Understanding the dynamics
and heterogeneity of responses
to immune checkpoint blockade:
opportunities to enhance
responses through translational
research
Jennifer A. Wargo MD MMSc
Associate Professor,
Departments of Surgical Oncology & Genomic
Medicine
UT, MD Anderson Cancer Center
Immune Checkpoint Inhibitors 2016
Boston, MA USA
March 7, 2016
Disclosure information
Immune Checkpoint Inhibitors 2016
Understanding the dynamics and heterogeneity of responses
to immune checkpoint blockade: opportunities to enhance
responses through translational research
Jennifer A. Wargo MD MMSc
• I have the following financial relationships to disclose:
- Speaker’s bureau: Imedex, Dava, BMS, Illumina
- Advisory board member: Genentech, GSK, Novartis
- Clinical trial support: Genentech, GSK, BMS
• I will discuss investigational use of biomarkers (and agents) in my
presentation
Background
We have made major advances in the treatment of
melanoma with targeted therapy and immunotherapy
FDA-approved agents for stage IV melanoma
Dacarbazine
(1976)
High-dose IL-2
(1998)
1990
Ipilimumab
(2011)
Vemurafenib
(2011)
2000
Dab, dabrafenib; FDA, Food and Drug Administration; IL-2, interleukin 2; Tram, trametinib.
www.FDA.gov.
2010
Vem + Cobi (2015)
TVEC (2015)
Nivolumab +
Dabrafenib
Ipilimumab
(2013)
(2015)
Trametinib Pembrolizumab
(2013)
(2014)
Dab+Tram
Nivolumab
(2014)
(2014)
2014
2015
These advances are associated with improved survival
1-year survival rates for stage IV melanoma
30–35%1,2
1990
47%3 56%4 70%5
2011 2012 2013
Can we improve
74%6 Dab+Tram
response rates
85%7 NIVO+IPI
even further, and
73%8 NIVO
bring these
68%9 Pembro (10mg/kg Q3w)a therapies to more
74%9 Pembro (10mg/kg Q3w)a
patients?
2014
2015
2016
Adapted from slide of G.V. Long
a2
mg/kg Q3W is the approved dosing.
Cobi=cobimetinib; Dab=dabrafenib; Ipi=ipilimumab; Nivo=nivolumab; Pembro=pembrolizumab;
Q2W=every 2 weeks; Q3W=every 3 weeks; Tram=trametinib; Vem=vemurafenib.
1. Middleton M, et al. Ann Oncol. 2007;18:1691-1697. 2. Balch CM, et al. J Clin Oncol. 2001;19:3635-3648. 3. Robert C, et al. N Engl J Med. 2011;64:2517-2526.
4. McArthur GA, et al. Lancet Oncol. 2014;15:323-332. 5. Grob JJ, et al. Presented at SMR 2014. 6. Long G, et al. Lancet. 2015;386:444-451. 7. Sznol M, et al, ASCO
2014, Abstract LBA 9003. 8. Robert C, et al. N Engl J Med. 2015;372:320-323. 9. Robert C, et al. N Engl J Med. 2015;372:2521-2532.
Despite these advances, responses are
heterogeneous and are not always durable…
BRAF-targeted therapy
Immune checkpoint (anti-PD1)
There is a critical need to better understand who will
benefit from therapy, as well as proper timing, sequence
and combination of different therapeutic agents
How can we better understand responses to
therapy and optimize treatment regimens?
The key to better understanding therapy and to
optimizing responses is through translational research
Translational research in patients with analysis of
longitudinal tissue and blood samples
Treatment 1
Treatment 2
Control
Treatment 1
Treatment 2
Murine models
(GEMM, PDX, etc)
Treatment 1 + treatment 2
Mechanistic studies
and therapeutic
optimization in
murine models
Insights gained in responses to targeted therapy
through translational research in human samples
A Landscape of Driver Mutations in Melanoma
Melanoma TCGA, Cell 2015
Treatment with a BRAF inhibitor often
results in rapid tumor regression
Before starting a
BRAF inhibitor
2 weeks later
Tumor Regression (Target Lesions)
Occurs in Majority of Patients
RECIST 30% Decrease
***
*** 7 patients had 100% tumor shrinkage, 3 of which had confirmed CR;
1 patient had unconfirmed CR and 3 patients had non-target lesions present
• 122 patients had baseline and ≥ 1post-baseline scan with measurable disease
Treatment with BRAF inhibitors results in a survival
benefit in patients with metastatic melanoma
but resistance develops in most patients
Median time to progression = 5.3 months
Key points:
Even with combined BRAF + MEK inhibition,
Most patients still progress within 1 year (though
some have a prolonged response)
There is a critical need to identify pretreatment
markers of response / resistance, as well as early
on-treatment markers of resistance (which are
potentially actionable)
Understanding response and resistance to
targeted therapy through translational research
Molecular and immune profiling
performed in tumors over the
course of therapy
Enrolled
onto
tissue
acquisition
& use
protocol
Patients going
onto trials with
targeted
