M. Kotlyar, C. A. Cumbaa, I. Jurisica Ontario Cancer Institute, Toronto, ON juris@ai.utoronto.ca...

M. Kotlyar, C. A. Cumbaa, I. JurisicaOntario Cancer Institute, Toronto, ON

juris@ai.utoronto.ca

Posters 35 & 10

Predisposition

DiagnosisPrognosis

Treatment plan

Early markers

Target List Interpretation/Prioritization

Lau et al., JCO, 2007

Source Interactions: 234,135Predicted Interactions: 211,604Total Interactions: 439,813

1,129 probesets associated with survival in lung cancer from 24 studies

Maps to 772 proteins in I2D Network comprises 5,925 proteins; 65,074 interactions

1,129 probesets associated with survival in lung cancer from 24 studies

Maps to 772 proteins in I2D Network comprises 5,925 proteins; 65,074 interactions

Zhu et al., Clin Lung Cancer, 2009

Network from analyzed signatures

Combined Signature and NIH Networks

(from Shedden et al., Nat Med, 2008)

Analyzing “Signatures from Validated Signatures“ (from Lau et al., JCO, 2007)

Boutros et al., PNAS, 2009

8 studies pooled589 patient samples

Signature Explosion 113 genes in 4 test datasets 10 M 6-gene permutations

16.4% of all 6-gene signatures are significant (P<0.05)3.28-fold greater than expected

by chance

1,789 signatures perform better across all 4 validation datasets

Boutros et al., PNAS, 2009

•

Solutions?

• Better analysis

–∫(data, methods)

• More protein interactions– ~30% of signature genes

do not have known PPIs

74,944 x 74,944 predictions 14,889 proteins, 100,083 interactions

16,259 proteins,151,312 interactions

Filter probability >75%

54,150 interactions5,725 proteins

Text Mining:43.2% sensitivity70.2% specificity

Evidence for ~67%New evidence for ~40%Avg. from 1.05 to 9.5

21,543 proteins,265,957 interactions

HumanIntact: 32K (8Kp)MINT: 21K (6.2Kp)HPRD: 38K (8.5Kp)DIP: 2.3K (1.5Kp)

310 lung, ovarian, prostate and head&neck cancer targets; network with 13,510 proteins and 104,765 interactions

Cancer – PDB – PSI

Conclusions Best signatures are not

created from the most differential genes“Sub-signatures” can be

even more accurate “Brute force” can identify

gold standard Integrative analysis and

heuristic approach may:Identify all good signaturesHelp to interpret biology

Acknowledgment

ophid.utoronto.ca/navigator

ophid.utoronto.ca/i2d

ophid.utoronto.ca/genecards

http://www.conquercancer.ca

http://www.worldcommunitygrid.org

D. Strumpf, P. Boutros, K. Brown, S. Der, W. Xie, D. Otasek, A. MuhammadF. Breard, R. LuY. Niu, K. Fortney, R. Yan, R. Ramnarine, S. Rahmati, E. Shirdel, D. Rosu, A. Ghavidel, J. Geraci, L. Waldron

ophid.utoronto.ca/cdip

M. S. Tsao, F. Shepherd, L. Penn , M. PintilieNIH Director’s Challenge Consortium I. Stagljar, I. DikicD. Wigle, T. Kislinger…

M. McGuffin, B. Devani, I. Van Toch M. Soloviev, S. Grant, J. Jiang,