1
|
Torland LA, Lai X, Kumar S, Riis MH, Geisler J, Lüders T, Tekpli X, Kristensen V, Sahlberg K, Tahiri A. Benign breast tumors may arise on different immunological backgrounds. Mol Oncol 2024. [PMID: 38757377 DOI: 10.1002/1878-0261.13655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/21/2023] [Accepted: 04/05/2024] [Indexed: 05/18/2024] Open
Abstract
Benign breast tumors are a nonthreatening condition defined as abnormal cell growth within the breast without the ability to invade nearby tissue. However, benign lesions hold valuable biological information that can lead us toward better understanding of tumor biology. In this study, we have used two pathway analysis algorithms, Pathifier and gene set variation analysis (GSVA), to identify biological differences between normal breast tissue, benign tumors and malignant tumors in our clinical dataset. Our results revealed that one-third of all pathways that were significantly different between benign and malignant tumors were immune-related pathways, and 227 of them were validated by both methods and in the METABRIC dataset. Furthermore, five of these pathways (all including genes involved in cytokine and interferon signaling) were related to overall survival in cancer patients in both datasets. The cellular moieties that contribute to immune differences in malignant and benign tumors were analyzed using the deconvolution tool, CIBERSORT. The results showed that levels of some immune cells were specifically higher in benign than in malignant tumors, and this was especially the case for resting dendritic cells and follicular T-helper cells. Understanding the distinct immune profiles of benign and malignant breast tumors may aid in developing noninvasive diagnostic methods to differentiate between them in the future.
Collapse
Affiliation(s)
- Lilly Anne Torland
- Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway
- Department of Research and Innovation, Vestre Viken HF, Drammen Hospital, Norway
| | - Xiaoran Lai
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Norway
| | - Surendra Kumar
- Department of Ocean Sciences, Memorial University of Newfoundland, St. John's, Canada
| | - Margit H Riis
- Department of Breast and Endocrine Surgery, Clinic of Cancer, Oslo University Hospital, Norway
| | - Jürgen Geisler
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway
- Department of Oncology, Akershus University Hospital, Lørenskog, Norway
| | - Torben Lüders
- Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway
| | - Xavier Tekpli
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Vessela Kristensen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Kristine Sahlberg
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway
- Department of Research and Innovation, Vestre Viken HF, Drammen Hospital, Norway
| | - Andliena Tahiri
- Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital, Lørenskog, Norway
- Department of Research and Innovation, Vestre Viken HF, Drammen Hospital, Norway
| |
Collapse
|
2
|
Huang F, Welner RS, Chen JY, Yue Z. PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis. FRONTIERS IN BIOINFORMATICS 2024; 4:1336135. [PMID: 38690527 PMCID: PMC11058213 DOI: 10.3389/fbinf.2024.1336135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/01/2024] [Indexed: 05/02/2024] Open
Abstract
Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA). Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells. Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.
Collapse
Affiliation(s)
- Fengyuan Huang
- Department of Biomedical Informatics and Data Science, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Robert S. Welner
- Hematology & Oncology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jake Y. Chen
- Department of Biomedical Informatics and Data Science, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Zongliang Yue
- Health Outcome Research and Policy Department, Harrison College of Pharmacy, Auburn University, Auburn, AL, United States
| |
Collapse
|
3
|
Weng Z, Yue Z, Zhu Y, Chen JY. DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features. Bioinformatics 2022; 38:i359-i368. [PMID: 35758816 PMCID: PMC9235497 DOI: 10.1093/bioinformatics/btac261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
SUMMARY In biology, graph layout algorithms can reveal comprehensive biological contexts by visually positioning graph nodes in their relevant neighborhoods. A layout software algorithm/engine commonly takes a set of nodes and edges and produces layout coordinates of nodes according to edge constraints. However, current layout engines normally do not consider node, edge or node-set properties during layout and only curate these properties after the layout is created. Here, we propose a new layout algorithm, distance-bounded energy-field minimization algorithm (DEMA), to natively consider various biological factors, i.e., the strength of gene-to-gene association, the gene's relative contribution weight and the functional groups of genes, to enhance the interpretation of complex network graphs. In DEMA, we introduce a parameterized energy model where nodes are repelled by the network topology and attracted by a few biological factors, i.e., interaction coefficient, effect coefficient and fold change of gene expression. We generalize these factors as gene weights, protein-protein interaction weights, gene-to-gene correlations and the gene set annotations-four parameterized functional properties used in DEMA. Moreover, DEMA considers further attraction/repulsion/grouping coefficient to enable different preferences in generating network views. Applying DEMA, we performed two case studies using genetic data in autism spectrum disorder and Alzheimer's disease, respectively, for gene candidate discovery. Furthermore, we implement our algorithm as a plugin to Cytoscape, an open-source software platform for visualizing networks; hence, it is convenient. Our software and demo can be freely accessed at http://discovery.informatics.uab.edu/dema. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Zhenyu Weng
- Communication and Information Security Lab, Institute of Big Data Technologies, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Zongliang Yue
- Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Yuesheng Zhu
- Communication and Information Security Lab, Institute of Big Data Technologies, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Jake Yue Chen
- Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| |
Collapse
|
4
|
Regan JL, Schumacher D, Staudte S, Steffen A, Lesche R, Toedling J, Jourdan T, Haybaeck J, Golob-Schwarzl N, Mumberg D, Henderson D, Győrffy B, Regenbrecht CR, Keilholz U, Schäfer R, Lange M. Identification of a Neural Development Gene Expression Signature in Colon Cancer Stem Cells Reveals a Role for EGR2 in Tumorigenesis. iScience 2022; 25:104498. [PMID: 35720265 PMCID: PMC9204726 DOI: 10.1016/j.isci.2022.104498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 02/28/2022] [Accepted: 05/26/2022] [Indexed: 11/12/2022] Open
Abstract
