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Molinari C, Solaini L, Rebuzzi F, Tedaldi G, Angeli D, Petracci E, Prascevic D, Ewald J, Rahm E, Canale M, Giovanni M, Tomezzoli A, Bencivenga M, Ambrosio MR, Marrelli D, Morgagni P, Ercolani G, Ulivi P, Saragoni L. Genomic events stratifying prognosis of early gastric cancer. Gastric Cancer 2024:10.1007/s10120-024-01536-z. [PMID: 39028418 DOI: 10.1007/s10120-024-01536-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 07/08/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND The purpose of the study was to conduct a comprehensive genomic characterization of gene alterations, microsatellite instability (MSI), and tumor mutational burden (TMB) in submucosal-penetrating (Pen) early gastric cancers (EGCs) with varying prognoses. METHODS Samples from EGC patients undergoing surgery and with 10-year follow-up data available were collected. Tissue genomic alterations were characterized using Trusight Oncology panel (TSO500). Pathway instability (PI) scores for a selection of 218 GC-related pathways were calculated both for the present case series and EGCs from the TCGA cohort. RESULTS Higher age and tumor location in the upper-middle tract are significantly associated with an increased hazard of relapse or death from any cause (p = 0.006 and p = 0.032). Even if not reaching a statistical significance, Pen A tumors more frequently present higher TMB values, higher frequency of MSI-subtypes and an overall increase in PI scores, along with an enrichment in immune pathways. ARID1A gene was observed to be significantly more frequently mutated in Pen A tumors (p = 0.006), as well as in patients with high TMB (p = 0.027). Tumors harboring LRP1B alterations seem to have a higher hazard of relapse or death from any cause (p = 0.089), being mutated mainly in relapsed patients (p = 0.093). CONCLUSIONS We found that the most aggressive subtype Pen A is characterized by a higher frequency of ARID1A mutations and a higher genetic instability, while LRP1B alterations seem to be related to a lower disease-free survival. Further investigations are needed to provide a rationale for the use of these markers to stratify prognosis in EGC patients.
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Affiliation(s)
- Chiara Molinari
- Biosciences Laboratory, IRCCS Istituto Romagnolo Per Lo Studio Dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy
| | - Leonardo Solaini
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
- General and Oncologic Surgery, Morgagni-Pierantoni Hospital, AUSL Romagna, Forlì, Italy.
| | - Francesca Rebuzzi
- Biosciences Laboratory, IRCCS Istituto Romagnolo Per Lo Studio Dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy
| | - Gianluca Tedaldi
- Biosciences Laboratory, IRCCS Istituto Romagnolo Per Lo Studio Dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy
| | - Davide Angeli
- Biostatistics and Clinical Trials Unit, IRCCS Istituto Romagnolo Per Lo Studio Dei Tumori (IRST), "Dino Amadori", Meldola, FC, Italy
| | - Elisabetta Petracci
- Biostatistics and Clinical Trials Unit, IRCCS Istituto Romagnolo Per Lo Studio Dei Tumori (IRST), "Dino Amadori", Meldola, FC, Italy
| | - Dusan Prascevic
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig University, Humboldtstr. 25, 04105, Leipzig, Germany
| | - Jan Ewald
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig University, Humboldtstr. 25, 04105, Leipzig, Germany
| | - Erhard Rahm
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig University, Humboldtstr. 25, 04105, Leipzig, Germany
| | - Matteo Canale
- Biosciences Laboratory, IRCCS Istituto Romagnolo Per Lo Studio Dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy
| | - Martinelli Giovanni
- Department of Hematology and Sciences Oncology, Institute of Haematology "L. and A. Seràgnoli", S. Orsola University Hospital, Bologna, Italy
| | - Anna Tomezzoli
- Department of Pathology, University of Verona, Verona, Italy
| | | | | | | | - Paolo Morgagni
- General and Oncologic Surgery, Morgagni-Pierantoni Hospital, AUSL Romagna, Forlì, Italy
| | - Giorgio Ercolani
- General and Oncologic Surgery, Morgagni-Pierantoni Hospital, AUSL Romagna, Forlì, Italy
| | - Paola Ulivi
- Biosciences Laboratory, IRCCS Istituto Romagnolo Per Lo Studio Dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy
| | - Luca Saragoni
- Pathology Unit, Morgagni-Pierantoni Hospital, Forlì, Italy
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Huang Y, Liu H, Liu B, Chen X, Li D, Xue J, Li N, Zhu L, Yang L, Xiao J, Liu C. Quantified pathway mutations associate epithelial-mesenchymal transition and immune escape with poor prognosis and immunotherapy resistance of head and neck squamous cell carcinoma. BMC Med Genomics 2024; 17:49. [PMID: 38331768 PMCID: PMC10854145 DOI: 10.1186/s12920-024-01818-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 01/23/2024] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND Pathway mutations have been calculated to predict the poor prognosis and immunotherapy resistance in head and neck squamous cell carcinoma (HNSCC). To uncover the unique markers predicting prognosis and immune therapy response, the accurate quantification of pathway mutations are required to evaluate epithelial-mesenchymal transition (EMT) and immune escape. Yet, there is a lack of