A decade of Genomic research

From bench to bedside

Promises of Genomic research in Cancer

• Rapid advance in our understanding of the molecular mechanisms determining tumor behavior

• Discovery of new therapeutic targets

• Better and less toxic target directed therapy

Director’s Challenge 1999-2005

• Employing the DNA microarray technology for large scale gene expression profiling – cDNA microarray: Pat Brown at Stanford

– Lymphochip: Lou Staudt at the NCI

• Collaborative study on B-cell lymphoma – UNMC

– Stanford

– BCCA

– NCI

First investigation produced promising results with DLBCL

Validation of the initial findings and identification of a third subtype

GCB and PM DLBCL have better

OS

Gene-Expression Predictors of Survival on DLBCL Patients Treated with R-CHOP

Mantle cell lymphoma: in situ, cyclinD1 positive and negative types

Prognosticator for MCL

Prognosticators: Bad guys and their neighborhood

Formation of the LLMPP

Getting the signatures from other lymphomas

Expansion into uncommon tumors the International T-cell Lymphoma study

AITL ATLL A LCL-ALK(+)

SpiB BTLA4 SYK

LILRB2 KYNU JAG1 SPOCK1 CD163 MS4A4A FCN1 LAMP1 TCF7L2 ZNF395 MS4A4A KYNU IQGAP2 MS4A6A VSIG4 RPL28 CUGBP2 CD9 KYNU ABCC3 CRIM1 MS4A6A CD209 KCNMA1 MARCKS DAB2 TNFAIP6 NASET2 CHPT1 HDAC9 NEDD9 NPL CCL8 PRDX1 IGFBP7 LMO2 CD55 EMG1 PLEKHA5 DUSP6 FYB TNFRSF4

MEG3 PDGFRA SPOCK1 HOXC6 C7 BASP1 APOD SEMA3C COL1A2 HNMT COL3A1 PTX3 ADCY7 SLC22A3 NPY1R CD44 RHOBTB3 MYH11 ABCA8 IGF1 CHRDL1 PTPRG CUGBP2 SRPX CXCL12 COL1A1 ASPA SFRP4 UST MGP TNFAIP6 OLFML1 COL14A1 FEZ1 MBP IGF1 GALNT12 FGF7 OMD OMD ANGPT1 CDKN2C TNXB ECM2

Tole

roge

nic

DC

ce

ll si

gnat

ure

C

D3

1 (

-) s

tro

mal

si

gnat

ure

RPL11 RPL13A RPL15 RPL28 RPL29 RPL4 RPL6 RPL7A RPLP0 RPS10 RPS11 RPS14 RPS19 RPS5 RPS6 RPS7 RRAS2

OS

EFS

P<0.001

A

B Good Poor

SEMA5A,

SEC16B,

LILRB2,

MSRB3,

RHOU

ADAMTS12,

VCAN

LOC728192,

BDNF

PRR5

PLEKHA6

MEG3

NCF4

ZBED2

IGFL2

Good Poor

Prognostic Groups

Prognostic groups

15 gene predictor

AITL cases Years

Years

P<0.001

B c

ell r

elat

ed

Pro

tein

syn

thes

is

Kaplan-Meier curves for risk groups obtained from cross validation:

The age of CHIPs: development of high resolution platforms for determining copy

number abnormalities (CNAs)

• NimbleGen: CGH oligonucleotide array; 386,165 probes

• Tumor sample labeled Cy3, control Cy5

• 203 de novo DLBCL patient samples and 30 DLBCL cell

lines

• Classification according to gene expression profiling:

72 GCB DLBCL

74 ABC DLBCL

31 PMBL

26 unclassified DLBCL

c-m

yc

1 M

b d

ele

tion

AmplificationDeletion

c-m

yc

1 M

b d

ele

tion

AmplificationDeletion

The marriage of GEP and aCGH

The mighty microRNA

Non-coding RNA • Ribosomal RNAs

• Transfer RNAs

• microRNAs

• Piwi-associated RNAs

• siRNA

• Various longer non-coding RNAs

– mRNA splicing

– Ribosome biogenesis

– Gene silencing, imprinting

• New members of these classes and new classes may be discovered by high throughput sequencing especially in unique cell types

