A systems approach to marker guided therapy
in breast cancer
Joe W. Gray, Ph.D.
Lawrence Berkeley National Laboratory
University of California, San Francisco
A systems approach to marker guided therapy
in breast cancer
Breast cancer overview and statement of the
problem
An in vitro systems approach to match treatment
to “ome”
Improving and testing the model
A systems approach to marker guided therapy
in breast cancer
Breast cancer overview and statement of the
problem
An in vitro systems approach to match treatment
to “ome”
Improving and testing the model
Stage Distribution and 5-year Relative Survival by Stage at
Diagnosis for 1999-2006, All Races, Females
Stage at Diagnosis
Localized (confined
to primary site)
Regional (spread to
regional
lymphnodes)
Distant (cancer has
metastasized)
Unknown (unstaged)
Stage
Distribution (%)
5-year
Relative Survival (%)
60
98.0
33
83.6
5
23.4
2
57.9
World wide incidence - 1,150,000/yr
Worldwide mortality - 410,000/yr
SEER Registry
Overall goal
Improve treatment by identifying molecular
subtype markers that
• predict resistance to existing therapies
• predict response to experimental therapies
Hundreds of compounds are approved or well along in
the developmental pipeline
How do we find the
most effective for
breast cancer?
International cancer genomics efforts are substantially
increasing the number of recognizable cancer subtypes that
may respond differentially to specific therapies
State of the breast cancer genome
• Remarkable genomic and epigenomic heterogeneity
between and within tumors
• Hundreds of genes and gene networks are deregulated in
ways that contribute to cancer pathophysiology
• Subtypes are defined by
aberrations at multiple levels:
mutation, structure, copy
number, chromatin
modification, ncRNA, …
• Subtypes defined by
recurrent aberrations are
associated with outcome
• Response varies with subtype
State of the breast cancer genome
• Remarkable genomic and epigenomic heterogeneity
between and within tumors
• Hundreds of genes and gene networks are deregulated in
ways that contribute to cancer pathophysiology
• Subtypes are defined by
aberrations at multiple levels:
mutation, structure, copy
number, chromatin
modification, ncRNA, …
• Subtypes defined by
recurrent aberrations are
associated with outcome
• Response varies with subtype
State of the breast cancer genome
• Remarkable genomic and epigenomic heterogeneity
between and within tumors
• Hundreds of genes and gene networks are deregulated in
ways that contribute to cancer pathophysiology
• Subtypes are defined by
aberrations at multiple levels:
mutation, structure, copy
number, chromatin
modification, ncRNA, …
• Subtypes defined by
recurrent aberrations are
associated with outcome
• Response varies with subtype
How do we make the optimal match
between drug and subtype?
A systems approach to marker guided therapy
in breast cancer
Breast cancer overview and statement of the
problem
An in vitro systems approach to match treatment
to “ome”
Improving and testing the model
We use a collection of 50+ breast cancer cell lines
to model the molecular diversity of primary tumors
• Therapeutic approaches can be tested quickly to
identify subtype specific responses
• Model can be characterized at great molecular
depth to identify predictive markers
• Model can be manipulated to test predictions
To what extent do the cell lines represent what we know
about breast cancer?
