Genomic Oncology and Personalized

Medicine

-Using lung cancers as a model

Chung-Che (Jeff) Chang, M.D., Ph.D.

Director, Hematology and Molecular Pathology Lab.

Florida Hospital

Professor of Pathology

College of Medicine

University of Central Florida

E-mail: c.jeff.chang.md@flhosp.org

Phone: 407-303-1879

Image courtesy of Nature,

issue: Feb. 15, 2001

Thirty Years

to create a

“Strategic

Inflection” in

Cancer

Research.

The -OMICS

Revolution

GENOMIC ONCOLOGY AND

PERSONALIZED MEDICINE --

DEFINITION

To optimize cancer patient care using specific

and targeted therapies applying human

genome data

Major Technologies Enabling Genomic

Oncology

cDNA microarray: profiling thousands of genes simultaneously (transcriptomics).

Array-based comparative genomic hybridization (Array CGH) or single nucleotide polymorphism array (SNP array): determining the gene copy number alternation/loss of heterozygosity across the whole genome (genomics).

Next generation sequencing technologies: point mutations, insertions, deletion, gene fusions across the whole genome (exomics, genomics)

Bioinformatics

Gene Expression

Profiling by cDNA

microarray

-Landmark paper for

genomic oncology

“Distinct Types of DLBCL IdentifiedBy Gene Expression Profiling.”

Nature, 2000; 403:503.

Diffuse large B-cell lymphoma

(DLBCL) B-cells

Non-neoplasticB-cells

GC BDLBCL

Activated BDLBCL

cDNA microarray

Germinal Center (GC) B-cell gene expression

profiles have better prognosis than Activated

B-cells.

Alizadeh et al. Nature, 2000, 403: 503-511.

GC BDLBCL

Activated BDLBCL

Microscopy Pathologists Microarray Pathologistsvs

Expression Pattern A: Germinal Center B-

cell

Positive for at least

one:

CD10

Bcl-6

Negative for

BOTH:

MUM-1

CD138

Expression Pattern B: Activated

Germinal Center B-cell

Positive for at

least one:

CD10

Bcl-6

Positive for at

least one:

MUM-1

CD138

Expression Pattern C: Activated non-Germinal Center B-cell

Negative for

BOTH:

CD10

Bcl-6

Positive for at

least one:

MUM-1

CD138

0

.2

.4

.6

.8

1

0 20 40 60 80 100 120

Pattern B or C

Pattern A

P = 0.055,

log-rank test

Time (months)

Cum

. S

urv

ival

Chang, AJSP, 2004;28:464

0

.2

.4

.6

.8

1

0 20 40 60 80 100 120

Time (months)

Pattern C

Pattern B

Pattern A

P < 0.008,

log-rank testCum

. S

urv

ival

All patients Low clinical risk patients

Array-based Comparative Genomic Hybridization (Array

CGH) or Single Nucleotide Polymorphism array (SNP array)

to Determine the Gene Copy Number Alternation in Cancers

Plasmablastic Lymphoma (PL)

HIV, oral cavity, described in 1997

Considered as a subtype of diffuse large B-cell

lymphoma (DLBCL)

Immunophenotypically identical to plasma cell

myeloma (PCM):

CD20-, CD138+, PAX5-, CD56+

(Vega, Chang et al, Mod Pathol 2005)

Mod Pathol,

2005;18:806

Plasmblastic

LymphomaExtramedullary

Plasm Cell

Myeloma

MIB1

Extramedullary

Plasm Cell

Myeloma

Plasmblastic

Lymphoma

Without clinical information, differentiation of

PL and extramedullary plasma cell myeloma is

very difficult, if not possible, based on

morphology and/or IHC

Clinically very important: treatment and

prognosis of myeloma and lymphoma are very

different

How about the relationship between DLBCL,

PL and PCM at genomic level?

10.78520.62660.228AIDS-DLBCL

0.785210.63530.1507DLBCL

0.62660.635310.1034PL

0.2280.15070.10341PCM

AIDS -DLBCLDLBCLPLPCM

10.78520.62660.228AIDS-DLBCL

0.785210.63530.1507DLBCL

0.62660.635310.1034PL

0.2280.15070.10341PCM

AIDS -DLBCLDLBCLPLPCM

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

1718

1920

2122

Chromo -

somePCM PL DLBCL AIDS -

DLBCL

0.0

0.2

- 0.4

- 0.2

0.4

0.6

0.8

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

1718

1920

2122

Chromo -

somePCM PL DLBCL AIDS -

DLBCL

0.2

0.4

0.6

0.8

Chang,

Br. J Hematol

Oncol,

2009;2:47

Gene copy

number

alternation

analysis

using array

CGH

At genomic level, PL is more closed to

DLBCL or DLBCL occurring in HIV+

patients than to PCM supporting the current

classification scheme and the treatment

approaches.

Next GenerationSequencing

(NGS)

Technologies

10 years to

complete

sequencing the

first human

genome

1 to 5days to

complete

a whole

genome

sequencing

Feero WG et al. N Engl J Med 2010;362:2001-2011.

Myelodysplastic Syndromes (MDS) Biomarker

and Mechanism Discovery by NGS

Clonal hematopoietic stem cell diseases

Peripheral cytopenias, hypercellular marrow and

dysplasia

No accurate diagnostic/prognostic biomarkers

for the early stage of MDSs

p38MAPK representing the hub of the 10 mutated genes (shaded ones)

detected by RNA-seq through IPA analysis. Chang Lab unpublished data

Control MDS patients

Shahjahan, Chang et al, Am J Clin Pathol, 2008;130:635

P38 MAPK is highly activated in MDS as compared to controls

The whole genome/transcriptome sequencing results

indicate that p38 MAPK pathway may play an

important role in the pathogenesis of MDS.

