Qingdao University, Qingdao, China
| | - Hui-Hua Chang
- Department of Pathology & Laboratory Medicine, University of California, Los Angeles (UCLA) Technology Center for Genomics & Bioinformatics, Los Angeles, CA, United States
| | - Mark Duhon
- Department of Pathology & Laboratory Medicine, University of California, Los Angeles (UCLA) Technology Center for Genomics & Bioinformatics, Los Angeles, CA, United States
| | - Xin Zhou
- Department of Medicine, Qiqihaer First Hospital, Qiqihar, China
| | - Gexin Zhao
- Department of Pathology & Laboratory Medicine, University of California, Los Angeles (UCLA) Technology Center for Genomics & Bioinformatics, Los Angeles, CA, United States
| | - Hong Chen
- Department of Medicine, Qiqihaer First Hospital, Qiqihar, China
| | - Xinmin Li
- Department of Pathology & Laboratory Medicine, University of California, Los Angeles (UCLA) Technology Center for Genomics & Bioinformatics, Los Angeles, CA, United States
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22
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Buzdin A, Tkachev V, Zolotovskaia M, Garazha A, Moshkovskii S, Borisov N, Gaifullin N, Sorokin M, Suntsova M. Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways. Adv Protein Chem Struct Biol 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] [What about the content of this article? (0)] [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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23
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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24
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Vladimirova U, Rumiantsev P, Zolotovskaia M, Albert E, Abrosimov A, Slashchuk K, Nikiforovich P, Chukhacheva O, Gaifullin N, Suntsova M, Zakharova G, Glusker A, Nikitin D, Garazha A, Li X, Kamashev D, Drobyshev A, Kochergina-Nikitskaya I, Sorokin M, Buzdin A. DNA repair pathway activation features in follicular and papillary thyroid tumors, interrogated using 95 experimental RNA sequencing profiles. Heliyon 2021; 7:e06408. [PMID: 33748479 PMCID: PMC7970325 DOI: 10.1016/j.heliyon.2021.e06408] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 12/22/2020] [Accepted: 02/26/2021] [Indexed: 12/12/2022] Open
Abstract
DNA repair can prevent mutations and cancer development, but it can also restore damaged tumor cells after chemo and radiation therapy. We performed RNA sequencing on 95 human pathological thyroid biosamples including 17 follicular adenomas, 23 follicular cancers, 3 medullar cancers, 51 papillary cancers and 1 poorly differentiated cancer. The gene expression profiles are annotated here with the clinical and histological diagnoses and, for papillary cancers, with BRAF gene V600E mutation status. DNA repair molecular pathway analysis showed strongly upregulated pathway activation levels for most of the differential pathways in the papillary cancer and moderately upregulated pattern in the follicular cancer, when compared to the follicular adenomas. This was observed for the BRCA1, ATM, p53, excision repair, and mismatch repair pathways. This finding was validated using independent thyroid tumor expression dataset PRJEB11591. We also analyzed gene expression patterns linked with the radioiodine resistant thyroid tumors (n = 13) and identified 871 differential genes that according to Gene Ontology analysis formed two functional groups: (i) response to topologically incorrect protein and (ii) aldo-keto reductase (NADP) activity. We also found RNA sequencing reads for two hybrid transcripts: one in-frame fusion for well-known NCOA4-RET translocation, and another frameshift fusion of ALK oncogene with a new partner ARHGAP12. The latter could probably support increased expression of truncated ALK downstream from 4th exon out of 28. Both fusions were found in papillary thyroid cancers of follicular histologic subtype with node metastases, one of them (NCOA4-RET) for the radioactive iodine resistant tumor. The differences in DNA repair activation patterns may help to improve therapy of different thyroid cancer types under investigation and the data communicated may serve for finding additional markers of radioiodine resistance.
