cardio gene panel experience - biosb research...

Jan JongbloedLaboratory Specialist Clinical GeneticsGenome DiagnosticsDepartment of GeneticsUMCG Groningen

CardioGene panelexperience

Cohorts:

Department of Genetics

AcknowledgmentsCardioGenetics:Rowida AlmomaniLudolf BovenAnne HerkertYvonne HoedemaekersIrene van LangenElisabetta LazzariniAnna PósafalviWouter te RijdtRichard SinkePeter van TintelenPaul van der Zwaag

Dutch Cardiology Depts:Maarten vd Berg (UMCG)Folkert Asselbergs (UMCU)Sebastiaan Piers (LUMC)Arthur Wilde (AMC)

Project 671239 Doelmatigheidsfonds UMCG

Genome diagnostics:Annemieke van der HoutJos DijkhuisenLennart JohanssonHenny LemminkMartine Meems-VeldhuisInge MulderRenée NiessenArjen ScheperMartijn VielYvonne Vos Dutch Clin Genet Depts:

Jasper vd Smagt (UMCU)Daniella Barge (LUMC)Karin van Spaendonck-Zwarts (AMC)

CGD:Terry VrijenhoekEdwin CuppenJoris VeltmanJohan den DunnenRaoul Hennekam

GCC (bionformatics):Lennart JohanssonJoeri van der VeldenPieter NierinckxMorris Schwertz

Genetics research:Eddy de BoerCleo van DiemenKrista van DijkRolf SijmonsBirgit Sikkema-RaddatzPieter vd VliesCisca Wijmenga

Dutch Diagnotics Sections:Dennis Dooijes (UMCURonald Lekanne (AMC)Marjon Slegtenhorst (EMC)Arthur vd Wijngaard (MUMC)

cardiomyopathies

DCM; dilatedcardiomyopathy

normalheart

HCM; hypertroficcardiomyopathy

ACM; aritmogeniccardiomyopathy

Wilde & Behr (2013) Nat Rev Cardiol 10:571-83

Rapid increase

van der Zwaag, thesis, 2012

Jongbloed (2011) Expert Opin Med Diagn 5:9-24van der Zwaag, thesis, 2012

Challenge for routine diagnostics:Extensive genetic heterogeneity

60+ genes involved

In multiple clinical phenotypes

no full penetrance

~20% diagnosis

Dutch cardio(myopathy) centers

Gene panel based appraochCardiomyopathies:

Groningen

5 Centers:

Dennis Dooijes (UMCU)Ronald Lekanne dit Deprez (AMC)Marjon Slegtenhorst (EMC)Arthur van de Wiingaard (MUMC)Jan Jongbloed (UMCG)

Utrecht

Amsterdam (AMC)

Rotterdam

Maastricht

Exome sequencing

MYBPC3

Exome sequencing 2011/2012:Agilent Sure Selectwhole exome kit vs 4:

Av cov: 30-60xGreen: >20xOrange: 10-20xRed: <10x

Rowida Almomani

MYBPC3

Exome sequencing 2013:Agilent Sure Selectwhole exome kit vs 5:

Av cov: 50-80xGreen: >20xOrange: 10-20xRed: <10x

Rowida Almomani

Exome sequencing:-High number of variants clinical interpretation-Insufficient coverage missing mutations

Targeted sequencing:capturing of exons of certain genes

Novel strategies: NGS

Candidate GeneScreening

Next GenerationSequencing

Which application?

Aim: apply one comprehensive test.Design and implement various targeted next generation sequencing (NGS) gene-panels

Targeted NGS

Sequencing:Illumina MiSeq machine151 bp sequencingPaired end

Enrichment:Agilent SureSelect

Data analysis:NextGene software

Data filtering/interpretation:Cartagenia software+ Alamut software

In addition Sanger sequenced amplicons: 15 for identification15 of badly covered regions

12 pat/MiSeq run!

