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Lu J, Cannizzaro E, Meier-Abt F, Scheinost S, Bruch PM, Giles HAR, Lütge A, Hüllein J, Wagner L, Giacopelli B, Nadeu F, Delgado J, Campo E, Mangolini M, Ringshausen I, Böttcher M, Mougiakakos D, Jacobs A, Bodenmiller B, Dietrich S, Oakes CC, Zenz T, Huber W. Multi-omics reveals clinically relevant proliferative drive associated with mTOR-MYC-OXPHOS activity in chronic lymphocytic leukemia. NATURE CANCER 2021; 2:853-864. [PMID: 34423310 PMCID: PMC7611543 DOI: 10.1038/s43018-021-00216-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 05/10/2021] [Indexed: 11/10/2022]
Abstract
Chronic Lymphocytic Leukemia (CLL) has a complex pattern of driver mutations and much of its clinical diversity remains unexplained. We devised a method for simultaneous subgroup discovery across multiple data types and applied it to genomic, transcriptomic, DNA methylation and ex-vivo drug response data from 217 Chronic Lymphocytic Leukemia (CLL) cases. We uncovered a biological axis of heterogeneity strongly associated with clinical behavior and orthogonal to the known biomarkers. We validated its presence and clinical relevance in four independent cohorts (n=547 patients). We find that this axis captures the proliferative drive (PD) of CLL cells, as it associates with lymphocyte doubling rate, global hypomethylation, accumulation of driver aberrations and response to pro-proliferative stimuli. CLL-PD was linked to the activation of mTOR-MYC-oxidative phosphorylation (OXPHOS) through transcriptomic, proteomic and single cell resolution analysis. CLL-PD is a key determinant of disease outcome in CLL. Our multi-table integration approach may be applicable to other tumors whose inter-individual differences are currently unexplained.
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Affiliation(s)
- Junyan Lu
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Molecular Medicine Partnership Unit (MMPU), Heidelberg, Germany
| | - Ester Cannizzaro
- Department of Medical Oncology and Hematology, University Hospital Zürich and University of Zürich, Zürich, Switzerland
| | - Fabienne Meier-Abt
- Department of Medical Oncology and Hematology, University Hospital Zürich and University of Zürich, Zürich, Switzerland
| | - Sebastian Scheinost
- Molecular Therapy in Hematology and Oncology, National Center for Tumor Diseases and German Cancer Research Centre, Heidelberg, Germany
| | - Peter-Martin Bruch
- Molecular Therapy in Hematology and Oncology, National Center for Tumor Diseases and German Cancer Research Centre, Heidelberg, Germany
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- University of Heidelberg, Heidelberg, Germany
| | - Holly AR Giles
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Molecular Medicine Partnership Unit (MMPU), Heidelberg, Germany
| | - Almut Lütge
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Jennifer Hüllein
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Molecular Therapy in Hematology and Oncology, National Center for Tumor Diseases and German Cancer Research Centre, Heidelberg, Germany
| | - Lena Wagner
- Molecular Therapy in Hematology and Oncology, National Center for Tumor Diseases and German Cancer Research Centre, Heidelberg, Germany
| | - Brian Giacopelli
- Division of Hematology, Department of Internal Medicine, The Ohio State University, Columbus, OH
| | - Ferran Nadeu
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Julio Delgado
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
- Hematopathology Unit, Hospital Clínic de Barcelona, University of Barcelona, Barcelona, Spain
| | - Elías Campo
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
- Hematopathology Unit, Hospital Clínic de Barcelona, University of Barcelona, Barcelona, Spain
| | - Maurizio Mangolini
- Wellcome Trust/MRC Cambridge Stem Cell Institute & Department of Haematology, University of Cambridge, Cambridge CB2 0AH, UK
| | - Ingo Ringshausen
- Wellcome Trust/MRC Cambridge Stem Cell Institute & Department of Haematology, University of Cambridge, Cambridge CB2 0AH, UK
| | - Martin Böttcher
- Department of Internal Medicine 5, Hematology and Oncology, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Dimitrios Mougiakakos
- Department of Internal Medicine 5, Hematology and Oncology, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Andrea Jacobs
- Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Sascha Dietrich
- Molecular Medicine Partnership Unit (MMPU), Heidelberg, Germany
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- University of Heidelberg, Heidelberg, Germany
- Translational Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christopher C. Oakes
- Division of Hematology, Department of Internal Medicine, The Ohio State University, Columbus, OH
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH
| | - Thorsten Zenz
- Department of Medical Oncology and Hematology, University Hospital Zürich and University of Zürich, Zürich, Switzerland
- Molecular Therapy in Hematology and Oncology, National Center for Tumor Diseases and German Cancer Research Centre, Heidelberg, Germany
| | - Wolfgang Huber
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Molecular Medicine Partnership Unit (MMPU), Heidelberg, Germany
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2
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Clark J, Avula V, Ring C, Eaves LA, Howard T, Santos HP, Smeester L, Bangma JT, O'Shea TM, Fry RC, Rager JE. Comparing the Predictivity of Human Placental Gene, microRNA, and CpG Methylation Signatures in Relation to Perinatal Outcomes. Toxicol Sci 2021; 183:269-284. [PMID: 34255065 DOI: 10.1093/toxsci/kfab089] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Molecular signatures are being increasingly integrated into predictive biology applications. However, there are limited studies comparing the overall predictivity of transcriptomic vs. epigenomic signatures in relation to perinatal outcomes. This study set out to evaluate mRNA and microRNA (miRNA) expression and cytosine-guanine dinucleotide (CpG) methylation signatures in human placental tissues and relate these to perinatal outcomes known to influence maternal/fetal health; namely, birth weight, placenta weight, placental damage, and placental inflammation. The following hypotheses were tested: (1) different molecular signatures will demonstrate varying levels of predictivity towards perinatal outcomes, and (2) these signatures will show disruptions from an example exposure (i.e., cadmium) known to elicit perinatal toxicity. Multi-omic placental profiles from 390 infants in the Extremely Low Gestational Age Newborns cohort were used to develop molecular signatures that predict each perinatal outcome. Epigenomic signatures (i.e., miRNA and CpG methylation) consistently demonstrated the highest levels of predictivity, with model performance metrics including R^2 (predicted vs. observed) values of 0.36-0.57 for continuous outcomes and balanced accuracy values of 0.49-0.77 for categorical outcomes. Top-ranking predictors included miRNAs involved in injury and inflammation. To demonstrate the utility of these predictive signatures in screening of potentially harmful exogenous insults, top-ranking miRNA predictors were analyzed in a separate pregnancy cohort and related to cadmium. Key predictive miRNAs demonstrated altered expression in association with cadmium exposure, including miR-210, known to impact placental cell growth, blood vessel development, and fetal weight. These findings inform future predictive biology applications, where additional benefit will be gained by including epigenetic markers.
