Building molecular handprints: a systems biology approach to complex diseases

Transcription

1 Building molecular handprints: a systems biology approach to complex diseases Bertrand De Meulder, PhD EISBM, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Lyon, France SIMONCO Workshop, 27th June 2016

2 Phenotypes and endotypes Clinical phenotypes Clinical physiologic characteristics Bio-clinical phenotypes Add pathobiologic processes at molecular level to clinical phenotype Endotypes Identifiable molecular pathway contributes to clinical characteristics 3

3 Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes Innovative Medicines Initiative call topic: Understanding Severe Asthma 4

4 U-BIOPRED hypothesis Biomarker profiles from high-dimensional molecular, physiological, and clinical data integrated by an innovative systems medicine approach into distinct handprints will enable the prediction of clinical course and therapeutic efficacy and identification of novel targets in the treatment of severe asthma 5

5 U-BIOPRED hypothesis Biomarker profiles from high-dimensional molecular, physiological, and clinical data integrated by an innovative systems medicine approach into distinct handprints will enable the prediction of clinical course and therapeutic efficacy and identification of novel targets in the treatment of severe asthma 6

6 Systems Biology / Medicine 6

7 Systems Biology / Medicine 7

8 Systems Biology / Medicine 8

9 Systems Biology / Medicine 9

10 Systems Biology / Medicine 10

11 Systems Biology / Medicine 11

12 Systems Biology / Medicine 12

13 Methods framework De Meulder et al, A bioinformatics and statistical framework for the generation of fingerprints and handprints of complex diseases, in preparation 16

14 Data subsetting De Meulder et al, A bioinformatics and statistical framework for the generation of fingerprints and handprints of complex diseases, in preparation 17

15 Example of handprint The Cancer Genome Atlas (TCGA): publicly available omics and clinical resource on several cancer types We chose the Ovarian Serous Cystadenocarcinoma dataset Ovarian serous cystadenocarcinoma Total Exome SNP Methylation mrna mirna Clinical Cases Selected for test case

16 Example of handprint The Cancer Genome Atlas (TCGA): publicly available omics and clinical resource on several cancer types We chose the Ovarian Serous Cystadenocarcinoma dataset Take home message Ovarian serous cystadenocarcinoma Total Exome SNP Methylation mrna mirna Clinical Cases Selected for test case #1: Plan ahead 21

17 Feature filtering De Meulder et al, A bioinformatics and statistical framework for the generation of fingerprints and handprints of complex diseases, in preparation 23

18 Feature filtering Aims at improving signal to noise ratio without losing information Two main categories of methods Comparative: using relative statistical methods to identify relevant features Needs a priori group definition to be performed (in this example: feature filtering based on p-value of survival status comparison) Absolute: using methods not relying on group definition Correlation Information content measurement Mean to standard deviation ratio filtering 24

19 Feature filtering: WGCNA Weighted gene correlation network analysis (Zhang & Horvath, 2005): finding groups of correlated genes without a priori, and crossing them with clinical variables. WGCNA: Zhang & Horvath, «A general framework for weighted gene co-expression network analysis, Statistical Applications in Genetics and Molecular Biology, 4(1),

20 Feature filtering: WGCNA Weighted gene correlation network analysis (Zhang & Horvath, 2005): finding groups of correlated genes without a priori, and crossing them with clinical variables. Take home message #2: Data filtering improves signal/noise WGCNA: Zhang & Horvath, «A general framework for weighted gene co-expression network analysis, Statistical Applications in Genetics and Molecular Biology, 4(1),

21 Omics-based clustering De Meulder et al, A bioinformatics and statistical framework for the generation of fingerprints and handprints of complex diseases, in preparation 27

22 28 Data integration SNF

23 Data integration SNF Take home message #3:Maximise participant overlap between omics 29

24 Clustering Stability assessment: consensus clustering (Monti et al., 2003) From B De Meulder et al, A bioinformatics and statistical framework for the generation of molecular fingerprints and phenotypic handprints of complex diseases, in preparation 32

25 Clustering Stability assessment: consensus clustering (Monti et al., 2003) Take home message #4:Good clusters are stable clusters From B De Meulder et al, A bioinformatics and statistical framework for the generation of molecular fingerprints and phenotypic handprints of complex diseases, in preparation 32