Targeted NGS

Sequencing:AMC: Illumina MiSeqEMC: Illumina MiSeqMUMC: Illumina HiSeq 2000UMCU: Solid 5500

Enrichment:AMC: Nimblegen SeqCap easy choiceEMC: Agilent SureSelect custom kitMUMC: Agilent SureSelect WESUMCU: Agilent SureSelect custom kit

Data analysis:AMC: BWA + Genome Analysis TKEMC: BWA + SeqPilotMUMC: MaasVar databaseUMCU: ?

Data filtering/interpretation:AMC: Cartagenia + AlamutEMC: SeqPilot + AlamutMUMC: MaasVar databaseUMCU: Cartagenia + Alamut

Sanger sequencing badly covered amplicons + confirmation

Ludolf Boven, Krista Bos,Lennart Johannson, Eddy de Boer

Cardio-panel v1; 48 genes

MiSeq capacity:1 channel 5 miljoen reads

Readlength 150 bp 5.000.000 x 150 bps = 750.000.000 bp

Paired-end 750.000.000 x2 = 1.500.000.000 bp

Accuracy 75% 75% x 1.500.000.000 = 1.125.000.000 bp

Size Cardio Custom 320.000bp 1.125.000.000 bp /320.000 bp = 3515 bp

12 patients multiplex 3515 /12 = 292

Average coverage 292x

Validationcriteria:

Coverage minimal 30 for each nucleotidecompared to Sanger: Specificity 100%

Sensitivity 98%

Ludolf Boven, Krista Bos,Lennart Johannson, Eddy de Boer

ABCC9, ACTC1, ACTN2, ANKRD1, BAG3, CALR3, CRYAB, CSRP3/MLP, DES, DMD, DSC2, DSG2, DSP, EMD, GLA, JPH2, JUP, LAMA4, LAMP2, LMNA, MYBPC3, MYH6, MYH7, MYL2, MYL3, MYPN, MYOZ1, MYOZ2, PKP2, PLN, PRKAG2, PSEN1, PSEN2, RBM20, RYR2, SCN5A, SGCD, TAZ, TBX20, TCAP, TMEM43, TNNC1, TNNI3, TNNT2, TPM1, TTN, VCL, ZASP/LDB3

Processing in NextGene (1)

Convert FastQ file to FastA

Alignment of reads

Check of read quality with criteria: (removal of unsuitable reads)

median of the read ↑Q20

if a >3bp stretch cannot be called: removal

at least 25 useable bp for mapping

if ≥ 3bp (adjacent) quality ↓Q16: removal or trimming

removal of duplicate reads

Processing in NextGene (2)

Calculating coverage per bp (report on badly covered regions)

Output: mutation report for identity check

Calling of variants in ≥ 20% of reads(Allele frequency >0.20)

Output: vcf-file with variants

Upload into

Birgit Sikkema-Raddatz, Ludolf BovenLennart Johannson, Eddy de Boer

Cardio-panel v1; 48 genes1. Technical validation:- Coverage (quality of sequence data)- Specificity (confirmation Sanger) (100%)- Sensitivity (false positive rate NGS) (98%)- Reproducibility

24 patients, with Sanger sequencing data (~6 genes) 5 patients, duplicate analysis

2. Clinical validation: novel patients multiple runs

- 48 genes, 1134 targets - Coverage >30: 99% (<30: 4,398 bps out of 323,651 bps)

Reproducibility

5 patients analysed twice (in different runs):

- 231 variants (198–268) were detected per sample

- on average, 10 unique variants (8–14) were reported

- in total, 1,007 variants were detected; 51 of these were differently reported; nonconcordance rate: 0.00315%

Due to:* 12/51: badly covered regions* 24/51: alignment problems; different annotation

same variant* 15/51: false positives; allele freq ~0.2; only in F or R

Conclusions

Resequencing of gene panels on the MiSeq can be used in routine diagnostics

99% of all bases of the target genes is of high quality

No false positives

No false negatives

12-16 patients can be multiplexed

Average coverage: >200x (currently ~400x)