Structure of mir-17-92 cluster

He et al., Nature 2005

Symbol Full name Total

Score

1 CD69 CD69 molecule -2.16

2 ATXN1 ataxin 1 -2.06

3 BMPR2 bone morphogenetic protein receptor, type II -1.71

4 CLIP4 CAP-GLY domain containing linker protein 4 -1.57

5 ITGB8 integrin, beta 8 -1.52

13 PHLPPL PH domain and leucine rich repeat protein

phosphatase-like -1.29

14 PTEN phosphatase and tensin homolog (mutated in

multiple advanced cancers 1) -1.28

38 BCL2L11 BCL2-like 11 (apoptosis facilitator) -1.07

Putative Targets of the miR-17~92

Negative regulator of the PI3K /AKT pathway

Proapoptotic factor

PTEN is a Direct Target of the miR17-92 Cluster

NIH 3T3 cells HEK293T cells

MiR-17~92 Overexpression Induces the PI3K/AKT Pathway Activation

pAKT-S473

Total AKT

GSK-3β

p70S6K

Tubulin

Ve

cto

r

Mir

-17

~92

Z138c Cells

Flow Cytometry Using anti-pAKT-S473

IgG Isotype

Vector

Mir-17~92

The unstable genome Legacy of generous AID

MYC BCL2 RHOH PIM1 PAX5 SOSC1 EZH2

BCL6

PRDM1

TP53

NF-kB pathway genes found mutated in DLBCL

Gene symbol (synonym) Number of mutated/tested cases (%)

ABC-DLBCL GCB-DLBCL Non-GC/NC- TNFAIP3 (A20) 9/37 (24.3) 1/44 (2.3) 4/20 (20)

CARD11 4/37 (10.8) 3/44 (6.8)** 2/20 (10)

TNFRSF11A (RANK) 3/37 (8.1)* 1/44 (2.3) 2/20 (10)

TRAF5 2/37 (5.4) 2/44 (4.5) 1/20 (5)

TRAF2 1/37 (2.7) 4/44 (9.1)*** 1/20 (5)

MAP3K7 (TAK1) 2/37 (5.4) 0/44 0/20

All genes 19/37 (51.3) 10/44 (22.7)# 7/20 (35)

* One additional cell line was found to carry a mutation in a minority of the population.

** Two of the three mutated samples are cell lines; analysis restricted to exons 4–9, encoding the

coiled-coil domain.

*** Three of the four mutated samples are cell lines.

# Four of the ten mutated samples are cell lines.

[From Compagno M et al Nature 459:717, 09]

CARD11 MUTATION IN DLBCL Georg Lenz, et al Science 319: 1676-1679, 2008

NF-kB pathway activated by gene mutations

MYD88 mutations in B-cell lymphoma

Ngo V et al Nature 470:115-9; 2011

CD79B and CD79A mutation

Next generation sequencing

• Many important mutations including novel translocations have been and will be discovered

• Interestingly, many of these affect the epigenome, eg

– EZH2

– MLL2

– IDH1,2

PRDM1a Promoter Methylation in NKL cases

Methylation=no expression? That is the question

Histone modifications in the genome modify gene expression

The tails of four histones

• Acetylation

• Methylation

• Phosphorylation

• Ubiquitination

• Sumoylation

• ADP- ribosylation

• Proline-isomerization

Verdone et al., (2005)

Lysine residues

JAK2 as a chromatin modifier:

H3Y41 phosphorylation prevents HP1a chromoshadow domain binding

Dawson MA Science 461:819; 2010

9p24 amplicon in PMBL

Rui et al Cancer Cell 18:590; 2010

JAK 2 dependent H3Y41 phosphorylation

Rui et al Cancer Cell 18:590; 2010

Rui et al Cancer Cell 18:590; 2010

JAK2 and JMJD2C in chromatin remodeling: Role in the pathobiology of PMBL

Bridging the gap between discovery and clinical practice

• Most of the studies and results obtained from cryopreserved tissue with high quality DNA and RNA

• Clinical setting with mostly fixed paraffin embedded tissue (FFPET)

• Either change the way tissue is acquired and processed or adapt the assays to FFPET

Wax, wax and more wax Using immunohistochemical assays

CD10 (≥30%)

GCB (29 cases)

Non-GCB (14 cases)

MUM1 (≥30%)

GCB (12 cases)

BCL6 (polyclonal)

(≥30%)

Non-GCB (29 cases)

+

-

+

+

-

(2/29 ABC by GEP)

(5/29 GCB by GEP)

