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Shashikant Kulkarni, M.S (Medicine)., Ph.D., FACMG

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1 Shashikant Kulkarni, M.S (Medicine)., Ph.D., FACMG
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

2 Why do we need NGS for clinical cancer diagnostics?

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”

4 Unique challenges for implementing NGS for clinical cancer diagnostics

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

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)

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 differences (characteristics of formalin fixation; duration of fixation, type of tissue processing, temperature of paraffin embedding, storage conditions of paraffin blocks)

8 Implementation of NGS for clinical cancer diagnostics

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

10 Example process of targeted sequencing panel in cancer
From “soup to nuts” Example process of targeted sequencing panel in cancer

11 Test overview

12 Cancer Gene Panel Genes Disease ALK 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 EGFR Colon, Lung FLT3 IDH1 AML, Brain IDH2 JAK2 Myeloproliferative Neoplasms KIT AML, GIST, Systemic Mastocytosis KRAS Colon, Endometrium, Lung, Melanoma, Pancreatic, Thyroid MAPK1(ERK) Lung, Melanoma MAPK2(MEK) MET MLL NPM1 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 TP53 Colon, Lung, Pancreatic WT1 AML, Renal, Wilms Tumor

13 Exons +/- 200 bp, plus 1000 bp +/- each gene
Target definitions Exons +/- 200 bp, plus 1000 bp +/- each gene AUG STOP TSS poly(A) promoter splice signals

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

15 First steps HapMap samples lung adenocarcinomas
Known genotypes lung adenocarcinomas frozen DNA sample + FFPE DNA sample Library prep, target enrichment Multiplex sequencing Analysis (coverage and comparison with genotypes)

16 Significant variation in coverage by gene
Capture baits Target region 1000x 500 bp Good coverage of EGFR Poor coverage of CEBPA

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

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

19 Re-designing of capture set

20 Defining clinical NGS guidelines

21 ACCE 21

22 Defining clinical validation
Accuracy Degree of agreement between the nucleic acid sequences derived from the assay and a reference sequence Precision Repeatability—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 Sensitivity The likelihood that the assay will detect a sequence variation, if present, in the targeted genomic region. Analytical Specificity The probability that the assay will not detect a sequence variation, if none are present, in the targeted genomic region. Diagnostic Specificity The probability that the assay will not detect a clinically relevant sequence variation, if none are present, in the targeted genomic region.

23 Reproducibility 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-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-Tech The 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.

24 Reproducibility

25 Major barriers for clinical implementation of NGS

26 Data tsunami

27 1. Need expertise in Biomedical Informatics
2. Need clinical grade “user-friendly-soup to nuts” software solution

28 3. Hardware

29 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

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

31 Order Intake

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

33 Inspection of coverage for each run

34 QC metrics (sample level)

35 QC metrics (exon level)

36 Tier 1 Informatics

37 Cancer specific analysis pipeline
Data Output FASTQ Sequence Output HiSeq MiSeq NovoalignTM SNV Calls Indel Translocation Validation GATK/Samtools Pindel Breakdancer SLOPE Parse Data Filtering Merged VCF file Read Alignment

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

39 Tier 2 Informatics

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 +

41 Tier 3 Informatics: Variant classificaiton

42 Clinical NGS process map

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

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!

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