Sequencing 60,000 Samples: An Innovative Large Cohort Study for Breast Cancer Risk

Sequencing 60,000 Samples: An Innovative Large Cohort Study for Breast Cancer Risk

Fergus J. Couch, Ph.D.Zbigniew and Anna M. Scheller Professor of Medical Research

Chair, Division of Experimental Pathology

Department of Laboratory Medicine and Pathology, Mayo Clinic

*For research use only, not for use in diagnostics procedures

Fergus J. Couch, PhD

GenomeWebinar July 21, 2016Sequencing 60,000 Samples: An Innovative Large Cohort Study for Breast Cancer Risk

Professor & Chair, Division of Experimental Pathology, Department of Laboratory Medicine & Pathology,

Mayo Clinic

Familial BC

Sporadic BC

93-95%

~25% BRCA1 & BRCA2

5-7%

ALL BREAST CANCER (BC)

Breast and Ovarian Cancer

ALL OVARIAN CANCER (OC)

Familial OC

Sporadic OC

90%

10%

majorityBRCA1 & BRCA2

Melchor Hum Genet 2013; Pennington Gynecol Oncol. 2012

Known breast cancer genes

Contribution of known genes to familial aggregation of breast cancer

Familial risk factors

BRCA1

BRCA2

TP53

ATMPTEN

CHEK2,PALB2

RAD51C/D, CDH1, STK11, BARD1, NBN, XRCC2, RAD50, MRE11A, MLH1, MSH2, MSH6

Gene Phenotype

BRCA1/2 Br & Br/Ov

TP53 Br & Br/Ov

PTEN Kidney & Br

ATM Br/Panc

CHEK2 Br (moderate)

PALB2 Br/Panc

BRIP1 Br (moderate)

RAD51C/D Ov & Br/Ov

CDH1 Lobular

STK11 Peutz Jeghers

BARD1 Br

NBN Br

XRCC2 Br (moderate)

MRE11A Br

RAD50 Br (moderate)

Panel Testing

• Gene panels are effective methods for germline mutation screening of predisposition genes

• Many mutations may only confer moderate risk of disease• The phenotypes (tumor pathology, contralateral disease,

other cancers) associated with mutations are not well understood

• Many VUS of unknown risk are being identified

Results of Panel Testing (Moderate-Penetrance)

Breast cancer risk estimates by panel gene

(Easton et al. NEJM, 2015)

Cancer Site

High Relative Risk (≥5.0)

Moderate(≥1.5 and <5.0)

Low Relative Risk(≥1.01 and <1.5)

Breast TP53, PTEN,

STK11, CDH1, BRCA1, BRCA2

CHEK2, ATM, PALB2AXIN2, BAP1, BARD1, MRE11A,

NBN, RAD50, RAD51C, RAD51D, XRCC2, BRIP1

Colonrectum

APC, MLH1, MSH2, MSH6, PMS2

CHEK2 AXIN2, BMPR1A, CDH1, DCC,

EPCAM, EXO1, MUTYH, PDGFRA, PMS1, PTEN, SMAD4,

STK11, TP53

OvaryRAD51D, RAD51C,

BRCA1, BRCA2, BRIP1

MSH2, MSH6, PALB2, PMS2, MLH1

ATM, AXIN2, BIP1, BARD1, CDH1, CHEK2, EPCAM, MRE11A,

MUTYH, NBN, PTEN, RAD50, , STK11, TP53, XRCC2

Cancer panel genes, stratified by relative risk

CAnceR RIsk Estimates Related to Susceptibility“CARRIERS”

Fergus Couch, Peter Kraft, David Goldgar, Kate Nathanson, Jeffrey Weitzel,

• Define population-based risks of cancers associated with mutations in known and candidate breast cancer predisposition genes

