the next generation of microsatellite instability...
TRANSCRIPT
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The next generation ofmicrosatellite instability testing
Richard Gallon PhD MBiochResearch Associate, Cancer Prevention Research GroupInstitute of Genetic Medicine, Newcastle [email protected]
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Overview and Disclosures
1. How and why we test for microsatellite instability (MSI)
2. A tumour-sequencing MSI assay, using as few as 6 markers for high throughput diagnostics
Newcastle University owns a patent covering the extended marker set used in the assayPatent ID: PCT/GB2017/052488, published 1st March 2018
Named inventor on a patent filed by Newcastle University covering a reduced marker setPCT application number: PCT/GB2019/052148, unpublished, filing date 31st July 2019
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Clinical Needs for Mismatch Repair Deficiency Testing
Mismatch repair (MMR) deficiency testing of colorectal cancers (CRCs) is recommended to screen for Lynch syndrome (LS) (Balmana et al, 2013; Stoffel et al, 2015; UK NICE DG 27, 2017)
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Pembrolizumab/Keytruda (immune checkpoint inhibitor) is FDA-approved as a second line treatment in any metastatic MSI-H cancer (MERCK & Co. Inc, 2017)
Screening for LS in endometrial cancers is also cost effective (Snowsill et al, 2019)
4-panel IHCOR
MSI testing
No further testing
MMRp / MSS
MLH1 promoter methylation testingOR
BRAF p.V600E testing
MMRd / MSI-H
No further testing
meth. / p.V600E
Germline MMR gene
testing
unmeth. / p.V600
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Tests for Mismatch Repair Deficiency
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(Promega MSI Analysis System v1.2; Alhilal PhD Thesis, 2016)
NR21 BAT26 BAT25 NR24 MONO27 Penta D & PentaC
2) MSI testing by PCR fragment length analysis:1) 4-panel IHC:
αMSH2
(LS adenoma; Gallon PhD Thesis, 2018)
3) MSI testing by next generation sequencing:
…TTGATTTTTTTTTTTTTTTTTTTTTTTTTGAGA…
…TTGATTTTTTTTTTTTTTTTTTTTTTTTTGAGA…
…TTGATTTTTTTTTTTTTTTTTTTTTTTGAGA…
…TTGATTTTTTTTTTTTTTTTTTTTTTTGAGA…
…TTGATTTTTTTTTTTTTTTTTTTTGAGA…
T23
T25
T20
…TTGATTTTTTTTTTTTTTTTTTTTTTTTTGAGA…
microsatellite lengthre
ad f
req
uen
cy
MSS CRC MSI-H CRC
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MSI by NGS
>95%
>95%
Low
High
Yes
Yes
One-step
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Test performance:
IHC MSI by FLA
Sensitivity >95% >95%
Specificity >95% >95%
Cost (inc. overheads) 235€ 226€
Throughput Low Low
Automated Not routinely Not routinely
Other markers tested No No
LS Screening Multi-step Multi-step
MSI by NGS
>95%
>95%
607(±207)€
High
Yes
Yes
One-step
(Shia, 2008; Zhang 2008; Kautto et al, 2016; Zhu et al, 2018; Hampel et al, 2018; Marino et al, 2018; Snowsill et al, 2019)
Only 43% of young (30-49yrs) CRC patients are screened for LS (US statistics; Shaikh et al, 2018)
…lack of testing attributed to cost (33.3%), unfamiliarity interpreting results (29.2%), and unavailable genetic counselling (24.9%) (questionnaire of 509 gastroenterologists; Noll et al, 2018)
Tests for Mismatch Repair Deficiency
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Considerations for MSI testing by NGS
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Sequencingplatform
Error rate (e.g. Ion Torrent technology has high error microsatellite sequencing)
Availability (e.g. the dominance of Illumina platforms)
Marker identity
Sensitivity and specificity:• Length and motif (e.g. mono-, di-, tri-, tetra- nucleotide repeats)• Genomic context• Error rate• Polymorphisms
Classification method
Calling instability at individual markers:• Matched normal DNAs, or reference set of control samples
Classification by proportion of markers that are “unstable”:• All markers are equal…
Marker number
Variable thresholds with different marker panels
Threshold uncertainty with small (<20) marker panels
large panels=
high cost
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Marker selection from TCGA data:
Criteria Reason
Mononucleotide repeats Greatest sensitivity and specificity (Bacher et al, 2004)
Detection of MSH6 deficiency (You et al, 2010)
Short (7-12bp) Reduced sequencing error (Fazekas et al, 2010)
Greatest discrimination between MSI-H and MSS samples by NGS (Maruvka et al, 2017)
Common SNP within 30bp To assess allelic distribution of microsatellite deletions
Monomorphic No need for matched normal DNA
LisaRedford
GhanimAlhilal
A Naïve Bayesian Approach to MSI Classification
120 candidate markers identified.
