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Interdisciplinary research collaboration to
unravel new insight on hematological
malignancies
Merja Heinäniemi, PhD
Academy Research Fellow, Adj. Prof
Institute of Biomedicine, School of Medicine
The problem
t(15;17) translocation in AML
Zhu et al. Blood 2001
Systems genomics research group
• We approach problems from a systems view
point
• With an interdisciplinary skill set
Biochemistry Genomics
Biomedicine
Computer science Systems biology
So what goes wrong in AML?
A complex dynamic system in its simplest form
Molecular interaction networks are central for cell function
Decisions &
memory
Principles from theoretical physics
A cell with two regulatory molecules
Molecular interaction networks are central for cell function
Decisions &
memory
Principles from theoretical physics
Regulatory molecules control the system state
Models of cell type dynamics were introduced by Prof. Stuart Kauffman and a
lot of experimental validation came from Prof. Sui Huang – two brilliant
scientists holding an MD degree who started applying theoretical physics in
their work
Decisions &
memory
Principles from theoretical physics
Prof Ilya Shmulevich
(left, Institute for Systems
Biology) and Prof Matti Nykter
(below, University of Tampere)
introduced me to
computational biology
Heinäniemi et al Nature Methods 2013
Together we discovered key regulators of normal
cell differentiation from data
Heinäniemi et al Nature Methods 2013
Coming back to our problem: Regulatory
molecules dysfunction in cancer
Transitions between
cellular states depend on
key regulatory proteins
Zhu et al. Blood 2001
t(15;17) translocation in AML
Can we observe and quantify from data what
happens in hematological malignancies?
- Regulators of normal cell
differentiation lost
Differentiation
blocked
manuscript in preparation
Can we observe and quantify from data what
happens in hematological malignancies?
Data-driven visualization and
clustering of patient data
- Identification of disease-specific regulators
Summary 1
• A theoretical framework exists for studying
complex dynamical systems which can be
applied to biomedical questions
• Biological systems are dynamic and complex:
diseases can be understood as network
perturbations and state transitions
The problem – Biology – Theoretical framework
The problem – Biology – Theoretical framework -
Data
How do we study this
ON A PRACTICAL LEVEL
A lot of our daily work deals with large datasets and therefore requires bioinformatics
gene expression level
Traditionally research has focused on measurements
quantifying specific genes and proteins from samples
Omics technologies and data
availability have changed biomedicine
Input from
computational
fields in data
analysis
Interdisciplinarity
Time in wet-lab is
only small part of
research projects
What has been measured
• Availability and ease of use
of the technologies matters:
many DNA ja RNA level
measurements (gene
expresssion levels, genetic
variants)
• Less data on proteins and
metabolites (so far)
Relevant information is obtained from various
measurement types – many large data tables!
ANALYSIS
Data integration:
current challenge
http://cancergenome.nih.gov/
Summary 2
• Omics technologies are available and generate
large volumes of data
• This has changed the way biomedical research
is conducted: we need new skills!
• Data matrices contain 104-105 measurement
values from each sample and many such
results need to be integrated
Data on hematological malignancies
10K transcriptomes were collected and curated
Can we diagnose patients based
on molecular profiles?
What can we learn about these cancers?
Challenge 1: data visualization
Gene Sample 1 …
Sample 15
…
Sample 22
…
A
10
…
9
…
2
…
B
0
…
1
…
12
…
C
1
…
3
…
2
…
Dimensionality of data
Gene A
Sample 1
Sample 15
Gene C
methods from
machine learning
Sample 22
2D
Gene B
gene expression level
Acute leukemia is one of the rare cancers that
develops at early age
Cells actively regulate which genes are read: genomics techniques
allow monitoring the molecular machinery involved in the process
e.g. binding of regulatory proteins: ChIP-seq
Recruitment of polymerase
-> activation of RNA production: GRO-seq
GRO-seq signal represents the level of actively transcribing RNA polymerases at a given genomic position
Studying leukemia initiation with regulatory genomics
Global run-on sequencing
primary transcripts
enhancer activity
Secondary genetic lesions in ALL
• Integration of whole-genome sequencing
(WGS) data on ALL structural variation (SV)
and Global Run-on (GRO)-sequencing signal
Additional genetic lesions in leukemia development
SV are frequent
TEL-AML1 patients
data from Papaemmanuil et al Nat Genet 2014
RAG functions in recombination
de Villartay et al. Nat Rev Immun 3, 962-972 2003
Which regions of the genome are
susceptible to SV?
The basic structural units of chromatin are topologically associated domains (TADs)
Topological domains in the mouse ES cell genome
(credit: Jesse R. Dixon et al./Nature)
Summary 3
Heinäniemi et al. manuscript in preparation
Making resources available: TCGA multilevel data & 10K
transcriptomes
Visualization, data management & software engineering
Stuart Kauffman
Acknowledgements
Ilya Shmulevich
Sui Huang
Acknowledgements
Ville Hautamäki
Arto Mannermaa
Matti Nykter
Anna-Liisa Levonen
Ritva Vanninen
Minna
Kaikkonen
Olli Lohi
Acknowledgements
Krista
Tapio
Petri
Juha
Maria