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Alontaga AY, Cano P, Ozakinci H, Puskas JA, Stewart PA, Welsh EA, Yoder SJ, Hicks JK, Saltos AN, Bossler AD, Haura EB, Koomen JM, Boyle TA. Implementation of a High-Accuracy Targeted Gene Expression Panel for Clinical Care. J Mol Diagn 2024; 26:685-699. [PMID: 38777037 PMCID: PMC11299514 DOI: 10.1016/j.jmoldx.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/29/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
This study describes the validation of a clinical RNA expression panel with evaluation of concordance between gene copy gain by a next-generation sequencing (NGS) assay and high gene expression by an RNA expression panel. The RNA Salah Targeted Expression Panel (RNA STEP) was designed with input from oncologists to include 204 genes with utility for clinical trial prescreening and therapy selection. RNA STEP was validated with the nanoString platform using remnant formalin-fixed, paraffin-embedded-derived RNA from 102 patients previously tested with a validated clinical NGS panel. The repeatability, reproducibility, and concordance of RNA STEP results with NGS results were evaluated. RNA STEP demonstrated high repeatability and reproducibility, with excellent correlation (r > 0.97, P < 0.0001) for all comparisons. Comparison of RNA STEP high gene expression (log2 ratio ≥ 2) versus NGS DNA-based gene copy number gain (copies ≥ 5) for 38 mutually covered genes revealed an accuracy of 93.0% with a positive percentage agreement of 69.4% and negative percentage agreement of 93.8%. Moderate correlation was observed between platforms (r = 0.53, P < 0.0001). Concordance between high gene expression and gene copy number gain varied by specific gene, and some genes had higher accuracy between assays. Clinical implementation of RNA STEP provides gene expression data complementary to NGS and offers a tool for prescreening patients for clinical trials.
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Affiliation(s)
- Aileen Y Alontaga
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Pedro Cano
- Department of Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Hilal Ozakinci
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - John A Puskas
- Department of Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Paul A Stewart
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Eric A Welsh
- Biostatistics and Bioinformatics Shared Resource, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Sean J Yoder
- Molecular Genomics Core Facility, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - J Kevin Hicks
- Department of Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Andreas N Saltos
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Aaron D Bossler
- Department of Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Eric B Haura
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - John M Koomen
- Department of Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida; Department of Molecular Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Theresa A Boyle
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida; Department of Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
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Gupta M, Kannappan S, Jain M, Douglass D, Shah R, Bose P, Narendran A. Development and validation of a 21-gene prognostic signature in neuroblastoma. Sci Rep 2023; 13:12526. [PMID: 37532697 PMCID: PMC10397261 DOI: 10.1038/s41598-023-37714-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 06/26/2023] [Indexed: 08/04/2023] Open
Abstract
Survival outcomes for patients with neuroblastoma vary markedly and reliable prognostic markers and risk stratification tools are lacking. We sought to identify and validate a transcriptomic signature capable of predicting risk of mortality in patients with neuroblastoma. The TARGET NBL dataset (n = 243) was used to develop the model and two independent cohorts, E-MTAB-179 (n = 478) and GSE85047 (n = 240) were used as validation sets. EFS was the primary outcome and OS was the secondary outcome of interest for all analysis. We identified a 21-gene signature capable of stratifying neuroblastoma patients into high and low risk groups in the E-MTAB-179 (HR 5.87 [3.83-9.01], p < 0.0001, 5 year AUC 0.827) and GSE85047 (HR 3.74 [2.36-5.92], p < 0.0001, 5 year AUC 0.815) validation cohorts. Moreover, the signature remained independent of known clinicopathological variables, and remained prognostic within clinically important subgroups. Further, the signature was effectively incorporated into a risk model with clinicopathological variables to improve prognostic performance across validation cohorts (Pooled Validation HR 6.93 [4.89-9.83], p < 0.0001, 5 year AUC 0.839). Similar prognostic utility was also demonstrated with OS. The identified signature is a robust independent predictor of EFS and OS outcomes in neuroblastoma patients and can be combined with clinically utilized clinicopathological variables to improve prognostic performance.
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Affiliation(s)
- Mehul Gupta
- Department of Pediatrics and Oncology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada
| | - Sunand Kannappan
- Department of Pediatrics and Oncology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada
| | - Mohit Jain
- Department of Pediatrics and Oncology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada
| | - David Douglass
- Department of Pediatrics, Hematology/Oncology Section, Arkansas Children's Hospital, University of Arkansas for Medical Sciences, Little Rock, AR, 72202, USA
| | - Ravi Shah
- Department of Pediatrics and Oncology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada
| | - Pinaki Bose
- Departments of Oncology and Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada.
- Cumming School of Medicine, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB, T2N 4N1, Canada.
| | - Aru Narendran
- Department of Pediatrics and Oncology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada.
- Departments of Oncology and Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada.
- Cumming School of Medicine, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB, T2N 4N1, Canada.
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Veneziani I, Infante P, Ferretti E, Melaiu O, Battistelli C, Lucarini V, Compagnone M, Nicoletti C, Castellano A, Petrini S, Ognibene M, Pezzolo A, Di Marcotullio L, Bei R, Moretta L, Pistoia V, Fruci D, Barnaba V, Locatelli F, Cifaldi L. Nutlin-3a Enhances Natural Killer Cell-Mediated Killing of Neuroblastoma by Restoring p53-Dependent Expression of Ligands for NKG2D and DNAM-1 Receptors. Cancer Immunol Res 2021; 9:170-183. [PMID: 33303573 DOI: 10.1158/2326-6066.cir-20-0313] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 09/17/2020] [Accepted: 12/04/2020] [Indexed: 11/16/2022]
Abstract
In this study, we explored whether Nutlin-3a, a well-known, nontoxic small-molecule compound antagonizing the inhibitory interaction of MDM2 with the tumor suppressor p53, may restore ligands for natural killer (NK) cell-activating receptors (NK-AR) on neuroblastoma cells to enhance the NK cell-mediated killing. Neuroblastoma cell lines were treated with Nutlin-3a, and the expression of ligands for NKG2D and DNAM-1 NK-ARs and the neuroblastoma susceptibility to NK cells were evaluated. Adoptive transfer of human NK cells in a xenograft neuroblastoma-bearing NSG murine model was assessed. Two data sets of neuroblastoma patients were explored to correlate p53 expression with ligand expression. Luciferase assays and chromatin immunoprecipitation analysis of p53 functional binding on PVR promoter were performed. Primary neuroblastoma cells were also treated with Nutlin-3a, and neuroblastoma spheroids obtained from one high-risk patient were assayed for NK-cell cytotoxicity. We provide evidence showing that the Nutlin-3a-dependent rescue of p53 function in neuroblastoma cells resulted in (i) increased surface expression of ligands for NK-ARs, thus rendering neuroblastoma cell lines significantly more susceptible to NK cell-mediated killing; (ii) shrinkage of human neuroblastoma tumor masses that correlated with overall survival upon adoptive transfer of NK cells in neuroblastoma-bearing mice; (iii) and increased expression of ligands in primary neuroblastoma cells and boosting of NK cell-mediated disaggregation of neuroblastoma spheroids. We also found that p53 was a direct transcription factor regulating the expression of PVR ligand recognized by DNAM-1. Our findings demonstrated an immunomodulatory role of Nutlin-3a, which might be prospectively used for a novel NK cell-based immunotherapy for neuroblastoma.
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Affiliation(s)
- Irene Veneziani
- Department of Paediatric Haematology/Oncology and of Cell and Gene Therapy, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
| | - Paola Infante
- Center for Life NanoScience@Sapienza, Istituto Italiano di Tecnologia, Rome, Italy
| | - Elisa Ferretti
- Department of Experimental Medicine, University of Genoa, Genova, Italy
- Centre of Excellence for Biomedical Research, University of Genoa, Genova, Italy
| | - Ombretta Melaiu
- Department of Paediatric Haematology/Oncology and of Cell and Gene Therapy, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
| | - Cecilia Battistelli
- Istituto Pasteur-Fondazione Cenci Bolognetti, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Valeria Lucarini
- Department of Paediatric Haematology/Oncology and of Cell and Gene Therapy, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
| | - Mirco Compagnone
- Department of Paediatric Haematology/Oncology and of Cell and Gene Therapy, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
| | - Carmine Nicoletti
- Istituto Pasteur-Fondazione Cenci Bolognetti, Rome, Italy
- DAHFMO-Unit of Histology and Medical Embryology, Sapienza University of Rome, Rome, Italy
| | - Aurora Castellano
- Department of Paediatric Haematology/Oncology and of Cell and Gene Therapy, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
| | - Stefania Petrini
- Confocal Microscopy, Core Facility, Research Laboratories, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
| | - Marzia Ognibene
- Laboratorio Cellule Staminali Post Natali e Terapie Cellulari, IRCCS Giannina Gaslini Institute, Genoa, Italy
| | - Annalisa Pezzolo
- Laboratorio Cellule Staminali Post Natali e Terapie Cellulari, IRCCS Giannina Gaslini Institute, Genoa, Italy
| | - Lucia Di Marcotullio
- Istituto Pasteur-Fondazione Cenci Bolognetti, Rome, Italy
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Roberto Bei
- Department of Clinical Sciences and Translational Medicine, University of Rome "Tor Vergata," Rome, Italy
| | - Lorenzo Moretta
- Department of Immunology, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Vito Pistoia
- Department of Immunology, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Doriana Fruci
- Department of Paediatric Haematology/Oncology and of Cell and Gene Therapy, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
| | - Vincenzo Barnaba
- Istituto Pasteur-Fondazione Cenci Bolognetti, Rome, Italy
- Cellular and Molecular Immunology Unit, Dipartimento di Scienze Cliniche, Internistiche, Anestesiologiche e Cardiovascolari, Sapienza University of Rome, Rome, Italy
| | - Franco Locatelli
- Department of Paediatric Haematology/Oncology and of Cell and Gene Therapy, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
- Department of Pediatrics, Sapienza University of Rome, Rome, Italy
| | - Loredana Cifaldi
- Department of Paediatric Haematology/Oncology and of Cell and Gene Therapy, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy.
- Department of Clinical Sciences and Translational Medicine, University of Rome "Tor Vergata," Rome, Italy
- Academic Department of Pediatrics (DPUO), Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
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Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma. Nat Commun 2020; 11:5992. [PMID: 33239635 PMCID: PMC7689423 DOI: 10.1038/s41467-020-19781-y] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 10/26/2020] [Indexed: 12/19/2022] Open
Abstract
Tumor-infiltrating lymphocytes play an essential role in improving clinical outcome of neuroblastoma (NB) patients, but their relationship with other tumor-infiltrating immune cells in the T cell-inflamed tumors remains poorly investigated. Here we show that dendritic cells (DCs) and natural killer (NK) cells are positively correlated with T-cell infiltration in human NB, both at transcriptional and protein levels, and associate with a favorable prognosis. Multiplex imaging displays DC/NK/T cell conjugates in the tumor microenvironment of low-risk NB. Remarkably, this connection is further strengthened by the identification of gene signatures related to DCs and NK cells able to predict survival of NB patients and strongly correlate with the expression of PD-1 and PD-L1. In summary, our findings unveil a key prognostic role of DCs and NK cells and indicate their related gene signatures as promising tools for the identification of clinical biomarkers to better define risk stratification and survival of NB patients. Tumour-infiltrating lymphocytes play a crucial role in neuroblastoma, but their relationship to other immune cells is poorly understood. Here the authors identify the cellular and gene signatures of intratumoural dendritic cells and natural killer cells that predict the clinical outcome of neuroblastoma.
