Download - Genomic oncology and personalized medicine
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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: [email protected]
Phone: 407-303-1879
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Image courtesy of Nature,
issue: Feb. 15, 2001
Thirty Years
to create a
“Strategic
Inflection” in
Cancer
Research.
The -OMICS
Revolution
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GENOMIC ONCOLOGY AND
PERSONALIZED MEDICINE --
DEFINITION
To optimize cancer patient care using specific
and targeted therapies applying human
genome data
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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
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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
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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.
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GC BDLBCL
Activated BDLBCL
Microscopy Pathologists Microarray Pathologistsvs
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Expression Pattern A: Germinal Center B-
cell
Positive for at least
one:
CD10
Bcl-6
Negative for
BOTH:
MUM-1
CD138
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Expression Pattern B: Activated
Germinal Center B-cell
Positive for at
least one:
CD10
Bcl-6
Positive for at
least one:
MUM-1
CD138
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Expression Pattern C: Activated non-Germinal Center B-cell
Negative for
BOTH:
CD10
Bcl-6
Positive for at
least one:
MUM-1
CD138
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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
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Array-based Comparative Genomic Hybridization (Array
CGH) or Single Nucleotide Polymorphism array (SNP array)
to Determine the Gene Copy Number Alternation in Cancers
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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)
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Mod Pathol,
2005;18:806
Plasmblastic
LymphomaExtramedullary
Plasm Cell
Myeloma
MIB1
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Extramedullary
Plasm Cell
Myeloma
Plasmblastic
Lymphoma
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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?
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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
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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.
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Next GenerationSequencing
(NGS)
Technologies
10 years to
complete
sequencing the
first human
genome
1 to 5days to
complete
a whole
genome
sequencing
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Feero WG et al. N Engl J Med 2010;362:2001-2011.
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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
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p38MAPK representing the hub of the 10 mutated genes (shaded ones)
detected by RNA-seq through IPA analysis. Chang Lab unpublished data
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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
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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.
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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.
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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
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My cancer genome
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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
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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
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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
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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
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Integrated genomic classification of
endometrial cancers
G Getz et al. Nature 497, 67-73
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Patel JP et al. N Engl J Med 2012;366:1079-1089
New Risk Stratification for
AML patients using
cytogenetic and NGS data
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Patel JP et al. N Engl J Med 2012;366:1079-1089
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C Kandoth et al. Nature 502, 333-339 (2013)
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Distribution of mutations in 127 SMGs across Pan-Cancer
cohort
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• 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.
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• 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
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12/25/2015
Molecular Taxonomy
Cell 2014 158, 929-944
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Identification of Cancer-Specific
Mutated genes or Chromosomal
Rearrangements from Sequencing of a Cancer Genome
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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