Crosstalk Between Cancer Inflammation and Immunity: Host Defense Webinar Series Part 4

Sample to Insight
The Crosstalk between Cancer Inflammation and Immunity:
Exploring Cancer Immune Responses
Wei Cao, PhD
Wei.Cao@qiagen.com
1

Sample to Insight
A four-part webinar series on host responses
2
Exploring the first line of defense: research tools for the innate
immune system
Toll-like receptors in inflammation
Studying the adaptive immune response: tools for T and B cell
research
The crosstalk between cancer inflammation and immunity:
exploring cancer immune responses
Explore host responses and defense mechanisms:
4
2
1
3

Sample to Insight
Legal disclaimer
3
QIAGEN products shown here are intended for molecular biology
applications. These products are not intended for the diagnosis,
prevention or treatment of a disease.
For up-to-date licensing information and product-specific
disclaimers, see the respective QIAGEN kit handbook or user
manual. QIAGEN kit handbooks and user manuals are available at
www.QIAGEN.com or can be requested from QIAGEN Technical
Services or your local distributor.

Sample to Insight
Agenda
4
Immunity in cancer biology
• Tumor immunoediting
• Cancer stem cells – immunological properties
• Tumor escape-mechanisms
• Anti-cancer immunotherapy
Solutions provided by QIAGEN
• Gene expression and regulation
• Microbial species identification
• Next-generating sequencing
• Single cell analysis
• Circulating tumor cells
Questions
1
2
3

Sample to Insight
Agenda
5
Questions
1
2
3

Sample to Insight
6
• Cancer is a chronic disease, similar to some viral infections
during which T cells are stimulated chronically
• The immune system plays an important role in the regulation
and outcome of cancer
• The relationship between cancer and the immune system is
highly complex and can take different paths
There are three main theories that shape our understanding of
the immune system in cancer
◦ Human protective cancer immunity by Paul Ehrlich
◦ Cancer immunosurveillance by Burnet and Thomas
◦ Cancer “immunoediting” – The three “E”s
− Elimination
− Equilibrium
− Escape
Dunn, G.P. (2004) The Immunobiology of Cancer Immunosurveillance and Immunoediting. Immunity 221(2), 137–148

Sample to Insight
7
The immune system’s response to cancer: immunoediting
Strausberg, R.L. (2005) Tumor microenvironments, the immune system and cancer survival. Genome Biology, 6, 211
1. Recognition, infiltrating,
recruiting to induce tumor
death
2. Elimination
3. Promote more tumor death
4. Destroy tumor cells
Tumor cells that
survive the
elimination phase
enter the equilibrium
phase
Tumor cells that have
acquired resistance to
equilibriumenter the
escape phase
Immunoediting – The three “E”s
• Elimination: The immune system has the ability to eliminate tumor cells
• Equilibrium: Poorly immunogenic and immunoevasive transformed cells enter a steady-state phase
• Escape: Edited tumor cells that survive the equilibriumphase enter the escape phase
Two mechanisms of
escape:
• Exhaustion of
immune system
• Acquisition of
mutations

Sample to Insight
Cancer stem cell theory: how do tumors escape the immune system?
Cancer stem cells (CSCs) have immune privileges:
• Serve as the backbone of tumorigenesis, which links the three “E”s of cancer immunoediting
• Are not only long-lived, but also express membrane-bound and soluble factors that enable them to
efficiently modulate immune responses and protect themselves from immune-mediated destruction
• Acquire metastatic potential by undergoing EMT
Bruttel, V.S., and Wischhusen, J. (2014) Cancer stem cell immunology: key to
understanding tumorigenesis and tumor immune escape. Frontiers in Immunology, 5(360),1
8

Sample to Insight
Tumor escape mechanisms: immunosuppression
Tumors can be classified into two groups:
• Immunologically-ignorant tumors (non-inflamed tumors)
◦ Absence of immune cell infiltration
◦ May be caused by
− Low mutation loads
− Immune tolerance against self-antigens
− Lack of essential chemokines and other molecules necessary for T cell
homing to tumor sites
• Immunologically-responsivetumors (T cell-inflamed tumors)
◦ Presence of immune cell infiltration
◦ May be caused by
− Intrinsic T cell immune-inhibition
− Extrinsic tumor-related T cell immunosuppression
9

