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Sajjad F, Jalal A, Jalal A, Gul Z, Mubeen H, Rizvi SZ, Un-Nisa EA, Asghar A, Butool F. Multi-omic analysis of dysregulated pathways in triple negative breast cancer. Asia Pac J Clin Oncol 2024. [PMID: 38899578 DOI: 10.1111/ajco.14095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/18/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024]
Abstract
The aggressive characteristics of triple-negative breast cancer (TNBC) and the absence of targeted medicines make TNBC a challenging clinical case. The molecular landscape of TNBC has been well-understood thanks to recent developments in multi-omic analysis, which have also revealed dysregulated pathways and possible treatment targets. This review summarizes the utilization of multi-omic approaches in elucidating TNBC's complex biology and therapeutic avenues. Dysregulated pathways including cell cycle progression, immunological modulation, and DNA damage response have been uncovered in TNBC by multi-omic investigations that integrate genomes, transcriptomics, proteomics, and metabolomics data. Methods like this pave the door for the discovery of new therapeutic targets, such as the EGFR, PARP, and mTOR pathways, which in turn direct the creation of more precise treatments. Recent developments in TNBC treatment strategies, including immunotherapy, PARP inhibitors, and antibody-drug conjugates, show promise in clinical trials. Emerging biomarkers like MUC1, YB-1, and immune-related markers offer insights into personalized treatment approaches and prognosis prediction. Despite the strengths of multi-omic analysis in offering a more comprehensive view and personalized treatment strategies, challenges exist. Large sample sizes and ensuring high-quality data remain crucial for reliable findings. Multi-omic analysis has revolutionized TNBC research, shedding light on dysregulated pathways, potential targets, and emerging biomarkers. Continued research efforts are imperative to translate these insights into improved outcomes for TNBC patients.
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Affiliation(s)
- Fatima Sajjad
- School of Interdisciplinary Engineering and Sciences, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Ahmer Jalal
- Faculty of Sciences and Technology, University of Central Punjab, Lahore, Pakistan
| | - Amir Jalal
- Department of Biochemistry, Sahara Medical College, Narowal, Pakistan
| | - Zulekha Gul
- Environmental and Biological Science, Nanjing University of Science and Technology, Nanjing, China
| | - Hira Mubeen
- Faculty of Sciences and Technology, University of Central Punjab, Lahore, Pakistan
| | - Seemal Zahra Rizvi
- Faculty of Sciences and Technology, University of Central Punjab, Lahore, Pakistan
| | - Ex Alim Un-Nisa
- Food and Biotechnology Research Centre, Pakistan Council of Scientific and Industrial Research, Lahore, Pakistan
| | - Andleeb Asghar
- Institute of Pharmaceutical Sciences, University of Veterinary and Animal Sciences Lahore, Lahore, Pakistan
| | - Farah Butool
- Institute of Pharmacy, Faculty of Pharmaceutical and Allied Health Sciences, Lahore College for Women University Lahore, Lahore, Pakistan
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2
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Dziubańska-Kusibab PJ, Nevedomskaya E, Haendler B. Preclinical Anticipation of On- and Off-Target Resistance Mechanisms to Anti-Cancer Drugs: A Systematic Review. Int J Mol Sci 2024; 25:705. [PMID: 38255778 PMCID: PMC10815614 DOI: 10.3390/ijms25020705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/22/2023] [Accepted: 12/28/2023] [Indexed: 01/24/2024] Open
Abstract
The advent of targeted therapies has led to tremendous improvements in treatment options and their outcomes in the field of oncology. Yet, many cancers outsmart precision drugs by developing on-target or off-target resistance mechanisms. Gaining the ability to resist treatment is the rule rather than the exception in tumors, and it remains a major healthcare challenge to achieve long-lasting remission in most cancer patients. Here, we discuss emerging strategies that take advantage of innovative high-throughput screening technologies to anticipate on- and off-target resistance mechanisms before they occur in treated cancer patients. We divide the methods into non-systematic approaches, such as random mutagenesis or long-term drug treatment, and systematic approaches, relying on the clustered regularly interspaced short palindromic repeats (CRISPR) system, saturated mutagenesis, or computational methods. All these new developments, especially genome-wide CRISPR-based screening platforms, have significantly accelerated the processes for identification of the mechanisms responsible for cancer drug resistance and opened up new avenues for future treatments.
