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!