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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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Webinar Series

Sponsored by

Webinar Series

Sponsored by

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”

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

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

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.)

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%

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

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

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0127353

Age Bins of Archived Ovarian Tumor FFPET

QC Metrics for WES of Archived Ovarian FFPET

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

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

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

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

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)

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

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)

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

Rules of Evidence for Actionable Variants Used for Treatment Selection

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

Feasibility DataReproducibility

Variant allele frequencies are very close across four lab replicates

Not detected

Variant Distribution in Sensitivity Study

SNV Indel Large Indel CNV Fusion Total

10 10 10 10

10 10 1013

10 10 10 10

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

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

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: match@jimmy.harvard.edu• 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.

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

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

Webinar Series

Sponsored by

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

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

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

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

~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

Webinar Series

Sponsored by

Jeremy Segal, M.D., Ph.D.University of Chicago Schoolof Medicine

Chicago, IL

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