Download Prioritization of Neoantigens without Predictions: Comprehensive T

Prioritization of Neoantigens
without Predictions:
Comprehensive T cell
Screening using ATLAS™
Jessica Baker Flechtner, PhD
CSO, Genocea Biosciences
15 November 2016
1
Safe Harbor Statement
This presentation contains “forward-looking” statements that are within the meaning of federal securities laws
and are based on our management’s beliefs and assumptions and on information currently available to
management. Forward-looking statements include information concerning our possible or assumed future
results of operations, business strategies, financing plans, competitive position, industry environment, potential
growth opportunities, potential market opportunities and the effects of competition.
Forward-looking statements include all statements that are not historical facts and can be identified by terms
such as “anticipates,” “believes,” “could,” “seeks,” “estimates,” “intends,” “may,” “plans,” “potential,”
“predicts,” “projects,” “should,” “will,” “would” or similar expressions and the negatives of those terms. Forwardlooking statements represent our management’s beliefs and assumptions only as of the date of this
presentation. Our operations involve risks and uncertainties, many of which are outside our control, and any
one of which, or combination of which, could materially affect our results of operations and whether the
forward-looking statements ultimately prove to be correct. Factors that may materially affect our results of
operations include, among other things, those listed in our Annual Report on Form 10-K, our Quarterly Report for
the third quarter of 2015 on Form 10-Q and other filings with the Securities and Exchange Commission (“SEC”).
Except as required by law, we assume no obligation to update these forward-looking statements publicly, or to
update the reasons actual results could differ materially from those anticipated in the forward-looking
statements, even if new information becomes available in the future.
You may get copies of our Annual Report on Form 10-K, Quarterly Report on Form 10-Q and our other SEC filings
for free by visiting EDGAR on the SEC website at http://www.sec.gov.
2
Introduction to Genocea
• We are a vaccine company
• Our ATLAS™ T cell antigen discovery platform is clinically validated
– GEN-003 - the first subunit vaccine to impact a chronic viral infection
– Expected to enter Phase 3 in 2017
• We have a comprehensive cancer vaccine solution:
– High-throughput ex vivo antigen selection for both CD4+ & CD8+ T cells
– Ability to discriminate between responders and non-responders
– Optimized vaccine delivery system includes adjuvant and
manufacturing/production expertise
• Advisors: a world class team of oncology and vaccine experts
• We plan to file an IND with a personalized cancer vaccine in 2017
3
Why is ATLAS™ Important for Neoantigen Vaccines?
On average, algorithms have a
~20% success rate at
predicting neoepitopes to
which T cells respond
4
Why is ATLAS™ Important for Neoantigen Vaccines?
On average, algorithms have a
Finding the
~20% success rate at
RIGHT TARGETS
predicting neoepitopes to
matters
which T cells respond
5
ATLAS™ Provides a Panoramic Perspective on Patient
T cell Responses to Identify True Neoantigens
Both CD4+
and CD8+
T cell
antigens
High
throughput
and rapid
All putative
antigens
Biological
relevance
ATLAS:
actual, not
predicted,
neoantigens
All HLA
types
6
The Core Invention: Accurate Identification of T cell
Antigens in vitro For Any Person
Identifying responses by both CD8+ T cells (shown below) and CD4+ T cells
• Accurately
recalls T cell
responses in
vitro
• For any
person, in any
indication
• Flexible
readout
Cytoplasmic form of listeriolysin O (cLLO) modified so that it can no longer be secreted. E. coli
expressing cLLO can deliver co-expressed protein to the cytosol of antigen presenting cells.
7
ATLAS™ Optimized as a Highly Industrialized Platform
Monocytes: + IL-4, + GM-CSF
Monocyte-derived
DC (APC)
Bead
purification
Putative
Cancer neoantigens
Effector T cells
Human Subjects
Protein
processing &
presentation
Ficoll gradient
“Human immune
system in a plate”
Whole blood
APC
Antigen plasmid
library
T cell
Cytokine readout
Identify priority
T cell targets
T cell Response
Profile
Antigen expression
library in E. coli
8
Establishing ATLAS™ Potential for Tumor Antigen
Profiling – Proof of Concept Study
• Question: Can ATLAS discern differential patterns of response to
tumor-associated antigens in melanoma?
