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Cancer Next Generation Sequencing - Clinical Implementation in CLIA - CAP facility PowerPoint Presentation

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Description : Check out this medical presentation on Desmoid Tumor, which is titled "Cancer Next Generation Sequencing - Clinical Implementation in CLIA/CAP facility", to know about the advantages of detecting mutations with next-generation sequencing.

Tags : Desmoid Tumor, Cancer, Next Generation Sequencing, Clinical Implementation, CLIA, CAP facility, detecting mutations

Published on : May 13, 2021 | Views : 2013 | Downloads : 1

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Slide 1-

Cancer Next Generation Sequencing Clinical Implementation in CLIA/CAP facility Shashikant Kulkarni, M.S (Medicine)., Ph.D., FACMG Associate Professor of Pediatrics, Genetics, Pathology and Immunology Medical 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 sequencing High throughput Test many genes at once Systematic, unbiased mutation detection All mutation types Single nucleotide variants (SNV), copy number alteration (CNA)-insertions, deletions and translocations Digital readout of mutation frequency Easier to detect and quantify mutations in a heterogeneous sample Cost 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 genomes Cancer genomes are extremely complex and diverse Mutation frequency Degree of variation in cancer cells compared to reference genome Copy number/ploidy Most tumors are aneuploid Bioinformatic software assume diploid status Genome structure
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Cancer-specific challenges Genomic alterations in cancer found at low-frequency Samples vary in quantity, quality and purity from constitutional samples Quantity Limiting for biopsy specimens Whole genome amplification not ideal Quality Most biopsies are formalin fixed, require special protocols Often include necrotic, apoptotic cells Purity (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 issues FFPE (formalin-fixed, paraffin-embedded) samples Age, temperature, processing Fresh tissues Not ideal without accompanying pathology investigation and marking of tumor cell population to guard against dilution effect on total DNA extracted Fine needle biopsies Very few cells available NGS 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 Cancer Goals High throughput, cost effective multiplexed sequencing assay with deep coverage Target clinically actionable regions in clinically relevant time Challenges Huge infrastructure costs Bioinformatic barriers Solution Leverage expertise and resources across Pathology, Bioinformatics and Genetics
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Example process of targeted sequencing panel in cancer From “soup to nuts”
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Test overview
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Cancer Gene Panel
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Target definitions
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Getting started Capture efficiency and coverage Overall and by gene Specimen type differences Fresh-frozen vs. FFPE specimens Detection of single nucleotide variants (SNVs) Methods Filters Detection of indels and other mutation types Methods
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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 Coverage Capture 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 gene Tumor 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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Defining clinical validation
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Reproducibility
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Reproducibility
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3. Hardware
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Informatics pipeline workflow
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Order Intake Patient samples accessioned in Cerner CoPath Gene panels ordered through CoPath Orders received will initiate workflow HL7
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Order Intake
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Tier 1 Informatics Optimized pipelines using several base callers, aligners, and variant calling algorithms to meet CAP/CLIA standards Easily customizable and updateable Facilitates new panel introduction and the rapid delivery of novel analytical tools and pipelines Seamless 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 Sequence Output 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 Informatics Deliver a clinical grade variant database that meets CAP/CLIA standards Requires combined expertise of informaticians and clinical genomocists/pathologists Future interoperability with (inter)national variant databases that meet CAP/CLIA standards
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Tier 2 Informatics
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Tier 3 Informatics EGFR (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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Conclusions Cancer NGS gene panel helps in multiplexing key actionable genes for a cost effective, accurate and sensitive assay Targeted cancer panel are useful for “drug repurposing” of small molecule inhibitors Clinical validation of NGS assays in cancer is complex and labor intensive but basic principles remain Bioinformatic 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!