Cell lines model gene expression subtypes, recurrent
copy number chances and mutations
We have assessed ~100 therapeutic
strategies in 50 cell lines
Emphasis on signaling pathways
Establishing associations between
response and molecular subtypes
UCSC Cancer Genome Browser
Molecular features
Biological features
Approximately half of compounds tested show
significant molecular subtype specificity
A
Luminal
Bas al
Claudin-low
Normal
Ambiguous
GI50
GI50Relative cell
1e-06
AKTi
We are especially interesting
in identifying
1e-07
genomic drivers for molecular response
Log drug concentration
CI1040
1e-05
GI50
1e-04
Associations
number at 3
days
1e-05
Lapatinib
1e-06
1e-05
1e-07
Associations
1e-06
1e-08
Cell line
Cell line
Kuo, Guan, Hu, Bayani 2007
Most effective targeted agents are linked to genomic markers that
predict response
Imatinib mesylate
CML
Imatinib mesylate
Sunitinib
Nilotinib
Dasatinib
GIST
Dermatofibrosarcoma
protuberans
Hypereosinophylic
syndrome
Melanoma
Trastuzumab
Pertuzumab
Lapatinib
Breast
Gefitinib, Erlotanib
Cetuxumab
Lung cancer
Bowel
PKC412, SU11248,
CMT53518
AML, ALL
PARP inhibitors
Breast Ovarian
PLX4032
Melanoma
Crizotinib
Lung
Tamoxifen, AIs
Breast cancer
BCR-ABL translocation
Oncogene addiction (1982)
c-KIT mutation
PDFGR mutation
Oncogene addiction (1999)
HER2 amplification
Oncogene addiction (1985)
EGFR mutation
Oncogene addiction (2004)
FLT-3 mutation, tandem
duplication
Oncogene addiction (1996)
BRCA1/2 mutation
Synthetic lethality (2005)
BRAF mutation
Oncogene addiction (2002)
EML-4 ALK translocation
Oncogene addiction (2007)
ER expression
Lineage (1800s)
*Except VEGFR and proteosome inhibitors
~25% of compounds are significantly associated
with genome copy number abnormalities
Spellman, Sadanandam, Kuo
Kuo, Spellman, Sadanandam
Platinum, anti-metabolites and antimitotic apparatus protein inhibitors
effective in basal subtype cells
Luminal
Basal
Claudin-low
Sensitive
Resistant
PI3K inhibitor
PI3K inhibitor
PI3K inhibitor
AURK inhibitor
PLK1 inhibitor
Response to mitotic apparatus inhibitors is associated with
transcriptional upregulation of a network of mitotic apparatus
genes
Mao, Hu et al
Why does this network exist?
Expression of mitotic apparatus genes is associated
with amplification of transcription factors that target
mitotic apparatus genes
Christina Clark, Carlos Caldas
All genes in the mitotic apparatus signature
are targeted by these transcription factors
Mao, Curtis, 2010
Kuo, Spellman, Sadanandam
EGFR, ERBB2, PI3K
inhibitors, HDACs effective
in luminal subtype cells
Luminal
Basal
Claudin-low
PI3K inhibitor
PI3K inhibitor
PI3K inhibitor
AURK inhibitor
PLK1 inhibitor
Hierarchical clustering
of 31 significant
subtype specific drugs
and BrCa cell lines.
Luminal subtype preference for ERBB2 and AKT
pathway inhibitors “explained” by the subtype
specificity of activating genomic aberrations
X
GI50
Lapatinib
GI50
AKTi
PIK3CA mutation
PTEN mutation
PIK3CA
PTEN
Aberrations interact - AKT inhibitors synergize with
lapatinib in ERBB2+, PIK3CAmt cells
Synergistic
Antagonistic?
PIK3CA mutation (blue
is mutant)
Drug combination dose
H1047R PIK3CA, E307K PTEN
K267fs*9 PTEN
H1047R PIK3CA
E545K PIK3CA
H1047R PIK3CA
E545K PIK3CA
K111N PIK3CA
Korkola, Cooper, et al 2010
A systems approach to marker guided therapy
in breast cancer
Breast cancer overview and statement of the
problem
An in vitro systems approach to match treatment
to “ome”
Improving and testing the model
Complicating factors
• Microenvironment
• Response not durable
• Response heterogeneity
The microenvironment modulates response to
ERBB2 targeted drugs
AU565 ERBB2 amp
SKBR3 ERBB2 amp
2D monolayer
3D matrigel
HCC1569 ERBB2 amp
BT549 ERBB2 norm
Wiegelt, et al., Breast Cancer Res Treat 122:35–43, 2010
The microenvironment modulates the
signaling network
HER3
HER2
PI3K a,b,g,d
IRS1
PDK1
RAF
Akt
MEK
TSC2
MAPK
Rheb
PRAS40
mTorC2
mTorC1
PKCα
S6K1
S6
Inhibition of microenv.
signaling also should
modulate response
HER3, PDK1, Akt, …
COX2, CREB, cJun, NFkB, ATF2,
ER, Tcf/Lef, Rb, AP1, cFos,
CXCR4, ETS, HIF1a,
MYC -> CBX5
Inhibition of b1-integrin signaling enhances response to
ERBB2 targeted drugs in 3D but not 2D
AU565 ERBB2 amp
SKBR3 ERBB2 amp
AIIB2
None
HCC1569 ERBB2 amp
BT549 ERBB2 norm
Wiegelt, et al., Breast Cancer Res Treat 122:35–43, 2010
Microenvironment dependent response
may explain why treatment of
metastatic disease is difficult
Can we identify microenvironment
independent therapies?