~15 exons require Sanger sequencing in parallelSikkema-Raddatz (2013) Hum Mutat

Onco-panel v1; 73 genesBRCA1, BRCA2, PTEN, NF1, CDK4, MUTYH, APC, MSH2, MSH6, MLH1, PMS2, CDH1, STK11, SDHB, RET, SDHD, WT1, SDHC, MEN1, SDHA, FLCN, VHL, NF2, PTCH, FH, BMPR1A, SMAD4, CHEK2, RAD51C, RAD51D, BRIP1, XRCC2, BARD1, HOXB13, KLLN, MITF, ENG, AXIN2, BMP4, TMEM127, CDC73, AIP, CDKN2B, CDKN2C, CDKN1A, CDKN1B, SDHAF2, MAX, PHOX2B, TERT, RUNX1, CEBPA, GATA2, PTCH2, MET, SUFU, TP53, CDKN2A, BAP1, PALB2, DICER1, SMARCB1, SMARCA4, BUB1B, PALLD, EGFR, PDGFRA, KIT, PRKAR1A, ATM, CEP57

Onco panel v1; 73 genesApplication on36 patients

9000 variants

40 variants- 35 substitutions- 5 indels

Filter onnovel variants

n = 40

Sanger Sequencingup to 6 genes

No false-positivesTotal: 105 variants

64 variants- 19 substitutions- 45 indels

Validation on24 patients

Targeted NGS for 73 genes

Sanger Sequencing

64/64 confirmed

Total no. of variants

No false-positives ornegatives

73 genes,996 targetsCoverage >30:99%

Onco panel v1; 73 genes

Class 2 (n = 32/70 ) e.g. RAD51C, MAX, ALK, …Preventive options available for the frequently associated tumor types

No official guidelines yet

Class 1 (n= 25/70 ) e.g. BRCA1, MLH1, RET,…

Preventive options available for the frequently associated

tumor types

Following national / international guidelines

Class 3 (n = 13/70) e.g. TP53, KIT, BAP1, ….

No preventive options available for frequently associated tumor

types (e.g. pancreatic cancer, sarcoma)

3 virtual sub-panels based on preventive options

Onco panel v1; 73 genes

Class 2 (n = 32/70 ) e.g. RAD51C, MAX, ALK, …Preventive options available for the frequently associated tumor types

No official guidelines yet

3 virtual sub-panels based on preventive options

Class 1 (n= 25/70 ) e.g. BRCA1, MLH1, RET,…

Preventive options available for the frequently associated

tumor types

Following national / international guidelines

Class 3 (n = 13/70) e.g. TP53, KIT, BAP1, ….

No preventive options available for frequently associated tumor

types (e.g. pancreatic cancer, sarcoma)

Most patients choose 1 + 2

Status of targeted gene panels

Panel No. of genes

Coverage > 20 for each

No. of patients

Cardio 55 99,3 >1000Onco 73 99,3 ~200Movement 88 99,4 ~100Skin 63 99,4 32Epilepsy 147 99,6 ~40Neuro Agilent IDLiver Agilent ID

Jos Dijkhuis, Martine Meems-Veldhuis, Inge Mulder, Paskal Norel, Arjen Scheper, Martijn Viel

Cardio-panel v2; 55 genesABCC9, ACTC1, ACTN2, ANKRD1, BAG3, CALR3,

CAV3, CRYAB, CSRP3/MLP, DES, DMD, DSC2, DSG2, DSP, DTNA, EMD, EYA4,

GATAD1, GLA, JPH2, JUP, LAMA4, LAMP2, LMNA,

MYBPC3, MYH6, MYH7, MYL2, MYL3, MYPN, MYOZ1, MYOZ2, NEXN, PKP2, PLN, PRKAG2,

PSEN1, PSEN2, RBM20, RYR2, SCN5A, SGCD, TAZ, TBX20,

TCAP, TMEM43, TNNC1, TNNI3, TNNT2, TPM1, TTN, TXNRD2, VCL, ZASP/LDB3

Since September 2012 in Routine Diagnostics:

