bhs annual meeting 2014 01.02 · implementing next-generation deep-sequencing assays in diagnostic...

Implementing next-generation deep-

sequencing assays in diagnostic

algorithms in hematological malignancies

Dr. Alexander Kohlmann

BHS Annual Meeting 2014 – 01.02.2014

1. Diagnosis/Classification

2. Prognosis

3. Targeted/individualized therapy

4. Minimal residual disease (MRD)

Medical Need for Molecular Characterization

Adopted from Lex Nederbragt http://dx.doi.org/10.6084/m9.figshare.100940

Developments in NGS and 2014 Outlook

Adopted from Lex Nederbragt http://dx.doi.org/10.6084/m9.figshare.100940

Developments in NGS and 2014 Outlook

MiSeqDx

NextSeq

HiSeq X Ten

MinION

Junior+

GeneReader

Editor's Summary: A Cancer Genome

“The technologies that made it possible to characterize individual

African and Chinese genomes have broad application in the biomedical

field. A demonstration of what can be achieved in a medical context is

the first comprehensive sequence of an individual cancer genome,

for a patient with AML”

6 Nov. 2008

NGS and Hematological Malignancies

• Chronic Lymphocytic Leukemia, Nature 2011: NOTCH1 mutations

• Chronic Lymphocytic Leukemia, NEJM 2011: SF3B1 mutations

• Hairy Cell Leukemia, NEJM 2011: BRAF mutations

• Multiple Myeloma, Nature 2011: BRAF mutations

• non-Hodgkin Lymphoma, Nature 2011: MLL2 mutations

• Burkitt Lymphoma, Nat Gen, Nature 2012: ID3 mutations

• MDS, Nature + NEJM 2011: SF3B1 + splicing machinery mutations

• AML, Blood 2011: BCOR mutations

• AML, Nature, Cell, NEJM 2012: clonal evolution, cohesin genes mutations

• WM, NEJM 2012: MYD88 mutations

• T-LGL, NEJM 2012: STAT3 mutations

• MPN, NEJM 2013: CALR mutations

MLL Munich Leukemia Laboratory

• 454 GS FLX [3]

• 454 GS Junior [4]

• Illumina MiSeq [3]

• Ion Torrent PGM [1]

• NimbleGen

• Fluidigm

• RainDance

• Beckman Coulter [4]

• IT (Cluster)

NGS platforms

www.mll.com

PatientGeneral

practioner

Pathologist

Technician

Cytogeneticist

Molecular

biologist

Biostatistician

Hematologist

Interactions in Routine Diagnostics

• 3.3 Gb region

• ~30-40X coverage

• Read: 2 x 100 bp

• Structural aberrations

Basic Principles of NGS Approaches [DNA]

“Whole”-exome (WES)

Gene panels

Whole-genome (WGS)

• >1000X coverage

• Deep-sequencing

• Up to few 100 kb region

• Read: 500 bp bidirectional

• Quantitative data

• ~60 Mb region

• ~100X coverage

• Read: 2 x 100 bp

• Coding regions

Example analyses:

• TET2

• CBL

• KRAS

• RUNX1

Accreditation: DIN EN ISO 15189:2007

www.mll.com

Amplicon deep-sequencing in

routine diagnostics operations

Mutation Analysis Using 454 Sequencing

Target (gene / region)

