ONLINE BIOMARKER VALIDATION OF SURVIVAL- ASSOCIATED BIOMARKERS IN BREAST AND OVARIAN CANCER USING MICROARRAY DATA OF 3,862 4,323 PATIENTS Balázs Győrffy.

Slides:

Advertisements

Similar presentations

Sino-Danish Breast Cancer Research Centre Beijing Genomic Institute, Shenzhen University of Copenhagen University of Århus University of Southern Denmark.

Advertisements

Introduction to BioConductor Friday 23th nov 2007 Ståle Nygård Statistical methods and bioinformatics for the analysis of microarray.

Glioblastoma Multiforme (GBM) – Subtype Analysis Lance Parsons.

PROGNOSTIC SIGNIFICANCE OF PRIMARY TUMORAL FDG UPTAKE MEASURED BY PET: Systematic Review and Meta-analysis Ben A. Dwamena, MD.

Total Lesion Glycolysis by 18 F-FDG PET/CT a Reliable Predictor of Prognosis in Soft Tissue Sarcoma Ilkyu Han Musculoskeletal Tumor Center, Seoul National.

References 1.Salazar R, Roepman P, Capella G et al. Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J.

Introduction The goal of translational bioinformatics is to enable the transformation of increasingly voluminous genomic and biological data into diagnostics.

PrognoScan A new database for meta-analysis of the prognostic value of genes 1 Hideaki Mizuno, Kunio Kitada, Kenta Nakai, Akinori Sarai BMC Med Genomics.

CS-1 Results of the Phase 3 Clinical Trials of Abraxane vs. Taxol in Metastatic Breast Cancer William J. Gradishar, MD, FACP Professor of Medicine Northwestern.

Taiwan 2000 PETACC 3 ASCO 2009 Molecular markers in colon cancer have a stage specific prognostic value. Results of the translational study on the PETACC.

Knowledgebase Creation & Systems Biology: A new prospect in discovery informatics S.Shriram, Siri Technologies (Cytogenomics), Bangalore S.Shriram, Siri.

A 14-gene prognosis signature predicts metastasis risk in node-negative, estrogen receptor-positive, Tamoxifen-treated breast cancer in different ethnogeographic.

The best of both worlds Pharma R&D IT - Informatics Rudi Verbeeck Guided analytics in the hands of the SME.

Surrogate Endpoints and Correlative Outcomes Hem/Onc Journal Club January 9, 2009.

Analysis of Molecular and Clinical Data at PolyomX Adrian Driga 1, Kathryn Graham 1, 2, Sambasivarao Damaraju 1, 2, Jennifer Listgarten 3, Russ Greiner.

Multiple Examples of tumor tissue (public data from Whitehead/MIT) SVM Classification of Multiple Tumor Types DNA Microarray Data Oracle Data Mining 78.25%

Enabling biomarker validation in breast cancer molecular subtypes: sensitivity and specificity of array-based subtype classification in 983 patients Balázs.

Supplemental Figures Loss of circadian clock gene expression is associated with tumor progression in breast cancer Cristina Cadenas 1*, Leonie van de Sandt.

PrognoChip Mediator, FORTH-ICS 1 Integrating Clinical and Genomic Information through the PrognoChip Mediator Implemented by Manos Kalaitzakis, Dimitris.

Sgroi DC et al. Proc SABCS 2012;Abstract S1-9.

1.DATABASE construction  n=1,715  Median OS=40.0 months, age: 64+/-10 yrs  Histology (adeno/squamous/large): 50% / 45% / 5%  Stage 1/2/3/4: 63% / 27%

A Quantitative Multi-Gene RT-PCR Assay for Prediction of Recurrence in Stage II Colon Cancer (CC): Selection of the Genes in 4 Large Studies and Results.

BioQUEST / SCALE-IT Module From Omics Data to Knowledge Case 1: Microarrays Namyong Lee Minnesota State University, Mankato Matthew Macauley Clemson University.

©Edited by Mingrui Zhang, CS Department, Winona State University, 2008 Identifying Lung Cancer Risks.

Michael Birrer Ian McNeish New Developments in Biology and Targets of Epithelial Ovarian Cancer.

Investigation of CA9 expression in pulmonary metastatic lesions from patients with clear cell renal cell carcinoma Pierre Tennstedt 1, Peter Schneider.

Gene Expression Signatures for Prognosis in NSCLC, Coupled with Signatures of Oncogenic Pathway Deregulation, Provide a Novel Approach for Selection of.

Ranjit Ganta, Raj Acharya, Shruthi Prabhakara Department of Computer Science and Engineering, Penn State University DATA WAREHOUSE FOR BIO-GEO HEALTH CARE.

Techniques for Analysing Microarrays Which genes are involved in ovarian and prostate cancer?

Using Predictive Classifiers in the Design of Phase III Clinical Trials Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute.

Poster Title ABSTRACT #59 Cell cycle progression genes differentiate indolent from aggressive prostate cancer. Steven Stone 1 Jack Cuzick 2, Julia Reid.

