NetBioSIG2014-Talk by Traver Hart
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Functional genomics and cancer subtyping with a human cancer coessentiality network Traver Hart Laboratory of Jason Moffat Donnelly Centre, U. Toronto NetBio SIG, 11 July 2014
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NetBioSIG2014 at ISMB in Boston, MA, USA on July 11, 2014
Transcript of NetBioSIG2014-Talk by Traver Hart
Functional genomics and cancer subtyping with a human cancer coessentiality network
Traver HartLaboratory of Jason Moffat
Donnelly Centre, U. TorontoNetBio SIG, 11 July 2014
GI correlation networks
Costanzo & Baryshnikova, et al., 2010
GI correlationnetworks
Gene aGene bGene cGene dGene e
Cell
line
1
Cell
line
2
Cell
line
3
Cell
line
4
Cell
line
5
Cell
line
12
Cell
line
6
Cell
line
7
Cell
line
8
Cell
line
9
Cell
line
10
Cell
line
11
EssentialNonessential
In yeast, highly correlated profiles imply shared gene function. Can be used to infer function of unknown genes.
Hypothesis: Correlated essentiality profiles across human cancer cell lines (bottom) are analogous to correlated GI profiles and imply shared function—even if we don’t know the query strain
Corollary: Gene clusters can help identify cell lines with similar vulnerabilities, possibly leading to novel classification
Dixon et al., 2009
Query strains
Arra
y ge
nes
Arra
y st
rain
sQuery strains
Pooled library shRNA screens
Marcotte et al., 2012
Bayesian essentiality scoring
Hart et al., 2014
The “daisy model” & core essential genes
Hart et al., 2014
A quantitative measure of sensitivity to RNAi perturbation
Correlation, Essentiality Score vs Expression
Den
sity
Gene aGene bGene cGene dGene e
Cell
line
1
Cell
line
2
Cell
line
3
Cell
line
4
Cell
line
5
Cell
line
12
Cell
line
6
Cell
line
7
Cell
line
8
Cell
line
9
Cell
line
10
Cell
line
11
EssentialNonessential
Query cell lines
Hart et al., 2014
Mean essentiality scoreSt
d. D
ev e
ssen
tialit
y sc
ore
F-measure
Number of Cell Lines
Num
ber o
f Cel
l Lin
es
Num
ber o
f Ess
entia
l Gen
es
Optimize LLS vs KEGG
30 BrCa Luminal + Her2
24 BrCa Basal
34 OvCa
15 PDAC
Filtered data:107 total screens
2,842 genes
Correlations at 1% FDR:1,086 genes
F65
F50
No hairpin norm.
Achilles
Correlation pair rank
Log
Like
lihoo
d Sc
ore
(vs
KEG
G)
4 other
866 genes1,877 edges
RibosomeProteasomeSpliceosomeOxPhos
The HumanCoessentiality
Network
Network ClusteringRibosomeProteasomeSpliceosomeOxPhos
BRCA Luminal/HER2
BrCa/LUM+Her2BrCa/BasalOvCaPDAC
2
Expression vs. Essentiality
Correlation coefficient
GENE CORR P-VALSPDEF 0.716 2.00e-17FOXA1 0.624 6.73e-13ERBB2 0.583 4.57e-11MDM2 0.529 7.87e-09TFAP2C 0.463 5.12e-07FUBP1 0.428 4.33e-06ESR1 0.411 9.39e-05CCND1 0.410 1.17e-05
OxPhos Cluster
BrCa/LUM+Her2BrCa/BasalOvCaPDAC
OxPhos Cluster
ATP5A1ATP5B
COX17
CYC1HCCS
UQCRC1
BCS1LICT1PNPT1SSBP1SUPV3L1TMED2TMEM79
ECSITNDUFA10NDUFA11NDUFAB1
NDUFS1NDUFV1
MRPL21MRPL22MRPL23MRPL51MRPS11ICT1
Functional genomics
(MLL2)
Homolog of GrpE, NEF of Hsp70-typeATPases
Mitochondrial Hsp70 family
Conclusions:• The human cancer coessentiality network
– Depends critically on the new scoring scheme derived from Hart et al, 2014– Optimized by lessons learned from the yeast GI network
• Clusters identify cell lines with common genetic vulnerabilities– Known and novel
• Co-essentiality implies Co-functionality– A unique functional genomics resource
Open questions:• Identify genomic drivers of validated clusters?
• Improve coverage?
• Improve accuracy? CRISPR?
18
Robert RottapelFabrice SirculombFernando SuarezMauricio MedranoJosee Normand
Jason MoffatTroy KetelaKevin BrownJudice KohGlauber BritoAzin SayidDina KaramboulasDewald Van DykDahlia KasimerChristine Misquitta
AcknowledgementsEssentiality Screens in Cancer Cell Lines
Yaroslav FedyshynMarianna LuhovaBohdana FedyshynPatricia MeroChristine Misquitta
Franco Vizeacoumar
Benjamin NeelRichard MarcotteAzin Sayad
CoEssential + CoElution
850 PPI
218 neg
313?
17 PPI
60 neg
236 ?
Vs CORUM
Vs GO_CC
GENE1 GENE2 CoEss CoElu GO_CC? NotesDLST OGDH 0.75 0.97 ? aKG dehydrogenaseMRPL22 MRPL23 0.66 0.52 0 MitochondrialEIF2B2 EIF2B3 0.62 0.91 1 EIF2B complex
MRPL23 SSBP1 0.59 0.70 ? MitochondrialHCCS SSBP1 0.57 0.42 ? MitochondrialNACA RPLP2 0.56 0.61 ? TranslationICT1 MRPL22 0.55 0.68 0 MitochondrialATP5B CYC1 0.53 0.59 ? Mitochondrial
ICT1 PTCD3 0.53 0.70 ? MitochondrialEML4 MAU2 0.52 0.41 0 Microtubule associated protein / sister
chromatid cohesion factor
NSMCE1 SMC5 0.52 0.51 1 SMC5/6 complexCOPB2 COPG1 0.51 0.98 1 Coatomer complexNUTF2 RAN 0.50 0.73 1 Nuclear pore/transportNUP205 NUP93 0.50 0.61 1 Nuclear pore/transportATP5A1 CYC1 0.50 0.79 ? MitochondrialARCN1 COPB1 0.49 0.99 1 Coatomer complexEBNA1BP2 NIFK 0.49 0.60 ? Ribosome biogenesis? Mitosis?BRIX1 UTP15 0.49 0.80 ? Ribosome biogenesisEIF3C PTBP3 0.48 0.47 ? EIF3 / Polypyrimidine (RNA) bindingGRPEL1 HSPA9 0.47 0.48 ? HSP70 + nucleotide exchange factorCYC1 PTCD3 0.47 0.68 ? MitochondrialEIF5A HNRNPK 0.46 0.68 0 Translation / SplicingCYC1 UQCRC1 0.45 0.41 ? MitochondrialCYC1 ECSIT 0.45 0.50 ? MitochondrialATP5B UQCRC1 0.44 0.57 0 Mitochondrial
Why Gene Essentiality?
• Context-sensitive essentials are candidate therapeutic targets
Kaelin WG, Nat Rev Cancer, 2005
Wildtype A
Oncogenic a
Targeted b
