1Shashikant Kulkarni, M.S (Medicine)., Ph.D., FACMG Cancer Next Generation Sequencing Clinical Implementation in CLIA/CAP facilityShashikant Kulkarni, M.S (Medicine)., Ph.D., FACMGAssociate Professor of Pediatrics, Genetics, Pathology and ImmunologyMedical Director of Genomics and Pathology Services
2Why do we need NGS for clinical cancer diagnostics?
3Advantages of detecting mutations with next-generation sequencing High throughputTest many genes at onceSystematic, unbiased mutation detectionAll mutation typesSingle nucleotide variants (SNV), copy number alteration (CNA)-insertions, deletions and translocationsDigital readout of mutation frequencyEasier 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”
4Unique challenges for implementing NGS for clinical cancer diagnostics
5Complexity of Cancer genomes Cancer genomes are extremely complex and diverseMutation frequencyDegree of variation in cancer cells compared to reference genomeCopy number/ploidyMost tumors are aneuploidBioinformatic software assume diploid statusGenome structure
6Cancer-specific challenges Genomic 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 protocolsOften 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)
7Sample procurement and pre-analytical issues FFPE (formalin-fixed, paraffin-embedded) samplesAge, temperature, processingFresh 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 DNAdifferences (characteristics of formalin fixation; duration of fixation, type of tissue processing, temperature of paraffin embedding, storage conditions of paraffin blocks)
8Implementation of NGS for clinical cancer diagnostics
9Clinical Next Generation Sequencing in Cancer GoalsHigh throughput, cost effective multiplexed sequencing assay with deep coverageTarget clinically actionable regions in clinically relevant timeChallengesHuge infrastructure costsBioinformatic barriersSolutionLeverage expertise and resources across Pathology, Bioinformatics and Genetics
10Example process of targeted sequencing panel in cancer From “soup to nuts”Example process of targeted sequencing panel in cancer
13Exons +/- 200 bp, plus 1000 bp +/- each gene Target definitionsExons +/- 200 bp, plus 1000 bp +/- each geneAUGSTOPTSSpoly(A)promotersplice signals
14Getting started Capture efficiency and coverage Overall and by geneSpecimen type differencesFresh-frozen vs. FFPE specimensDetection of single nucleotide variants (SNVs)MethodsFiltersDetection of indels and other mutation types
15First steps HapMap samples lung adenocarcinomas Known genotypeslung adenocarcinomasfrozen DNA sample+FFPE DNA sampleLibrary prep, target enrichmentMultiplex sequencingAnalysis(coverage and comparison with genotypes)
16Significant variation in coverage by gene Capture baitsTarget region1000x500 bpGood coverage of EGFRPoor coverage of CEBPA
17Significant 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
18Fresh vs. FFPE: Coverage by gene Tumor 1 normalized coverage, by gene(solid = frozen, hatched = FFPE)Only minor differences are apparent between fresh-frozen and FFPE data
22Defining clinical validation AccuracyDegree 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.
23Reproducibility Test Type Definitions Inter-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-TechThe 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-TechThe lanes are the same. In these experiments, the techs were different.
32Tier 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
37Cancer specific analysis pipeline Data OutputFASTQ SequenceOutputHiSeqMiSeqNovoalignTMSNVCallsIndelTranslocationValidationGATK/SamtoolsPindelBreakdancerSLOPEParse DataFilteringMergedVCF fileReadAlignment
38Tier 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
43ConclusionsCancer 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
44Karen Seibert, John Pfiefer, Skip Virgin, Jeffrey Millbrandt, Rob Mitra, Rich HeadRakesh Nagarajan and his Bioinf. teamDavid Spencer, Eric Duncavage, Andy Bredm.Hussam Al-Kateb, Cathy CottrellDorie Sher, Jennifer StratmanTina Lockwood, Jackie PaytonMark Watson, Seth Crosby, Don ConradAndy Drury, Kris Rickoff, Karen NovakMike Isaacs and his IT TeamNorma Brown, Cherie Moore, Bob FeltmannHeather Day, Chad Storer, George BijoyDayna Oschwald, Magie O Guin, GTAC teamJane Bauer and Cytogenomics &Mol path teamMANY MORE!