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Yan W, Jamal M, Tan SH, Song Y, Young D, Chen Y, Katta S, Ying K, Ravindranath L, Woodle T, Kohaar I, Cullen J, Kagan J, Srivastava S, Dobi A, McLeod DG, Rosner IL, Sesterhenn IA, Srinivasan A, Srivastava S, Petrovics G. Molecular profiling of radical prostatectomy tissue from patients with no sign of progression identifies ERG as the strongest independent predictor of recurrence. Oncotarget 2019; 10:6466-6483. [PMID: 31741711 PMCID: PMC6849651 DOI: 10.18632/oncotarget.27294] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 10/19/2019] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND As a major cause of morbidity and mortality among men, prostate cancer is a heterogenous disease, with a vast heterogeneity in the biology of the disease and in clinical outcome. While it often runs an indolent course, local progression or metastasis may eventually develop, even among patients considered "low risk" at diagnosis. Therefore, biomarkers that can discriminate aggressive from indolent disease at an early stage would greatly benefit patients. We hypothesized that tissue specimens from early stage prostate cancers may harbor predictive signatures for disease progression. METHODS We used a cohort of radical prostatectomy patients with longitudinal follow-up, who had tumors with low grade and stage that revealed no signs of future disease progression at surgery. During the follow-up period, some patients either remained indolent (non-BCR) or progressed to biochemical recurrence (BCR). Total RNA was extracted from tumor, and adjacent normal epithelium of formalin-fixed-paraffin-embedded (FFPE) specimens. Differential gene expression in tumors, and in tumor versus normal tissues between BCR and non-BCR patients were analyzed by NanoString using a customized CodeSet of 151 probes. RESULTS After controlling for false discovery rates, we identified a panel of eight genes (ERG, GGT1, HDAC1, KLK2, MYO6, PLA2G7, BICD1 and CACNAID) that distinguished BCR from non-BCR patients. We found a clear association of ERG expression with non-BCR, which was further corroborated by quantitative RT-PCR and immunohistochemistry assays. CONCLUSIONS Our results identified ERG as the strongest predictor for BCR and showed that potential prognostic prostate cancer biomarkers can be identified from FFPE tumor specimens.
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Affiliation(s)
- Wusheng Yan
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
- These authors contributed equally to this work
| | - Muhammad Jamal
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
- These authors contributed equally to this work
| | - Shyh-Han Tan
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
- These authors contributed equally to this work
| | - Yingjie Song
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Denise Young
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Yongmei Chen
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Shilpa Katta
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Kai Ying
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Lakshmi Ravindranath
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Tarah Woodle
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Indu Kohaar
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Jennifer Cullen
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
- John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Jacob Kagan
- Division of Cancer Prevention, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Sudhir Srivastava
- Division of Cancer Prevention, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Albert Dobi
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
- John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - David G. McLeod
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
- John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Inger L. Rosner
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
- John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | | | - Alagarsamy Srinivasan
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Shiv Srivastava
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
- John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Gyorgy Petrovics
- Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
- Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD, USA
- John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, MD, USA
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McQuerry JA, Jenkins DF, Yost SE, Zhang Y, Schmolze D, Johnson WE, Yuan Y, Bild AH. Pathway activity profiling of growth factor receptor network and stemness pathways differentiates metaplastic breast cancer histological subtypes. BMC Cancer 2019; 19:881. [PMID: 31488082 PMCID: PMC6727561 DOI: 10.1186/s12885-019-6052-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 08/19/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Gene expression profiling of rare cancers has proven challenging due to limited access to patient materials and requirement of intact, non-degraded RNA for next-generation sequencing. We customized a gene expression panel compatible with degraded RNA from formalin-fixed, paraffin-embedded (FFPE) patient cancer samples and investigated its utility in pathway activity profiling in patients with metaplastic breast cancer (MpBC). METHODS Activity of various biological pathways was profiled in samples from nineteen patients with MpBC and 8 patients with invasive ductal carcinoma with triple negative breast cancer (TNBC) phenotype using a custom gene expression-based assay of 345 genes. RESULTS MpBC samples of mesenchymal (chondroid and/or osteoid) histology demonstrated increased SNAI1 and BCL2L11 pathway activity compared to samples with non-mesenchymal histology. Additionally, late cornified envelope and keratinization genes were downregulated in MpBC compared to TNBC, and epithelial-to-mesenchymal transition (EMT) and collagen genes were upregulated in MpBC. Patients with high activity of an invasiveness gene expression signature, as well as high expression of the mesenchymal marker and extracellular matrix glycoprotein gene SPARC, experienced worse outcomes than those with low invasiveness activity and low SPARC expression. CONCLUSIONS This study demonstrates the utility of gene expression profiling of metaplastic breast cancer FFPE samples with a custom counts-based assay. Gene expression patterns identified by this assay suggest that, although often histologically triple negative, patients with MpBC have distinct pathway activation compared to patients with invasive ductal TNBC. Incorporation of targeted therapies may lead to improved outcome for MpBC patients, especially in those patients expressing increased activity of invasiveness pathways.
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Affiliation(s)
- Jasmine A. McQuerry
- Department of Oncological Sciences, School of Medicine, University of Utah, 2000 Circle of Hope Drive, Salt Lake City, UT 84112 USA
- Department of Medical Oncology and Therapeutics Research, City of Hope, 1218 S Fifth Ave, Monrovia, CA 91016 USA
| | - David F. Jenkins
- Division of Computational Biomedicine, School of Medicine, Boston University, 72 East Concord Street, Boston, MA 02218 USA
| | - Susan E. Yost
- Department of Medical Oncology and Therapeutics, City of Hope, 1500 East Duarte Road, Duarte, CA 91010 USA
| | - Yuqing Zhang
- Division of Computational Biomedicine, School of Medicine, Boston University, 72 East Concord Street, Boston, MA 02218 USA
| | - Daniel Schmolze
- Department of Pathology, City of Hope, 1500 East Duarte Road, Duarte, CA 91010 USA
| | - W. Evan Johnson
- Division of Computational Biomedicine, School of Medicine, Boston University, 72 East Concord Street, Boston, MA 02218 USA
| | - Yuan Yuan
- Department of Medical Oncology and Therapeutics, City of Hope, 1500 East Duarte Road, Duarte, CA 91010 USA
| | - Andrea H. Bild
- Department of Medical Oncology and Therapeutics Research, City of Hope, 1218 S Fifth Ave, Monrovia, CA 91016 USA
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Morgenstern DA, Bagatell R, Cohn SL, Hogarty MD, Maris JM, Moreno L, Park JR, Pearson AD, Schleiermacher G, Valteau-Couanet D, London WB, Irwin MS. The challenge of defining "ultra-high-risk" neuroblastoma. Pediatr Blood Cancer 2019; 66:e27556. [PMID: 30479064 DOI: 10.1002/pbc.27556] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 10/08/2018] [Accepted: 10/27/2018] [Indexed: 12/17/2022]
Abstract
Given the biological and clinical heterogeneity of neuroblastoma, risk stratification is vital to determining appropriate treatment. Historically, most patients with high-risk neuroblastoma (HR-NBL) have been treated uniformly without further stratification. Attempts have been made to identify factors that can be used to risk stratify these patients and to characterize an "ultra-high-risk" (UHR) subpopulation with particularly poor outcome. However, among published data, there is a lack of consensus in the definition of the UHR population and heterogeneity in the endpoints and statistical methods used. This review summarizes our current understanding of stratification of HR-NBL and discusses the complex issues in defining UHR neuroblastoma.
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Affiliation(s)
| | - Rochelle Bagatell
- Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
| | | | - Michael D Hogarty
- Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
| | - John M Maris
- Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
| | - Lucas Moreno
- Hospital Universitario Niño Jesus, Madrid, Spain
| | - Julie R Park
- Seattle Children's Hospital and University of Washington School of Medicine, Seattle, Washington
| | - Andrew D Pearson
- Institute of Cancer Research and Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, Surrey, UK
| | | | | | - Wendy B London
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, Massachusetts
| | - Meredith S Irwin
- Hospital for Sick Children and University of Toronto, Toronto, Canada
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8
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Maggio V, Chierici M, Jurman G, Furlanello C. Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma. PLoS One 2018; 13:e0208924. [PMID: 30532223 PMCID: PMC6285384 DOI: 10.1371/journal.pone.0208924] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 11/26/2018] [Indexed: 02/07/2023] Open
Abstract
We introduce the CDRP (Concatenated Diagnostic-Relapse Prognostic) architecture for multi-task deep learning that incorporates a clinical algorithm, e.g., a risk stratification schema to improve prognostic profiling. We present the first application to survival prediction in High-Risk (HR) Neuroblastoma from transcriptomics data, a task that studies from the MAQC consortium have shown to remain the hardest among multiple diagnostic and prognostic endpoints predictable from the same dataset. To obtain a more accurate risk stratification needed for appropriate treatment strategies, CDRP combines a first component (CDRP-A) synthesizing a diagnostic task and a second component (CDRP-N) dedicated to one or more prognostic tasks. The approach leverages the advent of semi-supervised deep learning structures that can flexibly integrate multimodal data or internally create multiple processing paths. CDRP-A is an autoencoder trained on gene expression on the HR/non-HR risk stratification by the Children’s Oncology Group, obtaining a 64-node representation in the bottleneck layer. CDRP-N is a multi-task classifier for two prognostic endpoints, i.e., Event-Free Survival (EFS) and Overall Survival (OS). CDRP-A provides the HR embedding input to the CDRP-N shared layer, from which two branches depart to model EFS and OS, respectively. To control for selection bias, CDRP is trained and evaluated using a Data Analysis Protocol (DAP) developed within the MAQC initiative. CDRP was applied on Illumina RNA-Seq of 498 Neuroblastoma patients (HR: 176) from the SEQC study (12,464 Entrez genes) and on Affymetrix Human Exon Array expression profiles (17,450 genes) of 247 primary diagnostic Neuroblastoma of the TARGET NBL cohort. On the SEQC HR patients, CDRP achieves Matthews Correlation Coefficient (MCC) 0.38 for EFS and MCC = 0.19 for OS in external validation, improving over published SEQC models. We show that a CDRP-N embedding is indeed parametrically associated to increasing severity and the embedding can be used to better stratify patients’ survival.
