The world leader in serving scienceFor Research Use Only. Not for use in diagnostic procedures.

World CDx Europe Panel Discussion: “The Future of Immuno-Oncology Testing in Europe”

Current practice, needs and future directions in immuno-oncology research testing

José Carlos Machado

Immune Therapies are Revolutionizing Oncology

Adapted from Hackl et al, Nat Rev Genetics 17:441-58 (2016)

Anti-PD-L1 in patients with NSCLC

Brahmer JR, et al. N Eng J Med 366, 2455-2465, 2012

Progression-free survival of NSCLC patients treated with anti-PD-L1 according to PD-L1 expression

Garon EB, et al. N Eng J Med 372, 2018-2028, 2015

RS Herbst et al. Nature 515, 563-567 (2014) doi:10.1038/nature14011

Programmed death-ligand 1 (PD-L1) prevalence and expression.

Cancer progression

Mutation load and survival in melanoma patients treated with immunemodulators

Snyder A, et al. NEJM 371:2189-2199, 2014

Mutation burden associated with clinical benefit of anti-PD-1 therapy in lung cancer

Rizvi NA, et al. Science 348, 124-128, 2015

Mutation load in tumors

LB Alexandrov et al. Nature 500:415-421, 2013

Oncomine Tumor Mutation Load Research Assay Workflow

For Research Use Only. Not for use in diagnostic procedures.* Two-day workflow requires overnight templating run.

DNA extraction from FFPE

AmpliSeq™ Library creation

Templating Sequencing Analysis

Invitrogen™ RecoverAll™ Total Nucleic Acid Isolation Kit for FFPE

Oncomine™ Tumor Mutation Load Research Assay Ion Chef™ System Ion S5™ System

Torrent Suite™

with Ion Reporter™

Two-day workflow *

409 genes (1.7Mb) for broad coverage

2 pool assay (DNA only) – 20 ng DNA in total

Configured for manual and automated library preparation

Optimized on Ion S5 and Ion 540 chip (8 samples/chip)

~60 Minutes Hands-On Time from DNA to Data, only 2 pipetting steps

Oncomine Tumor Mutation Load Research Assay

Optimized variant caller parameters (SNP calling at 5%)

Germ-line filtering using 1000 Genomes, 5000 Exomes, UCSC Common SNPs, ExAC databases

UI ReportAnalysis

Ion Reporter™

Variant Calling

Germ-line Filtering

Ion Reporter™

QC Run

Input BAM

from TS

Mutation Load per

Mb PDF Report,

Variant TSV

Single Sample (Tumor Only) Analysis

* Two-day workflow requires overnight templating run.

10ng

DNA /

pool

Mutation load associated with MSI status

CRC21(MSI-)

MSI-(CRC15)

MSI+(CRC5)

Somatic mutations type may indicate FFPE artefacts

C>T alterations are consistent FFPE processing (Wong SQ et al. BMC Medical Genomics. 2014)

(very) Preliminary results on therapy response

0

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LC1 LC2 LC3 LC4 LC5 LC6 LC7

nº

no

nsy

n.

mu

tati

on

s

84

Somatic mutations type may indicate smoking statusLung cancer smoker vs ex-smoker

IPA6-114 (smoker)

IPA6-67(ex-smoker)

C>A alterations are consistent with smoking damage (Alexandrov LB et al. Cancer Etiology. 2016)

(very) Preliminary results on therapy response

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LC1 LC2 LC3 LC4 LC5 LC6 LC7

nº

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Partial response Stable disease

Current needs

• A single test that detects all classes of genomic alterations for solid tumors.

• Results include microsatellite instability (MSI), tumor mutational burden (TMB) and other measurements, which may help inform immunotherapy decisions

• Sensitive and specific detection of alterations at low frequency

AmpliSeq TCR Beta Long Read Assay - CDR1, 2 and 3

RNA/cDNALeader FR1 FR2

Diversity(D) Joining (J) Constant

Variable gene (V) CDR3

FR3

CDR1 CDR2

AmpliSeq Primers ~325-400 bp

Spectratyping plot highlighting clonal proliferation in a severely inflamed distal CRC biopsy

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20

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60

80

TCRB V−gene usage and maximum clone frequency for TCR11_RNA_v1_20170824012056576C

DR

3 le

ng

th (

nt)

