BIOL 520 Advanced Immunology W2009 Lecture 1 Overview Immunology Lecture 1 Overview Immunology.
Systems Immunology in IO: A View from the Parker...
Transcript of Systems Immunology in IO: A View from the Parker...
Systems Immunology in IO: A View from the Parker Institute
Nikesh Kotecha, PhD
VP, Informatics
Disclosures
OUR MISSION
To accelerate the development of breakthrough immune
therapies to turn cancer into a curable disease.
A New Network
60+ Laboratorie
s
7 Research
Institutions
300+ Nation’s
Top
Researchers
40+ Industry
& Non-profit
Partners
April, 2016: Billionaire tech entrepreneur Sean Parker
announced a $250 million donation to establish the
Parker Institute for Cancer Immunotherapy to speed
research into innovative cancer treatments.
PROJECT-BASED
COLLABORATORS
Fortune Health
The Institute Leadership
Institute Leaders
JEFFREY
BLUESTONE, PhD
President + CEO
Parker Institute for
Cancer Immunotherapy
JEDD
WOLCHOK, MD, PhD
Memorial Sloan
Kettering Cancer Center
JAMES
ALLISON, PhD
MD Anderson
Cancer Center
CARL
JUNE, MD
The University of
Pennsylvania
LEWIS
LANIER, PhD
UCSF
ANTONI
RIBAS, MD, PhD
UCLA
CRYSTAL
MACKALL, MD
Stanford Medicine
SEAN
PARKER
Founder + Chairman
Parker Institute for
Cancer Immunotherapy
LAURIE
GLIMCHER, MD
Dana-Farber
Cancer Institute
Scientific Steering Committee Programmatic Collaborators
ELLIOTT SIGAL, MD, PhD
NEA
DAN LITTMAN, MD, PhD
NYU
NIR HACOHEN, PhD
Broad Institute
ELIZABETH JAFFEE, MD
Johns Hopkins
NINA BHARDWAJ, MD, PhD
Mount Sinai
LARRY TURKA, MD
Harvard Medical School
STEPHEN SHERWIN, MD
UCSF
JEFF HUBER
GRAIL
THOMAS DANIEL
Arch Venture Partners ROBERT
SCHREIBER, PhD
Washington University
PHIL
GREENBERG, MD
Fred Hutch
JIM
HEATH, PhD
Institute for Systems Biology
NINA
BHARDWAJ,
MD, PhD
Mount Sinai
STEPHEN
FORMAN, MD
City of Hope
Our Research Agenda
Large collaborative efforts focus on four areas with great potential.
Best-in-class T-cells Advance the next generation of T-cell therapies to provide targeted, safe,
long-lasting treatments to conquer cancer.
Immune Response Uncover why some patients respond to immunotherapy while others may not
to overcome cancer drug resistance.
Tumor Antigen Discovery Pinpoint novel cancer cell markers that will become the foundation for new
therapies and personalized treatments.
Tumor Microenvironment Discover how tumors impair immune cells, which will jumpstart the creation of
therapies that can fight hard-to-treat solid tumors.
INFORMATICS
Informatics is involved in all aspects of translational clinical research:
• Providing context through public data integration
• Evaluating new experimental technologies
• Supporting clinical trial design
• Sample tracking and data collection
• Clinical data entry and management
• Correlative assays data management
• Integrative data analysis
• Developing and scaling analytical methods
• Supporting computational efforts across the
consortium
I EXECUTION
I IDEATION
I ANALYSIS
Informatics supports PICI Clinical and Research studies with systems, programming, and biostats
o PICI Sponsored Clinical Studies
• Clinical Programming and Systems Setup for PICI trials (8+)
• Clinical Report Generation & Biostats
• Global library project
Towards a standardized set of CRFs/collection parameters to use across PICI studies
o PICI Sponsored Research Studies
• Programming and Systems Setup for Research Studies
Prospective collection underway for studying immune-related AEs after IO
• Novel deployment of REDCap environment
We are running it on a GCE environment that has others in the community excited.