Sample Preparation Options for NGS

96-well plates Access Arrays Microdroplets

µL reaction volume

2000 ng / plate

up to 95 amplicons / case

nL reaction volume

50 ng / reaction

48 amplicons x 48 cases

pL reaction volume

2000 ng / sample

up to 4000 amplicons

E33,409bp

E4 91bp

E10355bp

E11 1,472bp

E594bp

E6209bp

E7151bp

E890bp

E9138bp

13 amplicons 6 amplicons

27 amplicons

• Median: 343 bp

• Minimum: 336 bp

• Maximum: 350 bp

bi-directional sequencing

454 Sequencing Candidate Genes: TET2

900,000 reads

454 Life Sciences Titanium PicoTiterPlate

Next-generation Sequencing

T A C G

Read: GS6YAAE01AK65W

rank=0000487

x=124.5

y=1266.5

Read length=412

TGTACTACTCTACGGTAGCAGAGACTTGGTCTG

ACCGGGATCTCCTCTCTGGTTTCTCCTCTTTAG

TAATCTCTATGGGCGTGTGTGGTATCAACATGG

GATGCACCATGCCCAACCCCAGGGCATCTTGGT

AGGTCACAAACTCTGGACGGCCGGTGGGAAGCC

CATAGGGCAACCCAGGCTTTGGGGCAAGGTGCC

CAGGAAACAGACTGCCATTGGGTAACAAAACTG

GGTGAGGGTAGACAGGTCCTTTGCCATGTAAGG

AGAGGGGACTTACAGCAATGCCCTCAGGGGCTG

GGTAAGGGAGGTAACTCCTGGGGTAGGGAATTG

GTGGGGACCTGAATGCCTCATTTGGAGACAGAA

ATATAGAGCTTGGTGGAAGGCCTGTAGAACCAT

GTCGTCAGTGTGAGTA

Sequencing Methodologies: 454 Life Science

Illumina MiSeq Instrument Flow Cell (FC)

16,000,000 reads

Sequencing Methodologies: Illumina

@M01261:54:000000000-

A4RR3:1:1101:15637:1456

1:N:0:1

CAGGAACTCACTGCCTCCCAGCTCTGA

AACATACCATTGTTCAAGTTGAACAGA

AAGCTGCACATGTATTTATCATACACT

TTCCCTCTTCTGTCAGCTTCATCTTGA

GAAATAATCTAAAAAGAAAGACACAGG

AGAAAATTCTTTTGGATAAAGGTGATC

AAGCCTGACAGTCAGATCGGAAGAGCA

CACGTCTGAACTCCAGTCACGAGTGGA

TCTCGTATGCCGTCTTCTGCTTGAAAA

AAAAAAAA

+

BBBBBFFFFFFFCGGGGGGGGGHHHFB

5FFFHHHHFHHHHHHBFGGGHFHHHHH

GHFHGBFFHGHHHFGHHHHHHHHHHHH

HHHHHHGHHHHHHHHHHHHHFHHHHB5

EFBGGHFHHHFHFHFGGGHHHFHHGHH

0FHFGHHHHHHHHHGHHHHGHFHHFHH

EFHGGHH2GFHHHHHGHGHG/F/<CFH

GHHGHHHGEDHHHHFFGFHHGDG<GFH

F0DGFEFHHGHGGGHGHHHGGGFF0FF

GGGG?=--

Quality by cycle

% Bases by cycle

A

C

G

T

Kohlmann A. et al., Br J Haematol. 2013;160(6):736-53.

NGS Data Analysis: MLL In-House Pipeline

Roche Amplicon Variant Analyzer (AVA) software

c. ??? p. ???

TET2 Variant Analysis Procedure (454)

JSI SeqPilot software

p.Glu1728GlyfsX10c.5183_5187delAGATG

TET2 Variant Analysis Procedure (454)

Prognostic

Information

Disease

Characterization

& Classification

Utility of Amplicon Deep-Sequencing Assays

Predictive

Information

• Amplicon design for deep-sequencing analysis

E8476bp

E3270bp

E4157bp

E5105bp

E6192bp

E7162bp

Transcript ID: ENST00000344691

7 amplicons

Median:

Minimum:

Maximum:

342 bp

341 bp

348 bp

454 sequencing fusion primers

0

100

200

300

400

500

600

700

800

900

[FU]

15 50 100 150 200 300 400 500 700 1500 [bp]

15

424

1500

RUNX1_11-39458_11-3978...

424 bp

Grossmann V. et al., Haematologica. 2011;96(12):1874-7.

