Ngs part ii 2013

Sample & Assay Technologies

Next Generation Sequencing for Cancer Research

Vikram Devgan, Ph.D., MBA
Director, R&D
vikram.devgan@qiagen.com


Welcome to the three-part webinar series
Next Generation Sequencing and its role in cancer biology

Webinar 1: Next-generation sequencing, an introduction to technology and
applications
Speaker:
Quan Peng, Ph.D.

Webinar 2:
Speaker:

Next-generation sequencing for cancer research
Vikram Devgan, Ph.D., MBA

Webinar 3:
Speaker:

Next-generation sequencing data analysis for genetic profiling
Ravi Vijaya Satya, Ph.D.

2


Agenda

1. Introduction to Next Generation Sequencing in Cancer Research
2. Need for change

3. QIAGEN solutions: sample-to-result experience

3


Cancer: A Disease of Genome

A C
T
G

Variation
4


Genetic Variations

AGCTCGTTGCTCAGCTC

Reference
genome

AGCTCGTTGCTCAGCGTTC Insertion

---GCTCAGCTC

AGCTC
Mutations

Indels

Deletion
Copy number variation

Challenge in Treating Cancer:
Structural variation
Every tumor is different
Every cancer patient is different

5


Cancer: Facts

In 2008 over 12.7 millions new cases were diagnosed across the planet and
approximately 7.6 million cancer death occurred
In 2030, these numbers will rise to an expected 21.4 millions new cases and 13.2
million cancer death………..

If our ability to prevent, diagnose and treat cancer doesn't improve
6


Need of Cancer Research

Identify new cancer susceptibility genes
Identify mutations that drive cancer progression
Assist in developing new diagnostic tests and targeted therapies

Science 339, 1546 (2013);
Bert Vogelstein et al.
Cancer Genome Landscapes

7


e.g. lung cancer

Need: Patient stratification / Personalized medicine

e.g. lung cancer


Unknown
37%

Current lung cancer biomarker landscape

KRAS
25%

EGFR (L858R)

+

KRAS (G12C)

EGFR
23%
NRAS
0.2%
MAP2K1
0.4%
ERBB2
1%

MET
2%

BRAF EML4-ALK
PIKC3A 3%
6%
3%

Response rates of >70% in
patients with non-small cell
lung cancer treated with
either erlotinib or gefitinib

EML4-ALK

Poor response rate in
patients with non-small cell
lung cancer treated with
either erlotinib or gefitinib

Crizotinib

Multiple mutation tests
If tested sequentially
– Not adequate tissue sample
– Takes time to rule out individual mutations
Need tool to conduct simultaneous genetic testing for multiple oncogenic
drivers


Need: Public health

Population screening for high risk preventable
disorders
~0.25% of US women (375,000) carry a mutation in BRCA1/2
At very high risk of breast and ovarian cancer
– 85% lifetime breast cancer risk
– 25-50% lifetime ovarian cancer
Knowledge of risk allows prevention


Basic Research

Need of Cancer Research

Clinical Research

Cancer susceptibility genes

Tumor sub-typing

Causative mutations for
multigenic diseases

Biomarker identification
Resistance marker
identification

Public Health
Population screening for
high risk preventable
disorders
Personalized treatment
Therapeutic monitoring

Patient stratification for
clinical trails

Need a tool to detect multiple mutations in multiple genes with multiple samples


Evolution of technologies for mutation detection

Few
ARMS PCR
Liquid bead array
Sequenom MALDI-TOF
Next Generation Sequencing

Many
Next Generation Sequencing: Multiple Mutation/Multiple Genes/Multiple Samples
12


NGS: the best method for mutation detection
High throughput
Test many genes at once

Cost effective
Drastic decrease in cost of sequencing

Systematic and unbiased
Detection of all mutation types

Quantitative
Easy to quantify mutation frequency

13


Challenges in translating NGS to oncogenomics
Quantity
Limiting for biopsy specimens

Purity (genetic heterogeneity)
Cancerous cells may be a minor fraction of total sample
Multiple sub-clones of cancer may be present in one tumor
sample
Genomic alterations in cancer found at low-frequency

Data analysis
Biologically interpretable data

Fragmented Workflow
Technologies are not jointly developed

14


QIAGEN solution

NGS workflow
Primary sample to
purified DNA

DNA preprocessing

Target
enrichment
Library
preparation

1. Accessing single cell genomes forfor next generation sequencing
Accessing single cell genomes next generation sequencing
2. Target enrichment enabling rare mutation detection and QC
3. Streamlined one-tube library preparation
4. Raw reads to completely annotated variants

Sequencing
preparation

NGS run

Data alignment /
analysis

15


REPLI-g Single Cell Kit

NGS workflow
Primary sample to
purified DNA
DNA / RNA
preprocessing
Target
enrichment
Library
preparation

Problem:
Isolation of NGS compatible DNA quantity from single to few
cells

Solution:
REPLI-g Single Cell Kit
– Unbiased amplification of ultra-low DNA amount