Recent evidence demonstrates that colon cancer stem cells (CSCs) can generate neurons that synapse with tumor innervating fibers required for tumorigenesis and disease progression. Greater understanding of the mechanisms that regulate CSC driven tumor neurogenesis may therefore lead to more effective treatments. RNA-sequencing analyses of ALDHPositive CSCs from colon cancer patient-derived organoids (PDOs) and xenografts (PDXs) showed CSCs to be enriched for neural development genes. Functional analyses of genes differentially expressed in CSCs from PDO and PDX models demonstrated the neural crest stem cell (NCSC) regulator EGR2 to be required for tumor growth and to control expression of homebox superfamily embryonic master transcriptional regulator HOX genes and the neural stem cell and master cell fate regulator SOX2. These data support CSCs as the source of tumor neurogenesis and suggest that targeting EGR2 may provide a therapeutic differentiation strategy to eliminate CSCs and block nervous system driven disease progression. Colon cancer stem cells (CSCs) are enriched for nervous system development genes Colon cancer cells express nerve cell markers EGR2 is required for CSC survival and tumor growth and regulates SOX2 and HOX genes Targeting EGR2 may block cancer neurogenesis and stop disease progression
Collapse
|
5
|
Yue Z, Slominski R, Bharti S, Chen JY. PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics. Front Genet 2022; 13:820361. [PMID: 35495152 PMCID: PMC9039620 DOI: 10.3389/fgene.2022.820361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/17/2022] [Indexed: 12/30/2022] Open
Abstract
Functional genomics studies have helped researchers annotate differentially expressed gene lists, extract gene expression signatures, and identify biological pathways from omics profiling experiments conducted on biological samples. The current geneset, network, and pathway analysis (GNPA) web servers, e.g., DAVID, EnrichR, WebGestaltR, or PAGER, do not allow automated integrative functional genomic downstream analysis. In this study, we developed a new web-based interactive application, “PAGER Web APP”, which supports online R scripting of integrative GNPA. In a case study of melanoma drug resistance, we showed that the new PAGER Web APP enabled us to discover highly relevant pathways and network modules, leading to novel biological insights. We also compared PAGER Web APP’s pathway analysis results retrieved among PAGER, EnrichR, and WebGestaltR to show its advantages in integrative GNPA. The interactive online web APP is publicly accessible from the link, https://aimed-lab.shinyapps.io/PAGERwebapp/.
Collapse
Affiliation(s)
- Zongliang Yue
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Radomir Slominski
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
- Graduate Biomedical Sciences Program, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Samuel Bharti
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jake Y. Chen
- Informatics Institute in the School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
- *Correspondence: Jake Y. Chen,
| |
Collapse
|
6
|
Liu X, Su L, Li J, Ou G. Molecular Subclassification Based on Crosstalk Analysis Improves Prediction of Prognosis in Colorectal Cancer. Front Genet 2021; 12:689676. [PMID: 34804112 PMCID: PMC8600263 DOI: 10.3389/fgene.2021.689676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 09/23/2021] [Indexed: 12/09/2022] Open
Abstract
The poor performance of single-gene lists for prognostic predictions in independent cohorts has limited their clinical use. Here, we employed a pathway-based approach using embedded biological features to identify reproducible prognostic markers as an alternative. We used pathway activity score, sure independence screening, and K-means clustering analyses to identify and cluster colorectal cancer patients into two distinct subgroups, G2 (aggressive) and G1 (moderate). The differences between these two groups with respect to survival, somatic mutation, pathway activity, and tumor-infiltration by immunocytes were compared. These comparisons revealed that the survival rates in the G2 subgroup were significantly reduced compared to that in the G1 subgroup; further, the mutational burden rates in several oncogenes, including KRAS, DCLK1, and EPHA5, were significantly higher in the G2 subgroup than in the G1 subgroup. The enhanced activity of the critical pathways such as MYC and epithelial-mesenchymal transition may also lead to the progression of colorectal cancer. Taken together, we established a novel prognostic classification system that offers meritorious insights into the hallmarks of colorectal cancer.
Collapse
Affiliation(s)
- Xiaohua Liu
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Lili Su
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Jingcong Li
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Guoping Ou
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China
| |
Collapse
|
7
|
Pathway-Based Personalized Analysis of Pan-Cancer Transcriptomic Data. Biomedicines 2021; 9:biomedicines9111502. [PMID: 34829731 PMCID: PMC8615289 DOI: 10.3390/biomedicines9111502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/04/2021] [Accepted: 10/17/2021] [Indexed: 11/17/2022] Open
Abstract
The occurrence of cancer is closely related to the deregulation of certain pathways. Based on pathway deregulation scores (PDS) inferred by the Pathifier algorithm, we analyzed transcriptomic data of 13 different cancer types in The Cancer Genome Atlas database to identify cancer-specific deregulated pathways and prognostic pathways. The results showed that the individual-specific pathway deregulation scores can clearly distinguish different cancer types and their tumor-adjacent tissues. In addition, the cancer-specific deregulated pathways and prognostic pathways of different cancer types had high heterogeneity, and the identified cancer prognostic pathways have been reported to be closely related to the corresponding cancers. Furthermore, we also found that cancers with more deregulation pathways tend to be malignant and have worse prognoses. Finally, a Cox proportional Hazards model was constructed based on the prognostic pathways; this model successfully predicted survival and prognosis based on data from cancer samples. In addition, the performance of the breast cancer prognostic model was validated with an independent data set in the METABRIC database. Therefore, the prognostic pathways we identified have the potential to become targets for the treatment of cancer.
Collapse
|
8
|
Su K, Yu Q, Shen R, Sun SY, Moreno CS, Li X, Qin ZS. Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis. CELL REPORTS METHODS 2021; 1:100050. [PMID: 34671755 PMCID: PMC8525796 DOI: 10.1016/j.crmeth.2021.100050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/07/2021] [Accepted: 06/16/2021] [Indexed: 02/08/2023]
Abstract
Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in individual tumor samples, the disruption of transcription homeostasis in key pathways or gene sets plays an important role in tumorigenesis and has profound implications for the patient's clinical outcome. We devised a computational method named iPath to identify, at the individual-sample level, which pathways or gene sets significantly deviate from their norms. We conducted a pan-cancer analysis and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications.