• Modifying effects of environmental risk factors in mutation carriers• Combined risks of rare mutations in predisposition genes and

common risk SNPs• Pathological correlates for breast tumors associated with

predisposition gene mutations

• Estimate risks and penetrance of mutations in high-risk families

• Characterize Variants of Uncertain Significance in predisposition genes

CARRIERS

Define population-based risks of cancers associated with mutations in known and candidate breast cancer predisposition genes

• Population based breast cancer case-control study

• Nested case-control studies from established cohorts• Population based case-control studies

• Germline DNA (blood, saliva, mouth swabs/mouthwash, tissues)

• Panel-based mutation screening of cancer predisposition genes

• Test associations with breast cancer for pooled inactivating mutations within each gene

Mutation Testing – QIAGEN v3 Amplicon panel

Samples for CARRIERS

QIAGEN V3 data coverage

Sample Names TotalReads TotalMappedReadsRatio of coverage by primer in target region across sample

s_NB965-32-1-1_S1 3,831,960 3,781,940 (98.7%) 76.72s_NB965-32-1-2_S13 3,776,884 3,738,063 (99.0%) 25.24s_NB965-32-10-1_S10 3,457,794 3,404,611 (98.5%) 25.19s_NB965-32-10-2_S22 3,677,962 3,625,869 (98.6%) 94.24s_NB965-32-11-1_S11 4,730,970 4,656,051 (98.4%) 18.35s_NB965-32-11-2_S23 3,287,438 3,257,735 (99.1%) 104.55s_NB965-32-12-1_S12 6,417,240 6,208,724 (96.8%) 14.40s_NB965-32-12-2_S24 3,651,456 3,616,001 (99.0%) 98.39s_NB965-32-2-1_S2 4,275,818 4,223,759 (98.8%) 17.01s_NB965-32-2-2_S14 4,018,914 3,974,366 (98.9%) 63.42s_NB965-32-3-1_S3 4,899,236 4,830,972 (98.6%) 17.83s_NB965-32-3-2_S15 4,274,702 4,218,914 (98.7%) 96.04s_NB965-32-4-1_S4 5,252,154 5,188,008 (98.8%) 20.12s_NB965-32-4-2_S16 6,077,506 5,890,458 (96.9%) 15.24s_NB965-32-5-1_S5 3,825,788 3,779,427 (98.8%) 84.32s_NB965-32-5-2_S17 4,283,438 4,218,903 (98.5%) 23.70s_NB965-32-6-1_S6 3,269,102 3,236,539 (99.0%) 21.93s_NB965-32-6-2_S18 3,116,104 3,080,993 (98.9%) 23.40s_NB965-32-7-1_S7 3,887,332 3,840,578 (98.8%) 77.00s_NB965-32-7-2_S19 4,550,598 4,319,764 (94.9%) 113.35s_NB965-32-8-1_S8 4,090,478 4,024,420 (98.4%) 20.45s_NB965-32-8-2_S20 3,962,954 3,908,139 (98.6%) 29.65s_NB965-32-9-1_S9 3,023,768 2,993,373 (99.0%) 28.96s_NB965-32-9-2_S21 4,029,892 3,947,294 (98.0%) 97.25

Pilot Studies

• 24 selected blood-based samples • 22 known point mutations or small indels• 2 large genomic rearrangements