Configuration is available at https://github.com/ParkerICI/redcap-k8s-templates
• Medidata • Veeva • Endpoint • Oracle Argus Safety • Kubernetes
Primary analysis pipelines in place for 15+ assays
Input from our investigators informs how we prioritize further development of these pipelines
Assays that can be
automatedPrototype stage:have run once or twice,
Advanced prototype:have run a few times,
Standard analysis:have run many times,
Mature analysis, mostly automated
WES
TCR
ATAC-seq
scATAC-seq
16S
RNA-seq
Nanostring
scRNA-seq
Assays that need a human in the loop
Prototype stage:have run once or twice,
Advanced prototype:have run a few times,
Standard analysis:have run many times,
Mature analysis,as automated as possible
Vectra
IMC
MIBI
CODEX
Cytokines/Luminex
Flow
CyTOF
Systems and infrastructure to support PICI trials and translational analyses (latest iteration)
Clinical & Research Ops Translational
Informatics Progress across PICI
o 30+ projects across 7 sites, 15+ data types, 10+ publications
o PICI worked with MD Anderson to identify TCR features in tumors associated with response to neoadjuvant nivolumab in high-risk resectable metastatic melanoma patients. o Amaria et al. Nature Medicine 2018
o PICI worked with UCLA to apply in-house CyTOF methodology, revealing differences in PD-1 expression patterns by anatomic site. o Davidson et al. Clinical Cancer Research 2018
o …and more
o Enabled novel work with PICI developed tools o https://github.com/parkerici
o “Deep” integrative analysis of multi-omic trials
Outline of talk/examples of progressing IO
• End-to-end clinical trial analyses
• Approaches to think about (PD1) Resistance and Toxicities
• Approaches to think about neoantigen prediction (TESLA)
• Bringing data together to answer key questions
Pancreatic Study Combining Nivo CD40 and Chemo (PRINCE)
Exploring safety and efficacy of chemotherapy and immunotherapy combinations
for metastatic pancreatic cancer
o Chemotherapy + CD40 Ab + anti-PD-1:
• Standard of care chemotherapy
Nab-paclitaxel
Gemcitabine
• Immunotherapies
CD40 Antibody APX005M (Apexigen)
Nivolumab anti-PD-1 checkpoint inhibitor (Bristol-Myers Squibb)
PRINCE Overview
PRINCE Overview
Gemcitabine + nab-Paclitaxel + CD40 Ab APX005M +/- PD-1 Ab Nivolumab
Immunotherapy
Pancreatic cancer
Chemotherapy Blood
vessel Lymph
node
Tumor
1 Release of
cancer antigens
(cancer cell death)
2 Cancer antigen
presentation
(dendritic cells/APC)
3 Priming and
activation
(APCs + T-cells)
4 Trafficking of T-cells
to tumors (CTLs) 5 Infiltration of T-cells
into tumors
(CTLs, endothelial cells)
6 Recognition of
cancer cells
by T-cells
(CTLs, cancer cells)
7
Killing of
cancer cells Chemotherapy
CD40
Antibody (APX005M)
PD-1
Antibody (Nivolumab)
CD40 antibody
(APX005M)
PD-1 antibody
(Nivolumab)
Deep Immune Profil ing
Pa
tien
t S
am
ple
s
Biomarker
Samples
Clinical Metadata
Blood
Tumor
Germline WES
Tumor WES
Multiparameter
Imaging
Immune Profile
CyTOF/Flow
ctDNA: Mutant
KRAS
Neo-epitope
Prediction
HLA Determination
(MHC Class I and II)
TME Gene
Expression Signature
Tumor Genome/TMB
Extensive
Computational
Analysis
Harmonized methods of collection and processing at a central biorepository
RNAseq
(tumor & blood)
Cytokine
(serum)
Clinical Snapshot date: 05MAR19
Safety-evaluable Population
PRINCE Phase 1b: Promising Efficacy Signals
Cohort B1: Gem/NP/APX005M 0.1 mg/kg
Cohort B2: Gem/NP/APX005M 0.3 mg/kg
Cohort C1: Gem/NP/APX005M 0.1 mg/kg + nivo
Cohort C2: Gem/NP/APX005M 0.3 mg/kg + nivo
Overall Response Rate (CR or PR) = 46.7% (14/30)
Historical ORR for Gem/NP: 23% Phase 2 ongoing
Tumor Immune Profi l ing with High -Dimensional Imaging
CD68 CD8 Ki67 PD-L1 FoxP3 panCK, DAPI
Imaging of baseline tumors with two technologies:
Vectra and 30-marker OHSU mIHC