score to accurately quantify pathway mutations. MATERIAL AND METHODS Firstly, we proposed Individualized Weighted Hallmark Gene Set Mutation Burden (IWHMB, https://github.com/YuHongHuang-lab/IWHMB ) which integrated pathway structure information and eliminated the interference of global Tumor Mutation Burden to accurately quantify pathway mutations. Subsequently, to further elucidate the association of IWHMB with EMT and immune escape, support vector machine regression model was used to identify IWHMB-related transcriptomic features (IRG), while Adversarially Regularized Graph Autoencoder (ARVGA) was used to further resolve IRG network features. Finally, Random walk with restart algorithm was used to identify biomarkers for predicting ICI response. RESULTS We quantified the HNSCC pathway mutation signatures and identified pathway mutation subtypes using IWHMB. The IWHMB-related transcriptomic features (IRG) identified by support vector machine regression were divided into 5 communities by ARVGA, among which the Community 1 enriching malignant mesenchymal components promoted EMT dynamically and regulated immune patterns associated with ICI responses. Bridge Hub Gene (BHG) identified by random walk with restart was key to IWHMB in EMT and immune escape, thus, more predictive for ICI response than other 70 public signatures. CONCLUSION In summary, the novel pathway mutation scoring-IWHMB suggested that the elevated malignancy mediated by pathway mutations is a major cause of poor prognosis and immunotherapy failure in HNSCC, and is capable of identifying novel biomarkers to predict immunotherapy response.
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Affiliation(s)
- Yuhong Huang
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China
| | - Han Liu
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China
| | - Bo Liu
- Institute for Genome Engineered Animal Models of Human Diseases, Dalian Medical University, Dalian, China
| | - Xiaoyan Chen
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
| | - Danya Li
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
| | - Junyuan Xue
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
| | - Nan Li
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China
| | - Lei Zhu
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China
| | - Liu Yang
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China
| | - Jing Xiao
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China.
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China.
| | - Chao Liu
- Department of Oral Pathology, Dalian Medical University School of Stomatology, Dalian, China.
- Academician Laboratory of Immunology and Oral Development & Regeneration, Dalian Medical University, Dalian, China.
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Arruda HS, Araújo MVL, Marostica Junior MR. Underexploited Brazilian Cerrado fruits as sources of phenolic compounds for diseases management: A review. FOOD CHEMISTRY. MOLECULAR SCIENCES 2022; 5:100148. [PMID: 36439937 PMCID: PMC9694390 DOI: 10.1016/j.fochms.2022.100148] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 11/04/2022] [Accepted: 11/19/2022] [Indexed: 04/18/2023]
Abstract
The Brazilian Cerrado is home to a large number of native and endemic species of enormous potential, among which we can highlight the cagaita, gabiroba, jatobá-do-cerrado, lobeira, and mangaba. In this review, we report the nutritional and phenolic composition, as well as bioactivities of these five Brazilian Cerrado fruits. The compiled data indicated that these fruits have high nutritional, functional, and economic potential and contribute to the daily intake of macro- and micronutrients, energy, and phenolic compounds by inhabitants of the Cerrado region. Phenolic-rich extracts obtained from these fruits have shown several bioactivities, including antioxidant, anti-inflammatory, antidyslipidemic, antidiabetic, analgesic, anticarcinogenic, hepatoprotective, gastrointestinal protective, and antimicrobial properties. Therefore, these fruits can be explored by the food industry as a raw material to develop food products of high value-added, such as functional foods, and can also be employed as plant sources to obtain bioactive compounds for food, cosmetic, and pharmaceutical purposes.
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Affiliation(s)
- Henrique Silvano Arruda
- Nutrition and Metabolism Laboratory, Department of Food Science and Nutrition, School of Food Engineering, University of Campinas, Monteiro Lobato Street 80, 13083-862 Campinas, São Paulo, Brazil
| | - Maria Vitória Lopes Araújo
- Nutrition and Metabolism Laboratory, Department of Food Science and Nutrition, School of Food Engineering, University of Campinas, Monteiro Lobato Street 80, 13083-862 Campinas, São Paulo, Brazil
| | - Mario Roberto Marostica Junior
- Nutrition and Metabolism Laboratory, Department of Food Science and Nutrition, School of Food Engineering, University of Campinas, Monteiro Lobato Street 80, 13083-862 Campinas, São Paulo, Brazil
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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. [PMID: 36316649 PMCID: PMC9623986 DOI: 10.1186/s12885-022-10177-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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.
