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Characterizing cancers from liquid biopsies and FFPE samples: The rise of targeted sequencing panelsApril 13, 2016
Webinar Series
Sponsored by
P. Mickey Williams, Ph.D.Frederick National Lab for Cancer ResearchFrederick, MD
Jeremy Segal, M.D., Ph.D.University of Chicago School of MedicineChicago, IL
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Characterizing cancers from liquid biopsies and FFPE samples: The rise of targeted sequencing panelsApril 13, 2016
Webinar Series
Sponsored by
Brought to you by the Science/AAAS Custom Publishing Office Participating experts
Characterizing cancers from liquid biopsies and FFPE samples: The rise of targeted sequencing panelsApril 13, 2016
Webinar Series
Sponsored by
P. Mickey Williams, Ph.D.Frederick National Lab for Cancer ResearchFrederick, MD
Jeremy Segal, M.D., Ph.D.University of Chicago School of MedicineChicago, IL
P. Mickey Williams, Ph.D.
Director of the Molecular Characterization Laboratory
Frederick National Laboratory for Cancer Research
“Characterizing cancers from liquid biopsies and FFPE samples: The rise of targeted
sequencing panels”
Frederick National Laboratory for Cancer Research
Topics Covered
• Basic assay development
• Defining the assay system and it’s intended use
• Developing targeted NGS assays
• Special considerations for formalin fixed tumor tissues
• Analytical performance testing
Frederick National Laboratory for Cancer Research
Defining the Assay Intended Use
• Assays applications:1. Pure research 2. Clinical
a) Integrated (all patients will be tested but assay results are not used to enroll or select treatment arms for a clinical study)
b) Integral (all patients tested and assay results used to enroll or select treatment for a clinical study)
• The assay application will determine the rigor which the assay must be undergo during development
• Will the assay test blood, tissue blocks or biopsies– Best to use the intended sample type during assay development
Frederick National Laboratory for Cancer Research
Defining the Assay System
• Consider all aspects of the assay that are critical for obtaining consistent results
• Specimens:– Formalin fixed, therefore nucleic acids are fragmented (this
generally does not interfere with probe capture or PCR based library preparations, but may add noise to sequencing results due to base damage which occurs as a result of fixation)
– Are specimens recently collected and use neutral buffered formalin, or are the older possibly with acidic formalin?
– Will tumor content be enriched or will specimens be used without enrichment (this will impact detection sensitivity)?
– What potential clinical details of specimens and how may this impact the results (surgical resections versus biopsies, etc.)
Frederick National Laboratory for Cancer Research
Enriching for Tumor ContentMacro or Micro Dissection
Hepatocellular cancer, 50%
Renal cell carcinoma, 100%
Acinic salivary gland tumor, 100%
Mesothelioma, 50%
Colorectal cancer, 70%
Leiomyosarcoma, 75%
Frederick National Laboratory for Cancer Research
Defining the Assay System (cont.)• Determine the method of nucleic extraction
– RNA and DNA versus DNA only
• Define and select the NGS method
– Library preparation methods
– Sequencing read depth
• Impacts detection sensitivity
• Determine the data analysis methods
– Select specific parameters (minimal read depth for variant reporting)
– What variants will be reported? Is this discovery effort where all variants are reported or clinical application (focus on variants of clinical significance)
– Will germline data be used for reporting variants
• Helps when identifying all somatic mutations are the goal
• Debatable if needed for clinical applications where only clinically relevant variants are reported
– Will variant fraction reporting cut-points be used?
– If assay will be used for clinical application, will a tumor board or rules based variant annotation occur?