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Affiliation(s)
- Uliana Vladimirova
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
- Pirogov Russian National Research Medical University, Moscow, 117997, Russia
| | - Pavel Rumiantsev
- Endocrinology Research Centre, Moscow, 117312, Russia
- Pirogov Russian National Research Medical University, Moscow, 117997, Russia
| | | | | | | | | | | | | | - Nurshat Gaifullin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119992, Russia
| | - Maria Suntsova
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | | | - Alexander Glusker
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Daniil Nikitin
- Omicsway Corp., Walnut, CA, 91789, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | | | - Xinmin Li
- Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Dmitriy Kamashev
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Alexei Drobyshev
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | | | - Maxim Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
- Omicsway Corp., Walnut, CA, 91789, USA
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Anton Buzdin
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
- Omicsway Corp., Walnut, CA, 91789, USA
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
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25
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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26
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Sharma A, Colonna G. System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration. Mol Diagn Ther 2021; 25:9-27. [PMID: 33475988 PMCID: PMC7847983 DOI: 10.1007/s40291-020-00505-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2020] [Indexed: 12/17/2022]
Abstract
Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.
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Affiliation(s)
- Ankush Sharma
- Department of Biosciences, University of Oslo, Oslo, Norway.
- Department of Informatics, University of Oslo, Oslo, Norway.
- Institute of Cancer Research, Institute of Clinical medicine, University of Oslo, Oslo, Norway.
| | - Giovanni Colonna
- Medical Informatics, AOU-Vanvitelli, Università della Campania, Naples, Italy
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27
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Borisov N, Ilnytskyy Y, Byeon B, Kovalchuk O, Kovalchuk I. System, Method and Software for Calculation of a Cannabis Drug Efficiency Index for the Reduction of Inflammation. Int J Mol Sci 2020; 22:ijms22010388. [PMID: 33396562 PMCID: PMC7795809 DOI: 10.3390/ijms22010388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 12/26/2020] [Accepted: 12/28/2020] [Indexed: 12/19/2022] Open
Abstract
There are many varieties of Cannabis sativa that differ from each other by composition of cannabinoids, terpenes and other molecules. The medicinal properties of these cultivars are often very different, with some being more efficient than others. This report describes the development of a method and software for the analysis of the efficiency of various cannabis extracts to detect the anti-inflammatory properties of the various cannabis extracts. The method uses high-throughput gene expression profiling data but can potentially use other omics data as well. According to the signaling pathway topology, the gene expression profiles are convoluted into the signaling pathway activities using a signaling pathway impact analysis (SPIA) method. The method was tested by inducing inflammation in human 3D epithelial tissues, including intestine, oral and skin, and then exposing these tissues to various extracts and then performing transcriptome analysis. The analysis showed a different efficiency of the various extracts in restoring the transcriptome changes to the pre-inflammation state, thus allowing to calculate a different cannabis drug efficiency index (CDEI).
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Affiliation(s)
- Nicolas Borisov
- Moscow Institute of Physics and Technology, 9 Institutsky lane, Dolgoprudny, Moscow Region 141701, Russia;
| | - Yaroslav Ilnytskyy
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (Y.I.); (B.B.); (O.K.)
- Pathway Rx., 16 Sandstone Rd. S., Lethbridge, AB T1K 7X8, Canada
| | - Boseon Byeon
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (Y.I.); (B.B.); (O.K.)
- Pathway Rx., 16 Sandstone Rd. S., Lethbridge, AB T1K 7X8, Canada
- Biomedical and Health Informatics, Computer Science Department, State University of New York, 2 S Clinton St, Syracuse, NY 13202, USA
| | - Olga Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (Y.I.); (B.B.); (O.K.)
- Pathway Rx., 16 Sandstone Rd. S., Lethbridge, AB T1K 7X8, Canada
| | - Igor Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (Y.I.); (B.B.); (O.K.)
