tp53 mutational analysis - methods and approaches€¦ · ssca2 + sanger sanger n=50 1) high...
TRANSCRIPT
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Jitka Malčíko á, Eugen Tausch
TP53 mutational analysis
- methods and approaches
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Sanger sequencing
Interpretation
Reporting
Sample collection
cDNA
Prescreening methods
dHPLC, SSCP, HRM FASAY
General scheme of TP53 analysis
gDNA RNA
NGS
Confirmation?
See the following presentations
Visit the practical workshop
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Sample collection
Peripheral blood – the most relevant in CLL
Bone marrow – not standard, but relevant material
Lymph nodes – difficult to obtain – may be relevant in specific cases (e.g. SLL or Richter)
Whole blood
Leukocytes
Mononuclear cells – the most appropriate
Separated CD19+ – time consuming and expensive – in diagnostic labs not necessary
• Material
• Cells
acceptable, fast processing
Results of ERIC - TP53 survey
Results of ERIC - TP53 survey
0%
20%
40%
60%
80%
100%
Peripheral blood Bone marrow Lymph nodes
0%
20%
40%
60%
80%
Whole blood Leukocytes Mononuclear
cells
CD19+
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Sample collection
• The result is influenced by lymphocytosis • In general practice information on lymphocytosis is mostly unavailable
• Physicians should be informed of the cell type used for analysis
• Negative result from non-separated leukocytes in patient with <50% lymphocytes is not reliable
• Generally no relevant information is obtained from samples in remission unless separated cells and/or sensitive methods are used
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Type of nucleic acid
+ Stable during shipment
- Multiple amplicons (exons), more sequencing reactions
+ The primary hit is identified
+Majority of mutations is detected except rare complex mutations
gDNA
-Prone to degradation
+ One amplicon, few sequencing reactions
- Identified change may not be the primary hit
- Truncating mutations may be missed due to nonsense-mediated RNA decay
RNA/cDNA
Results of ERIC - TP53 survey
0%
20%
40%
60%
80%
100%
gDNA RNA
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Nonsense-mediated RNA decay (NMD)
• Surveillance mechanism reducing errors in gene expression by eliminating transcripts that contain premature termination codons (PTC)
• Mutations leading to PTC formation = truncating mutations
• Nonsense mutations
• Frameshift deletions and insertions
• Splice-site mutations
• Factors influencing the effectivity of NMD
• Position of the stop codon (e.g. exon junction distance)
• Cell condition: stress, malignant transfromation.…other u k o fa tors
http://muehlemann.dcb.unibe.ch/
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Type of nucleic acid
• gDNA is preferable
• cDNA is still an option, but:
• More sensitive methods should be used – direct Sanger sequencing of cDNA results in lower sensitivity for detection of truncating mutations
• Special care during shipment - must be processed in limited time period after the sampling
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Which exons
N=
28
71
7
Ze
nz
et
al
20
09
IA
RC
CLL
Ca
nce
r
2 3 4 5 6 7 8 9 10 11 splice
site
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Which exons Z
en
z e
t a
l 2
00
9
2 3 4 5 6 7 8 9 10 11 splice
site Minimal Covered Region
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Which exons Z
en
z e
t a
l 2
00
9
Cover at least exons 4-9, but better 4-10 to
detect all mutated CLL cases
2 3 4 5 6 7 8 9 10 11 splice
site Minimal Covered Region
Optimal Covered Region
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Which exons Z
en
z e
t a
l 2
00
9
Minimal Covered Region
Optimal Covered Region
Cover at least exons 4-9, but better 4-10 to
detect all mutated CLL cases
2 3 4 5 6 7 8 9 10 11 splice
site
Include splice junctions (+/-2 intronic bp)
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Which exons
• Outside DNA binding domain truncating mutations are more frequent
Bullock & Fersht 2001
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Choosing the right method in the lab
• Methods established in the lab and used for other diagnostic purposes
• Available instrumentation
• Number of samples analyzed in the lab
• Financial and personal capacity
• Gene and disease specificities • Clinical impact of low-burden mutations in CLL – importance of
method sensitivity
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Methods used in other labs
54%
10%
10%
6%
14%
4% 2%
FASAY5 + Sanger
NGS3 + Sanger
dHPLC4 + Sanger
HRM1-PCR + Sanger
NA
SSCA2 + Sanger
Sanger
n=50
1) High resolution melting analysis
2) Single strand confirmation analysis
3) Next generation Sequencing
4) Denaturating high perfomance liquid chromatography
5) Functional analysis of separated alleles in yeast
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dHPLC (denaturing high-performance liquid chromatography) Oefner & Underhill (1995)
DNA Extraction from PBMCs
Amplification of Exons 4, 5, 6, 7, 8/9, 10
Denaturation and renaturation of mutated and wildtype
DNA fragments results in heteroduplex DNA fragments.