This motivates assessment of pathway
function in situ
Britt Marie Ljung
TOF-SIMS “ome” imaging
Immunohistochemistry
or in
Primary
Ion Beam
situ hybridization with mass
tag labeled reagents. Each
tag is a color.
Tag 1 Map
Total Area Spectrum
Tag 2 map
m/z
Sample
More complications
ERBB2 inhibition is not durable
Amin et al, Science TM 2010; 2: 16ra7.
Understanding response dynamics
HER3
HER2
PI3K a,b,g,d
IRS1
PDK1
RAF
Akt
MEK
TSC2
MAPK
Rheb
PRAS40
Mills, Moasser et al
mTorC2
mTorC1
PKCα
S6K1
S6
HER3, PDK1, Akt, …
COX2, CREB, cJun, NFkB, ATF2,
ER, Tcf/Lef, Rb, AP1, cFos,
CXCR4, ETS, HIF1a,
MYC -> CBX5
Statistical and dynamic
modeling to understand
long term behavior
Center for Cancer Systems Biology
–
–
ODE model for short term
effects (Soulaiman Itani)
A hybrid Boolean-ODE
model
Signaling
occurs in 3 dimensions
using to model longer term
effects[Chen 2009] (YoungNetwork behavior is context dependent
Hwan Chang)
Need to understand the emergent properties
of complex, cross coupled systems
Tomlin lab
Molecular responses are heterogeneous – a
partial explanation for lack of durability?
Digital v. analogue drug responses
Sorger et al
A systems approach to marker guided therapy
in breast cancer
TCGA/ICGC projects are defining a growing
number of distinct subtypes
In vitro systems suggest at least half of all
therapeutic compounds show subtype specificity
Improving the model - Modeling the
microenvironment, heterogeneity and long term
durability
Collaborators
Clinical science (I SPY etc)
Laura Esserman (UCSF)
Laura Van’t Veer (UCSF)
Rick Baehner (UCSF)
Nola Hylton (UCSF)
John Park (UCSF)
Hope Rugo (UCSF)
Britt Marie Ljung (UCSF)
Hubert Stoppler (UCSF)
Fred Waldman (UCSF)
Mitotic apparatus networks
Zhi Hu
Jian Hua Mao
Shenda Gu
Barbara Weber (GSK – then)
Richard Wooster (GSK)
Christina Clark (Cambridge)
Carlos Caldas (Cambridge)
Cell line system biology
Wen-Lin Kuo
Jim Korkola
Nick Wang
Nora Bayani
Brian Cooper
Mara Jeffress
Anna Lapuk
Demetris Iacovides
Mina Bissell
Martha Stampfer
Terry Speed (UCB)
Claire Tomlin (UCB)
Michael Korn (UCSF)
Frank McCormick (UCSF)
Gordon Mills (MDACC)
Yiling Lu (MDACC)
Peter Sorger (Harvard)
Genome biology
Paul Spellman
Anguraj Sadanandam
Laura Heiser
Shannon Dorton
Jing Huang
Steffen Durinck
Obi Griffith
Lakshmi Jakkula
Francois Pepin
Andy Wyrobek
David Haussler (UCSC)
Josh Stuart (UCSC)
Project management
Heidi Feiler
Shradda Ravani
NCI Center for Cancer Systems Biology, The Cancer Genome Atlas,
CPTAC, Bay Area Breast Cancer SPORE, Atwater foundation, GSK,
Roche, Millenium, Pfizer, Progen, Cytokinetics, Cell Biosciences, DOD
Innovator, SU2C