Sample to Insight
Tumor escape mechanisms: immunosuppression
Extrinsic tumor-related T cell immunosuppression:
• Involves extrinsic inhibitory molecules, such as TGF-β,
IL-10 and indoleamine 2,3-dioxyenase (IDO)
• Negatively impacts T cell function in the TME and the
recruitment of anti-inflammatory cells, including
◦ Tolerogenic antigen presentingcells (APCs)
◦ Regulatory T cells (Tregs)
◦ Myeloid derived suppressor cells (MDSCs)
Intrinsic T cell immunosuppression:
• T cell “exhaustion” with cancer endogenous immune
checkpoint molecules, such as
◦ Cytotoxic T lymphocyte-associated antigen 4 (CTLA-4)
◦ Programmed cell death 1 (PD-1)
◦ T cell immunoglobulin mucin-3 (Tim-3)
◦ Lymphocyte-activation gene 3 (LAG-3)
• Serves as immunotherapy candidate to increase
endogenous anti-tumor immunity
• An important feature is the loss of secretion of important
molecules, such as IL-2, IFN-γ and TNF-α
Immune system exhaustion
Additional mutation acquisition
Yuan, J. et al. (2016) Novel technologies and emerging biomarkers for personalized cancer immunotherapy. J Immunother Cancer, 4:3
10

Sample to Insight
Regulatory T cells (Tregs) and human cancer
Are Tregs regulating for or against tumor progression?
• Inducible Tregs (iTregs) play a key but dual role in cancer, and their plasticity is
controlled and driven by the microenvironment:
◦ iTregs expand and accumulate in tissues and the peripheral blood of cancer patients
◦ They may suppress anti-tumor immune responses and be responsible for tumor escape
◦ On the other hand, they may down-regulateexcessive inflammation and protect the
patient from tumor development
Regulatory T cells serve as potential
biomarkers
• Monitoring for the frequency and
functions of iTregs (FOXP3) is important
in cancer
• Are post-therapy iTregs beneficial or
harmful for patients undergoing
potentially curative cancer therapies?
Whiteside, T.L. (2014) Regulatory T cell subsets in human cancer: are they regulating
for or against tumor progression? Cancer Immunol Immunother, 63(1), 67–72
11

Sample to Insight
Title, Location,Date 12
Immune-targeted therapy focused on T cells
Cancer immunotherapy relied on two principal mechanisms of action:
• ‘‘Passive’’ immunotherapy:
◦ Anti-tumor antibodies, e.g. Trastuzumab (aHER2 mAB) or Rituximab (aCD20 mAB)
◦ Adoptive transfer of cytotoxic T and NK cells
• ‘‘Active’’ immunotherapy (mobilize the patient’s immune cells):
◦ Immune checkpoint blockade, eliminating negative signals that block T-cell function
by targeting:
− Cytotoxic-T-lymphocyte-associated protein 4 (CTLA-4)
− Programmed cell death protein 1 (PD-1)
− Combination therapies targeting the two checkpoints, CTLA-4 and PD-1
◦ Vaccines: administer vaccines to expand antigen-specific T cells
DC-based and viral-vector-based vaccines
Strausberg, R.L. (2005) Tumor microenvironments, the immune system and cancer survival. Genome Biology, 6, 211
Patient microbiomes play a regulatory role in cancer risk
and response to therapy
Need to be added to the precision medicine equation

