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Immuno-Oncology Solutions

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• Our I-O Technology Platforms and Service Portfolio

• Highlighted Services:

• I-O Biomarker Discovery & Clinical Applications

• Neoantigen Identification & Clinical Applications

• Regulatable CAR-T Development

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ICI Efficacy Prediction

Cancer Vaccine

Checkpoint Inhibitor & drug targeting

Regulatable CAR-T

Neoantigen Identification

Immune Repertoire

• Tumor Mutational Burden• Microbiome• dMMR, MSI-H• Checkpoint Inhibitor

Expression

• Neoantigen• Immune Repertoire• MHC Binding/Prediction• Epigenetic Analysis

• Transcriptome Seq• Exome Seq• Epigenetic Analysis• Single-cell profiling• MHC Binding• scRNA-seq

AptaNxTM RegCAR-TTM

Biomarker Discovery

• Exome Seq• Transcriptome Seq• Epigenetic Analysis • HLA Typing

• Exome Seq• Transcriptome Seq• Epigenetic Analysis• AptaNxTM

• Immune Repertoire• MHC Binding• Checkpoint Inhibitor

Discovery• Exome Seq• Transcriptome Seq• scRNA-seq

Solution

Technology

DiscoveryClinical

Translation

Therapeutics

Immuno-OncologySolutions

Legend

Stem cell Transplantation

• HLA Matching• Transcriptome Seq

Minimal Residual Disease • Transcriptome Seq

• Exome Seq

Tumor Escape & Resistance

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Distribution and Growth of Cumulative Immuno-Oncology Biomarker

Mentions by Test Purpose 2014 – 17Growth of Cumulative Mentions of Top 30 Immuno-Oncology Biomarkers; 2014 – ’17

https://www.decibio.com

Increasing Importance of Biomarkers in I-O

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J Yuan et al. Journal for ImmunoTherapy of Cancer20164:3

Biomarker Solutions for Personalized Immunotherapy

Technology

Whole Exome Sequencing

Gene signature/RNA Seq

Epigenetic Analysis

Antigen/Neoantigen Identification

B/T-cell receptor repertoire

Flow cytometry/WES/RNA Seq

Multicolor IHC

Therapeutic strategy

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Putative I-O Biomarkers in the TME

• PD-L1 expression

• Tumor-infiltrating lymphocytes (TILs)

• Mutational load and neoantigens

• Immunosuppressive cell types

• Macrophage and DC polarization

• Immunosuppressive molecules

• Cytokine signatures

Tumor cell Dead tumor cell MDSC CD8+ T cells CD4+ T cells Immature

dendritic cell

Primed

dendritic cell

M1

macrophage

M2

macrophage

PD-L1 PD-1 MHC I CTLA-4 TIM-3 LAG-3 Tumor Neoantigens IDO IFNγ M-CSF T-regulatory cell

antigens

© 2017 American Association for Cancer Research

M1

M2

IFNγ

M-CSF

iDC

CD8+

CD8+

CD8+

MDSC

CD8+

MDSC

MDSC

TIL

TIL

TIL

TIL

IDO

M2

IDO

iDC

Mutational

load

pDC

CD4+

Treg Treg

Multiple Biomarkers Needed for Understanding TME

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Clinical Application Examples

Efficacy Biomarkers of ICI (Immunotherapy Checkpoint Inhibitors)

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Barnhart C. J Adv Pract Oncol. 2015 May-Jun;6(3):234-8.

PD-1 inhibitors:• Pembrolizumab (Keytruda)

• Nivolumab (Opdivo)

PD-L1 inhibitors:• Atezolizumab (Tecentriq)

• Avelumab (Bavencio)

• Durvalumab (Imfinzi)

CTLA-4 inhibitors:• Ipilimumab (Yervoy)

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28%

18%

27%

Melanoma NSCLC Renal-cell cancer

Response rate (Nivolumab)

33%

27%

13%

Melanoma NSCLC PD-L1–positive endometrial

cancer

Response rate (Pembrolizumab)

10.90%

Melanoma

Response Rate (Ipilimumab)

SL Topalian, FS Hodi, JR Brahmer , etal N Engl J Med 366: 2443– 2454,2012

A Ribas, et al. JAMA. 2016 Apr 19. doi: 10.1001/jama.2016.4059

R Hui, EB Garon, et al. Ann Oncol. 2017 Apr 1;28(4):874-881. doi: 10.1093/annonc/mdx008.

Ott et al. Journal of Clinical Oncology 35, no. 22 (August 2017) 2535-2541.

F Stephen Hodi et al. N Engl J Med. 2010 Aug 19;363(8):711-23. doi: 10.1056/NEJMoa1003466.

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• TMB (Tumor Mutational Burden)

• RNA signature

• PD-1, PD-L1 expression

• dMMR, MSI-H

• Microbiome

• Neoantigen

Hugo W. et al. Cell. 2017;168:542. doi: 10.1016/j.cell.2017.01.010.

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Snyder, A. et al. N. Engl. J. Med. 371, 2189–2199, doi:10.1056/NEJMoa1406498 (2014).