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Morgenstern DA, Pötschger U, Moreno L, Papadakis V, Owens C, Ash S, Pasqualini C, Luksch R, Garaventa A, Canete A, Elliot M, Wieczorek A, Laureys G, Kogner P, Malis J, Ruud E, Beck-Popovic M, Schleiermacher G, Valteau-Couanet D, Ladenstein R. Risk stratification of high-risk metastatic neuroblastoma: A report from the HR-NBL-1/SIOPEN study. Pediatr Blood Cancer 2018; 65:e27363. [PMID: 30015396 DOI: 10.1002/pbc.27363] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 06/18/2018] [Accepted: 06/23/2018] [Indexed: 12/29/2022]
Abstract
BACKGROUND Risk stratification is crucial to treatment decision-making in neuroblastoma. This study aimed to explore factors present at diagnosis affecting outcome in patients aged ≥18 months with metastatic neuroblastoma and to develop a simple risk score for prognostication. PROCEDURE Data were derived from the European high-risk neuroblastoma 1 (HR-NBL1)/International Society for Paediatric Oncology European Neuroblastoma (SIOPEN) trial with analysis restricted to patients aged ≥18 months with metastatic disease and treated prior to the introduction of immunotherapy. Primary endpoint was 5-year event-free survival (EFS). Prognostic factors assessed were sex, age, tumour MYCN amplification (MNA) status, serum lactate dehydrogenase (LDH)/ferritin, primary tumour and metastatic sites. Factors significant in univariate analysis were incorporated into a multi-variable model and an additive scoring system developed based on estimated log-cumulative hazard ratios. RESULTS The cohort included 1053 patients with median follow-up 5.5 years and EFS 27 ± 1%. In univariate analyses, age; serum LDH and ferritin; involvement of bone marrow, bone, liver or lung; and >1 metastatic system/compartment were associated with worse EFS. Tumour MNA was not associated with worse EFS. A multi-variable model and risk score incorporating age (>5 years, 2 points), serum LDH (>1250 U/L, 1 point) and number of metastatic systems (>1, 2 points) were developed. EFS was significantly correlated with risk score: EFS 52 ± 9% for score = 0 versus 6 ± 3% for score = 5 (P < 0.0001). CONCLUSIONS A simple score can identify an "ultra-high risk" (UHR) cohort (score = 5) comprising 8% of patients with 5-year EFS <10%. These patients appear not to benefit from induction therapy and could potentially be directed earlier to alternative experimental therapies in future trials.
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Affiliation(s)
- Daniel A Morgenstern
- Paediatric Haematology/Oncology, Hospital for Sick Children and University of Toronto, Toronto, Canada.,Paediatric Haematology/Oncology, Great Ormond Street Hospital and UCL Institute of Child Health, London, UK
| | - Ulrike Pötschger
- Studies and Statistics on Integrated Research and Projects, St Anna Kinderkrebsforschung, Vienna, Austria
| | - Lucas Moreno
- Paediatric Haematology/Oncology, Hospital Nino Jesús, Madrid, Spain
| | - Vassilios Papadakis
- Paediatric Haematology/Oncology, Agia Sofia Children's Hospital, Athens, Greece
| | - Cormac Owens
- Paediatric Haematology/Oncology, Our Lady's Children's Hospital, Crumlin, Dublin, Ireland
| | - Shifra Ash
- Paediatric Haematology/Oncology, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
| | - Claudia Pasqualini
- Department of Paediatric and Adolescent Oncology, Institut Gustav Roussy, Viellejuif, France
| | - Roberto Luksch
- Dipartimento di Ematologia e Onco-ematologia Pediatrica, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | - Adela Canete
- Pediatric Oncology Unit, Hospital Universitario y Politecnico La Fe, Valencia, Spain
| | - Martin Elliot
- Paediatric Oncology, Leeds Teaching Hospital NHS Trust, Leeds, UK
| | - Aleksandra Wieczorek
- Department of Pediatric Oncology and Hematology, Institute of Pediatrics, Jagiellonian University Medical College, Krakow, Poland
| | - Geneviève Laureys
- Department of Pediatric Hematology/Oncology and Stem Cell Transplantation University Hospital Ghent, Ghent, Belgium
| | - Per Kogner
- Department of Women's and Children's Health, Karolinska Institutet, Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Josef Malis
- Department of Pediatric Hematology and Oncology, University Hospital Motol, Prague, Czech Republic
| | - Ellen Ruud
- Department of Paediatric Medicine, Rikshospitalet, Oslo University Hospital, Oslo, Norway
| | - Maja Beck-Popovic
- Department of Pediatrics and Pediatric Surgery, Pediatric Haematology Oncology Unit, University Hospital Lausanne, Lausanne, Switzerland
| | | | | | - Ruth Ladenstein
- Paediatric Haematology/Oncology, St Anna Kinderspital and St Anna Kinderkrebforschung, Vienna, Austria
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Giordano TJ. 65 YEARS OF THE DOUBLE HELIX: Classification of endocrine tumors in the age of integrated genomics. Endocr Relat Cancer 2018; 25:T171-T187. [PMID: 29980645 DOI: 10.1530/erc-18-0116] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 05/31/2018] [Indexed: 12/26/2022]
Abstract
The classification of human cancers represents one of the cornerstones of modern pathology. Over the last century, surgical pathologists established the current taxonomy of neoplasia using traditional histopathological parameters, which include tumor architecture, cytological features and cellular proliferation. This morphological classification is efficient and robust with high reproducibility and has served patients and health care providers well. The most recent decade has witnessed an explosion of genome-wide molecular genetic and epigenetic data for most cancers, including tumors of endocrine organs. The availability of this expansive multi-dimensional genomic data, collectively termed the cancer genome, has catalyzed a re-examination of the classification of endocrine tumors. Here, recent cancer genome studies of various endocrine tumors, including those of the thyroid, pituitary and adrenal glands, pancreas, small bowel, lung and skin, are presented with special emphasis on how genomic insights are impacting endocrine tumor classification.
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Affiliation(s)
- Thomas J Giordano
- Divisions of Anatomic Pathology and Molecular & Genomic PathologyDepartments of Pathology and Internal Medicine, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
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Fletcher JI, Ziegler DS, Trahair TN, Marshall GM, Haber M, Norris MD. Too many targets, not enough patients: rethinking neuroblastoma clinical trials. Nat Rev Cancer 2018; 18:389-400. [PMID: 29632319 DOI: 10.1038/s41568-018-0003-x] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Neuroblastoma is a rare solid tumour of infancy and early childhood with a disproportionate contribution to paediatric cancer mortality and morbidity. Combination chemotherapy, radiation therapy and immunotherapy remains the standard approach to treat high-risk disease, with few recurrent, actionable genetic aberrations identified at diagnosis. However, recent studies indicate that actionable aberrations are far more common in relapsed neuroblastoma, possibly as a result of clonal expansion. In addition, although the major validated disease driver, MYCN, is not currently directly targetable, multiple promising approaches to target MYCN indirectly are in development. We propose that clinical trial design needs to be rethought in order to meet the challenge of providing rigorous, evidence-based assessment of these new approaches within a fairly small patient population and that experimental therapies need to be assessed at diagnosis in very-high-risk patients rather than in relapsed and refractory patients.
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Affiliation(s)
- Jamie I Fletcher
- Children's Cancer Institute Australia, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, Australia
- School of Women's and Children's Health, UNSW Sydney, Kensington, NSW, Australia
| | - David S Ziegler
- Children's Cancer Institute Australia, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, Australia
- School of Women's and Children's Health, UNSW Sydney, Kensington, NSW, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Toby N Trahair
- Children's Cancer Institute Australia, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, Australia
- School of Women's and Children's Health, UNSW Sydney, Kensington, NSW, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Glenn M Marshall
- Children's Cancer Institute Australia, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, Australia
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia
| | - Michelle Haber
- Children's Cancer Institute Australia, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, Australia
- School of Women's and Children's Health, UNSW Sydney, Kensington, NSW, Australia
| | - Murray D Norris
- Children's Cancer Institute Australia, Lowy Cancer Research Centre, UNSW Sydney, Kensington, NSW, Australia.
- University of New South Wales Centre for Childhood Cancer Research, UNSW Sydney, Kensington, NSW, Australia.
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Yoon N, Ahn S, Yong Yoo H, Jin Kim S, Seog Kim W, Hyeh Ko Y. Cell-of-origin of diffuse large B-cell lymphomas determined by the Lymph2Cx assay: better prognostic indicator than Hans algorithm. Oncotarget 2017; 8:22014-22022. [PMID: 28423544 PMCID: PMC5400642 DOI: 10.18632/oncotarget.15782] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 01/29/2017] [Indexed: 01/20/2023] Open
Abstract
Diffuse large B-cell lymphomas (DLBCLs) are clinically heterogeneous and need a biomarker that can predict the outcome of treatments accurately. To assess the prognostic significance of the cell-of-origin type for DLBCLs, we applied the Lymph2Cx assay using a NanoString gene expression platform on formalin-fixed paraffin wax-embedded pretreatment tissues obtained from 82 patients with de novo DLBCL, not otherwise specified. All patients were treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) as the first line of chemotherapy. Based on the expression levels of Bcl-6, CD10, and MUM-1 measured by immunohistochemistry, cases were subdivided into germinal center B-cell (GCB) and non-GCB types according to the Hans algorithm. NanoString assay was performed on 82 cases. The Lymph2Cx assay successfully classified 82 cases into three categories: activated B-cell (ABC), GCB, and unclassified types. The concordance rate between the Lymph2Cx assay and the Hans algorithm was 73.6%. The Lymph2Cx-defined ABC type had significantly poorer outcomes compared with the GCB type (5-year overall survival, GCB vs. ABC, 96.6% vs. 77.1%, P = 0.020; 5-year disease-free survival, GCB vs. ABC, 96.6% vs. 79.2%, P = 0.018). In contrast, no significant differences were observed in survival between the two patient subgroups with DLBCL types classified by the Hans algorithm. The Lymph2Cx assay is a robust, reliable method for predicting the outcome of patients with DLBCL treated with R-CHOP chemotherapy.