Sample to Insight
Gut microbiota modulate cancer therapy
13
• Commensal microbes have a beneficial
role in the fight against cancer
• Gut microbiota composition is
dramatically impacted by common
anti-neoplastic drugs
• Gut microbiota contributes to the efficacy
of cancer therapies with chemotherapy
drugs, T cell transfers and immune
checkpoint blockers
Perez-Chanona, E. And Trinchieri, G. (2016) The role of microbiota in cancer therapy. Curr Opin Immunol 39,75–81
Marchesi, J.R. et al. (2016) The gut microbiota and host health: a new clinical frontier. Gut. 65(2), 330-9
Anti-tumor immune response of
anti-CTLA-4 treatment
Anti-tumor
effect of
anti-PD-L1

Sample to Insight
Precision medicine strategies for cancer management
14
Optimize immune-mediated tumor control with multi-modal biomarker-based approaches
Analysis needed:
• Whole-exome sequencing (WES)
of tumor samples
• Whole genome (WGA) or whole
transcriptome (WTA)
• Circulating cell-free DNAprofile
• Epigenetic analysis
• Microbiome phenotype and
composition analysis
Analytical techniques needed:
• NGS library prep and sequencing
• Sanger sequencing
• Microarrays
• Real-time PCR
Therapy
Surgery Targeted Tx Cytotoxic Tx ICBVaccines
Monitor tumor state based on
Biomarkers Microbiome
phenotype
Circulating
Cell-free DNA
(cfDNA)
Circulating
cytokine
levels
Circulating
tumor cells
Outcome
Escape Control Regression

Sample to Insight
Precision medicine strategies for cancer management
15
Immune assays for biomarker discovery and personalized cancer immunotherapy
Yuan, J. et al. (2016) Novel technologies and emerging biomarkers for personalized cancer immunotherapy. J Immunother Cancer, 4:3

Sample to Insight
Agenda
16
Questions
1
2
3

Sample to Insight
Tools for precision cancer immunotherapy
17
• Measurement of gene expression with qPCR
◦ mRNA
◦ lncRNA
◦ miRNA
• Identification of microbial DNA
• Next-generation sequencing: targeted enrichment and WGS
• Single cell analysis: WGA and WTA
• Enrichment and detection of circulating tumor cells (CTCs)

Sample to Insight
- 18 -
Real-time PCR based approaches – sensitive and quantitative for RNAs expressed at
low levels and small gene changes
Gene expression with qPCR – mRNA and lncRNA
Three simple steps: Isolate
RNA
Run PCR
using PCR
arrays
Analyze
data
18

Sample to Insight
Gene expression with qPCR: mRNA and lncRNA
19
RT2 Profiler Arrays
• 84 of the most relevant genes in biological and disease pathways
• Gene lists identified through state-of-the-art bioinformatics and text-mining tools
• Integrated controls for genomic DNA contamination, normalization and PCR processes
• Web-based data analysis software at no additional cost
• Compatible with most real-time PCR instruments
Featured pathways:
• Cancer Inflammation and Immunity
• Tumor Metastasis
• Cancer Stem Cells
• T Helper Cell Differentiation
• TH17 Response
• TH1 and TH2 Responses
• T Cell Anergy and Immune Tolerance
• T cell and B cell Activation
• Toll-Like Receptors(TLRs)
Species: human, mouse, rat and up to
14 different species
cDNA
conversion
RNA QC
Data
analysis
Data
interpretation
Assays
and arrays