>1000 patients received~1000 sequenced

-> ~1000 reports sent-> ~2-4 MiSeq run (12 patients) per week-> Of these 2 cardiomyopathy runs

Dutch cardio panels

AMC:23 genes (454): 350 patients41 genes (Solid): 140 patients41/46 (MiSeq): 270 patientsTTN: 60 (454), 20 (S), 50 (Mi)Aritmie: 130 (S), 50 (Mi)

Groningen

Utrecht

Amsterdam (AMC)

Rotterdam

Maastricht

EMC:45 genes (cardiochip): 500 patients

UMCU:CM, 64 genes: 300 patientsConduction panel, 33 genes: 60 patientsCongenital, 34 genes: 50 patientsTTN: 100 patientsConnective tissue, 18 genes: 60 patients

MUMC:34 genes (cardiochip): 260 patients34 genes (454): 100 patients45 (HiSeq): 220 patients

Analysis: workflow

FASTQ-file FASTA-file

VCF-file

Challenge: Data interpretationPer patient ± 250 variants

Benign -- Pathogenic

Filtering:- BED file (exons +/- 20 bp)- Quality (>20x)- 1000 genomes (≥0.02 MAF; ≥200 observation)- GoNL (≥0.02 MAF; ≥200 observation)- ESP (≥0.05 MAF; ≥200 observation)- SNP database (≥0.02 MAF; ≥200 observation)- “managed variant lists”

Cardio-panel v2; filteringPer patient 5 – 15 variants

Annemieke van der Hout, Henny Lemmink, Renée Niessen, Yvonne Vos

Cardio-panel v2; filtering

Coverage >20

MVL* poly MVL

artefactGONL 2%

1000 genomes2% >200 observations

ESP5%>200 observations

dbSNP2% >200 observations

MVL Likely Benign

*MVL = managed variant list

Annemieke van der Hout, Henny Lemmink,Renée Niessen, Yvonne Vos

Cardio‐panel v2; interpretationFields Gene Variant Previous Classification HGMD How often found Relevante isoforms Grantham Score Allele frequency Population frequency

(1000 G, GoNL)

Conclusion Category:

Alamut: PhyloP score Mutation Taster Polyphen SIFT Align GVGD Conservation Splicing Google Scholar

BenignLikely BenignVOUSLikely PathogenicPathogenic

Pathogenic:-truncating mutations in “usual suspects” -missense mutations with sufficient proof

Likely Pathogenic:-truncating mutations in genes less studied-missense mutations fullfilling:

*conserved (at least up to chicken)*most or all prediction programs: pathogenic*not or <0.0005 MAF in control populations

Exception: not fullfilling the above,but additional data available

Diagnostic ReportReport includes

Only (likely) pathogenic mutations(with disclaimer that not all variants are reported)

Conclusion regarding: genotype – phenotype correlation

All tested genes

Total coverage (% of total region of interest covered with >20x)

Average read depth

Request for affected family members for segregation analysis

Normal reports: made by senior technicians, authorized by staff

Anna Pósafalvi

Cardio-panel v2; diagnostic yield

• Yield LP +P = 45%• Note: ~15% >1 P/LP• ~40% P/LP in “usual suspects” • Truncating TTN mutations: 36 (9%) of cases

-> 28 (13%) DCM patients-> 5 (5%) HCM patients-> 1 ARVC, 1 NCCM, 1 CM patient

43 pathogenic; 11%

134 likely pathogenic; 34%

213 VOUS/likely benign; 55%

390 patients

Diagnostic yield:

Anna Pósafalvi

Cardio-panel v2; diagnostic yield

• Yield LP +P = 45%• Note: ~15% >1 P/LP• ~40% P/LP in “usual suspects” • Truncating TTN mutations: 36 (9%) of cases

-> 28 (13%) DCM patients-> 5 (5%) HCM patients-> 1 ARVC, 1 NCCM, 1 CM patient

43 pathogenic; 11%

134 likely pathogenic; 34%

213 VOUS/likely benign; 55%

390 patients

Diagnostic yield:

Conclusion implementation

Improvements:* Reducing Turn-Around-Times1. Further robotization of sample processing

2. Optimizing the “pipe line” (Filtering parameters)

3. Automation of interpretation process

* Detection of exon deletions/duplications to avoid additional

MLPA analyses.