therapy
Blood draw
and tumor
biopsy
Pretreatment
(pre-treatment, on-treatment,
progression time points)
Start of
therapy Blood draw
and tumor
biopsy
Ontreatment
Restaging
as
indicated
per trial /
standard
of care
(SOC)
Blood draw and
tumor biopsy
at progression,
if applicable
With Keith Flaherty, Ryan Sullivan, Levi Garraway, Steve Hodi, Arlene Sharpe et al – Harvard / MGH / BWH / DFCI
Multiple molecular mechanisms of response and
resistance to targeted therapy identified
Receptor activation (PDGFRb, IGF1R, cMET, EGFR)
BRAF
amplification,
splice variants
BRAF
CRAF
BRAF
BRAF
MEK
ERK
MEK1 mutations
CDK2
CDK4
p16
CyclinD
NRAS
NRAS
mutations
COT
PI3K
PTEN PTEN loss
AKT
leading to
AKT
mTOR
activation
Over-expression of
COT (MEK kinase)
Over-expression of
CyclinD
With Keith Flaherty, Ryan Sullivan, Levi Garraway, David Solit, and many investigators worldwide
Oncogenic mutations contribute to tumor escape via
multiple mechanisms including immune evasion
and blocking mutations can make tumors more immunogenic
X
Uncontrolled
proliferation
BRAF CRAF
X
MEK
ERK
Resistance to
apoptosis
Angiogenesis
Invasion &
More
Metastasis
Immunogenic
Immune
Evasion
Understanding responses to melanoma therapy:
lessons learned from mouse and man
Molecular profiling
(WES, RNAseq, etc) and
immune profiling performed in
tumors over the course of therapy
Enrolled
onto tissue
acquisition
& use
protocol
Patients going
onto trials with
targeted therapy
Blood draw
and tumor
biopsy
Pre-treatment
(pre-treatment, on-treatment,
progression time points)
Tumors assayed for T cell infiltrate, markers of
cytotoxicity, immunosuppressive cytokines and VEGF,
and PD-1 / PD-L1 expression
Start of
therapy Blood draw
and tumor
biopsy
On-treatment
Restaging
as indicated
per trial /
standard of
care (SOC)
Blood draw and
tumor biopsy
at progression, if
applicable
Immune mechanisms of response and resistance
to targeted therapy also identified
( antigens & CD8+T cells immunosuppressive cytokines & VEGF)
CD8
MART-1
BUT with a concurrent increase in PD-1 & PD-L1
On-treatment (Day 10-14)
PDL1
Pre-treatment (Day 0)
These favorable immune
changes are NOT likely
to be solely related to
increased melanoma
antigen expression, but to
overall changes in the
microenvironment
(and maybe neoantigens)
Frederick, et al.
Clinical Cancer
Research 2013
Pre-treatment (Day 0)
On-treatment (Day 10-14)
1.4
% o f p r e - e x is it in g c lo n e s
100
80
60
100
T o p 5 % C lo n e s
40
( S % o f T o t a l C lo n e s
D
(S
p
6
-1
t
D
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(S
3
6
-1
%
D
(S
3
)
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-1
D
) p
9
-1
t
p
.5
9
9
t
.5 p
%
9
(P
%
t
p
)
(P
R
t
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)
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9
-4
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-4
(P
5
(P
5
%
R
%
R
)
)p
p
4
t
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t
8
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8
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.7
.7
4
4
%p
(P
%
p
(P
t
)t
R
)
R
1
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4
-5
4
(P
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3
(P
3
%
R
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R
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p
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6
t
p
4
1
6
t
.9
1
4
1
%
.9
(P
1
)
p
%
(P
R
t
)
p
-8
7
R
t
0
(C
-8
7
%
R
0
(C
)
%
-1
R
)
0
0
1
%
0
)
0
%
)
1.0
1.2
O n - t r e a t m e n t D o m in a n t C lo n e s a s a % o f t o t a l p o p u la t io n
80
20
0
60
T o p 2 .5 % C lo n e s
T o p 1 % C lo n e s
T o p 0 .5 % C lo n e s
40
20
t
1
0
p
p
t
1
0
0.8
change 'CLONALITY'
on RX Inhibitor
Change infoldClonality
after BRAF
Treatment with targeted therapy in melanoma
patients is associated with a more clonal T cell
response (TIL)
n=8,
p< 0.05
n=8 p=0.04
p
t
1
0
Clonality= 1-(entropy)/log2(#of productive uniques)
Cooper et al, OncoImmunology 2013
Zac Cooper PhD
Addition of Immune Checkpoint Blockade to BRAFi
Enhances TIL in a Murine Model of Melanoma
BRAFi + isotype
CD8
DAPI
BRAFi + anti-PD-1
Multiple trials combining targeted therapy
and immune checkpoint blockade are
currently underway
(for melanoma and other cancers)
Cooper et al. Cancer Immunol Res. 2014 (with Arlene Sharpe et al)
Zac Cooper PhD
Insights gained in responses to immunotherapy
through translational research in human samples
How can we best predict responses to
immune checkpoint blockade?