Sample to Insight
Immune & Inflammatory Responses
Immunostimulatory Factors: IFNG, IL2, IL12A, IL12B, IL15, TNF.
Immunosuppressive Factors: CD274 (PD-L1), CSF2 (GM-CSF), CTLA4, CXCL12 (SDF1), CXCL5 (ENA-78, LIX), IDO1 (IDO), IL10, IL13, IL4,
CXCL8, MIF, NOS2 (INOS), PDCD1 (PD1), PTGS2 (COX2), TGFB1,VEGFA.
Pro-Inflammatory Genes: CCL2 (MCP-1), CCL20 (MIP-3a), IFNG, IL1A, IL1B, IL2, IL6, IL12A, IL12B, IL17A,IL23A, PTGS2(COX2), TLR4, TNF,
VEGFA.
Anti-Inflammatory Genes: IL4, IL10, IL13, TGFB1.
Enzymatic Modulators of Inflammation & Immunity:AICDA (AID), GZMA, GZMB, IDO1 (IDO), NOS2 (INOS),PTGS2 (COX2).
Antigen Presentation
HLA-A, HLA-B, HLA-C, MICA, MICB.
Chemokines
CCL2 (MCP-1), CCL4 (MIP-1B), CCL5 (RANTES), CCL18 (PARC), CCL20 (MIP-3a), CCL21 (MIP-2),CCL22 (MDC), CCL28, CXCL1 (GRO1,
GROa, SCYB1), CXCL2 (GRO2, GROb, SCYB2), CXCL5 (ENA-78, LIX), CXCL9 (MIG), CXCL10 (INP10), CXCL11 (I-TAC, IP-
9), CXCL12 (SDF1).
Chemokine Receptors
ACKR3 (CXCR7), CCR1, CCR2, CCR4, CCR7, CCR9, CCR10, CXCR1 (IL8RA), CXCR2 (IL8RB), CXCR3,CXCR4, CXCR5.
Interleukins
IL1A, IL1B, IL2, IL4, IL6, CXCL8, IL10, IL12A, IL12B, IL13, IL15, IL17A, IL23A.
Other Cytokines
KITLG (SCF), MIF, SPP1, TNF, TNFSF10 (TRAIL).
Growth Factors& Receptors
CSF1 (MCSF), CSF2 (GM-CSF), CSF3 (GCSF), EGF, EGFR (ERBB1), IGF1, TGFB1, VEGFA.
Signal Transduction
Interferon Signaling: GBP1, IFNG, IL6, IRF1.
Interferon-Responsive Genes: CCL2 (MCP-1), CCL5 (RANTES), CXCL9 (MIG), CXCL10 (INP10), GBP1,IRF1, MYD88, STAT1, TLR3,
TNFSF10 (TRAIL).
NFκB Targets: BCL2L1 (BCLXL), CCL2 (MCP-1), CCL5 (RANTES), CSF1 (MCSF), CSF2(GM-CSF), CSF3(GCSF), IFNG, CXCL8, TNF.
STAT Targets: CCL2 (MCP-1), CCL4 (MIP-1B), CCL5 (RANTES), CSF1 (MCSF), CSF2 (GM-CSF), CSF3(GCSF), CXCL9 (MIG),
CXCL10 (INP10), CXCL11 (I-TAC, IP-9), CXCL12 (SDF1), IL1B, IL6, CXCL8, IL10,IL17A, IL23A, MYC.
Toll-Like Receptor Signaling: TLR2, TLR3, TLR4, MYD88.
Transcription Factors: FOXP3, HIF1A, IRF1, MYC, NFKB1, STAT1, STAT3, TP53 (p53).
Apoptosis
Pro-Apoptotic: FASLG (TNFSF6), TNF, TNFSF10 (TRAIL), TP53 (p53).
Anti-Apoptotic: BCL2, BCL2L1 (BCLXL), MYC, STAT3.
http://www.qiagen.com/search/rt2-profiler-pcr-arrays?catno=PAHS-181Z#geneglobe
Functional Gene Grouping
Cancer Inflammation & Immunity Crosstalk RT2 Profiler PCR Array
20

Sample to Insight
Application data: gene expression
• Identify immunological and vascularization gene-expression clusters
and correlations between clusters
• Determine the factors that are correlated with patient survival in
cervical carcinoma
Punt, S. et al. (2015) Correlations between immuneresponse and vascularizationqRT-PCR gene
expression clusters in squamous cervical cancer.Molecular Cancer 14:71
Free download: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4752653/pdf/gutjnl-2015-309990.pdf
21

Sample to Insight
22
Experimental setup
• Material: fresh frozen squamous cervical cancer
samples from 52 patients who underwent primary
surgical treatment for cervical cancer between
1989 and 2005
• RNA purification with Trizol and QIAGEN RNeasy
Mini columns
• cDNA conversion with RT2 First Strand Kit
◦ Integrated DNase step
◦ Proprietary spike in RNA
◦ Priming with both oligo-dTs and randomhexamers
• Profile 42 genes using custom RT2 Profiler PCR
Array and RT2 SYBR Green master mix
• Analyze gene expression data

