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

Why do we need NGS for clinical cancer diagnostics?

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”

Unique challenges for implementing NGS for clinical cancer diagnostics

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

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)

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)

Implementation of NGS for clinical cancer diagnostics

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

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

Test overview

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

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

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

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)

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

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

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

Re-designing of capture set

Defining clinical NGS guidelines

ACCE http://www.cdc.gov/genomics/gtesting/ACCE/ 21

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.

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.

Reproducibility

Major barriers for clinical implementation of NGS

Data tsunami

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

3. Hardware

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

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

Order Intake

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

Inspection of coverage for each run

QC metrics (sample level)

QC metrics (exon level)

Tier 1 Informatics

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

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

Tier 2 Informatics

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 +

Tier 3 Informatics: Variant classificaiton

Clinical NGS process map

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

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!