26 Biomarkers identification De Meulder et al, A bioinformatics and statistical framework for the generation of fingerprints and handprints of complex diseases, in preparation 35

27 Finding biomarkers Machine learning: selection of small list of features that still explain the groups & make accurate predictions for future participants (a.k.a. biomarkers) Caret R package (Kuhn et al, 2012) 37

28 Finding biomarkers Machine learning: selection of small list of features that still explain the groups & make accurate predictions for future participants (a.k.a. biomarkers) Caret R package (Kuhn et al, 2012) Take home message #5: Biomarkers have to be predictive 38

29 Biomarker identification Extracting list of differentially abundant/expressed features Making sense of the results Functional analysis (GO, KEGG, REACTOME, MetaCore, IPA) Gene set analysis Disease maps (based on Fujita et al, 2014) g:profiler: Reimand et al, Nucleic Acids Res.,

30 Biomarker identification Extracting list of differentially abundant/expressed features Making sense of the results Functional analysis (GO, KEGG, REACTOME, MetaCore, IPA) Gene set analysis Disease maps (based on Fujita et al, 2014) Take home message #6:Make sense of the results with multilayer interpretation g:profiler: Reimand et al, Nucleic Acids Res.,

31 Lessons learned from U-BIOPRED Combinations of Multiple Fingerprints (Molecular and Clinical data Partial or Complete Handprints of Mild to Severe Asthma Study Participants U-BIOPRED succeeded by integrating and implementing all key actions in its workplan: Plan ahead experimental design (biological question and model) and statistical power Standardise SOPs, sample collection and biobanking, data housing and analysis plan Improve signal to noise ratio inclusion/exclusion criteria, stringent QC, data filtering Maximise overlap focus on participants with maximum omics/biological measurements Ensure robustness internal replication (resampling, repeatability, reproducibility) Generalize experimental validation using additional methods and independent cohorts Make sense of the results multi-layer biological interpretation, computerized disease maps 45

32 Partners Integrated and explorable data are valuable data!

33 Funded by the European Union University of Amsterdam, University of Southampton, Imperial College London, University of Manchester, University of Nottingham, Fraunhofer Institute Hannover, CNRS-EISBM Lyon, Université de Méditerranee Montpellier, Karolinska Institute Stockholm, University Hospital Umea, University Tor Vergata Rome, Università Cattolica del Sacro Cuore Rome, University of Catania, Hvidore Hospital Copenhagen, University Hospital Copenhagen, Haukeland University Bergen, Semmelweis University Budapest, Jagiellonian University Krakow, University Hospital Bern, University of Ghent EFPIA Partners Novartis Almirall Amgen AstraZeneca Boehringer Ingelheim SME s Aerocrine BioSci Consulting Synairgen Philips Chiesi Research GlaxoSmithKline Johnson & Johnson / Janssen Merck UCB Roche /Genentech Patient organisations Asthma UK European Lung Foundation EFA Int Primary Care Respiratory Group Lega Italiano Anti Fumo Netherlands Asthma Foundation website hosted by the ELF:

34 Funded by the European Union University of Amsterdam, University of Southampton, Imperial College London, University of Manchester, University of Nottingham, Fraunhofer Institute Hannover, CNRS-EISBM Lyon, Université de Méditerranee Montpellier, Karolinska Institute Stockholm, University Hospital Umea, University Tor Vergata Rome, Università Cattolica del Sacro Cuore Rome, University of Catania, Hvidore Hospital Copenhagen, University Hospital Copenhagen, Haukeland University Bergen, Semmelweis University Budapest, Jagiellonian University Krakow, University Hospital Bern, University of Ghent Thank you! Any questions? EFPIA Partners Novartis Almirall Amgen AstraZeneca Boehringer Ingelheim SME s Aerocrine BioSci Consulting Synairgen Philips Chiesi Research GlaxoSmithKline Johnson & Johnson / Janssen Merck UCB Roche /Genentech Patient organisations Asthma UK European Lung Foundation EFA Int Primary Care Respiratory Group Lega Italiano Anti Fumo Netherlands Asthma Foundation website hosted by the ELF: