immunotherapy biomarker identification with ngs technology · 3 immunotherapy biomarker...

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Immunotherapy Biomarker Identification with NGS Technology

Andrew FeltonVP Marketing and Product Management, Ion Torrent Business

Monday, September 4, 2017

2


• Challenges with immuno-oncology clinical trials

• Ion NGS platform offers integrated solution for multidimensional approach

• Characterizing gene expression in tumor microenvironment for 36 immune response pathways

• Sequencing of T cell receptors to characterize immune status

• Characterizing tumor mutation burden to stratify clinical trial research samples

• Integrating biomarker research assays can improve clinical trial success

• Example 1: Can TRBV allele polymorphisms be potential future biomarkers for immune-mediated

adverse events?

• Example 2: How do immune cell co-infiltrates influence response to immunotherapy?

• Immunotherapy biomarker identification with NGS technology

3


Source: clinicaltrials.gov

Summary: pipeline radar chart

Recent

setbacks in

immuno-

oncology

clinical trials

4

… Opens Door to Important Questions for Any Trial Going Forward

Can characterizing

the tumor micro-

environment (TME)

predict immune

response?

Can we improve a

selection strategy

for immune therapy

clinical research

trials?

Can we identify

population subsets

that are

predisposed to

immune-mediated

adverse events?

5

No Single Biomarker for Mono and Combination Therapies Is Fully Predictive

Source: OmniSeq

Isolated use of individual

markers does not provide

confidence that all the bases are

being covered for a given patient.

100%

100%

Prevalence

Po

sit

ive

pre

dic

tive

va

lue

fo

r

ch

ec

kp

oin

t in

hib

ito

r re

sp

on

se

Patients positive by biomarker

Comprehensive immune profiling

Ideal

biomarker(s)

Microsatellite

Instability (MSI)

PD-L1/2 Copy

Number Variation

PD-L1 IHC

TPS >50%

Mutational Burden

(MuB) High

PD-L1 IHC

TPS >1%

CD3+/CD8+

Tumor-Infiltrating

Lymphocytes (TILs)

6

Tumor-Immune Environment Is Complex and Warrants Multimarker Approach

1

2

3

4

5

6

7

Gene expression of >350 immune-related

genes for tumor-infiltrating lymphocytes

and T cell receptor signaling

Up to 400 genes for

mutational burden

Microsatellite

instability (MSI) for

DNA mismatch repair

deficiency

Sequencing of T cell receptors

to characterize immune

repertoire of the sample

1. Release and capture tumor-associated antigens

2. Cancer antigen presentation

3. Priming and activation of effector T cells

4. Trafficking of cytotoxic T cells

5. Infiltration of cytotoxic T cells into tumor

6. T cell recognition / binding to T cell receptors (TCRs)

7. Killing of cancer cells

7

Ion NGS Platform Offers Integrated Solution for Multidimensional Approach

Can characterizing the

tumor microenvironment

(TME) predict immune

response?

Can we improve a

selection strategy for

immune therapy clinical

research trials?

Can we identify population

subsets that are

predisposed to immune-

mediated adverse events?

Sample prep AnalysisSequencing

Characterizing gene

expression in TME for immune

response pathways

Sequencing of T cell receptors

to characterize immune

repertoire of the sample

Characterizing somatic

mutations to assess

tumor mutation burden

RNA-Seq TCR-Seq DNA-Seq

+ +

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

8

Characterizing Gene Expression in TME for 36 Immune Response Pathways

Ion Torrent™ Oncomine™ Immune Response Research AssayRNA-Seq

*Genes associated with T cell activation in tumor-infiltrating lymphocytes (TIL)

• Profile 395 genes associated with

immune response

• Accurately quantify gene expression with

wide dynamic range

• Capture low-expressing genes (INFγ)

with greater sensitivity

The box-and-whisker plot depicts the log2 ratios of the

reads per million for the genes shown on the x-axis in 8

NSCLC samples.

The box represents the interquartile range, the line within

the box represents the median value, whiskers represent

the 10 values, and outliers are represented by dots.