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Affiliation(s)
- Jeliyah Clark
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Vennela Avula
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Lauren A Eaves
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Thomas Howard
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Hudson P Santos
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Biobehavioral Laboratory, School of Nursing, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Lisa Smeester
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jacqueline T Bangma
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - T Michael O'Shea
- Department of Pediatrics, Division of Neonatal-Perinatal Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Rebecca C Fry
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Julia E Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
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Guo C, Gao YY, Ju QQ, Zhang CX, Gong M, Li ZL. HELQ and EGR3 expression correlate with IGHV mutation status and prognosis in chronic lymphocytic leukemia. J Transl Med 2021; 19:42. [PMID: 33485349 PMCID: PMC7825181 DOI: 10.1186/s12967-021-02708-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 01/16/2021] [Indexed: 11/16/2022] Open
Abstract
Background IGHV mutation status is a crucial prognostic biomarker for CLL. In the present study, we investigated the transcriptomic signatures associating with IGHV mutation status and CLL prognosis. Methods The co-expression modules and hub genes correlating with IGHV status, were identified using the GSE28654, by ‘WGCNA’ package and R software (version 4.0.2). The over-representation analysis was performed to reveal enriched cell pathways for genes of correlating modules. Then 9 external cohorts were used to validate the correlation of hub genes expression with IGHV status or clinical features (treatment response, transformation to Richter syndrome, etc.). Moreover, to elucidate the significance of hub genes on disease course and prognosis of CLL patients, the Kaplan–Meier analysis for the OS and TTFT of were performed between subgroups dichotomized by the median expression value of individual hub genes. Results 2 co-expression modules and 9 hub genes ((FCRL1/FCRL2/HELQ/EGR3LPL/LDOC1/ZNF667/SOWAHC/SEPTIN10) correlating with IGHV status were identified by WGCNA, and validated by external datasets. The modules were found to be enriched in NF-kappaB, HIF-1 and other important pathways, involving cell proliferation and apoptosis. The expression of hub genes was revealed to be significantly different, not only between CLL and normal B cell, but also between various types of lymphoid neoplasms. HELQ expression was found to be related with response of immunochemotherapy treatment significantly (p = 0.0413), while HELQ and ZNF667 were expressed differently between stable CLL and Richter syndrome patients (p < 0.0001 and p = 0.0278, respectively). By survival analysis of subgroups, EGR3 expression was indicated to be significantly associated with TTFT by 2 independent cohorts (GSE39671, p = 0.0311; GSE22762, p = 0.0135). While the expression of HELQ and EGR3 was found to be associated with OS (p = 0.0291 and 0.0114 respectively).The Kras, Hedgehog and IL6-JAK-STAT3 pathways were found to be associating with the expression of hub genes, resulting from GSEA. Conclusions The expression of HELQ and EGR3 were correlated with IGHV mutation status in CLL patients. Additionally, the expression of HELQ/EGR3 were prognostic markers for CLL associating with targetable cell signaling pathways.
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Affiliation(s)
- Chao Guo
- Department of Hematology, China-Japan Friendship Hospital, Yinghua East Street, Beijing, 100029, China
| | - Ya-Yue Gao
- Department of Hematology, China-Japan Friendship Hospital, Yinghua East Street, Beijing, 100029, China
| | - Qian-Qian Ju
- Department of Hematology, China-Japan Friendship Hospital, Yinghua East Street, Beijing, 100029, China
| | - Chun-Xia Zhang
- Department of Hematology, China-Japan Friendship Hospital, Yinghua East Street, Beijing, 100029, China
| | - Ming Gong
- Department of Hematology, China-Japan Friendship Hospital, Yinghua East Street, Beijing, 100029, China
| | - Zhen-Ling Li
- Department of Hematology, China-Japan Friendship Hospital, Yinghua East Street, Beijing, 100029, China.
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Kreuzberger N, Damen JA, Trivella M, Estcourt LJ, Aldin A, Umlauff L, Vazquez-Montes MD, Wolff R, Moons KG, Monsef I, Foroutan F, Kreuzer KA, Skoetz N. Prognostic models for newly-diagnosed chronic lymphocytic leukaemia in adults: a systematic review and meta-analysis. Cochrane Database Syst Rev 2020; 7:CD012022. [PMID: 32735048 PMCID: PMC8078230 DOI: 10.1002/14651858.cd012022.pub2] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND Chronic lymphocytic leukaemia (CLL) is the most common cancer of the lymphatic system in Western countries. Several clinical and biological factors for CLL have been identified. However, it remains unclear which of the available prognostic models combining those factors can be used in clinical practice to predict long-term outcome in people newly-diagnosed with CLL. OBJECTIVES To identify, describe and appraise all prognostic models developed to predict overall survival (OS), progression-free survival (PFS) or treatment-free survival (TFS) in newly-diagnosed (previously untreated) adults with CLL, and meta-analyse their predictive performances. SEARCH METHODS We searched MEDLINE (from January 1950 to June 2019 via Ovid), Embase (from 1974 to June 2019) and registries of ongoing trials (to 5 March 2020) for development and validation studies of prognostic models for untreated adults with CLL. In addition, we screened the reference lists and citation indices of included studies. SELECTION CRITERIA We included all prognostic models developed for CLL which predict OS, PFS, or TFS, provided they combined prognostic factors known before treatment initiation, and any studies that tested the performance of these models in individuals other than the ones included in model development (i.e. 'external model validation studies'). We included studies of adults with confirmed B-cell CLL who had not received treatment prior to the start of the study. We did not restrict the search based on study design. DATA COLLECTION AND ANALYSIS We developed a data extraction form to collect information based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Independent pairs of review authors screened references, extracted data and assessed risk of bias according to the Prediction model Risk Of Bias ASsessment Tool (PROBAST). For models that were externally validated at least three times, we aimed to perform a quantitative meta-analysis of their predictive performance, notably their calibration (proportion of people predicted to experience the outcome who do so) and discrimination (ability to differentiate between people with and without the event) using a random-effects model. When a model categorised individuals into risk categories, we pooled outcome frequencies per risk group (low, intermediate, high and very high). We did not apply GRADE as guidance is not yet available for reviews of prognostic models. MAIN RESULTS From 52 eligible studies, we identified 12 externally validated models: six were developed for OS, one for PFS and five for TFS. In general, reporting of the studies was poor, especially predictive performance measures for calibration and discrimination; but also basic information, such as eligibility criteria and the recruitment period of participants was often missing. We rated almost all studies at high or unclear risk of bias according to PROBAST. Overall, the applicability of the models and their validation studies was low or unclear; the most common reasons were inappropriate handling of missing data and serious reporting deficiencies concerning eligibility criteria, recruitment period, observation time and prediction performance measures. We report the results for three models predicting OS, which had available data from more than three external validation studies: CLL International Prognostic Index (CLL-IPI) This score includes five prognostic factors: age, clinical stage, IgHV mutational status, B2-microglobulin and TP53 status. Calibration: for the low-, intermediate- and high-risk groups, the pooled five-year survival per risk group from validation studies corresponded to the frequencies observed in the model development study. In the very high-risk group, predicted survival from CLL-IPI was lower than observed from external validation studies. Discrimination: the pooled c-statistic of seven external validation studies (3307 participants, 917 events) was 0.72 (95% confidence interval (CI) 0.67 to 0.77). The 95% prediction interval (PI) of this model for the c-statistic, which describes the expected interval for the model's discriminative ability in a new external validation study, ranged from 0.59 to 0.83. Barcelona-Brno score Aimed at simplifying the CLL-IPI, this score includes three prognostic factors: IgHV mutational status, del(17p) and del(11q). Calibration: for the low- and intermediate-risk group, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of four external validation studies (1755 participants, 416 events) was 0.64 (95% CI 0.60 to 0.67); 95% PI 0.59 to 0.68. MDACC 2007 index score The authors presented two versions of this model including six prognostic factors to predict OS: age, B2-microglobulin, absolute lymphocyte count, gender, clinical stage and number of nodal groups. Only one validation study was available for the more comprehensive version of the model, a formula with a nomogram, while seven studies (5127 participants, 994 events) validated the simplified version of the model, the index score. Calibration: for the low- and intermediate-risk groups, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of the seven external validation studies for the index score was 0.65 (95% CI 0.60 to 0.70); 95% PI 0.51 to 0.77. AUTHORS' CONCLUSIONS Despite the large number of published studies of prognostic models for OS, PFS or TFS for newly-diagnosed, untreated adults with CLL, only a minority of these (N = 12) have been externally validated for their respective primary outcome. Three models have undergone sufficient external validation to enable meta-analysis of the model's ability to predict survival outcomes. Lack of reporting prevented us from summarising calibration as recommended. Of the three models, the CLL-IPI shows the best discrimination, despite overestimation. However, performance of the models may change for individuals with CLL who receive improved treatment options, as the models included in this review were tested mostly on retrospective cohorts receiving a traditional treatment regimen. In conclusion, this review shows a clear need to improve the conducting and reporting of both prognostic model development and external validation studies. For prognostic models to be used as tools in clinical practice, the development of the models (and their subsequent validation studies) should adapt to include the latest therapy options to accurately predict performance. Adaptations should be timely.