Collapse
Affiliation(s)
- Kenong Su
- Department of Computer Science, Emory University, Atlanta, GA 30322, USA
| | - Qi Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Ronglai Shen
- Department of Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10017, USA
| | - Shi-Yong Sun
- Department of Hematology & Medical Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Carlos S. Moreno
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Xiaoxian Li
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Zhaohui S. Qin
- Department of Computer Science, Emory University, Atlanta, GA 30322, USA
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30322, USA
| |
Collapse
|
9
|
Rintala TJ, Federico A, Latonen L, Greco D, Fortino V. A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery. Brief Bioinform 2021; 22:6350885. [PMID: 34396389 PMCID: PMC8575038 DOI: 10.1093/bib/bbab314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/05/2021] [Accepted: 07/20/2021] [Indexed: 12/14/2022] Open
Abstract
Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers.
Collapse
Affiliation(s)
- Teemu J Rintala
- Institute of Biomedicine University of Eastern Finland, Yliopistonranta 1 E, 70210 Kuopio, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology Tampere University, Kalevantie, 4 33100 Tampere, Finland.,BioMediTech Institute Tampere University, Kalevantie 4, 33100 Tampere, Finland
| | - Leena Latonen
- Institute of Biomedicine University of Eastern Finland, Yliopistonranta 1 E, 70210 Kuopio, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology Tampere University, Kalevantie, 4 33100 Tampere, Finland.,BioMediTech Institute Tampere University, Kalevantie 4, 33100 Tampere, Finland.,Institute of Biotechnology University of Helsinki, Viikinkaari 5d, 00014 Helsinki, Finland
| | - Vittorio Fortino
- Institute of Biomedicine University of Eastern Finland, Yliopistonranta 1 E, 70210 Kuopio, Finland
| |
Collapse
|
10
|
Park KS, Kim SH, Oh JH, Kim SY. Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning. Brief Bioinform 2021; 22:bbaa336. [PMID: 33341874 PMCID: PMC8599295 DOI: 10.1093/bib/bbaa336] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 01/27/2023] Open
Abstract
Thyroid nodules are neoplasms commonly found among adults, with papillary thyroid carcinoma (PTC) being the most prevalent malignancy. However, current diagnostic methods often subject patients to unnecessary surgical burden. In this study, we developed and validated an automated, highly accurate multi-study-derived diagnostic model for PTCs using personalized biological pathways coupled with a sophisticated machine learning algorithm. Surprisingly, the algorithm achieved near-perfect performance in discriminating PTCs from non-tumoral thyroid samples with an overall cross-study-validated area under the receiver operating characteristic curve (AUROC) of 0.999 (95% confidence interval [CI]: 0.995-1) and a Brier score of 0.013 on three independent development cohorts. In addition, the algorithm showed excellent generalizability and transferability on two large-scale external blind PTC cohorts consisting of The Cancer Genome Atlas (TCGA), which is the largest genomic PTC cohort studied to date, and the post-Chernobyl cohort, which includes PTCs reported after exposure to radiation from the Chernobyl accident. When applied to the TCGA cohort, the model yielded an AUROC of 0.969 (95% CI: 0.950-0.987) and a Brier score of 0.109. On the post-Chernobyl cohort, it yielded an AUROC of 0.962 (95% CI: 0.918-1) and a Brier score of 0.073. This algorithm also is robust against other various types of clinical scenarios, discriminating malignant from benign lesions as well as clinically aggressive thyroid cancer with poor prognosis from indolent ones. Furthermore, we discovered novel pathway alterations and prognostic signatures for PTC, which can provide directions for follow-up studies.
Collapse
Affiliation(s)
| | | | - Jung Hun Oh
- Department of Medical Physics at Memorial Sloan Kettering Cancer Center, USA
| | | |
Collapse
|
11
|
Molecular analysis of encapsulated papillary carcinoma of the breast with and without invasion. Hum Pathol 2021; 111:67-74. [DOI: 10.1016/j.humpath.2021.02.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 12/14/2022]
|
12
|
Hernández-Lemus E, Martínez-García M. Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics. Front Oncol 2021; 10:605680. [PMID: 33520715 PMCID: PMC7841291 DOI: 10.3389/fonc.2020.605680] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 11/24/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors-let us call that the silver bullet approach to cancer therapeutics-to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns-we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases "shrapnel gunshots" may become more effective than "silver bullets". Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.
Collapse
Affiliation(s)
- Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Mireya Martínez-García
- Sociomedical Research Unit, National Institute of Cardiology “Ignacio Chávez”, Mexico City, Mexico
| |
Collapse
|
13
|
Benor G, Fuks G, Chin S, Rueda OM, Mukherjee S, Arandkar S, Aylon Y, Caldas C, Domany E, Oren M. Transcriptional profiling reveals a subset of human breast tumors that retain wt TP53 but display mutant p53-associated features. Mol Oncol 2020; 14:1640-1652. [PMID: 32484602 PMCID: PMC7400784 DOI: 10.1002/1878-0261.12736] [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: 04/01/2020] [Revised: 05/12/2020] [Accepted: 05/28/2020] [Indexed: 11/25/2022] Open
Abstract
TP53 gene mutations are very common in human cancer. While such mutations abrogate the tumor suppressive activities of the wild-type (wt) p53 protein, some of them also endow the mutant (mut) protein with oncogenic gain of function (GOF), facilitating cancer progression. Yet, p53 may acquire altered functionality even without being mutated; in particular, experiments with cultured cells revealed that wtp53 can be rewired to adopt mut-like features in response to growth factors or cancer-mimicking genetic manipulations. To assess whether such rewiring also occurs in human tumors, we interrogated gene expression profiles and pathway deregulation patterns in the METABRIC breast cancer (BC) dataset as a function of TP53 gene mutation status. Harnessing the power of machine learning, we optimized a gene expression classifier for ER+Her2- patients that distinguishes tumors carrying TP53 mutations from those retaining wt TP53. Interestingly, a small subset of wt TP53 tumors displayed gene expression and pathway deregulation patterns markedly similar to those of TP53-mutated tumors. Moreover, similar to TP53-mutated tumors, these 'pseudomutant' cases displayed a signature for enhanced proliferation and had worse prognosis than typical wtp53 tumors. Notably, these tumors revealed upregulation of genes which, in BC cell lines, were reported to be positively regulated by p53 GOF mutants. Thus, such tumors may benefit from mut p53-associated activities without having to accrue TP53 mutations.