- Pathway Rx., 16 Sandstone Rd. S., Lethbridge, AB T1K 7X8, Canada
- Correspondence:
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28
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Knyazeva M, Korobkina E, Karizky A, Sorokin M, Buzdin A, Vorobyev S, Malek A. Reciprocal Dysregulation of MiR-146b and MiR-451 Contributes in Malignant Phenotype of Follicular Thyroid Tumor. Int J Mol Sci 2020; 21:E5950. [PMID: 32824921 PMCID: PMC7503510 DOI: 10.3390/ijms21175950] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 08/15/2020] [Accepted: 08/17/2020] [Indexed: 01/08/2023] Open
Abstract
Over the last few years, incidental thyroid nodules are being diagnosed with increasing frequency with the use of highly sensitive imaging techniques. The ultrasound thyroid gland examination, followed by the fine-needle aspiration cytology is the standard diagnostic approach. However, in cases of the follicular nature of nodules, cytological diagnosis is not enough. Analysis of miRNAs in the biopsy presents a promising approach. Increasing our knowledge of miRNA's role in follicular carcinogenesis, and development of the appropriate the miRNA analytical technologies are required to implement miRNA-based tests in clinical practice. We used material from follicular thyroid nodes (n.84), grouped in accordance with their invasive properties. The invasion-associated miRNAs expression alterations were assayed. Expression data were confirmed by highly sensitive two-tailed RT-qPCR. Reciprocally dysregulated miRNAs pair concentration ratios were explored as a diagnostic parameter using receiver operation curve (ROC) analysis. A new bioinformatics method (MiRImpact) was applied to evaluate the biological significance of the observed expression alterations. Coupled experimental and computational approaches identified reciprocal dysregulation of miR-146b and miR-451 as important attributes of follicular cell malignant transformation and follicular thyroid cancer progression. Thus, evaluation of combined dysregulation of miRNAs relevant to invasion and metastasis can help to distinguish truly malignant follicular thyroid cancer from indolent follicular adenoma.
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Affiliation(s)
- Margarita Knyazeva
- Subcellular technology Lab., N. N. Petrov National Medical Center of Oncology, 197758 Saint Petersburg, Russia; (M.K.); (E.K.)
- Oncosystem Company Limited, 121205 Moscow, Russia
- Institute of Biomedical Systems and Biotechnologies, Peter the Great Saint. Petersburg Polytechnic University (SPbPU), 195251 Saint Petersburg, Russia
| | - Ekaterina Korobkina
- Subcellular technology Lab., N. N. Petrov National Medical Center of Oncology, 197758 Saint Petersburg, Russia; (M.K.); (E.K.)
- Oncosystem Company Limited, 121205 Moscow, Russia
| | - Alexey Karizky
- Information Technologies and Programming Faculty, Information Technologies, Mechanics and Optics (ITMO) University, 197101 Saint-Petersburg, Russia;
| | - Maxim Sorokin
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (A.B.)
- Omicsway Corporation, Walnut, CA 91789, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
| | - Anton Buzdin
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (A.B.)
- Omicsway Corporation, Walnut, CA 91789, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
| | - Sergey Vorobyev
- National Center of Clinical Morphological Diagnostics, 192283 Saint Petersburg, Russia;
| | - Anastasia Malek
- Subcellular technology Lab., N. N. Petrov National Medical Center of Oncology, 197758 Saint Petersburg, Russia; (M.K.); (E.K.)
- Oncosystem Company Limited, 121205 Moscow, Russia
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29
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Sorokin M, Poddubskaya E, Baranova M, Glusker A, Kogoniya L, Markarova E, Allina D, Suntsova M, Tkachev V, Garazha A, Sekacheva M, Buzdin A. RNA sequencing profiles and diagnostic signatures linked with response to ramucirumab in gastric cancer. Cold Spring Harb Mol Case Stud 2020; 6:a004945. [PMID: 32060041 PMCID: PMC7133748 DOI: 10.1101/mcs.a004945] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/03/2020] [Indexed: 02/06/2023] Open
Abstract
Gastric cancer (GC) is the fifth-ranked cancer type by associated mortality. The proportion of early diagnosis is low, and most patients are diagnosed at the advanced stages. First-line therapy standardly includes fluoropyrimidines and platinum compounds with trastuzumab for HER2-positive cases. For recurrent disease, there are several alternative options including ramucirumab, a monoclonal therapeutic antibody that inhibits VEGF-mediated tumor angiogenesis by binding with VEGFR2, alone or in combination with other cancer drugs. However, overall response rate following ramucirumab or its combinations is 30%-80% of the patients, suggesting that personalization of drug prescription is needed to increase efficacy of treatment. We report here original tumor RNA sequencing profiles for 15 advanced GC patients linked with data on clinical response to ramucirumab or its combinations. Three genes showed differential expression in the tumors for responders versus nonresponders: CHRM3, LRFN1, and TEX15 Of them, CHRM3 was up-regulated in the responders. Using the bioinformatic platform Oncobox we simulated ramucirumab efficiency and compared output model results with actual tumor response data. An agreement was observed between predicted and real clinical outcomes (AUC ≥ 0.7). These results suggest that RNA sequencing may be used to personalize the prescription of ramucirumab for GC and indicate potential molecular mechanisms underlying ramucirumab resistance. The RNA sequencing profiles obtained here are fully compatible with the previously published Oncobox Atlas of Normal Tissue Expression (ANTE) data.
