How about a Masters project in the area of cancer systems biology

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How about a Masters 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
• nelander_at_cbio.mskcc.org (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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Multiple Perturbation Analysis of Cancer Pathways

• Sven Nelander
• Computational Biology Center / Chris Sander group
• Memorial Sloan-Kettering Cancer Center
• New York

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Outline

• Perturbing cancer cells - key questions
• Building models for combinatorial perturbation of
  breast cancer pathways
• Whole-genome RNAi screening to extend the
  TGF-beta pathway
• Work in cancer genomics and related areas

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Cancer

• A broad class of diseases exhibiting uncontrolled
  growth, tissue invasion and metastasis.
• Gradual progression towards a more malignant
  phenotype
• Acquisition of mutations that affect a specific
  set of processes
• Growth factor signaling
• Apoptosis
• DNA repair
• Cell cycle regulation
• Differentiation

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Selective targeting of cancer pathways by small
compounds

• Force differentiation
• Inhibit anti-apoptotic signals
• Inhibit growth-stimulating signals

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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.

Gain of DNA Loss of DNA
200 Patients
genomic position (3000 megabases)
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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?
• Whats 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.

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Implementing a systems biology cycle for combined
perturbations
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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

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Simple dynamical models

• Similar models used for
• Analysis of microarray time series (DHaeseler
  2000, Xiong 2004)
• Network inference from perturbed microarray
  profiles (Yeung 2002, Tegner 2003)
• Inference of mechanism of action (diBernardo et
  al 2006)

Similar models used for Analysis of microarray
time series (DHaeseler, 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)
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Prediction of perturbation responses
COMPOUNDS
PHENOTYPE
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Prediction of perturbation responses
Experimental data from Kaufman et al, PLoS Comp
Biol, 2006
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Parameter fitting / system identification

• For all experiments minimize

Sum of squares error
Structural complexity
Solmaz Shahalizadeh, Masters thesis
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Algorithms used to minimize E

• Recurrent backpropagation (Pineda, 1988)
• Backpropagation through time (Pearlmutter)
• Gennemark and Wedelin, 2007

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Inference from steady state perturbation
responses, hypothetical experiment with 40 dual
perturbations and 10 readouts
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Inference from perturbation responses,
experimental data
Data from Janes et al, Science 2005
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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

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Efficient proteomics technique will make large
perturbation studies possible.
SILAC technology (Jens Andersen group, Odense)
Reverse phase protein array (Weiqing Wang)
Dilution of Lysate

• 1) One grid for one sample
• 2) One antibody blot for one slide
• 3) Relative quantification, positive controls on
  each slide
• 4) Quantitative peptide and phosphopeptide
  controls

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
Duplicates
Duplicates
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RNAi screening for TGF-beta pathway
components (Niki Schultz)
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21000 siRNA duplexes were scored for their effect
on TGF-beta signaling
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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.

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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.

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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

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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
• Søren Schandorff, Jens Andersen
• Björn Nilsson