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Affiliation(s)
- Nara Yoon
- Departments of Pathology, The Catholic University of Korea Incheon St. Mary's Hospital, Incheon, Korea
| | - Soomin Ahn
- Departments of Patholgy, Ewha Womans University Medical Center, Ewha Womans University School of Medicine, Seoul, Korea
| | - Hae Yong Yoo
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea
| | - Suk Jin Kim
- Division of Hematology and Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Won Seog Kim
- Division of Hematology and Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Young Hyeh Ko
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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13
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Yang XH, Tang F, Shin J, Cunningham JM. Incorporating genomic, transcriptomic and clinical data: a prognostic and stem cell-like MYC and PRC imbalance in high-risk neuroblastoma. BMC SYSTEMS BIOLOGY 2017; 11:92. [PMID: 28984200 PMCID: PMC5629556 DOI: 10.1186/s12918-017-0466-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Previous studies suggested that cancer cells possess traits reminiscent of the biological mechanisms ascribed to normal embryonic stem cells (ESCs) regulated by MYC and Polycomb repressive complex 2 (PRC2). Several poorly differentiated adult tumors showed preferentially high expression levels in targets of MYC, coincident with low expression levels in targets of PRC2. This paper will reveal this ESC-like cancer signature in high-risk neuroblastoma (HR-NB), the most common extracranial solid tumor in children. METHODS We systematically assembled genomic variants, gene expression changes, priori knowledge of gene functions, and clinical outcomes to identify prognostic multigene signatures. First, we assigned a new, individualized prognostic index using the relative expressions between the poor- and good-outcome signature genes. We then characterized HR-NB aggressiveness beyond these prognostic multigene signatures through the imbalanced effects of MYC and PRC2 signaling. We further analyzed Retinoic acid (RA)-induced HR-NB cells to model tumor cell differentiation. Finally, we performed in vitro validation on ZFHX3, a cell differentiation marker silenced by PRC2, and compared cell morphology changes before and after blocking PRC2 in HR-NB cells. RESULTS A significant concurrence existed between exons with verified variants and genes showing MYCN-dependent expression in HR-NB. From these biomarker candidates, we identified two novel prognostic gene-set pairs with multi-scale oncogenic defects. Intriguingly, MYC targets over-represented an unfavorable component of the identified prognostic signatures while PRC2 targets over-represented a favorable component. The cell cycle arrest and neuronal differentiation marker ZFHX3 was identified as one of PRC2-silenced tumor suppressor candidates. Blocking PRC2 reduced tumor cell growth and increased the mRNA expression levels of ZFHX3 in an early treatment stage. This hypothesis-driven systems bioinformatics work offered novel insights into the PRC2-mediated tumor cell growth and differentiation in neuroblastoma, which may exert oncogenic effects together with MYC regulation. CONCLUSION Our results propose a prognostic effect of imbalanced MYC and PRC2 moderations in pediatric HR-NB for the first time. This study demonstrates an incorporation of genomic landscapes and transcriptomic profiles into the hypothesis-driven precision prognosis and biomarker discovery. The application of this approach to neuroblastoma, as well as other cancer more broadly, could contribute to reduced relapse and mortality rates in the long term.
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Affiliation(s)
- Xinan Holly Yang
- Section of Hematology and Oncology, Departments of Pediatrics, University of Chicago, Chicago, IL, 60637, USA.
| | - Fangming Tang
- Section of Hematology and Oncology, Departments of Pediatrics, University of Chicago, Chicago, IL, 60637, USA
| | - Jisu Shin
- Section of Hematology and Oncology, Departments of Pediatrics, University of Chicago, Chicago, IL, 60637, USA
| | - John M Cunningham
- Section of Hematology and Oncology, Departments of Pediatrics, University of Chicago, Chicago, IL, 60637, USA.
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14
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Cangelosi D, Pelassa S, Morini M, Conte M, Bosco MC, Eva A, Sementa AR, Varesio L. Artificial neural network classifier predicts neuroblastoma patients' outcome. BMC Bioinformatics 2016; 17:347. [PMID: 28185577 PMCID: PMC5123344 DOI: 10.1186/s12859-016-1194-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Background More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly vascularized tissues, which activates specific genes and contributes to the acquisition of the tumor aggressive phenotype. We defined a gene expression signature (NB-hypo), which measures the hypoxic status of the neuroblastoma tumor. We aimed at developing a classifier predicting neuroblastoma patients’ outcome based on the assessment of the adverse effects of tumor hypoxia on the progression of the disease. Methods Multi-layer perceptron (MLP) was trained on the expression values of the 62 probe sets constituting NB-hypo signature to develop a predictive model for neuroblastoma patients’ outcome. We utilized the expression data of 100 tumors in a leave-one-out analysis to select and construct the classifier and the expression data of the remaining 82 tumors to test the classifier performance in an external dataset. We utilized the Gene set enrichment analysis (GSEA) to evaluate the enrichment of hypoxia related gene sets in patients predicted with “Poor” or “Good” outcome. Results We utilized the expression of the 62 probe sets of the NB-Hypo signature in 182 neuroblastoma tumors to develop a MLP classifier predicting patients’ outcome (NB-hypo classifier). We trained and validated the classifier in a leave-one-out cross-validation analysis on 100 tumor gene expression profiles. We externally tested the resulting NB-hypo classifier on an independent 82 tumors’ set. The NB-hypo classifier predicted the patients’ outcome with the remarkable accuracy of 87 %. NB-hypo classifier prediction resulted in 2 % classification error when applied to clinically defined low-intermediate risk neuroblastoma patients. The prediction was 100 % accurate in assessing the death of five low/intermediated risk patients. GSEA of tumor gene expression profile demonstrated the hypoxic status of the tumor in patients with poor prognosis. Conclusions We developed a robust classifier predicting neuroblastoma patients’ outcome with a very low error rate and we provided independent evidence that the poor outcome patients had hypoxic tumors, supporting the potential of using hypoxia as target for neuroblastoma treatment. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1194-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Davide Cangelosi
- Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy
| | - Simone Pelassa
- Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy
| | - Martina Morini
- Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy
| | - Massimo Conte
- Department of Hematology-Oncology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy
| | - Maria Carla Bosco
- Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy
| | - Alessandra Eva
- Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy
| | - Angela Rita Sementa
- Department of Pathology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy
| | - Luigi Varesio
- Laboratory of Molecular Biology, Gaslini Institute, Largo G. Gaslini 5, 16147, Genoa, Italy.
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15
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Ghatalia P, Yang ES, Lasseigne BN, Ramaker RC, Cooper SJ, Chen D, Sudarshan S, Wei S, Guru AS, Zhao A, Cooper T, Della Manna DL, Naik G, Myers RM, Sonpavde G. Kinase Gene Expression Profiling of Metastatic Clear Cell Renal Cell Carcinoma Tissue Identifies Potential New Therapeutic Targets. PLoS One 2016; 11:e0160924. [PMID: 27574806 PMCID: PMC5004806 DOI: 10.1371/journal.pone.0160924] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Accepted: 07/27/2016] [Indexed: 01/05/2023] Open
Abstract
Kinases are therapeutically actionable targets. Kinase inhibitors targeting vascular endothelial growth factor receptors (VEGFR) and mammalian target of rapamycin (mTOR) improve outcomes in metastatic clear cell renal cell carcinoma (ccRCC), but are not curative. Metastatic tumor tissue has not been comprehensively studied for kinase gene expression. Paired intra-patient kinase gene expression analysis in primary tumor (T), matched normal kidney (N) and metastatic tumor tissue (M) may assist in identifying drivers of metastasis and prioritizing therapeutic targets. We compared the expression of 519 kinase genes using NanoString in T, N and M in 35 patients to discover genes over-expressed in M compared to T and N tissue. RNA-seq data derived from ccRCC tumors in The Cancer Genome Atlas (TCGA) were used to demonstrate differential expression of genes in primary tumor tissue from patients that had metastasis at baseline (n = 79) compared to those that did not develop metastasis for at least 2 years (n = 187). Functional analysis was conducted to identify key signaling pathways by using Ingenuity Pathway Analysis. Of 10 kinase genes overexpressed in metastases compared to primary tumor in the discovery cohort, 9 genes were also differentially expressed in TCGA primary tumors with metastasis at baseline compared to primary tumors without metastasis for at least 2 years: EPHB2, AURKA, GSG2, IKBKE, MELK, CSK, CHEK2, CDC7 and MAP3K8; p<0.001). The top pathways overexpressed in M tissue were pyridoxal 5'-phosphate salvage, salvage pathways of pyrimidine ribonucleotides, NF-kB signaling, NGF signaling and cell cycle control of chromosomal replication. The 9 kinase genes validated to be over-expressed in metastatic ccRCC may represent currently unrecognized but potentially actionable therapeutic targets that warrant functional validation.
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Affiliation(s)
- Pooja Ghatalia
- Department of Internal Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL, United States of America
| | - Eddy S. Yang
- Department of Radiation Oncology, UAB, Birmingham, AL, United States of America
| | | | - Ryne C. Ramaker
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States of America
- Department of Genetics, UAB, Birmingham, AL, United States of America
| | - Sara J. Cooper
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States of America
| | - Dongquan Chen
- UAB Department of Preventive Medicine, Birmingham, AL, United States of America
| | - Sunil Sudarshan
- UAB Department of Urology, Birmingham, AL, United States of America
| | - Shi Wei
- UAB Department of Urologic Pathology, Birmingham, AL, United States of America
| | - Arjun S. Guru
- Department of Radiation Oncology, UAB, Birmingham, AL, United States of America
| | - Amy Zhao
- Department of Radiation Oncology, UAB, Birmingham, AL, United States of America
| | - Tiffiny Cooper
- Department of Radiation Oncology, UAB, Birmingham, AL, United States of America
| | | | - Gurudatta Naik
- UAB Department of Medicine, Section of Hematology-Oncology and the UAB Comprehensive Cancer Center, Birmingham, AL, United States of America
| | - Richard M. Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, United States of America
| | - Guru Sonpavde
- UAB Department of Medicine, Section of Hematology-Oncology and the UAB Comprehensive Cancer Center, Birmingham, AL, United States of America
- * E-mail:
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Transcript signatures that predict outcome and identify targetable pathways in MYCN-amplified neuroblastoma. Mol Oncol 2016; 10:1461-1472. [PMID: 27599694 DOI: 10.1016/j.molonc.2016.07.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Revised: 07/22/2016] [Accepted: 07/27/2016] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND In the pediatric cancer neuroblastoma (NB), patients are stratified into low, intermediate or high-risk subsets based in part on MYCN amplification status. While MYCN amplification in general predicts unfavorable outcome, no clinical or genomic factors have been identified that predict outcome within these cohorts of high-risk patients. In particular, it is currently not possible at diagnosis to determine which high-risk neuroblastoma patients will ultimately fail upfront therapy. EXPERIMENTAL DESIGN We analyzed the prognostic potential of most published gene expression signatures for NB and developed a new prognostic signature to predict outcome for patients with MYCN amplification. Network and pathway analyses identified candidate therapeutic targets for this MYCN-amplified patient subset with poor outcome. RESULTS Most signatures have a high capacity to predict outcome of unselected NB patients. However, the majority of published signatures, as well as most randomly generated signatures, are highly confounded by MYCN amplification, and fail to predict outcome in subpopulations of high-risk patients with MYCN-amplified NB. We identify a MYCN module signature that predicts patient outcome for those with MYCN-amplified tumors, that also predicts potential tractable therapeutic signaling pathways and targets including the DNA repair enzyme Poly [ADP-ribose] polymerase 1 (PARP1). CONCLUSION Many prognostic signatures for NB are confounded by MYCN amplification and fail to predict outcome for the subset of high-risk patients with MYCN amplification. We report a MYCN module signature that is associated with distinct patient outcomes, and predicts candidate therapeutic targets in DNA repair pathways, including PARP1 in MYCN-amplified NB.
