Keeping Pace with Immuno-Oncology Breakthroughs and .../media/q2labs/library/presentation… · innovation, FDA collaborations, operational excellence and Bioinformatic Expertise

Copyright ©2016 Q2 Solutions. All rights reserved.

COMPANY CONFIDENTIAL

Keeping Pace with Immuno-Oncology Breakthroughs

and Biomarker Identification

Adrian Benjamin, Sr. Commercial Development Manager, Oncology, Illumina

Kimberly Robasky, Ph.D., Lead Scientist, Bioinformatics and Clinical Systems, EA Genomics, a Q2 Solutions Company

2 COMPANY CONFIDENTIAL

• Immunotherapy Overview

• Immuno-Oncology relevance

• RNA-Seq Applications, comparison to other technologies

• Assay reports

Overview


Immunotherapy Overview


Growing Interest in Immunotherapy Publications in immunotherapy by year since 2000

Source: Thompson Reuters ISI Web of Science (search: immune+cancer+sequencing+clinical from 2000-2016).

600

500

400

300

200

100

0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015


Classification of Current Anticancer Immunotherapies

Reference: Galluzzi L, Vacchelli E, Bravo-San Pedro JM. Classification of current anticancer immunotherapies. Oncotarget. 2014;

5(24):12472-12508.


Immunotherapy in Cancer Offers Tremendous Promise


But the Data Often Reveal Variability in Patient Responses

A study of CTLA4 blockade via the mAb ipilimumab yielded a response rate of only 20%

Source: Van Allen et al. Science. 2015;350(6257):207-211.

However, the 20% who responded experienced a long-term clinical benefit


Oncology’s Complexity Requires Comprehensive Understanding

Immunology

Tumor & Cell Biology

Protein Chemistry

Genomics

Microbiology

Genetics

Clinical

Oncology

Researcher

For Research Use Only. Not for use in diagnostic procedure.


NGS is Helping to Answer Complex Biological and Clinical Questions



Relevance


Relevance: Oncology Drugs in the Market

• Over 800 drugs in trials

• Challenges:

> Early detection

> Drug resistance

• Immunotherapies hold great promise but still

have the following challenges:

> Response rates

> Toxicities

• Molecular testing can determine, per-patient,

which therapy will be:

> Most effective

> Safest

Therapy Response

Rate

Toxicity Long term

Survival

“Standard”

chemotherapy

Lower Higher Lower

Targeted

chemotherapy

Higher Lower Moderate

Immunotherapy Moderate Lower Higher

Source: Sharma, Allison: Cell, April 2015


Relevance: Clinical Results Molecular testing is the foundation for targeted therapy

Personalized/ Precision Approach

Prescreened

Population

Predictive Bio-

marker Testing

Responders

Adverse Event

Patients

Non-responders

Source: http://www.personalgenome.com/translating-cancer-genome-analyses

McDermott et al, N Engl J Med. 2011 Jan 27;364(4):340-50

Of the 189 oncology therapies currently

under development in the period of 2012-

2015, 56% have an associated biomarker.

/wikipedia/commons/4/44/Sanger_sequencing_read_display.gif

http://www.personalgenome.com/translating-cancer-genome-analyses









http://www.ncbi.nlm.nih.gov/pubmed/21268726




2011 2012 2013 2014 2015 2016 Today

Interest over past 5 years***

Relevance: Emerging Opportunities in Immuno-Oncology

• 546 registered clinical trials relevant

to PD1/PDL1 checkpoint inhibitors*

• Increasing number of relevant

publications annually**

**PubMed; keywords ‘cancer immunotherapy

*** Google Trends, 23-Apr-2016’ keyword immunotherapy

Source: Larkin et al, N Engl J Med 2015; 373:23-34

*Cancer.gov NCI-supported trials, keywords ‘PD1,PDL1,PD-1,PD-L1’ (Apr 2016)


Genomics provide quantitative and economic advantages for biomarker analysis

Shift toward genomics to bolster other methodologies with broadly quantitative

accuracy, sensitivity, and high throughput

Requirement Technology/Method

Somatic Mutation Analysis DNA deep Sequencing, Breakpoint

analysis and Fusion Detection

Gene Expression Profiling RNA-Seq, Arrays, RNA panels

Germ Line Variant Analysis CNV, Structural and Small Variant

Detection

HLA Characterization HLA Calling (DNA, RNA, Arrays)

Antigen-Specific Immune

Response

B/T Cell Repertoire (DNA/RNA),

VDJ mutation (RNA)

Relevance: Economic Efficiencies


Relevance: Drug Development I/O assays find biomarkers for biological understanding as well as population profiling