* Improving data interpretation

1. Targeted NGS can replace Sanger Sequencing

2. Improved diagnostic yield (~50% for cardiomyopathies)

Improvements/challenges

MLPA analyses.

Annemieke van der Hout, Henny Lemmink,Renée Niessen, Yvonne Vos

Cardio‐panel v2; interpretationFields Gene Variant Previous Classification HGMD How often found Relevante isoforms Grantham Score Allele frequency Population frequency

(1000 G, GoNL)

Conclusion Category:

Alamut: PhyloP score Mutation Taster Polyphen SIFT Align GVGD Conservation Splicing Google Scholar

BenignLikely BenignVOUSLikely PathogenicPathogenic

Jos Dijkhuis, Inge Mulder, Jerbic

Cardio‐panel v2; interpretation

Dit zit er nog ondercartagenia alamut

MLPA analyses.

Exon deletion/duplications

Average coverage per target

Targets from X chromosome

One serie of 12 samples

Another serie of 12 samples

Instead of MLPANumber of reads: deletion, duplication compared to normal

Normalisation to avoid false positives

Lennart Johansson, Birgit Raddatz

1. Best match: determine the control group

0 200 400 600 800 1000 1

Reeks2

Reeks3

Reeks4

Reeks5

Reeks6

Reeks7

Reeks8

Reeks9

Reeks10

Reeks11

Reeks12

20 per. Z

Controles with the most similar pattern compared to the sample

2. Normalisation per sample and per gen

Compare sample with control group:

Deletion: Ratio 0.65, Z-score <-3 Duplication: Ratio 1.25, Z-score >3

Quality control (calculation of variation)

Threshold to exclude

Bad samples

Bad genes (targets)

0 200 400 600 800 1000 1

Reeks2

Reeks3

Reeks4

Reeks5

Reeks6

Reeks7

Reeks8

Reeks9

Reeks10

Reeks11

Reeks12

20 per. Z

Validation of 120 samples, including 10 known deletions/ duplications

On average 907 of the 930 targets of the onco panel pass the thresholds.No false negative results.

Results

2 positive controls in bad samples1 positive control bad target

1 positive control bad target

CardioOnco

MLPA analyses.

Interpretation

29 support pathogenicity (affected carrier)

13 partly support pathogenicity

6 support no pathogenicity (affected not carrier)

81 families

Outcome cosegregation analysis:

33 not decisive (presymptomatic; 1st degree rel)

Data sharing

Database/sharing (groep 3, Richard Sinke, UMCG)

•Project 1: VKGL, open source (Morris Swertz)Sharing of all data (vcf files); focus on technical aspects first

•Project 2: Cartagenia (Renée Niessen)Sharing data cardiomyopathy panels

•Project 3: pre-NGS data:Via managed variant lists Cartagenia?

Renée Niessen, Dennis Dooijes, Marjon Slegtenhorst,Ronald Lekanne dit Deprez, Arthur van de Wijgaard

Data sharing; cardiomyopathies

Gene panel based appraochCardiomyopathies:

Groningen

5 Centers:

Dennis Dooijes (UMCU)Ronald Lekanne dit Deprez (AMC)Marjon Slegtenhorst (EMC)Arthur van de Wiingaard (MUMC)Jan Jongbloed *UMCG)

Utrecht

Amsterdam (AMC)

Rotterdam

Maastricht

Guidelines NGS

Apply to diagnostic guidelines and recommendations

Minimal gene set:

As of may2013:

Core disease gene listCardiomyopathieën:

46 genes

• Proof of concept• Identify potential issues• Guide development of complete NGS

consortium solution

Renée Niessen, Dennis Dooijes, Marjon Slegtenhorst,Ronald Lekanne dit Deprez, Arthur van de Wijgaard

benignbenignpathogeniclikely benignbenign

(Limited) phenotype:Hypertrophic CMRestrictive CMDilated CMRight ventricular CM

One‐clickSubmission

nt1234

Curation, Validation

Phenotype, filter, assess, interpret, classify, report

• Frequency statistics• Curation information• hom/het; affected

ClinicalUse

NGS consortium solution

• Lessons learned: Lowlands consortium for CNV– >20k cases; already solved diagnostic cases!