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LisaRedford
GhanimAlhilal
Discovery cohort:
• 58 CRCs of known MSI status*
• Selection of 17 markers based on ROC AUCs
*MSI by FLA (Promega)
PCR1: Singleplexamplification
Amplicon pooling per sample
Amplicon purification(AMPure XP Beads, Beckman Coulter)
PCR2: Addition of sample indexes and sequencing adaptors
Amplicon purification,dilution and pooling
Library preparation:
A Naïve Bayesian Approach to MSI Classification
Amplicon sequencing, target depth of 5000x
(MiSeq, Illumina)
Sequencing and analysis:
Read alignment to reference genome
(BWA, Li & Durbin, 2010)
Detection of microsatellite deletion frequency and
allelic distribution
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MMRproficient
MMRproficient
MMRdeficient
MMRdeficient
Marker 1
3M
arker 20
SNP allele
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LisaRedford
GhanimAlhilal
A Naïve Bayesian Approach to MSI Classification
Classifier training cohort:
• 139 CRCs of known MSI status*
• MSI classification using microsatellite deletionfrequency and allelic distribution
*MSI by FLA (Promega)
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LisaRedford
GhanimAlhilal
A Naïve Bayesian Approach to MSI Classification
Classifier training cohort:
• 139 CRCs of known MSI status*
• MSI classification using microsatellite deletionfrequency and allelic distribution
*MSI by FLA (Promega)
allelic biasof deletions
95%
5%
5.9%
94.1%
16.0%
84.0%
73.7%
26.3%
microsatellite deletion frequency
Unique probabilities per marker accounts for unique sensitivity to MMR deficiency
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LisaRedford
GhanimAlhilal
A Naïve Bayesian Approach to MSI Classification
Classifier training cohort:
• 139 CRCs of known MSI status*
• MSI classification using microsatellite deletionfrequency and allelic distribution
*MSI by FLA (Promega)
• 100% sensitivity and 100% specificity
ሻ𝑝(𝑂|𝑀𝑆𝐼
ሻ𝑝(𝑂|𝑀𝑆𝑆= ෑ
𝑖=1
𝑁 ሻ𝑝(𝑂𝑖|𝑀𝑆𝐼
ሻ𝑝(𝑂𝑖|𝑀𝑆𝑆
Probability from assay observations:
𝑠𝑐𝑜𝑟𝑒 = log10ሻ𝑝(𝑀𝑆𝐼
ሻ𝑝(𝑀𝑆𝑆.
ሻ𝑝(𝑂|𝑀𝑆𝐼
ሻ𝑝(𝑂|𝑀𝑆𝑆
Calculating assay score:
prior probability = 0.15/0.85
Validation cohort:
• 70 CRCs of known MSI status*
• 97% sensitivity and 97% specificity
• Discordance resolved in 3 of 4 samples
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A Low Cost Sequencing-based MSI Assay for Clinical Diagnostics
24 MNRs and BRAF multiplexed by single molecule molecular inversion probes (smMIPs):
Followed recommendations for the validation of NGS-based oncology assays for clinical diagnostics (Association of Molecular Pathology, College of American Pathologists; Jennings et al, 2017).