NCI-MATCH Assay System & Work Flow
BiopsyBiopsy
Review and Sign off
Review and Sign off
Ion ReporterIon Reporter
Shipped to MDACC
Tissue Processing/Enrichment
Archive• Tissue Blocks• Slides• Nucleic Acid
PTEN IHC NA Extraction
Tissue Accession
NA Shipped
BAM File Storage
MDACC MGH YaleMoCha
MOI Annotation
Library Prep and
Sequencing
Library Prep and
Sequencing
Final ReportFinal Report Clinical DBMATCHbox applies rules basedvariant annotation/treatment selection
Frederick National Laboratory for Cancer Research
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0127353
Frederick National Laboratory for Cancer Research
Age Bins of Archived Ovarian Tumor FFPET
Frederick National Laboratory for Cancer Research
QC Metrics for WES of Archived Ovarian FFPET
Frederick National Laboratory for Cancer Research
Conclusions of Paper
• Sufficient quality data was retrieved from all FFPE specimens regardless of repository site or age
• There was a trend poorer quality sequence and increasing age of specimen
• In general archived FFPET provide a resource for biological research
Frederick National Laboratory for Cancer Research
NGS Assays and Oncology
• NGS provides a powerful tool for massively parallel sequencing of patient tumors
• NGS assays are being developed, applied and acted upon for patient management in oncology……“Everybody is doing it”
• There is a need for:– Assay Standards (e.g., Genome in a Bottle)
– Guidance on clinical relevance of detected variants, levels of evidence
– Minimal data reporting standards such that others can understand assay bias and repeat data
– Public data sharing with assay details and clinical outcome
– Continued efforts to demonstrate clinical utility of sequencing panels
Frederick National Laboratory for Cancer Research
The Power of NGS Comes Along with Complexities
• Tumor Specimen:– Is the specimen archival pre-treatment or recent post treatment
(resistance mutations)
– How does the specimen reflects whole tumor mass; i.e., tumor heterogeneity
– How much viable tumor is represented in sample used for sequencing (20% versus 80%)
• Sequencing choices:– Read depth (10X versus 200X)
– Lower limit of detection i.e., allele frequency reported
• All of the above impact result interpretation
Frederick National Laboratory for Cancer Research
NCI-MATCH Trial
• MATCH Trial:– A national study using NGS and IHC assays to select a
“matched” treatment
– Variants used for treatment selection are based on levels of evidence
• We attempted to follow a quality system approach for development and analytical validation of our NGS assays
Frederick National Laboratory for Cancer Research
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0% 20.0%
SMO or PTCH1 ‐ (<2%)
ROS1 ‐ (<2%)
PTEN mutation or deletion ‐ (11%)
PTEN loss ‐ (11%)
NF2 ‐ (2%)
MET ‐ (4%)
HER2 mutation ‐ (2‐5%)
HER2 amplification ‐ (5%)
FGFR ‐ (5%)
EGFR T790M ‐ (1‐2%)
EGFR ‐ (1‐4%)
cKIT ‐ (2%)
ALK ‐ (<2%)
DDR2 ‐ (2%)
NF1 ‐ (7.7%)
GNAQ ‐ (2%)
GNA11 ‐ (1.6%)
BRAF V600E or V600K ‐ (1‐12%)
BRAF ‐ (2.79)
TSC1 or TSC2 ‐ (2.6 and 3.5%)
PIK3CA ‐ (17‐18%)
mTOR ‐ (5%)
AKT ‐ (1‐10%)
aMOIs in NCI-MATCH and Estimated Prevalence
(TCGA, c-BIOPortal, My Cancer Genome, MDACC, ETC)
Frederick National Laboratory for Cancer Research
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0% 20.0%
SMO or PTCH1 ‐ (<2%)
ROS1 ‐ (<2%)
PTEN mutation or deletion ‐ (11%)
PTEN loss ‐ (11%)
NF2 ‐ (2%)
MET ‐ (4%)
HER2 mutation ‐ (2‐5%)
HER2 amplification ‐ (5%)
FGFR ‐ (5%)
EGFR T790M ‐ (1‐2%)
EGFR ‐ (1‐4%)
cKIT ‐ (2%)
ALK ‐ (<2%)