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Affiliation(s)
- Maxim Sorokin
- I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- Moscow Institute of Physics and Technology, 141701 Moscow Region, Russia
- OmicsWay Corp, 91789 Walnut, CA USA
| | | | - Daniil Nikitin
- OmicsWay Corp, 91789 Walnut, CA USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
| | - Maria Suntsova
- World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Elena Poddubskaya
- I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
- Clinical Center Vitamed, 121309 Moscow, Russia
| | - Alexander Glusker
- I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | | | - Alexey Moisseev
- I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Xinmin Li
- Department of Pathology and Laboratory Medicine, University of California, 90095 Los Angeles, CA USA
| | - Marina Sekacheva
- World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | | | | | - Ye Wang
- Core Laboratory, The Affiliated Qingdao Central Hospital of Qingdao University, Qingdao, China
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, 141701 Moscow Region, Russia
- 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
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Borisov N, Sergeeva A, Suntsova M, Raevskiy M, Gaifullin N, Mendeleeva L, Gudkov A, Nareiko M, Garazha A, Tkachev V, Li X, Sorokin M, Surin V, Buzdin A. Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles. Front Oncol 2021; 11:652063. [PMID: 33937058 PMCID: PMC8083158 DOI: 10.3389/fonc.2021.652063] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 03/19/2021] [Indexed: 12/17/2022] Open
Abstract
Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): FGFR3, MAF, IGHA2, IGHV1-69, and GRB14. Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity.
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Affiliation(s)
- Nicolas Borisov
- Moscow Institute of Physics and Technology, Laboratory for Translational Genomic Bioinformatics, Dolgoprudny, Russia
| | - Anna Sergeeva
- National Research Center for Hematology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Maria Suntsova
- I.M. Sechenov First Moscow State Medical University, Institute of Personalized Medicine, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Group for Genomic Analysis of Cell Signaling Systems, Moscow, Russia
| | - Mikhail Raevskiy
- Moscow Institute of Physics and Technology, Laboratory for Translational Genomic Bioinformatics, Dolgoprudny, Russia
| | - Nurshat Gaifullin
- Department of Pathology, Faculty of Medicine, Lomonosov Moscow State University, Moscow, Russia
| | - Larisa Mendeleeva
- National Research Center for Hematology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Alexander Gudkov
- I.M. Sechenov First Moscow State Medical University, Institute of Personalized Medicine, Moscow, Russia
| | - Maria Nareiko
- National Research Center for Hematology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Andrew Garazha
- Omicsway Corp., Research Department, Walnut, CA, United States
- Oncobox Ltd., Research Department, Moscow, Russia
| | - Victor Tkachev
- Omicsway Corp., Research Department, Walnut, CA, United States
- Oncobox Ltd., Research Department, Moscow, Russia
| | - Xinmin Li
- Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Maxim Sorokin
- I.M. Sechenov First Moscow State Medical University, Institute of Personalized Medicine, Moscow, Russia
- Omicsway Corp., Research Department, Walnut, CA, United States
- Oncobox Ltd., Research Department, Moscow, Russia
| | - Vadim Surin
- National Research Center for Hematology, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Anton Buzdin
- I.M. Sechenov First Moscow State Medical University, Institute of Personalized Medicine, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Group for Genomic Analysis of Cell Signaling Systems, Moscow, Russia
- Omicsway Corp., Research Department, Walnut, CA, United States
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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] [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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Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments. BMC Med Genomics 2020; 13:111. [PMID: 32948183 PMCID: PMC7499993 DOI: 10.1186/s12920-020-00759-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 07/27/2020] [Indexed: 12/18/2022] Open
Abstract
Background Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics. Methods We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories. Results We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases. Conclusions We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.
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Sorokin M, Ignatev K, Barbara V, Vladimirova U, Muraveva A, Suntsova M, Gaifullin N, Vorotnikov I, Kamashev D, Bondarenko A, Baranova M, Poddubskaya E, Buzdin A. Molecular Pathway Activation Markers Are Associated with Efficacy of Trastuzumab Therapy in Metastatic HER2-Positive Breast Cancer Better than Individual Gene Expression Levels. BIOCHEMISTRY (MOSCOW) 2020; 85:758-772. [DOI: 10.1134/s0006297920070044] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Sorokin M, Ignatev K, Poddubskaya E, Vladimirova U, Gaifullin N, Lantsov D, Garazha A, Allina D, Suntsova M, Barbara V, Buzdin A. RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens. Biomedicines 2020; 8:E114. [PMID: 32397474 PMCID: PMC7277916 DOI: 10.3390/biomedicines8050114] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/02/2020] [Accepted: 05/07/2020] [Indexed: 12/11/2022] Open
Abstract
RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman's rho 0.65-0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.