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dHPLC (denaturing high-performance liquid chromatography)
100% wt homoduplex
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dHPLC (denaturing high-performance liquid chromatography)
homoduplex
heteroduplex
60% wt 40% mut
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homoduplex 100% mut
dHPLC (denaturing high-performance liquid chromatography)
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dHPLC (denaturing high-performance liquid chromatography)
homoduplex 100% mut
mix wt DNA (i.e. 20%)
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dHPLC (denaturing high-performance liquid chromatography) Example 1:
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Example 2:
dHPLC (denaturing high-performance liquid chromatography)
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Example 3:
dHPLC (denaturing high-performance liquid chromatography)
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dHPLC - fragment collection Example 4: Mutation SMALL
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dHPLC - fragment collection Example 4: Mutation SMALL
mutated fragment wildtype fragment
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FASAY - Functional analysis of separated alleles in yeast
• Gene-specific – designed specifically for TP53
• Detects mutations based on the disturbed transactivation capabilities of mutant protein
Ishioka et al., 1993
Flaman et al., 1994
Smardova et al., 2002 Provided by prof. Smardova
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RNA from PBMNCs
FASAY principle
Medium with minimal amount of adenine
ADE2
ADE2
RGC
1
LEU2
393
TP53
RGC
ADE2
ADE2
1
LEU2
393
TP53
Mut p53
RT-PCR (proofreading polymerase)
393 1
346 67 5´ TP53 gene 3´ TP53 gene
42 374 TP53 gene
+
Transformation
wt p53
Smardova et al., 2014 modified
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NEGATIVE - Red colonies below established threshold
(10% in our lab)
POSITIVE Red colonies above established threshold
(10% in our lab)
FASAY results
Validation & mutation identification
(Sanger sequencing)
• cDNA: >30 % red colonies
• DNA from red colonies
• Pooling of DNA
• Individual colonies
• Sequencing of gDNA – optional
• Further confirmation
Report „fu ctio al p53“
Report „ uta t p53“
Clonal mutation identified
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FASAY positive & no clonal mutation found
• Suboptimal establishment of the method • Non-proofreading polymerase usage
• Inappropriately prepared vector (self-ligation)
• Low quality of input material • RNA degradation – long deletions, complex indels
• Prolonged sample processing leads to higher frequency of aberrantly spliced transcripts • TP53-beta variant
• intron insertions/deletions
• True presence of multiple minor mutations in the sample • Typically multiple missense mutations identified in FASAY colonies
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Multiple mutations – case report
• Sampling 03/2010: FASAY: 20% red colonies
• Sampling 04/2011: FASAY: 18% red colonies
c.817C>T p.R273C 2x
c.416A>C p.K139T
c.530C>T p.P177L
c.730G>A p.G244S
c.824G>T p.C275F
c.830G>A p.C277Y
c.753_881del, c.895_919del
c.720_938del
c.781_783dupAGT
c.817C>T p.R273C
c.329G>C p.R110P
c.517G>T p.V173L
c.581T>G p.L194R
c.722C>T p.S241F
c.724T>C p.C242R
c.733G>C p.G245R
c.823T>C p.C275R
c.195_315del
c.375_396del
4.2%
0.4%
0.3%
0.4%
0.3%
0.3%
0.6%
Ultra-deep NGS
+ 13 additional mutations –
compound variant allelic
burden: 16%
% variant reads Mutations in FASAY colonies
c.376-1G>A 0.7%
c.395A>G p.K132R 0.7%
c.488A>G p.Y163C 0.4%
c.527G>T p.C176F 0.3%
c.535C>T p.H179Y 1.0%
c.659A>G p.Y220C 0.7%
c.673-2A>C 0.6%
c.673-2A>T 1.4%
c.701A>G p.Y234C 1.8%
c.716A>G p.N239S 0.3%
c.730G>T p.G244C 0.5%
c.743G>A p.R248Q 0.4%
c.823T>C p.C275R 0.6%
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FASAY - conclusion
• Sensitivity
• Price
• Functional readout – detects biologically important mutations
• Distinguishes allelic constitution
• Cumulative readout
• RNA based
• Not easy to establish
0
1000
2000
3000
4000
5000
6000
Sanger sequencing FASAY
EU
R
Price per 100 patients with 10 being positive
for mutation
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Sanger sequencing
• Widely used, almost each lab has a sequencing machine
• Minimum time for establishment required
• With or without prescreening
Confirmation and identification of the mutation suggested by any prescreening method
The only method used
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Sanger sequencing • Choosing the right primers
• Establishment of PCR
• Optimization of sequencing to minimize the noise
http://p53.iarc.fr/ProtocolsAndTools.aspx
• Analysis of the Sanger sequencing data (visit the Practical workshop)
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To prescreen or not to prescreen?