Collapse
Affiliation(s)
- Gal Benor
- Department of Physics of Complex SystemsThe Weizmann Institute of ScienceRehovotIsrael
| | - Garold Fuks
- Department of Physics of Complex SystemsThe Weizmann Institute of ScienceRehovotIsrael
| | - Suet‐Feung Chin
- Cancer Research UK Cambridge Institute and Department of OncologyLi Ka Shing CentreUniversity of CambridgeCambridgeUK
| | - Oscar M. Rueda
- Cancer Research UK Cambridge Institute and Department of OncologyLi Ka Shing CentreUniversity of CambridgeCambridgeUK
| | - Saptaparna Mukherjee
- Department of Molecular Cell BiologyThe Weizmann Institute of ScienceRehovotIsrael
| | - Sharathchandra Arandkar
- Department of Molecular Cell BiologyThe Weizmann Institute of ScienceRehovotIsrael
- Advanced Centre for Treatment, Research and Education in Cancer (ACTREC)Tata Memorial CentreKhargharIndia
| | - Yael Aylon
- Department of Molecular Cell BiologyThe Weizmann Institute of ScienceRehovotIsrael
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute and Department of OncologyLi Ka Shing CentreUniversity of CambridgeCambridgeUK
| | - Eytan Domany
- Department of Physics of Complex SystemsThe Weizmann Institute of ScienceRehovotIsrael
| | - Moshe Oren
- Department of Molecular Cell BiologyThe Weizmann Institute of ScienceRehovotIsrael
| |
Collapse
|
14
|
Saux M, Ponnaiah M, Langlade N, Zanchetta C, Balliau T, El-Maarouf-Bouteau H, Bailly C. A multiscale approach reveals regulatory players of water stress responses in seeds during germination. PLANT, CELL & ENVIRONMENT 2020; 43:1300-1313. [PMID: 31994739 DOI: 10.1111/pce.13731] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 01/23/2020] [Accepted: 01/23/2020] [Indexed: 06/10/2023]
Abstract
Seed germination is regulated by environmental factors, particularly water availability. Water deficits at the time of sowing impair the establishment of crop plants. Transcriptome and proteome profiling was used to document the responses of sunflower (Helianthus annuus) seeds to moderate water stress during germination in two hybrids that are nominally classed as drought sensitive and drought tolerant. Differences in the water stress-dependent accumulation reactive oxygen species and antioxidant enzymes activities were observed between the hybrids. A pathway-based analysis of the hybrid transcriptomes demonstrated that the water stress-dependent responses of seed metabolism were similar to those of the plant, with a decreased abundance of transcripts encoding proteins associated with metabolism and cell expansion. Moreover, germination under water stress conditions was associated with increased levels of transcripts encoding heat shock proteins. Exposure of germinating seeds to water stress specifically affected the abundance of a small number of proteins, including heat shock proteins. Taken together, these data not only identify factors that are likely to play a key role in drought tolerance during seed germination, but they also demonstrate the importance of the female parent in the transmission of water stress tolerance.
Collapse
Affiliation(s)
- Marine Saux
- CNRS, Laboratoire de Biologie du Développement, Sorbonne Université, Paris, France
| | - Maharajah Ponnaiah
- CNRS, Laboratoire de Biologie du Développement, Sorbonne Université, Paris, France
| | | | | | - Thierry Balliau
- PAPPSO, GQE-Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | | | - Christophe Bailly
- CNRS, Laboratoire de Biologie du Développement, Sorbonne Université, Paris, France
| |
Collapse
|
15
|
Nygård S, Lingjærde OC, Caldas C, Hovig E, Børresen-Dale AL, Helland Å, Haakensen VD. PathTracer: High-sensitivity detection of differential pathway activity in tumours. Sci Rep 2019; 9:16332. [PMID: 31704995 PMCID: PMC6841931 DOI: 10.1038/s41598-019-52529-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 10/14/2019] [Indexed: 01/23/2023] Open
Abstract
Gene expression profiling of tumours is an important source of information for cancer patient stratification. Detecting subtle alterations of gene expression remains a challenge, however. Here, we propose a novel tool for high-sensitivity detection of differential pathway activity in tumours. For a pathway defined by a collection of genes, the samples are projected onto a low-dimensional manifold in the subspace spanned by those genes. For each sample, a score is next found by calculating the distance between each projected sample and the projection of a subgroup of reference samples. Depending on the aim of the analysis and the available data, the reference samples may represent e.g. normal tissue or tumour samples with a particular genotype or phenotype. The proposed tool, PathTracer, is demonstrated on gene expression data from 1952 invasive breast cancer samples, 10 DCIS, 9 benign samples and 144 tumour adjacent normal breast tissue samples. PathTracer scores are shown to predict survival, clinical subtypes, cellular proliferation and genomic instability. Furthermore, predictions are shown to outperform those obtained with other comparable methods.
Collapse
Affiliation(s)
- Ståle Nygård
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
- Bioinformatics core facility, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Ole Christian Lingjærde
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Carlos Caldas
- Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
| | - Eivind Hovig
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Åslaug Helland
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Department of Oncology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Vilde D Haakensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.