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Neagu MR, Reardon DA. An Update on the Role of Immunotherapy and Vaccine Strategies for Primary Brain Tumors. Curr Treat Options Oncol 2016; 16:54. [PMID: 26454859 DOI: 10.1007/s11864-015-0371-3] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Existing therapies for glioblastoma (GBM), the most common malignant primary brain tumor in adults, have fallen short of improving the dismal patient outcomes, with an average 14-16-month median overall survival. The biological complexity and adaptability of GBM, redundancy of dysregulated signaling pathways, and poor penetration of therapies through the blood-brain barrier contribute to poor therapeutic progress. The current standard of care for newly diagnosed GBM consists of maximal safe resection, followed by fractionated radiotherapy combined with concurrent temozolomide (TMZ) and 6-12 cycles of adjuvant TMZ. At progression, bevacizumab with or without additional chemotherapy is an option for salvage therapy. The recent FDA approval of sipuleucel-T for prostate cancer and ipilumimab, nivolumab, and pembrolizumab for select solid tumors and the ongoing trials showing clinical efficacy and response durability herald a new era of cancer treatment with the potential to change standard-of-care treatment across multiple cancers. The evaluation of various immunotherapeutics is advancing for GBM, putting into question the dogma of the CNS as an immuno-privileged site. While the field is yet young, both active immunotherapy involving vaccine strategies and cellular therapy as well as reversal of GBM-induced global immune-suppression through immune checkpoint blockade are showing promising results and revealing essential immunological insights regarding kinetics of the immune response, immune evasion, and correlative biomarkers. The future holds exciting promise in establishing new treatment options for GBM that harness the patients' own immune system by activating it with immune checkpoint inhibitors, providing specificity using vaccine therapy, and allowing for modulation and enhancement by combinatorial approaches.
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Affiliation(s)
- Martha R Neagu
- Dana-Farber Cancer Institute, G4200, 44 Binney St, Boston, MA, 02115, USA
- Pappas Center for Neuro-Oncology, Massachusetts General Hospital, WACC 8-835m 55 Fruit St, Boston, MA, 02114, USA
| | - David A Reardon
- Dana-Farber Cancer Institute, G4200, 44 Binney St, Boston, MA, 02115, USA.
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Hahn CM, Iwanowicz LR, Cornman RS, Mazik PM, Blazer VS. Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays. COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY D-GENOMICS & PROTEOMICS 2016; 20:27-40. [PMID: 27497300 DOI: 10.1016/j.cbd.2016.07.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 06/04/2016] [Accepted: 07/08/2016] [Indexed: 12/14/2022]
Abstract
Environmental studies increasingly identify the presence of both contaminants of emerging concern (CECs) and legacy contaminants in aquatic environments; however, the biological effects of these compounds on resident fishes remain largely unknown. High throughput methodologies were employed to establish partial transcriptomes for three wild-caught, non-model fish species; smallmouth bass (Micropterus dolomieu), white sucker (Catostomus commersonii) and brown bullhead (Ameiurus nebulosus). Sequences from these transcriptome databases were utilized in the development of a custom nCounter CodeSet that allowed for direct multiplexed measurement of 50 transcript abundance endpoints in liver tissue. Sequence information was also utilized in the development of quantitative real-time PCR (qPCR) primers. Cross-species hybridization allowed the smallmouth bass nCounter CodeSet to be used for quantitative transcript abundance analysis of an additional non-model species, largemouth bass (Micropterus salmoides). We validated the nCounter analysis data system with qPCR for a subset of genes and confirmed concordant results. Changes in transcript abundance biomarkers between sexes and seasons were evaluated to provide baseline data on transcript modulation for each species of interest.
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Affiliation(s)
- Cassidy M Hahn
- West Virginia University, School of Natural Resources, 322 Percival Hall, Morgantown, WV 26506, USA; US Geological Survey, Leetown Science Center, 11649 Leetown Road, Kearneysville, WV 25430, USA.
| | - Luke R Iwanowicz
- US Geological Survey, Leetown Science Center, 11649 Leetown Road, Kearneysville, WV 25430, USA
| | - Robert S Cornman
- US Geological Survey, Leetown Science Center, 11649 Leetown Road, Kearneysville, WV 25430, USA
| | - Patricia M Mazik
- West Virginia University, School of Natural Resources, 322 Percival Hall, Morgantown, WV 26506, USA
| | - Vicki S Blazer
- US Geological Survey, Leetown Science Center, 11649 Leetown Road, Kearneysville, WV 25430, USA
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19
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Das K, Chan XB, Epstein D, Te Teh B, Kim KM, Kim ST, Park SH, Kang WK, Rozen S, Lee J, Tan P. NanoString expression profiling identifies candidate biomarkers of RAD001 response in metastatic gastric cancer. ESMO Open 2016; 1:e000009. [PMID: 27843583 PMCID: PMC5070203 DOI: 10.1136/esmoopen-2015-000009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 01/15/2016] [Accepted: 01/19/2016] [Indexed: 12/25/2022] Open
Abstract
Background Gene expression profiling has contributed greatly to cancer research. However, expression-driven biomarker discovery in metastatic gastric cancer (mGC) remains unclear. A gene expression profile predicting RAD001 response in refractory GC was explored in this study. Methods Total RNA isolated from 54 tumour specimens from patients with mGC, prior to RAD001 treatment, was analysed via the NanoString nCounter gene expression assay. This assay targeted 477 genes representing 10 different GC-related oncogenic signalling and molecular subtype-specific expression signatures. Gene expression profiles were correlated with patient clinicopathological variables. Results NanoString data confirmed similar gene expression profiles previously identified by microarray analysis. Signature I with 3 GC subtypes (mesenchymal, metabolic and proliferative) showed approximately 90% concordance where the mesenchymal and proliferative subtypes were significantly associated with signet ring cell carcinoma and the WHO classified tubular adenocarcinoma GC, respectively (p=0.042). Single-gene-level correlations with patient clinicopathological variables showed strong associations between FHL1 expression (mesenchymal subtype) and signet ring cell carcinoma, and NEK2, OIP5, PRC1, TPX2 expression (proliferative subtype) with tubular adenocarcinoma (adjusted p<0.05). Increased BRCA2 (p=0.040) and MMP9 (p=0.045) expression was significantly associated with RAD001 good response and longer progression-free survival outcome (BRCA2, p=0.012, HR 0.370 95% CI (0.171 to 0.800); MMP9, p=0.010, HR 0.359 95% CI (0.166 to 0.779)). In contrast, increased BTC (p=0.035) expression was significantly associated with RAD001 poor response and poor progression-free survival (p=0.031, HR 2.336 95% CI (1.079 to 5.059) by univariate Cox regression analysis. Conclusions Microarray results are highly reproducible with NanoString nCounter gene expression profiling. Additionally, BRCA2 and MMP9 expression are potential predictive biomarkers for good response in RAD001-treated mGC.
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Affiliation(s)
- Kakoli Das
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School.
| | - Xiu Bin Chan
- Genome Institute of Singapore, Biopolis, Singapore
| | - David Epstein
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School
| | - Binan Te Teh
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School; Laboratory of Cancer Epigenome, Division of Medical Sciences, National Cancer Centre Singapore, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore
| | - Kyoung-Mee Kim
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seung Tae Kim
- Department of Medicine, Division of Hematology-Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Se Hoon Park
- Department of Medicine, Division of Hematology-Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Won Ki Kang
- Department of Medicine, Division of Hematology-Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Steve Rozen
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School
| | - Jeeyun Lee
- Department of Medicine, Division of Hematology-Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Patrick Tan
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School; Genome Institute of Singapore, Biopolis, Singapore; Cancer Science Institute of Singapore, National University of Singapore, Singapore
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20
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Abstract
PURPOSE OF REVIEW Major advances in our understanding of the genetic basis of neuroblastoma, and the role somatic alterations play in driving tumor growth, have led to improvements in risk-stratified therapy and have provided the rationale for targeted therapies. In this review, we highlight current risk-based treatment approaches and discuss the opportunities and challenges of translating recent genomic discoveries into the clinic. RECENT FINDINGS Significant progress in the treatment of neuroblastoma has been realized using risk-based treatment strategies. Outcome has improved for all patients, including those classified as high-risk, although survival remains poor for this cohort. Integration of whole-genome DNA copy number and comprehensive molecular profiles into neuroblastoma classification systems will allow more precise prognostication and refined treatment assignment. Promising treatments that include targeted systemic radiotherapy, pathway-targeted small molecules, and therapy targeted at cell surface molecules are being evaluated in clinical trials, and recent genomic discoveries in relapsed tumor samples have led to the identification of new actionable mutations. SUMMARY The integration of refined treatment stratification based on whole-genome profiles with therapeutics that target the molecular drivers of malignant behavior in neuroblastoma has the potential to dramatically improve survival, with decreased toxicity.
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21
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Rudd J, Zelaya RA, Demidenko E, Goode EL, Greene CS, Doherty JA. Leveraging global gene expression patterns to predict expression of unmeasured genes. BMC Genomics 2015; 16:1065. [PMID: 26666289 PMCID: PMC4678722 DOI: 10.1186/s12864-015-2250-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2015] [Accepted: 11/27/2015] [Indexed: 12/31/2022] Open
Abstract
Background Large collections of paraffin-embedded tissue represent a rich resource to test hypotheses based on gene expression patterns; however, measurement of genome-wide expression is cost-prohibitive on a large scale. Using the known expression correlation structure within a given disease type (in this case, high grade serous ovarian cancer; HGSC), we sought to identify reduced sets of directly measured (DM) genes which could accurately predict the expression of a maximized number of unmeasured genes. Results We developed a greedy gene set selection (GGS) algorithm which returns a DM set of user specified size based on a specific correlation threshold (|rP|) and minimum number of DM genes that must be correlated to an unmeasured gene in order to infer the value of the unmeasured gene (redundancy). We evaluated GGS in the Cancer Genome Atlas (TCGA) HGSC data across 144 combinations of DM size, redundancy (1–3), and |rP| (0.60, 0.65, 0.70). Across the parameter sweep, GGS allows on average 9 times more gene expression information to be captured compared to the DM set alone. GGS successfully augments prognostic HGSC gene sets; the addition of 20 GGS selected genes more than doubles the number of genes whose expression is predictable. Moreover, the expression prediction is highly accurate. After training regression models for the predictable gene set using 2/3 of the TCGA data, the average accuracy (ranked correlation of true and predicted values) in the 1/3 testing partition and four independent populations is above 0.65 and approaches 0.8 for conservative parameter sets. We observe similar accuracies in the TCGA HGSC RNA-sequencing data. Specifically, the prediction accuracy increases with increasing redundancy and increasing |rP|. Conclusions GGS-selected genes, which maximize expression information about unmeasured genes, can be combined with candidate gene sets as a cost effective way to increase the amount of gene expression information obtained in large studies. This method can be applied to any organism, model system, disease, or tissue type for which whole genome gene expression data exists. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2250-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- James Rudd
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, One Medical Center Drive, 7927 Rubin Building, Lebanon, NH, 03756, USA.
| | - René A Zelaya
- Department of Genetics, Geisel School of Medicine at Dartmouth College; Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, 10-131 SCTR, 34th & Civic Center Boulevard, Philadelphia, PA, 19104-5158, USA.
| | - Eugene Demidenko
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, One Medical Center Drive, 7927 Rubin Building, Lebanon, NH, 03756, USA.
| | - Ellen L Goode
- Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA.
| | - Casey S Greene
- Department of Genetics, Geisel School of Medicine at Dartmouth College; Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, 10-131 SCTR, 34th & Civic Center Boulevard, Philadelphia, PA, 19104-5158, USA.
| | - Jennifer A Doherty
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, One Medical Center Drive, 7927 Rubin Building, Lebanon, NH, 03756, USA.