Vectra tumor imaging with 3 panels reveals low overall immune infiltrate with
higher macrophages and low CD8 T Cells
Frac
tio
n o
f n
ucl
eate
d c
ells
Cell-free DNA in blood
• Plasma cfDNA concentration measured before mutation analysis
KRAS G12V, D, R mutations
• Droplet digital PCR (ddPCR) assay to detect KRAS mutations in cfDNA
• Measured the portion of cfDNA that is confirmed to contain one or more KRAS mutations
Circulating Tumor DNA: Mutant KRAS Early analysis suggests that KRAS fraction in cfDNA correlates with tumor diameter
in patients who have measurable KRAS mutations at baseline (67% of patients)
Timepoint
KR
AS
Var
ian
t A
llelic
Fra
ctio
n
PR SD
PR
PR PR PR
SD
Tumor Sum Diameter
KRAS Allelic Fraction
Tum
or Su
m D
iameter (cm
)
PR
B2: Gem/NP + APX005M 0.1mg/kg
C2: Gem/NP + APX005M 0.1mg/kg + Nivo
High-dimensional cytometry for prof i l ing of immune dynamics in blood Treg
CD4 Central memory
CD4 Naive
CD4 EM
CD8 Naive
CD8 Central memory
CD8 Effector memory
TEMRA
CyTOF Immune phenotyping
(37 markers)
Early observations of immune changes with treatment: - Activation of B cells during the course of therapy - Activation of Tregs at the early timepoints
FACS Symphony T cell panel
(28 markers)
Current work: Unbiased clustering analysis of cellular population dynamics
Memory B cells (CD19+ CD27+ CD38-)
Plasmablasts (CD19+ CD27-)
Treg-like > CD38+ (CD4+ FoxP3+)
Outline of talk/examples of progressing IO
• End-to-end clinical trial analyses
• Approaches to think about (PD1) Resistance and Toxicities
• Approaches to think about neoantigen prediction (TESLA)
• Bringing data together to answer key questions
The problem of the majority
Tang et. al. (2018) Nat Rev. Drug Discovery
2,250 active trials testing anti-PD1/PDL1 agents as of September 2018,
totaling about 380,900 patient volunteers
~247,585 patients will experience resistance in trials alone (not accounting for SOC patients)
PD-1
Total=100
Response
Resistance
~35%
~65%
PD-1 “Responders”
PD-1 “Non-Responders” PD-1 “Non-Responders”
~65%
Intrinsic & extrinsic factors: What’s known
Havel et. al. (2019) Nature Reviews
Important questions in breaking down & harmonizing biomarker studies of PD -1
Biomarker Clinical
Response
to PD-1
At what clinical timepoint(s) was this measured?
How was this measured?
At what clinical timepoint was this measured?
How was this measured?
PICI trials addressing the problem of PD -1 resistance
Spencer, Wells & LaValle (2019) Trends in Cancer
PICI trial name Challenge addressed in design
MAHLER
Melanoma Tx with Ipi/Nivo Enrolled at
Multiple Centers Melanoma
- Trial exclusively in patients who progressed on PD-1
- Prior PD-1 resistance type taken into account in analysis
- Longitudinal, multi-omic biomarker studies, standard processing
- Working with partners to build out reference dataset (expand
sample size)
PRINCE
Pancreatic Study Combining Nivo,
CD-40 and Chemo
- Novel combinations in Pancreatic cancer, a novel tumor type
- Longitudinal, multi-omic biomarker studies, standard processing
MCGRAW
Melanoma CP and Gut Microbiome
Alteration With Microbiome
Intervention
- Novel biomarker (microbiome) as combination with PD-1
- Patients stratified by baseline microbiome (pro-resistance vs pro-
response)
- Longitudinal biomarker studies, standard processing
PORTER
Prostate Researching Translational
Endpoints Correlated to Response to
Inform Use of Novel Combos
- Novel combinations in castrate-resistance prostate cancer
(CRPC), a novel tumor type
- Platform trial design
- Longitudinal, multi-omic biomarker studies, standard processing
We Ran A Workshop
“Translational Approaches to PD-1 Resistance Workshop”
• Brought pharma + non-profit science + academia together to discuss how PD1 resistance can be addressed through translational science.
