How about a Master’s project in the area of cancer systems biology? • We offer projects in the following areas: • Reverse engineering of cancer pathways • Control of the cancer cell phenotype using drug combinations • Predicting phenotype responses to cellular perturbation from array data • Contact for more info: • [email protected](please attach a CV) • Previous project workers: • Frank Eriksson, Mat Stat, Chalmers • Darima Lamazhapova, Cambridge University • Erik Larsson, Wallenberg lab, GU • Tanya Lobovkina, Chemistry, Chalmers
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How about a Master’s project in the area of cancer systems biology?
• We offer projects in the following areas: • Reverse engineering of cancer pathways• Control of the cancer cell phenotype using drug
combinations• Predicting phenotype responses to cellular perturbation
Selective targeting of cancer pathways by small compounds
1. Force differentiation2. Inhibit anti-apoptotic signals3. Inhibit growth-stimulating signals
Tumors contain multiple genetic abnormalities
• Copy number alterations (MSKCC study).
• Sequence alterations. 90 mutated genes per tumor in breast and colon cancer (Sjöblom et al, Science 2006)
• Promoter hypermethylation.
genomic position (3000 megabases)
200 Patients
Gain of DNA
Loss of DNA
A role for systems biology?
• Key types of question:
• How will a melanoma cell line with mutation X respond to drug Y?• Will drug X synergize with drug Y?• Which regulatory interactions are implicated by the observed responses?• What’s the mechanism of action of drug X?
• Pathway maps are ambiguous, incomplete and have unclear predictive value.
• Expert intuition is likely to fail in complicated cases.
• To facilitate prediction and inference, mathematical models can be employed.
• Efficient identification of promising drug combinations
• Potentially faster progression to clinical trials
SkMel28:
BRAF V600EEGFR P753STP53 L145R
Cell lines with differentmutational profiles
SkMel28:
BRAF V600EEGFR P753STP53 L145R
Implementing a systems biology cycle for combined perturbations
Desirable properties of a model for our purposes
Representational capability:• Pathway-like and biochemically plausible• Quantitative or semi-quantitative predictions• Nonlinear interaction effects (epistasis and synergism) are possible
Experimental implications:• Both temporal and steady state perturbation responses• Incomplete readout possible
Algorithms:• Reverse engineering is computationally tractable
Simple dynamical models
Similar models used forAnalysis of microarray time series (D’Haeseler 2000, Xiong 2004) Network inference from perturbed microarray profiles (Yeung 2002, Tegner 2003)Inference of mechanism of action (diBernardo et al 2006)€
dx idt= ( W ij x j
j
∑ ) −α ix i + Pi
€
dx idt= β i f ( W ij x j
j
∑ + Pi) −α ix i
Similar models used for
Analysis of microarray time series (D’Haeseler, 2000),
Modeling of lambda phage gene regulation (Vohradsky, 2001),
Robustness analysis of the yeast cell cycle (Li et al 2004).
Discussed as a model for signaling in (Bhalla 2003).
DNA switch network - synthetic biology (Kim et al 2004 and 2006)
Prediction of perturbation responsesCOMPOUNDS
PHENOTYPE
Prediction of perturbation responses
Experimental data from Kaufman et al, PLoS Comp Biol, 2006
Parameter fitting / system identification
• For all experiments minimize
Sum of squares error
Structural complexity€
E = ESSQ + ESTRUCT
Solmaz Shahalizadeh, Master’s thesis
Algorithms used to minimize E
• Recurrent backpropagation (Pineda, 1988)
• Backpropagation through time (Pearlmutter)
• Gennemark and Wedelin, 2007
Inference from steady state perturbation responses, hypothetical experiment with 40 dual perturbations
and 10 readouts
Inference from perturbation responses, experimental data
Data from Janes et al, Science 2005
Experimental pilot studies (ongoing)
• Two breast epithelial cell lines• MCF7 - cancer• MCF10A - transformed noncancer
• Initial focus on mitogenic pathways and low molecular weight compound perturbation
• Database of 2200 compound-gene links
• Experiment 1: predict triplet perturbations from dual perturbations • Experiment 2: crosstalk detection and explanation
Reverse phase protein array (Weiqing Wang)
0 1/2 1/4 1/8
0 1/2 1/4 1/8
1/16 1/32 1/64 1/128
1/16 1/32 1/64 1/128
Dilution of Lysate
Duplicates
Duplicates
1) One grid for one sample 2) One antibody blot for one slide3) Relative quantification, positive
controls on each slide4) Quantitative peptide and
phosphopeptide controls
SILAC technology (Jens Andersen group, Odense)
Efficient proteomics technique will make large perturbation studies possible.
RNAi screening for TGF-beta pathway components
(Niki Schultz)
21000 siRNA duplexes were scored for their effect on TGF-beta signaling
Work in genomics
• Sarcoma genome project• Collaboration with MSKCC surgery dept and Broad Inst.• 140 sarcoma patients• Large-scale genomic characterization:
– Transcriptional arrays
– Copy number arrays
– Exon sequencing
• Aberrant processes? Therapy targets?
• DNA copy number alteration in nonmalignant lesions• Collaboration with Columbia pathology dept.
Summary
• Methodology to analyze combinatorial perturbation experiments using differential equation models.
• Preliminary data suggest applicability to real experimental data• No assumptions of linearity or complete observation• The methodology generalizes genetic epistasis analysis in that
it handles higher order perturbations and feedback loops.
• We are proceeding to a study of combinatorial drug effects on the phenotype of breast cancer cells.
Future perspectives
• Using perturbation to pinpoint mutations and regulatory differences between tumors
• Cancer genomics data as an endogenous perturbation experiment
• Phenotype control in non-malignant disease conditions
Acknowledgements
• Weiqing Wang, Nikolaus Schultz, Christine Pratilas, Barry Taylor, Dina Marenstein, Sam Singer, Joan Massague, Neal Rosen, Chris Sander
• Solmaz Shahalizadeh, Peter Gennemark, Frank Eriksson, Darima Lamazhapova