P38 MAPK inhibitors may help a subset of MDS

patients who carry mutations leading to over-

activation of the p38 MAPK pathway.

Genomic Oncology Diagnosis of Lung Cancers

Morphologic diagnosis is

the base for characterizing

cancers but more genomic

info is needed for patient

management

EGFR/ALK/ROS1/KRAS

etc mutation status is

needed for the

individualized treatment

for lung cancer patients.

EGFR Tyrosine Kinase Domain

Mutations

TK domain

Exons 18-24

Amino acids 718-94

200 mutations have

been identified

90% are in exon 19 or

21

My cancer genome

Tumor

proliferation

EGFR TKIs inhibit the proliferation and

survival signaling pathway

MAPK

Ras

Sos

Grb2

Raf

MEK

EGFR:EGFR EGFR:HER3

AK

T

PI3K

Tumor survival

PDK1

BAD

Bax FOXO1

Caspase 9

1. Wheeler et al. Oncogene. 2008;27:3944-3956. 2. Mukohara et al. J Natl Cancer Inst. 2005;97:1185-1194.3. Tarceva [package insert]. Melville, NY: OSI Pharmaceuticals Inc; 2009

Tumor

proliferation

EGFR TKIs inhibit the survival/proliferation

signaling pathway

MAPK

Ras

Sos

Grb2

Raf

MEK

EGFR:EGFR EGFR:HER3

AK

T

Tumor survival

PDK1

BAD

Bax FOXO1

Caspase 9

1. Wheeler et al. Oncogene. 2008;27:3944-3956. 2. Mukohara et al. J Natl Cancer Inst. 2005;97:1185-1194.3. Tarceva [package insert]. Melville, NY: OSI Pharmaceuticals Inc; 2009

Progression-Free Survival in EGFR Mutation

Positive and Negative Patients

EGFR mutation positive EGFR mutation negative

Treatment by subgroup interaction test, p<0.0001

HR (95% CI) = 0.48 (0.36, 0.64)

p<0.0001

No. events gefitinib, 97 (73.5%)

No. events C / P, 111 (86.0%)

Gefitinib (n=132)

Carboplatin / paclitaxel (n=129)

HR (95% CI) = 2.85 (2.05, 3.98)

p<0.0001

No. events gefitinib , 88 (96.7%)

No. events C / P, 70 (82.4%)

132 71 31 11 3 0129 37 7 2 1 0

108103

0 4 8 12 16 20 24

GefitinibC / P

0.0

0.2

0.4

0.6

0.8

1.0

Pro

babili

ty o

f pro

gre

ssio

n-f

ree s

urv

ival

At risk :91 4 2 1 0 085 14 1 0 0 0

2158

0 4 8 12 16 20 24

0.0

0.2

0.4

0.6

0.8

1.0

Pro

babili

ty o

f pro

gre

ssio

n-f

ree s

urv

ival

Gefitinib (n=91)

Carboplatin / paclitaxel (n=85)

Months Months

60

40

20

0

–20

–40

–60

–80

–100

Progressive disease

Stable disease

Confirmed partial response

Confirmed complete response

Maxim

um

ch

an

ge i

n t

um

or

siz

e (

%)

–30%

Tumor Responses to Crizotinib for

Patients with ALK-positive NSCLC

Integrated genomic classification of

endometrial cancers

G Getz et al. Nature 497, 67-73

Patel JP et al. N Engl J Med 2012;366:1079-1089

New Risk Stratification for

AML patients using

cytogenetic and NGS data

Patel JP et al. N Engl J Med 2012;366:1079-1089

C Kandoth et al. Nature 502, 333-339 (2013)

Distribution of mutations in 127 SMGs across Pan-Cancer

cohort

• Average number of driver mutations varies across tumor

types

• Most tumors have two to six, indicating that the number of

driver mutations required during oncogenesis is relatively

small.

• Highest (6 mutations per tumor) in UCEC, LUAD and

LUSC, and the lowest (2 mutations per tumor) in AML,

BRCA, KIRC and OV.

• Clinical association analysis identifies genes having a

significant effect on survival.

• Laying the groundwork for developing new diagnostics

and individualizing cancer treatment.

• Cluster-of-cluster

assignments (COCA)

• 11/28 lung squamous

samples reclassified as

lung adenoCa

• Merging of colon and

rectal Ca into a single

group

• BRCA: (BRCA/

Luminal, ER+/HER+) and

(BRCA/basal, Triple-)

• COCA classification

differs from tissue-of-

origin-classification in

only 10% of all samples.

• Reflecting tumor biology

and clinical outcome.

Cell. 2014

V158;p929

12/25/2015

Molecular Taxonomy

Cell 2014 158, 929-944

Identification of Cancer-Specific

Mutated genes or Chromosomal

Rearrangements from Sequencing of a Cancer Genome

AcknowledgementChang’s Lab

Albert Mo, BS

Joe Conway, MD

Wan-Ting Huang, MD

Jianguo Wen, PhD

Yongdong Feng, MD, PhD

David Choi, PhD

Collaborators

Lawrence Rice, MD

Kyriacos A. Athanasiou, PhD

Helen Heslop, MD

Jessica Shafer, MD

Funding Agency

NIH/NCI