• Included whole genome amplified DNA, blood DNA, saliva DNA

• 2 x 12 batches on Amplicon panel

• Individual bar-coding

• Sequence on Illumina MiSeq

• Informatics blinded to mutation status

CARRIERS Bioinformatics Pipeline

Target sequence coverage

Sample Names Total Reads% Mapped to genome

% Mapped to targets

Minimum coverage

Maximum coverage

Ratio of coverage

s_NB965-32-1-1_S1 3,831,960 98.70% 80.10% 60 4603 76.7s_NB965-32-1-2_S13 3,776,884 99.00% 188 4745 25.2s_NB965-32-10-1_S10 3,457,794 98.50% 73.60% 172 4332 25.2s_NB965-32-10-2_S22 3,677,962 98.60% 74.90% 46 4335 94.2s_NB965-32-11-1_S11 4,730,970 98.40% 75.40% 315 5779 18.3s_NB965-32-11-2_S23 3,287,438 99.10% 75.80% 40 4182 104.6s_NB965-32-12-1_S12 6,417,240 96.80% 65.40% 539 7759 14.4s_NB965-32-12-2_S24 3,651,456 99.00% 76.10% 44 4329 98.4s_NB965-32-2-1_S2 4,275,818 98.80% 79.00% 308 5238 17.0s_NB965-32-2-2_S14 4,018,914 98.90% 77.20% 76 4820 63.4s_NB965-32-3-1_S3 4,899,236 98.60% 79.40% 338 6025 17.8s_NB965-32-3-2_S15 4,274,702 98.70% 75.40% 52 4994 96.0s_NB965-32-4-1_S4 5,252,154 98.80% 77.00% 304 6115 20.1s_NB965-32-4-2_S16 6,077,506 96.90% 63.90% 471 7179 15.2s_NB965-32-5-1_S5 3,825,788 98.80% 78.40% 57 4806 84.3s_NB965-32-5-2_S17 4,283,438 98.50% 72.50% 226 5356 23.7s_NB965-32-6-1_S6 3,269,102 99.00% 77.00% 173 3794 21.9s_NB965-32-6-2_S18 3,116,104 98.90% 75.90% 156 3560 22.8s_NB965-32-7-1_S7 3,887,332 98.80% 76.50% 59 4543 77.0s_NB965-32-7-2_S19 4,550,598 94.90% 56.70% 46 5214 113.3s_NB965-32-8-1_S8 4,090,478 98.40% 74.70% 240 4907 20.4s_NB965-32-8-2_S20 3,962,954 98.60% 75.70% 174 5159 29.6s_NB965-32-9-1_S9 3,023,768 99.00% 75.40% 118 3417 29.0s_NB965-32-9-2_S21 4,029,892 98.00% 70.90% 51 4960 97.3

#CHROM POS REF ALT Samples CAVA_CSN CAVA_GENE Sample_AD Sample_DP Sample_AF2 47637513 T C s_NB965-32-4-1_S4 c.645+2T>C MSH2 141 295 48%

2 215595181 T

TCATACTTTTCTTCCTGTTCA s_NB965-32-8-1_S8 c.1935_1954dup20 BARD1 145 336 43%

2 48033791 GTAAC G s_NB965-32-2-2_S14 c.4001+12_4001+15delACTA MSH6 160 320 50%3 37035159 G A s_NB965-32-1-2_S13 c.116+5G>A MLH1 145 298 49%7 152346193 A T s_NB965-32-2-1_S2 c.377T>A_p.Leu126X XRCC2 80 198 40%8 90983441 ATTTGT A s_NB965-32-12-2_S24 c.657_661del5 NBN 259 567 46%10 89690828 G A s_NB965-32-1-1_S1 c.235G>A_p.Ala79Thr PTEN 209 501 42%11 108183151 G T s_NB965-32-3-2_S15 c.5932G>T_p.Glu1978X ATM 180 372 48%11 108153564 CTTTTA C s_NB965-32-10-1_S10 c.3712_3716del5 ATM 132 260 51%13 32907420 G GA s_NB965-32-6-1_S6 c.1813dupA BRCA2 114 271 42%13 32912466 C CTGCT s_NB965-32-9-2_S21 c.3975_3978dupTGCT BRCA2 111 229 48%13 32944557 C T s_NB965-32-12-1_S12 c.8350C>T_p.Arg2784Trp BRCA2 121 254 48%14 45644539 TAAAA T s_NB965-32-11-2_S23 c.2586_2589delAAAA FANCM 164 370 44%16 3632367 GC G s_NB965-32-6-2_S18 c.5480delG SLX4 109 185 59%16 23649206 GACAA G s_NB965-32-7-1_S7 c.172_175delTTGT PALB2 170 328 52%17 7577069 C T s_NB965-32-3-1_S3 c.869G>A_p.Arg290His TP53 156 312 50%17 33428374 TG T s_NB965-32-5-2_S17 c.748delC RAD51D 170 350 49%17 41246039 CTTTAA C s_NB965-32-5-1_S5 c.1504_1508del5 BRCA1 132 303 44%17 56787218 A G s_NB965-32-9-1_S9 c.706-2A>G RAD51C 132 243 54%17 59857686 G T s_NB965-32-11-1_S11 c.1871C>A_p.Ser624X BRIP1 214 407 53%22 29091178 C A s_NB965-32-1-1_S1 c.1312G>T_p.Asp438Tyr CHEK2 267 523 51%22 29091856 AG A s_NB965-32-10-2_S22 c.1100delC CHEK2 149 382 39%22 29091856 AG A s_NB965-32-12-2_S24 c.1100delC CHEK2 127 366 35%

