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A mouse

Expression signatures as biomarkers: solving combinatorial problems with gene networks Andrey Alexeyenko Department of Medical Epidemiology and Biostatistics, Karolinska Institute. High-throughput evidence. A mouse. Find orthologs. Human. ?. Rat. Fly. B mouse. Yeast.

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A mouse

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  1. Expression signatures as biomarkers: solving combinatorial problems with gene networks Andrey AlexeyenkoDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute

  2. High-throughput evidence Amouse Find orthologs Human ? Rat Fly Bmouse Yeast FunCoup is a data integration framework to discover functional coupling in eukaryotic proteomes with data from model organisms Andrey Alexeyenkoand Erik L.L. Sonnhammer. Global networks of functional coupling in eukaryotes from comprehensive data integration.Genome Research. Published in Advance February 25, 2009

  3. FunCoup • Each piece of data isevaluated • Data FROM many eukaryotes (7) • Practical maximum of data sources (>50)‏ • Predicted networks FOR a number of eukaryotes (10…)‏ • Organism-specific efficient and robust Bayesian frameworks • Orthology-based information transfer and phylogenetic profiling • Networks predicted for different types of functional coupling (metabolic, signaling etc.)‏ http://FunCoup.sbc.su.se

  4. TGFβ<-> cancer pathway cross-talk FunCoup was queried for any links between members of TGFβ pathway (left blue circle) and habituées of known cancer pathways (members of at least 7 out of 18 groups; right blue circle). MAPK1 and MAPK3 belonged to both categories. http://FunCoup.sbc.su.se

  5. FunCoup: recapitulation of known cancer pathways Figure 5 from: The Cancer Genome Atlas Research Network Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008 Sep 4.[Epub ahead of print] The same genes submitted to FunCoup No TCGA data were used. Outgoing links are not shown.

  6. Single molecular markers are (often) far from perfect. Combinations (signatures) should perform better. The problem:How to select optimal combinations? × Outcome, Optimal treatment, Severity/urgency etc.

  7. Biomarker discovery in network context • The idea: • Construct multi-gene predictors with regard to network context • Reduce the computational complexity • Make marker sets biologically sound • Accounting for network context is taking either: • network neighbors or • genes at remote network positions

  8. “Rotterdam” dataset (Wang et al., 2005): 286 patients Clinical data: Estrogen receptor status: +/ – Lymph. node status: all – Relapse : yes/no and time (days) × Expression: ~22000 probes Split data: 75% to train, 25% to test. Produce a linear regression equation (weight terms step-wise, reward for performance, penalize for complexity) on the train sub-set. Apply the equation to the test set to predict outcome (relapse yes/no). Record the specificity/sensitivity (Type I/II error rates) as ROC curve. Repeat m times Procedure Individual probe p-values (~22000): Estrogen receptor-specific ability to predict relapse Select most significant probes (1000): Candidate members for marker signatures Compile set of probes: N probes at a time (e.g. N=20 or N=50) RELAPSE = γ1g1+ γ2g2 + γ3g3 + … + γNgN

  9. Split data: 75% to train, 25% to test. Produce a linear regression equation (weight terms step-wise, reward for performance, penalize for complexity) on the train sub-set. Apply the equation to the test set to predict outcome (relapse yes/no). Record the specificity/sensitivity (Type I/II error rates) as ROC curve. Repeat m times Procedure Select most significant probes (1000): Candidate members for marker signatures Compile set of probes: N probes at a time (e.g. N=20 or N=50) Test X randomly retieved sets Account for the network context Take the best ones RELAPSE = γ1g1+ γ2g2 + γ3g3 + … + γNgN

  10. Candidate signature in the network Biomarker candidates

  11. Ready signature in the network RELAPSE = γ1EIF3S9+ γ2CRHR1+ γ3LYN+ … + γNKCNA5

  12. Testing “top”, “free”, and “network” approaches Top Top

  13. Signature involves genes mutated in cancer

  14. Cancer individuality: each tumor is unique in its molecular state and set of mutated/disordered genes Tumourtcga-02-0114-01a-01w

  15. Partial correlations:a way to get rid of spurious links 0.7 0.6 0.4

  16. Functional coupling transcription ? transcription transcription ? methylation methylation ? methylation mutation  methylation mutation  transcription mutation ? mutation + mutated gene Cancer individuality via network view

  17. is a framework for biomarker discovery: • Markers can be discovered and presented in the network dimension. • Choice of data types to incorporate is unlimited – from metabolite profiling to patient phenotypes. Useful features: • Web-based resource ready for further expansion and presenting new research results in an interactome perspective; • Cross-species network comparison of human and model organisms. • Efficient query system to retrieve network environments of interest. http://FunCoup.sbc.su.se

  18. Thank you for attention!

  19. Decomposing biological context Develomental rPLC = 0.95 Common rPLC = 0.88 ANOVA (Analysis Of VAriance): Look at F-ratios: Signal of interest / Residual (“error”) variance Dioxin-enabled rPLC = 0.76

  20. Accounting for edge features:dioxin-enabled vs. dioxin-sensitive links Andrey Alexeyenko, Deena M Wassenberg, Edward K Lobenhofer, Jerry Yen, Erik LL Sonnhammer, Elwood Linney, Joel N Meyer Transcriptional response to dioxin in the interactome of developing zebrafish. submitted.

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