– Antigen frequency
– Breadth of response
– CD4+ and CD8+ T cell responses
• DFCI collaboration*
– Retrospective analysis of 20 checkpoint inhibitor recipient subjects’
T cell responses to 23 pre-selected TAAs
– Ten cytokine readout
*funded by Ludwig Trust, collaboration between DFCI (Hodi/Dranoff), HMS (Higgins/Grubaugh), Genocea
9
Profiling Response vs. Non-Response to Checkpoint
Inhibitor Therapy (Melanoma)
Tumor
Regression
Tumor Regression
Tumor Progression
Antigen
Tumor
Progression
10
e
C y t o k in e
-1
3
C y t o k in e
IL
0
-8
-1
IL
-6
-4
e
IL
IL
-1
3
0
-8
-6
-4
-1
IL
IL
a
ta
h
IL
b
0
a
-2
7
IL
p
m
lp
2
m
a
-1
F
IL
N
-1
a
P<0.0412
IL
a
ta
h
IL
b
lp
T
IL
-g
100
IL
a
0
a
-2
7
IL
p
m
N
P<0.0156
-1
F
2
m
IF
N u m b e r o f T c e ll A n t ig e n s
P<0.0351
IL
N
a
-1
-g
IL
N
T
IF
N u m b e r o f T c e ll A n t ig e n s
ATLAS Discriminates Responder versus Non-Responder
CD4
P r o g re s s o r
R esponder
10
P<0.0252
1
0 .1
CD8
100
P<0.0002
10
1
0 .1
11
The Application of ATLAS to Neoantigen Prioritization
Immune
memory
cell
Response
Response
Autologous
APC and T cells
Tumor
Neoantigen
library
immune
Antigen
Use antigen prioritization
criteria defined through
ongoing retrospective analysis
of CPI-treated subjects
12
Construction and Screening of Personalized Neoantigen
Expression Libraries
• Identify tumor-specific somatic mutations
• Clone DNA sequences encoding ~100 aa spanning each
mutation into ATLAS expression construct
Shine-Dalgarno 6 x His
T7 Promoter
attR1
ATLASTM Expression construct
SIINFEKL T7 terminator
Neoantigen
attR2
Ampicillin
1
6204
• Validate expression using a high-throughput surrogate T cell
assay
• Derive DC from monocytes and sort and expand CD4+ and CD8+
T cell subsets from blood, recombine with ATLAS library
• Measure T cell responses through meso scale discovery
13
Clones Arrayed Randomly and Screened in Duplicate
add Confidence to Data Analysis
Example patient data from a NSCLC patient who was treated with pembrolizumab
14
Robust Platform for Identification of Neoantigens for
CD8+ T cells
C D 8 T c e ll R e s p o n s e s P r e - T r e a t m e n t
800
Ag pre-treatment
Ag post-treatment
Inhibitory Ag
Negative controls
IF N  p g /m L
600
400
M e a n + 3 *S D
200
0
C D 8 T c e ll R e s p o n s e s P o s t-T re a tm e n t
800
IF N  p g /m L
600
400
M e a n + 3 *S D
200
0
15
Robust Platform for Identification of Neoantigens for
CD4+ T cells
CD4
+
T c e ll R e s p o n s e s P r e - T r e a t m e n t
10000
Ag pre-treatment
Ag post-treatment
Inhibitory Ag
Negative controls
p g /m L IF N 
8000
6000
M e a n + 4 *S D
4000
2000
0
CD4
+
T c e ll R e s p o n s e s P o s t - T r e a t m e n t
p g /m L IF N 
3000
2000
M e a n + 4 *S D
1000
0
16
Stringent Assay Identifies Truly Immunogenic T cell
Targets
CD8+ T cell Responses
• Number of mutations identified as
neoantigens (IFNγ)
– Pre-treatment: 13 (6%)
– Post-treatment: 20 (10%)
– Both pre- and post-treatment: 6
(3%)
• Number of ‘inhibitory’
neoantigens increases posttreatment
CD4+ T cell Responses
• Number of mutations identified
neoantigens (IFNγ)
– Pre-treatment: 16 (8%)
– Post-treatment: 38 (19%)
– Both pre- and post-treatment: 9
(5%)
• Number of ‘inhibitory’
neoantigens declines posttreatment
• 15 neoantigens were positive both pre- and post-treatment for either
cell type, one for both cell types
• Six neoantigens were identified by both subsets at any time point
17
Comparison of Results with in silico Predictions
• 64 of 77 (83%) of neoAg predicted by
algorithms show no detectable
response with ATLAS
• 8 of 77 (10%) of neoAg were
predicted by both algorithms and
ATLAS
• 5 of 77 (6.5%) of neoAg were missed
by all three algorithms but found by
ATLAS
• 3 of 13 (23%) of neoAg detected by
ATLAS were predicted by all three
algorithms
ATLAS increases useful NeoAg found by 38% and eliminated
83% of false positives predicted by algorithms
18
Summary of the ATLAS Potential in Neoantigen
Discovery
• Identifies the majority of neoantigens in any patient
• Every mutation from a tumor separately screened to identify true
neoantigens
• CD4+ and CD8+ T cell antigens can be separately identified,
those that stimulate both may be valuable
• Multiple cytokines measured simultaneously
• Inhibitory neoantigens can be identified
19
Our Working Hypotheses for Successful Neoantigen
Vaccination
• It is a lower bar to boost pre-existing responses than create de
novo responses
• Re-educate the immune system by delivering an antigen with a
strong adjuvant
– Proof of concept established in our GEN-003 chronic virus
immunotherapy
• With the right adjuvant and ATLAS-prioritized antigens, an IND is
anticipated in 2017
20
21
Biologically Verified Antigens are Worth It
• ATLAS adds ~2 weeks to personalized vaccine development
• The value added:
– Certainty that T cell responses exist and are not deleterious
– Confidence that targets are cancer antigens in the individual
– Likelihood that T cell responses can be boosted with the right
vaccine
– Potential to redirect mis-educated T cells to be productive
22
Acknowledgements
• Memorial Sloan Kettering Cancer
Center
–
–
–
–
–
–
Timothy Chan
Jedd Wolchok
Jonathan Havel
Vladimir Makarov
Taha Merghoub
Matthew Hellman
• Dana-Farber Cancer Institute
– F. Stephen Hodi
– [Glenn Dranoff]
• Harvard Medical School
– Darren Higgins
– Daniel Grubaugh
• Genocea Biosciences
–
–
–
–
–
–
–
–
–
–
–
Lila Ghamsari
Emilio Flano
Judy Jacques
Biao Liu
Wendy Broom
Zheng Yan
Christine Kelley
Aula Alami
LeeAnn Blalock
Jean-Luc Bodmer
Ning Wu
• Funding
– Ludwig Trust (TAA study)
23