• Cost-effectiveness
• Sensitivity
• Require experience
• Need time to optimize the method
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How far is the time when all the labs will use Next Generation
Sequencing (NGS)?
...is this generation
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Next Generation Sequencing (NGS) - Why?
(visit the Practical workshop)
• To increase sensitivity
• To increase sample throughput
• To combine with mutation analysis in SF3B1, BIRC3 etc
• To determine variant allele fraction
Rossi et al., Blood, 2014
TP53 wild-type
Sanger -negative mutation
Sanger positive mutation
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NGS – Experience from Brno
• Designed for retrospective analysis
• Aim – sensitivity below 1%
• Very high coverage, mean 25000, minimum 5000
• Sufficient DNA input – 5000 cells (~30ng)
• Proofreading polymerase
UNTREATED RELAPSE
THERAPY OR?
FASAY positive FASAY negative
0
2
4
6
8
10
12
Average error rate
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NGS – workflow
• Amplicon sequencing
• Exons 2-11 in 7 amplicons
• Library preparation – Nextera XT
• Format 24/96 samples per run
• Sequencing on Miseq – kit v2
• In-house bioinformatics pipeline
• Sensitivity 0.2%
• Validation
• Repeat NGS from independent PCR of particular exon
Malcikova et al., Leukemia 2015
Illumina.com
Nextera XT principle
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Bioinformatics pipelines
- the tricky part of NGS
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Base calling Reads pre-
processing
Mapping on
reference
Local realignment
Variant
annotation
Variant detection
Biological
interpretation
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FASAY vs. ultra-deep NGS
FASAY
inconclusive,
7
FASAY-mut,
33
FASAY-wt
retrospective
analysis; 20
FASAY-wt -
prospective
samples; 60
Examined patients n=120
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FASAY vs. ultra-deep NGS
FASAY
inconclusive,
7
FASAY-mut,
33
FASAY-wt
retrospective
analysis; 20
FASAY-wt -
prospective
samples; 60
Examined patients n=120
NGS minor-
clone mut,
18
NGS wt,
2
FASAY-wt
retrospective samples
with known clonal evolution
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FASAY vs. ultra-deep NGS
FASAY
inconclusive,
7
FASAY-mut,
33
FASAY-wt
retrospective
analysis; 20
FASAY-wt -
prospective
samples; 60
Examined patients n=120
NGS minor-
clone mut,
18
NGS wt,
2
FASAY-wt
retrospective samples
with known clonal evolution
FASAY mut
confirmed +
additional
mut
detected;
20
FASAY mut
confirmed;
13
FASAY-mut
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FASAY vs. ultra-deep NGS
FASAY
inconclusive,
7
FASAY-mut,
33
FASAY-wt
retrospective
analysis; 20
FASAY-wt -
prospective
samples; 60
Examined patients n=120
FASAY mut
confirmed +
additional
mut
detected;
20
FASAY mut
confirmed;
13
FASAY-mut
NGS minor-
clone mut,
18
NGS wt,
2
FASAY-wt
retrospective samples
with known clonal evolution
NGS-mut, 5
NGS-wt, 2
FASAY inconclusive
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FASAY vs. ultra-deep NGS
NGS-wt, 54
minor-clone
mut, 4
functional
germline mut, 1
nonsense-
mediated RNA
decay, 1
NGS-mut; 6
FASAY-wt
prospective samples
before treatment initialisation
FASAY false negativity
FASAY
inconclusive,
7
FASAY-mut,
33
FASAY-wt
retrospective
analysis; 20
FASAY-wt
prospective
samples; 60
Examined patients n=120
NGS-mut, 5
NGS-wt, 2
FASAY inconclusive
FASAY mut
confirmed +
additional
mut
detected;
20
FASAY mut
confirmed;
13
FASAY-mut
NGS minor-
clone mut,
18
NGS wt,
2
FASAY-wt
retrospective samples
with known clonal evolution
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Conclusions FASAY vs. NGS
• NGS detects truncating mutations underrepresented by FASAY
• 1/60 FASAY-wt patients
• NGS detects minor clone TP53 mutations that represent risk for being selected by standard treatment regimens
• Mutation load – 0.2-4%
• In our prospective FASAY-negative
cohort of patients entering treatment
– 4/60 patients carried minor mutation
• Sensitivity matters - the deeper you go the more you get
• What sensitivity do we really need?