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Affiliation(s)
- Maxim Sorokin
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Omicsway Corp., Walnut, CA 91789, USA;
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
| | - Kirill Ignatev
- Karelia Republic Oncological Hospital, 185000 Petrozavodsk, Russia;
| | - Elena Poddubskaya
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Vitamed Oncological Clinical Center, 121309 Moscow, Russia
| | - Uliana Vladimirova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
| | - Nurshat Gaifullin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia;
| | - Dmitriy Lantsov
- Kaluga Regional Oncological Hospital, 248007 Kaluga, Russia;
| | | | - Daria Allina
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
| | - Maria Suntsova
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
| | - Victoria Barbara
- Oncological Dispensary of the Republic of Karelia, 185002 Petrozavodsk, Russia;
| | - Anton Buzdin
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Omicsway Corp., Walnut, CA 91789, USA;
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
- Moscow Institute of Physics and Technology, 141701 Moscow, Russia
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10
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Chang H, Sasson A, Srinivasan S, Golhar R, Greenawalt DM, Geese WJ, Green G, Zerba K, Kirov S, Szustakowski J. Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer. Mol Diagn Ther 2020; 23:507-520. [PMID: 31250328 PMCID: PMC6675777 DOI: 10.1007/s40291-019-00408-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Introduction Tumor mutational burden (TMB) has emerged as a clinically relevant biomarker that may be associated with immune checkpoint inhibitor efficacy. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment. Objective Here we describe the in-depth evaluation of bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples from a phase III clinical trial. Methods In the CheckMate 026 clinical trial, TMB was retrospectively assessed in 312 patients with non-small-cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or standard-of-care chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne® CDx assay. Results TMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman’s r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. WES and FoundationOne CDx outputs were highly correlated (Spearman’s r = 0.90). Conclusions Parameter variation can impact TMB calculations, highlighting the need for standardization. Encouragingly, differences between assays could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms, and centers is achievable.
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Affiliation(s)
- Han Chang
- Translational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA
| | - Ariella Sasson
- Translational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA
| | - Sujaya Srinivasan
- Translational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA
| | - Ryan Golhar
- Translational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA
| | | | - William J Geese
- Translational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA
| | - George Green
- Translational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA
| | - Kim Zerba
- Global Biometric Sciences, Bristol-Myers Squibb, Princeton, NJ, USA
| | - Stefan Kirov
- Translational Medicine, Bristol-Myers Squibb, Princeton, NJ, 08648, USA
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11
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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] [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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12
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Zolotovskaia MA, Sorokin MI, Petrov IV, Poddubskaya EV, Moiseev AA, Sekacheva MI, Borisov NM, Tkachev VS, Garazha AV, Kaprin AD, Shegay PV, Giese A, Kim E, Roumiantsev SA, Buzdin AA. Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology. Int J Mol Sci 2020; 21:E1580. [PMID: 32111026 PMCID: PMC7084891 DOI: 10.3390/ijms21051580] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 02/17/2020] [Accepted: 02/19/2020] [Indexed: 02/07/2023] Open
Abstract
Inter-patient molecular heterogeneity is the major declared driver of an expanding variety of anticancer drugs and personalizing their prescriptions. Here, we compared interpatient molecular heterogeneities of tumors and repertoires of drugs or their molecular targets currently in use in clinical oncology. We estimated molecular heterogeneity using genomic (whole exome sequencing) and transcriptomic (RNA sequencing) data for 4890 tumors taken from The Cancer Genome Atlas database. For thirteen major cancer types, we compared heterogeneities at the levels of mutations and gene expression with the repertoires of targeted therapeutics and their molecular targets accepted by the current guidelines in oncology. Totally, 85 drugs were investigated, collectively covering 82 individual molecular targets. For the first time, we showed that the repertoires of molecular targets of accepted drugs did not correlate with molecular heterogeneities of different cancer types. On the other hand, we found that the clinical recommendations for the available cancer drugs were strongly congruent with the gene expression but not gene mutation patterns. We detected the best match among the drugs usage recommendations and molecular patterns for the kidney, stomach, bladder, ovarian and endometrial cancers. In contrast, brain tumors, prostate and colorectal cancers showed the lowest match. These findings provide a theoretical basis for reconsidering usage of targeted therapeutics and intensifying drug repurposing efforts.
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Affiliation(s)
- Marianna A. Zolotovskaia
- Oncobox ltd., Moscow, 121205, Russia; (I.V.P.); (A.A.B.)
- Department of Oncology, Hematology and Radiotherapy of Pediatric Faculty, Pirogov Russian National Research Medical University, Moscow, 117997, Russia;
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia;
| | - Maxim I. Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
- Omicsway Corp., Walnut, CA, 91789, USA; (V.S.T.); (A.V.G.)