DDR2 ‐ (2%)
NF1 ‐ (7.7%)
GNAQ ‐ (2%)
GNA11 ‐ (1.6%)
BRAF V600E or V600K ‐ (1‐12%)
BRAF ‐ (2.79)
TSC1 or TSC2 ‐ (2.6 and 3.5%)
PIK3CA ‐ (17‐18%)
mTOR ‐ (5%)
AKT ‐ (1‐10%)
Each Targeted Rx Becomes a Basket Trial and Many Basket Trials are Combined Under MATCH Umbrella
Frederick National Laboratory for Cancer Research
Selection of NGS Platform and Laboratory Network
• NGS platform chosen after evaluation of RFI: – Ion Torrent PGM and the Oncomine Cancer Research Panel
– 143 genes & 4066 annotated variants
– SNV, indel, CNV, targeted translocations
• Competitively chosen lab sites:– MDACC (Hamilton)
– MGH (Iafrate)
– Yale (Sklar)
– FNLCR - MoCha (Williams)
Frederick National Laboratory for Cancer Research
MATCH Assay ‐ Oncomine Cancer Panel Gene List
Hotspot genes, n=73(hotspot coverage)
ABL1AKT1ALKARARAFBRAFBTKCBLCDK4CHEK2CSF1RCTNNB1DDR2DNMT3AEGFRERBB2ERBB3ERBB4ESR1EZH2FGFR1FGFR2FGFR3FLT3FOXL2GATA2
GNA11GNAQGNASHNF1AHRASIDH1IDH2IFITM1IFITM3JAK1JAK2JAK3KDRKITKNSTRNKRASMAGOHMAP2K1MAP2K2MAPK1MAXMED12METMLH1MPLMTOR
MYD88NFE2L2NPM1NRASPAX5PDGFRAPIK3CAPPP2R1APTPN11RAC1RAF1RETRHEBRHOASF3B1SMOSPOPSRCSTAT3U2AF1XPO1
Copy gain, n=49
ACVRL1AKT1APEX1ARATP11BBCL2L1BCL9BIRC2BIRC3CCND1CCNE1CD274CD44CDK4CDK6CSNK2A1DCUN1D1EGFRERBB2FGFR1FGFR2FGFR3FGFR4FLT3GAS6
IGF1RIL6KITKRASMCL1MDM2MDM4METMYCMYCLMYCNMYO18ANKX2-1NKX2-8PDCD1LG2PDGFRAPIK3CAPNPPPARGRPS6KB1SOX2TERTTIAF1ZNF217
CDS, n=26(full gene)
APCATMBAP1BRCA1BRCA2CDH1CDKN2AFBXW7GATA3MSH2NF1NF2NOTCH1PIK3R1PTCH1PTENRB1SMAD4SMARCB1STK11TET2TP53TSC1TSC2VHLWT1
Fusion drivers, n=22 (183 assays)
ALKRETROS1NTRK1ABL1AKT3AXLBRAFCDK4EGFRERBB2ERGETV1ETV4ETV5FGFR1FGFR2FGFR3NTRK3PDGFRAPPARGRAF1
SNV/Indel CNV Gene Fusion
Courtesy of Thermo Fisher 143 Genes
Frederick National Laboratory for Cancer Research
Rules of Evidence for Actionable Variants Used for Treatment Selection
Frederick National Laboratory for Cancer Research
Feasibility Testing
• Non harmonized SOPs used by each lab• IR v 4.2 used for data analysis• 44 FFPE clinical samples tested within 4 laboratories• 10 Cancer cell line genomes x4 labs• 3 Hapmap genomes x3 replicates x 4 labs
Frederick National Laboratory for Cancer Research
Feasibility DataReproducibility
Variant allele frequencies are very close across four lab replicates
Not detected
Frederick National Laboratory for Cancer Research
Variant Distribution in Sensitivity Study
0
10
20
30
40
50
60
70
SNV Indel Large Indel CNV Fusion Total
25
10 10 10 10
65
25
10 10 10 10
65
25
10 10 1013
68
27
10 10 10 10
67
MDACCMGHNCIYale
Total Variant Number in each variant type
149 Unique Variants
SNV, 76
Large indel, 19
Indel, 26
Fusion , 13 CNV, 15
265 total variants 149 unique variants
Frederick National Laboratory for Cancer Research
Multiple Tissues Tested for Performance
186 Unique clinical specimen samples from 15 tissue types in in sensitivity study
Bladder, 8 Blood, 1Bone, 2
Brain, 38
Breast, 8
Colon, 13
Esophagus, 3
GI Tract, 15Head and Neck, 2Liver, 2
Lung, 69
Ovary, 4Pancreas, 2 Skin, 10
Soft Tissue, 3Stomach, 1 Thyroid, 2
Source unknown, 2
Frederick National Laboratory for Cancer Research
Resources for NCI-MATCH
• Main Webpages: cancer.gov/nci-matchecog-acrin.org/nci-match-eay131
• Protocol Documents: ctsu.org (password required)• Spanish: cancer.gov/espanol/nci-match• Email Inquiries: [email protected]• Patient Brochure: EA website (above)• Site Process Brochure: EA website (above)• NCI’s Cancer Information Service: 1-800-4-CANCER and
cancer.gov/contact
This slide presentation is updated regularly. For the latest version, visit ecog-acrin.org.