Pilot Study – Known Variant Detection

chr start.pos stop.pos gene NB965-32-7-2-pval NB965-32-7-2-CNV.log2ratio SNR.dbchr17 41256010 41256379 BRCA1 7.09E-13 -1.02 25.19chr17 41243344 41244253 BRCA1 1.38E-08 -0.92 23.18chr17 41247750 41248099 BRCA1 1.93E-05 -1.09 20.11chr17 41258337 41258686 BRCA1 2.06E-09 -1.07 21.79chr17 41267581 41267930 BRCA1 1.20E-07 -1.02 20.64chr17 41256749 41257098 BRCA1 7.90E-06 -1.01 18.35chr17 41249110 41249459 BRCA1 1.65E-07 -1.13 16.06chr17 41244261 41245170 BRCA1 0.000584913 -0.87 16.76chr17 41246093 41247002 BRCA1 9.35E-08 -0.92 22.32chr17 41245177 41246086 BRCA1 5.37E-06 -0.95 19.46chr17 41242850 41243199 BRCA1 5.94E-09 -1.16 16.72chr14 45624457 45624806 FANCM 4.32E-10 -0.97 23.844chr9 33675926 33676765 PTENP1,PTENP1-AS 0.000761584 -1.29 11.09

PATTERN CNV

Study Design

• 1300 primers along with 36x24 dual barcoded adaptors

• Post-PCR evaluation of libraries by eGel and/or QPCR

• Post-PCR libraries batched by 768 on Illumina 4000 sequencer

• Qiagen v3 amplicon panel protocol fully automated on Janus pre-PCR and post-PCR robots

• 96 sample testing


Global Product Manager, NGS Solutions

Qiagen

Raed Samara, PhD

Sample to Insight

Raed N. Samara, PhD

Global Product Manager

23

QIAseq targeted DNA Panels*: Get more out of your sequencer

*For research use only, not for use in diagnostics procedures

Sample to Insight

Current challenges of targeted DNA sequencing

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Inability to detect low frequency mutations

Inefficient sequencing of

GC-rich regions

Due to PCR duplicates• Limited panel analytical sensitivity

Due to suboptimal enrichment chemistry and primer design strategy• Decreased panel breadth of coverage

QIAseq targeted DNA panels; GenomeWeb webinar 20160721; Raed N. Samara, PhD

Sample to Insight


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PCR and sequencing errors (artifacts) limit variant calling accuracy

A mutation is seen in 1 out of 5 reads that map to EGFR exon 21.Cannot accurately tell whether the mutation is:

1. A PCR or sequencing error (artifact) (False positive), or2. A true low-frequency mutations

EGFR exon 21

*

Variant calling based on non-unique reads does not reflect the mutational status of original DNA molecules


Sample to Insight


QIAseq targeted DNA panels; GenomeWeb webinar 20160721; Raed N. Samara, PhD 26

PCR duplicates limit accurate quantification

Five reads OR library fragments that look exactly the same.Cannot tell whether they represent:1. Five unique DNA molecules, or