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
| | - Ivan V. Petrov
- Oncobox ltd., Moscow, 121205, Russia; (I.V.P.); (A.A.B.)
| | - Elena V. Poddubskaya
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
| | - Alexey A. Moiseev
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
| | - Marina I. Sekacheva
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
| | - Nicolas M. Borisov
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia;
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
- Omicsway Corp., Walnut, CA, 91789, USA; (V.S.T.); (A.V.G.)
| | | | | | - Andrey D. Kaprin
- National Medical Research Radiological Centre of the Ministry of Health of the Russian Federation, Moscow 125284, Russia;
| | - Peter V. Shegay
- Center for Innovative Radiological and Regenerative Technologies of the Ministry of Health of the Russian Federation, Obninsk 249030, Russia;
| | - Alf Giese
- Orthocentrum Hamburg, Hamburg, Germany; or
| | - Ella Kim
- Johannes Gutenberg University Mainz, Mainz, Germany;
| | - Sergey A. Roumiantsev
- Department of Oncology, Hematology and Radiotherapy of Pediatric Faculty, Pirogov Russian National Research Medical University, Moscow, 117997, Russia;
| | - Anton A. Buzdin
- Oncobox ltd., Moscow, 121205, Russia; (I.V.P.); (A.A.B.)
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region 141701, Russia;
- I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia (E.V.P.); (A.A.M.)
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia
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13
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Intratumoral Heterogeneity and Longitudinal Changes in Gene Expression Predict Differential Drug Sensitivity in Newly Diagnosed and Recurrent Glioblastoma. Cancers (Basel) 2020; 12:cancers12020520. [PMID: 32102350 PMCID: PMC7072286 DOI: 10.3390/cancers12020520] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 02/21/2020] [Accepted: 02/21/2020] [Indexed: 12/22/2022] Open
Abstract
Background: Inevitable recurrence after radiochemotherapy is the major problem in the treatment of glioblastoma, the most prevalent type of adult brain malignancy. Glioblastomas are notorious for a high degree of intratumor heterogeneity manifest through a diversity of cell types and molecular patterns. The current paradigm of understanding glioblastoma recurrence is that cytotoxic therapy fails to target effectively glioma stem cells. Recent advances indicate that therapy-driven molecular evolution is a fundamental trait associated with glioblastoma recurrence. There is a growing body of evidence indicating that intratumor heterogeneity, longitudinal changes in molecular biomarkers and specific impacts of glioma stem cells need to be taken into consideration in order to increase the accuracy of molecular diagnostics still relying on readouts obtained from a single tumor specimen. Methods: This study integrates a multisampling strategy, longitudinal approach and complementary transcriptomic investigations in order to identify transcriptomic traits of recurrent glioblastoma in whole-tissue specimens of glioblastoma or glioblastoma stem cells. In this study, 128 tissue samples of 44 tumors including 23 first diagnosed, 19 recurrent and 2 secondary recurrent glioblastomas were analyzed along with 27 primary cultures of glioblastoma stem cells by RNA sequencing. A novel algorithm was used to quantify longitudinal changes in pathway activities and model efficacy of anti-cancer drugs based on gene expression data. Results: Our study reveals that intratumor heterogeneity of gene expression patterns is a fundamental characteristic of not only newly diagnosed but also recurrent glioblastomas. Evidence is provided that glioblastoma stem cells recapitulate intratumor heterogeneity, longitudinal transcriptomic changes and drug sensitivity patterns associated with the state of recurrence. Conclusions: Our results provide a transcriptional rationale for the lack of significant therapeutic benefit from temozolomide in patients with recurrent glioblastoma. Our findings imply that the spectrum of potentially effective drugs is likely to differ between newly diagnosed and recurrent glioblastomas and underscore the merits of glioblastoma stem cells as prognostic models for identifying alternative drugs and predicting drug response in recurrent glioblastoma. With the majority of recurrent glioblastomas being inoperable, glioblastoma stem cell models provide the means of compensating for the limited availability of recurrent glioblastoma specimens.