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Affiliation(s)
- Maxim Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
- Omicsway Corp., Walnut, California 91789, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Elena Poddubskaya
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Madina Baranova
- N.N. Blokhin Russian Cancer Research Center, Moscow, 115478, Russia
- Clinical Center Vitamed, Moscow, 121309, Russia
| | - Alex Glusker
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Lali Kogoniya
- M.F. Vladimirsky Moscow Regional Research Clinical Institute, Moscow, 129110, Russia
| | - Ekaterina Markarova
- M.F. Vladimirsky Moscow Regional Research Clinical Institute, Moscow, 129110, Russia
| | - Daria Allina
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Maria Suntsova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | | | | | - Marina Sekacheva
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Anton Buzdin
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
- Omicsway Corp., Walnut, California 91789, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
- Moscow Institute of Physics and Technology, Moscow Region, 141701, Russia
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30
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Moisseev A, Albert E, Lubarsky D, Schroeder D, Clark J. Transcriptomic and Genomic Testing to Guide Individualized Treatment in Chemoresistant Gastric Cancer Case. Biomedicines 2020; 8:biomedicines8030067. [PMID: 32210001 PMCID: PMC7148467 DOI: 10.3390/biomedicines8030067] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 03/20/2020] [Accepted: 03/20/2020] [Indexed: 12/20/2022] Open
Abstract
Gastric cancer is globally the fifth leading cause of cancer death. We present a case report describing the unique genomic characteristics of an Epstein–Barr virus-negative gastric cancer with esophageal invasion and regional lymph node metastasis. Genomic tests were performed first with the stomach biopsy using platforms FoundationOne, OncoDNA, and Oncopanel at Dana Farber Institute. Following neoadjuvant chemotherapy, residual tumor was resected and the stomach and esophageal residual tumor samples were compared with the initial biopsy by whole exome sequencing and molecular pathway analysis platform Oncobox. Copy number variation profiling perfectly matched the whole exome sequencing results. A moderate agreement was seen between the diagnostic platforms in finding mutations in the initial biopsy. Final data indicate somatic activating mutation Q546K in PIK3CA gene, somatic frameshifts in PIH1D1 and FBXW7 genes, stop-gain in TP53BP1, and a few somatic mutations of unknown significance. RNA sequencing analysis revealed upregulated expressions of MMP7, MMP9, BIRC5, and PD-L1 genes and strongly differential regulation of several molecular pathways linked with the mutations identified. According to test results, the patient received immunotherapy with anti-PD1 therapy and is now free of disease for 2 years. Our data suggest that matched tumor and normal tissue analyses have a considerable advantage over tumor biopsy-only genomic tests in stomach cancer.
