Endogenous Perturbation Analysis of Cancer Sven Nelander, - - PowerPoint PPT Presentation

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Endogenous Perturbation Analysis of Cancer Sven Nelander, - - PowerPoint PPT Presentation

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Endogenous Perturbation Analysis of Cancer Sven Nelander, Wallenberg laboratory / Sahlgrenska-CMR University of Gothenburg onsdag, 2010 oktober 06 Previous work at MSKCC: the CoPIA method Fraction of newly FDA-approved cancer drugs that

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Endogenous Perturbation Analysis of Cancer

Sven Nelander, Wallenberg laboratory / Sahlgrenska-CMR University of Gothenburg

  • nsdag, 2010 oktober 06
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Previous work at MSKCC: the CoPIA method

Nelander et al, MSB 2008

Fraction of newly FDA-approved cancer drugs that target signalling

  • nsdag, 2010 oktober 06
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Comprehensive molecular profiling of primary cancer tumors and cancer cell lines

DNA

RNA

Genetic profile ~10s of mutations affecting protein seq ~100s of copy-number altered genes Epigenetic profile ~100s of hypermethylated promoters

Transcriptional profile ~100s-1000s altered message RNA levels ~10s-100s altered micro-RNA levels

  • 1. Tumor

samples

(Representative numbers for glioblastoma shown)

  • 3. Molecular

profiles

  • 2. Arrays,

sequencing

Agilent Affymetrix Illumina Solexa etc..

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Can we use perturbation models to ‘mine’ the cancer genome project data?

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The cancer genome atlas project: data from 186 cases of glioma

Molecular profi Contain 1: Copy Number 2:

DNA mRNA

Genes Tumors

White: unchanged, Red: gain/upregulation, Blue: loss/downregulation

Data generated and prepared by the Cancer Genome Atlas Consortium (MSKCC, Broad and other centers)

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Relating copy number aberrations to mRNA levels is not so different from other ‘perturbation problems’

Perturbation-response pairs (e.g. siRNA → mRNA) Inherited genotype → phenotype pairs (e.g. CNV → mRNA) Acquired genotype → phenotype pairs (CNA → mRNA)

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Intuitive model State space model Linear steady state equation

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Linear matrix equations for n genes and p patients

Solve for A or G using the Lasso penalty (Tibshirani 1995) and cyclic coordinate descent method (matlab/mex implemnentation, c.a. 30 sec for 10000 genes, 200 patients). For convex methods and gene methods, see e.g. early work by Yeung/Collins 2001, recent work by Boyd et al

n x n n x p n x p n x p

n x n n x p

n x p

n x p n x n n x n

‘System’ ‘Transfer’

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Relationship between A and G=-inv(A)

Transfer matrix G System matrix A

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EPoC net (G matrix) of glioma

(identified in at least 200/1000 bootstrap runs on 10672 genes)

GO analysis: GO:0007267 cell signaling (p=10-12), GO:0007399 nervous system development (p=10-10) What about the A matrix?: dominated by inflammatory genes and and blood cell markers

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Main hubs

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Does the glioma model contain any ‘drug targets’?

amplified hub deleted hub stimulated target suppressed target

5 8 2 2

Numbers in boxes: # targets in each category

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Test 1: is the network consistent between replicate datasets?

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Test 2: does the network overlap with IntAct, NCI, HPRD, and Reactome ‘pathways’?

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Test 3: does the network hold up to testing in glioma cell lines?

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Can the geometry of G be used for survival predictions?

CNA perturbations mRNA responses

projection of mRNA profiles onto the main axis of G gives a ‘signal amplification score’ Blue group: score<0, Red group: score >0

G

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Conclusion

  • The key idea is to reverse engineer human transcription using CNA

perturbations and mRNA responses

  • Combination of two datatypes resulted in more consistent models,

and a more relevant selection of genes (GO, inspection, survival)

  • Fast enough method that gives concrete, testable, predictions from

cancer genome data.

  • We identified a new glioma tumor suppressor (p53-interacting

protein NDN).

  • There are additional datatypes that were not yet exploited in our
  • model. Several challenges remain.
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Credits

  • Teresia Kling, Tobias Abenius (EPoC method)
  • Linnéa Schmidt, Linda Lindahl, Keiko Funa (experiments)
  • Rebecka Jörnsten, Torbjörn Nordling (theory)
  • The Cancer Genome Atlas Project and Chris Sander
  • Work was supported by: Vetenskapsrådet, Cancerfonden,

Barncancerfonden, Assar Gabrielssons stiftelse, EMBO, Kungliga vetenskapsakademin, Sahlgrenska akademin

  • nsdag, 2010 oktober 06