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14
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Negro G, Aschenbrenner B, Brezar SK, Cemazar M, Coer A, Gasljevic G, Savic D, Sorokin M, Buzdin A, Callari M, Kvitsaridze I, Jewett A, Vasileva-Slaveva M, Ganswindt U, Skvortsova I, Skvortsov S. Molecular heterogeneity in breast carcinoma cells with increased invasive capacities. Radiol Oncol 2020; 54:103-118. [PMID: 32061169 PMCID: PMC7087425 DOI: 10.2478/raon-2020-0007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 01/30/2020] [Indexed: 01/10/2023] Open
Abstract
Background Metastatic progression of breast cancer is still a challenge in clinical oncology. Therefore, an elucidation how carcinoma cells belonging to different breast cancer subtypes realize their metastatic capacities is needed. The aim of this study was to elucidate a similarity of activated molecular pathways underlying an enhancement of invasiveness of carcinoma cells belonging to different breast carcinoma subtypes. Materials and methods In order to reach this aim, parental and invasive (INV) MDA-MB-231 (triple-negative), T47D (hormone receptor-positive), and Au565 (Her2-positive) breast carcinoma cells were used and their molecular phenotypes were compared using a proteomic approach. Results Independently from breast cancer subtypes, INV cells have demonstrated fibroblast-like morphology accompanied by enhancement of invasive and migratory capacities, increased expression of cancer stem cell markers, and delayed tumor growth in in vivo animal models. However, the global proteomic analysis has highlighted that INV cells were different in protein expressions from the parental cells, and Her2-positive Au565-INV cells showed the most pronounced molecular differences compared to the triple-negative MDA-MB-231-INV and hormone receptor-positive T47D-INV cells. Although Au565-INV breast carcinoma cells possessed the highest number of deregulated proteins, they had the lowest overlapping in proteins commonly expressed in MDA-MB-231-INV and T47D-INV cells. Conclusions We can conclude that hormone receptor-positive cells with increased invasiveness acquire the molecular characteristics of triple-negative breast cancer cells, whereas Her2-positive INV cells specifically changed their own molecular phenotype with very limited partaking in the involved pathways found in the MDA-MB-231-INV and T47D-INV cells. Since hormone receptor-positive invasive cells share their molecular properties with triple-negative breast cancer cells, we assume that these types of metastatic disease can be treated rather equally with an option to add anti-hormonal agents. In contrast, Her2-positive metastasis should be carefully evaluated for more effective therapeutic approaches which are distinct from the triple-negative and hormone-positive metastatic breast cancers.
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Affiliation(s)
- Giulia Negro
- Medical University of Innsbruck, Therapeutic Radiology and Oncology, Innsbruck, Austria
- Tyrolean Cancer Research Institute, Innsbruck, Austria
- EORTC PathoBiology GroupBrussels, Belgium
| | - Bertram Aschenbrenner
- Medical University of Innsbruck, Therapeutic Radiology and Oncology, Innsbruck, Austria
- Tyrolean Cancer Research Institute, Innsbruck, Austria
- EORTC PathoBiology GroupBrussels, Belgium
| | - Simona Kranjc Brezar
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
| | - Maja Cemazar
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
- University of Primorska, Faculty of Health Sciences, Izola, Slovenia
- EORTC PathoBiology GroupBrussels, Belgium
| | - Andrej Coer
- University of Primorska, Faculty of Health Sciences, Izola, Slovenia
- Orthopaedic Hospital Valdoltra, Ankaran, Slovenia
| | - Gorana Gasljevic
- Department of Experimental Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia
| | - Dragana Savic
- Medical University of Innsbruck, Therapeutic Radiology and Oncology, Innsbruck, Austria
- Tyrolean Cancer Research Institute, Innsbruck, Austria
- EORTC PathoBiology GroupBrussels, Belgium
| | - Maxim Sorokin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Omicsway Corp., Walnut, USA
- EORTC PathoBiology GroupBrussels, Belgium
| | - Anton Buzdin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Oncobox ltd., Moscow, Russia
- EORTC PathoBiology GroupBrussels, Belgium
| | - Maurizio Callari
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- EORTC PathoBiology GroupBrussels, Belgium
| | - Irma Kvitsaridze
- Medical University of Innsbruck, Therapeutic Radiology and Oncology, Innsbruck, Austria
- Tyrolean Cancer Research Institute, Innsbruck, Austria
- EORTC PathoBiology GroupBrussels, Belgium
| | - Anahid Jewett
- Division of Oral Biology and Medicine Microbiology, Immunology, & Molecular Genetics, Tumor Immunology Laboratory, College of Letters & Science, UCLA School of Dentistry and Medicine, Los Angeles, USA
| | - Mariela Vasileva-Slaveva
- Medical University of Innsbruck, Therapeutic Radiology and Oncology, Innsbruck, Austria
- Tyrolean Cancer Research Institute, Innsbruck, Austria
- EORTC PathoBiology GroupBrussels, Belgium
| | - Ute Ganswindt
- Medical University of Innsbruck, Therapeutic Radiology and Oncology, Innsbruck, Austria
| | - Ira Skvortsova
- Medical University of Innsbruck, Therapeutic Radiology and Oncology, Innsbruck, Austria
- Tyrolean Cancer Research Institute, Innsbruck, Austria
- EORTC PathoBiology GroupBrussels, Belgium
| | - Sergej Skvortsov
- Medical University of Innsbruck, Therapeutic Radiology and Oncology, Innsbruck, Austria
- Tyrolean Cancer Research Institute, Innsbruck, Austria
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15
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Mutation Enrichment and Transcriptomic Activation Signatures of 419 Molecular Pathways in Cancer. Cancers (Basel) 2020; 12:cancers12020271. [PMID: 31979117 PMCID: PMC7073226 DOI: 10.3390/cancers12020271] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/16/2020] [Accepted: 01/20/2020] [Indexed: 12/13/2022] Open
Abstract
Carcinogenesis is linked with massive changes in regulation of gene networks. We used high throughput mutation and gene expression data to interrogate involvement of 278 signaling, 72 metabolic, 48 DNA repair and 47 cytoskeleton molecular pathways in cancer. Totally, we analyzed 4910 primary tumor samples with individual cancer RNA sequencing and whole exome sequencing profiles including ~1.3 million DNA mutations and representing thirteen cancer types. Gene expression in cancers was compared with the corresponding 655 normal tissue profiles. For the first time, we calculated mutation enrichment values and activation levels for these pathways. We found that pathway activation profiles were largely congruent among the different cancer types. However, we observed no correlation between mutation enrichment and expression changes both at the gene and at the pathway levels. Overall, positive median cancer-specific activation levels were seen in the DNA repair, versus similar slightly negative values in the other types of pathways. The DNA repair pathways also demonstrated the highest values of mutation enrichment. However, the signaling and cytoskeleton pathways had the biggest proportions of representatives among the outstandingly frequently mutated genes thus suggesting their initiator roles in carcinogenesis and the auxiliary/supporting roles for the other groups of molecular pathways.