There was a significant difference in mutational load between patients with a long-term clinical

benefit and those with a minimal benefit or no benefit.

Our Solution:

TMB analysis by WES (Whole Exome Sequencing) or our OncoGx gene panel

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Hugo W. et al. Cell. 2017;168:542. doi: 10.1016/j.cell.2017.01.010.

Identification of transcriptomic features (IPRES:

innate anti-PD-1 resistance) associated with anti-

PD-1 resistance

Our Solution:

RNA expression signature analysis by RNA-Seq

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Sunshine, J. & Taube, J. M. 23, 32–38, doi:10.1016/j.coph.2015.05.011 (2015).

Association of PD-L1 expression in pre-

treatment tumor specimens with

objective response to anti-PD-1/PD-L1

therapy

Our Solution:

Expression analysis of PD-1 and PD-L1

in tumor tissue by IHC

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Le DT, et al. N Engl J Med. 2015;372:2509–2520. doi: 10.1056/NEJMoa1500596.

Mismatch repair–deficient tumors are more responsive to

PD-1 blockade than are mismatch repair–proficient tumors

Our Solution:

MSI-H and dMMR status testing by our MSI-H/dMMR or

OncoGx gene panels.

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Metagenomics of cancer

patient stools revealed

correlations between clinical

responses to ICI.

Routy B. et al. Science. 2017 Nov 2. pii: eaan3706. doi: 10.1126/science.aan3706.

Our Solution:

Gut microbiome analysis by metagenomics

or our FloraCheck™ assay.

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A peptide signature from the candidate neoepitopes is generated. This set of neoepitopes

defines a signature linked to a benefit from CTLA-4 blockade.

Snyder, A. et al. N. Engl. J. Med. 371, 2189–2199, doi:10.1056/NEJMoa1406498 (2014).

Our Solution:

Neoantigen signature analysis by WES (whole exome sequencing),

RNA seq, MHC binding prediction and bioinformatics analysis

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• Solid tumor test panel designed to provide comprehensive genomic analysis for

cancer therapy

• Provides clinically actionable genomic aberrations as well as HLA-typing and

MMR (mismatch repair) information

• 333 genes

• point mutations, small insertions/deletions, fusions, copy number variations

• Covers all coding exons and UTRs, as well as select intronic regions

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Matched

normal

Tumor

Sample

WES data from 376 TCGA COAD tumor samples

Somatic

variant data

from TCGA

TCGA

pipeline

OncoGxOne

Plus

pipeline

All variant data

without matched

normal

● # of potential somatic variants (n_somatic)

● # of deleterious variants (n_deleterious)

● # of CADD-score high variants (n_CADDphred20)

● # of COSMIC variants (n_COSMIC)

● # of MMR damaging variants (n_MMR)

● existence of BRAF V600E (V600E)

333 genes

w/o matched normal

WES

w/ matched normal

# of somatic variants

/ MB

prediction

Filter out low confident variants

(DP, AD, QUAL, Allele Freq.)

Filter out common variants

(Pop. Freq.: ExAC, 1000GP, ESP6500)

Filter out variants in intronic regions

Filter out homozygous germline variants

(Allele Freq. >0.9)

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TMB-H (73)

TMB-L (303)

10 somatic mutations / MB

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● # of potential somatic variants (n_somatic)

● # of deleterious variants (n_deleterious)

● # of CADD-score high variants (n_CADDphred20)

● # of COSMIC variants (n_COSMIC)

● # of MMR damaging variants (n_MMR)

● existence of BRAF V600E (V600E)

# of somatic

variants

/ MB

SVM classifier

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Fold 0

Fold 1

Fold 2

Fold 3

Fold 4

Training Validation

# of TMB-H samples

# of TMB-L samples

Cross validation design

Accuracy:

0.95 ±0.04

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• High accuracy (95% vs 90% F1CDx)

• Cost effective (targeted panel vs WES)

• Fast TAT (7-10 Days)

• Without matched normal

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Highlighted Service II

Neoantigen Identification & Clinical Applications

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Neoantigen in Cancer Immunotherapy

Ton N. Schumacher, Robert D. Schreiber Science 03 Apr 2015: Vol. 348, Issue 6230, pp. 69-74

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Solution for Neoantigen Discovery

H. Hackl, et al. Nature Reviews Genetics 17, 441–458 (2016)

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• Whole-exome sequencing (WES) - identified neoantigen

• RNA-seq - Validate and assess the expression of neoantigen

• HLA binding - predict

• Vaccine synthesize and administration

At a median of 25 months after vaccination

4 patients: no disease recurrence

2 patients with lung metastases: disease recurrence

ICI treatment

complete responses

Ott PA et al. Nature. 2017 Jul 13;547(7662):217-221..

Cancer vaccineClinical Application

ConfidentialHinrichs CS. et al. Immunol Rev. 2014 Jan; 257(1): 56–71

Clinical Application

Tumor

Normal TissueWES

RNA Seq

Non-synonymous Mutation

Expression ConfirmHLA Typing

MHC Binding Neoantigen

T-cell Activation

Reintroduce to Patient

TCR Clone/Construct

T-cell Expression

Transfect to T-cell

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DMM classification analysis of 35 cancer samples based

on SNV, Indel, fusion, CNV, total mutation loading for

each mutated gene and overall mutated genes.