Malcikova et al., Leukemia 2015
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NGS – Experience from Ulm
Customized Illumina TSCA Panel for standard diagnostics
1st version for 8 genes including TP53
2nd version for 11 genes (ERIC panel)
96 sample library
48 samples pooled per MiSeq run
2d 2d 1d 5d
library prep MiSeq run Bioinformatics Second look
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NGS – Experience from Ulm
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NGS – Experience from Ulm
CLL2o: Comparison Wave vs. NGS
Cohort: 110 patients within a GCLLSG trial for Alemtuzumab treatment in
1) Fludarabine refractory or
2) 17p deleted untreated or
3) 17 deleted pretreated patients
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NGS vs Wave: 110 pat. from CLL2o
Cohort: 110 patients within a GCLLSG trial for Alemtuzumab treatment in
1) Fludarabine refractory or
2) 17p deleted untreated or
3) 17 deleted pretreated patients
TP53mut
n=80
TP53wt
n=30
dHPLC+Sanger
TP53mut
n=80
TP53wt
n=30
NGS
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NGS vs Wave: 110 pat. from CLL2o
Cohort: 110 patients within a GCLLSG trial for Alemtuzumab treatment in
1) Fludarabine refractory or
2) 17p deleted untreated or
3) 17 deleted pretreated patients
dHPLC+Sanger NGS
96,4% overlap!
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NGS vs Wave: 110 pat. from CLL2o
Discrepancies
Pat-ID dHPLC+Sanger NGS Varian fractions Comment
LB 0 R248Q; Y220C 0.077; 0.584
AB 0 P213R 0.982
DA p.N311KfsX33 0 ? subclonal deletion
NG p.G59VfsX60 0 ? subclonal deletion
PS p.H179Y H179Y; P177R 0.655; 0.089 subclonal SNV
SC p.L265P L265P; V216M 0.680; 0.109 subclonal SNV
PM p.G245V G245V; R145H 0.246; 0.082 subclonal SNV
WK
p.W53E62delinsX,
p.P278R P278R 0.996 subclonal deletion
CC p.S241SfsX6 C242F 0.216 wrong annotation
CH p.E258G, p.D281E D281E; E258G; P177R 0.446; 0.318; 0.181 subclonal SNV
DR R248W, V143 R248W; Y234C; V143M 0.081; 0.082; 0.099 sublconal SNV
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NGS vs Wave: 110 pat. from CLL2o
Conclusions for NGS:
Need for improvement in detection of deletions &
insertions
Using IGV to manually check for missed variants
Improve annotation
Conclusions for dHPLC+Sanger:
Subclonal mutations not well covered
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NGS vs Wave: CLL14 study of GCLLSG
Initial plan: TP53 analysis via NGS!
Combined with other genes
Higher Sensitivity / Output of variant fraction
Less hands on time
But: TP53 analysis within 4 weeks after sampling!
With ~7 samples per week no chance to get 48 sample
batches and thus to use the current NGS setting!
3d 2d 1d 2d
Shipping Prep & Run Bioinformatics Final look &
Reporting
2d 14d
Pooling
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Acknowledgement
• Šárka Pospíšilo á
• Šárka Pavlová
• Jana Š ardo á
• Martin Tr ušek
• Nikola Tom
• Boris Tichý
• Barbara Kantorová
• Martin Fox
• Stephan Stilgenbauer
• Hartmut Döhner
• Julia Sempf
• Doris Winter
• Melanie Flauger
• Christina Galler