(1/12 ABC by GEP)

Hans’ Algorithm

-

(4/14 GCB by GEP)

(all match with GEP)

(2/19 GCB by GEP)

GCET1(≥80%)

MUM1 (≥80%)

CD10 (≥30%)

ABC (3 cases)

GCB (27 cases)

GCB (12 cases)

FOXP1 (≥80%)

ABC (19 cases)

BCL6 (monoclonal)

(≥30%)

GCB (10 cases)

ABC(13 cases)

+

-

+

-

+

-

+

-

+

-

(all match with GEP)

(all match with GEP)

(1/12 ABC by GEP)

(3/10 ABC by GEP)

Choi’s Algorithm

MicroRNA in molecular diagnosis: Correlation of miRNA profiles between paired frozen and FFPE

samples

0

5

10

15

20

25

30

0 5 10 15 20 25 30

0

5

10

15

20

25

30

0 5 10 15 20 25 30

0

5

10

15

20

25

30

0 5 10 15 20 25 30

0

5

10

15

20

25

30

0 5 10 15 20 25 30

5 4 7 3

Fre

sh F

roze

n s

amp

les

(Δ

Ct)

FFPE samples ( ΔCt)

r=0.94 r=0.95 r=0.94

r=0.95

FFPE block in years

Training set

LOOCV

(% probability)

35 m

iRN

A

Probability in BL Probability in DLBCL

GEP classification

BL DLBCL

miRNA classifier for Burkitt Lymphoma

Other platforms

• qRT-PCR – Possible to perform in a high throughput format

• GEP on FFPET – Technically feasible

– Degree of loss of information acceptable

– Still a rather complicated platform

• Non-PCR based transcript quantitation – Nanostring

– High Throughput Genomics

Complexity, Complexity, Complexity

• Can we have one platform that provides all the relevant information?

• Can high throughput seq covers all/most of the grounds? Genomic or transcriptome seq?

• How are we going to make sense of all these data?

• Can GEP/pathway analysis integrate the functional effects of diverse pathogenic mechanisms?

Testing the concept • Accurately diagnose and classify the tumor

• Identify the critical molecular abnormalities present

• Apply treatment directed at the proper molecular targets

Caveat:

Up or down regulation of a pathway, even when it seems very compelling biologically as a critical pathway, does not necessary mean that it would be a good therapeutic target

GSEA analysis of STAT3 hi/lo ABC DLBCL

STAT3 as a therapeutic target

Cross-talk between STAT3 and NF-kB pathway in DLBCL

JAK and/NF-kB inhibition in ABC DLBCL

Activation of the Stat3 Pathway Predicts Poor Survival in Non-GCB-DLBCL but not in GCB-DLBCL

Non-GCB DLBCL

GCB DLBCL

Pathway analysis for pathway directed therapy

A B C D

E F G H

pYSTAT3 BCL6 MUM1 H&E

pYSTAT3 and CD20 double staining

Bench

Bedside

Molecular Pathogenesis

(identify)

Target Discovery/Validation

(authenticate/develop)

Clinical Trials

(delivery)

Research Goal

ACKNOWLEDGEMENT LLMPP Consortium

• University of Nebraska Medical Center – D. Weisenburger, T. Greiner, K. Fu, W. Sanger, B.Dave, J. Vose, J. Armitage

• National Cancer Institute L. Staudt, E. Jaffe, W. Wilson

• Southwest Oncology Group – University of Arizona L Rimsza

– University of Rochester R. Fisher

– University of Oregon R. Braziel

– Cleveland Clinics R. Tubbs

• British Columbia Cancer Agency R. Gascoyne, J. Connors

• University of Wurzburg, Germany K. Muller-Hermelink, A. Rosenwald

• University of Barcelona E. Campos

• Radium Hospital, Norway E. Smeland, J. Delabie

• St. Bart, London, UK A. Lister, J. FrizGibbon

Chan lab and collaborators

McKeithan Alyssa Bourska Himabindu Ramachandrareddy

Fu Chunsun Jiang Wenfeng Cao Jirong Bai Xin Huang Chengfeng (Andy) Bi

Iqbal Can Kucuk Zhongfeng Liu Sharathkumar M Bhagavathi Xiaozhou Hu

Deffenbacher Cindy Lachel Peter Julius

Yulei Shen Yanyan Liu Cuiling Liu

Greiner Deb Lytle