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22
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Hingorani P, Missiaglia E, Shipley J, Anderson JR, Triche TJ, Delorenzi M, Gastier-Foster J, Wing M, Hawkins DS, Skapek SX. Clinical Application of Prognostic Gene Expression Signature in Fusion Gene-Negative Rhabdomyosarcoma: A Report from the Children's Oncology Group. Clin Cancer Res 2015; 21:4733-9. [PMID: 26473193 PMCID: PMC4610152 DOI: 10.1158/1078-0432.ccr-14-3326] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE Pediatric rhabdomyosarcoma (RMS) has two common histologic subtypes: embryonal (ERMS) and alveolar (ARMS). PAX-FOXO1 fusion gene status is a more reliable prognostic marker than alveolar histology, whereas fusion gene-negative (FN) ARMS patients are clinically similar to ERMS patients. A five-gene expression signature (MG5) previously identified two diverse risk groups within the fusion gene-negative RMS (FN-RMS) patients, but this has not been independently validated. The goal of this study was to test whether expression of the MG5 metagene, measured using a technical platform that can be applied to routine pathology material, would correlate with outcome in a new cohort of patients with FN-RMS. EXPERIMENTAL DESIGN Cases were taken from the Children's Oncology Group (COG) D9803 study of children with intermediate-risk RMS, and gene expression profiling for the MG5 genes was performed using the nCounter assay. The MG5 score was correlated with clinical and pathologic characteristics as well as overall and event-free survival. RESULTS MG5 standardized score showed no significant association with any of the available clinicopathologic variables. The MG5 signature score showed a significant correlation with overall (N = 57; HR, 7.3; 95% CI, 1.9-27.0; P = 0.003) and failure-free survival (N = 57; HR, 6.1; 95% CI, 1.9-19.7; P = 0.002). CONCLUSIONS This represents the first, validated molecular prognostic signature for children with FN-RMS who otherwise have intermediate-risk disease. The capacity to measure the expression of a small number of genes in routine pathology material and apply a simple mathematical formula to calculate the MG5 metagene score provides a clear path toward better risk stratification in future prospective clinical trials.
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Affiliation(s)
- Pooja Hingorani
- Center for Cancer and Blood Disorders, Phoenix Children's Hospital, Phoenix, Arizona.
| | | | - Janet Shipley
- Divisions of Molecular Pathology and Cancer Therapeutics, The Institute of Cancer Research, London, United Kingdom
| | - James R Anderson
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska
| | - Timothy J Triche
- Department of Pathology, Children's Hospital of Los Angeles, Los Angeles, California
| | - Mauro Delorenzi
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. Ludwig Center for Cancer Research, Lausanne, Switzerland. Oncology Department, University of Lausanne, Lausanne, Switzerland
| | | | - Michele Wing
- The Research Institute at Nationwide Children's Hospital, Columbus, Ohio
| | - Douglas S Hawkins
- Division of Hematology/Oncology, Seattle Children's Hospital, Fred Hutchinson Cancer Research Center, University of Washington, Seattle, Washington
| | - Stephen X Skapek
- Pauline Allen Gill Center for Cancer and Blood Disorders, Children's Medical Center, Dallas, Texas. Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, Texas
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23
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Cortés C, Kozma SC, Tauler A, Ambrosio S. MYCN concurrence with SAHA-induced cell death in human neuroblastoma cells. Cell Oncol (Dordr) 2015; 38:341-52. [PMID: 26306783 DOI: 10.1007/s13402-015-0233-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/22/2015] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND In the past, the HDAC inhibitor suberoylanilide hydroxamic acid (SAHA) has been shown to induce apoptosis in several human tumor types, including neuroblastomas. Amplification and over-expression of the MYCN oncogene is a diagnostic hallmark and a poor prognostic indicator in high-risk neuroblastomas. Here, we studied the relationship between MYCN amplification and over-expression and the anti-tumor effect of SAHA to assess whether this drug may serve as a treatment option for high-risk neuroblastomas. METHODS Different human neuroblastoma cell lines, over-expressing or not over-expressing MYCN, were used in this study. Targeted knockdown and exogenous over-expression of MYCN were employed to examine correlations between MYCN expression levels and SAHA responses. After various time periods and concentration exposures to the drug, cell viability was measured by MTS assay, and variations in MYCN mRNA and protein levels were assessed by qPCR and Western blotting, respectively. RESULTS We found that SAHA decreased cell viability in all cell lines tested through apoptosis induction, and that SAHA had a stronger effect on cell lines carrying an amplified MYCN gene. A decrease in MYCN mRNA and protein levels was observed in the SAHA treated cell lines. Subsequent silencing and exogenous over-expression of MYCN changed the proliferation rate of the cells, but did not have any significant impact on the effect of SAHA on the viability of the cells. We also found that SAHA blocked the expression of MYCN and, by doing so, reduced the effects mediated by this protein. CONCLUSIONS Our results suggest that SAHA may be used as a single-drug treatment option for neuroblastomas with an amplified MYCN gene, and as an adjuvant treatment option for all neuroblastomas.
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Affiliation(s)
- Constanza Cortés
- Departament de Bioquímica i Biologia Molecular, Facultat de Farmàcia, Universitat de Barcelona, 08028, Barcelona, Catalunya, Spain.,Laboratory of Cancer Metabolism, IDIBELL, Hospital Duran i Reynals, L'Hospitalet de Llobregat, Catalunya, 08907, Spain
| | - Sara C Kozma
- Laboratory of Cancer Metabolism, IDIBELL, Hospital Duran i Reynals, L'Hospitalet de Llobregat, Catalunya, 08907, Spain
| | - Albert Tauler
- Departament de Bioquímica i Biologia Molecular, Facultat de Farmàcia, Universitat de Barcelona, 08028, Barcelona, Catalunya, Spain.,Laboratory of Cancer Metabolism, IDIBELL, Hospital Duran i Reynals, L'Hospitalet de Llobregat, Catalunya, 08907, Spain
| | - Santiago Ambrosio
- Unitat de Bioquímica, Dep. Ciències Fisiològiques II, Facultat de Medicina, Campus Universitari de Bellvitge - IDIBELL, Universitat de Barcelona, L'Hospitalet de Llobregat, Catalunya, 08907, Spain.
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24
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Veldman-Jones MH, Brant R, Rooney C, Geh C, Emery H, Harbron CG, Wappett M, Sharpe A, Dymond M, Barrett JC, Harrington EA, Marshall G. Evaluating Robustness and Sensitivity of the NanoString Technologies nCounter Platform to Enable Multiplexed Gene Expression Analysis of Clinical Samples. Cancer Res 2015; 75:2587-93. [PMID: 26069246 DOI: 10.1158/0008-5472.can-15-0262] [Citation(s) in RCA: 229] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Accepted: 03/19/2015] [Indexed: 11/16/2022]
Abstract
Analysis of clinical trial specimens such as formalin-fixed paraffin-embedded (FFPE) tissue for molecular mechanisms of disease progression or drug response is often challenging and limited to a few markers at a time. This has led to the increasing importance of highly multiplexed assays that enable profiling of many biomarkers within a single assay. Methods for gene expression analysis have undergone major advances in biomedical research, but obtaining a robust dataset from low-quality RNA samples, such as those isolated from FFPE tissue, remains a challenge. Here, we provide a detailed evaluation of the NanoString Technologies nCounter platform, which provides a direct digital readout of up to 800 mRNA targets simultaneously. We tested this system by examining a broad set of human clinical tissues for a range of technical variables, including sensitivity and limit of detection to varying RNA quantity and quality, reagent performance over time, variability between instruments, the impact of the number of fields of view sampled, and differences between probe sequence locations and overlapping genes across CodeSets. This study demonstrates that Nanostring offers several key advantages, including sensitivity, reproducibility, technical robustness, and utility for clinical application.
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Affiliation(s)
- Margaret H Veldman-Jones
- AstraZeneca Oncology and Discovery Sciences Innovative Medicines, Alderley Park, Macclesfield, United Kingdom.