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Affiliation(s)
- Alexey Moisseev
- Institute for personalized medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia;
- Correspondence: ; Tel.: +7(926)1443639
| | - Eugene Albert
- Institute for personalized medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia;
| | | | - David Schroeder
- Wellesley Internal Medicine, 372 Washington St Ste 2, Wellesley Hills, MA 02481, USA;
| | - Jeffrey Clark
- Department of Hematology and Oncology, Massachusetts General Hospital, 55 Fruit Street Boston, MA 02114, USA;
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31
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Kalasauskas D, Sorokin M, Sprang B, Elmasri A, Viehweg S, Salinas G, Opitz L, Rave-Fraenk M, Schulz-Schaeffer W, Kantelhardt SR, Giese A, Buzdin A, Kim EL. Diversity of Clinically Relevant Outcomes Resulting from Hypofractionated Radiation in Human Glioma Stem Cells Mirrors Distinct Patterns of Transcriptomic Changes. Cancers (Basel) 2020; 12:cancers12030570. [PMID: 32121554 PMCID: PMC7139840 DOI: 10.3390/cancers12030570] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 02/12/2020] [Accepted: 02/22/2020] [Indexed: 12/17/2022] Open
Abstract
Hypofractionated radiotherapy is the mainstay of the current treatment for glioblastoma. However, the efficacy of radiotherapy is hindered by the high degree of radioresistance associated with glioma stem cells comprising a heterogeneous compartment of cell lineages differing in their phenotypic characteristics, molecular signatures, and biological responses to external signals. Reconstruction of radiation responses in glioma stem cells is necessary for understanding the biological and molecular determinants of glioblastoma radioresistance. To date, there is a paucity of information on the longitudinal outcomes of hypofractionated radiation in glioma stem cells. This study addresses long-term outcomes of hypofractionated radiation in human glioma stem cells by using a combinatorial approach integrating parallel assessments of the tumor-propagating capacity, stemness-associated properties, and array-based profiling of gene expression. The study reveals a broad spectrum of changes in the tumor-propagating capacity of glioma stem cells after radiation and finds association with proliferative changes at the onset of differentiation. Evidence is provided that parallel transcriptomic patterns and a cumulative impact of pathways involved in the regulation of apoptosis, neural differentiation, and cell proliferation underly similarities in tumorigenicity changes after radiation.
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Affiliation(s)
- Darius Kalasauskas
- Laboratory for Experimental Neurooncology, Clinic for Neurosurgery, Johannes Gutenberg University Medical Centre, 55131 Mainz, Germany; (D.K.); (B.S.); (A.E.); (S.V.)
- Clinic for Neurosurgery, Johannes Gutenberg University Medical Centre, 55131 Mainz, Germany;
| | - Maxim Sorokin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia; (M.S.); (A.B.)
- I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- Omicsway Corp., Walnut, CA 91789, USA
| | - Bettina Sprang
- Laboratory for Experimental Neurooncology, Clinic for Neurosurgery, Johannes Gutenberg University Medical Centre, 55131 Mainz, Germany; (D.K.); (B.S.); (A.E.); (S.V.)
| | - Alhassan Elmasri
- Laboratory for Experimental Neurooncology, Clinic for Neurosurgery, Johannes Gutenberg University Medical Centre, 55131 Mainz, Germany; (D.K.); (B.S.); (A.E.); (S.V.)
| | - Sina Viehweg
- Laboratory for Experimental Neurooncology, Clinic for Neurosurgery, Johannes Gutenberg University Medical Centre, 55131 Mainz, Germany; (D.K.); (B.S.); (A.E.); (S.V.)
| | - Gabriela Salinas
- NGS Integrative Genomics Core Unit (NIG), Institute for Human Genetics, University Medical Centre, 37077 Göttingen, Germany; (G.S.); (L.O.)
| | - Lennart Opitz
- NGS Integrative Genomics Core Unit (NIG), Institute for Human Genetics, University Medical Centre, 37077 Göttingen, Germany; (G.S.); (L.O.)
| | - Margret Rave-Fraenk
- Department of Radiotherapy and Radiooncology, University Medical Centre, 37077 Göttingen, Germany;
| | | | - Sven Reiner Kantelhardt
- Clinic for Neurosurgery, Johannes Gutenberg University Medical Centre, 55131 Mainz, Germany;
| | - Alf Giese
- OrthoCentrum Hamburg, Department of Tumor Spinal Surgery, 20149 Hamburg, Germany;
| | - Anton Buzdin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia; (M.S.); (A.B.)
- I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- Oncobox ltd., 121205 Moscow, Russia
- Moscow Institute of Physics and Technology (National Research University), 141700 Moscow, Russia
| | - Ella L. Kim
- Laboratory for Experimental Neurooncology, Clinic for Neurosurgery, Johannes Gutenberg University Medical Centre, 55131 Mainz, Germany; (D.K.); (B.S.); (A.E.); (S.V.)
