Inferring cancer pathways from Retroviral Insertional Mutagenesis Screens Jeroen de Ridder1,2,*, Anthony Uren3, Jaap Kool3, Jan Bot1, Marcel Reinders1, Lodewyk Wessels1,2 *[email protected] 1Delft Bioinformatics Lab, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. 2Bioinformatics and Statistics, Dept. of Molecular Biologyivision of Molecular Biology, 3Division of Molecular Genetics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands

Summary In this study, 100 splenic tumors - that were induced by retroviral insertional mutagenesis - are expression profiled, resulting in a dataset for which both the initiating events (the viral in- l1 l2 l3 l4 l5 g t1 tegration sites) as well as the consequent expression profiles are available.

t2 t3 To capture complex associations that arise due to interaction among insertion target , t4 we infer small Boolean logic networks that explicitly incorporate operators to model the po- tential parallel alternatives (’or-’ and ‘exclusive-or’ gates) as well as the potential coopera- activation tion between mutations (‘and’ gates).

250 Interaction network l1 g l1 l2 g Gfi-1 200 not inserted CIS inserted 150 Ccnd3 Rasgrp1

100 up Rras2 mmu-mir106a-36 down

Smooth insertion count insertion Smooth Myb/Ahi-1 Pik3r5/Ntn1 50 Pim-1 Notch1 Ikaros Nmyc1 Lfng Jundm2 g g Lmo2 Gaussian Kernel Convolution 0 de Ridder et al., PLoS Comp. Biol., 2006 CISs Chr. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 X Y Uren and Kool et al., Cell, 2008 AND l1 l1 l2 Uren and Kool et al., Cell, 2008 Poor association Good association Common Insertion Sites indicate putative cancer genes Significantly co-occurrent Significantly mutually exclusive Interactions destroy correlation

0.5

p<10-15

z-score 0.4 l5 0.3 4 position relative to TSS l 0.2 l3 2 p<.01 l 0.1 l1 1 t 0 g Binned average z-score Binned average

t2 −0.1 p<.01

−0.2 t3 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 Position relative to Transcription Start Site Mbp S B (l1 l2 ( l3 Insertions affect expression Use Combinatorial Association Logic to incorporate interactions

12 8.5

11 8 11.5

11 (many possible targets ) Slamf7/Cd48/Slamf6 Notch1 Zeb2 Kit/Kdr Gfi1/Evi5 Ccdc63/Ppp1cc Mark4 Rras2 6330512M04Rik/Ctsd Jund (many possible targets) Gse1 Zfp280d/Mns1 Ccr9 Ptbp1/Arid3a/Polr2e Ikzf1 Cmah Zmiz1 Rcbtb2/Itm2b/Rb1 Ly6e Ttll12 Map3k7ip1 Atp13a3 Erg 10 7.5 mmu−mir−674 Spred1 2610510H03Rik X 19 10.5 Rasgrp1 18 9 7 17 16 10 Expression of gene: Ccnd1

15 Expression of gene: Rundc3b 8 6.5 14 Runx1 9.5 13 Gtf2h4

12 7 6 Gtf2h4 expression 9 11 Pde3b 10

6 5.5 Rras2 Copb1 9 −0.5 0 0.5 1 1.5 −0.5 0 0.5 1 1.5 Psma1 8.5 Insertion locus: Ccnd1 Insertion locus: Notch1 8

7 Fgf4 Oraov1 8 Probe positions Probe Fgf3 Fgf15 Rras2 Runx1 6 ENSMUSG00000066092 Rasgrp1 Network 5 Rras2

4 Tmem16a Ccnd1 Tpcn2 Runx1 3 Gpsm1 Pmpca Egfl7 B230317C12Rik 2 Rasgrp1

Lhx3 C030048H21Rik Inpp5e Notch1 Agpat2 Lcn4 1 Qsox2 4932418E24Rik Sec16a y optimal A2ALE2_MOUSE 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 171819 X D2Bwg1335e Card9 y predicted Common Insertion Site location Snapc4 Sdccag3

Many direct associations are observed Discover new putative cancer pathways