| | - Roz Brant
- AstraZeneca Oncology and Discovery Sciences Innovative Medicines, Alderley Park, Macclesfield, United Kingdom
| | - Claire Rooney
- AstraZeneca Oncology and Discovery Sciences Innovative Medicines, Alderley Park, Macclesfield, United Kingdom
| | - Catherine Geh
- AstraZeneca Oncology and Discovery Sciences Innovative Medicines, Alderley Park, Macclesfield, United Kingdom
| | - Hollie Emery
- AstraZeneca Oncology and Discovery Sciences Innovative Medicines, Alderley Park, Macclesfield, United Kingdom
| | - Chris G Harbron
- AstraZeneca Oncology and Discovery Sciences Innovative Medicines, Alderley Park, Macclesfield, United Kingdom
| | - Mark Wappett
- AstraZeneca Oncology and Discovery Sciences Innovative Medicines, Alderley Park, Macclesfield, United Kingdom
| | - Alan Sharpe
- AstraZeneca Oncology and Discovery Sciences Innovative Medicines, Alderley Park, Macclesfield, United Kingdom
| | - Michael Dymond
- AstraZeneca Oncology and Discovery Sciences Innovative Medicines, Alderley Park, Macclesfield, United Kingdom
| | | | - Elizabeth A Harrington
- AstraZeneca Oncology and Discovery Sciences Innovative Medicines, Alderley Park, Macclesfield, United Kingdom
| | - Gayle Marshall
- AstraZeneca Oncology and Discovery Sciences Innovative Medicines, Alderley Park, Macclesfield, United Kingdom
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25
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Zhang W, Yu Y, Hertwig F, Thierry-Mieg J, Zhang W, Thierry-Mieg D, Wang J, Furlanello C, Devanarayan V, Cheng J, Deng Y, Hero B, Hong H, Jia M, Li L, Lin SM, Nikolsky Y, Oberthuer A, Qing T, Su Z, Volland R, Wang C, Wang MD, Ai J, Albanese D, Asgharzadeh S, Avigad S, Bao W, Bessarabova M, Brilliant MH, Brors B, Chierici M, Chu TM, Zhang J, Grundy RG, He MM, Hebbring S, Kaufman HL, Lababidi S, Lancashire LJ, Li Y, Lu XX, Luo H, Ma X, Ning B, Noguera R, Peifer M, Phan JH, Roels F, Rosswog C, Shao S, Shen J, Theissen J, Tonini GP, Vandesompele J, Wu PY, Xiao W, Xu J, Xu W, Xuan J, Yang Y, Ye Z, Dong Z, Zhang KK, Yin Y, Zhao C, Zheng Y, Wolfinger RD, Shi T, Malkas LH, Berthold F, Wang J, Tong W, Shi L, Peng Z, Fischer M. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biol 2015; 16:133. [PMID: 26109056 PMCID: PMC4506430 DOI: 10.1186/s13059-015-0694-1] [Citation(s) in RCA: 247] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Accepted: 06/12/2015] [Indexed: 12/22/2022] Open
Abstract
Background Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. Results We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. Conclusions We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0694-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wenqian Zhang
- BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong, 518083, China
| | - Ying Yu
- Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, 201203, China
| | - Falk Hertwig
- Department of Pediatric Oncology and Hematology, University Children's Hospital of Cologne, Kerpener Strasse 62, D-50924, Cologne, Germany.,University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Kerpener Strasse 62, D-50924, Cologne, Germany
| | - Jean Thierry-Mieg
- NIH/NCBI, Bldg 38A/Room 8S808, 8600 Rockville Pike, Bethesda, MD, 20894, USA
| | - Wenwei Zhang
- BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong, 518083, China
| | | | - Jian Wang
- Eli Lilly and Company Research Informatics, Lilly Corporate Center, Drop Code 0725, Indianapolis, IN, 46285, USA
| | - Cesare Furlanello
- Fondazione Bruno Kessler (FBK), Via Sommarive 18, 38123, Trento Povo, TN, Italy
| | - Viswanath Devanarayan
- AbbVie Inc., Global Pharmaceutical R&D, 32 Knights Crest Court, Souderton, PA, 18964, USA
| | - Jie Cheng
- GlaxoSmithKline, Discovery Analytics, Mailstop UP4335, 1250 South Collegeville Rd, Collegeville, PA, 19426, USA
| | - Youping Deng
- Department of Internal Medicine, Rush University Cancer Center, 1725 W. Harrison Street, Chicago, IL, 60612, USA
| | - Barbara Hero
- Department of Pediatric Oncology and Hematology, University Children's Hospital of Cologne, Kerpener Strasse 62, D-50924, Cologne, Germany
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Meiwen Jia
- Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, 201203, China
| | - Li Li
- SAS Institute Inc., SAS Campus Drive, Cary, NC, 27513, USA
| | - Simon M Lin
- Marshfield Clinic Research Foundation, Biomedical Informatics Research Center, 1000 N Oak Avenue, Marshfield, WI, 54449, USA
| | - Yuri Nikolsky
- Thomson Reuters IP & Science, 5901 Priesty Drive, Carlsbad, CA, 92008, USA
| | - André Oberthuer
- Department of Pediatric Oncology and Hematology, University Children's Hospital of Cologne, Kerpener Strasse 62, D-50924, Cologne, Germany
| | - Tao Qing
- Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, 201203, China
| | - Zhenqiang Su
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Ruth Volland
- Department of Pediatric Oncology and Hematology, University Children's Hospital of Cologne, Kerpener Strasse 62, D-50924, Cologne, Germany
| | - Charles Wang
- Center for Genomics and Division of Microbiology & Molecular Genetics, School of Medicine, Loma Linda University, Loma Linda, CA, 92350, USA
| | - May D Wang
- Department of Biomedical Engineering, GeorgiaTech and Emory University, 313 Ferst Drive, Atlanta, GA, 30332, USA
| | - Junmei Ai
- Department of Internal Medicine, Rush University Cancer Center, 1725 W. Harrison Street, Chicago, IL, 60612, USA
| | - Davide Albanese
- Fondazione Edmund Mach, CRI-CBC, San Michele all'Adige, TN, Italy
| | | | - Smadar Avigad
- Department of Pediatric Hematology-Oncology, Molecular Oncology, Felsenstein Medical Research Center, Schneider Children's Medical Center of Israel, Petach Tikva, 49202, Israel
| | - Wenjun Bao
- SAS Institute Inc., SAS Campus Drive, Cary, NC, 27513, USA
| | - Marina Bessarabova
- Thomson Reuters IP & Science, 5901 Priesty Drive, Carlsbad, CA, 92008, USA
| | - Murray H Brilliant
- Marshfield Clinic Research Foundation, Center of Human Genetics, 1000 N Oak Avenue, Marshfield, WI, 54449, USA
| | - Benedikt Brors
- Department of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
| | - Marco Chierici
- Fondazione Bruno Kessler (FBK), Via Sommarive 18, 38123, Trento Povo, TN, Italy
| | - Tzu-Ming Chu
- SAS Institute Inc., SAS Campus Drive, Cary, NC, 27513, USA
| | - Jibin Zhang
- BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong, 518083, China
| | - Richard G Grundy
- University of Nottingham, Children's Brain Tumour Research Centre, Queen's Medical Centre, University of Nottingham, D Floor Medical School, Nottingham, NG7 2UH, UK
| | - Min Max He
- Marshfield Clinic Research Foundation, Biomedical Informatics Research Center, 1000 N Oak Avenue, Marshfield, WI, 54449, USA
| | - Scott Hebbring
- Marshfield Clinic Research Foundation, Center of Human Genetics, 1000 N Oak Avenue, Marshfield, WI, 54449, USA
| | - Howard L Kaufman
- Department of Internal Medicine, Rush University Cancer Center, 1725 W. Harrison Street, Chicago, IL, 60612, USA
| | - Samir Lababidi
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, WOC1 RM400S, HFM-210, 1401 Rockville Pike, Rockville, MD, 20852, USA
| | - Lee J Lancashire
- Thomson Reuters IP & Science, 5901 Priesty Drive, Carlsbad, CA, 92008, USA
| | - Yan Li
- Department of Internal Medicine, Rush University Cancer Center, 1725 W. Harrison Street, Chicago, IL, 60612, USA
| | - Xin X Lu
- AbbVie Inc., Global Pharmaceutical Research and Development, 1 North Waukegan Road, North Chicago, IL, 60064, USA
| | - Heng Luo
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA.,University of Arkansas at Little Rock, UALR/UAMS Joint Bioinformatics Graduate Program, 2801 South University Avenue, Little Rock, AR, 72204, USA
| | - Xiwen Ma
- Eli Lilly and Company, Discovery Statistics, Lilly Corporate Center, Drop Code 2036, Indianapolis, IN, 46285, USA
| | - Baitang Ning
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Rosa Noguera
- Department of Pathology, University of Valencia, Medical School, Avda. Blasco Ibáñez, 17, 46010, Valencia, Spain
| | - Martin Peifer
- University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Kerpener Strasse 62, D-50924, Cologne, Germany.,Department of Translational Genomics, University of Cologne, D-50924, Cologne, Germany
| | - John H Phan
- Department of Biomedical Engineering, GeorgiaTech and Emory University, 313 Ferst Drive, Atlanta, GA, 30332, USA
| | - Frederik Roels
- Department of Pediatric Oncology and Hematology, University Children's Hospital of Cologne, Kerpener Strasse 62, D-50924, Cologne, Germany.,University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Kerpener Strasse 62, D-50924, Cologne, Germany
| | - Carolina Rosswog
- Department of Pediatric Oncology and Hematology, University Children's Hospital of Cologne, Kerpener Strasse 62, D-50924, Cologne, Germany
| | - Susan Shao
- SAS Institute Inc., SAS Campus Drive, Cary, NC, 27513, USA
| | - Jie Shen
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Jessica Theissen
- Department of Pediatric Oncology and Hematology, University Children's Hospital of Cologne, Kerpener Strasse 62, D-50924, Cologne, Germany
| | - Gian Paolo Tonini
- Neuroblastoma Laboratory, Onco/Hematology Laboratory, SDB Department, University of Padua, Pediatric Research Institute, Padua, Italy
| | - Jo Vandesompele
- Department of Pediatrics and Genetics, Ghent University, Center for Medical Genetics, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium
| | - Po-Yen Wu
- Georgia Institute of Technology, School of Electrical and Computer Engineering, 777 Atlantic Drive NW, Atlanta, GA, 30332, USA
| | - Wenzhong Xiao
- Harvard Medical School, Massachusetts General Hospital, 51 Blossom Street, Boston, MA, 02114, USA
| | - Joshua Xu
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Weihong Xu
- Stanford University, Stanford Genome Technology Center, 855 South California Avenue, Palo Alto, CA, 94304, USA
| | - Jiekun Xuan
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Yong Yang
- Eli Lilly and Company Research Informatics, Lilly Corporate Center, Drop Code 0725, Indianapolis, IN, 46285, USA
| | - Zhan Ye
- Marshfield Clinic Research Foundation, Biomedical Informatics Research Center, 1000 N Oak Avenue, Marshfield, WI, 54449, USA
| | - Zirui Dong
- BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong, 518083, China
| | - Ke K Zhang
- Department of Pathology, University of North Dakota School of Medicine, 501 N. Columbia Road RM 3573, Grand Forks, ND, 58202-9037, USA
| | - Ye Yin
- BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong, 518083, China
| | - Chen Zhao
- Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, 201203, China
| | - Yuanting Zheng
- Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, 201203, China
| | | | - Tieliu Shi
- East China Normal University, Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, 500 Dongchuan Road, Shanghai, 200241, China
| | - Linda H Malkas
- Department of Molecular & Cellular Biology, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, 91010, USA
| | - Frank Berthold
- Department of Pediatric Oncology and Hematology, University Children's Hospital of Cologne, Kerpener Strasse 62, D-50924, Cologne, Germany.,University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Kerpener Strasse 62, D-50924, Cologne, Germany
| | - Jun Wang
- BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong, 518083, China.,Department of Biology, University of Copenhagen, Copenhagen, DK-2200, Denmark.,King Abdulaziz University, Jeddah, 21589, Saudi Arabia.,Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, DK-2200, Denmark
| | - Weida Tong
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Leming Shi
- Collaborative Innovation Center for Genetics and Development, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and School of Pharmacy, Fudan University, Shanghai, 201203, China. .,National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA.
| | - Zhiyu Peng
- BGI-Shenzhen, Main Building, Bei Shan Industrial Zone, Yantian District, Shenzhen, Guangdong, 518083, China. .,BGI-Guangzhou, Guangzhou Higher Education Mega Center, No. 280, Waihuan East Rd., Guangzhou, 510006, China.
| | - Matthias Fischer
- Department of Pediatric Oncology and Hematology, University Children's Hospital of Cologne, Kerpener Strasse 62, D-50924, Cologne, Germany. .,University of Cologne, Center for Molecular Medicine (CMMC), Medical Faculty, Kerpener Strasse 62, D-50924, Cologne, Germany.