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Affiliation(s)
| | | | - Bernard Haendler
- Research and Early Development Oncology, Pharmaceuticals, Bayer AG, Müllerstr. 178, 13353 Berlin, Germany; (P.J.D.-K.); (E.N.)
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3
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Spick M, Muazzam A, Pandha H, Michael A, Gethings LA, Hughes CJ, Munjoma N, Plumb RS, Wilson ID, Whetton AD, Townsend PA, Geifman N. Multi-omic diagnostics of prostate cancer in the presence of benign prostatic hyperplasia. Heliyon 2023; 9:e22604. [PMID: 38076065 PMCID: PMC10709398 DOI: 10.1016/j.heliyon.2023.e22604] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/01/2023] [Accepted: 11/15/2023] [Indexed: 09/11/2024] Open
Abstract
There is an unmet need for improved diagnostic testing and risk prediction for cases of prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum proteome and lipidome of 262 study participants by liquid chromatography-mass spectrometry, including participants diagnosed with PCa, benign prostatic hyperplasia (BPH), or otherwise healthy volunteers, with the aim of improving biomarker specificity. Although a two-class machine learning model separated PCa from controls with sensitivity of 0.82 and specificity of 0.95, adding BPH resulted in a statistically significant decline in specificity for prostate cancer to 0.76, with half of BPH cases being misclassified by the model as PCa. A small number of biomarkers differentiating between BPH and prostate cancer were identified, including proteins in MAP Kinase pathways, as well as in lipids containing oleic acid; these may offer a route to greater specificity. These results highlight, however, that whilst there are opportunities for machine learning, these will only be achieved by use of appropriate training sets that include confounding comorbidities, especially when calculating the specificity of a test.
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Affiliation(s)
- Matt Spick
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7YH, United Kingdom
| | - Ammara Muazzam
- The Hospital for Sick Children (SickKids), 555 University Ave, Toronto, ON M5G 1X8, Canada
- Division of Cancer Sciences, Manchester Cancer Research Center, Manchester Academic Health Sciences Center, University of Manchester, Manchester, M20 4GJ, United Kingdom
| | - Hardev Pandha
- School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom
| | - Agnieszka Michael
- School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom
| | - Lee A. Gethings
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7YH, United Kingdom
- Waters Corporation, Wilmslow, Cheshire, SK9 4AX, United Kingdom
- Manchester Institute of Biotechnology, Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, United Kingdom
| | | | | | - Robert S. Plumb
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, Burlington Danes Building, Du Cane Road, London, W12 0NN, United Kingdom
| | - Ian D. Wilson
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, Burlington Danes Building, Du Cane Road, London, W12 0NN, United Kingdom
| | - Anthony D. Whetton
- Veterinary Health Innovation Engine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7YH, United Kingdom
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7YH, United Kingdom
- Division of Cancer Sciences, Manchester Cancer Research Center, Manchester Academic Health Sciences Center, University of Manchester, Manchester, M20 4GJ, United Kingdom
| | - Paul A. Townsend
- School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom
- Division of Cancer Sciences, Manchester Cancer Research Center, Manchester Academic Health Sciences Center, University of Manchester, Manchester, M20 4GJ, United Kingdom
| | - Nophar Geifman
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, GU2 7YH, United Kingdom
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4
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Dudka I, Lundquist K, Wikström P, Bergh A, Gröbner G. Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes. J Transl Med 2023; 21:860. [PMID: 38012666 PMCID: PMC10683247 DOI: 10.1186/s12967-023-04747-7] [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: 05/22/2023] [Accepted: 11/21/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis. METHODS A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity). RESULTS Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B. CONCLUSIONS The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.