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A Low Cost Sequencing-based MSI Assay for Clinical Diagnostics
Validation of diagnostic accuracy:
• Sample number and type
• Independent cohorts
• Reproducibility
Quality controls (QCs):
• Detection limits
• Internal quality checks of library complexity
Clinical utility:
• Sample identification
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A Low Cost Sequencing-based MSI Assay for Clinical Diagnostics
Classifier training and validation:
10100% sensitivity and 100% specificity using either 24 or 6 markers
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A Low Cost Sequencing-based MSI Assay for Clinical Diagnostics
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Assessing detection limits using sample mixtures:
3-6% MSI-H cell line DNA can be detected in mixtures – equivalent to MSI by FLA
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A Low Cost Sequencing-based MSI Assay for Clinical Diagnostics
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Molecular barcodes (MBs) provide an internal QC of sequencing:
10 MBs 15 MBs
>75 MBs/marker ensures results reliability
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Assessing assay reproducibility and portability:
Northern Genetics Service (Newcastle Hospitals):Research laboratory
• 32 CRCs repeat tested from amplification through to classification
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A Low Cost Sequencing-based MSI Assay for Clinical Diagnostics
24 marker panel 6 marker panel
100% concordance 100% concordance
Score correlation R2 = 0.97 Score correlation R2 = 0.97
3 samples had <75 MBs/marker but still correctly classified, in-line with in silico predictions.
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Assay summary:
Future work:
• Commercialisation (Cancer Research UK Commercial Partnerships)
• Continued validation and accreditation (Northern Genetics Service, Newcastle Hospitals)
• Application to extra-colonic cancers
A Low Cost Sequencing-based MSI Assay for Clinical Diagnostics
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24 MNRs plus BRAF 6 MNRs plus BRAF
Accuracy >95% sens. + spec. >95% sens. + spec.
Detection limit >3% MSI-H DNA >6% MSI-H DNA
Quality control >75 MBs/marker >75 MBs/marker
LS screening One-step One-step
Sample identification pr(SNP match) = 3.6x10-10 pr(SNP match) = 3.8x10-3
Reagent cost <20€ per sample <10€ per sample
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Tumour Type Percentage MSI-H (95% CI)
Breast 50.0% (21.5% - 78.4%)
Colorectal 94.0% (83.8% - 97.9%)
Endometrial 74.4% (58.9% - 85.4%)
Other GI 50.0% (2.6% - 97.4%)
Ovarian 85.7% (48.7% - 99.3%)
Prostate 0.0% (0.0% - 94.9%)
Sebaceous Adenoma 100.0% (67.6% - 100.0%)
Other Skin 40.9% (38.7% - 76.7%)
Urothelial 100.0% (56.6% - 100.0%)
MSI Detection in LS Extra-Colonic Cancers
Peter Gawthorpe
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Detection of Constitutional Mismatch Repair Deficiency
Notes:
† homozygous for a hypomorphic PMS2 variant
§ patient 8 (3 samples all with low scores) was recovering from aplasia at sample collection
ROC AUC: 1.00
Dotted lines represent a priori score thresholds of 1.30 (95% probability not a control sample), and 2.00 (99% probability not a control sample).
Using a threshold of 2.00, the assay has 97% sensitivity, and 100% specificity.
Low-level MSI in non-neoplastic tissues is detectable with an alternative analysis pipeline:
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What is the next generation of MSI testing?
Heterogeneous, due to:
• New developments – e.g. automation of IHC
• Compatibility with established clinical pathways
• Cost versus resource
• Local expertise
• Local infrastructure
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Diagnostic niche for low cost sequencing-based MSI assays
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References
Gallon et al, 2019. A sensitive and scalable microsatellite instability assay to diagnose constitutional mismatch repair deficiency by sequencing of peripheral blood leukocytes. Human Mutation, 40(5):649-655. doi: 10.1002/humu.23721.
Gallon et al, 2019. Sequencing-based microsatellite instability testing using as few as six markers for high throughput clinical diagnostics. Human Mutation, doi: 10.1002/humu.23906
Redford et al, 2018. A novel panel of short mononucleotide repeats linked to informative polymorphisms enabling effective high volume low cost discrimination between mismatch repair deficient and proficient tumours. PLoS One, 13(8):e0203052. doi: 10.1371/journal.pone.0203052
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Special thanks to:
The Barbour Foundation for funding my PhD.
My PhD supervisory team, Mauro Santibanez-Koref,
Mike Jackson, and John Burn.
My colleagues Christine Hayes, and Harsh Sheth.
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Additional thanks to:
The CaPP3 clinical trial team: Gill Borthwick, Donna Job,
Jackie Greenwood, Lynn Reed, Lynne Longstaff, and Amy McAllister.
The Northern Genetics Service: Ottie O’Brien,
Amanda Waltham, Helena Spiewak, and Ciaron McAnulty.