Sequencing
preparation

NGS run

Data alignment /
analysis

16


Technology
Multiple displacement based whole gemome amplification

Primers anneal to template DNA
Phi 29 DNA polymerase moves along the DNA
template strand displacing the complementary strand
The displaced strand becomes a template for
replication

Advantages
High yield of high-molecular-weight DNA
High fidelity amplification – minimal error rate
No effect of secondary structure - minimal bias

17


How REPLI-g Single Cell Kit work
Simple three step protocol

(Yield: 20-40 µg)
18


Comparable NGS results with gDNA & amplified single cells

REPLI-g Single Cell technology provide high genome coverage & high fidelity

19


Unbiased amplification with REPLI-g Single Cell Kit

No allele drop out after REPLI-g Single Cell WGA

20


QIAGEN solution

NGS workflow
Primary sample to
purified DNA

DNA preprocessing

Target
enrichment
Library
preparation

1. Accessing single cell genomes for next generation sequencing

Sequencing
preparation

NGS run

Data alignment /
analysis

21


NGS: DNA sequencing

Gene Panels

Increase in
•Cost
•Data complexity

Exome
Decrease in
•Deep sequencing
•Multiplexing
•Reimbursements

Genome

Gene Panel Sequencing: Efficient, Effective and Economical method
22


Clinical utility requires targeted analysis
Linking genetic variants with biology

Title, Location, Date

23


Shrink the Genome
Focus on Genes of interest relevant to research topic

Sample

Information

Analysis Time

Coverage Depth

Sample Size

Genome

3 x 109 bps

7 days

10X

1 ug

20 Genes

6 x 104 bps

8 hours

1000X

10 ng

24


Hybridization

Which is most suitable target enrichment technology?
Hybridization, Ligation + PCR

Multiplex PCR

25


Hybridization

Multiplex PCR technology
Hybridization, Ligation + PCR

Multiplex PCR

Workflow:

Workflow:

Workflow: (SIMPLE)

Library construction(4 hrs)

Hybridization with probe (16 hrs)

PCR amplification (3 hrs)

Hybridization with probes (24-48 hrs)

Ligation (1 hr)

Library construction (4 hrs)

PCR & Indexing (2 hrs)

PCR & Indexing (2 hrs)

DNA input:
1-3 ug

DNA input:
200-400 ng

DNA input: (LOW)
<100 ng

Time from DNA sample to NGS library:
2-3 days

2 days

1 days (RAPID TAT)

26


GeneRead DNAseq Gene Panel

Multiplex PCR technology based targeted enrichment for DNA sequencing
Cover all human exons (coding region + UTR)
Division of gene primers sets into 4 tubes; up to 1500 plex in each tube

27


Focus on your Disease of Interest
Comprehensive Cancer Panel (124 genes)
Cancer-specific Focused Gene Panels (20 genes)
Breast cancer

Liver cancer

Colon Cancer

Lung Cancer

Gastric cancer

Ovarian Cancer

Leukemia

Prostate Cancer

How Genes on Panels Are Selected
Genes Involved in Disease
Clinically/Biologically relevant
Multiple Publically accessible databases
Text mining tools
Manually curated

Genes with High Relevance

Technically relevant
Most frequently mutated genes
Specific feedback from the thought leaders
28


Breast Cancer Gene Panel

Somatic: All
Germline and Somatic: APC, BRCA1, BRAC2, TP53
With approved FDA inhibitor: BRAF, EGFR, ERBB2

29


Advantages of Focused and Curated Gene Panel

Deep sequencing enabling detection of low prevalence mutations
Ion Torrent 316 Chip

Competitor A
CCP (400 genes)

Median Coverage Depth

GeneRead
Lung Cancer Panel (20 genes)

87X
39.0%

Region with >100X coverage

944X
91.2%

Multiplex more samples (cost saving)

Coverage Depth

Competitor A
CCP (400 genes)
1000X

GeneRead
1000X

Number of Samples

1

8

MiSeq

Without missing potentially important mutations
COSMIC database
Lung cancer specific mutations

Competitor A
CCP (400 genes)
79.9%

GeneRead
71.4%

30


Gene Read DNASeq Gene Panel
>10 years of proven expertise in primer design: GeneRead algorithm

Simple
Any gene
Any sequencer
Any sample: average amplicon size is 145 bp

Superior
Wet bench verified gene panels
– High design rate: >90%
– High specificity: >85%
– High uniformity (0.1X of median coverage depth): >85%

31


GeneRead DNAseq Custom Panel

32

Simple Protocol

PCR primer mix + control primers

Add GeneRead Mastermix
and genomic DNA (20 ng/rxn)

2 hours


PCR amplification

Pool reactions for each sample
and purify (QIAquick PCR Purification Kit)

33


GeneRead DNASeq Library Quant Array

Serial dilution of DNA standard
with primer assays for
library quantification

primer assays for
library QC

primer assays for
Library
Quantification

34


Quantification and QC in a single PCR run

GeneRead DNAseq Library Quant Array

QC
Score

1-4

4-8

>8

Results

Pass

Marginal

Fail

Proceed

Caution!