Deep-sequencing of RUNX1 mutations

Robustness of Amplicon Deep Sequencing

Grossmann V. et al., J Mol Diagn. 2013;15(4):473-84.


Robustness of Mutation Calling Study

IRON-I Study: Participants and Laboratories

Austria

GB

Belgium

Germany

Germany

Italy

Austria

USA

Nether-

lands

Dr. Gabriel, Blood Bank, Linz

Dr. Garicochea, Pontifícia Universidade

Católica do Rio Grande do Sul, Porto Alegre

Dr. Simen, 454 Life Sciences, Branford

Prof. Vandenberghe, UZ Leuven, Belgium

Prof. Martinelli, University of Bologna

Dr. JH Jansen, Radboud University

Medical Centre, Nijmegen

Brazil Italy

Prof. Haferlach, Munich

Leukemia Laboratory, Munich

Dr. Timmermann, Max Planck Institute for Molecular Genetics, Berlin

Prof. Basso, Università degli studi di Padova, Padova

Prof. Young, St. Bartholomews,

London

A. Kohlmann et al., Leukemia; 25: 1840-1848, 2011

% mutated Coverage

TET2 Thr1096Serfs*7

IRON-I: Inter-laboratory Reproducibility

Longitudinal Study on Detection Robustness

TP53 p.Leu194Arg

TP53 mutation (p.Leu194Arg) studied during 11 distinct runs

- routine operations from 19. June 2013 – 03. September 2013

- 33 independent PCR assays

- three distinct molecular barcodes (MID-066, MID-067, MID-068)

- three distinct sequencing instruments

Performance: Reproducibility & Linearity

KRAS: p.Gly13Asp CEBPA

Serial dilutions of amplicons yield

consistent results

Distinct sample preparation assays

are leading to robust results


p.Gln235*

p.Arg139_Ser140insPro

p.Leu294Serfs*7

p.Arg444Profs*128

Kohlmann A. et al., Leukemia. 2014 Jan;28(1):129-37.

Serial Analyses of RUNX1 Mutations

Detection of Residual Disease in AML

RUNX1 double mutation (p.Asp133*, p.Pro157Thrfs*29)

c.396dupT 38.0%

37.0%

2.0%

2.0%

0.4%

0.8%

1.4%

1.6%

9.0%

12.0%c.467dupC

Mu

tati

on

Lo

ad

(%

)

3.61%

good responders

(n=76)

poor responders

(n=27)


Implication of residual RUNX1 mutations

Mutation load reduction analysis of 103 cases at follow-up stage


Prognostic Model of Residual Mutation Load

Significant differences in (a) EFS (median 21.0 vs 5.7 months, P<0.001)

and (b) OS (median 56.9 vs 32.0 months, P=0.002)

a b

poor responders

(n=27; median 5.7 months)

good responders

(n=76; median 21.0 months)

poor responders

(n=27; median

32.0 months)

good responders

(n=76; median

56.9 months)

p<0.001 p=0.002

Baccarani et al., Haematologica. 2008;93(2):161-9.

t(9;22)(q34;q11) in CML and

targeted treatment regimens

Molecular Detection of Mutations in CML

Assay Design for Deep-Sequencing in CML

Soverini S. et al., Blood. 2013;122(9):1634-48.

710 1027

939 1255

1142 1409

1361 1657

P-loop A-loop

T315I

F317L

G250E/R

Q252R/H

E255K/V

Y253F/HM244V

L248V V299L

M351TF359V

H396P/R

L387M/F

TK domain

F486S

1. cDNA Synthesis: 1 µg total RNA required

2. First amplification: BCR-ABL1 transcript breakpoint & kinase domain (KD)

3. Second amplification of first PCR product with 454 barcoded fusion primers

IRON Study Phase II: BCR-ABL1 Data

BCR-ABL1 TKD screening performed in Bologna, Brno, Istanbul, London,

Madrid, Prague, Rome, Salamanca, Vienna (n=615)