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MESH Headings
- Adult
- Age Factors
- Bias
- Biomarkers, Tumor
- Calibration
- Confidence Intervals
- Discriminant Analysis
- Disease-Free Survival
- Female
- Genes, p53/genetics
- Humans
- Immunoglobulin Heavy Chains/genetics
- Immunoglobulin Variable Region/genetics
- Leukemia, Lymphocytic, Chronic, B-Cell/mortality
- Leukemia, Lymphocytic, Chronic, B-Cell/pathology
- Male
- Models, Theoretical
- Neoplasm Staging
- Prognosis
- Progression-Free Survival
- Receptors, Antigen, B-Cell/genetics
- Reproducibility of Results
- Tumor Suppressor Protein p53/genetics
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Affiliation(s)
- Nina Kreuzberger
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Johanna Aag Damen
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Lise J Estcourt
- Haematology/Transfusion Medicine, NHS Blood and Transplant, Oxford, UK
| | - Angela Aldin
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lisa Umlauff
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | | | - Karel Gm Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ina Monsef
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Farid Foroutan
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Karl-Anton Kreuzer
- Center of Integrated Oncology Cologne-Bonn, Department I of Internal Medicine, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Nicole Skoetz
- Cochrane Cancer, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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5
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Chen XK, Gu CL, Fan JQ, Zhang XM. P-STAT3 and IL-17 in tumor tissues enhances the prognostic value of CEA and CA125 in patients with lung adenocarcinoma. Biomed Pharmacother 2020; 125:109871. [PMID: 32187953 DOI: 10.1016/j.biopha.2020.109871] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 01/03/2020] [Accepted: 01/10/2020] [Indexed: 12/24/2022] Open
Abstract
AIM The present study aimed to examine the capability of p- signal transducer and activator of transcription (STAT)3 and interleukin-17 (IL-17), along with two known tumor markers carcinoembryonic antigen (CEA) and carbohydrate antigen 125 (CA125), for disease prognosis. Moreover, the associations among biomarkers and clinicopathological parameters were evaluated to uncover the potential mechanisms responsible for their correlations with lung adenocarcinoma (LAD) prognosis. METHODS Five LAD-related parameters were used in the study: CEA, CA125, STAT3, p-STAT3, and IL-17. Spearman and chi-square correlation tests were used to explore the relationships between some clinicopathological variables and parameter expression levels and the associations among these five parameters. RESULTS The disease-specific survival decreased with the positive expression of CEA, CA125, p-STAT3, and IL-17, with no significant difference in the expression level of STAT3. Combinations of p-STAT3 and IL-17, CEA and p-STAT3, CEA and IL-17, CA125 and p-STAT3, and CA125 and IL-17 had higher predictive values in LAD prognosis. The correlation analyses indicated the synergic activities of STAT3, p-STAT3, and IL-17 and the coordinated expression of CEA, CA125, p-STAT3, and IL-17. The tumor-node-metastasis (TNM) stage significantly correlated with the levels of CA125 and p-STAT3. CONCLUSIONS Elevated levels of CEA, CA125, p-STAT3, and IL-17 alone and/or combinations of p-STAT3 and IL-17, CEA and p-STAT3, CEA and IL-17, CA125 and p-STAT3, and CA125 and IL-17 were recommended as the prognostic predictors of unfavorable clinical outcomes in patients with postoperative LAD. Also, p-STAT3 and IL-17 combined with CA125 and CEA helped in predicting the overall survival of patients with LAD and informing the TNM stage.
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Affiliation(s)
- Xiao-Ke Chen
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chuan-Long Gu
- Department of Anatomy, Zhejiang University School of Medicine, Hangzhou, China
| | - Jun-Qiang Fan
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Xiao-Ming Zhang
- Department of Anatomy, Zhejiang University School of Medicine, Hangzhou, China.
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6
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Friedman DR, Carson KR, Weinberg JB. Reflections on and future of hematologic malignancies research in the Veterans Health Administration. Semin Oncol 2019; 46:346-350. [PMID: 31699443 DOI: 10.1053/j.seminoncol.2019.09.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 09/17/2019] [Indexed: 11/11/2022]
Abstract
Research in the Veterans Health Administration (VHA) has played an integral part in learning about cancer biology and treatment. Here we provide examples of past research performed in the VHA focusing on hematologic malignancies, and identify future opportunities for areas of research in this group of uncommon diseases that have specific importance for Veterans and the VHA. Veterans treated in the VHA and in the private sector deserve information that is focused on them, and is not an extrapolation from the larger population. Only by building upon and expanding existing research within the VHA can Veteran-specific results be collected and best practices be developed. In turn, such advances will benefit Veterans affected by these cancers with an improved quality of life and a longer lifespan.
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Affiliation(s)
- Daphne R Friedman
- Department of Medicine, Duke University School of Medicine, Durham, NC; Medicine Service, Durham VA Medical Center, Durham, NC.
| | - Kenneth R Carson
- Department of Medicine, St. Louis VA, St. Louis, MO; Division of Medical Oncology, Washington University School of Medicine, St. Louis, MO.
| | - J Brice Weinberg
- Department of Medicine, Duke University School of Medicine, Durham, NC; Medicine Service, Durham VA Medical Center, Durham, NC.
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7
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Mosquera Orgueira A, Antelo Rodríguez B, Alonso Vence N, Bendaña López Á, Díaz Arias JÁ, Díaz Varela N, González Pérez MS, Pérez Encinas MM, Bello López JL. Time to Treatment Prediction in Chronic Lymphocytic Leukemia Based on New Transcriptional Patterns. Front Oncol 2019; 9:79. [PMID: 30828568 PMCID: PMC6384245 DOI: 10.3389/fonc.2019.00079] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 01/29/2019] [Indexed: 11/13/2022] Open
Abstract
Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome in western countries. CLL evolution is frequently indolent, and treatment is mostly reserved for those patients with signs or symptoms of disease progression. In this work, we used RNA sequencing data from the International Cancer Genome Consortium CLL cohort to determine new gene expression patterns that correlate with clinical evolution.We determined that a 290-gene expression signature, in addition to immunoglobulin heavy chain variable region (IGHV) mutation status, stratifies patients into four groups with notably different time to first treatment. This finding was confirmed in an independent cohort. Similarly, we present a machine learning algorithm that predicts the need for treatment within the first 5 years following diagnosis using expression data from 2,198 genes. This predictor achieved 90% precision and 89% accuracy when classifying independent CLL cases. Our findings indicate that CLL progression risk largely correlates with particular transcriptomic patterns and paves the way for the identification of high-risk patients who might benefit from prompt therapy following diagnosis.
