Download Discovering conserved DNA

Immuno and
Epigenetic Therapies
Xiaole Shirley Liu
STAT115, STAT215, BIO298, BIST520
Using Single Agent to Treat Cancer
Cancer Immunology
• Would tumor grow in another individual?
Effector Lympocytes
• Lymphocytes express highly specific antigen
receptors on their surface, recognize specific
structural (AA) motif
• Usually CD8+ cells which kill target cells by
recognizing foreign peptide-MHC molecules on
the target cell membrane.
• Cancer cells express neoantigen from mutations
that are recognizable and accessible to the
immune system -- tumor-specific “antigenicity”
• The immune system is able to mount a response
against cells bearing such antigens
Cytokines
• Low molecular weight protein mediators
involved in cell growth, inflammation, immunity,
differentiation and repair
• Production triggered by presence of foreign
particles
• Interleukins (ex. IL-2) and interferons
• Acts as a potent immunomodulator and
antitumor element, but might have extensive
multiorgan toxicity
Active Immonotherapy
• High dose IL-2 (FDA approved for kidney cancer and
melanoma)
• Boost overall immune cells inside the patient body
CAR
• Chimeric antigen receptors: proteins that allow the T
cells to recognize specific antigen (e.g. CD19 on
lymphoma) on tumors
• Side effects: rapid and massive release of cytokines into
the bloodstream
Using Antibodies to Boost
Immune Systems
• Checkpoint blockade antibodies can activate Tcell and boost immune to kill tumor
Adoptive Immunotherapy
• Isolate tumor-infiltrating lymphocytes (TILs)
• Expand their number artificially in cell culture to
recognize the tumor-specific neoantigens
• Infuse TIL back into the bloodstream, recognize and
destroy the tumor cells
• Find mutations from exome sequencing
• Use bioinformatics program to find mutations
that might be immunogenic
• Create vectors expressing the small peptides
containing the mutations
• Co-culture to activate TIL
Personalized ImmunoTherapy
• Great for melanoma, lung and colon cancer
• Immunotherapy specific to each patients’ tumor
mutations
Bioinformatics of Immunotherapy
• How much of different immune cells are around
each tumor
• Which peptide will be presented on the cell
surface
• Which mutations are immunogenic
• How does tumor evade immune system
• How do the patient T-cells respond to the tumor
(TCR)
• Biomarkers of immunotherapy response
Deconvolve Tumor Immune Infiltrates
1111
LogR
LogR
LogR
LogR
Ratio
Ratio
Ratio
Ratio
LRR
LRR
LRR
LRR
aa
000 0 0.5
0.5
0.5
0.5 111 1 -1
-1
-1-100001111
Figure
Figure 11
0000
LRR
LRR
LRR
LRR
Index
Index
Index
Index
-1
-1
-1-1
BAF
BAF
BAF
BAF
BBAllele
B
Allele
Allele
B Allele
Frequency
Frequency
Frequency
Frequency
Genomic
estimates
Genomic
Genomic estimates
estimates
of
tumor
purity can beobtained using referencedataset or
of
of
tumor
purity
or
of tumor
tumor purity
purity can
can beobtained
beobtained using
using referencedataset
referencedataset
or
p=0.7
p=0.7
log
transformation
on
both
sides
of
Eq.(1)
p=0.7
log
transformation
on
both
sides
of
Eq.(1)
p=0.7
p=1.0
log transformation on both sides of Eq.(1)
p=1.0
p=1.0
0
0
0.25
0.25
DNA-based estimation of
tumor
purity
from
from external
external sources.