- Department of Oncology, Oslo University Hospital, Oslo, Norway.
| |
Collapse
|
16
|
Ponnaiah M, Gilard F, Gakière B, El-Maarouf-Bouteau H, Bailly C. Regulatory actors and alternative routes for Arabidopsis seed germination are revealed using a pathway-based analysis of transcriptomic datasets. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 99:163-175. [PMID: 30868664 DOI: 10.1111/tpj.14311] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 02/07/2019] [Accepted: 03/05/2019] [Indexed: 06/09/2023]
Abstract
Regulation of seed germination by dormancy relies on a complex network of transcriptional and post-transcriptional modifications during seed imbibition that controls seed adaptive responses to environmental cues. High-throughput technologies have brought significant progress in the understanding of this phenomenon and have led to identify major regulators of seed germination, mostly by studying the behaviour of highly differentially expressed genes. However, the actual models of transcriptome analysis cannot catch additive effects of small variations of gene expression in individual signalling or metabolic pathways, which are also likely to control germination. Therefore, the comprehension of the molecular mechanism regulating germination is still incomplete and to gain knowledge about this process we have developed a pathway-based analysis of transcriptomic Arabidopsis datasets, to identify regulatory actors of seed germination. The method allowed quantifying the level of deregulation of a wide range of pathways in dormant versus non-dormant seeds. Clustering pathway deregulation scores of germinating and dormant seed samples permitted the identification of mechanisms involved in seed germination such as RNA transport or vitamin B6 metabolism, for example. Using this method, which was validated by metabolomics analysis, we also demonstrated that Col and Cvi seeds follow different metabolic routes for completing germination, demonstrating the genetic plasticity of this process. We finally provided an extensive basis of analysed transcriptomic datasets that will allow further identification of mechanisms controlling seed germination.
Collapse
Affiliation(s)
- Maharajah Ponnaiah
- Laboratoire de Biologie du Développement, Sorbonne Université, CNRS, F-75005, Paris, France
| | - Françoise Gilard
- Institute of Plant Sciences Paris-Saclay (IPS2), UMR 9213/UMR1403, CNRS, INRA, Université d'Evry, Université Paris-Diderot, Université Paris-Sud, Sorbonne Paris-Cité, Saclay Plant Sciences, Orsay, France
| | - Bertrand Gakière
- Institute of Plant Sciences Paris-Saclay (IPS2), UMR 9213/UMR1403, CNRS, INRA, Université d'Evry, Université Paris-Diderot, Université Paris-Sud, Sorbonne Paris-Cité, Saclay Plant Sciences, Orsay, France
| | | | - Christophe Bailly
- Laboratoire de Biologie du Développement, Sorbonne Université, CNRS, F-75005, Paris, France
| |
Collapse
|
17
|
Vitali F, Li Q, Schissler AG, Berghout J, Kenost C, Lussier YA. Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes. Brief Bioinform 2019; 20:789-805. [PMID: 29272327 PMCID: PMC6585155 DOI: 10.1093/bib/bbx149] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 10/06/2017] [Indexed: 12/13/2022] Open
Abstract
The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.
Collapse
Affiliation(s)
| | - Qike Li
- BIO5 Institute, University of Arizona, Tucson, AZ, USA
| | | | | | | | | |
Collapse
|
18
|
Fa B, Luo C, Tang Z, Yan Y, Zhang Y, Yu Z. Pathway-based biomarker identification with crosstalk analysis for robust prognosis prediction in hepatocellular carcinoma. EBioMedicine 2019; 44:250-260. [PMID: 31101593 PMCID: PMC6606892 DOI: 10.1016/j.ebiom.2019.05.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 05/06/2019] [Accepted: 05/06/2019] [Indexed: 02/07/2023] Open
Affiliation(s)
- Botao Fa
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; SJTU-Yale Joint Centre for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Chengwen Luo
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; SJTU-Yale Joint Centre for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Zhou Tang
- SJTU-Yale Joint Centre for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuting Yan
- SJTU-Yale Joint Centre for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; SJTU-Yale Joint Centre for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; SJTU-Yale Joint Centre for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.
| |
Collapse
|
19
|
Abstract
Reliable identification of molecular biomarkers is essential for accurate patient stratification. While state-of-the-art machine learning approaches for sample classification continue to push boundaries in terms of performance, most of these methods are not able to integrate different data types and lack generalization power, limiting their application in a clinical setting. Furthermore, many methods behave as black boxes, and we have very little understanding about the mechanisms that lead to the prediction. While opaqueness concerning machine behavior might not be a problem in deterministic domains, in health care, providing explanations about the molecular factors and phenotypes that are driving the classification is crucial to build trust in the performance of the predictive system. We propose Pathway-Induced Multiple Kernel Learning (PIMKL), a methodology to reliably classify samples that can also help gain insights into the molecular mechanisms that underlie the classification. PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an approach that has demonstrated excellent performance in different machine learning applications. After optimizing the combination of kernels to predict a specific phenotype, the model provides a stable molecular signature that can be interpreted in the light of the ingested prior knowledge and that can be used in transfer learning tasks. The reliable classification of biomedical samples to predict phenotypic differences requires not only robust methods, but also interpretable approaches that can explain the reasons behind a prediction. A team led by María Rodríguez Martínez at IBM Research - Zürich has developed PIMKL, a methodology that exploits prior knowledge and enables the integration of multiple types of data with varying predictive power. Even when noisy datasets are simultaneously analyzed, PIMKL is able to discard uninformative data and achieve strong prediction power. Importantly, PIMKL produces a molecular signature that enables the interpretation of the results in terms of known biological functions. This signature can be transferred to other cohorts without loss of performance, demonstrating surprising robustness across cohorts. Interpretable algorithms can effectively help gain insights about disease mechanisms and build trust in a model.
Collapse
|
20
|
Ozturk K, Dow M, Carlin DE, Bejar R, Carter H. The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine. J Mol Biol 2018; 430:2875-2899. [PMID: 29908887 PMCID: PMC6097914 DOI: 10.1016/j.jmb.2018.06.016] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/30/2018] [Accepted: 06/06/2018] [Indexed: 12/19/2022]
Abstract
Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.
