high throughput functional genomics approaches to identifying novel drivers of melanoma - brian...

High throughput functional

genomics approaches to identifying novel drivers of melanoma

Brian Gabrielli

Cell Cycle Group

ARVEC

Functional genomics screens

Unbiased approaches to identify new genes and pathways

gene expression phenotype gene function

(↓siRNA, shRNA) (↑cDNA, ORF) (Cell-based assays) (therapeutic targets)

sequence function

cells

Gene vectors: ORF, siRNA

ASSAY: add drugs, change growth conditions, etc.

LABEL: (antisera, dyes)

COLLECT AND ANALYSE DATA : Plate reader, high content imager

X ~23 000

HIGH CONTENT IMAGE ANALYSIS

ARVEC arrayed libraries Gene overexpression

~ 17 033 ORFs as Gateway entry clones

Cloned into a lentiviral expression vector by ARVEC 96-well plates

Screens

•G1 phase progression factors

•Bypass of p16-induced senescence

•ER response genes in BC (Liz Musgrove, Garvan)

•EMT genes in BC (Rik Thompson, SVI)

•Drivers of resistance to Brafi + MEKi in melanoma (Helen Rizos, WMI)

•Inhibitors of p53 induction in DBA (Ross Hannan, Peter Mac)

•Driver mutations in melanoma

•UV-induced G2 phase checkpoint and repair

ARVEC arrayed libraries Gene silencing

siRNA libraries: Dharmacon Smart Pool whole-genome

Lentiviral shRNA libraries: (Open Biosystems) whole genome

Screens

•Ca signaling in BC (Greg Monteith, UQ)

•Synthetic lethality with HPV (Nigel McMillan, UQ)

•Synthetic lethality with checkpoint defect

•G2 phase regulators (Andrew Burgess CNRS)

•miR/anti-miRs of EGFRi resistance/sensitivity (Nicole Cloonan, QIMR)

•Synthetic lethality with RA in neuroblastoma (Bellamy Cheung, CCIA)

•UV-induced G2 phase checkpoint and repair

Melanoma

• 10% Australian cancers

• 3% Australian cancer-related fatalities

• Highest incident cancer in 19-40 year old Australians

• Risk factors: UVR exposure, B-Raf, MiTF, MC1R, p16INK4A

• Treatment: chemo ineffective, B-Rafi-MEKi, immune checkpoint

drugs?

• Early diagnosis has >90% long term survival

Genome wide over expression screen to identify genes that bypass p16

induced senescence

Chuaire-Noack et al., nt. J. Morphol., 28(1):37-50, 2010

Naevus

p16 Ki-67

Melanoma

p16-induced Senescence is a Barrier to Melanoma

Experimental Approach

2 days

5 days

IPTG

p16 ↑

GFP-only lentivirus

Potential hORF “hit”

or +ve control lentivirus •CCNE1 •CDK4

• CDK4-R24C

IPTG

Imaging-based assay for: • Gene transduction (GFP) • Proliferation (Ki-67) •Cell size (-tubulin) • S and G2/M DNA content

p16 ↑

DNA (DAPI) GFP Ki-67 (Cy3) -tubulin (Cy5)

pLV

41

1 +

IPTG

C

DK

4-R

24

C +

IPTG

Note: same 10 x magnification for all fields

Assay Development

Screened a human ORFeome library (~17,000 unique ORFs) using HCS in collaboration with UQDI ARVEC Facility (www.di.uq.edu.au/arvechcsfacility).

~200 x

Primary Screen Hits

Unstained wells

HPV18E7

Secondary Screen Validated Hits

HMGB2 and CDK6 increased in melanoma

CDK6

0

3

6

9

12

H-s

co

re

HMGB2

0

3

6

9

12

H-s

co

re

How can we stratify the validated hits?

0

10

20

30

40

50

60

70

80

90

100

%RB positive (GFP cells only)

Do hits deplete RB?

Does depletion of hits affect proliferation/viability?

CDK6 Hi Lo Hi p16 Wt Methylated Mut

ACKNOWLEDGEMENTS

Gab Lab Won Jae Lee

Kelly Brooks

Sandra Pavey

Kee Ming Chia

Carly Fox

Weili Wang

Alex Pinder

Loredana Spoerri

Vanessa Oakes

Brooke Edwards

James Chen

Sheena Daignault

Alex Stevenson

Weili Wang

Fawzi Bokhari

ARVEC Facility

Tom Gonda

Duka Skalamera

Max Ranall

Mareike Dahmer

Bioinformatics

Konstantin Shakhbazov

Pamela Mukhopadhyay

Paul Leo

Collaborators

Nigel McMillan, Griffith

Nick Hayward, QIMR

Chris Schmidt, QIMR

Rick Sturm IMB

Peter Soyer UQ SOMS

Duncan Lambie PAH

Graeme Walker QIMR

Richard Scolyer MIA

Helen Rizos, Westmead

Grant McArthur PMCI

Petranel Ferrao PMCI

Functional genomics screens

- populating pathways

Unbiased approach to identify

new genes and pathways