Cancer Next Generation Sequencing Clinical Implementation in CLIA/CAP facility

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

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

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

11. Test overview

12. Cancer Gene Panel

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

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

15. First steps HapMapsamples lung adenocarcinomas Known genotypes Known genotypes 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 500 bp 500 bp 1000x 1000x Coverage Coverage Capture baits Capture baits Target region Target region 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 http://www.cdc.gov/genomics/gtesting/ACCE/

22. Defining clinical validation

23. Reproducibility

24. Reproducibility

25. Major barriers for clinicalimplementation 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 Sample Sequence Order Patient Physician Tier 2: Genome Annotation Medical Knowledgebase Tier 1: Base Calling Alignment Variant Calling EHR Tier 3: Clinical Report

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

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

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) KRAS (G12C) + Response rates of >70% in patients with non-small cell lung cancer treated with either erlotinib or gefitinib Poor response rate in patients with non-small cell lung cancer treated with either erlotinib or gefitinib

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 RakeshNagarajan and his Bioinf. team David Spencer, Eric Duncavage, Andy Bredm. Hussam Al-Kateb, Cathy Cottrell DorieSher, 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 DaynaOschwald, Magie O Guin, GTAC team Jane Bauer and Cytogenomics&Mol path team MANY MORE!