Sample to Insight
Key findings:
• Identified CD3E, IL6, VEGFA and a high IL6/IL17 ratio combined with low IL5 expression
as the most prognostic factors in squamous cervical cancer
• High expression of T cell markers was correlated with improved prognosis
• High expression of angiogenesis marker VEGFA was correlated with poor prognosis
• Th17 counteracts the effect of IL6
Support for the development of combined anti-IL-6 and anti-VEGF therapies
23
Punt, S. et al. (2015) Correlations between immuneresponse and vascularizationqRT-PCR gene
expression clusters in squamous cervical cancer.Molecular Cancer 14:71
Free download: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4752653/pdf/gutjnl-2015-309990.pdf

Sample to Insight
RT2 lncRNA qPCR system for lncRNA profiling
• lncRNA databases: in-house database at QIAGEN GeneGlobe covers
over 28,000 human and 16,000 mouse lncRNA targets
• RT2 lncRNA assays: laboratory-verified for optimal qPCR performance
with high specificity, amplification efficiency and sensitivity
• RT2 lncRNA qPCR Arrays: pathway- or disease-relevant lncRNA assays
◦ RT2 lncFinder PCR Array (human and mouse)
◦ RT2 Inflammatory Response & Autoimmunity (human and mouse)
◦ RT2 Cancer PathwayFinder (human and mouse)
• New Custom Builder: flexible, custom designs from the lncRNA and
qPCR databases to profile mRNA and lncRNA simultanously
• lncRNA isolation: miRNeasy or exoRNeasy kits
• Data analysis: free online data analysis tool
http://www.qiagen.com/lncrna/
24

Sample to Insight
RT2 lncRNA qPCR Array applications
25
Flexible layout and patented controls
• Each 96-well plate has 84 lncRNA-specific assays 12
control assays (five reference genes, one genomic DNA,
three reverse transcriptionand three positive PCR)
• Arrays are also available in 384-well plates and 100-well
ring discs for the Rotor-Gene Q
http://www.qiagen.com/us/landing-pages/lncrna/
Volcano plot of lncRNA gene expression changes in stage II
prostate cancer tissue compared with normal tissue

Sample to Insight
miRNA expression — miScript miRNA PCR Arrays
• miRNome
◦ Human: miRBase v21, covers 2,402 primer assays
◦ Mouse: miRBase v21, covers 1,765 primer assays
◦ Rat: 653 primer assays
◦ Dog: 277 primer assays
◦ Rhesus macaque: 469 primer assays
◦ Cow: 744 primer assays
• Pathway-focused arrays (over 20 arrays)
◦ miFinder
◦ Serum and plasma miRNAs
◦ Cancer stem cells
◦ Immunopathology
◦ Inflammatory response and autoimmunity
◦ T cell and B cell activation
• miScript PreAMP Kit
◦ Optional step for small or precious samples
◦ Full miRNome profiling from as little as 1 ng RNA
http://www.qiagen.com/products/catalog/assay-technologies/mirna/miscript-mirna-pcr-arrays
Pre-formatted, single-use PCR arrays with wet lab-verified assays
26

Sample to Insight
• RNA-seq verification
• Biomarker discovery
• CRISPR/RNAi knockdown verification
• Bioprocessing optimization
• Biological response investigation
• Interspecies study (14 species available)
Email: BRC.Custom@QIAGEN.com
Custom array builder - custom qPCR arrays
https://www.qiagen.com/shop/genes-and-pathways/custom-products/custom-array-products/custom-rt2array/
Design custom PCR arrays from our collection of laboratory-verified
mRNA and lncRNAs SYBR® Green qPCR assays
27

Sample to Insight
- 28 -
Real-time PCR based approaches – sensitive and quantitative for RNAs expressed at
low levels and small gene changes
Gene expression with qPCR – mRNA and lncRNA
Three simple steps: Isolate
RNA
Run PCR
using PCR
arrays
Analyze
data
28