Genomic factors
• Mutational load and neoantigens may help
explain varied response to therapy
Snyder, et al N Engl J Med. 2014;371:2189-2199.
Distribution of CD8+ T cells
• Immune differences seen in responders and
non-responders to PD-1 therapy (namely, CD8+
cells at invasive margin in responders before
treatment and in tumor while on therapy)
Tumeh, et al. Nature. 2014;515:568-571.
Presence of immune related gene signatures (IFNg
signaling, antigen presentation) correlates with
outcome
Predictive Value of IFNγ Signatu
PFS and OS in Patients With Melanoma and<br />IFNγ Signature Score Above and Below the Cutoff
PFS and OS in Patients With Melanoma and<br />IFNγ Signature Score Above and Below the Cutoff
• “T cell-inflamed” characterized by
chemokines and type I IFN signature
• CD8+ T cell-driven IFN upregulates
PD-L1 and IDO
• Checkpoint blockade may
preferentially benefit this subset
Presented by Thomas Gajewski at 2015 ASCO Annual Meeting.
IFNg, interferon gamma; ROC, receiver operating characteristic.
Presented By Antoni Ribas a
Presented by Antoni Ribas at 2015 ASCO Annual Meeting.
Presented
By Antoni
AntoniRibas
Ribasatat2015
2015
ASCO
Annual
Meeting
Presented By
ASCO
Annual
Meeting
Methods
Responders
(7)
Responders
(13)
CTLA-4 blockade
Progressors
(46)
PD-1 blockade
Melanoma patients
(53)
Biopsy
Biopsy
Biopsy
Biopsy
Progressors
(33)
Biopsy
Molecular profiling (whole exome sequencing, nanostring, RPPA)
Immune profiling (IHC, flow cytometry, TCR sequencing) at each time point
With Jim Allison, Pam Sharma, Jorge Blando, Lynda Chin, Andy Futreal, and Moon Shot team
Hypothesis
• Molecular and immune “signatures” exist in pretreatment (and early on-treatment) samples of
patients receiving CTLA-4 and PD-1 blockade
and may be predictive of response
• Deep analysis of these signatures will allow us
to derive actionable strategies to enhance
response to therapy
We may have acceptable predictable biomarkers at present
but may simply be looking at the wrong time point
Perhaps rather than putting an emphasis on pretreatment markers for checkpoint therapy,
We should be looking at adaptive immune
responses in early on-treatment samples
(and should be incorporating this into clinical trials)
At least until we identify better pre-treatment biomarkers
What is the role of tumor heterogeneity in
influencing responses to therapy?
What is the relationship of the relationship of
genomic and immune heterogeneity,
and their relationship to response to therapy?
What are some novel approaches to enhance
responses to cancer therapy?
Targeting the microbiome…
Bacteria within the gut of patients with cancer
may modulate responses to therapy
Evidence for the role of the microbiome in animal
models of melanoma published in Science 2015
Bacteria within the gut of patients with cancer
may also modulate responses to therapy
Tumor biopsies and blood draws
incorporating molecular and immune
profiling (+ microbiome)
Treatment 1
Treatment 2
We now have evidence for the role of the microbiome
in patients on immune checkpoint blockade
(bacteria censored, submitting for IP)
Deepak Gopalakrishnan MS, confidential unpublished data,
manuscript in preparation
Acknowledgements
Immune Checkpoint Inhibitor 2016
MDACC Collaborators
• David Snowdon, MD
• Lynda Chin MD, Ron DePinho MD
• Other ICI staff
• Jim Allison PhD, Pam Sharma MD PhD
Laboratory Investigation (Wargo lab)
• Patrick Hwu MD, Michael Daveis MD PhD
• Zachary Cooper PhD
• Andrew Futreal PhD, Giulio Draetta MD PhD
• Alexandre Reuben PhD
• Jeffrey E. Lee MD, Jeff Gershenwald MD
• Pei-Ling Chen MD PhD
• Michael Tetzlaff MD PhD, Alex Lazar MD
• Hong Jiang PhD
• TIL lab
• Jacob Austin-Breneman, BS
• Immunotherapy Platform
• Peter Prieto, MD MPH
• Wei-Shen Chen, MD PhD
• Faculty in Melanoma Medical Oncology and
Surgical Oncology
• Sangeetha Reddy MD PhD
Other Mentors/Collaborators
• Christine Spencer MS
• Steven A. Rosenberg, MD PhD
• Vascheswaran (Deepak) Gopalakrishnan, MS
• James Ecnonou MD PhD
Harvard / MGH / DFCI Collaborators
• Toni Ribas MD PhD
• Keith Flaherty, MD, Ryan Sullivan, MD
Philanthropic/Grant Support
• Marty Mihm, MD, Steve Hodi, MD
• NIH (K08, U54), MRA, MRF, BSF
• Levi Garraway, MD, PhD
• MDACC internal support
• David Fisher, MD, PhD
• Arlene Sharpe, MD, PhD
Industry Sponsors/Collaborators
• Nir Hacohen, MD, PhD
• Bill Hahn, MD, PhD
Patients and their families
Thank you for your attention!
Questions?
The Wargo lab