R2: web-based genomics analysis and visualization platform Overview Jan Koster Department of Oncogenomics Academic Medical Center (AMC) UvA, the Netherlands jankoster@amc.uva.nl jankoster@amc.uva.nl 1
The problem High throughput technologies have become part of the every day biomedical laboratory standards Working with these data can be challenging and is often done via external collaboration (bioinformatics group) As a consequence the wetlab researchers are out of touch with their own data, while external data analists that are not in touch with the experiment are analyzing the data in stead. jankoster@amc.uva.nl 2
What is R2? R2 genomics analysis and visualization platform Web based (http://r2platform.com) No installation required Works everywhere (lab / home / conference room) (Public) collection datasets Uniform normalization Analysis/Visualization tools Intended users Bio(medical) researchers Wetlab biologists Queries Targeted by users interests Graphical representations jankoster@amc.uva.nl 3
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Overview of R2 core jankoster@amc.uva.nl 5
Neuroblastoma Childhood tumor Before we start MYCN oncogene amplified (++ DNA copies) in 20% patients Bad prognosis on their survival Amplification is measured in clinical setting and used in risk stratification Most of the options in R2 will be demonstrated in a neuroblastoma dataset generated within the department of Oncogenomics (NB88), and in addition focus on the MYCN gene or its amplification status jankoster@amc.uva.nl 6
R2 main window jankoster@amc.uva.nl 7
R2 main window jankoster@amc.uva.nl 8
R2 main window jankoster@amc.uva.nl 9
View a gene jankoster@amc.uva.nl 10
View a gene jankoster@amc.uva.nl 11
View a gene jankoster@amc.uva.nl 12
View a gene jankoster@amc.uva.nl 13
TranscriptView jankoster@amc.uva.nl 14
Correlations with a gene jankoster@amc.uva.nl 15
Correlations with a gene jankoster@amc.uva.nl 16
Correlations with a gene jankoster@amc.uva.nl 17
Two gene view jankoster@amc.uva.nl 18
Two gene view jankoster@amc.uva.nl 19
Pubsniffer 20 jankoster@amc.uva.nl 20
Two gene view jankoster@amc.uva.nl 21
TimeSeries FLAG ChIP MYCN ChIP 22 jankoster@amc.uva.nl 22
Two gene view jankoster@amc.uva.nl 23
2D distribution jankoster@amc.uva.nl 24
Differential Expression 25 jankoster@amc.uva.nl 25
Differential Expression jankoster@amc.uva.nl 26
Differential Expression jankoster@amc.uva.nl 27
Detailed View jankoster@amc.uva.nl 28
Detailed View 29 jankoster@amc.uva.nl 29
Detailed View jankoster@amc.uva.nl 30
KaplanScan jankoster@amc.uva.nl 31
Differential Expression jankoster@amc.uva.nl 32
2 groups plotter jankoster@amc.uva.nl 33
Differential Expression jankoster@amc.uva.nl 34
Gene Ontology 35 jankoster@amc.uva.nl 35
Differential Expression jankoster@amc.uva.nl 36
Scavenger / gene set analysis jankoster@amc.uva.nl 37
Differential Expression jankoster@amc.uva.nl 38
End of overview jankoster@amc.uva.nl 39
Custom Themes Core overview Gene Expression Signatures acgh-like / QDNAseq MegaSampler / MegaSearch Integration Gex / Methylation Integration Gex / mirna WGS Personalized Med WGS FOXR1 End jankoster@amc.uva.nl 40
Across datasets One of the Powers of R2 is the large database of diverse (public) datasets and the possibility to combine them for analysis / visualizations Cell Lines Pediatric Cancers Adult Cancers Normal Tissues Leukemia jankoster@amc.uva.nl 41 Ewing GBM MB NRBL OS RMS
MegaSampler views jankoster@amc.uva.nl 42
MegaSearch (Find Marker genes) 3 neuroblastoma vs 2 medulloblastoma datasets Identify marker genes jankoster@amc.uva.nl 43
Visual Fisher s Exact test Interactive annotation view Make Exact test visual Identify every sample Add layer of information with color Mandriota et al, Oncotarget, 2015 jankoster@amc.uva.nl 44
Pathway signatures Pathway Signatures Use genes from canonical pathway (like KEGG database) Problem Pathways often work op protein modification level (phosphorylation) Not neccesarily change on mrna level 1 out of all genes in a pathway may excert a measurable effect jankoster@amc.uva.nl 45