0.0

0.025

0.05

0.15

>0.20

Largest Clone Frequency

435002 readsClone Shannon Diversity: 7.1772Clone Evenness: 0.7102 1102 clones

Mean CDR3 NT length: 37.3449 +/− 7.5772V−gene Shannon Diversity: 4.28

V−CDR3 Frequency

0.10

0.05

●0.01

●0.005● ●

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The following slide will lookat this expansion in detail

CDR3 amino acid convergence within TME: Evidence for tumor antigen-driven T cell responses

Variable Gene

Joining Gene CDR3AA CDR3NT Freq

TRBV6-5

*01

TRBJ1-5

*01

ASSPSQNQPQH GCCAGCAGTCCGTCACAAAATCAGCCCCAGCAT .1257

TRBV6-5

*01B

TRBJ1-5

*01

ASSPSQNQPQH GCCAGCAGTCCTTCCCAGAATCAGCCCCAGCAT .0017

• Variable and Joining gene contribution to CDR3 highlighted in yellow and blue.

• Clones differ at positions deriving from addition of non-templated bases by TdT.

• This individual also possesses a synonymous allele variant of TRBV6-5 that is absent from the IMGT database (denoted *01B).

This tumor repertoire is enriched for T cells containing the ASSPSQNQPQH CDR3 amino

acid sequence. Two clones having this sequence were detected in this sample.

• We can quantify convergence using the formula:

where the total clone pairs for n clones is defined as C(n,2).

• The CRC repertoire shows greater evidence of antigen-driven TCR convergence than T cell repertoires derived from healthy donor PBL.

The CRC repertoire is highly convergent compared to healthy PBL repertoires

Identical Clone Pairs

Total Clone PairsConv =

CRC Healthy PBL

-7.0

-6.5

-6.0

-5.5

-5.0

Convergence Scores

for CRC and PBL samples

Con

verg

ence S

core

p=4.5E-6

• Libraries were prepared from 14 colorectal biopsies included 4 sets of paired samples from the same donor.

• Libraries were sequenced in multiplex. We expect to observe clone overlap between related sample pairs but not unrelated samples.

• We only observe overlap between related samples.

Clone overlap analysis identifies same-donor sample pairs

Morista clone overlap between 14 CRC biopsies

including 4 sample pairs

1.00

0.65

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CRC11_pair1

CRC18_pair1

CRC10_pair2

CRC3_pair2

CRC4_pair3

CRC5_pair3

CRC15_pair4

CRC2_pair4

CRC14

CRC9

CRC13

CRC1

CRC12

CRC16

0

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1

Oncomine Immune Response Research Assay Panel Content

Thermo Fisher All Rights ReservedFor Research Use Only. Not for use in diagnostic procedures.

Function Number of genes Function Number of genes

Antigen presentation 3 B cell marker 11

Antigen processing 19 Dendritic cell 7

Innate immune response 11 Dendritic cell, macrophage 6

Leukocyte inhibition 2 Helper T cells 8

Leukocyte migration 5 Macrophage 5

Lymphocyte activation 2 Myeloid marker 7

Lymphocyte development 3 Neutrophil 5

Lymphocyte infiltrate 46 NK activation 8

B cell receptor signaling 3 NK cell marker 4

T cell receptor signaling 6 T cell differentiation 2

T cell regulation 9

TCR coexpression 19 Checkpoint pathway 30

PD-1 signaling 9

Chemokine signaling 10 Drug target 21

Cytokine signaling 15

Interferon signaling 8 Adhesion, migration 14

Type I interferon signaling 8 Apoptosis 4

Type II interferon signaling 23 Proliferation 10

Tumor antigen 17

Housekeeping 11 Tumor marker 27

• 395 genes

• 394 primer sets

• 36 functional annotation groups

• Lymphocyte regulation

• Cytokine signaling

• Lymphocyte markers

• Checkpoint pathway

• Tumor characterization

• Housekeeping

Red arrows are EBV negative cases

Gene expression correlation of gastric cancer samples according to EBV status

Ion NGS Platform Offers Integrated Solution for Multidimensional Approach

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?

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

+ +

Thermo Fisher All Rights ReservedFor Research Use Only. Not for use in diagnostic procedures.

The cancer immunogram

Blank CU, et al. Science 352:658, 2016

Cancer immunotherapy and precision oncology

Adapted from Hackl et al, Nat Rev Genetics 17:441-58 (2016)