Cancer Next Generation Sequencing - PowerPoint Presentation

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Cancer Next Generation Sequencing Clinical Implementation in CLIA/CAP facility

Shashikant Kulkarni, M.S (Medicine)., Ph.D., FACMGAssociate Professor of Pediatrics, Genetics, Pathology and ImmunologyMedical Director of Genomics and Pathology Services

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Why do we need NGS for clinical cancer diagnostics?

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Advantages of detecting mutations with next-generation sequencingHigh throughputTest many genes at once

Systematic, unbiased mutation detectionAll mutation typesSingle nucleotide variants (SNV), copy number alteration (CNA)-insertions, deletions and translocationsDigital readout of mutation frequency

Easier to detect and quantify mutations in a heterogeneous sampleCost effective precision medicine“Right drug at right dose to the right patient at the right time”

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Unique challenges for implementing NGS for clinical cancer diagnostics

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Complexity of Cancer genomesCancer genomes are extremely complex and diverseMutation frequencyDegree of variation in cancer cells compared to reference genomeCopy number/ploidyMost tumors are aneuploidBioinformatic software assume diploid status Genome structure

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Cancer-specific challengesGenomic alterations in cancer found at low-frequencySamples vary in quantity, quality and purity from constitutional samplesQuantityLimiting for biopsy specimensWhole genome amplification not idealQualityMost biopsies are formalin fixed, require special protocols Often include necrotic, apoptotic cellsPurity (tumor heterogeneity)Admixture with normal cells (need pathologists to ensure test is performed on DNA from tumor cell)Within cancer heterogeneity (different clones)

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Sample procurement and pre-analytical issuesFFPE (formalin-fixed, paraffin-embedded) samplesAge, temperature, processing Fresh tissuesNot ideal without accompanying pathology investigation and marking of tumor cell population to guard against dilution effect on total DNA extractedFine needle biopsiesVery few cells availableNGS methods will need to work by decreasing minimum inputs of DNA

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Implementation of NGS for clinical cancer diagnostics

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Clinical Next Generation Sequencing in CancerGoalsHigh throughput, cost effective multiplexed sequencing assay with deep coverage

Target clinically actionable regions in clinically relevant time ChallengesHuge infrastructure costsBioinformatic barriers

SolutionLeverage expertise and resources across Pathology, Bioinformatics and Genetics

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Example process of targeted sequencing panel in cancerFrom “soup to nuts”

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Test overview

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Cancer Gene Panel Genes

DiseaseALK

Lymphoma, Lung

BRAF

Brain, Colon, Lung, Melanoma, Thyroid

CEBPA

AML

CTNNB1

Colon, Desmoid Tumor, Liver, Lung, Prostate, Renal, Thyroid

CHIC2

Myeloid Neoplasms w/Eosinophilia

CSF1R

AML, GIST

DNMT3A

AML

EGFR

Colon, Lung

FLT3

AML

IDH1

AML, Brain

IDH2

AML, Brain

JAK2

Myeloproliferative Neoplasms

KIT

AML, GIST, Systemic Mastocytosis

KRAS

Colon,

Endometrium

, Lung, Melanoma, Pancreatic, Thyroid

MAPK1(ERK)

Lung, Melanoma

MAPK2(MEK)

Lung, Melanoma

MET

Lung, Melanoma

MLL

AML

NPM1

AML

NRAS

Colon, Lung, Melanoma, Pancreatic, Thyroid

PDGFRA

GIST, Sarcoma

PIK3CA

Colon, Lung, Melanoma, Pancreatic

PTEN

Brain, Endometrium, Melanoma, Ovarian, Prostate, Testis

PTPN11

JMML, MDS

RET

MEN2A/2B (adrenal), Thyroid

RUNX1

AML

TP53

Colon, Lung, Pancreatic

WT1

AML, Renal,

Wilms

Tumor

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Target definitions

Exons +/- 200 bp, plus 1000 bp +/- each gene

AUG

STOP

TSS

poly(A)

promoter

splice signals

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Getting startedCapture efficiency and coverageOverall and by geneSpecimen type differencesFresh-frozen vs. FFPE specimensDetection of single nucleotide variants (SNVs)Methods

FiltersDetection of indels and other mutation typesMethods

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First steps

HapMap

samples

Known

genotypes

lung

adenocarcinomas

Known

genotypes

frozen

DNA sample

+

FFPE

DNA sample

Library prep, target enrichment

Multiplex

sequencing

Analysis

(coverage and comparison with genotypes)

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Significant variation in coverage by gene

CoverageCapture baits

Target region

1000x

500 bp

Coverage

1000x

Capture baits

Target region

500 bp

Good coverage of EGFR

Poor coverage of CEBPA

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Significant variation in coverage by gene