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16
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Borisov N, Sorokin M, Garazha A, Buzdin A. Quantitation of Molecular Pathway Activation Using RNA Sequencing Data. Methods Mol Biol 2020; 2063:189-206. [PMID: 31667772 DOI: 10.1007/978-1-0716-0138-9_15] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Intracellular molecular pathways (IMPs) control all major events in the living cell. IMPs are considered hotspots in biomedical sciences and thousands of IMPs have been discovered for humans and model organisms. Knowledge of IMPs activation is essential for understanding biological functions and differences between the biological objects at the molecular level. Here we describe the Oncobox system for accurate quantitative scoring activities of up to several thousand molecular pathways based on high throughput molecular data. Although initially designed for gene expression and mainly RNA sequencing data, Oncobox is now also applicable for quantitative proteomics, microRNA and transcription factor binding sites mapping data. The Oncobox system includes modules of gene expression data harmonization, aggregation and comparison and a recursive algorithm for automatic annotation of molecular pathways. The universal rationale of Oncobox enables scoring of signaling, metabolic, cytoskeleton, immunity, DNA repair, and other pathways in a multitude of biological objects. The Oncobox system can be helpful to all those working in the fields of genetics, biochemistry, interactomics, and big data analytics in molecular biomedicine.
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Affiliation(s)
- Nicolas Borisov
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Omicsway Corp., Walnut, CA, USA
| | - Maxim Sorokin
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Omicsway Corp., Walnut, CA, USA
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | - Anton Buzdin
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
- Omicsway Corp., Walnut, CA, USA.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
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17
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Tkachev V, Sorokin M, Garazha A, Borisov N, Buzdin A. Oncobox Method for Scoring Efficiencies of Anticancer Drugs Based on Gene Expression Data. Methods Mol Biol 2020; 2063:235-255. [PMID: 31667774 DOI: 10.1007/978-1-0716-0138-9_17] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
We describe here the Oncobox method for scoring efficiencies of anticancer target drugs (ATDs) using high throughput gene expression data. The method rationale, design, and validation are given along with the examples of its practical applications in biomedicine. The method is based on the analysis of intracellular molecular pathways activation and measuring expressions of molecular target genes for every ATD under consideration. Using Oncobox method requires collection of normal (control) expression profiles and annotated databases of molecular pathways and drug target genes. Both microarray and RNA sequencing profiles are acceptable, although the latter type of data prevails in the most recent applications of this technique.
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Affiliation(s)
| | - Maxim Sorokin
- Omicsway Corp., Walnut, CA, USA
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | | | - Nicolas Borisov
- Omicsway Corp., Walnut, CA, USA
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Anton Buzdin
- Omicsway Corp., Walnut, CA, USA.
- Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
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18
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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: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [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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19
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Nikitin D, Kolosov N, Murzina A, Pats K, Zamyatin A, Tkachev V, Sorokin M, Kopylov P, Buzdin A. Retroelement-Linked H3K4me1 Histone Tags Uncover Regulatory Evolution Trends of Gene Enhancers and Feature Quickly Evolving Molecular Processes in Human Physiology. Cells 2019; 8:cells8101219. [PMID: 31597351 PMCID: PMC6830109 DOI: 10.3390/cells8101219] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 09/25/2019] [Accepted: 10/01/2019] [Indexed: 12/20/2022] Open
Abstract
Background: Retroelements (REs) are mobile genetic elements comprising ~40% of human DNA. They can reshape expression patterns of nearby genes by providing various regulatory sequences. The proportion of regulatory sequences held by REs can serve a measure of regulatory evolution rate of the respective genes and molecular pathways. Methods: We calculated RE-linked enrichment scores for individual genes and molecular pathways based on ENCODE project epigenome data for enhancer-specific histone modification H3K4me1 in five human cell lines. We identified consensus groups of molecular processes that are enriched and deficient in RE-linked H3K4me1 regulation. Results: We calculated H3K4me1 RE-linked enrichment scores for 24,070 human genes and 3095 molecular pathways. We ranked genes and pathways and identified those statistically significantly enriched and deficient in H3K4me1 RE-linked regulation. Conclusion: Non-coding RNA genes were statistically significantly enriched by RE-linked H3K4me1 regulatory modules, thus suggesting their high regulatory evolution rate. The processes of gene silencing by small RNAs, DNA metabolism/chromatin structure, sensory perception/neurotransmission and lipids metabolism showed signs of the fastest regulatory evolution, while the slowest processes were connected with immunity, protein ubiquitination/degradation, cell adhesion, migration and interaction, metals metabolism/ion transport, cell death, intracellular signaling pathways.