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26
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Liew M, Mao R, Wittwer CT, Salama ME. Detection of chromosomal translocations in formalin-fixed paraffin-embedded (FFPE) leukemic specimens by digital expression profiling. Int J Lab Hematol 2015; 37:690-8. [DOI: 10.1111/ijlh.12388] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Accepted: 04/14/2015] [Indexed: 02/02/2023]
Affiliation(s)
- M. Liew
- ARUP Institute for Clinical and Experimental Pathology; Salt Lake City UT USA
| | - R. Mao
- ARUP Institute for Clinical and Experimental Pathology; Salt Lake City UT USA
- Department of Pathology; University of Utah School of Medicine; Salt Lake City UT USA
| | - C. T. Wittwer
- ARUP Institute for Clinical and Experimental Pathology; Salt Lake City UT USA
- Department of Pathology; University of Utah School of Medicine; Salt Lake City UT USA
| | - M. E. Salama
- ARUP Institute for Clinical and Experimental Pathology; Salt Lake City UT USA
- Department of Pathology; University of Utah School of Medicine; Salt Lake City UT USA
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27
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Biomarkers for glioma immunotherapy: the next generation. J Neurooncol 2015; 123:359-72. [PMID: 25724916 DOI: 10.1007/s11060-015-1746-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 02/16/2015] [Indexed: 12/11/2022]
Abstract
The term "biomarker" historically refers to a single parameter, such as the expression level of a gene or a radiographic pattern, used to indicate a broader biological state. Molecular indicators have been applied to several aspects of cancer therapy: to describe the genotypic and phenotypic state of neoplastic tissue for prognosis, to predict susceptibility to anti-proliferative agents, to validate the presence of specific drug targets, and to evaluate responsiveness to therapy. For glioblastoma (GBM), immunohistochemical and radiographic biomarkers accessible to the clinical lab have informed traditional regimens, but while immunotherapies have emerged as potentially disruptive weapons against this diffusely infiltrating, heterogeneous tumor, biomarkers with strong predictive power have not been fully established. The cancer immunotherapy field, through the recently accelerated expansion of trials, is currently leveraging this wealth of clinical and biological data to define and revise the use of biomarkers for improving prognostic accuracy, personalization of therapy, and evaluation of responses across the wide variety of tumors. Technological advancements in DNA sequencing, cytometry, and microscopy have facilitated the exploration of more integrated, high-dimensional profiling of the disease system-incorporating both immune and tumor parameters-rather than single metrics, as biomarkers for therapeutic sensitivity. Here we discuss the utility of traditional GBM biomarkers in immunotherapy and how the impending transformation of the biomarker paradigm-from single markers to integrated profiles-may offer the key to bringing predictive, personalized immunotherapy to GBM patients.
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28
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von Stedingk K, De Preter K, Vandesompele J, Noguera R, Øra I, Koster J, Versteeg R, Påhlman S, Lindgren D, Axelson H. Individual patient risk stratification of high-risk neuroblastomas using a two-gene score suited for clinical use. Int J Cancer 2015; 137:868-77. [PMID: 25652004 DOI: 10.1002/ijc.29461] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Accepted: 01/08/2015] [Indexed: 11/11/2022]
Abstract
Several gene expression-based prognostic signatures have been described in neuroblastoma, but none have successfully been applied in the clinic. Here we have developed a clinically applicable prognostic gene signature, both with regards to number of genes and analysis platform. Importantly, it does not require comparison between patients and is applicable amongst high-risk patients. The signature is based on a two-gene score (R-score) with prognostic power in high-stage tumours (stage 4 and/or MYCN-amplified diagnosed after 18 months of age). QPCR-based and array-based analyses of matched cDNAs confirmed cross platform (array-qPCR) transferability. We also defined a fixed cut-off value identifying prognostically differing subsets of high-risk patients on an individual patient basis. This gene expression signature independently contributes to the current neuroblastoma classification system, and if prospectively validated could provide further stratification of high-risk patients, and potential upfront identification of a group of patients that are in need of new/additional treatment regimens.
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Affiliation(s)
- Kristoffer von Stedingk
- Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden.,Department of Pediatric Oncology and Hematology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Katleen De Preter
- Center for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium
| | - Jo Vandesompele
- Center for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium
| | - Rosa Noguera
- Department of Pathology, Medical School, University of Valencia, Valencia, Spain
| | - Ingrid Øra
- Department of Pediatric Oncology and Hematology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Jan Koster
- Department of Oncogenomics, Academic Medical Center, Amsterdam, The Netherlands
| | - Rogier Versteeg
- Department of Oncogenomics, Academic Medical Center, Amsterdam, The Netherlands
| | - Sven Påhlman
- Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - David Lindgren
- Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Håkan Axelson
- Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
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29
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Abstract
Neuroblastoma (NB) is the third most common pediatric cancer. Although NB accounts for 7% of pediatric malignancies, it is responsible for more than 10% of childhood cancer-related mortality. Prognosis and treatment are determined by clinical and biological risk factors. Estimated 5-year survival rates for patients with non-high-risk and high-risk NB are more than 90% and less than 50%, respectively. Recent clinical trials have continued to reduce therapy for patients with non-high-risk NB, including the most favorable subsets who are often followed with observation approaches. In contrast, high-risk patients are treated aggressively with chemotherapy, radiation, surgery, and myeloablative and immunotherapies.
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30
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Volchenboum SL, Andrade J, Huang L, Barkauskas DA, Krailo M, Womer RB, Ranft A, Potratz J, Dirksen U, Triche TJ, Lawlor ER. Gene Expression Profiling of Ewing Sarcoma Tumors Reveals the Prognostic Importance of Tumor-Stromal Interactions: A Report from the Children's Oncology Group. JOURNAL OF PATHOLOGY CLINICAL RESEARCH 2015; 1:83-94. [PMID: 26052443 PMCID: PMC4457396 DOI: 10.1002/cjp2.9] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Relapse of Ewing sarcoma (ES) can occur months or years after initial remission, and salvage therapy for relapsed disease is usually ineffective. Thus, there is great need to develop biomarkers that can predict which patients are at risk for relapse so that therapy and post‐therapy evaluation can be adjusted accordingly. For this study, we performed whole genome expression profiling on two independent cohorts of clinically annotated ES tumours in an effort to identify and validate prognostic gene signatures. ES specimens were obtained from the Children's Oncology Group and whole genome expression profiling performed using Affymetrix Human Exon 1.0 ST arrays. Lists of differentially expressed genes between survivors and non‐survivors were used to identify prognostic gene signatures. An independent cohort of tumours from the Euro‐Ewing cooperative group was similarly analysed as a validation cohort. Unsupervised clustering of gene expression data failed to segregate tumours based on outcome. Supervised analysis of survivors versus non‐survivors revealed a small number of differentially expressed genes and several statistically significant gene signatures. Gene‐specific enrichment analysis demonstrated that integrin and chemokine genes were associated with survival in tumours where stromal contamination was present. Tumours that did not harbour stromal contamination showed no association of any genes or pathways with clinical outcome. Our results reflect the challenges of performing RNA‐based assays on archived bone tumour specimens. In addition, they reveal a key role for tumour stroma in determining ES prognosis. Future biological and clinical investigations should focus on elucidating the contribution of tumour:micro‐environment interactions on ES progression and response to therapy.
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Affiliation(s)
- Samuel L Volchenboum
- Center for Research Informatics, University of Chicago ; Department of Pediatrics, University of Chicago
| | - Jorge Andrade
- Center for Research Informatics, University of Chicago
| | - Lei Huang
- Center for Research Informatics, University of Chicago
| | - Donald A Barkauskas
- Department of Preventive Medicine, Keck School of Medicine of the University of Southern
| | - Mark Krailo
- Department of Preventive Medicine, Keck School of Medicine of the University of Southern
| | - Richard B Womer
- Children's Hospital of Philadelphia and University of Pennsylvania
| | - Andreas Ranft
- University Hospital Muenster, Department of Pediatrics and Pediatric Hematology and Oncology
| | - Jenny Potratz
- University Hospital Muenster, Department of Pediatrics and Pediatric Hematology and Oncology
| | - Uta Dirksen
- University Hospital Muenster, Department of Pediatrics and Pediatric Hematology and Oncology
| | - Timothy J Triche
- Department of Pathology, Keck School of Medicine of the University of Southern California
| | - Elizabeth R Lawlor
- Department of Pediatrics and Department of Pathology, University of Michigan
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31
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Stricker TP, Morales La Madrid A, Chlenski A, Guerrero L, Salwen HR, Gosiengfiao Y, Perlman EJ, Furman W, Bahrami A, Shohet JM, Zage PE, Hicks MJ, Shimada H, Suganuma R, Park JR, So S, London WB, Pytel P, Maclean KH, Cohn SL. Validation of a prognostic multi-gene signature in high-risk neuroblastoma using the high throughput digital NanoString nCounter™ system. Mol Oncol 2014; 8:669-78. [PMID: 24560446 DOI: 10.1016/j.molonc.2014.01.010] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2013] [Revised: 12/24/2013] [Accepted: 01/21/2014] [Indexed: 10/25/2022] Open
Abstract
Microarray-based molecular signatures have not been widely integrated into neuroblastoma diagnostic classification systems due to the complexities of the assay and requirement for high-quality RNA. New digital technologies that accurately quantify gene expression using RNA isolated from formalin-fixed paraffin embedded (FFPE) tissues are now available. In this study, we describe the first use of a high-throughput digital system to assay the expression of genes in an "ultra-high risk" microarray classifier in FFPE high-risk neuroblastoma tumors. Customized probes corresponding to the 42 genes in a published multi-gene neuroblastoma signature were hybridized to RNA isolated from 107 FFPE high-risk neuroblastoma samples using the NanoString nCounter™ Analysis System. For classification of each patient, the Pearson's correlation coefficient was calculated between the standardized nCounter™ data and the molecular signature from the microarray data. We demonstrate that the nCounter™ 42-gene panel sub-stratified the high-risk cohort into two subsets with statistically significantly different overall survival (p = 0.0027) and event-free survival (p = 0.028). In contrast, none of the established prognostic risk markers (age, stage, tumor histology, MYCN status, and ploidy) were significantly associated with survival. We conclude that the nCounter™ System can reproducibly quantify expression levels of signature genes in FFPE tumor samples. Validation of this microarray signature in our high-risk patient cohort using a completely different technology emphasizes the prognostic relevance of this classifier. Prospective studies testing the prognostic value of molecular signatures in high-risk neuroblastoma patients using FFPE tumor samples and the nCounter™ System are warranted.
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Affiliation(s)
- Thomas P Stricker
- Department of Pathology, Microbiology and Immunology, Vanderbilt University, Nashville, TN, USA
| | | | - Alexandre Chlenski
- Department of Pediatrics, Comer Children's Hospital, University of Chicago, Chicago, IL, USA
| | - Lisa Guerrero
- Department of Pediatrics, Comer Children's Hospital, University of Chicago, Chicago, IL, USA
| | - Helen R Salwen
- Department of Pediatrics, Comer Children's Hospital, University of Chicago, Chicago, IL, USA
| | - Yasmin Gosiengfiao
- Department of Pediatrics, Ann and Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Elizabeth J Perlman
- Department of Pathology, Ann and Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Wayne Furman
- Department of Hematology/Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Armita Bahrami
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jason M Shohet
- Department of Pediatrics, Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Peter E Zage
- Department of Pediatrics, Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - M John Hicks
- Department of Pathology, Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Hiroyuki Shimada
- Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Rie Suganuma
- Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Julie R Park
- Department of Pediatrics, Seattle Children's Hospital, University of Washington, Seattle, WA, USA; Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Sara So
- Department of Pediatrics, Seattle Children's Hospital, University of Washington, Seattle, WA, USA; Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Wendy B London
- Children's Oncology Group Statistics and Data Center, Boston, MA, USA; Boston Children's Hospital/Dana-Farber Cancer Institute, Boston, MA, USA
| | - Peter Pytel
- Department of Pathology, Comer Children's Hospital, University of Chicago, Chicago, IL, USA
| | | | - Susan L Cohn
- Department of Pediatrics, Comer Children's Hospital, University of Chicago, Chicago, IL, USA.
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