9

Sequencing of T Cell Receptors to Characterize Immune Status

• Sequence information on variable gene segments covering CDR1, 2, and 3 regions

• Flexible input requirements covering samples with low, medium, and high clonal diversity

• Clonality assessment with high accuracy without interference from primer bias

Coming soon: Ion AmpliSeq™ Immune Repertoire Assay Plus, TCR βTCR-Seq

Constant

RNA/cDNALeader FR1 FR2

Diversity (D) Joining (J)

Variable (V) CDR3

FR3

CDR1 CDR2

Ion AmpliSeq™ primers ~325–400 bp

10

Rich Repertoire Analyses on Ion Reporter

QC metrics V-gene and allele identification

Clone sizes per variable gene

Clonotype identification

Perfect read

Quality

trimmed

Not full length

Read c

ount Representation of different alleles

Variable Joining CDR3 AA CDR3 NT Counts Frequency Rank

TRBV3-1 TRBJ2-3 ASSQDGGQNTDTQY

GCCAGCAGCCAAGATGGGGGA

CAGAACACAGATACGCAGTAT 421059 0.1626341 1

TRBV3-1 TRBJ2-1 ASSQQLGEQF

GCCAGCAGCCAACAATTAGGT

GAGCAGTTC 216586 0.0836564 2

TRBV11-2 TRBJ2-3 ASSLTALGRSPDTQY

GCCAGCAGCTTAACCGCCCTA

GGCAGGAGTCCAGATACGCAG

TAT 39654 0.0153164 3

TRBV28 TRBJ1-2 ASSLHHKSNYGYT

GCCAGCAGTTTACATCACAAAT

CTAACTATGGCTACACC 34338 0.0132631 4

TRBV29-1 TRBJ2-2 SIIIQNTGELF

AGCATCATAATTCAGAACACCG

GGGAGCTGTTT 24600 0.0095018 5

Expanded clones

In color

11

Characterizing Tumor Mutation Burden to Stratify Clinical Trial Research Samples

r = 0.9197

Correlation of somatic mutations

Tumor-normal analysis

Sin

gle

-sa

mp

le a

na

lysis

Mu

tatio

ns/M

b

Bla

dd

er

Bra

in

Colo

n

Hea

d &

ne

ck

Lu

ng

Ova

ry

Sto

ma

ch

Ute

rus

NC

I-H

64

7

Reproducible mutation counts

MSI MSS

P-value = 1.984 x 10–9

Mutation burden in MSI/MSS research samples

So

ma

tic m

uta

tio

ns


*The content provided herein may relate to products that have not been officially released and is subject to change without notice.

Tumor mutation burden analysis* (in development)DNA-Seq

• Accurate quantification of somatic mutations to assess tumor mutation burden in research samples

• Single-sample workflow (tumor only) with low input requirement

• Targeted NGS panel with high multiplexing ability

12

Integrating Biomarker Research Assays Can Improve Clinical Trial Success


*The content provided herein may relate to products that have not been officially released and is subject to change without notice.

Example 1: Can TRBV allele polymorphisms be potential future biomarkers for immune-mediated

adverse events?

Example 2: How do immune cell co-infiltrates influence response to immunotherapy?

Oncomine Immune Response

Research Assay

In development

Tumor Mutation Burden Analysis*

Introducing

Ion AmpliSeq Immune Repertoire

Assay Plus, TCR β

A more informative perspective

13

Example 1

Can TRBV allele polymorphisms be potential future biomarkers for immune-mediated adverse events?

Certain TRBV alleles may increase TCR recognition of auto-antigens

CDR 1 2 3Allelic variants alter

interaction of CDR1

and 2 with HLA

CDR

loops

TCR

HLA

Source: www.frontiersin.org/files/Articles/55250

September 6, 2017 | 2–4 p.m.

Room: Elicium 1 | Session: OFP-13 Molecular Pathology

Evaluating overlap of circulating and tumor-

infiltrating T cells using AmpliSeq-based Ion Torrent

TCR β immune repertoire sequencing

Dr. Tim Looney, Sr. Scientist, Thermo Fisher Scientific, USA

Rheumatoid arthritis

(McDermott 1995, Maskmowych 1992)

Multiple sclerosis

(Hockertz 1998, Hibbard 1992, Seboun 1989)

Narcolepsy

(Han 2013, Hallmayer 2009)

Type 1 diabetes

(Hughes, 2015, Pierce 2013)

Asthma

(Cho 2001, Moffatt 1997)

14

Example 2

Identifying immune cell co-infiltrates that influence T cell function in the tumor microenvironment

Repressive

tumor microenvironment

Permissive

tumor microenvironment

Inhibits T cell responses to tumor Permits T cell expansion and

anti-tumor activity

Myeloid suppressor?