Collapse
Affiliation(s)
- Kivilcim Ozturk
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Michelle Dow
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Daniel E Carlin
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Rafael Bejar
- Moores Cancer Center, Division of Hematology and Oncology, University of California San Diego, La Jolla, CA 92093, USA
| | - Hannah Carter
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center and Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA; CIFAR, MaRS Centre, West Tower, 661 University Ave., Suite 505, Toronto, ON M5G 1M1, Canada.
| |
Collapse
|
21
|
Mejía-Pedroza RA, Espinal-Enríquez J, Hernández-Lemus E. Pathway-Based Drug Repositioning for Breast Cancer Molecular Subtypes. Front Pharmacol 2018; 9:905. [PMID: 30158869 PMCID: PMC6104565 DOI: 10.3389/fphar.2018.00905] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 07/24/2018] [Indexed: 12/20/2022] Open
Abstract
Breast cancer is a major public health problem which treatment needs new pharmacological options. In the last decades, during the postgenomic era new theoretical and technological tools that give us novel and promising ways to address these problems have emerged. In this work, we integrate several tools that exploit disease-specific experimental transcriptomic results in addition to information from biological and pharmacological data bases obtaining a contextual prioritization of pathways and drugs in breast cancer subtypes. The usefulness of these results should be evaluated in terms of drug repurposing in each breast cancer molecular subtype therapy. In favor of breast cancer patients, this methodology could be further developed to provide personalized treatment schemes. The latter are particularly needed in those breast cancer subtypes with limited therapeutic options or those who have developed resistance to the current pharmacological schemes.
Collapse
Affiliation(s)
- Raúl A Mejía-Pedroza
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Jesús Espinal-Enríquez
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.,Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
| |
Collapse
|
22
|
Zhang CL, Xu YJ, Yang HX, Xu YQ, Shang DS, Wu T, Zhang YP, Li X. sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses. Sci Rep 2017; 7:15322. [PMID: 29127397 PMCID: PMC5681640 DOI: 10.1038/s41598-017-15631-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 10/30/2017] [Indexed: 02/07/2023] Open
Abstract
MicroRNAs (miRNAs) regulate biological pathways by inhibiting gene expression. However, most current analytical methods fail to consider miRNAs, when inferring functional or pathway activities. In this study, we developed a model called sPAGM to infer subpathway activities by integrating gene and miRNA expressions. In this model, we reconstructed subpathway graphs by embedding miRNA components, and characterized subpathway activity (sPA) scores by simultaneously considering the expression levels of miRNAs and genes. The results showed that the sPA scores could distinguish different samples across tumor types, as well as samples between tumor and normal conditions. Moreover, the sPAGM model displayed more specificities than the entire pathway-based analyses. This model was applied to melanoma tumors to perform a prognosis analysis, which identified a robust 55-subpathway signature. By using The Cancer Genome Atlas and independently verified data sets, the subpathway-based signature significantly predicted the patients’ prognoses, which were independent of clinical variables. In the prognostic performance comparison, the sPAGM model was superior to the gene-only and miRNA-only methods. Finally, we dissected the functional roles and interactions of components within the subpathway signature. Taken together, the sPAGM model provided a framework for inferring subpathway activities and identifying functional signatures for clinical applications.
Collapse
Affiliation(s)
- Chun-Long Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yan-Jun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Hai-Xiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Ying-Qi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - De-Si Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Tan Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yun-Peng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| |
Collapse
|
23
|
Zhang Q, Li J, Wang D, Wang Y. Finding disagreement pathway signatures and constructing an ensemble model for cancer classification. Sci Rep 2017; 7:10044. [PMID: 28855608 PMCID: PMC5577098 DOI: 10.1038/s41598-017-10258-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 08/07/2017] [Indexed: 12/02/2022] Open
Abstract
Cancer classification based on molecular level is a relatively routine research procedure with advances in high-throughput molecular profiling techniques. However, the number of genes typically far exceeds the number of the sample size in gene expression studies. The existing gene selection methods are almost based on statistics and machine learning, overlooking relevant biological principles or knowledge while working with biological data. Here, we propose a robust ensemble learning paradigm, which incorporates multiple pathways information, to predict cancer classification. We compare the proposed method with other methods, such as Elastic SCAD and PPDMF, and estimate the classification performance. The results show that the proposed method has the higher performances on most metrics and robust performance. We further investigate the biological mechanism of the ensemble feature genes. The results demonstrate that the ensemble feature genes are associated with drug targets/clinically-relevant cancer. In addition, some core biological pathways and biological process underlying clinically-relevant phenotypes are identified by function annotation. Overall, our research can provide a new perspective for the further study of molecular activities and manifestations of cancer.
Collapse
Affiliation(s)
- Qiaosheng Zhang
- Harbin Institute of Technology, School of Computer Science and Technology, Harbin, 150001, P.R. China.,Heilongjiang Bayi Agricultural University, College of Science, Daqing, 163319, P.R. China
| | - Jie Li
- Harbin Institute of Technology, School of Computer Science and Technology, Harbin, 150001, P.R. China.