Project of CZ5225 Zhang Jingxian:

A comparative study of survival models for breast cancer prognostication based on microarray data: a single gene beat them all? B. Haibe-Kains, C. Desmedt,

Multilevel and multifrailty models. Overview  Multifrailty versus multilevel Only one cluster, two frailties in cluster e.g., prognostic index (PI) analysis,

GeWorkbench Overview Support Team Molecular Analysis Tools Knowledge Center Columbia University and The Broad Institute of MIT and Harvard.

Pan-cancer analysis of prognostic genes Jordan Anaya Omnes Res, In this study I have used publicly available clinical and.

A B C Supplementary Figure S1. Time-dependent assessment of grade, GGI and PAM50 in untreated patients Landmark analyses of the Kaplan-Meier estimates.

Discussant Slides: Title: Radiosensitivity Index Shows Promise for Predicting Outcomes with Adjuvant Radiation in Resected Pancreatic Cancer Patients Poster.

Date of download: 5/29/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Gene Expression Signatures, Clinicopathological Features,

Introduction to Oncomine Xiayu Stacy Huang. Oncomine is a cancer-specific microarray database and has a web-based data-mining platform aimed at facilitating.

GENOME WIDE PROGNOSTIC STUDY IN BLADDER CANCER Antoni Picornell, Stephen J Chanock, Montserrat García-Closas, Guillermo Pita, Daniel Rico, Alfonso Valencia,

CellExpress Tutorial A Comprehensive Microarray-Based Cancer Cell Line and Clinical Sample Gene Expression Analysis Online System :8080 NTU.

Figure 1. FinHER dataset: distribution of tumor-infiltrating lymphocytes in breast cancer according to the (A) three breast cancer subtypes and (B) HER2.

Comparison between Pathologic Characteristics of Her2 Negative and Positive Breast Cancer in a Single Cancer Center in Jordan DR Majdi A. Al Soudi, MD,

C Supplemental Figure S2.. C Supplemental Figure S2.

San Antonio Breast Cancer Symposium - December 6-10, 2011 Pre-operative haematological markers and prognosis in early breast cancer Cordiner RL1, Mansell.

TBCRC (the translational breast cancer research consortium) 005 Prospective study

Hallett, et al., - Supplementary Figure 1

CellExpress Examples A Comprehensive Microarray-Based Cancer Cell Line and Clinical Sample Gene Expression Analysis Online System :8080 NTU.

High-level TNFSF13 predict a good response to post-operative chemotherapy in patients with basal-like breast cancer: A systematic review 林惠鈺1,2 歸家豪1,3.

Development and internal-external validation of a multivariable prediction model for pre-operative assessment of positive lymph nodes during robot assisted.

Prognostic significance of DDB2 in ovarian cancer.

ECM29 mediates palbociclib‐induced proteasomal activation and may function as a putative biomarker for palbociclib treatment efficacy in breast cancer.

Human triple‐negative breast cancers (TNBC) express WNT10B, show active Wnt signalling and have high proliferation, and WNT10B has clinical relevance and.

Volume 17, Issue 1, Pages (January 2010)

Suppl Table 1. Database has been randomly split in two groups

Volume 5, Issue 6, Pages e3 (December 2017)

Recurrence-Associated Long Non-coding RNA Signature for Determining the Risk of Recurrence in Patients with Colon Cancer Meng Zhou, Long Hu, Zicheng.

Identification, Review, and Systematic Cross-Validation of microRNA Prognostic Signatures in Metastatic Melanoma Kaushala Jayawardana, Sarah-Jane Schramm,

PD-L1 as a Companion Biomarker for Immune Checkpoint Inhibitors in NSCLC: Should RNA ISH (RISH) Be Considered? Steven G. Gray, PhD Journal of Thoracic.

Single Sample Expression-Anchored Mechanisms Predict Survival in Head and Neck Cancer Yang et al Presented by Yves A. Lussier MD PhD The University.

Figure 1 Overview of the imaging biomarker roadmap

János Fekete, Balázs Győrffy

GOCS GRUPO ONCOLÓGICO COOPERATIVO DEL SUR

(A) Distribution of LATS2 mRNA expression levels in different breast cancer subtypes (PAM50, METABTIC dataset); ***P-value < 0.001, t test comparing lumB.

Figure 1. Identification of three tumour molecular subtypes in CIT and TCGA cohorts. We used CIT multi-omics data ( Figure 1. Identification of.

MiR-520d-3p is an independent positive prognostic factor in ovarian cancer. miR-520d-3p is an independent positive prognostic factor in ovarian cancer.

Immune signatures of patients with short-, medium-, and long-term survival. Immune signatures of patients with short-, medium-, and long-term survival.