OncoGxOne PlusTM panel for mutation analysis

Tang et al. Chin Med Biotechnol, April 2016, Vol. 11, No. 2

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333 oncogene heatmap with clustering from 35

cancer samples (labelled with cancer clinical

type, MMR deficiency type and DMM groups)

Barplot of HLA-I genes mutation loading of each

samples (Line indicated the HLA-I mutation

loading was 6 mutations)

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HLA-I genes mutation loading statistical analysis in DMM groups from 35 cancer samples

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ID LQL SJS HJL LKS

Cancer type Lung adenocarcinoma Esophageal cancer Renal pelvic carcinoma Lung adenocarcinoma

MMR Mutation

MLH1 5 0 0 0

MSH2 4 0 0 0

MSH6 14 0 0 0

PMS2 7 0 0 0

MMR result* 1 0 0 0

Mutation loading 3243 86 130 141

Neoantigen prediction

Point mutation improved MHC-I - 17 19 24

Neoantigen improved TCR affinity - 3 6 6

HLA-I gene mutation loading

HLA-A 20 4 1 1

HLA-B 14 1 0 0

HLA-C 21 3 1 1

B2M 2 0 0 0

DMM 1 1 0 0

Clinical data

PD-1（X/times） 3X - 5X 5X

MASCT（X/times） 4X 3X 5X -

Response# PD PD PR PR

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• Neoantigen prediction

• HLA-I genes mutation loading evaluation

• Patients predicted as for their response for MHC-I

restricted immunotherapy

• Precise immunotherapy through NGS for cancer

associated mutation

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Confidential 37

CAR generation 1st 2nd 3rd

Chronology 1989 2002 2009

Li H et al,2017, PMID: 28434147

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Clinical trial 1

63 r/r B-cell ALL

(3-21 yrs old)

Clinical trial 2

101 NHL

(77 DLBCL + 24 TFL/PMBCL)]

CR: complete remission; ORR: objective response rate; NHL: Non-Hodgkin's Lymphoma;

DLBCL: Diffuse Large B-Cell Lymphoma; TFL: transformed follicular lymphoma;

PMBCL: Primary Mediastinal Large B-Cell Lymphoma

Data from public news release

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Clinical Trial 1

63 patients with r/r B-cell ALL

(3-21 yrs old)

Clinical Trial 2

101 patients with NHL

(77 DLBCL + 24 TFL/PMBCL)

CR: complete remission; ORR: objective response rate; CRS: cytokine release syndrome

ALL: Acute Lymphoblastic Leukemia; NHL: Non-Hodgkin's Lymphoma;

DLBCL: Diffuse Large B-Cell Lymphoma; TFL: transformed follicular lymphoma;

PMBCL: Primary Mediastinal Large B-Cell Lymphoma;

Data from public news release

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ON-switch OFF-switch

Li H et al,2017, PMID: 28434147

Dose tuning to reduce CRS and avoid long-term B-cell aplasia while maintaining CAR-T

efficacy (PMID: 26759369; 26759368).

Tagged Ab

Tumor cell

Y

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• Limited options of the split proteins with the capability of

chemical induced dimerization (CID)

• The function of tagged antibodies may be affected by the

tagging position, tagging efficiency and tissue penetration

• The construct is too large to allow multi-layer T-cell

engineering

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ON-Switch ( Turn on CAR by a ligand)

OFF-Switch ( Turn off CAR by a ligand)

AAACAR

AAACAR

AAACAR

AAACAR

Aptazyme-ligand as a switch Potential Advantages

• Control CAR expression at mRNA level, no cell

stress due to constitutive overexpression of

CAR components.

• Aptazymes may be developed against

intracellular and external ligands, allowing

multiple layers of control.

• Aptazyme is small (~100 nt), allowing multiple

layer engineering of CAR constructs.

Aptazyme = Aptamer + Ribozyme

↑

Ligand

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Combinatory

Library (1015)

Ligand

Selection (n=10-30)

Partition

NGS Aptazymes

Nn

Our AptaNxTM Technology

ON-Switch ( Turn on CAR by a ligand)

AAACAR

AAACAR

Aptazyme-ligand as a switch

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Aptazyme Enrichment in Selection Pools

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Decrease IC50 of individual aptazymes with selection progress

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Ligand-dependent inhibition of aptazyme cleavage

0%

20%

40%

60%

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R18

Cle

ava

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(%

)

Drug (µM)

0N

Drug (µM)

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30%

40%

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R26

Cle

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Drug (µM)

0N

Drug (µM)

0N

Drug (µM)

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R9

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ava

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(%

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Drug (µM)

AAACAR

AAACARON-Switch ( Turn on CAR by a ligand)

Full length

Cleaved

aptazyme

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CAR-T AAACAR

RegCAR-TTM

Efficacy/toxicity

uncontrollable

AAACAR controllable

AptaNxTM