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Affiliation(s)
- Daniil Nikitin
- Group for genomic analysis of cell signaling systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia.
- Omicsway Corp., Walnut, CA 91789, USA.
| | | | | | - Karina Pats
- ITMO University, 195251 Saint-Petersburg, Russia.
| | | | | | - Maxim Sorokin
- Omicsway Corp., Walnut, CA 91789, USA.
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia.
| | - Philippe Kopylov
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia.
| | - Anton Buzdin
- Group for genomic analysis of cell signaling systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia.
- Omicsway Corp., Walnut, CA 91789, USA.
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia.
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20
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Buzdin A, Sorokin M, Garazha A, Glusker A, Aleshin A, Poddubskaya E, Sekacheva M, Kim E, Gaifullin N, Giese A, Seryakov A, Rumiantsev P, Moshkovskii S, Moiseev A. RNA sequencing for research and diagnostics in clinical oncology. Semin Cancer Biol 2019; 60:311-323. [PMID: 31412295 DOI: 10.1016/j.semcancer.2019.07.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 07/16/2019] [Indexed: 12/26/2022]
Abstract
Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.
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Affiliation(s)
- Anton Buzdin
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Omicsway Corp., Walnut, CA, USA; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
| | - Maxim Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Omicsway Corp., Walnut, CA, USA; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | | | - Alex Aleshin
- Stanford University School of Medicine, Stanford, 94305, CA, USA
| | - Elena Poddubskaya
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Vitamed Oncological Clinics, Moscow, Russia
| | - Marina Sekacheva
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Ella Kim
- Johannes Gutenberg University Mainz, Mainz, Germany
| | - Nurshat Gaifullin
- Lomonosov Moscow State University, Faculty of Medicine, Moscow, Russia
| | | | | | | | - Sergey Moshkovskii
- Institute of Biomedical Chemistry, Moscow, 119121, Russia; Pirogov Russian National Research Medical University (RNRMU), Moscow, 117997, Russia
| | - Alexey Moiseev
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
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21
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Buzdin A, Sorokin M, Poddubskaya E, Borisov N. High-Throughput Mutation Data Now Complement Transcriptomic Profiling: Advances in Molecular Pathway Activation Analysis Approach in Cancer Biology. Cancer Inform 2019; 18:1176935119838844. [PMID: 30936679 PMCID: PMC6434430 DOI: 10.1177/1176935119838844] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 02/22/2019] [Indexed: 12/19/2022] Open
Abstract
We recently reviewed the current progress in the use of high-throughput molecular “omics” data for the quantitative analysis of molecular pathway activation. These quantitative metrics may be used in many ways, and we focused on their application as tumor biomarkers. Here, we provide an update of the most recent conceptual findings related to pathway analysis in tumor biology, which were not included in the previous review. The major novelties include a method enabling calculation of pathway-scale tumor mutation burden termed “Pathway Instability” and its application for scoring of anticancer target drugs. A new technique termed Shambhala emerged that enables accurate common harmonization of any number of gene expression profiles obtained using any number of experimental platforms. This may be helpful for merging various gene expression data sets and for comparing their pathway activation characteristics. Another recent bioinformatics method, termed FLOating-Window Projective Separator (FloWPS), has the potential to significantly enhance the value of pathway activation profiles as biomarkers of cancer response to treatments. It reduces the minimum required number of training samples needed to construct a machine-learning-based classifier. Finally, several documented clinical cases have been recently published, in which gene-expression-based pathway analysis was successfully used for personalized off-label prescription of target drugs to metastatic cancer patients.
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Affiliation(s)
- Anton Buzdin
- Institute for Personalized Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Omicsway Corp., Walnut, CA, USA.,Group for Genome Analysis of Cell Signaling Systems,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Maxim Sorokin
- Institute for Personalized Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Omicsway Corp., Walnut, CA, USA.,Group for Genome Analysis of Cell Signaling Systems,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Elena Poddubskaya
- Institute for Personalized Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Vitamed Oncological Clinics, Moscow, Russia
| | - Nicolas Borisov
- Institute for Personalized Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Omicsway Corp., Walnut, CA, USA
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