- Correspondence:
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Kim EL, Sorokin M, Kantelhardt SR, Kalasauskas D, Sprang B, Fauss J, Ringel F, Garazha A, Albert E, Gaifullin N, Hartmann C, Naumann N, Bikar SE, Giese A, Buzdin A. Intratumoral Heterogeneity and Longitudinal Changes in Gene Expression Predict Differential Drug Sensitivity in Newly Diagnosed and Recurrent Glioblastoma. Cancers (Basel) 2020; 12:E520. [PMID: 32102350 DOI: 10.3390/cancers12020520] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [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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Zolotovskaia MA, Tkachev VS, Seryakov AP, Kuzmin DV, Kamashev DE, Sorokin MI, Roumiantsev SA, Buzdin AA. Mutation Enrichment and Transcriptomic Activation Signatures of 419 Molecular Pathways in Cancer. Cancers (Basel) 2020; 12:E271. [PMID: 31979117 DOI: 10.3390/cancers12020271] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/16/2020] [Accepted: 01/20/2020] [Indexed: 12/13/2022] Open
Abstract
Carcinogenesis is linked with massive changes in regulation of gene networks. We used high throughput mutation and gene expression data to interrogate involvement of 278 signaling, 72 metabolic, 48 DNA repair and 47 cytoskeleton molecular pathways in cancer. Totally, we analyzed 4910 primary tumor samples with individual cancer RNA sequencing and whole exome sequencing profiles including ~1.3 million DNA mutations and representing thirteen cancer types. Gene expression in cancers was compared with the corresponding 655 normal tissue profiles. For the first time, we calculated mutation enrichment values and activation levels for these pathways. We found that pathway activation profiles were largely congruent among the different cancer types. However, we observed no correlation between mutation enrichment and expression changes both at the gene and at the pathway levels. Overall, positive median cancer-specific activation levels were seen in the DNA repair, versus similar slightly negative values in the other types of pathways. The DNA repair pathways also demonstrated the highest values of mutation enrichment. However, the signaling and cytoskeleton pathways had the biggest proportions of representatives among the outstandingly frequently mutated genes thus suggesting their initiator roles in carcinogenesis and the auxiliary/supporting roles for the other groups of molecular pathways.
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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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Zolotovskaia M, Sorokin M, Garazha A, Borisov N, Buzdin A. Molecular Pathway Analysis of Mutation Data for Biomarkers Discovery and Scoring of Target Cancer Drugs. Methods Mol Biol 2020; 2063:207-234. [PMID: 31667773 DOI: 10.1007/978-1-0716-0138-9_16] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
DNA mutations govern cancer development. Cancer mutation profiles vary dramatically among the individuals. In some cases, they may serve as the predictors of disease progression and response to therapies. However, the biomarker potential of cancer mutations can be dramatically (several orders of magnitude) enhanced by applying molecular pathway-based approach. We developed Oncobox system for calculation of pathway instability (PI) values for the molecular pathways that are aggregated mutation frequencies of the pathway members normalized on gene lengths and on number of genes in the pathway. PI scores can be effective biomarkers in different types of comparisons, for example, as the cancer type biomarkers and as the predictors of tumor response to target therapies. The latter option is implemented using mutation drug score (MDS) values, which algorithmically rank the drugs capacity of interfering with the mutated molecular pathways. Here, describe the mathematical basis and algorithms for PI and MDS values calculation, validation and implementation. The example analysis is provided encompassing 5956 human tumor mutation profiles of 15 cancer types from The Cancer Genome Atlas (TCGA) project, that totally make 2,316,670 mutations in 19,872 genes and 1748 molecular pathways, thus enabling ranking of 128 clinically approved target drugs. Our results evidence that the Oncobox PI and MDS approaches are highly useful for basic and applied aspects of molecular oncology and pharmacology research.
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Affiliation(s)
- Marianna Zolotovskaia
- Omicsway Corp., Walnut, CA, USA
- Department of Oncology, Hematology and Radiotherapy of Pediatric Faculty, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Maxim Sorokin
- 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
| | | | - Nikolay 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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