High T cell evenness (Normalized Shannon entropy)

Low T cell evenness

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Example 2

Myeloid-derived suppressor cell signature correlates with T cell evenness


0.2

0.4

0.6

0.8

1.0

1

19 NSCLC biopsies

T cell evenness from

Ion AmpliSeq Immune Repertoire Plus, TCR β

1 = most even sizes

0 = least even sizes

Correlation with Oncomine Immune Response Research Assay

Pro

life

ratio

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Gene function correlation with clone evenness

Corr

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-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

Gene categories

16

Ion NGS Platform Offers Integrated Solution for Multidimensional Approach

An Algorithmic Approach to Cancer Immune Profiling

Conroy J1,2, He J1, Glenn S1,2, Pabla S1, Qin M1, Burgher B1, Giamo V1, Andreas J1, Gardner M1, Papanicolau-Sengos A1, Bshara W2, Omilian A2, Lugade A2, Kelly B3, Mongan A3, Pankov A3, Sun Y3, Ganpule G3, Morrison C1,2

OmniSeq LLC1, Buffalo, NY; Center for Personalized Medicine, Roswell Park Cancer Institute2; ThermoFisher Scientific3, Carlsbad, CA

Specimen Characteristics

Figure 2: Principal component analysis shows that sample characteristicsin the reference population have no significant impact on the threeexpresser groups.

Training PopulationBackground:Immune checkpoint blockade with monoclonal antibodiesdirected at the inhibitory immune receptors has emerged as asuccessful treatment for cancer patients. Evaluation of tumorcheckpoint blockade by IHC is subjective, not reproducible, andimportantly not scalable. We have designed a custom 64 geneRNA-Seq immune profiling panel that evaluate T-cell receptorsignaling (TCRS) and tumor infiltrating lymphocytes (TILs). Thecombination of TCRS and TILs along with mutation burden (MuB)was used as a multi-analyte algorithmic analysis (MAAA) todevelop a proprietary predictor of response to checkpointinhibitors (CPIs).

Methods:A custom 64 gene RNA-Seq immune profiling panel based uponthe OncomineTM Immune Response Research Assay and 1.5 MbDNA-seq of 400 known cancer genes was performed for >300FFPE samples that included lung cancer, bladder cancer, kidneycancer, H&N cancer, and melanoma. Subsets of samples wereused to develop a reference (n= 167) and training (n= 88)databases. The latter was used to develop a MAAA to predictresponse to CPIs. Predictions were reported for a continuousscale of 1-100 and grouped as 3 clinically relevant groups as high,indeterminate, or low overall response rate to CPIs.

Inter-run reproducibility.

Figure 1: Principal component analysis identified 3 subgroups in thereference population including low, indeterminate, and high expressers.

Reference Population

Reference Population

Tumor Type # Samples

Melanoma 86

Lung Cancer 50

Kidney Cancer 27

Bladder Cancer 3

H&N Cancer 1

Figure 4: Predictive value of the Immune Score for response to Check PointInhibitors: The clinical utility study included 88 patients with eithermelanoma, kidney cancer, bladder cancer, head and neck cancer, or NSCLCtreated with one or more FDA-approved CPIs.

o Immune Score >75 representing 26% of the tested populationhas a 95% predictive value for response to CPIs.

o Immune Score <50 representing 49% of the tested populationhas a 95% predictive value for no response to CPIs.

o Immune Score >50 to <75 representing 25% of the testedpopulation is an indeterminate score without benefit forpredicting response to CPI.

Immune Score

Training DatasetTumor Type # Samples

Melanoma 49

Lung cancer 21

Kidney cancer 16

H&N cancer 1

Bladder cancer 1

Clinical benefit # SamplesNo 56

Yes 32

Clinical benefit =

CR, PR, or SD with >1 year OS

CPI # Samplesnivolumab 30

pembrolizumab 25

ipilimumab 25

ipilimumab, nivolumab 7

nivolumab, ipilimumab 1

PCA Dimension 1: Specimen CharacteristicsVariable Correlation p.value

Normal Tissue 0.182 0.025Percent Necrosis -0.042 0.607

Percent Neoplastic -0.398 0.000Primary Metastatic Recurrent 0.043 0.037

Tissue Amount 0.045 0.074Tissue site 0.084 0.075

Specimen Year 0.120 0.142Specimen Type 0.049 0.185Normal Tissue 0.006 0.354

Tumor Type 0.029 0.357Architecture 0.020 0.391

Stroma Quality 0.006 0.636Stroma Quantity 0.002 0.971

Figure 3: ROC curve for Immune Advance shows an area under the curve (AUC) of >95%.

Performance of the Prediction Model

Conclusion:

o Immune Advance is pending submission to New York State ClinicalLaboratory Evaluation Program as high complexity test (MAAA).

o Additional training datasets will refine the accuracy of ImmuneAdvance for predicting response to CPIs.

Low Indeterminate High

p = 0.39

p = 0.97

p = 0.074

Specimen Characteristics

Indeterminate50-74

Non-Responders

100%

Responders

100%

High75-100

8.7% (2/23)

91.3% (21/23)93.0% (40/43)

54.5% (12/22)

100% 100%

45.5% (10/22)

Low0-49

7.0% (3/43)

Responders and Non-Responders by Immune Activation ScorePercentage of Samples, n = 88

NPV =PPV =

Responder and non-responder research samples by immune activation score

Immune score

Predictive value of the immune score for response to checkpoint inhibitors: The clinical

utility study included 88 tumor research samples with either melanoma, kidney cancer, bladder

cancer, head and neck cancer, or NSCLC, treated with one or more FDA-approved CPIs.