Sample to Insight
Agenda
29
Questions
1
2
3

Sample to Insight
30
Allprotect Tissue
Reagents
• QIAamp DNA
Microbiome
Kit
• QIAamp UCP
Pathogen Mini
Kit
• QIAamp DNA
Stool Mini Kit
REPLI-g Single
Cell Kit
• GeneRead
Library Prep
Kits
• GeneRead
Size Selection
Kit
• GeneRead
Library Quant
System
• Microbial DNA
qPCR Arrays
• Microbial DNA
qPCR Assays
• Custom
Microbial DNA
qPCR Arrays
Detecting microbial metagenomes – a complete solution
QIAGEN provides next-generation sequencing technologies for:
• Metagenomics
• qPCR assays and arrays to:
◦ verify sequencing results and
◦ screen for specific bacterial species, virulence factor genes and antibiotic resistance genes
Sample
collection
DNA
purification
DNA
amplification
Library
preparation
Verification
By PCRSample Insight

Sample to Insight
QIAGEN’s microbial DNA qPCR assays and arrays
31
Reveal the mysteries of the microbiome with over 600 assays
that target species-specific or gene-specific microbial DNA
• >600 assays to identify bacteria
• 8 assays to identify fungi
• One protest-identification assay
• 87 antibiotic resistance genes
• 87 virulence factor genes
• 18 Arrays
• Antibiotic Resistance Genes
• Bacterial Vaginosis
• Biodefense
• Food testing: Dairy
• Food testing: Meat
• Food testing: Poultry
• Food testing: Seafood
• Food testing: Vegetable
• Intestinal Infections
• Intestinal Infections 2
• Metabolic Disorders
• Oral Disease
• Respiratory Infections
• Respiratory viral
• Sepsis
• Urinary Tract Infections
• Vaginal Flora
• Water Analysis
Microbiome: From Identification to Characterization
DNA
purification
Detection
by qPCR
Data
analysis

Sample to Insight
Agenda
32
Questions
1
2
3

Sample to Insight
Addressing the challenges of NGS workflows:
Ensure equal library
loading for all samples
Rescue low-quality DNA
samples, don’t discard them
Sensitive assays to detect
low-frequency variants
• GeneRead
DNA FFPE Kit
• REPLI-g Single
Cell Kit
• REPLI-g Cell
WGA & WTAKit
• GeneRead
DNAseq Target
enrichment
panels
• Expanded
Content in V2
(cancer,
Inherited
diseases, cardio-
myopathy)
• GeneRead
DNA Library
Core Kit
• GeneRead
DNA Amp Kit
• GeneRead
Size
Selection Kit
GeneRead data
analysis portal
CLC-Bio
Ingenuity System’s
Ingenuity® Variant
Analysis
GeneRead NGS solutions from Sample to Insight
33
Streamlined, standardized and automated sample-to-insight workflow

Sample to Insight
Experimental performance metrics
Application Panel name # genes
Target region
(bases)
Coverage
(%)
Specificity
(%)
Uniformity
(%)
Solid tumors
Tumor Actionable Mutations 8 7,104 100.0 98.2 91
Clinically Relevant Tumor 24 39,603 98.1 95.3 90
Hematologic
malignancies
Myeloid Neoplasms 50 236,319 98.1 97.4 94
Disease-specific
Breast Cancer 44 268,621 98.2 96.8 91
Colorectal Cancer 38 182,851 98.7 98.3 95
Liver Cancer 33 191,170 99.0 96.4 96
Lung Cancer 45 332,999 97.5 98.1 90
Ovarian Cancer 32 189,058 98.9 96.6 96
Prostate Cancer 32 167,195 98.4 97.3 94
Gastric Cancer 29 222,333 98.1 98.5 93
Cardiomyopathy 58 249,727 96.3 96.7 87
Comprehensive
Carrier Testing 157 664,735 97.5 97.9 91
Cancer Predisposition 143 620,318 98.3 96.8 93
Comprehensive Cancer 160 744,835 98.0 97.7 92
Panel optimization results in outstanding experimental performance metrics
GeneRead DNASeq Targeted Panel V2
Cancer stem cells 34