Tumor – Immune Biology

• Target protein expression – IHC

• Serum proteins – ELISA

• Circulating cell populations – Flow

• RNA expression – Multiplex PCR, RNA-Seq

Patient – Tumor Profile

• Transcriptome – RNA-Seq

• Serum proteins – proteomics

• Genomics– NGS


Complementary Technologies


Complementary Technologies Value proposition

RNA-Seq

Response Signature

Neoantigen/HLA biomarkers

Prioritized Biomarker Activities 1. Develop next-gen IHC tests

2. RNA-Seq – gene expression analysis

3. Immunoassay serum protein proteomics

4. Flow cytometry immune cell population profiling

5. Genomic profiling for DNA repair defects/mutation

load and microbiome profile

IHC Test

Multiplex IHC Test

Targeted RNA-Seq

Immunoscore

Patient Monitoring

Flow cellular profiling (blood)

Clinical Trial

Assays and

Diagnostics

CRO & Dx

Manufacturer

Partnerships

Preclinical &

Clinical

Research

Patient

Samples &

Clinical Data

Novel Drug Combination w.r.t. time

NGS IO CDx

Genomic profiling

Microbiome

TIL Analysis

Serum proteomics

Immunoassay Test

1

2

3

4

5

Opportunities for synergies

Non-biomarker research & bioinformatics

Bioinformatics


Assay Reports


RNA-Seq (targeted and transcriptome) applications in I/O:

• Gene signature – Breast Cancer/prognostic of overall survival

• IGHV mutation status

– CLL/associated with overall survival and drug response

• Immune repertoire

– Se´zary Syndrome (a type of T-Cell Lymphoma) immune response biomarkers

• HLA alleles

– Biomarkers for autoimmunity and other adverse reactions

• Neoantigens/neoepitopes – Researchers find cytolytic expression, neoantigen and mutational load were significantly

associated with clinical benefit

Assay Reports


Assay Report: Expression Profiling Immunity status and Immune Checkpoint gene expression signatures

Source: Iglesia et. al. (2014) Clinical Cancer Research 20(14):3818

Basal-like

Luminal

IGG

B Cell

CD8

TCell

CD68

MacTh1

Immune Checkpoint expression cluster together


Assay Report: Expression Profiling Immunity status and Immune Checkpoint gene expression signatures

B-cell gene signature more significantly associates

with improved Overall Survival

than classical clinical variables.

Source: Iglesia et. al. (2014) Clinical Cancer Research 20(14):3818


Assay Report: Immune Repertoire and IGHV Patients with IGHV type = “mutated” have better overall survival

Source: Zenz, T et al. “From pathogenesis to treatment of chronic lymphocytic leukaemia” Nature Reviews Cancer 2010 (10) 37-50.


Assay Report: Immune Repertoire and IGHV IGHV type = “unmutated” status is correlated with response to Ibrutinib

Source: Byrd JC et al. N Engl J Med 2013;369:32-42,

Dias et al. (2016) Cardiovascular & Hematological Agents in Medicinal

Chemistry 11.4 : 265–271

.

“The only factor

associated with a

response was the

mutation status of

the IGHV. ”

-- Dias et al


Assay Report: Immune Repertoire and IGHV Healthy appears “mutated” and clonally diverse

Source: Brown et al. Presented at AMP 2015


Assay Report: Immune Repertoire and IGHV IGHV typing conveys relative health of cellular receptors

Low diversity



Assay Report: Immune Repertoire and IGHV Sanger can only report consensus sequence, misses less frequent mutation events



Assay Report: Immune Repertoire RNA-Seq (“TCR-LA-MC PCR”) for understanding T-cell response

• Pathogenesis unknown, no reliable diagnostic biomarkers

• Targeted RNA Sequencing elucidates restricted clonal repertoire

• Promise for diagnostic biomarker development

Source: Ruggiero et al. (2015) Nature Communications 6. 801


Assay Report: Identifying Self vs. Non-self (HLA genes)

• Post-treatment autoimmunity

or hepatotoxicity

• As much as 30%

immunotherapy patients can

develop autoimmunity and

DILI is the top cause of post

market withdrawal.

• Biomarker analysis can

identify adverse drug

reaction associations in the

affected population

• HLA is a challenging

genomic locus to analyze

but has turned up many

automimmunity associations

and putative risk alleles.

Situation

Solution

Drug Safety Issue Biomarker

Abacavir Hypersensitivity HLA-B*57:01

Carbamazepine SJS/TEN HLA-B*15:02

Phenytoin SJS/TEN HLA-B*15:02

Lumiracoxib Hepatotoxicity HLA-DQA1*01:02

Lapatinib Hepatotoxicity HLA-DQA1*02:01

Lumiracoxib Hepatotoxicity HLA-DQA1*01:02

Flucloxacillin Hepatotoxicity HLA-DRB1*15:01

Amox/clav Hepatotoxicity HLA-DQA1*02:01

Ximelagatran Hepatotoxicity HLA-DRB1*07:01

Ticlopidine Hepatotoxicity HLA -A*33:03

Clozapine Agranulocytosis HLA-DRB5*02:01

Allopurinol Skin reactions HLA-B*58:01

Erythropoietin PRCA HLA-DRB1*9

Source: Kaur et al, Presented at ACR 2015


Emerging Technologies for HLA Analysis Targeted solutions and mining of genomic profiling data

