Cancer ImmuneNet Biomarker Profiling AssayCancer ImmuneNet Biomarker Profiling AssayAlex Chenchik¹, Mikhail Makhanov¹, Leonid Iakoubov¹, Sunitha Sastry¹, and Costa Frangou¹.

¹Cellecta, Inc., Mountain View, CA USA

www.cellecta.com

info@cellecta.com

ImmuneNet Assay Gene Composition

The ImmuneNet 2500 panel includes a comprehensive set of 2500 genes specific for detection and profiling of different types of immune cells including T cells, B cells, fibroblasts, stromal and endothelial cells in the tumor microenvironment, activated immune cells suggestive of adaptive and innate immunity, immunity-related genes from 16 predictive and prognostic core gene signatures that have been validated in recent chemo- and immunotherapy clinical trials across several tumor types, including melanoma, colorectal, breast, and lung cancers.

Immunotherapy drug targets in

clinical & preclinical studies

FDA Drug Targets

Immune mechanisms & activation

status

28 Immune cell type specific

signatures

Immunotherapy BIOMARKERS

from 18 clinical trials

540 1,200

400

200450

600

CancerDriverGenes

ImmuneNet2500

Introduction

Link between the immune infiltrate and several human carcinoma types and prognosis and/or response to therapy

Increasing evidence suggests that the number, type, and location of tumor-infiltrating lymphocytes in primary tumors harbor prognostic value, and this has led to the development of a “tumor immunoscore/ immune” index

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Develop comprehensive expression assay for analysis of cellular composition

Molecular profiling of key immune-related genes, including drug targets, known biomarkers, and immune mechanisms

Unbiased discovery of most informative biomarkers that can be analyzed by conventional IHC/FACS assays

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Detect cellular composition of immune / stromal / cancer cells in tumor microenvironment

Identify immunity status and immunoediting mechanisms

Discover novel biomarkers for immunotherapy

Profiling of immunotherapy targets and all FDA-approved drug targets

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Background:

Aims:

What is ImmuneNet?

Figure 1. Tumor Microenvironment. Adapted from Joyce, J. A., & Pollard, J. W. (2009). Microenvironmental regulation of metastasis. Nature Reviews. Cancer, 9(4), 239–252.

Signaling Networks Detected by ImmuneNet

0 5 10 15 20 25

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Networks

-log(pValue)

1. Immune response Antigen presentation

2. Cell adhesion / Leukocyte chemotaxis

3. Inflammation / NK cell cytotoxicity

4. Chemotaxis

5. Proliferation / Leukocyte proliferation

6. Immune response / TCR signaling

7. Inflammation / Jak-STAT Pathway

8. Inflammation / Interferon signaling

9. Inflammation / IL-10 anti-inflammatory response

10. Immune response / T helper cell differentiation

Figure 6. Validation of ImmuneNet 2500 assay with TNBC clinical samples. (A) Tumor immune cell infiltration visualized by H&E staining (40X magnification). (B) Heat map showing differentially expressed genes detected by ImmuneNet assay using immune-infiltrated and control tumor samples. (C) Example immune enrichment score for immune infiltrating dendritic cells.

Validation of ImmuneNet Assay with TNBC Samples

Immune CellsTumor

Highly Infiltrated tumor cellsBREAST CANCER PATIENT 166N

RES

1.0

0.5

0.0

-0.5

Immune Enrichment score Dendritic cell signatures

A.B.

C.

166N

170N

B-Cell

CD8 T-Cell

Checkpoint

Cytotoxic cell

DC

Macrophages

Mast cellNK CD56dim cellNK cell

T helper cell

T-Cell

TFH

TcmTem

Th1 cell

Th17 cellTh2 cellTreg

aDC

iDC

Next Generation Targeted RNAseq

Multiplex PCR withAnchor primers

Directional1st and 2nd

StrandSynthesis

cDNA

GSPAmplicon

80-200 nt Gene-specific fragment

Next-Gen Sequencing (NGS)

FwdGSP

GSPAnchor1

GSP

GSP

Anchor1

Anchor2

RevGSP

GSP

Anchor2GSP

GSP

Index1P7

Anchor2

Anchor1

P7 Index1

P5

Index2

Index2 P5

Biopsy, Xenograft, PBMC, FFPE, FNA

RTReaction Total RNA +

CalibrationStandard

AAAA....