Sample to Insight
Agenda
35
Questions
1
2
3

Sample to Insight
REPLI-g Single Cell technology
36
Easy-to-use single-cell WGAand WTAmethod
• Offers complete genome/transcriptome coverage
• Minimizes sequence bias, allowing for discovery of cell heterogeneity
• Consistent yields of up to 40 μg from 1–1000 cells (average product length >10 kb)
15 min
Whole genome amplification workflowThe REPLI-g WGA Single Cell Kit: for whole genome
amplification (WGA) from samples as small as a single cell
(1 tumor cell).
The REPLI-g WTA Single Cell Kit: for transcriptome
amplification (WTA) from samples as small as a single cell
(1 tumor cell).
The REPLI-g Cell WGA & WTA Kit: enables uniform WGA
and WTA in parallel from a single sample. Start with the lysis
of 25–1000 cells and easily get up to 40 µg of gDNA and
cDNA, which can be used for comparative genome and
transcriptome analysis via NGS, qPCR or microarray
analysis.
REPLI-g Single
Cell WGA
REPLI-g Single
Cell WTA
REPLI-g Single
Cell WGA &
WTA
Learn about the technology: http://www.qiagen.com/us/resources/technologies/wga
Read a white paper: “Genomic analysis of individual cells by NGS and real-time PCR”

Sample to Insight
Applications of REPLI-g Single Cell Kits
37
WGA
or
WTA
Whole Genome Sequencing
• Detect variability in genome sequence (SNV, microsatellites, etc.)
• Variability in genome structure (CNV, structural rearrangements, aneuploidy)
• De novo sequencing of new, unidentified and unculturable organisms
TargetedDNASequencing
• Detect variability in a target set of genes or region of the genome
Microarrays
• Use SNP-chips to genotype thousands of loci
RNA-seq
• Detect variability in transcript abundance for all expressed genes
• Detect variability in isoform structure and abundance
qRT-PCR profiling
• Profile gene expression for a targeted set of transcripts
• Accurately quantify specific splice-junctions, isoforms or other structural features

Sample to Insight
Agenda
38
Questions
1
2
3

Sample to Insight
AdnaTest: the circulating tumor cell (CTC) revolution
Blood sampling Determination of
prognostic or
predictive
biomarkers (liquid
biopsy)
Turn around time: 5h
CTC enrichment
using multi
antibody-labelled
magnetic beads
Lysis of the
enriched cells
RT and multiplex
PCR
Two-step procedure for the enrichment of CTCs from blood samples:
1. Immunomagnetic-capturing using a multi-antibody mix
2. Multiplex RT-PCR to determine the number of tumor-associated transcripts
• Every AdnaTest usesdifferent antibody mixtures optimizedfor each tumor entity
• The method is easy and straight forward manual assay
Type AdnaTestin questions box to request further information
39

Sample to Insight
Agenda
40
Questions
1
2
3

Sample to Insight
Thank you for attending
41
Thank you for attending today’s webinar!
Contact QIAGEN
Call: 1-800-426-8157
Email: BRCsupport@QIAGEN.com
QIAwebinars@QIAGEN.com
Wei Cao, PhD
Wei.Cao@QIAGEN.com
Questions?

Crosstalk Between Cancer Inflammation and Immunity: Host Defense Webinar Series Part 4

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (7)

Similar to Crosstalk Between Cancer Inflammation and Immunity: Host Defense Webinar Series Part 4

Similar to Crosstalk Between Cancer Inflammation and Immunity: Host Defense Webinar Series Part 4 (20)

More from QIAGEN

More from QIAGEN (20)

Recently uploaded

Recently uploaded (20)

Crosstalk Between Cancer Inflammation and Immunity: Host Defense Webinar Series Part 4