Cancer Next Generation Sequencing Clinical Implementation in CLIA/CAP facility

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

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”

Test overview

Cancer Gene Panel

Genes DiseaseALK Lymphoma, LungBRAF Brain, Colon, Lung, Melanoma, Thyroid

CEBPA AMLCTNNB1 Colon, Desmoid Tumor, Liver, Lung, Prostate, Renal, ThyroidCHIC2 Myeloid Neoplasms w/EosinophiliaCSF1R AML, GISTDNMT3A AMLEGFR Colon, LungFLT3 AMLIDH1 AML, BrainIDH2 AML, BrainJAK2 Myeloproliferative NeoplasmsKIT AML, GIST, Systemic MastocytosisKRAS Colon, Endometrium, Lung, Melanoma, Pancreatic, ThyroidMAPK1(ERK) Lung, MelanomaMAPK2(MEK) Lung, MelanomaMET Lung, MelanomaMLL AMLNPM1 AMLNRAS Colon, Lung, Melanoma, Pancreatic, ThyroidPDGFRA GIST, SarcomaPIK3CA Colon, Lung, Melanoma, PancreaticPTEN Brain, Endometrium, Melanoma, Ovarian, Prostate, TestisPTPN11 JMML, MDSRET MEN2A/2B (adrenal), ThyroidRUNX1 AMLTP53 Colon, Lung, PancreaticWT1 AML, Renal, Wilms Tumor

Target definitions

Exons +/- 200 bp, plus 1000 bp +/- each gene

AUG STOPTSS 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 Methods

First steps

HapMap samplesKnown genotypes

lung adenocarcinomasKnown genotypes

frozen DNA sample+

FFPE DNA sample

Library prep, target enrichment

Multiplex sequencing

Analysis (coverage and comparison with genotypes)

Significant variation in coverage by geneC

over

age

Capture baits

Target region

1000x

500 bp

Cov

erag

e

1000x

Capture baits

Target region

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

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

ACCE

Defining clinical validation

AccuracyDegree 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

Inter-Tech (Stringent)

Inter-Tech (Relaxed)

Intra-Tech Inter-Lane (All)

Inter-Lane & Intra-Tech

Intra-Lane & Inter-Tech

90.0%

92.0%

94.0%

96.0%

98.0%

100.0%

98.1% 97.9%97.1%

98.6% 98.7% 98.4%

Reproducibility

Variability Method

Perc

ent A

gree

men

t

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

OrderSequence

Tier 1:Base CallingAlignment

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

HiSeqMiSeq

NovoalignTM

SNVCalls

IndelCalls

TranslocationValidation

GATK/Samtools

Pindel

Breakdancer SLOPE

Parse Data

SNVFiltering

MergedVCF file

TranslocationCalls

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 with non-small cell lung cancer treated with either erlotinib or gefitinib

+

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 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 team

MANY MORE!