sources. Taking
Taking
from external sources. Taking
and
(2) leads
to:
and
to:
leadsto:
and (2)
(2) leads
0.5
0.5
KIRP
HNSC
CESC
KIRC
LUAD
SKCM
LUSC
OV
LIHC
BRCA
UCEC
BLCA
GBM
STAD
ACC
UCS
KICH
COAD
THCA
READ
PRAD
LGG
DLBC
Odds
Odds
Odds
Odds
Ratio
Ratio
Ratio
Ratio
g
00
0.25
0.5
s
s
s
0.25g
0.5 g
g
g
g
g
log(Y
log(⌃ RRr=
⇥
µ
e
R
gsss )) =
gsss
R 1ff r,i
gsss )) +
rg
ii g
Y
log(Y
=
log(⌃
⇥
µ
+
e
r,i
r=
1
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Y
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)
=
log(⌃
f
⇥
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)
+
e
r,i
r=
rr
ii Immune
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r= 1
1 r,i
YR
Immune
b
g
s
Immune
R
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b
⌃
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R
R
g
rg
=
ssff r,i
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µ
g
s Merge
Cell
g
s
TCGA tumor
r,i
Cell= log{f ⇥ µggsss ⇥ (1+ ⌃ r6
r
r6
=
f
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ss
r
r6
=
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r,i
g
+
e
r
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g
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sss ⇥ (1+
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=
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+
e
g
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ss
s,i
g
f
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s
Y
s,i
s
g
s
Y
Batch
effect removal
ff s,i
⇥µ
s,i
Batch
µssss g
s,i
Batch effect
effect removal
removal Tumor
s,i ⇥ expression
with reference
Tumor
R
s
Tumor
R
g
⌃
ff r,i
⇥
µ
ss
R
g
R
g
r
r6
=
s
⌃
⇥
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g
s
g
s
r,i
s
r6
=
g
=
r,i ⇥gµrrr + eg
g
'' log(µ
)) +
gs ) +
r6
= sssf r,i
gsss
Y
+ log(f
log(f
+ ⌃ r6
+
e
s
immune
dataset
s,i
g
Y
ss
s,i
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log(µsgsssss )) +
log(f s,i
+
e
g
ff s,i
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s
g
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s
⇥
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s,i
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s,i
s,i
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ss ) + log(f s,i ) + ∆ s,i + eg
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=
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(3)
g
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ccc
sg
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+ log(f
)
+
∆
+
e
(3)
s,i
s,i
ssss )) +
Y
s,i
s,i
=
log(µ
log(f
)
+
∆
+
e
(3)
Y
s,i
s,i
Y
g
g
g
s
s
s
g
Select
genes
overexpressed
in
g
g
R≤
-0.2
ss
g
s
s
log(X
)
=
log(µ
)
+
e
(4)
g
g
s
s
g
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s
ssgs ) = log(µsg
X
log(X
(4)
Purity
based
gene
R≤ -0.2
-0.2
ssss )) + e
X
log(X
log(µ
e
(4)
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ss )
X
Purity
based
gene
X
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Purityselection
based gene
R> -0.2
selection
the
microenvironment
selection
In
theaboveequations,
new
error
terms
follow
normal
distributions
with
In
new
error
terms
follow
normal
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In theaboveequations,
theaboveequations,
new
error
terms
follow
normal
distributions
g
g
s
s with gs
g
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g
zero
means.
Let
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denotethelog-transformed
observation
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and
Xsgggsss
s
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g
s
s
ii denotethelog-transformed
zero
zero means.
means. Let
Let Y
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denotethelog-transformed observation
observation of
of Y
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and X
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g
s
denotefor
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Thejoint
likelihood
of
observations
regarding
geneset
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ssg
denotefor
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).