• In parallel: similar NGS pilot in US– 5 labs (CHOP hospital lead); panels & exomes

One-clickSubmission

Curation, Validation• Frequency statistics• Curation information• hom/het; affected

ClinicalUse

• First Phase (Finished..)

–Groningen (UMCG) & Utrecht (UMCG)–Both using Bench Lab NGS platform

• Second Phase (Started…)

–Add Rotterdam (EMC), Amsterdam (AMC) & Maastricht (MUMC)

Pilot: Who?

• Analyzed variants– Per analysis => Frequency information– Inconsistent or incomplete labeling possible

• Curated variants– In Bench: Managed Variant List (MVL)– Analysis independent

Two levels of Data

• UMCG– # patients: 1000– # analyzed variants: 230k (6k unique)– # curated variants: 2000 (1800

unique)

• UMCU– # patients: 150– # analyzed variants: 40k 2.5k unique)– # curated variants: 530 (500

unique)

First Phase: Some Numbers

• Only 4 pathogenic variants common between the two labs:

– p.C796R PKP2– p.R79* PKP2– p.Q791fs MYBPC3– p.P955fs MYBPC3

First Phase: Findings

• Both g-, c- and p-notation important!

• Example:–Utrecht

• 11_47359280_-/C c.2373dupG p.Q791fs

–Groningen• 11_47359282_T/CT c.2372delAinsAG p.Q791fs• 11_47359282_-/C c.2371_2372insG p.Q791fs

First Phase: Findings

• Sharing immediately provides new and valuable information.

• Identified important issues.–Notation–Labeling methodology

• e.g. What exactly means ‘likely pathogenic’ ?• e.g. When and how often do you label variants?

Intermediate Conclusions

Conclusions

Gene panel based NGS succesful in clinical diagnostics

Challenges (technical, interpretation, reporting) however

still remain, even in gene-panel based approaches

Deletion/duplication detection from targeted NGS data

possible

Data sharing important for further interpretation

Cohorts:

AcknowledgmentsCardioGenetics:Rowida AlmomaniLudolf BovenAnne HerkertYvonne HoedemaekersIrene van LangenElisabetta LazzariniAnna PósafalviWouter te RijdtRichard SinkePeter van TintelenPaul van der Zwaag

Dutch Cardiology Depts:Maarten vd Berg (UMCG)Folkert Asselbergs (UMCU)Sebastiaan Piers (LUMC)Arthur Wilde (AMC)

Project 671239 Doelmatigheidsfonds UMCG

Genome diagnostics:Annemieke van der HoutJos DijkhuisenLennart JohanssonHenny LemminkMartine Meems-VeldhuisInge MulderRenée NiessenArjen ScheperMartijn VielYvonne Vos Dutch Clin Genet Depts:

Jasper vd Smagt (UMCU)Daniella Barge (LUMC)Karin van Spaendonck-Zwarts (AMC)

CGD:Terry VrijenhoekEdwin CuppenJoris VeltmanJohan den DunnenRaoul Hennekam

GCC (bionformatics):Lennart JohanssonPieter NierinckxMorris Schwertz

Genetics research:Eddy de BoerCleo van DiemenKrista van DijkRolf SijmonsBirgit Sikkema-RaddatzPieter vd VliesCisca Wijmenga

Dutch Diagnotics Sections:Dennis Dooijes (UMCURonald Lekanne (AMC)Marjon Slegtenhorst (EMC)Arthur vd Wijngaard (MUMC)

cardio gene panel experience - biosb research...

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