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Affiliation(s)
- Adrián Mosquera Orgueira
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.,Division of Hematology, Complexo Hospitalario Universitario de Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Department of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Beatriz Antelo Rodríguez
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.,Division of Hematology, Complexo Hospitalario Universitario de Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Department of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Natalia Alonso Vence
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.,Division of Hematology, Complexo Hospitalario Universitario de Santiago de Compostela, SERGAS, Santiago de Compostela, Spain
| | - Ángeles Bendaña López
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.,Division of Hematology, Complexo Hospitalario Universitario de Santiago de Compostela, SERGAS, Santiago de Compostela, Spain
| | - José Ángel Díaz Arias
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.,Division of Hematology, Complexo Hospitalario Universitario de Santiago de Compostela, SERGAS, Santiago de Compostela, Spain
| | - Nicolás Díaz Varela
- Division of Hematology, Complexo Hospitalario Universitario de Santiago de Compostela, SERGAS, Santiago de Compostela, Spain
| | - Marta Sonia González Pérez
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.,Division of Hematology, Complexo Hospitalario Universitario de Santiago de Compostela, SERGAS, Santiago de Compostela, Spain
| | - Manuel Mateo Pérez Encinas
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.,Department of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - José Luis Bello López
- Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.,Division of Hematology, Complexo Hospitalario Universitario de Santiago de Compostela, SERGAS, Santiago de Compostela, Spain.,Department of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain
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8
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Nørgaard CH, Jakobsen LH, Gentles AJ, Dybkær K, El-Galaly TC, Bødker JS, Schmitz A, Johansen P, Herold T, Spiekermann K, Brown JR, Klitgaard JL, Johnsen HE, Bøgsted M. Subtype assignment of CLL based on B-cell subset associated gene signatures from normal bone marrow - A proof of concept study. PLoS One 2018; 13:e0193249. [PMID: 29513759 PMCID: PMC5841735 DOI: 10.1371/journal.pone.0193249] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 02/07/2018] [Indexed: 11/26/2022] Open
Abstract
Diagnostic and prognostic evaluation of chronic lymphocytic leukemia (CLL) involves blood cell counts, immunophenotyping, IgVH mutation status, and cytogenetic analyses. We generated B-cell associated gene-signatures (BAGS) based on six naturally occurring B-cell subsets within normal bone marrow. Our hypothesis is that by segregating CLL according to BAGS, we can identify subtypes with prognostic implications in support of pathogenetic value of BAGS. Microarray-based gene-expression samples from eight independent CLL cohorts (1,024 untreated patients) were BAGS-stratified into pre-BI, pre-BII, immature, naïve, memory, or plasma cell subtypes; the majority falling within the memory (24.5-45.8%) or naïve (14.5-32.3%) categories. For a subset of CLL patients (n = 296), time to treatment (TTT) was shorter amongst early differentiation subtypes (pre-BI/pre-BII/immature) compared to late subtypes (memory/plasma cell, HR: 0.53 [0.35-0.78]). Particularly, pre-BII subtype patients had the shortest TTT among all subtypes. Correlates derived for BAGS subtype and IgVH mutation (n = 405) revealed an elevated mutation frequency in late vs. early subtypes (71% vs. 45%, P < .001). Predictions for BAGS subtype resistance towards rituximab and cyclophosphamide varied for rituximab, whereas all subtypes were sensitive to cyclophosphamide. This study supports our hypothesis that BAGS-subtyping may be of tangible prognostic and pathogenetic value for CLL patients.
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MESH Headings
- Adult
- Aged
- Aged, 80 and over
- Antineoplastic Agents, Alkylating/therapeutic use
- Antineoplastic Agents, Immunological/therapeutic use
- B-Lymphocyte Subsets/metabolism
- Bone Marrow/metabolism
- Cyclophosphamide/therapeutic use
- Drug Resistance, Neoplasm/physiology
- Female
- Gene Expression Regulation, Neoplastic
- Humans
- Leukemia, Lymphocytic, Chronic, B-Cell/classification
- Leukemia, Lymphocytic, Chronic, B-Cell/metabolism
- Leukemia, Lymphocytic, Chronic, B-Cell/therapy
- Male
- Microarray Analysis
- Middle Aged
- Prognosis
- Proof of Concept Study
- Retrospective Studies
- Rituximab/therapeutic use
- Survival Analysis
- Time-to-Treatment
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Affiliation(s)
| | - Lasse Hjort Jakobsen
- Department of Haematology, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Andrew J. Gentles
- Departments of Medicine and Biomedical Data Science, Stanford, California, United States of America
| | - Karen Dybkær
- Department of Haematology, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark
| | - Tarec Christoffer El-Galaly
- Department of Haematology, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark
| | - Julie Støve Bødker
- Department of Haematology, Aalborg University Hospital, Aalborg, Denmark
- Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark
| | - Alexander Schmitz
- Department of Haematology, Aalborg University Hospital, Aalborg, Denmark
| | - Preben Johansen
- Department of Pathology, Aalborg University Hospital, Aalborg, Denmark
| | - Tobias Herold
- Department of Internal Medicine 3, University of Munich, Munich, Germany
| | | | - Jennifer R. Brown
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States of America
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Josephine L. Klitgaard
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States of America
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Hans Erik Johnsen
- Department of Haematology, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark
| | - Martin Bøgsted
- Department of Haematology, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Clinical Cancer Research Center, Aalborg University Hospital, Aalborg, Denmark
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9
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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.
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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.
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10
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Aberrantly expressed LGR4 empowers Wnt signaling in multiple myeloma by hijacking osteoblast-derived R-spondins. Proc Natl Acad Sci U S A 2016; 114:376-381. [PMID: 28028233 DOI: 10.1073/pnas.1618650114] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The unrestrained growth of tumor cells is generally attributed to mutations in essential growth control genes, but tumor cells are also affected by, or even addicted to, signals from the microenvironment. As therapeutic targets, these extrinsic signals may be equally significant as mutated oncogenes. In multiple myeloma (MM), a plasma cell malignancy, most tumors display hallmarks of active Wnt signaling but lack activating Wnt-pathway mutations, suggesting activation by autocrine Wnt ligands and/or paracrine Wnts emanating from the bone marrow (BM) niche. Here, we report a pivotal role for the R-spondin/leucine-rich repeat-containing G protein-coupled receptor 4 (LGR4) axis in driving aberrant Wnt/β-catenin signaling in MM. We show that LGR4 is expressed by MM plasma cells, but not by normal plasma cells or B cells. This aberrant LGR4 expression is driven by IL-6/STAT3 signaling and allows MM cells to hijack R-spondins produced by (pre)osteoblasts in the BM niche, resulting in Wnt (co)receptor stabilization and a dramatically increased sensitivity to auto- and paracrine Wnts. Our study identifies aberrant R-spondin/LGR4 signaling with consequent deregulation of Wnt (co)receptor turnover as a driver of oncogenic Wnt/β-catenin signaling in MM cells. These results advocate targeting of the LGR4/R-spondin interaction as a therapeutic strategy in MM.
