Analysis and Integration of Large-scale Molecular
and Clinical Data in Cancers
Sampsa Hautaniemi, DTech
Systems Biology LaboratoryInstitute of Biomedicine
Genome-Scale Biology Research ProgramCentre of Excellence in Cancer Genetics
Faculty of MedicineUniversity of Helsinki
Table of Contents The essence of systems biology: Iteration
and collaboration. Iteration in ovarian cancer.
The essence of systems biology II: Multi-level data. Multi-levelity of breast cancer.
The essence of systems biology III: Computation. Anduril computational framework &
glioblastoma multiforme.
Systems Biology: Iteration
Adapted from a slide by Peter Sorger
Ovarian Cancer Epithelial ovarian cancer is the fifth most
frequent cause of female cancer deaths, with an overall 5-year survival rate below 50%.
The standard chemotherapy for high-grade serous ovarian cancer (HGS-OvCa) is platinum-taxane combination. Majority of patients suffer relapse <18 months. No clinically applicable methods to predict the
prognostic outcome or even to identify the patients unresponsive to current therapies.
Aims of the HGS-OvCa Study To identify poor response and good response
subtypes of HGS-OvCa. Report biomarkers that allow to identify
whether a HGS-OvCa patient responds to the platinum treatment. We developed a computational method that
integrates transcriptomics and clinical data in subtype finding step.
We used transcriptomics and clinical data from 184 HGS-OvCa patients treated with platinum and taxane from TCGA repository.
Three Subtypes of HGS-OvCa
Chen et al. In preparation.
Validation, validation, validation We also used an independent prospective
HGS-OvCa cohort of 29 patients. Data measured with qRT-PCR.
Chen et al. In preparation.
Pathway Analysis Our pathway analysis (too) identified TR3
as a potential driver for platinum resistance.
TR3 Inhibition with Two Drugs We identified two signaling pathway
regulators for TR3 and associated inhibitors. The use of two inhibitors should transform the
HGS-OvCa cells sensitive to platinum.
Chen et al. In preparation.
AKT inh
+ AKT inh + ERK5 inh
Systems Biology II: Multi-level Data
eAtlas of Pathology
While cancer cells are clearly visible the exact molecular causes for are still unknown. Need to study cancer samples at multiple levels.
Multiple Levels of Data
Clinical
Genetics
Transcriptome
Epigenetics
Proteomics
100 samples lead to~200 million data points.
Multiple level data: Estrogen Receptor
Nuclear receptor:Estrogen receptor
Gene regulation
Transcription factor
Genomic action Non-genomic action
Why Is This Important? Estrogen receptor is the most
important clinical variable in determining how to treat a breast cancer patient.
There are several anti-cancer drugs targeting estrogen receptor pathway. Currently unknown which tumors
do not response to therapy. Finding genes respond to
estrogen receptor stimulus may give clues which genes are important in ER inhibition resistance.
Hugo Simberg: Garden of Death
Data We used chromatin immunoprecipitation
combined with massive parallel sequencing (ChIP-seq) to determine genome-wide occupancy (eight time points) after estradiol stimuli in MCF-7 breast cancer cell line: Estrogene receptor RNA polymerase II
Histone marks (H3K4me3, H2A.Z)
These experiments resulted in >2.0 billion data points to the initial analysis.
SYNERGY database SYNERGY database is available and fully
operational. http://csblsynergy.fimm.fi/
Finding ER Responsive Genes
Results We identified 777 estrogen receptor early
responding genes. Interestingly, the major estrogen receptor
related changes in cells were due to non-genomic action.
Results Next we searched for genes that have
survival association in a breast cancer cohort of 150 ER+/HER2-/postmenopausal patients in The Cancer Genome Atlas (TCGA) cohort. Based on Kaplan-Meier analysis we identified
23 genes with survival p<0.05. The best survival associated gene was ATAD3B.
Kaplan-Meier for ATAD3B
Intermission Pol2 activity is much better way of
searching for responsive genes to a cue that mRNA.
In deep sequencing, the sequencing depth is important (with our 200 mill. short-read Pol2 data, we found many ER responsive genes not found in 20 mill. short-read GRO-seq).
How to systematically analyze multi-level data?
Multi-level Cancer Research Requires Computational Methods Storing the data and computing power are
the first (but relatively small) hurdles. Analysis of large-scale, heterogeneous
data is much more challenging than single genomics or proteomics data analysis.
There is a need for computational infrastructure. Writing an analysis program fast without
proper infrastructure will lead to delays and errors in larger projects.
Infrastructure: Anduril Anduril is a computational framework to integrate
large-scale and heterogeneous data, knowledge in bio-databases and analysis tools.
The main design principles are: Modular pipeline analysis approach Scalable Open source, thorough documentation
http://www.anduril.org/ Method written in any programming language
executable from the command prompt can be included.
Produces automatically the result PDF and website containing the results.
Complex Pipelines Are Fragile
Glioblastoma Multiforme (GBM) Glioblastoma multiforme (GBM) is one of the
deadliest cancers. The Cancer Genome Atlas (TCGA) has
published data from >500 GBM patients: comparative genomic hybridization arrays single nucleotide polymorphism arrays exon and gene expression arrays microRNA arrays methylation arrays clinical data
Which genes or genetic regions have survival effect?
GBM Results in Anduril Website
Latest on moesin in GBM
(Sequence) Component Libraries Over 400 Anduril components already available. Pipelines:
ChIP-seq (EMBO J 2011, Cancer Res 2012, ...) RNA-seq (not published) miRNA-seq (not published) DNA methylation-seq (not published) Whole-genome sequence & exome-sequence (not
published) Image analysis (manuscript)
Summary Characterization of a complex disease first requires
identifying the key variables. This requires integration data from multiple levels, iterative
mode of research and collaboration. Multi-level data integration requires computational
infrastructure and data-intensive computing. We have developed Anduril to organize large-scale data
analysis projects (imaging, deep sequencing, database usage, conversions, etc.)
The need for computational infrastructure is evident in particular when analyzing deep sequencing data.
All our methods are (will be) freely available.
http://research.med.helsinki.fi/gsb/hautaniemi/software.html
AcknowledgementsSystems Biology Lab
FundingAcademy of FinlandFinnish Cancer OrganizationsSigrid Jusélius FoundationEU FP7ERA-NET SysBio+Biocenter FinlandBiocentrum Helsinki
CollaboratorsOlli Carpén Henk StunnenbergGeorge ReidJukka Westermarck