Frederick National Laboratory for Cancer Research
MATCH AcknowledgementsMOCHA @ FNLCRJason LihDavid SimsRobin HarringtonKneshay HarperPatty RungeVivekananda DattaJoyAnn Phillips RohanCourtney Bouk
NCI CBIITBrent CoffeyMary AndersonFrank SpinaDavid Patton
NCI DCTDBarbara ConleyEric PolleyLisa McShaneLarry RubensteinErin SouhanSanita BhartiRita MisraAlice ChenJeff AbramsJim Doroshow
28
MDACCRajesh Singh Johnny Yao Raja LuthraMark RoutbortGeeta ManthaStanley Hamilton
YaleKayn RonskiSandra CanosaJeff Sklar
MGHHayley Robinson Amelia RaymondJohn Iafrate
Thermo FisherLeslie EvansJingwei NiPeter WyngaardSeth SadisJeff SmithOncomine team
ECOG-ACRINKeith FlahertyPeter O’DwyerBob ComisShuli LiBob GrayKamalia SazaliJeff ZhangDonna Marinucci
AND MANY OTHERS
Brought to you by the Science/AAAS Custom Publishing Office Participating experts
Characterizing cancers from liquid biopsies and FFPE samples: The rise of targeted sequencing panelsApril 13, 2016
Webinar Series
Sponsored by
P. Mickey Williams, Ph.D.Frederick National Lab for Cancer ResearchFrederick, MD
Jeremy Segal, M.D., Ph.D.University of Chicago School of MedicineChicago, IL
NGS Cancer Profiling:Options, Best Practices and Pitfalls
Jeremy P. Segal, MD, PhDCo-Director, Clinical Genomics LaboratoryDivision of Genomic and Molecular PathologyUniversity of Chicago
Inborn genetics: • Genetic disease • Risk factors
Disease Genetics: • Diagnosis• Prognosis• Therapy
Disease Genetics: • Residual disease testing• Resistance mutation surveillance
Disease Genetics: • Early screening
Indications for Clinical Cancer Genomics
MutationsPoint mutationsInsertions and deletions (indels)
EpigeneticsAltered DNA methylationAltered histone methylationAltered DNA-protein interactionsAltered chromatin structure
RNA AnalysisAltered expressionPathway activationMicroRNAsLncRNAsAlternative SplicingAllele-specific expressionRNA binding protein interactions
Structural VariationsLarge scale deletions/duplicationsFusions/RearrangementsAneuploidyChromothripsis
Cancer Genomics Targets
MutationsPoint mutationsInsertions and deletions (indels)
EpigeneticsAltered DNA methylationAltered histone methylationAltered DNA-protein interactionsAltered chromatin structure
RNA AnalysisAltered expressionPathway activationMicroRNAsLncRNAsAlternative SplicingAllele-specific expressionRNA binding protein interactions
Structural VariationsLarge scale deletions/duplicationsFusions/RearrangementsAneuploidyChromothripsis
Cancer Genomics Targets
NGS
Lung Adenocarcinoma
Point MutationsEGFR L858R, T790M,KRAS, PIK3CA
BRAF, etc.
Small DeletionsEGFR exon 19
Copy Number AlterationsMET amplification
EGFR amplificationGene Fusions
ALK (e.g. EML4-ALK)RET
ROS1NTRK1
Clinical Example: Lung Cancer
• Demanding Clinicians!• This is now too many tests to do in the traditional manner.• NGS advantage: cover all these analytes with a single test process.
Large DeletionsMET exon 14 deletion
What’s the right assay?