Figure courtesy of OmniSeq.

Sample prep AnalysisSequencing

Tumor Mutation

Burden Analysis

Predictive model

Oncomine Immune

Response Research Assay

Ion AmpliSeq Immune

Repertoire Assay Plus,

TCR β

17

Summary: Immunotherapy Biomarker Identification with NGS Technology


• Immunotherapies have the potential to positively impact patient response in the future

• There is much work to do to understand the variability in observed response among various types of cancer

research samples, to improve clinical research

• Progress in the field of immuno-oncology will rely on a multidisciplinary and multiparametric approach to unlock

a deeper understanding of the immune system in the context of a complex and heterogeneous disease

• To address the complexity of cancer and the immune system, Thermo Fisher Scientific offers multiple cancer

research assays to provide comprehensive coverage of targets associated with key genes in immune response

biomarker research, in order to:

• Better understand the tumor microenvironment

• Better stratify clinical trial research samples

• More accurately and rapidly determine response to therapy, and reduce adverse drug reactions

Appendix


20

Long-Read Capability Offers Advantages of Full V-Gene Characterization


CDR1 CDR2 CDR3

Clonotype

assignment

confidence score

• High throughput: >50,000 clones/sample generated from up

to 16 samples with hands-on time < 30 min, TAT under 48 hr

• Unbiased output: V-gene primers are optimized to minimize

bias and maximize coverage of possible rearrangements of the

VDJ region

• Comprehensive: up to 400 bp read length offers complete

characterization/sequencing of all 3 CDR domains (1, 2, and 3)

• Highly accurate: sequencing and amplification error correction

using proprietary statistical models leveraging unique insights

about TCR and BCR mRNA

21

Example 1

Can TRBV allele polymorphisms be potential future biomarkers for immune-mediated adverse events?

CDR 1 2 3

Allelic variants alter interaction of CDR1

and 2 with HLA

Certain TRBV alleles may increase TCR

recognition of auto-antigens, leading to

immune-mediated adverse events


Rheumatoid arthritis

(McDermott 1995, Maskmowych 1992)

Multiple sclerosis

(Hockertz 1998, Hibbard 1992, Seboun 1989)

Narcolepsy

(Han 2013, Hallmayer 2009)

Type 1 diabetes

(Hughes, 2015, Pierce 2013)

Asthma

(Cho 2001, Moffatt 1997)

Allele nameLocation of AA

variant

Number of research

samples having alleleIn Lym1k?

In NCBI NR

database?Potential genetic mechanism

TRBV11-2*32k FR3 1 No No

TRBV11-3*1k FR2 18 No YesGene conversion between

TRBV11-3*01 and *02

TRBV12-4*46k FR2 1 No NoGene conversion between

TRBV12-4*01 and *02


TRBV19*17k FR2 1 No No


TRBV24-1*1k FR2 43 No Yes



TRBV6-2*156kFR1, CDR1, FR2,

CDR2, FR31 No No

Gene conversion between

TRBV6-1*01 and TRBV6-2*01

TRBV6-5*106k CDR2 1 No No

TRBV11-1*11p CDR1, FR2/CDR2 1 Yes No

TRBV30*6p FR3 1 Yes No

TRBV5-5*9p FR3 2 Yes No

TRBV5-6*11p FR3 4 Yes No

16 of 85 research samples carry an uncommon, novel TRBV allele

22


Overview of Oncology Portfolio

Oncology research specimen

Routine research Translational

Molecular profiling solutions Liquid biopsy solutions Immuno-oncology solutions+ +

Full characterization of

oncology samples

Oncomine™ Focus Assay

Oncomine™ BRCA Research

Assay

Oncomine™ Comprehensive

Assay

Oncomine™ Solid Tumor

Research AssayOncomine™ Pan-Cancer

Cell-Free Research Assay

Oncomine™ cfDNA Assays

for Lung, Breast, Colon

Oncomine™ Mutation Load

Research Assay

Ion AmpliSeq™ Immune

Repertoire Assay Plus, TCR

Oncomine™ Immune Response

Research Assay

Commercial product

Product in development

Molecular profiling solutions

Liquid biopsy solutions

Immuno-oncology solutions

Oncomine™ assays are Ion Torrent™ Oncomine™ assays

Oncomine™ Lung cfDNA

Research Assay

Oncomine™ Breast cfDNA

Research Assay v2

immunotherapy biomarker identification with ngs technology · 3 immunotherapy biomarker...

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