genomic oncology and personalized medicine
TRANSCRIPT
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
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