NA19129 coverage distribution by gene (black bar = median; box = 25-75%ile)*

*

Capture for genes with poor coverage have been redesigned

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Fresh vs. FFPE: Coverage by geneTumor 1 normalized coverage, by gene(solid = frozen, hatched = FFPE)

Only minor differences are apparent between fresh-frozen and FFPE data

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Re-designing of capture set

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Defining clinical NGS guidelines

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http://www.cdc.gov/genomics/gtesting/ACCE/ACCE

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Defining clinical validationAccuracyDegree of agreement between the nucleic acid sequences derived from the assay and a reference sequencePrecisionRepeatability—degree to which the same sequence is derived in sequencing multiple reference samples, many times. Reproducibility—degree to which the same sequence is derived when sequencing is performed by multiple operators and by more than one instrument.Analytical SensitivityThe likelihood that the assay will detect a sequence variation, if present, in the targeted

genomic region.Analytical SpecificityThe probability that the assay will not detect a sequence variation, if none are present, in the targeted genomic region.Diagnostic SpecificityThe probability that the assay will not detect a clinically relevant sequence variation, if none are present, in the targeted genomic region.

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ReproducibilityTest TypeDefinitionsInter-Tech (Stringent)The technicians performing the run were different, but the experiment and lanes were the same.Inter-Tech (Relaxed)The technicians performing the run were different for each comparison. We did not control for the experiment or lane. Intra-Tech

The technician performing the run was the same. The experiment was different. Inter-Lane (All)The lanes are different. These experiments, the techs were different in two, and the same in two.Inter-Lane & Intra-TechThe lanes are different. In these experiments, the techs were the same.Intra-Lane & Inter-Tech

The lanes are the same. In these experiments, the techs were different.

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Reproducibility

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Major barriers for

clinical implementation of NGS

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Data tsunami

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1. Need expertise in Biomedical Informatics

2. Need clinical grade “user-friendly-soup to nuts” software solution

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3. Hardware

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Informatics pipeline workflow

Patient

Physician

Sample

Order

Sequence

Tier 1:

Base Calling

Alignment

Variant Calling

Tier 2:

Genome Annotation

Medical Knowledgebase

Tier 3: Clinical Report

EHR

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Order IntakePatient samples accessioned in Cerner CoPathGene panels ordered through CoPathOrders received will initiate workflow

HL7

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Order Intake

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Tier 1 InformaticsOptimized pipelines using several base callers, aligners, and variant calling algorithms to meet CAP/CLIA standardsEasily customizable and updateableFacilitates new panel introduction and the rapid delivery of novel analytical tools and pipelinesSeamless to the clinical genomicist

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Inspection of coverage for each run

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QC metrics (sample level)

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QC metrics (exon level)

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Tier 1 Informatics

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Cancer specific analysis pipeline

Data Output

FASTQ SequenceOutput

HiSeq

MiSeq

NovoalignTM

SNV

Calls

Indel

Calls

Translocation

Validation

GATK/

Samtools

Pindel

Breakdancer

SLOPE

Parse Data

SNV

Filtering

Merged

VCF file

Translocation

Calls

Read

Alignment

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Tier 2 InformaticsDeliver a clinical grade variant database that meets CAP/CLIA standardsRequires combined expertise of informaticians and clinical genomocists/pathologistsFuture interoperability with (inter)national variant databases that meet CAP/CLIA standards

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Tier 2 Informatics

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Tier 3 InformaticsEGFR (L858R)

Response rates of >70% in patients with non-small cell lung cancer treated with either erlotinib or

gefitinib

KRAS (G12C)

Poor response rate in patients with non-small cell lung cancer

treated with either erlotinib or

gefitinib

+

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Tier 3 Informatics: Variant classificaiton

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Clinical NGS process map

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ConclusionsCancer NGS gene panel helps in multiplexing key actionable genes for a cost effective, accurate and sensitive assayTargeted cancer panel are useful for “drug repurposing” of small molecule

inhibitorsClinical validation of NGS assays in cancer is complex and labor intensive but basic principles remainBioinformatic barriers are the most challenging

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Karen Seibert,

John Pfiefer

, Skip Virgin, Jeffrey Millbrandt

, Rob Mitra, Rich Head

Rakesh Nagarajan and his

Bioinf. team

David Spencer, Eric

Duncavage

, Andy

Bredm

.

Hussam

Al-

Kateb

, Cathy Cottrell

Dorie

Sher

, Jennifer

Stratman

Tina Lockwood

, Jackie Payton

Mark Watson, Seth Crosby, Don Conrad

Andy Drury,

Kris

Rickoff

, Karen Novak

Mike Isaacs and his IT Team

Norma Brown, Cherie Moore, Bob

Feltmann

Heather Day, Chad

Storer

, George

Bijoy

Dayna

Oschwald

,

Magie

O

Guin

, GTAC team

Jane Bauer and

Cytogenomics

&

Mol

path

team

MANY MORE!