Smaller Targeted Assays Larger Comprehensive Assays
• Some clinicians• Cancer specimens• Validation effort• Cost• Reimbursement
• Most clinicians• Clinical requirements• Translational research• Lab Competition• Technology
ClinicalGenomicsLaboratory
COVERAGE
DEPTH
COVERAGE x DEPTH = Sequencing $$
Depth: Key Consideration #1
Cancer – Low % Mutations
Tumor cell percentage Tumor heterogeneity
Low mutation allelic percentage
Yates and Campbell, Nat. Rev. Genetics. 2012.
Increased Depth Improves Mutation Detection
Sequencing Depth
Estim
ated
Sen
sitiv
ity
Sampling: Key Consideration #2
Library Prep&
Sequencing
GOOD Sampling
If QC is adequate, this is a believable/reproducible result.
But….don’t be fooled!BAD Sampling…
Library Prep&
Sequencing
There is no cure for a badly controlled wet-lab assay.
…any amplification error becomes dangerous
Variable Specimens, Total DNA Loading
Multiplex PCR Product
QPCR Assessment of Effective/Amplifiable DNA
Multiplex PCR Product
Variable Specimens, “Amplifiable” DNA Loading
Amplicon vs. Hybrid Capture
modified from Nature Reviews Genetics 2011; 12: 745-755
Probe Design
Template
Simplified Assay Type Comparisons
Amplicon Systems Hybrid Capture
Low DNA Input
Small/Medium Indels (<100 bp)
Copy Number Alterations
Structural Alterations
Broad Coverage
1 mm
~5 ng FFPE DNA, 10-15% tumor cells:EGFR mutation negativeKRAS c.34G>T, p.G12C (NM_033360) – 5% MAFTP53 c.818G>T, p.R273L (NM_000546) – 5% MAF
Amplicon Assays for Minute Specimens
50 gene amplicon panel
KRAS G13C
FNAs: 1 ng DNA from Diffquik Smear
OncoPlus Comprehensive Cancer PanelTier 1 = 316 genes Tier 2 = 896 genes
I. Actionable II. Possibly
Actionable
III. Cancer
Research Interest
Validated by CLIA standardsIncluded in Clinical Report
Discovery content (masked without IRB approval and patient consent)
~1200 genes
Tier 1
Tier 2
Progressive Validation
Copy Number Events Gene Fusions
Mutations/Indels Rearrangements
EGFR Exon 19del
TP5311kb deletion
EGFR/MET/other amplification KIF5B-RET Fusion
Capture Assay Flexibility
Scant Mutations• A critical emerging target in cancer
diagnostics:• Mutation detection in plasma
(ctDNA), urine, pancreatic secretions, etc.
• Minimal residual disease detection (heme, etc.)
Bettegowda C et al. Sci Transl Med 2014
NGS Data Is Not Perfect
Kinde I et al. PNAS 2011;108:9530-9535
Molecular Barcode Proof-Reading
Without Molecular Barcode Proof-Reading
NPM1 c.860_863dupAF = 0.1%
With Molecular Barcode Proof-Reading
Summary: NGS in Cancer Diagnostics
• NGS offers affordable, broad coverage of many anomaly types.• Optimal assay selection depends on a variety of institutional and
laboratory-related factors.• Many options for preparation of targeted sequencing libraries, each with
different pros and cons:– Amplicon: Low input, less expensive, more targeted, limited applications– Capture: Higher input, more expensive, more scalable, more applications
• DNA sampling is ALWAYS a critical concern.• Every assay produces assay-specific data with assay-specific artifacts.
– NGS oncology assays are ALWAYS critically dependent on high quality informatics systems.
• Low % mutation targets (e.g. ctDNA) are addressable with NGS using molecular barcodes to increase specificity.
• The majority of described NGS applications have not yet reached the clinic, and are only awaiting demonstration of clinical relevance.
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Participating experts
Characterizing cancers from liquid biopsies and FFPE samples: The rise of targeted sequencing panelsApril 13, 2016
Webinar Series
Sponsored by
P. Mickey Williams, Ph.D.Frederick National Lab for Cancer ResearchFrederick, MD
Jeremy Segal, M.D., Ph.D.University of Chicago Schoolof Medicine
Chicago, IL
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Characterizing cancers from liquid biopsies and FFPE samples: The rise of targeted sequencing panelsApril 13, 2016
Webinar Series
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