1

NovNov MayMayFebFeb MarMar AprApr JulJul AugAug SepSep OctOctIM DAS

M351T

59.55%

male, 64 yrs

DAS after IM failure

M351T

89.99%

Cytogenetic relapse

46.56%

M351T

3.50%

100%

20%

0.05%

JunJun NovNov DecDec JanJan FebFeb MarMar

M351T

0.53%

53.27%

JanJanDecDec

85.32%M351T+F317L

M351T

M351T+F317L

CCyR

Lower limit of Sanger Sequencing

The M351T-positive clone re-emerges

The M351T-positive clone acquires an F317L mutation

which confers greater selective advantage

MMolR

Data provided by Dr. Simona Soverini, Univ. of Bologna

What is the Future of NGS Diagnostics?

exome or

genome

sequencing

deep-sequencing

of gene panels

• Gene expression

• Methylation

• Digital PCR

Myeloid malignancies are clinical and biological heterogeneous diseases

Gene Panels in Myeloid Malignancies

Cazzola M. et al., Blood. 2013;122(25):4021-34.

SF3B1 mutation:refractory anemia with

ring sideroblasts

Mutations in MDS and MDS/MPN

SF3B1/JAK2 or SF3B1/MPL co-mutation: refractory anemia with

ring sideroblasts associated withMarked thrombocytosis

Miscellaneous drivermutations: refractory

cytopenia with unilineagedysplasia (refractory anemia)

Refractorycytopenia withmultilineage

dysplasia

Refractoryanemia with

excess blasts

TET2/SRSF2 co-mutation: chronicmyelomonocyticleukemia

Various combinations of foundingdriver mutations involving genes of

RNA splicing (SRSF2, U2AF1) orDNA methylation (TET2, DNMT3A),

and subclonal driver mutations involving genes like ASXL1, EZH2,

RUNX1, or TP53

Activating GSF3Rmutation: chronicneutrophilic leukemia

Various foundingmutations plus subclonal SETBP1mutation: atypicalchronic myeloid leukemia

CALR (Calreticulin) Mutations in MPN

CALR positive myeloproliferative neoplasms suggested to have a more

benign clinical course than the corresponding disorders associated with

JAK2 or MPL mutations

Klampfl T. et al., N Engl J Med. 2013 Dec 19;369(25):2379-90.

“Evaluation of the mutation status of TP53, EZH2, ETV6, RUNX1, and ASXL1

would add the most information to clinical prognostic scores as currently assessed

in patients with myelodysplastic syndromes.”

Rafael Bejar et al., NEJM 2011: 18 genes in 439 MDS patients

Bejar R. et al., N Engl J Med. 2011;364(26):2496-506.

Clinical Effect of Point Mutations in MDS

“Our whole-exome sequencing study unexpectedly unmasked a complexity of novel

pathway mutations found in approximately 45% to 85% of myelodysplasia patients

depending on the disease subtypes.”

Kenichi Yoshida et al., Nature 2011: spliceosome in 582 MDS patients

Yoshida K. et al., Nature. 2011;478(7367):64-9.

E/A splicing complex:

• SF3B1

• SRSF2

• U2AF1

• ZRSR2

• SF3A1

• SF1

• U2AF2

• PRPF40B

Splicing Machinery Mutations in MDS

• Patients with cytogenetic aberrations: 33%

• Patients with molecular oncogenic aberrations: 78%


Elli Papaemmanuil et al., BLOOD 2013: 111 genes in 738 MDS patients

Papaemmanuil E. et al., Blood. 2013;122(22):3616-27.

Haferlach T. et al., Leukemia. 2013 Nov 13. [Epub]

Torsten Haferlach et al., LEUKEMIA 2013: 104 genes in 944 MDS patients

• Patients with cytogenetic aberrations: 31.4%

• Patients with molecular oncogenic aberrations: 89.5%


Papaemmanuil et al. Haferlach et al.