2. Quintuplets of the same DNA molecule (PCR duplicates)

EGFR exon 21

Quantification based on non-unique reads does not reflect quantities of original DNA molecules

Sample to Insight

The necessary evil: PCR amplification

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While PCR amplification is required for target enrichment, it results in PCR duplicates

DNA

dsDNA

PCR Amplification under uniform conditions

PCR duplicates & errors

Since PCR duplicates cannot be physically eliminated, correct for them


Sample to Insight

How can PCR duplicates be corrected for?

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Ligate molecularly-barcoded adapters to unique DNA molecules before amplification

dsDNA

Molecularly-barcoded adapter TATCGTACAGAT

Incorporate this random barcode (signature) into the original DNA or RNA molecules (before amplification) to preserve their uniqueness

Correct for PCR duplicates & errors


DNA


Sample to Insight

The necessary evil: PCR amplification

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Suboptimal conditions result in inefficient enrichment of GC-rich regions

dsDNA


Inefficient sequencing of GC-rich regions

Suboptimal chemistry withtwo primers per target region

DNA


Sample to Insight

Optimal chemistry & SPE approach for robust enrichment

30

Single primer extension uses one, not two, target-specific primers

dsDNA

Efficient sequencing of GC-rich regions


Unique, optimal PCR chemistrySingle primer to define target region

DNA


Sample to Insight

Performance: Coverage of GC-rich regions

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CEBPA GC content

Coverage

GC content

Coverage

CCND1

Chemistry used in the QIAseq targeted DNA panels enables efficient coverage of regions high in GC content


Sample to Insight

Solutions to overcome challenges

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Target-specific primers for enrichment (based on SPE)

All required buffers and enzymes Magnetic beads

Molecularly-barcoded library adapters, primers to prepare sample indexed-, sequencing platform-specific libraries

Two boxes are needed to support the workflow

Panel box (kit) Index box (kit)


Sample to Insight

Achieve accurate variant calling with Molecular barcodes

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Count and analyze single original molecules (not total reads) = digital sequencing

A mutation is seen in 1 out of 5 reads that map to EGFR exon 21.Cannot accurately tell whether the mutation is:

1. A PCR or sequencing error (artifact) (False positive), or2. A true low-frequency mutations

False variant is present in some fragments carrying the same molecular barcode

True variant is present in all fragments carrying the same

molecular barcode

Molecular barcode

Molecular barcodes before any amplification

EGFR exon 21

*

* *****

EGFR exon 21 readsEGFR exon 21 reads


Sample to Insight

Achieve accurate quantification with Molecular barcodes

34

Count and analyze single original molecules (not total reads) = digital sequencing

Five reads OR library fragments that look exactly the same.Cannot tell whether they represent:1. Five unique DNA molecules, or

2. Quintuplets of the same DNA molecule (PCR duplicates)

Five unique DNA moleculessince 5 molecular barcodes are detected

Quintuplets of the same DNA molecule (PCR duplicates)since 1 molecular barcode is detected

Molecular barcode

Molecular barcodes before any amplification

EGFR exon 21

EGFR exon 21 readsEGFR exon 21 reads


Sample to Insight

QIAseq targeted DNA panels: Sample-to-Insight solution

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Panels, molecularly-barcoded adapters, and data analysis algorithms

Sample isolation

Library construction & Targeted enrichment

NGS run Data analysis InterpretationSample Insight

Panels and molecularly-

barcoded adapters

Barcode-aware variant

calling pipeline


Sample to Insight

QIAseq Workflow

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Compatible with both Illumina and Ion Torrent platforms

End repair and A tailing

Adapter ligation/Library construction (incorporation of adapters, molecular barcodes, and sample indexes)