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likelihood
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observations
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geneset
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d
* *
d
{G
bewritten
as:
Filter in purity-selected genes
d
ss}} 9can
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can
bewritten
as:
{Gss} can bewritten
as:
*
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6
g
s
* * *
g
s
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for
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* * * * *
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Select
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s,i
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Select immune
{{ g
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gs }} p
L
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2
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ss 2G
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⇧
⇧
exp{−
}
ss } p 2⇡ σ2
2
g
2σ
i
{
g
2G
signature
genes
g
2
s
g
1
s
s
g
2σ
signature genes
2
0
2σYYY2 gggsss
2⇡⇡ σ
σYYY2 gggssss
gs
g
s 2
g
1
(X
log(µ
ss ))2
gsss −
rrg
rg
s ))2
1
(X
−
log(µ
r
1
(X
−
log(µ
)) }}
q
⇥
⇧
⇧
exp{−
(5)
rr ⇧{{ g
r
r
2G
}
s
g
q
⇥
⇧
exp{−
(5)
s
2
}
g
q
⇥ ⇧r ⇧{ ggsss 2G
exp{−
}
(5)
s
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gs
2Ggss }
2
2
2σ
g
2
⇡
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X
gs
s
g
r
s
2
2σ
gs
2
⇡
σ
X
g
X
s
r
r
g
s
r
2⇡ σXX rrrgs
X r Linearly deconvolve six
e
ee
Constrained
least square
fit
Constrained
Constrained least
least square
square fit
fit
Statistical
Statistical
Statistical
deconvolution
deconvolution
deconvolution
6
X
X
6
6
X
X
g
g
X
X
g
g 2
2
ffˆˆˆ =
argmin
(Y
−
ff rr X
g−
rrg))2
=
argmin
(Y
X
f = argmin
(Y
−
f
X
r r)
8rf r > 0
8rf
G}
8rf rrr >
>0
0 g2{
g2{
g2{ G}
G}
Inlinemode: max
r=
1
r=
1
r= 1
, max .
immune cells: B cell, CD4 T
cell, CD8 T cell,(6)
neutrophil,
(6)
(6)
macrophage and dendritic cell
13
TIL Association With Survival
TIL
• Adjusted for age, gender, tumor stage and viral infection
status
• CD8 T cell with better survival and macrophage with
worse outcome
• Single cell analysis?
14
CYT: Rooney et al, Cell 2015
MHC Presentation
• MHC presents peptides (~9 aa) in the
cell to the surface for immune
examination (self vs non-self),
important for transplanation
• MHC presents tumor neoantigen for
immune elimination
• Different HLA have different affinity
to different peptides
• Immune evasion: HLA mutation,
antigen mutation
NetMHC: Predict HLA / peptide affinity
• Predict which peptide in the
cell will be presented on the
cell surface by which HLA
• There are 2500 different
HLA alleles
• ~70 HLA alleles are
characterized by binding
data
• Reliable MHC class I
binding predictions for ~50
HLA A and B molecules
HLA-A*3001
HLA-A*3002
Polysolver HLA Somatic Mutations
Shukla et al,
NBT 2015
T-Cell Repertoire and VDJ Recombination
• Assembled α- (light) and β- (heavy) chains form the αβTCR expressed on T cells
• Traditional TCR repertoire: PCR amply and sequence
the rearranged VDJ region in the T cells
18
Infer T-Cell Repertoire and CDR3
Sequence from Tumor RNA-seq
Figure 2
V region
(i)
J region
s1
s2
s1
(ii)
(iii)
CDR3
s2
s1
s2
19
Response BioMarkers
• High CD8 T-cell infiltrates
• High immune checkpoint
gene expression
• High tumor mutation load
Epigenetic Drugs
• HDAC inhibitor to delay drug resistance
• Minimum 5-aza (DNA demethylation)
21
Treat Cell Lines
Directly Treating Mice
Effect of 5-aza
• Minimum dosage and toxicity, well tolerated
• Activate suppressed immune genes
• Can use DNA methylation status at these immune
genes to predict patient response
• Small % of patients directly cured.
• Others re-sensitized for chemotherapy
• Can be used with other drugs?
Targeted Epigenetic Drug
• DOT1L inhibitor for MLL Leukemia
Meyer et al, Nat 2013
Targeted Epigenetic Drug
• EZH2 inhibitors
• Diffuse large B-cell
lymphoma
• Rhabdoid tumor with
SNF5 mutation
• Hormone independent
prostate cancer
Martinez-Garcia & Licht, Nat Genetics 2010
Targeted Epigenetic Drug
• JQ1 as a BET domain inhibitor, also works on
MLL leukemia
Summary
• Immunotherapy: a living drug!
• Different immunotherapy options
• Bioinformatics challenges of immunotherapy
– Immune infiltrate
– MHC presentation and HLA typing
– T-cell receptor repertoire
• Epigenetic therapy: 5-aza immune response
• Targeted epigenetic therapy: DOT1L, EZH2,
BRD4