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11
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Polk A, Lu Y, Wang T, Seymour E, Bailey NG, Singer JW, Boonstra PS, Lim MS, Malek S, Wilcox RA. Colony-Stimulating Factor-1 Receptor Is Required for Nurse-like Cell Survival in Chronic Lymphocytic Leukemia. Clin Cancer Res 2016; 22:6118-6128. [PMID: 27334834 DOI: 10.1158/1078-0432.ccr-15-3099] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 05/31/2016] [Accepted: 06/04/2016] [Indexed: 01/08/2023]
Abstract
PURPOSE Monocytes and their progeny are abundant constituents of the tumor microenvironment in lymphoproliferative disorders, including chronic lymphocytic leukemia (CLL). Monocyte-derived cells, including nurse-like cells (NLC) in CLL, promote lymphocyte proliferation and survival, confer resistance to chemotherapy, and are associated with more rapid disease progression. Colony-stimulating factor-1 receptor (CSF-1R) regulates the homeostatic survival of tissue-resident macrophages. Therefore, we sought to determine whether CSF-1R is similarly required for NLC survival. EXPERIMENTAL DESIGN CSF-1R expression by NLC was examined by flow cytometry and IHC. CSF-1R blocking studies were performed using an antagonistic mAb to examine its role in NLC generation and in CLL survival. A rational search strategy was performed to identify a novel tyrosine kinase inhibitor (TKI) targeting CSF-1R. The influence of TKI-mediated CSF-1R inhibition on NLC and CLL viability was examined. RESULTS We demonstrated that the generation and survival of NLC in CLL is dependent upon CSF-1R signaling. CSF-1R blockade is associated with significant depletion of NLC and consequently inhibits CLL B-cell survival. We found that the JAK2/FLT3 inhibitor pacritinib suppresses CSF-1R signaling, thereby preventing the generation and survival of NLC and impairs CLL B-cell viability. CONCLUSIONS CSF-1R is a novel therapeutic target that may be exploited in lymphoproliferative disorders, like CLL, that are dependent upon lymphoma-associated macrophages. Clin Cancer Res; 22(24); 6118-28. ©2016 AACR.
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Affiliation(s)
- Avery Polk
- Department of Internal Medicine, Division of Hematology and Oncology, University of Michigan, Ann Arbor, Michigan
| | - Ye Lu
- Department of Internal Medicine, Division of Hematology and Oncology, University of Michigan, Ann Arbor, Michigan
| | - Tianjiao Wang
- Department of Internal Medicine, Division of Hematology and Oncology, University of Michigan, Ann Arbor, Michigan
| | - Erlene Seymour
- Department of Internal Medicine, Division of Hematology and Oncology, University of Michigan, Ann Arbor, Michigan
| | | | | | - Philip S Boonstra
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Megan S Lim
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | - Sami Malek
- Department of Internal Medicine, Division of Hematology and Oncology, University of Michigan, Ann Arbor, Michigan
| | - Ryan A Wilcox
- Department of Internal Medicine, Division of Hematology and Oncology, University of Michigan, Ann Arbor, Michigan.
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12
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Yasrebi H. Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients. Brief Bioinform 2015; 17:771-85. [PMID: 26504096 PMCID: PMC5863785 DOI: 10.1093/bib/bbv092] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Indexed: 11/16/2022] Open
Abstract
Microarray gene expression data sets are jointly analyzed to increase statistical power.
They could either be merged together or analyzed by meta-analysis. For a given ensemble of
data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works
better. In this article, three joint analysis methods, Z -score
normalization, ComBat and the inverse normal method (meta-analysis) were selected for
survival prognosis and risk assessment of breast cancer patients. The methods were applied
to eight microarray gene expression data sets, totaling 1324 patients with two clinical
endpoints, overall survival and relapse-free survival. The performance derived from the
joint analysis methods was evaluated using Cox regression for survival analysis and
independent validation used as bias estimation. Overall, Z -score
normalization had a better performance than ComBat and meta-analysis. Higher Area Under
the Receiver Operating Characteristic curve and hazard ratio were also obtained when
independent validation was used as bias estimation. With a lower time and memory
complexity, Z -score normalization is a simple method for joint analysis
of microarray gene expression data sets. The derived findings suggest further assessment
of this method in future survival prediction and cancer classification applications.
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13
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Di Paolo A, Polillo M, Lastella M, Bocci G, Del Re M, Danesi R. Methods: for studying pharmacogenetic profiles of combination chemotherapeutic drugs. Expert Opin Drug Metab Toxicol 2015; 11:1253-67. [PMID: 26037261 DOI: 10.1517/17425255.2015.1053460] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
INTRODUCTION Molecular and genetic analysis of tumors and individuals has led to patient-centered therapies, through the discovery and identification of genetic markers predictive of drug efficacy and tolerability. Present therapies often include a combination of synergic drugs, each of them directed against different targets. Therefore, the pharmacogenetic profiling of tumor masses and patients is becoming a challenge, and several questions may arise when planning a translational study. AREAS COVERED The review presents the different techniques used to stratify oncology patients and to tailor antineoplastic treatments according to individual pharmacogenetic profiling. The advantages of these methodologies are discussed as well as current limits. EXPERT OPINION Facing the rapid technological evolution for genetic analyses, the most pressing issues are the choice of appropriate strategies (i.e., from gene candidate up to next-generation sequencing) and the possibility to replicate study results for their final validation. It is likely that the latter will be the major obstacle in the future. However, the present landscape is opening up new possibilities, overcoming those hurdles that have limited result translation into clinical settings for years.
