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Department of Genetics,

University Medical Center Groningen

The Netherlands

Next-generation sequencing can replace Sanger sequencing in clinical diagnostics.

Birgit Sikkema-Raddatz

Department of Genetics

Groningen

~190,000 inhabitants ~50,000 students

GRONINGEN


University Medical Center Groningen

No. beds: 1,339 No. employees: >11,0000 Department of Genetics

~ 300 employees Many different nationalities Counseling Diagnostics Research Teaching

Section Genome Diagnostics 10 laboratory specialists, 70 technicians >20,000 genetics tests

Karyotyping SNP & Oligo Arrays FISH Sequencing analysis


NGS into diagnostics

Genome Diagnostics Richard Sinke Eddy de Boer Krista van Dijk-Bos Jan Jongbloed Yvonne Vos Eva van den Berg-de Ruiter Trijnie Dijkhuizen Annemieke van der Hout Henny Lemmink Renee Niessen Jos Dijkhuis Annelies ten Berge Margaret Burton Martine Meems-Veldhuis Inge Mulder Arjen Scheper Martijn Viel

Clinical Genetics Rolf Sijmons Tom de Koning Conny van Ravenswaaij Peter van Tintelen Joke Verhey Jan Oosterwijk Corien Verschuuren-Bemelmans Irene van Langen Beike Leegte

Cisca Wijmenga Genomics coordination center Morris Swertz Freerk van Dijk Pieter Neerincx Martijn Dijkstra Rowida Almomani Lennart Johansson Patrick Deelen

Department of Genetics Department of Genetics

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Many diseases are heterogeneous

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Cardiomyopathies


DCM; dilated cardiomyopathy

normal heart

HCM; hypertrofic cardiomyopathy

ACM; aritmogenic cardiomyopathy

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


Cardiomyopathies

Paul van der Zwaag, thesis, 2012

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Cardiomyopathies

Genetic overlap Up to 50 genes involved

Up to 6 genes in current Diagnostics


Arg294Stop CGA-TGA

Data analyses

9

Sanger sequencing

Sanger Sequencing: analysis of 1 gene Growing number of genes need to be tested for a particular disease


Novel strategies

Candidate Gene Screening

Next Generation Sequencing

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


Novel strategies

Candidate Gene Screening

Next Generation Sequencing

What kind of sequencing machine?

Which technique?


Exome Sequencing (Agilent)

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Exome Library

Fragments of 120bp labeled with Biotine. Contains coding exons of all genes (+/- 20.000)


Exome vs. Targeted

-  Coverage performance in exome data varied significantly between exons and (for some exons) insufficient to rely on exome sequencing only

-  Insufficient coverage may result in missing clinically relevant mutations

Exome sequencing: High number of variants ! clinical interpretation Insufficient coverage ! missing mutations Targeted resequencing = capturing of exons of certain genes


Targeted NGS

What kind of gene panels should we construct to replace

Sanger sequencing?

Based on the indication;

Cardiomyopathies

Hereditary cancer (breast cancer, Lynch syndrome)

Epilepsy

Neurological /movement disorders

Skin disorders

(mental retardation)


!

!Custom made library

Custom made Library Contains all coding exons of 48 cardio genes.

Baits: fragments of 120bp labeled with Biotine.


Cardio: Genes enriched

ABCC9, 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


Cardio: validation

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 nucleotide compared to Sanger: Specificity 100% Sensitivity 98%


Results

1.  Technical validation: Coverage Specificity/ Sensitivity compared to Sanger Sequencing

2. Clinical validation:

Diagnostic yield


48 genes, 1134 targets:

Coverage >30: 99% (<30: 4,398 bps out of 323,651 bps)

Coverage; cardio


Coverage; on average 250x (151 bp paired-end reads )

29 targets out of 1134 in at least one sample

15 exons with insufficient coverage

Results - Coverage gaps


Cardio: Sensitivity/ specificity


Conclusions

Resequencing of gene panels on the MiSeq can be

used in routine diagnostics

99% of all bases coverage >30x

No false positives/ negatives

12-16 patients can be multiplexed

Average coverage: 250x

~15 exons per require Sanger sequencing in parallel


Analysis - Workflow

FASTQ-file FASTA-file

VCF-file


Interpretation tree Cartagenia

Filtering: - quality - 1000 genomes - GoNL - ESP - SNP database - “managed variant lists”


Interpretation tree Cartagenia

Filtering: Quality 1000 genomes GoNL ESP SNP database “managed variant lists”

Per

Per patient 5 – 10 variants


Transfer to results form

Previous Classification Present in HGMD Relevante isoforms Allele frequency Population frequency (1000 G, GoNL) Conservation Splicing effects Conclusion Category:

Prediction programas: Alamut (PhyloP score, Mutation Taster, Polyphen,SIFT)

Benign Likely Benign VOUS Likely Pathogenic Pathogenic


Cardio-panel v2; 55 genes Clinical yield

Since September 2012 in Routine Diagnostics: >800 patients received >450 finished reports >1 MiSeq run per week

Note: ~15% >1 P/LP

43 pathogenic; 11%

134 likely pathogenic; 34%

213 no mutations/ potentially benign; 55%

390 patients

Diagnostic yield:


Challenges

Data sharing!

Existing LOVDs Cartagenia Users Dutch UMCs

Other LSDBs

MMDB

ClinVar


Targeted NGS panels

Panel No. of genes Coverage > 30 for each base

No. of patients analyzed

Cardio 55 99,6 >800 Onco 73 99.5 120 Movement 88 99.37 28 Skin 63 in progres not yet Epilepsy 147 99.63 not yet Neuro Construction Liver Construction


Onco; genes enriched

BRCA1, 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


Targeted NGS; onco

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)


Targeted NGS; onco

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)

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Detection of exon deletions/ duplications

Average coverage per target

Normalisation (first) per sample and (second) per target Calculation of variation coefficient

Targets from X chromosome

One serie of 12 samples Another serie of 12 samples


Detection of exon deletions/ duplications

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.


Conclusion

Obsolete at a coverage of >30 per nucleotide.

In parallel with NGS at a coverage structural <20x.

Exon deletions/ duplication can be detected.

Targeted NGS can replace Sanger Sequencing


Improvements

Reducing Turn-Around-Times

1.  Further robotization of sample processing

2.  Optimizing the “pipe line”

- Filtering parameters

- Generation of reporting files

3.  Automation of interpretation process

How to deal with homologe/ repetitive sequences and

pseudogenes.


Targeted NGS, what else !.

(1) Non invasive prenatal diagnostics

(2) Hematology :

•  amplicon panels for mutation detection

•  TLA panels for structural abnormalities

(3) Neonatology: 72 h Whole Genome

Sequencing

next-generation sequencing can replace - welcome to the ... · next-generation sequencing can...

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