N6N6

Profiling of up to 10,000 genes in a single tube assay

Requires 10-100 ng of total RNA from biopsy, blood, FFPE, or FNA samples

Built-in calibration standards for QC and normalization of expression data

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Figure 5. Our novel workflow leverages the power of quantitative PCR (upstream) and Next-Gen Sequencing (downstream read-out). Experimentally validated gene-specific primers are used to amplify selected target regions. Synthetic calibration controls act as an effective QC metric.

Conclusions

We present the novel Cancer ImmuneNet 2500 assay panel, a quantitative, multiplexed, high throughput targeted RNASeq approach that leverages the power of multiplex PCR technologies and NGS and allows you to obtain the transcriptome profile of the tumor microenvironment.

The ImmuneNet Panel includes 2,500 immunity-related genes. In order to develop cell-type specific gene signatures, we developed a non-probabilistic binary linear classifier algorithm to infer the level of infiltrating immune cells in tumor tissues. Our panel can distinguish hematopoietic cell phenotypes from bulk tissues and tumor cells and offers a unique approach to identify tumor-infiltrating cells.

In this study, we present the design and algorithms of our panel that can accurately resolve relative fractions of diverse immune cell types from complex tissues.

Intended Applications:

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The platform is applicable for novel predictive and prognostic biomarker discovery.

Comprehensive profiling of tumor-associated immune cell composition will provide important insights into cancer immunoediting mechanisms

Has the potential to provide a new molecular stratification approach applicable to cancer immunotherapy. Currently, a portfolio (Driver-Map™) of such assays are in development to address specific disease areas.

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Development of Functionally Validated RT-PCR Primers

Figure 4. Testing Primer Efficiency. Primers designed for the select panel of genes are functionally validated to be compatible with our high-throughput, multiplex protocol. SyntheticRNA control (Lane 1) and Human Universal RNA control (Lane 2) help demonstrate that the primers work poorly in Fig 4A and efficiently as in Fig 4B. Mouse Universal RNA control (Lane 3)denotes cross-reactivity. Validated Primers with greater efficiency are selected and are subsequently used in the Targeted RNA Seq assay. 24 cancer model cell lines were profiled against a select panel of genes (125). Red denotes upregulated genes and green denotes low expression in the heat-map presented (Fig. 4C).

Primer Target Selection

Filtering

HT Experimental Validation

Targeted RNAseqPipeline

Failed Primers go through

another iteration

Validated Primers

Ranking of Primer Sets

1. Control RNA (synthetic)2. Human Universal RNA3. Mouse Universal RNA

1 2 31 2 3 ZR75

-30

MCF

10A

SK-B

R3M

B157

MCF

7H

s606

TH

s742

TH

s281

TM

B157

Hs7

39H

CC13

95BT

549

MB4

36H

CC11

87H

s274

TH

CC38

HCC

1937

HCC

1954

HCC

1569

BT20

Hs5

78T

MB4

53T4

7DM

B231

Expr

essi

on

(A)GoodPrimers

(B)PoorPerformingPrimers

(C) Experimental Validation

ImmuneNet Assay Workflow

Detect and enumerate immune cell types using

reference cell type-specific signatures

RelativeFraction andEnrichment

Score

Classifier

ImmuneNetDeconvolution

Immune Cell Enrichment Score

TargetedRNAseqProfile

SamplesFractionated Tumor

Bulk TumorBloodFFPEFNA

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0.2

0.4

0.6

0.8

1.0

Cell Populations in Tumor Microenvironment

Immune Cells

Targeted RNAseq expression profile of 2,500 immune-associated genes

Figure 2. Summary of the ImmuneNet 2500 pipeline and application to infiltrating tumor immune cell deconvolution. ImmuneNet uses a novel, multiplex, quantitative, targeted RNAseq assay and classifier algorithm to measure the absolute digital expression for ~2500 genes and accurately resolve relative fractions of diverse immune cells types from complex tissues.

Figure 3. Cluster Analysis with ImmuneNet 2500: Model reference gene expression data by principal component analysis using ImmuneNet differentially expressed (DE) genes successfully distinguishes hematopoietic cell phenotypes from bulk tissues and tumor cells. Diagram to right describes major cell types detected by the assay.

InfiltratingImmune

Cells

Solid TumorSamples

Immune CellTranscriptome Profiles

TH1

TH2

CMPMEP

Early ERY

MEGAGMP

GRAN

MONOEOSBASODEND2DEND1

PBCELL

BCELL

NK

TCELL

Late ERY

HSC1(CD34+)

HSC2

Development of Classifier Model for DifferentImmune Cell Types in Tumor Microenvironment