Do not
proceed

35


An Example

Segregate bad libraries before sequencing; save time and money

36


DNAseq Panels compatible with NGS platforms
No manipulation of target enrichment protocols needed

Illumina

•
•
•
•
•
•

MiSeq
GA Iix
HiSeq 1000
HiSeq 2000
HiSeq 1500
HiSeq 100

Ion

•

•

Torrent PGM
• 314
• 316
• 318
Proton


QIAGEN solution

NGS workflow
Primary sample to
purified DNA

DNA preprocessing

Target
enrichment
Library
preparation


Sequencing
preparation

NGS run

Data alignment /
analysis

38


NGS workflow
Primary sample to
purified DNA

GeneRead DNA Library Prep Kits
Library preparation

Problem
Inefficient, complex and error-prone workflow

DNA preprocessing

Target
enrichment

Solution
Combine various enzymatics steps in single tube

Library
preparation

Fast procedure that allows up to 50% time savings
High yields from minimal amounts of starting material

Sequencing
preparation

Unbiased and high-fidelity amplification mastermix (optional)
Can be automated on QIAcube

NGS run

For Illumina & Ion Torrent platforms

Data alignment /
analysis

39


Faster, less variable & more efficient workflow

40


GeneRead Size Selection Kit
Remove DNA fragments <150 bp
Before size selection

GeneRead size selection

Adapterdimers

Adaptermonomers

Precise size selection of DNA fragments
Fast procedure, based on QIAGEN’s proven silica-column
technology
An easy-to-follow protocol, automatable on the QIAcube
41


Input DNA as little as 50 ng

High yield of Library DNA

Uniform coverage distribution

42


HiFi Polymerase Comparison: Experimental Design

43


GeneRead Library Prep Kit
Low error rate with minimal sequence bias

High-fidelity amplification

Greater and more even coverage
in GC- and AT- rich areas of DNA

44


QIAGEN solution

NGS workflow
Primary sample to
purified DNA

DNA preprocessing

Target
enrichment
Library
preparation


Sequencing
preparation

NGS run

Data alignment /
analysis

45


Data Analysis: Free, Complete and Easy to Use

46


Results in Multiple Analysis Format

Run Summary
Specificity
Coverage
Uniformity
Numbers of SNPs and Indels

Summary By Gene
Specificity
Coverage
Uniformity
# of SNPs and Indels

47


SNP detection
Indel detection

Features of Variant Report

Predicted amino
acid change

dbSNP and
COSMIC ID

Effect of SNP

Impact of SNP

Link to qPCR
somatic mutation
assay

48


Genetic variant analysis in FFPE lung adenocarcinoma samples
Experimental Design

gDNA isolated from 3 FFPE lung adenocarcinoma and one FFPE normal lung samples

GeneRead Lung Cancer Gene Panel was used to enrich 20 genes

Library preparation, quantification and sequencing

QIAGEN NGS Data Analysis Web Portal

49


Run Summary

Sample

Total number of reads

Tumor
Sample-1
1,629,983

Tumor
Sample-1
1,892,621

Tumor
Sample-1
1,851,514

Normal
Sample
1,625,119

Specificity
(Reads on target)

92%

92%

90%

90%

Median Coverage

608

744

703

577

Uniformity
(regions with >10% of
median coverage)

93%

92%

90%

93%

No. of SNPs

84

72

87

83

No. of Indels

8

11

9

6

50



Snap shot of variant report

51


Streamlined Sample-to-Result Workflow

PyroMark Assays

52


Experimental Design

gDNA isolated from 3 FFPE lung adenocarcinoma and one FFPE normal lung samples

GeneRead Lung Cancer Gene Panel was used to enrich 20 genes

Library preparation, quantification and sequencing

QIAGEN NGS Data Analysis Web Portal

53


Run Summary

Sample

Total number of reads

Tumor
Sample-1
1,629,983

Tumor
Sample-1
1,892,621

Tumor
Sample-1
1,851,514

Normal
Sample
1,625,119

Specificity
(Reads on target)

92%

92%

90%

90%

Median Coverage

608

744

703

577

Uniformity
(regions with >10% of
median coverage)

93%

92%

90%

93%

No. of SNPs

84

72

87

83

No. of Indels

8

11

9

6

54


Variant Filtering

55


Detection of low frequency variant in lung adenocarcinoma

56


Data Validation: PyroMark Assay
Validation of KRAS:G12V somatic mutation by pyrosequencing assay

Tumor sample-1
(KRAS c.35G>T 35%)

Normal sample-1
(KRAS c.35G>T 0%)

Mutation analysis of codons 12 of KRAS using PyroMark Q24. Upper Pyrogram show G to T mutation in position 1 of
codon 12 of KRAS in lung adenocarcinoma sample. The mutation rate (35%) is similar to the NGS results (38%) confirming
the reliability of GeneRead DNAseq Gene Panel. The lower Pyrogram shows normal genotype in normal sample-1.

57


Streamlined sample-to-result workflow

PyroMark Assays

Gene Reader
Eco Solution

2013

Streamlined, standardized and automated sample-to-result workflow

58

Ngs part ii 2013

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