current practice, needs and future directions in immuno ......current practice, needs and future...

Current practice, needs and future directions in immuno-oncology

research testing

Jose Carlos Machado IPATIMUP - Porto, Portugal

ESMO 2017- THERMO FISHER SCIENTIFIC SYMPOSIUM

Immune Therapies are Revolutionizing Oncology erapy

Checkpoint inhibitors on market

• Ipilimumab (CTLA4)

• Nivolumab (PD-1) • Pembrolizumab (PD-1)

• Atezolizumab (PD-L1) Adapted from Chen and Mellman, Immunity 39:1 (2013)

Traditional pathway for chemotherapies and targeted therapies

Cancer vaccines and adjuvants

Chemokines and homing receptor modulators

Adoptive T cell therapies

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

Thermo Fisher All Rights Reserved

Hodi FS et al. N Engl J Med 2010;363:711-723.

Kaplan–Meier Curves for Overall Survival and Progression-free Survival in the Intention-to-Treat Population.

Anti-PD-L1 in patients with NSCLC

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

PD-L1 expression in NSCLC

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

<1% 1-49% >=50%

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

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.

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

Antitumour activity of MPDL3280A by immunohistochemistry (IHC) tumour-infiltrating immune cell (IC) and biomarker status.

Cancer somatic mutations

• On average > 10.000 mutations per case

• On average > 50 non-synonymous mutations per case

Non-synonymous mutations > neo-antigens

Mutation load and immunemodulation therapy benefit in patients with melanoma

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

Mutation load and survival in melanoma patients treated with immunemodulators


Association of a Neoepitope Signature with a Clinical Benefit from CTLA-4 Blockade


Clinical benefit of Pembrolizumab treatment according to MMR status

Le DT, et al. NEJM 371: 2509-2510, 2015

Current needs

• Better predictors to currently available immunotherapies. • Predictors to future immunotherapies targeting mechanisms

other than immune checkpoints. • Predictors that work in immunoedited tumours and in

immunosubversive tumours. • Assays targeting the tumour genome and the tumour immune

profile.

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 Analysis Sequencing

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

Characterizing Tumor Mutation Burden to Stratify Samples


For Research Use Only. Not for use in diagnostic procedures. *The content provided herein may relate to products that have not been officially released and is subject to change without notice.

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

• 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

Oncomine Immune Response Research Assay Panel Content

Thermo Fisher All Rights Reserved For 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

V(D)J Recombination Creates Tremendous CDR3 Diversity

• Chewback of the ends of the V-D-J

genes at the CDR3 junction

• Addition of non-templated bases (N-additions) by TdT

• Tandemly arranged variable, diversity and joining genes

Immune Repertoire: The collection of B and T cell VDJ rearrangements present in an individual

CDR3 CDR1&2


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

Leader FR1 FR2

Diversity(D) Joining (J) Constant

Variable gene (V) CDR3 FR3

CDR1

CDR2

AmpliSeq Primers ~325-400 bp

Immune Response Characterization Cell Characterization for T cell Therapies

Autoimmunity Biomarker Research

Thermo Fisher All Rights Reserved For research use only. Not for use in diagnostic procedures

• Simple and Flexible Workflow: Prepare libraries using from 10ng to 1ug of RNA starting material. Sequence up to 16 samples per chip with <48hrs turnaround.

• Unbiased output: V-gene primers are optimized to reproduce results from 5’RACE (no primer bias)

• Comprehensive: 400bp read length offers complete characterization of CDR1,2,3

• Highly accurate: Correction of sequencing and PCR errors leverages unique insights about TCR mRNA