• Attendees from Merck, BMS, Genentech, Pfizer, Amgen, Regeneron; SITC, PICI, ISB; MDA/PICI, Yale, MSKCC/PICI
• Outcomes: core questions focus questions:
• How can data sharing be enabled between pharma?
• What should be the standard clinical definition of PD1 resistance?
• What are the molecular phenotypes of cancer resistance to immune killing?
What’s Coming – come join us!
• Molecular Phenotypes of Immune Resistance in Cancer • Working group based out of, but not restricted to, the workshop
• Initial goals
• Draft white paper/review on mechanisms of immune resistance (non-clinical)
• Retrospective, integrative analysis of gene expression and WES data from published cohorts
• Lead by SITC/TimIOs & ISB/UNC & PICI
• A follow up to “The Immune Landscape of Cancer”
• Actively seeking collaborators with large molecular data sets from IO trials!
What about toxicities?
PICI Launches the Autoimmunity and Cancer Program in Partnership with JDRF and Helmsley
CONSORTIUM:
PICI sites, academic labs, cancer centers, research hospitals, foundations, pharma and government institutions
GOALS:
Generate insight into the mechanisms behind irAEs following immune checkpoint inhibition
Determine overlap in mechanism with “classic” forms of autoimmune disease
Identify at-risk patients early to reduce the incidence and/or severity of such events
Understand irAE drug selectivity
Determine target antigen specificity
NERVOUS SYSTEM
Guillaine–Barré Syndrome
Myasthenia Gravis Encephalitis
THYROID
Hypothyroid
Hyperthyroid
HEART
Myocarditis
ADRENAL
Insuff iciency
GASTRO INTESTINAL
Colitis
SKIN
Vitiligo Psoriasis
PANCREAS
T1D
PITUITARY
Hypophysitis
LUNGS
Pneumonitis
LIVER
Autoimmune Hepatitis
RHEUMATOLOGIC
Vasculitis Arthritis
Autoimmune Events Resulting frOm Systemic Modulation by ImmunoTherapy (AEROSMITH)
• Aim: To collect clinical data and blood samples on patients before, at the time of, and after irAEs during checkpoint blockade therapy for cancer
• Goal: Enroll 1000+ patients prospectively and follow-up for 1.5yrs
• Progress: 200+ patients enrolled to date across 35 sites (April 2019)
Distribution of Cancer Types collected so far:
~50% melanoma + lung ~50% other
Distribution of AE Grades collected so far:
~50% are Grade 1
Outline of talk/examples of progressing IO
• End-to-end clinical trial analyses
• Approaches to think about (PD1) Resistance and Toxicities
• Approaches to think about neoantigen prediction (TESLA)
• Bringing data together across PICI to answer key questions
Tumor-specif ic neoepitopes are at t ract ive targets for cancer therapeutics
• Truly tumor-specific
o Novel peptides
o No pre-existing tolerance
o Immunogenic
• Safer therapeutic profile
o Minimal risk of autoimmunity
• Possibility of targeting multi-neoantigens could be a response to tumor heterogeneity and evolution
Hypothesis: if we can identify and target neoantigens with therapeutic agents, we can have personalized, safe, highly active therapies.
Yarchoan, & al. , 2017
Neoantigens can be targeted by therapeutic vaccines
Neoantigen Discovery Workflow
WES + RNA Sequencing
Variant Identification
Neoantigen identification
MHC I Binding prediction
Deep machine learning
Antigen processing models
Tumor cells
Normal
tissue
Ranked neoepitopes
• SNV • MNV • FS • Indel
Mass Spec.