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Affiliation(s)
- Antonello Di Paolo
- University of Pisa, Department of Clinical and Experimental Medicine, Via Roma 55, 56126 Pisa , Italy +39 050 2218755 ; +39 050 2218758 ;
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14
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Burmeister DW, Smith EH, Cristel RT, McKay SD, Shi H, Arthur GL, Davis JW, Taylor KH. The expression of RUNDC3B is associated with promoter methylation in lymphoid malignancies. Hematol Oncol 2015; 35:25-33. [PMID: 26011749 PMCID: PMC5363240 DOI: 10.1002/hon.2238] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Revised: 04/13/2015] [Accepted: 04/21/2015] [Indexed: 11/26/2022]
Abstract
DNA methylation is an epigenetic modification that plays an important role in the regulation of gene expression. The function of RUNDC3B has yet to be determined, although its dysregulated expression has been associated with malignant potential of both breast and lung carcinoma. To elucidate the potential of using DNA methylation in RUNDC3B as a biomarker in lymphoid malignancies, the methylation status of six regions spanning the CpG island in the promoter region of RUNDC3B was determined in cancer cell lines. Lymphoid malignancies were found to have more prominent methylation and did not express RUNDC3B compared with myeloid malignancies and solid tumours, supporting the potential use of DNA methylation in this region as a biomarker for lymphoid malignancies. RUNDC3B contains a RUN domain in its N‐terminal region that mediates interaction with Rap2, an important component of the mitogen‐activated protein kinase (MAPK) cascade, which regulates cellular proliferation and differentiation. The protein sequence of RUNDC3B also contains characteristic binding sites for MAPK intermediates. Therefore, it is possible that RUNDC3B serves as a mediator between Rap2 and the MAPK signalling cascade. Three genes with MAPK‐inducible expression were downregulated in a methylated leukaemia cell line (HSPA5, Jun and Fos). Jun and Fos combine to form the activating protein 1 transcription factor, and loss of this factor is associated with the dysregulation of genes involved in differentiation and proliferation. We hypothesize that the loss of RUNDC3B secondary to aberrant hypermethylation of the early growth response 3 transcription factor binding site results in dysregulated MAPK signalling and carcinogenesis in lymphoid malignancies. © 2015 The Authors. Hematological Oncology published by John Wiley & Sons Ltd
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Affiliation(s)
- Dane W Burmeister
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, USA
| | - Emily H Smith
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, USA.,Department of Dermatology, University of Michigan Health System, Ann Arbor, MI, USA
| | - Robert T Cristel
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, USA
| | - Stephanie D McKay
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, USA.,Department of Animal Science, University of Vermont, Burlington, VT, USA
| | - Huidong Shi
- Department of Biochemistry and Molecular Biology, Georgia Regents University, Augusta, GA, USA
| | - Gerald L Arthur
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, USA
| | - Justin Wade Davis
- Department of Health Management and Informatics, University of Missouri, Columbia, MO, USA.,Department of Statistics, University of Missouri, Columbia, MO, USA
| | - Kristen H Taylor
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO, USA
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15
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Yang L, Ainali C, Tsoka S, Papageorgiou LG. Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework. BMC Bioinformatics 2014; 15:390. [PMID: 25475756 PMCID: PMC4269079 DOI: 10.1186/s12859-014-0390-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Accepted: 11/19/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or outcome. Traditional approaches where expression of genes were treated independently suffer from low prediction accuracy and difficulty of biological interpretation. Current research efforts focus on integrating information on protein interactions through biochemical pathway datasets with expression profiles to propose pathway-based classifiers that can enhance disease diagnosis and prognosis. As most of the pathway activity inference methods in literature are either unsupervised or applied on two-class datasets, there is good scope to address such limitations by proposing novel methodologies. RESULTS A supervised multiclass pathway activity inference method using optimisation techniques is reported. For each pathway expression dataset, patterns of its constituent genes are summarised into one composite feature, termed pathway activity, and a novel mathematical programming model is proposed to infer this feature as a weighted linear summation of expression of its constituent genes. Gene weights are determined by the optimisation model, in a way that the resulting pathway activity has the optimal discriminative power with regards to disease phenotypes. Classification is then performed on the resulting low-dimensional pathway activity profile. CONCLUSIONS The model was evaluated through a variety of published gene expression profiles that cover different types of disease. We show that not only does it improve classification accuracy, but it can also perform well in multiclass disease datasets, a limitation of other approaches from the literature. Desirable features of the model include the ability to control the maximum number of genes that may participate in determining pathway activity, which may be pre-specified by the user. Overall, this work highlights the potential of building pathway-based multi-phenotype classifiers for accurate disease diagnosis and prognosis problems.
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Affiliation(s)
- Lingjian Yang
- Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, London, WC1E 7JE, UK.
| | - Chrysanthi Ainali
- Department of Informatics, School of Natural and Mathematical Sciences, King's College London, London, WC2R 2LS, UK.
| | - Sophia Tsoka
- Department of Informatics, School of Natural and Mathematical Sciences, King's College London, London, WC2R 2LS, UK.
| | - Lazaros G Papageorgiou
- Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, London, WC1E 7JE, UK.
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16
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Reddy V, Leandro M. Variability in clinical and biological response to rituximab in autoimmune diseases: an opportunity for personalized therapy? ACTA ACUST UNITED AC 2014. [DOI: 10.2217/ijr.14.18] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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17
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A three step network based approach (TSNBA) to finding disease molecular signature and key regulators: a case study of IL-1 and TNF-alpha stimulated inflammation. PLoS One 2014; 9:e94360. [PMID: 24747419 PMCID: PMC3991618 DOI: 10.1371/journal.pone.0094360] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Accepted: 03/13/2014] [Indexed: 12/11/2022] Open
Abstract
A disease molecular signature is a set of biomolecular features that are prognostic of clinical phenotypes and indicative of underlying pathology. It is of great importance to develop computational approaches for finding more relevant molecular signatures. Based upon the hypothesis that various components in a molecular signature are more likely to share similar patterns, we introduced a novel three step network based approach (TSNBA) to identify the molecular signature and key pathological regulators. Protein-protein interaction (PPI) network and ranking algorithm were integrated in the first step to find pathology related proteins with high accuracy. It was followed by the second step to further screen with co-expression patterns for better pathology enrichment. Context likelihood of relatedness (CLR) algorithm was used in the third step to infer gene regulatory networks and identify key transcription regulators. We applied this approach to study IL-1 (interleukin-1) and TNF-alpha (tumor necrosis factor-alpha) stimulated inflammation. TSNBA identified inflammatory signature with high accuracy and outperformed 5 competing methods namely fold change, degree, interconnectivity, neighborhood score and network propagation based approaches. The best molecular signature, with 80% (40/50) confirmed inflammatory genes, was used to predict inflammation related genes. As a result, 8 out of 10 predicted inflammation genes that were not included in the benchmark Entrez Gene database were validated by literature evidence. Furthermore, 23 of the 32 predicted inflammation regulators were validated by literature evidence. The rest 9 were also validated with TF (transcription factor) binding site analysis. In conclusion, we developed an efficient strategy for disease molecular signature finding and key pathological regulator identification.
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18
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Ferreira PG, Jares P, Rico D, Gómez-López G, Martínez-Trillos A, Villamor N, Ecker S, González-Pérez A, Knowles DG, Monlong J, Johnson R, Quesada V, Djebali S, Papasaikas P, López-Guerra M, Colomer D, Royo C, Cazorla M, Pinyol M, Clot G, Aymerich M, Rozman M, Kulis M, Tamborero D, Gouin A, Blanc J, Gut M, Gut I, Puente XS, Pisano DG, Martin-Subero JI, López-Bigas N, López-Guillermo A, Valencia A, López-Otín C, Campo E, Guigó R. Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia. Genome Res 2013; 24:212-26. [PMID: 24265505 PMCID: PMC3912412 DOI: 10.1101/gr.152132.112] [Citation(s) in RCA: 153] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Chronic lymphocytic leukemia (CLL) has heterogeneous clinical and biological behavior. Whole-genome and -exome sequencing has contributed to the characterization of the mutational spectrum of the disease, but the underlying transcriptional profile is still poorly understood. We have performed deep RNA sequencing in different subpopulations of normal B-lymphocytes and CLL cells from a cohort of 98 patients, and characterized the CLL transcriptional landscape with unprecedented resolution. We detected thousands of transcriptional elements differentially expressed between the CLL and normal B cells, including protein-coding genes, noncoding RNAs, and pseudogenes. Transposable elements are globally derepressed in CLL cells. In addition, two thousand genes—most of which are not differentially expressed—exhibit CLL-specific splicing patterns. Genes involved in metabolic pathways showed higher expression in CLL, while genes related to spliceosome, proteasome, and ribosome were among the most down-regulated in CLL. Clustering of the CLL samples according to RNA-seq derived gene expression levels unveiled two robust molecular subgroups, C1 and C2. C1/C2 subgroups and the mutational status of the immunoglobulin heavy variable (IGHV) region were the only independent variables in predicting time to treatment in a multivariate analysis with main clinico-biological features. This subdivision was validated in an independent cohort of patients monitored through DNA microarrays. Further analysis shows that B-cell receptor (BCR) activation in the microenvironment of the lymph node may be at the origin of the C1/C2 differences.