Advantages

CDR1 CDR2 CDR3

Clonotype assignment confidence score


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

cou

nt Representation of different alleles

Variable Joining CDR3 AA CDR3 NT Counts Frequency Rank

TRBV3-1 TRBJ2-3 ASSQDGGQNTDTQY GCCAGCAGCCAAGATGGGGGACAGA

ACACAGATACGCAGTAT 421059 0.1626341 1

TRBV3-1 TRBJ2-1 ASSQQLGEQF GCCAGCAGCCAACAATTAGGTGAGCA

GTTC 216586 0.0836564 2

TRBV11-2 TRBJ2-3 ASSLTALGRSPDTQY GCCAGCAGCTTAACCGCCCTAGGCAG

GAGTCCAGATACGCAGTAT 39654 0.0153164 3

TRBV28 TRBJ1-2 ASSLHHKSNYGYT GCCAGCAGTTTACATCACAAATCTAAC

TATGGCTACACC 34338 0.0132631 4

TRBV29-1 TRBJ2-2 SIIIQNTGELF AGCATCATAATTCAGAACACCGGGGA

GCTGTTT 24600 0.0095018 5

Expanded clones In color


1. Characterize the tumor infiltrating T cell repertoire for a set of diverse CRC samples

2. Evaluate evidence for tumor antigen driven T cell expansion within TME

Evaluation of AmpliSeq TCR Beta Long Read Assay Objectives

Sample Reads Clones Shannon diversity Evenness TCR1 328291 1151 79487 0.7817 TCR2 691712 3960 88760 0.7427 TCR3 717124 4270 102732 0.8518 TCR4 254859 775 72418 0.7545 TCR5 750191 2121 72314 0.6544 TCR6 38932 359 75725 0.8922 TCR9 204565 1303 92901 0.8978

TCR10 206573 1492 89373 0.8477 TCR11 435002 1102 71772 0.7102 TCR12 105172 510 76904 0.8550 TCR13 1128870 3044 88973 0.7689 TCR14 403981 2490 98213 0.8705 TCR15 705170 2971 87989 0.7627 TCR16 719898 1641 73977 0.6926 TCR17 23722 502 73939 0.8242 TCR18 264786 1345 85182 0.8196

TCRbeta sequencing of fresh frozen CRC samples

Definitions: CRC

• Mild • Moderate • Severe

• Still waiting for the Pathologist to provide me the proper

description.

Description of inflammation grading methodology

• T cell clone refers to the set of T cells having the same VDJ rearrangement. They are related to each other by descent.

• Evenness is a measurement of the similarity of clone sizes. It is derived from the Shannon Diversity of the clone population. – Evenness of 1 indicates that all clones

are found at the same frequency. – Evenness approaches 0 if there are a

small number of dominating clones.

Definitions: T cell clone richness and evenness

High Evenness Low Evenness

V D J


Inflammation classification correlates with T cell clone richness

Clones detected vs inflammation status for 20 CRC

biopsies

* p=.038

T cell clone richness is elevated in distal CRC biopsies

Clones detected vs tumor localization for 20 CRC

biopsies

Sample 13 is outlier

No correlation of clone richness with tumor grade

Clones detected vs tumor grade for 20 CRC biopsies

Minimal T cell infiltration detected in metastatic CRC Clones detected vs

metastatic status for 20 CRC biopsies

*** p=.0001

TCR11 same patient as TCR18 but different block

• Tumor neoantigens within the TME may stimulate the proliferation or recruitment of T cells possessing a specific CDR3 amino acid binding motif. – Due to the degeneracy of the amino acid code, T cell clones having the same

CDR3 amino acid sequence may have different CDR3 nucleotide sequences.

• This phenomenon is often described as a “convergent” T cell response to

antigen.

• We can identify such events using TCR repertoire profiling.

Objective 2: Detecting tumor antigen driven T cell expansion

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

The following slide will look at 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.

Preliminary (very) conclusions

• The TCR repertoire in CRC recapitulates differences in tumor inflammation.

• There is evidence of convergence of T cell selection towards specific antigens.

Immuno Oncology Consortium

For research use only. Not for use in diagnostic procedures

Thermo Fisher Scientific and its affiliates are not endorsing, recommending, or promoting any use or application of Thermo Fisher Scientific products presented by third parties during this seminar. Information and materials presented or provided by third parties are provided as-is and without warranty of any kind, including regarding intellectual property rights and reported results. Parties presenting images, text and material represent they have the rights to do so.

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