Universal PCR amplificationSample indexing and amplification

Sequencing-ready library

Target enrichment by SPE

Enzyme-based random DNA fragmentation


Sample to Insight

QIAseq targeted DNA panels: Solutions for your sequencing challenges


Get more out analytical performance from your sequencer

Confidently detect low-frequency mutations

Efficiently sequence GC-

rich regions

Molecular barcodes• Correct for PCR duplicates and errors• Unmatched analytical sensitivity & specificity

Proprietary enrichment chemistry• Enrich all genomic regions • Uniform sequencing

Sample to Insight

Specifications of QIAseq targeted DNA panels


DNA input As little as 20 ng DNA

Primer multiplexing level 9,600 primers (DNA)

Number of primer pools 1

Enrichment technology SPE-based with molecularly-barcoded adapters

Amplicon size Average 150 bp

Sample multiplexing level 384 (Illumina), 96 (Ion Torrent)

Total workflow time 8-9 hours

Number of libraries per sample 1

Sequencer compatibility Illumina and Ion Torrent platforms

Variant allele frequency called 1%

Sample to Insight

QIAseq DNA Product offerings


DNA Cataloged Custom Extended Booster

Illumina 12-index 96-index (4 sets, for up to 384-plex)

Ion Torrent 12-index 96-index

Panels Indexes

At a glance

Sample to Insight

QIAseq targeted DNA panels

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List of panels


Panel Number of genes Number of primers Type of coverage

Breast cancer panel 93 4831 1

Colorectal cancer panel 71 2929 1

Myeloid Neoplasms panel 141 5887 1

Lung cancer panel 72 4149 1

Actionable solid tumor panel 23 651 2

BRCA1 and BRCA2 panel 2 223 1

BRCA1 and BRCA2 Plus panel 6 348 1

Pharmacogenomics panel 39 146 3

Mitochondria panel Chromosome M 222 4

Inherited diseases panel 298 11579 1

Comprehensive cancer panel 275 11311 1

Types of coverage:1. Exonic regions of genes plus 5 bases to cover intron/exon junctions2. Mix of type of coverage 1 (for tumor suppressor genes) and HotSpots for

Oncogenes3. SNPs4. Full chromosome

Sample to Insight

QIAseq Indexes


For sample indexing

Name Sequencing platform Use to multiplex up to Each kit (SAP ID) is

enough to process

QIAseq 12-Index I (48) Illumina 12 samples per sequencing run 48 samples

QIAseq 96-Index I set A (384) Illumina 96 samples per sequencing run 384 samples

QIAseq 96-Index I set B (384) Illumina 96 samples per sequencing run (192 if used with Set A) 384 samples

QIAseq 96-Index I set C (384) Illumina 96 samples per sequencing run(288 if used with Sets A, B) 384 samples

QIAseq 96-Index I set D (384) Illumina 96 samples per sequencing run (384 if used with Sets A, B, C) 384 samples

QIAseq 12-Index L (48) Ion Torrent 12 samples per sequencing run 48 samples

QIAseq 96-Index L (384) Ion Torrent 96 samples per sequencing run 384 samples

Sample to Insight

QIAseq solutions to detect all Biomarkers using NGS


Go beyond DNA!

Biomarkers

Gene Expression

Copy number variants

Indels

Mutations

miRNA expression

Fusions

QIAseq targeted RNA panels

QIAseq miRNA sequencing system

QIAseq targeted RNAscan panels




Sample to Insight

QIAseq targeted DNA Panels: Get more out of your sequencer

Raed N. Samara, PhD

Global Product Manager

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Thank you for your participation!

Please be sure to fill out our post-webinar survey to let us know how we did!


Sample to Insight

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Questions?

Contact QIAGEN

All our solutions from Sample to Insight on:QIAGEN.com

Contact QIAGEN Technical ServiceCall: 1-800-426-8157 for US

Call: +49 2103-29-12400 EU

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Innovative NGS library prep methods

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Health & Medicine

Sequencing 60,000 Samples: An Innovative Large Cohort Study for Breast Cancer Risk