• PCR-based amplification

• Choice of Class I and Class II genes

Target HLA

• Prepare NGS library

• Sequence using Illumina MiSeq*

Next Generation Sequencing

• Genotyping Results

• HLA allele assignment

• Can identify new alleles

Data Analysis

• Start with total RNA

• HLA genes are expressed in most tissues

RNA Sequencing

• Prepare NGS library

• Sequence using Illumina HiSeq*

Next Generation Sequencing

• Whole transcriptome for biomarker discovery and evaluation

• HLA determination

Data Analysis

• Defined content GWAS array

• Includes HLA loci

Axiom BioBank

Genotyping Array

• Standard Affymetrix genotyping chemistry

• Automated system

GeneTitan Workflow

• Genotyping Results suitable for GWAS

• HLA allele assignment

Data Analysis

Targeted Next Generation

Sequencing • Highly concordant with reference

methods

RNA Sequencing • Add-on analysis to standard

RNA Sequencing analysis

• HLA analysis in addition to

transcriptome profile

• FFPE with total RNA

Genotyping Array • Add-on analysis to Axiom

BioBank Genotyping Array

• HLA analysis in addition to

genotyping data for GWAS

*For research use only. Not for use in diagnostic procedures


RNA-Seq DNA-Seq/MiSeq

75bp 50bp

HLA-A 98.6% 97.3% 99%

HLA-B 94.0% 94.0% 99%

HLA-C 98.2% 94.6% 99%

HLA-DQB1 70.0% 80.0% 99%

HLA-DRB1 90.5% 93.2% 95%

Overall 89.2% 91.3% 98%

Assay Report: Identifying Self vs. Non-self (HLA genes) Common HLA alleles can be determined as a cost-efficient add-on to expression profiling

Concordance with gold standard, 40 Lymphoblastoid cell lines (LCL).

HapMap samples were procured from the gEUVADIS project and clipped using

Q2 Solutions standard delivery pipelines. Both datasets use paired-end reads, the 50bp

dataset is created from trimming the 75bp dataset. Results were compared to SBT.

Source: Robasky et al. (2016) AACR Poster #412


Assay Report: Neoepitope Identification Neoantigen identification using RNA-Seq and complementary technologies

Source: Van Allen et. al. (2015) Science 350(6257):207

• In most cases, somatic mutation calling

from Exomes provides early evidence of

malformed antigens

• Coupling with RNA-Seq is essential to

confer expression of that variant allele

• Clinical group cluster by mutational

burden within antigens

• Creating custom antibodies to restore

recognition increases survivability


Conclusions


Routinely collected clinical samples

Data can be mined for molecular immune-oncology characterization

Genomics in Immuno-Oncology Innovation New opportunities for molecular characterization

Samples Analysis Potential Application How these characterizations can be used in drug studies

Saliva

Blood

Biopsy

Slides

Self-recognition HLA and KIR genotyping

Immune activation B and T-cell repertoire,

Immune gene signature

Tumor

characterization DNA and RNA

Cancer Vaccines and Tumor-

Specific Immune Responses,

(including neoantigen targets

i.e., NY-ESO-1, MAGE-3, gp100)

Optimized Patient Selection

Refinement of Immuno-

modulatory Therapies

Exploitation of Innate and

Adaptive Immune Response to

Tumors ( including immune

checkpoint targets, e.g., CTLA4,

PD1/PDL1, IDO1, OX40)


• RNA-Seq is relevant to the market, to drug development and clinical trials

• RNA-Seq has strong value propositions in applications, as a stand-in for other

technologies, and also as a complement to other technologies

• RNA-Seq has proven value in immuno-oncology applications

Conclusions


36

Q2 Solutions EA Genomics A comprehensive suite of genomic services supports your clinical trial and research needs

Genomic Know-How ® for your drug development needs

Bioinformatics Nucleic Acid

Isolation

2nd & 3rd

Generation

Sequencing

Expression

Profiling

Genotyping &

Copy Number

Variation

Sequence

Enrichment

Our Key Differentiators

• Key Opinion Leader with history of

innovation, FDA collaborations, operational

excellence and Bioinformatic Expertise

• Technical Expertise across broad

technological platforms offering a

comprehensive genomic service solution in

clinical trials and research

• Best in class infrastructure with robust and

proven quality system and the first

microarray facility in industry to implement

GLP compliant procedures

• Consultative and solutions based, leading to

longstanding relationships with top pharma

and biotech companies

• CLIA certification in 2010

Copyright ©2015 Q2 Solutions. All rights reserved.

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Keeping Pace with Immuno-Oncology Breakthroughs and .../media/q2labs/library/presentation… · innovation, FDA collaborations, operational excellence and Bioinformatic Expertise