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Affiliation(s)
- Pedro G Ferreira
- Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain
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19
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Sung J, Kim PJ, Ma S, Funk CC, Magis AT, Wang Y, Hood L, Geman D, Price ND. Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures. PLoS Comput Biol 2013; 9:e1003148. [PMID: 23935471 PMCID: PMC3723500 DOI: 10.1371/journal.pcbi.1003148] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2012] [Accepted: 06/05/2013] [Indexed: 12/23/2022] Open
Abstract
We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein – Identification of Structured Signatures and Classifiers (ISSAC) – that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood. From a multi-study, integrated transcriptomic dataset, we identified a marker panel for differentiating major human brain cancers at the gene-expression level. The ISSAC molecular signatures for brain cancers, composed of 44 unique genes, are based on comparing expression levels of pairs of genes, and phenotype prediction follows a diagnostic hierarchy. We found that sufficient dataset integration across multiple studies greatly enhanced diagnostic performance on truly independent validation sets, whereas signatures learned from only one dataset typically led to high error rate. Molecular signatures of brain cancers, when obtained using all currently available gene-expression data, achieved 90% phenotype prediction accuracy. Thus, our integrative approach holds significant promise for developing organ-level, comprehensive, molecular signatures of disease.
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Affiliation(s)
- Jaeyun Sung
- Institute for Systems Biology, Seattle, Washington, United States of America
- Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, Illinois, United States of America
| | - Pan-Jun Kim
- Asia Pacific Center for Theoretical Physics, Pohang, Gyeongbuk, Republic of Korea
- Department of Physics, POSTECH, Pohang, Gyeongbuk, Republic of Korea
| | - Shuyi Ma
- Institute for Systems Biology, Seattle, Washington, United States of America
- Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, Illinois, United States of America
| | - Cory C. Funk
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Andrew T. Magis
- Institute for Systems Biology, Seattle, Washington, United States of America
- Center for Biophysics and Computational Biology, University of Illinois, Urbana, Illinois, United States of America
| | - Yuliang Wang
- Institute for Systems Biology, Seattle, Washington, United States of America
- Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, Illinois, United States of America
| | - Leroy Hood
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Donald Geman
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, Washington, United States of America
- * E-mail:
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20
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Friedman DR, Lucas JE, Weinberg JB. Clinical and biological relevance of genomic heterogeneity in chronic lymphocytic leukemia. PLoS One 2013; 8:e57356. [PMID: 23468975 PMCID: PMC3585365 DOI: 10.1371/journal.pone.0057356] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2012] [Accepted: 01/21/2013] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Chronic lymphocytic leukemia (CLL) is typically regarded as an indolent B-cell malignancy. However, there is wide variability with regards to need for therapy, time to progressive disease, and treatment response. This clinical variability is due, in part, to biological heterogeneity between individual patients' leukemias. While much has been learned about this biological variation using genomic approaches, it is unclear whether such efforts have sufficiently evaluated biological and clinical heterogeneity in CLL. METHODS To study the extent of genomic variability in CLL and the biological and clinical attributes of genomic classification in CLL, we evaluated 893 unique CLL samples from fifteen publicly available gene expression profiling datasets. We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups. RESULTS Using an unsupervised approach, we determined that approximately 600 CLL samples are needed to define the spectrum of diversity in CLL genomic expression. We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes. CONCLUSIONS Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology. These findings may have important implications in identifying patients who should be treated with specific targeted therapies, which could have efficacy against CLL cells that rely on specific biological pathways.
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Affiliation(s)
- Daphne R Friedman
- Department of Medicine, Duke University, Durham, North Carolina, USA.
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21
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Roy J, Winter C, Isik Z, Schroeder M. Network information improves cancer outcome prediction. Brief Bioinform 2012; 15:612-25. [PMID: 23255167 DOI: 10.1093/bib/bbs083] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression.
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22
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Emerging real-time technologies in molecular medicine and the evolution of integrated ‘pharmacomics’ approaches to personalized medicine and drug discovery. Pharmacol Ther 2012; 136:295-304. [DOI: 10.1016/j.pharmthera.2012.08.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2012] [Accepted: 08/07/2012] [Indexed: 01/05/2023]
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23
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Sampson ER, McMurray HR, Hassane DC, Newman L, Salzman P, Jordan CT, Land H. Gene signature critical to cancer phenotype as a paradigm for anticancer drug discovery. Oncogene 2012; 32:3809-18. [PMID: 22964631 DOI: 10.1038/onc.2012.389] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2011] [Revised: 04/25/2012] [Accepted: 07/20/2012] [Indexed: 02/06/2023]
Abstract
Malignant cell transformation commonly results in the deregulation of thousands of cellular genes, an observation that suggests a complex biological process and an inherently challenging scenario for the development of effective cancer interventions. To better define the genes/pathways essential to regulating the malignant phenotype, we recently described a novel strategy based on the cooperative nature of carcinogenesis that focuses on genes synergistically deregulated in response to cooperating oncogenic mutations. These so-called 'cooperation response genes' (CRGs) are highly enriched for genes critical for the cancer phenotype, thereby suggesting their causal role in the malignant state. Here, we show that CRGs have an essential role in drug-mediated anticancer activity and that anticancer agents can be identified through their ability to antagonize the CRG expression profile. These findings provide proof-of-concept for the use of the CRG signature as a novel means of drug discovery with relevance to underlying anticancer drug mechanisms.
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Affiliation(s)
- E R Sampson
- Department of Biomedical Genetics, University of Rochester Medical Center, Rochester, NY 14642, USA
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24
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Sung J, Wang Y, Chandrasekaran S, Witten DM, Price ND. Molecular signatures from omics data: from chaos to consensus. Biotechnol J 2012; 7:946-57. [PMID: 22528809 PMCID: PMC3418428 DOI: 10.1002/biot.201100305] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Revised: 02/14/2012] [Accepted: 03/08/2012] [Indexed: 01/17/2023]
Abstract
In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.
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Affiliation(s)
- Jaeyun Sung
- Institute for Systems BiologySeattle, WA, USA
- Department of Chemical and Biomolecular Engineering, University of IllinoisUrbana, IL, USA
| | - Yuliang Wang
- Institute for Systems BiologySeattle, WA, USA
- Department of Chemical and Biomolecular Engineering, University of IllinoisUrbana, IL, USA
| | - Sriram Chandrasekaran
- Institute for Systems BiologySeattle, WA, USA
- Center for Biophysics and Computational Biology, University of IllinoisUrbana, IL, USA
| | - Daniela M Witten
- Department of Biostatistics, University of WashingtonSeattle, WA, USA
| | - Nathan D Price
- Institute for Systems BiologySeattle, WA, USA
- Department of Chemical and Biomolecular Engineering, University of IllinoisUrbana, IL, USA
- Center for Biophysics and Computational Biology, University of IllinoisUrbana, IL, USA
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25
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Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression. Blood 2012; 120:2639-49. [PMID: 22837534 DOI: 10.1182/blood-2012-03-416461] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for CLL progression. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection, more accurately than established markers. The prognostic power of these subnetworks then was validated on 2 other cohorts of patients. We noted reduced divergence in gene expression between leukemia cells of CLL patients classified at diagnosis with aggressive versus indolent disease over time. The predictive subnetworks vary in levels of expression over time but exhibit increased similarity at later time points before therapy, suggesting that degenerate pathways apparently converge into common pathways that are associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL.
