BHS Annual Meeting 2014 01.02 · Implementing next-generation deep-sequencing assays in diagnostic...
Transcript of 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.
Grossmann V. et al., J Mol Diagn. 2013;15(4):473-84.
Robustness of Mutation Calling Study
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
Grossmann V. et al., J Mol Diagn. 2013;15(4):473-84.
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)
Kohlmann A. et al., Leukemia. 2014 Jan;28(1):129-37.
Implication of residual RUNX1 mutations
Mutation load reduction analysis of 103 cases at follow-up stage
Kohlmann A. et al., Leukemia. 2014 Jan;28(1):129-37.
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%
Clinical Effect of Point Mutations in MDS
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%
Clinical Effect of Point Mutations in MDS
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