| | - Dong Wang
- Harbin Institute of Technology, School of Computer Science and Technology, Harbin, 150001, P.R. China
| | - Yadong Wang
- Harbin Institute of Technology, School of Computer Science and Technology, Harbin, 150001, P.R. China
| |
Collapse
|
24
|
Cui DN, Wang X, Chen JQ, Lv B, Zhang P, Zhang W, Zhang ZJ, Xu FG. Quantitative Evaluation of the Compatibility Effects of Huangqin Decoction on the Treatment of Irinotecan-Induced Gastrointestinal Toxicity Using Untargeted Metabolomics. Front Pharmacol 2017; 8:211. [PMID: 28484391 PMCID: PMC5399027 DOI: 10.3389/fphar.2017.00211] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 04/05/2017] [Indexed: 12/14/2022] Open
Abstract
Huangqin decoction (HQD), a traditional Chinese medicine (TCM), has been widely used to treat gastrointestinal syndrome in China for thousands of years. Chemotherapy drug irinotecan (CPT-11) is used clinically to treat various kinds of cancers but limited by its side effects, especially delayed diarrhea. Nowadays, HQD has been proved to be effective in attenuating the intestinal toxicity induced by CPT-11. HQD consists of four medicinal herbs including Scutellaria baicalensis Georgi, Glycyrrhiza uralensis Fisch, Paeonia lactiflora Pall, and Ziziphus jujuba Mill. Due to its complexity, the role of each herb and the multi-herb synergistic effects of the formula are poorly understood. In order to quantitatively assess the compatibility effects of HQD, mass spectrometry-based untargeted metabolomics studies were performed. The serum metabolic profiles of rats administered with HQD, single S. baicalensis decoction, S. baicalensis-free decoction and baicalin/baicalein combination were compared. A time-dependent trajectory upon principal component analysis was firstly used to visualize the overall differences. Then metabolites deregulation score and relative area under the curve were calculated and used as parameters to quantitatively evaluate the compatibility effects of HQD from the aspect of global metabolic profile and the specifically altered metabolites, respectively. The collective results indicated that S. baicalensis played a crucial role in the therapeutic effect of HQD on irinotecan-induced diarrhea. Both HQD and SS decoction regulated glycine, serine and threonine pathway. This study demonstrated that metabolomics was a promising tool to elucidate the compatibility effects of TCM or combinatorial drugs.
Collapse
Affiliation(s)
- Dong-Ni Cui
- MOE Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical UniversityNanjing, China.,State Key Laboratory of Natural Medicine, China Pharmaceutical UniversityNanjing, China
| | - Xu Wang
- MOE Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical UniversityNanjing, China.,State Key Laboratory of Natural Medicine, China Pharmaceutical UniversityNanjing, China
| | - Jia-Qing Chen
- MOE Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical UniversityNanjing, China.,State Key Laboratory of Natural Medicine, China Pharmaceutical UniversityNanjing, China
| | - Bo Lv
- MOE Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical UniversityNanjing, China.,State Key Laboratory of Natural Medicine, China Pharmaceutical UniversityNanjing, China
| | - Pei Zhang
- MOE Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical UniversityNanjing, China.,State Key Laboratory of Natural Medicine, China Pharmaceutical UniversityNanjing, China
| | - Wei Zhang
- State Key Laboratory for Quality Research in Chinese Medicines, Macau University of Science and TechnologyMacau, China
| | - Zun-Jian Zhang
- MOE Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical UniversityNanjing, China.,State Key Laboratory of Natural Medicine, China Pharmaceutical UniversityNanjing, China
| | - Feng-Guo Xu
- MOE Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical UniversityNanjing, China.,State Key Laboratory of Natural Medicine, China Pharmaceutical UniversityNanjing, China
| |
Collapse
|
25
|
Kourou K, Papaloukas C, Fotiadis DI. Integration of Pathway Knowledge and Dynamic Bayesian Networks for the Prediction of Oral Cancer Recurrence. IEEE J Biomed Health Inform 2017; 21:320-327. [DOI: 10.1109/jbhi.2016.2636448] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
26
|
Dadiani M, Bossel Ben-Moshe N, Paluch-Shimon S, Perry G, Balint N, Marin I, Pavlovski A, Morzaev D, Kahana-Edwin S, Yosepovich A, Gal-Yam EN, Berger R, Barshack I, Domany E, Kaufman B. Tumor Evolution Inferred by Patterns of microRNA Expression through the Course of Disease, Therapy, and Recurrence in Breast Cancer. Clin Cancer Res 2016; 22:3651-62. [PMID: 26957561 DOI: 10.1158/1078-0432.ccr-15-2313] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 02/21/2016] [Indexed: 11/16/2022]
Abstract
PURPOSE Molecular evolution of tumors during progression, therapy, and metastasis is a major clinical challenge and the main reason for resistance to therapy. We hypothesized that microRNAs (miRNAs) that exhibit similar variation of expression through the course of disease in several patients have a significant function in the tumorigenic process. EXPERIMENTAL DESIGN Exploration of evolving disease by profiling 800 miRNA expression from serial samples of individual breast cancer patients at several time points: pretreatment, posttreatment, lymph nodes, and recurrence sites when available (58 unique samples from 19 patients). Using a dynamic approach for analysis, we identified expression modulation patterns and classified varying miRNAs into one of the eight possible temporal expression patterns. RESULTS The various patterns were found to be associated with different tumorigenic pathways. The dominant pattern identified an miRNA set that significantly differentiated between disease stages, and its pattern in each patient was also associated with response to therapy. These miRNAs were related to tumor proliferation and to the cell-cycle pathway, and their mRNA targets showed anticorrelated expression. Interestingly, the level of these miRNAs was lowest in matched recurrent samples from distant metastasis, indicating a gradual increase in proliferative potential through the course of disease. Finally, the average expression level of these miRNAs in the pretreatment biopsy was significantly different comparing patients experiencing recurrence to recurrence-free patients. CONCLUSIONS Serial tumor sampling combined with analysis of temporal expression patterns enabled to pinpoint significant signatures characterizing breast cancer progression, associated with response to therapy and with risk of recurrence. Clin Cancer Res; 22(14); 3651-62. ©2016 AACR.