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Affiliation(s)
- Ilona Dudka
- Department of Chemistry, Umeå University, Umeå, Sweden
| | | | - Pernilla Wikström
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden.
| | - Anders Bergh
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
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5
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Zhan C, Tang T, Wu E, Zhang Y, He M, Wu R, Bi C, Wang J, Zhang Y, Shen B. From multi-omics approaches to personalized medicine in myocardial infarction. Front Cardiovasc Med 2023; 10:1250340. [PMID: 37965091 PMCID: PMC10642346 DOI: 10.3389/fcvm.2023.1250340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/17/2023] [Indexed: 11/16/2023] Open
Abstract
Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.
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Affiliation(s)
- Chaoying Zhan
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Tang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Erman Wu
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Yuxin Zhang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- KeyLaboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Mengqiao He
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Rongrong Wu
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Cheng Bi
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- KeyLaboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Jiao Wang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Yingbo Zhang
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
| | - Bairong Shen
- Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
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6
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Rocha SM, Santos FM, Socorro S, Passarinha LA, Maia CJ. Proteomic analysis of STEAP1 knockdown in human LNCaP prostate cancer cells. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2023; 1870:119522. [PMID: 37315586 DOI: 10.1016/j.bbamcr.2023.119522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 06/02/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023]
Abstract
Prostate cancer (PCa) continues to be one of the most common cancers in men worldwide. The six transmembrane epithelial antigen of the prostate 1 (STEAP1) protein is overexpressed in several types of human tumors, particularly in PCa. Our research group has demonstrated that STEAP1 overexpression is associated with PCa progression and aggressiveness. Therefore, understanding the cellular and molecular mechanisms triggered by STEAP1 overexpression will provide important insights to delineate new strategies for PCa treatment. In the present work, a proteomic strategy was used to characterize the intracellular signaling pathways and the molecular targets downstream of STEAP1 in PCa cells. A label-free approach was applied using an Orbitrap LC-MS/MS system to characterize the proteome of STEAP1-knockdown PCa cells. More than 6700 proteins were identified, of which a total of 526 proteins were found differentially expressed in scramble siRNA versus STEAP1 siRNA (234 proteins up-regulated and 292 proteins down-regulated). Bioinformatics analysis allowed us to explore the mechanism through which STEAP1 exerts influence on PCa, revealing that endocytosis, RNA transport, apoptosis, aminoacyl-tRNA biosynthesis, and metabolic pathways are the main biological processes where STEAP1 is involved. By immunoblotting, it was confirmed that STEAP1 silencing induced the up-regulation of cathepsin B, intersectin-1, and syntaxin 4, and the down-regulation of HRas, PIK3C2A, and DIS3. These findings suggested that blocking STEAP1 might be a suitable strategy to activate apoptosis and endocytosis, and diminish cellular metabolism and intercellular communication, leading to inhibition of PCa progression.
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Affiliation(s)
- Sandra M Rocha
- CICS-UBI-Health Sciences Research Center, Universidade da Beira Interior, 6201-506 Covilhã, Portugal
| | - Fátima M Santos
- CICS-UBI-Health Sciences Research Center, Universidade da Beira Interior, 6201-506 Covilhã, Portugal; Functional Proteomics Laboratory, Centro Nacional de Biotecnología (CNB-CSIC), Calle Darwin 3, Campus de Cantoblanco, 28029 Madrid, Spain
| | - Sílvia Socorro
- CICS-UBI-Health Sciences Research Center, Universidade da Beira Interior, 6201-506 Covilhã, Portugal
| | - Luís A Passarinha
- CICS-UBI-Health Sciences Research Center, Universidade da Beira Interior, 6201-506 Covilhã, Portugal; Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2819-516 Caparica, Portugal; UCIBIO-Applied Molecular Biosciences Unit, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2819-516 Caparica, Portugal; Laboratório de Fármaco-Toxicologia-UBIMedical, Universidade da Beira Interior, 6201-284 Covilhã, Portugal
| | - Cláudio J Maia
- CICS-UBI-Health Sciences Research Center, Universidade da Beira Interior, 6201-506 Covilhã, Portugal.