Medgenome
From Personalis Website
Neoantigen discovery pipelines are becoming more diverse and complex
Genome Medicine20168:11
Speedy and accurate identification of neoantigen has been identified as a critical bottleneck for the delivery of neoantigen
promises
The need for benchmarking predict ion algori thms is becoming more pressing
Benchmarking predict ion a lgor i thms is a press ing need
TESLA : a community-based effort to optimizing neoepitope discovery
The Tumor neoEpitope SeLect ion All iance (TESLA) Program Goals
o TESLA is a community-based initiative that aims to support the field’s efforts to develop
safe and efficacious neoantigen-based therapeutics/vaccines for cancer by:
• Delineating the variation of neoepitope predictions in existing computational pipelines
• Generating high quality epitope validation sets that provide a basis to assess and
improve prediction pipelines
• Elucidating the key factors for accurate neo-epitope prediction
TESLA Par t i c i pat i ng G roups and Cont r i but ors
• 24 Academia/Non-Profits • 22 Pharma/Biotech
Advaxis Immunotherapies Agenus AMGEN Biontech BGI Genomics Bristol-Myers Squibb EpiVax Genentech Illumina ISA Pharmaceuticals MSD MedImmune Medgenome Neon Therapeutics Oncolmmunity Personalis Seven Bridges TEMPUS Vaccibody Yu Bio
Schematic of TESLA
Validation of the predicted peptides is central to algori thm improvement
o Validation aims at determining whether patient’s T cells are able to recognize the predicted
neoepitope.
• Focus on peptides binding to Class I MHC (pMHC)
o Goal: To validate predicted peptides in at least 2 assays guided by
• HLA restriction
• Availability of biological material
N. Bhardwaj, Mt Sinai A. Sette, LIAI
1. Peptide:MHC Binding 2. ex vivo stimulation
TESLA Functional Validation Methods
J. Heath, Caltech/ISB R. Schreiber, WUSTL P. Kvistborg, NKI
3. Tetramer detection (FACS) 4. NP-tetramer isolation
TESLA functional val idation methods
There is no “consensus” neoantigen identif icat ion pipeline.
Conclusions
• Only few steps were
commonly used across all
teams
• TESLA teams use a wide
variety of approaches, tools,
filters, etc. in their process.
• Most teams use 20-25
features for predictions
Manuscript in preparation Confidential – Do Not Post
• Overlap in ranked neoepitope are limited
• In the majority of the patients, reactive T cells could be detected against
some of the predicted neoepitopes
• Substantial variability exists between teams for predicted and ranked the
neoepitopes
o There are a set of teams that can consistently identify and rank highly peptide
MHC (pMHC) for which binding T-cells can be identified
o Filtering seems to play in important role in the quality of the results
• Pipelines are complex and diverse across teams
• Overall 600 pMHC were tested for T-cell binding in a tetramer-based
assays o 37 of these pMHC were found to have binding T-cells
o Majority of tested pMHC also have in-vitro MHC binding measurement
• in-depth analyses and elucidation of the key factors for accurate neo-
epitope prediction are underway
Summary
Outline of talk/examples of progressing IO
• End-to-end clinical trial analyses
• Approaches to think about (PD1) Resistance and Toxicities
• Approaches to think about neoantigen prediction (TESLA)
• Bringing data together across PICI to answer key questions
How we engage with sites and partners…
• Ask broad questions of interest to PICI and the field
• Find stakeholders who are interested in asking the question with us and can bring data and expertise to the table
• Align on a question of interest.
• Recent example:
“What features of the immune repertoire are associated with particular somatic tumor alterations, and how does this interaction shape the response to checkpoint inhibitor therapy?”
Bring PICI together to answer the question
PICI Informatics Brings Together:
MSK Melanoma:
• Clinical (R/NR)
• WES/TCR
MDA Melanoma:
• Clinical (R/NR)
• WES/TCR/RNA
MSK NCLC:
• Clinical (R/NR)
• WES/TCR
Tools from Broad
Institute:
• GATK, Mutect2,
Polysolver,
Oncotator,
Tools from
Stanford
Immunology:
GLIPH
(TCR Clustering)
• 1000s of unique samples
• Data from 13+ trials, 6+ published papers
• 300+ WES samples
• 1M+ TCR from 550 unique samples
• Bioinformatic methods for:
• Variant calling, HLA, HLA mutations. HLA copy
number, neoantigens, mutational signature ID,...