TMEscore is a prognostic biomarker and predicts immunotherapeutic benefit. TMEscore is a prognostic biomarker and predicts immunotherapeutic benefit. A,

Vylyny Chat, Robert Ferguson, Tomas Kirchhoff

Presentation transcript:

ONLINE BIOMARKER VALIDATION OF SURVIVAL- ASSOCIATED BIOMARKERS IN BREAST AND OVARIAN CANCER USING MICROARRAY DATA OF 3,862 4,323 PATIENTS Balázs Győrffy 1, András Lánczky 2, Zoltán Szállási 3,4 1 Research Laboratory of Pediatrics and Nephrology, Hungarian Academy of Sciences, Budapest, Hungary; 2 Pázmány Péter University, Budapest, Hungary; 3 Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark; 4 Children's Hospital Informatics Program, Harvard Medical School, Boston, USA. AIMS The pre-clinical validation of prognostic gene candidates in large independent patient cohorts is a pre-requisite for the development of robust biomarkers. In present study we expanded our online Kaplan-Meier plotter tool to assess the effect of genes on ovarian cancer prognosis. CONCLUSIONS We extended our global biomarker validation platform to assess the prognostic power of 22,277 genes in 2,977 breast and 1,346 ovarian cancer patients. Online access at: METHODS Gene expression data and survival information of breast and ovarian cancer patients were downloaded from GEO and TCGA. To analyze the prognostic value of the selected gene in the various cohorts the patients are divided into two groups according to the quantile expression of the gene. Filtering is implemented for stage, grade, and histology subtypes. Follow-up threshold is implemented to exclude long-term effects. A Kaplan-Meier survival plot is generated and significance is computed in the R statistical environment using Bioconductor packages. The combination of several probe sets can be employed to assess the mean of their expression as a multigene predictor of survival. RESULTS All together 1,346 ovarian cancer patients and 2,977 breast cancer patients were entered into the database. These groups can be compared using relapse free survival or overall survival. We used this integrative data analysis tool to validate the prognostic power of 37 biomarkers identified in the literature. Of these, CA125 (p=3.7e-5, HR=1.4), CDKN1B (p=5.4e-5, HR=1.4), KLK6 (p=0.002,HR=0.79), IFNG (p=0.004, HR=0.81), P16 (p=0.02, HR=0.66) and BIRC5 (p= , HR=0.75) were associated with survival. Analysis at Analysis at Raw data n=5,032 Raw data n=5,032 PostgreSQL database PostgreSQL database Remaining n=4,323 Clinical data Real time computation in R Graphical feedback (KM-plot, hazard ratio and p-value) Graphical feedback (KM-plot, hazard ratio and p-value) Filtering for gene expression 1.Quality control 2.Normalization 3.Combination of platforms 1.Quality control 2.Normalization 3.Combination of platforms GEO, TCGA TOP2A in breast cancer CA125 in ovarian cancer Distribution of CA125 Figure 1. The online query pages Figure 2. Overview of the system SymbolSurv.Analyzed in:Affymetrix IDHRp CA125PFSAll patients220196_atn.s _s_at * _s_at1.43.7e-05* KRT19PFSDebulk = subopt _atn.s. KLK6PFSAll patients216699_s_at * _atn.s. KLK10PFSStage = _s_atn.s. IL6OSAll patients205207_atn.s. FASPFSAll patients204780_s_at _s_atn.s _s_at _x_atn.s _x_atn.s. VEGFROSAll patients203934_at CCND1 OSStage = _s_at n.s _at n.s. CCND3 OSAll patients _at n.s. CCNEOSDebulk = subopt _atn.s _atn.s. P15PFSAll patients204599_s_atn.s _x_at * _s_at _atn.s. P16PFSDebulk = subopt _at * _x_atn.s. CDKN1APFSHistology = serous202284_s_atn.s. CDKN1BPFSAll patients209112_at1.45.4e-05* RB1OSStage = _atn.s. E2F1PFSAll patients2028_s_at E2F4PFSAll patients38707_r_atn.s. TP53PFSStage = _s_atn.s _at BAXPFS Therapy = contains Taxol _s_atn.s _s_atn.s. BCL2L1PFSAll patients212312_at _s_atn.s. BIRC2OSStage = _atn.s. BIRC5PFSAll patients210334_x_at * _at _s_at EGFRPFSStage = _s_atn.s _s_atn.s _atn.s. ERBB2PFSHistology = serous216836_s_atn.s. METOSStage = _atn.s _atn.s _x_atn.s _x_atn.s. MMP2PFSHistology = endom _at MMP9OSStage = _s_atn.s. MMP14OSStage = _atn.s _s_atn.s _s_atn.s. HE4PFSAll patients203892_atn.s. SERPINB5PFSDebulk = subopt _atn.s. BRCA1OSAll patients204531_s_atn.s. ERCC1 PFS Stage = 3 Therapy=Tax+Plat _at n.s _s_at n.s. Table 1. The association between prognostic markers and survival. The markers were analyzed in subsets of patients with equivalent clinical characteristics to the cohorts in which the association has previously been described. GRANT SUPPORT: OTKA PD 83154; TAMOP B-09/1/KMR ; The PREDICT consortium (EU grant no )