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7
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Zhu Z, Jiang L, Ding X. Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels. Cancers (Basel) 2023; 15:4164. [PMID: 37627192 PMCID: PMC10452610 DOI: 10.3390/cancers15164164] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/23/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.
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Affiliation(s)
- Zijian Zhu
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai 200030, China;
| | - Lai Jiang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200025, China;
| | - Xianting Ding
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai 200030, China;
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200025, China;
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8
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Mohammed MA, Lakhan A, Abdulkareem KH, Garcia-Zapirain B. A hybrid cancer prediction based on multi-omics data and reinforcement learning state action reward state action (SARSA). Comput Biol Med 2023; 154:106617. [PMID: 36753981 DOI: 10.1016/j.compbiomed.2023.106617] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/21/2023] [Accepted: 01/28/2023] [Indexed: 02/05/2023]
Abstract
These days, the ratio of cancer diseases among patients has been growing day by day. Recently, many cancer cases have been reported in different clinical hospitals. Many machine learning algorithms have been suggested in the literature to predict cancer diseases with the same class types based on trained and test data. However, there are many research rooms available for further research. In this paper, the studies look into the different types of cancer by analyzing, classifying, and processing the multi-omics dataset in a fog cloud network. Based on SARSA on-policy and multi-omics workload learning, made possible by reinforcement learning, the study made new hybrid cancer detection schemes. It consists of different layers, such as clinical data collection via laboratories and tool processes (biopsy, colonoscopy, and mammography) at the distributed omics-based clinics in the network. The study considers the different cancer classes such as carcinomas, sarcomas, leukemias, and lymphomas with their types in work and processes them using the multi-omics distributed clinics in work. In order to solve the problem, the study presents omics cancer workload reinforcement learning state action reward state action "SARSA" (OCWLS) schemes, which are made up of an on-policy learning scheme on different parameters like states, actions, timestamps, reward, accuracy, and processing time constraints. The goal is to process multiple cancer classes and workload feature matching while reducing the time it takes to process in clinical hospitals that are spread out. Simulation results show that OCWLS is better than other machine learning methods regarding+ processing time, extracting features from multiple classes of cancer, and matching in the system.
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Affiliation(s)
- Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq; eVIDA Lab, University of Deusto, 48007 Bilbao, Spain.
| | - Abdullah Lakhan
- Department of Computer Science, Dawood University of Engineering and Technology, Pakistan.
| | - Karrar Hameed Abdulkareem
- College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq; College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq.
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9
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Golubova A, Lanekoff I. Surface sampling capillary electrophoresis-mass spectrometry for a direct chemical characterization of tissue and blood samples. Electrophoresis 2023; 44:387-394. [PMID: 36330562 PMCID: PMC10107203 DOI: 10.1002/elps.202200183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022]
Abstract
Capillary electrophoresis (CE) is a powerful separation tool for non-targeted analysis of chemically complex samples, such as blood, urine, and tissue. However, traditionally CE requires samples in solution for analysis, which limits information on analyte distribution and heterogeneity in tissue. The recent development of surface sampling CE-mass spectrometry (SS-CE-MS) brings these advantages of CE to solid samples and enables chemical mapping directly from the tissue surface without laborious sample preparation. Here, we describe developments of SS-CE-MS to increase reproducibility and stability for metabolite, lipid, and protein extraction from tissue sections and dried blood spots. Additionally, we report the first electrokinetic sequential sample injection for high throughput analysis. We foresee that the wide molecular coverage from a distinct tissue region in combination with higher throughput will provide novel information on biological function and dysfunction.