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26
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Yang X, Regan K, Huang Y, Zhang Q, Li J, Seiwert TY, Cohen EEW, Xing HR, Lussier YA. Single sample expression-anchored mechanisms predict survival in head and neck cancer. PLoS Comput Biol 2012; 8:e1002350. [PMID: 22291585 PMCID: PMC3266878 DOI: 10.1371/journal.pcbi.1002350] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2011] [Accepted: 11/28/2011] [Indexed: 12/11/2022] Open
Abstract
Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These “causality challenges” hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of “Oncogenic FAIME Features of HNSCC” (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume). Clinical utilization of multi-gene expression signatures that are predictive of therapeutic response has been steadily increasing, however, interpretation of such results remains challenging because multi-gene signatures, generated from analyzing different patient cohorts, tend to be equally predictive but contain minimal overlap. Whereas pathway-level analyses of expression arrays show promise for generating clinically meaningful mechanistic signatures, current approaches do not permit single-patient based analyses that are independent of cross-group calculations. To bridge the gap between deterministic biological mechanisms of single-gene biomarkers and the statistical predictive power of multi-gene signatures that are disconnected from mechanisms, we developed FAIME, a novel method that transforms microarray gene expression data into individualized patient profiles of molecular mechanisms. We have validated its capability for predicting clinical outcomes, including cancer patient samples derived from six different clinical trial cohorts of head and neck cancers. This method provides opportunities to harness an untapped resource for personal genomics: clinical evaluation and testing of individually interpretable mechanistic profiles derived from gene expression arrays.
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Affiliation(s)
- Xinan Yang
- Center for Biomedical Informatics, The University of Chicago, Chicago, Illinois, United States of America
- Section of Genetic Medicine, The University of Chicago, Chicago, Illinois, United States of America
| | - Kelly Regan
- Center for Biomedical Informatics, The University of Chicago, Chicago, Illinois, United States of America
| | - Yong Huang
- Center for Biomedical Informatics, The University of Chicago, Chicago, Illinois, United States of America
- Section of Genetic Medicine, The University of Chicago, Chicago, Illinois, United States of America
| | - Qingbei Zhang
- Center for Biomedical Informatics, The University of Chicago, Chicago, Illinois, United States of America
- Section of Genetic Medicine, The University of Chicago, Chicago, Illinois, United States of America
| | - Jianrong Li
- Center for Biomedical Informatics, The University of Chicago, Chicago, Illinois, United States of America
- Section of Genetic Medicine, The University of Chicago, Chicago, Illinois, United States of America
| | - Tanguy Y. Seiwert
- Section of Hematology/Oncology of the Department of Medicine, The University of Chicago, Chicago, Illinois, United States of America
- Comprehensive Cancer Center, The University of Chicago, Chicago, Illinois, United States of America
| | - Ezra E. W. Cohen
- Section of Hematology/Oncology of the Department of Medicine, The University of Chicago, Chicago, Illinois, United States of America
- Comprehensive Cancer Center, The University of Chicago, Chicago, Illinois, United States of America
| | - H. Rosie Xing
- Comprehensive Cancer Center, The University of Chicago, Chicago, Illinois, United States of America
- Departments of Pathology and of Cellular and Radiation Oncology, The University of Chicago, Chicago, Illinois, United States of America
- Ludwig Center for Metastasis Research, The University of Chicago, Chicago, Illinois, United States of America
| | - Yves A. Lussier
- Center for Biomedical Informatics, The University of Chicago, Chicago, Illinois, United States of America
- Section of Genetic Medicine, The University of Chicago, Chicago, Illinois, United States of America
- Comprehensive Cancer Center, The University of Chicago, Chicago, Illinois, United States of America
- Departments of Pathology and of Cellular and Radiation Oncology, The University of Chicago, Chicago, Illinois, United States of America
- Ludwig Center for Metastasis Research, The University of Chicago, Chicago, Illinois, United States of America
- Computation Institute, Institute for Translational Medicine, and Institute for Genomics and Systems Biology, The University of Chicago, Chicago, Illinois, United States of America
- * E-mail:
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Moreno C, Montserrat E. Genetic lesions in chronic lymphocytic leukemia: what's ready for prime time use? Haematologica 2011; 95:12-5. [PMID: 20065079 DOI: 10.3324/haematol.2009.016873] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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28
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Tentler JJ, Nallapareddy S, Tan AC, Spreafico A, Pitts TM, Morelli MP, Selby HM, Kachaeva MI, Flanigan SA, Kulikowski GN, Leong S, Arcaroli JJ, Messersmith WA, Eckhardt SG. Identification of predictive markers of response to the MEK1/2 inhibitor selumetinib (AZD6244) in K-ras-mutated colorectal cancer. Mol Cancer Ther 2010; 9:3351-62. [PMID: 20923857 DOI: 10.1158/1535-7163.mct-10-0376] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Mutant K-ras activity leads to the activation of the RAS/RAF/MEK/ERK pathway in approximately 44% of colorectal cancer (CRC) tumors. Accordingly, several inhibitors of the MEK pathway are under clinical evaluation in several malignancies including CRC. The aim of this study was to develop and characterize predictive biomarkers of response to the MEK1/2 inhibitor AZD6244 in CRC in order to maximize the clinical utility of this agent. Twenty-seven human CRC cell lines were exposed to AZD6244 and classified according to the IC(50) value as sensitive (≤ 0.1 μmol/L) or resistant (>1 μmol/L). All cell lines were subjected to immunoblotting for effector proteins, K-ras/BRAF mutation status, and baseline gene array analysis. Further testing was done in cell line xenografts and K-ras mutant CRC human explants models to develop a predictive genomic classifier for AZD6244. The most sensitive and resistant cell lines were subjected to differential gene array and pathway analyses. Members of the Wnt signaling pathway were highly overexpressed in cell lines resistant to AZD6244 and seem to be functionally involved in mediating resistance by shRNA knockdown studies. Baseline gene array data from CRC cell lines and xenografts were used to develop a k-top scoring pair (k-TSP) classifier, which predicted with 71% accuracy which of a test set of patient-derived K-ras mutant CRC explants would respond to AZD6244, providing the basis for a patient-selective clinical trial. These results also indicate that resistance to AZD6244 may be mediated, in part, by the upregulation of the Wnt pathway, suggesting potential rational combination partners for AZD6244 in CRC.
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Affiliation(s)
- John J Tentler
- Division of Medical Oncology, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado 80045, USA.
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Abstract
An increasing number of neoplasms are associated with variably specific genetic abnormalities. This is best exemplified by hematological malignancies, in which there is a growing list of entities that are defined by their genetic lesion(s); this is not (yet) the case in mature B-cell lymphomas. However, enhanced insights into the pathogenesis of this large and diverse group of lymphomas have emerged with the ongoing unraveling of a plethora of fascinating genetic abnormalities. The purpose of this review is to synthesize well-recognized data and nascent discoveries in our understanding of the genetic basis of a spectrum of mature B-cell lymphomas, and how this may be applied to contemporary clinical practice. Despite the explosion of new and exciting knowledge in this arena, with the potential for enhanced diagnostic and prognostic strategies, it is essential to remain cognizant of the limitations (and complexity) of genetic investigations, so that assays can be developed and used both judiciously and rationally.
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