• TCR clustering
• Expertise in cancer immunotherapy:
• Renowned experts in cancer immunotherapy,
immunology, immunogenomics, …
Industry Partners
Funneling Data
300+ IO patients
Missing clinical data
Obviously incorrect
clinical data
Low TCR Quality
Low WES Quality
Final unified cohort
Singular patients incompatible with remaining cohort
TMB status is associated with TCR clonality in pretreatment.
Wells, et. al. 2019. In Preparation.
Bringing it all together: how does TMB shape TCR repertoire?
Summary:
• TMB High is associated with a “short, flat” repertoire
distribution
• TMB Low is associated with a “long, skewed” repertoire
distribution Wells, et. al. 2019. In Preparation.
Organize ourselves to answer broad questions working with PICI investigators + industry
oWhat are the genomic and immunologic features which predispose a patient to having an immune-related adverse event?
oWhich features of the pre-infusion product are associated with durable clinical response to cell therapy?
oWhat are the immune characteristics of the tumor microenvironment in castration-resistant prostate cancer, and how do they depend on previous treatment
oWhat are the molecular subtypes of cancer resistance to immune killing? How do these intersect with aPD1 therapy?
Data organized to enable the PICI community to ask further questions
• TCGA, published data, collaborator data, and PICI trial data.
Example question, answered in seconds:
“Which non-metastatic
head and neck cancer samples have
CNVs in PD-L1 and CD8 percentage > 15%?”
23,536 Samples
12,671 Subjects
33 Cancer Types
9 Technologies
286,954,730 Measurements
Thank you!
What about cell therapy?
Parker Institute: Next generation cell therapy
Novel CARs and vectors for clinical trials
NK cell evaluation and engineering
Endogenous T cell priming and therapeutics
CAR-T persistence and pediatric clinical trials
T cell trafficking and glioma targeting
Non-viral methods for T cell engineering
Novel cell therapy programs in GBM
CASSIAN YEE, MD
MD Anderson
CARL JUNE, MD
The University of
Pennsylvania
LEWIS LANIER, PhD
UCSF
HIDEHO OKADA, MD, PhD
UCSF
STEPHEN FORMAN, MD
City of Hope
CRYSTALL MACKALL, MD
Stanford Medicine
ALEXANDER MARSON, MD, PhD
UCSF
56
Efforts at PICI informatics are focused in 4 areas
Molecular Data Clinical Data
FLOW
FISH
SEQUENCING
MICROBIOME
IMAGING
CTMS
NOTES
REPORTS
SAMPLES/LIS
LABS
EMR
GENOMIC TESTS
DICOM
”Deep” Immune Profiling
From Tumor
From Blood
From both
GENETIC/
EPIGENETICS
WES
TCR
ATAC-seq
scATAC-seq
Methylation
PROTEIN
Cytokines
IHC (Vectra)
HD Imaging
HD FLow
GENE
EXPRESSION
Nano String
RNA Seq
scRNA-seq
GENE
EXPRESSION
Nano String
RNA Seq
scRNA-seq
MICROBIOME
Incorporate Prior Knowledge + Public Data
PRIOR KNOWLEDGE
PubMed
External databases
Past Sites’ Trials
Tech
Enthusiasts
Technology Evaluation & Standardization
Visionaries
Pragmatists
Conservatives
Skeptics
You can’t afford to ignore the outside world
Each sample is annotated with multiple molecular measurements (features)
Gene expression
(RNAseq)
Cytokines
(Luminex)
Cell populations
(CyTOF)
Responders
Non-responders
Categorical endpoint Continuous endpoint
Progression-free survival
No matter what, real-world data is incomplete
Features from all assays : 30,000 total
All
su
bje
cts
: 6
0 t
ota
l
Post-
treatm
ent.
P
re-t
reatm
ent
Number of patients
with all features: 8
The Core Questions
• How frequently do different mechanisms of immune evasion/suppression occur (in different cancers)?
• How do different mechanisms of immune evasion or suppression correlate (in different cancers)?
• How does aPD-(L)1 alter the tumor resistance
landscape?
• What are the subtypes of resistance to aPD-(L)1 therapy?