1 SF3B1 TET2

2 TET2 SF3B1

3 SRSF2 ASXL1

4 ASXL1 SRSF2

5 DNMT3A DNMT3A

6 RUNX1 RUNX1

7 U2AF1 U2AF1

8 TP53 ZRSR2

9 EZH2 STAG2

10 IDH2 TP53

11 STAG2 EZH2

12 ZRSR2 CBL

The Top Twelve Genes Mutated in MDS

Haferlach T. et al., Leukemia. 2013 Nov 13. [Epub]Papaemmanuil E. et al., Blood. 2013 Nov 21;122(22):3616-27.

Prognostic Models in MDS Beyond IPSS-R

Model 1 Model 2

14 genes + age + WBC, Hb,

Plt, % blasts, Cytogenetics

according IPSS-R

14 genes only

(13/14 from Model 1)

Haferlach T. et al., Leukemia. 2013 Nov 13. [Epub]

27-gene panel in routine Dx

• ASXL1

• BCOR

• BRAF

• CBL

• CEBPA

• DNMT3A

• ETV6

• EZH2

• FLT3 (TKD)

• GATA1

• GATA2

• IDH1

• IDH2

• JAK2

• KIT

• KRAS

• MPL

• NPM1

• NRAS

• PHF6

• RUNX1

• SF3B1

• SRSF2

• TET2

• TP53

• U2AF1

• WT1

Deep-Sequencing Myeloid Gene Panel

Turn-around time: 6-7 days

Input: 2.5 µg DNA

Single-plex PCR libraries

Pre-made customized library of individual PCR primer-pair droplets:

Tewhey R. et al., Nat Biotechnol. 2009;27(11):1025-31.

Merging patient DNA with primer library:

Emulsion plate collects

PCR droplets for amplification

Genomic DNA

Template Mix

29-gene Primer

Pair Library

Template Preparation Using Microdroplets

29 genes selected

Amplicon design pipeline

450 amplicons

Median length: 164 bp

Range: 108-207 bp

Primer synthesis

Picoliter-volume droplets

Single-plex PCR reaction

SBS Chemistry SBS SequencingAmplicon Generation

Assay overview for massively parallel deep-sequencing:

Deep-Sequencing Using SBS Chemistry

Turn-around time: 6-7 days

Input: 2.2 µg DNA

Single-plex PCR libraries

323 amplicons

13-gene panel:

• ATM

• BIRC3

• BRAF (V600)

• FBXW7

• KLHL6

• KRAS

• NOTCH1 (PEST)

• NRAS

• MYD88

• POT1

• SF3B1 (HEAT)

• TP53

• XPO1

RainDance

MiSeq

Quesada V. et al., Nat Genet. 2011;44(1):47-52.

Puente XS. et al., Nature. 2011;475(7354):101-5.

Wang L. et al., N Engl J Med. 2011;365(26):2497-506.

Landau DA., et al., Cell. 2013;152(4):714-26.

Rossi D. et al., Blood. 2013;121(8):1403-12.

Targeted Deep-Sequencing Gene Panel

What is the Future of NGS Diagnostics?

exome or

genome

sequencing

deep-sequencing

of gene panels

• Gene expression

• Methylation

• Digital PCR

GenomeWeb online, October 2012

Panels vs. Whole-exome & Whole-Genome

“Next-generation sequencing” can be routinely applied in

hematological malignancies in diagnosis and prognosis

Amplicon deep-sequencing is a technically challenging and

complex workflow, including the need for bioinformatics data

analysis support

Laboratory-developed assays enable the characterization of

hematological malignancies for targeted regions, mostly distinct

genes or exons, e.g. RUNX1, BCR-ABL1, TP53, EZH2, KRAS, …

Whole-exome and whole-genome sequencing efforts in cancer

patients lead to novel actionable gene panels for diagnostics

Summary and Conclusions

At this stage, whole-exome/whole-genome diagnostic assessment

either too costly, too difficult to analyze or not thoroughly regulated

concerning ethical topics

bhs annual meeting 2014 01.02 · implementing next-generation deep-sequencing assays in diagnostic...

Documents