Functional pathway Signatures Functional pathway signatures 1. Manipulate pathway experimentally 2. determine mrna response 3. use these mrna response profiles as proxy for pathway activity jankoster@amc.uva.nl 46
K-means clustering on signature Responsive genes upon experimental MYCN reduction in cell line (R2 timeseries analysis) Use these as proxy for MYCN activity (R2 signature creation) Use signature on patient cohort to identify MYCN active tumors EXPECTED: (R2 k-means) MYCN amplified patients have high activity UNEXPECTED: Subset of patients show activity without amplification Valentijn et al, PNAS, 2012 jankoster@amc.uva.nl 47
Continuous: Signature Score Avg of up reg. genes Avg of down reg. genes - Up - Down corrected for group size = jankoster@amc.uva.nl 48
Signature scores Signatures can also be used for the creation of meta-genes Used for correlations with genes / other signatures, to identify new relations Database of (public) signatures jankoster@amc.uva.nl 49
mrna vs mirna Assess, which mirnas are correlated with LIN28B mrna expression Cell Lines Pediatric Cancers Adult Cancers Normal Tissues RMS OS NRBL MB GBM Ewing Leukemia Molenaar et al, Nat Genet, 2012 jankoster@amc.uva.nl 50
mrna vs mirna DNA amplification? Molenaar et al, Nat Genet, 2012 jankoster@amc.uva.nl 51
mrna vs acgh Indeed focal amplification Around LIN28B jankoster@amc.uva.nl 52 Molenaar et al, Nat Genet, 2012
Methylation arrays jankoster@amc.uva.nl 53
Integration Methylation vs Gene Expression + Hovestadt et al, Nature, 2014 jankoster@amc.uva.nl 54
acgh / acgh-like Visualization of acgh(-like) data in the embedded genome browser of R2 acgh, NGS coverage based (Whole Genome or QDNAseq) jankoster@amc.uva.nl 55
acgh / acgh-like cohort jankoster@amc.uva.nl 56
acgh / acgh-like cohort dataset Efficient CBSbin datasets Circular Binary Segmentation on raw reporter values Extend segments to meet halfway Superimpose (SI) all segments within a cohort Annotate SI segments by overlap with gene bodies jankoster@amc.uva.nl 57
30k profiles CGH patterns as dataset jankoster@amc.uva.nl 58
Tumor board Patient overview jankoster@amc.uva.nl 59
Patient overview jankoster@amc.uva.nl 60
Patient overview jankoster@amc.uva.nl 61
Patient overview jankoster@amc.uva.nl 62
Patient overview jankoster@amc.uva.nl 63
Patient overview jankoster@amc.uva.nl 64
Patient overview jankoster@amc.uva.nl 65
Whole genome sequencing data in R2 Molenaar & Koster et al, Nature, 2012 jankoster@amc.uva.nl 66
Whole genome sequencing data in R2 Only ~12 aa affecting mutations Chromothripsis frequent in high stage disease Structural variations affecting single genes Molenaar & Koster et al, Nature, 2012 jankoster@amc.uva.nl 67
Whole genome sequencing data in R2 Neuroblastoma Tumor DNA Landscape jankoster@amc.uva.nl 68
Neuroblastoma patient with single event Santo et al, Oncogene, 2012 jankoster@amc.uva.nl 69
Neuroblastoma patient with single event Santo et al, Oncogene, 2012 jankoster@amc.uva.nl 70
FOXR1 only expressed in combination with Structural Variation Neuroblastoma Ped. cancer cell lines Other cancer Normal tissues Santo et al, Oncogene, 2012 jankoster@amc.uva.nl 71
-dct FOXR1 shrna in HOS cell line FOXR1 qpcr No virus SHC control FOXR1 shrna B4 FOXR1 shrna B8 Microscopy Cell growth (3T3 assay) FACS SHC FOXR1 shrna B4 NV SHC FOXR1 shrna B8 SHC control FOXR1 shrna B4 FOXR1 shrna B8 jankoster@amc.uva.nl 72 Santo et al, Oncogene, 2012 FOXR1 shrna B4 FOXR1 shrna B8
FOXR1 causes growth maintenance in non malignant mouse neuroblasts Immortalized by 4-oht regulated cmyc expression Removal of 4-oht > terminal differentiation Santo et al, Oncogene, 2012 jankoster@amc.uva.nl 73
R2 usage overview http://r2platform.com jankoster@amc.uva.nl 74
Acknowledgements Development/Concepts/Support Jan Koster Richard Volckmann Danny Zwijnenburg Piet Molenaar Jan Molenaar Marcel Kool Linda Valentijn Rogier Versteeg Ext. Data shown from Volker Hovestadt Stefan Pfister Frederike Dijk jankoster@amc.uva.nl 75
Amsterdam Monday April 18 th Den Haag Sept 3/4