Download Free Medical Cancer Next Generation Sequencing - Clinical Implementation in CLIA

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

DOWNLOAD MEDICAL POWERPOINT PRESENTATION

About This Presentation

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 : 2081 | Downloads : 1

Shareable Url :

Embed Code :

Comment

Transcript

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
Slide 2-

Why do we need NGS for clinical cancer diagnostics?
Slide 3-

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”
Slide 4-

Unique challenges for implementing NGS for clinical cancer diagnostics
Slide 5-

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
Slide 6-

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)
Slide 7-

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
Slide 8-

Implementation of NGS for clinical cancer diagnostics
Slide 9-

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
Slide 10-

Example process of targeted sequencing panel in cancer From “soup to nuts”
Slide 11-

Test overview
Slide 12-

Cancer Gene Panel
Slide 13-

Target definitions
Slide 14-

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
Slide 15-

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)
Slide 16-

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
Slide 17-

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
Slide 18-

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
Slide 19-

Re-designing of capture set
Slide 20-

Defining clinical NGS guidelines
Slide 21-

ppt slide no 21 content not found
Slide 22-

Defining clinical validation
Slide 23-

Reproducibility
Slide 24-

Reproducibility
Slide 25-

ppt slide no 25 content not found
Slide 26-

ppt slide no 26 content not found
Slide 27-

ppt slide no 27 content not found
Slide 28-

3. Hardware
Slide 29-

Informatics pipeline workflow
Slide 30-

Order Intake Patient samples accessioned in Cerner CoPath Gene panels ordered through CoPath Orders received will initiate workflow HL7
Slide 31-

Order Intake
Slide 32-

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
Slide 33-

Inspection of coverage for each run
Slide 34-

QC metrics (sample level)
Slide 35-

QC metrics (exon level)
Slide 36-

Tier 1 Informatics
Slide 37-

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
Slide 38-

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
Slide 39-

Tier 2 Informatics
Slide 40-

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 +
Slide 41-

Tier 3 Informatics: Variant classificaiton
Slide 42-

Clinical NGS process map
Slide 43-

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
Slide 44-

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!