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Affiliation(s)
| | - Ingela Lanekoff
- Department of Chemistry-BMC, Uppsala University, Uppsala, Sweden
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10
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Raith F, O’Donovan DH, Lemos C, Politz O, Haendler B. Addressing the Reciprocal Crosstalk between the AR and the PI3K/AKT/mTOR Signaling Pathways for Prostate Cancer Treatment. Int J Mol Sci 2023; 24:ijms24032289. [PMID: 36768610 PMCID: PMC9917236 DOI: 10.3390/ijms24032289] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/17/2023] [Accepted: 01/20/2023] [Indexed: 01/27/2023] Open
Abstract
The reduction in androgen synthesis and the blockade of the androgen receptor (AR) function by chemical castration and AR signaling inhibitors represent the main treatment lines for the initial stages of prostate cancer. Unfortunately, resistance mechanisms ultimately develop due to alterations in the AR pathway, such as gene amplification or mutations, and also the emergence of alternative pathways that render the tumor less or, more rarely, completely independent of androgen activation. An essential oncogenic axis activated in prostate cancer is the phosphatidylinositol-3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) pathway, as evidenced by the frequent alterations of the negative regulator phosphatase and tensin homolog (PTEN) and by the activating mutations in PI3K subunits. Additionally, crosstalk and reciprocal feedback loops between androgen signaling and the PI3K/AKT/mTOR signaling cascade that activate pro-survival signals and play an essential role in disease recurrence and progression have been evidenced. Inhibitors addressing different players of the PI3K/AKT/mTOR pathway have been evaluated in the clinic. Only a limited benefit has been reported in prostate cancer up to now due to the associated side effects, so novel combination approaches and biomarkers predictive of patient response are urgently needed. Here, we reviewed recent data on the crosstalk between AR signaling and the PI3K/AKT/mTOR pathway, the selective inhibitors identified, and the most advanced clinical studies, with a focus on combination treatments. A deeper understanding of the complex molecular mechanisms involved in disease progression and treatment resistance is essential to further guide therapeutic approaches with improved outcomes.
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Affiliation(s)
- Fabio Raith
- Research & Development, Pharmaceuticals, Bayer AG, Müllerstr. 178, 13353 Berlin, Germany
| | - Daniel H. O’Donovan
- Research & Development, Pharmaceuticals, Bayer AG, Müllerstr. 178, 13353 Berlin, Germany
| | - Clara Lemos
- Bayer Research and Innovation Center, Bayer US LLC, 238 Main Street, Cambridge, MA 02142, USA
| | - Oliver Politz
- Research & Development, Pharmaceuticals, Bayer AG, Müllerstr. 178, 13353 Berlin, Germany
| | - Bernard Haendler
- Research & Development, Pharmaceuticals, Bayer AG, Müllerstr. 178, 13353 Berlin, Germany
- Correspondence: ; Tel.: +49-30-2215-41198
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11
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Comparative Proteomic and Transcriptomic Analysis of the Impact of Androgen Stimulation and Darolutamide Inhibition. Cancers (Basel) 2022; 15:cancers15010002. [PMID: 36611998 PMCID: PMC9817687 DOI: 10.3390/cancers15010002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 11/22/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Several inhibitors of androgen receptor (AR) function are approved for prostate cancer treatment, and their impact on gene transcription has been described. However, the ensuing effects at the protein level are far less well understood. We focused on the AR signaling inhibitor darolutamide and confirmed its strong AR binding and antagonistic activity using the high throughput cellular thermal shift assay (CETSA HT). Then, we generated comprehensive, quantitative proteomic data from the androgen-sensitive prostate cancer cell line VCaP and compared them to transcriptomic data. Following treatment with the synthetic androgen R1881 and darolutamide, global mass spectrometry-based proteomics and label-free quantification were performed. We found a generally good agreement between proteomic and transcriptomic data upon androgen stimulation and darolutamide inhibition. Similar effects were found both for the detected expressed genes and their protein products as well as for the corresponding biological programs. However, in a few instances there was a discrepancy in the magnitude of changes induced on gene expression levels compared to the corresponding protein levels, indicating post-transcriptional regulation of protein abundance. Chromatin immunoprecipitation DNA sequencing (ChIP-seq) and Hi-C chromatin immunoprecipitation (HiChIP) revealed the presence of androgen-activated AR-binding regions and long-distance AR-mediated loops at these genes.
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