Collapse
Affiliation(s)
- Maya Dadiani
- Cancer Research Center, Sheba Medical Center, Tel-Hashomer, Israel.
| | - Noa Bossel Ben-Moshe
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel
| | | | - Gili Perry
- Cancer Research Center, Sheba Medical Center, Tel-Hashomer, Israel
| | - Nora Balint
- Pathology Institute, Sheba Medical Center, Tel-Hashomer, Israel
| | - Irina Marin
- Pathology Institute, Sheba Medical Center, Tel-Hashomer, Israel
| | - Anya Pavlovski
- Pathology Institute, Sheba Medical Center, Tel-Hashomer, Israel
| | - Dana Morzaev
- Cancer Research Center, Sheba Medical Center, Tel-Hashomer, Israel
| | | | - Ady Yosepovich
- Pathology Institute, Sheba Medical Center, Tel-Hashomer, Israel
| | | | - Raanan Berger
- Oncology Institute, Sheba Medical Center, Tel-Hashomer, Israel
| | - Iris Barshack
- Pathology Institute, Sheba Medical Center, Tel-Hashomer, Israel. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eytan Domany
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel
| | - Bella Kaufman
- Oncology Institute, Sheba Medical Center, Tel-Hashomer, Israel. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
27
|
Wang D, Gu J. Integrative clustering methods of multi-omics data for molecule-based cancer classifications. QUANTITATIVE BIOLOGY 2016. [DOI: 10.1007/s40484-016-0063-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
28
|
Liu C, Srihari S, Lal S, Gautier B, Simpson PT, Khanna KK, Ragan MA, Lê Cao KA. Personalised pathway analysis reveals association between DNA repair pathway dysregulation and chromosomal instability in sporadic breast cancer. Mol Oncol 2016; 10:179-93. [PMID: 26456802 PMCID: PMC5528935 DOI: 10.1016/j.molonc.2015.09.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 08/19/2015] [Accepted: 09/04/2015] [Indexed: 01/05/2023] Open
Abstract
The Homologous Recombination (HR) pathway is crucial for the repair of DNA double-strand breaks (DSBs) generated during DNA replication. Defects in HR repair have been linked to the initiation and development of a wide variety of human malignancies, and exploited in chemical, radiological and targeted therapies. In this study, we performed a personalised pathway analysis independently for four large sporadic breast cancer cohorts to investigate the status of HR pathway dysregulation in individual sporadic breast tumours, its association with HR repair deficiency and its impact on tumour characteristics. Specifically, we first manually curated a list of HR genes according to our recent review on this pathway (Liu et al., 2014), and then applied a personalised pathway analysis method named Pathifier (Drier et al., 2013) on the expression levels of the curated genes to obtain an HR score quantifying HR pathway dysregulation in individual tumours. Based on the score, we observed a great diversity in HR dysregulation between and within gene expression-based breast cancer subtypes, and by using two published HR-defect signatures, we found HR pathway dysregulation reflects HR repair deficiency. Furthermore, we identified a novel association between HR pathway dysregulation and chromosomal instability (CIN) in sporadic breast cancer. Although CIN has long been considered as a hallmark of most solid tumours, with recent extensive studies highlighting its importance in tumour evolution and drug resistance, the molecular basis of CIN in sporadic cancers remains poorly understood. Our results imply that HR pathway dysregulation might contribute to CIN in sporadic breast cancer.
Collapse
Affiliation(s)
- Chao Liu
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4067, Australia
| | - Sriganesh Srihari
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4067, Australia
| | - Samir Lal
- The University of Queensland, UQ Centre for Clinical Research, Herston, QLD 4029, Australia
| | - Benoît Gautier
- University of Queensland Diamantina Institute, Translational Research Institute, Woolloongabba, QLD 4102, Australia
| | - Peter T Simpson
- The University of Queensland, UQ Centre for Clinical Research, Herston, QLD 4029, Australia; School of Medicine, The University of Queensland, Herston, QLD 4006, Australia
| | - Kum Kum Khanna
- QIMR-Berghofer Medical Research Institute, Herston, Brisbane, QLD 4029, Australia
| | - Mark A Ragan
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4067, Australia.
| | - Kim-Anh Lê Cao
- University of Queensland Diamantina Institute, Translational Research Institute, Woolloongabba, QLD 4102, Australia.
| |
Collapse
|
29
|
Livshits A, Git A, Fuks G, Caldas C, Domany E. Pathway-based personalized analysis of breast cancer expression data. Mol Oncol 2015; 9:1471-83. [PMID: 25963740 PMCID: PMC5528809 DOI: 10.1016/j.molonc.2015.04.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 03/23/2015] [Accepted: 04/16/2015] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION Most analyses of high throughput cancer data represent tumors by "atomistic" single-gene properties. Pathifier, a recently introduced method, characterizes a tumor in terms of "coarse grained" pathway-based variables. METHODS We applied Pathifier to study a very large dataset of 2000 breast cancer samples and 144 normal tissues. Pathifier uses known gene assignments to pathways and biological processes to calculate for each pathway and tumor a Pathway Deregulation Score (PDS). Individual samples are represented in terms of their PDSs calculated for several hundred pathways, and the samples of the data set are analyzed and stratified on the basis of their profiles over these "coarse grained", biologically meaningful variables. RESULTS We identified nine tumor subtypes; a new subclass (comprising about 7% of the samples) exhibits high deregulation in 38 PKA pathways, induced by overexpression of the gene PRKACB. Another interesting finding is that basal tumors break into two subclasses, with low and high deregulation of a cluster of immune system pathways. High deregulation corresponds to higher concentrations of Tumor Infiltrating Lymphocytes, and the patients of this basal subtype have better prognosis. The analysis used 1000 "discovery set" tumors; our results were highly reproducible on 1000 independent "validation" samples. CONCLUSIONS The coarse-grained variables that represent pathway deregulation provide a basis for relevant, novel and robust findings for breast cancer. Our analysis indicates that in breast cancer reliable prognostic signatures are most likely to be obtained by treating separately different subgroups of the patients.
Collapse
Affiliation(s)
- Anna Livshits
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel.
| | - Anna Git
- Cancer Research UK Cambridge Research Institute, Cambridge CB2 0RE, UK; Department of Oncology, University of Cambridge, Cambridge CB2 2XZ, UK.
| | - Garold Fuks
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel.
| | - Carlos Caldas
- Cancer Research UK Cambridge Research Institute, Cambridge CB2 0RE, UK; Department of Oncology, University of Cambridge, Cambridge CB2 2XZ, UK.
| | - Eytan Domany
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel.
| |
Collapse
|