TP53 mutational analysis - methods and approaches€¦ · SSCA2 + Sanger Sanger n=50 1) High...

Jitka Malčíko á, Eugen Tausch

TP53 mutational analysis

- methods and approaches

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

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


0%

20%

40%

60%

80%

100%

Peripheral blood Bone marrow Lymph nodes

0%

20%

40%

60%

80%

Whole blood Leukocytes Mononuclear

cells

CD19+

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

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


0%

20%

40%

60%

80%

100%

gDNA RNA

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/

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

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

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

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

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)

Which exons

• Outside DNA binding domain truncating mutations are more frequent

Bullock & Fersht 2001

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

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

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.

dHPLC (denaturing high-performance liquid chromatography)

100% wt homoduplex


homoduplex

heteroduplex

60% wt 40% mut

homoduplex 100% mut



homoduplex 100% mut

mix wt DNA (i.e. 20%)

dHPLC (denaturing high-performance liquid chromatography) Example 1:

Example 2:


Example 3:


dHPLC - fragment collection Example 4: Mutation SMALL

dHPLC - fragment collection Example 4: Mutation SMALL

mutated fragment wildtype fragment

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

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

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

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

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%

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

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

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)

To prescreen or not to prescreen?

• Cost-effectiveness

• Sensitivity

• Require experience

• Need time to optimize the method

How far is the time when all the labs will use Next Generation

Sequencing (NGS)?

...is this generation

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

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

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

Bioinformatics pipelines

- the tricky part of NGS

Base calling Reads pre-

processing

Mapping on

reference

Local realignment

Variant

annotation

Variant detection

Biological

interpretation

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

inconclusive,

7

FASAY-mut,

33

FASAY-wt

retrospective

analysis; 20

FASAY-wt -

prospective

samples; 60


NGS minor-

clone mut,

18

NGS wt,

2

FASAY-wt

retrospective samples

with known clonal evolution


FASAY

inconclusive,

7

FASAY-mut,

33

FASAY-wt

retrospective

analysis; 20

FASAY-wt -

prospective

samples; 60


NGS minor-

clone mut,

18

NGS wt,

2

FASAY-wt



FASAY mut

confirmed +

additional

mut

detected;

20

FASAY mut

confirmed;

13

FASAY-mut


FASAY

inconclusive,

7

FASAY-mut,

33

FASAY-wt

retrospective

analysis; 20

FASAY-wt -

prospective

samples; 60


FASAY mut

confirmed +

additional

mut

detected;

20

FASAY mut

confirmed;

13

FASAY-mut

NGS minor-

clone mut,

18

NGS wt,

2

FASAY-wt



NGS-mut, 5

NGS-wt, 2

FASAY inconclusive


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


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



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

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


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

NGS vs Wave: 110 pat. from CLL2o





TP53mut

n=80

TP53wt

n=30

dHPLC+Sanger

TP53mut

n=80

TP53wt

n=30

NGS






dHPLC+Sanger NGS

96,4% overlap!


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


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

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

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

TP53 mutational analysis - methods and approaches€¦ · SSCA2 + Sanger Sanger n=50 1) High...

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