Molecular and Genomic Pathology at UNC

1. Molecular and Genomic Pathology at UNC

2. Molecular and Genomic Pathology at UNC David A. Eberhard, MD, PhD Dept. of Pathology and Laboratory Medicine Director, Preclinical Genomic Pathology Lab Lineberger Comprehensive Cancer Center University of North Carolina – Chapel Hill ADASP Annual Meeting March 2, 2013

3. In accordance with ACCME guidelines, any individual in a position to influence and/or control the content of this ASCP CME activity has disclosed all relevant financial relationships within the past 12 months with commercial interests that provide products and/or services related to the content of this CME activity. The individual below has responded that he/she has no relevant financial relationship(s) with commercial interest(s) to disclose: David A. Eberhard, MD, PhD Notice of Faculty Disclosure

4. World Rank of NGS Centers From http://topsequence500.org/

5. Different NGS Platforms Have Different Capabilities DNA copy number variations DNA rearrangements Sequence alterations DNA and RNA Text RNA splicing variants Methylation RNA expression profiles A single method is suitable for some of these, but not others – must consider cost, specimen type, & application

6. NGS Applications in Cancer Human Genome Project: reference genome and large-scale compilation of tumor variants from various sources (http://cancercommons.org, www.icgc.org, http://cancergenome.nih.gov/, http://www.sanger.ac.uk/genetics/CGP/cosmic/ • Mutation Panels (Genotyping or resequencing) • Exome or transcriptome screening • Genome sequencing (compare to normal or reference sample)

7. Genome Res. 2012 Nov;22(11):2101-8 Erlotinib (2004) Crizotinib (2011) Text Vandetinib Vemurafinib Resistance to Erlotinib?

8. Headline

9. NGS For Cancer Diagnostics • Potentially improve Economy, Efficiency, Sensitivity • No one size fits all: must consider desired end use • Considerations for technical platform: • Broad vs Deep: More genes vs more sensitivity • Turnaround time & cost: single samples vs multiplex batches • PCR vs non-PCR libraries: implications for sample amount, false positives • Sample preanalytical variables (FFPE, amount, etc) • References (T/N), standards • What results does it provide? What results do we report?

10. Making NGS Accessible

11. NGS In Clinical Cancer Diagnostics How much do you need? Broad coverage = more complexity and cost; more unknown variants; overkill for clinical care? What do you need to find? Large indels, rearrangements with variable breakpoints are difficult

12. Deep Coverage Improves Mutation Detection

13. Small Sample Size and Low Tumor Content May Result In False Negatives WT WT Mu Detection Limit WT Mu - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Mu

14. NGS Mutation Detection Issues Mutation confirmation Usually by Sanger sequencing- will platform evolution eliminate? NGS platform-dependent false positives May overlap with NGS false positive rate and background noise Low level mutations- not easily confirmed by Sanger sequencing (higher detection threshold ≈ 15-20%) May need more sensitive method – DGGE, dHPLC, pyrosequencing or mutation enrichment- i.e. COLD PCR FFPE background noise Variable % tumor cells and variable % tumor cells with secondary mutation

15. What to Test? • Quality and quantity are key determinants • A cellular FNA is better than a necrotic resection • Decal; Bouin’setc degrade quality • Primary vs. metastasis • May be changes during interval therapy • If metastasis after initial response, then test metastasis • Multiple primaries • If histologies differ, then test BOTH/ALL • Patients benefit even if 1 of multiple tumors responds • Testing multiple areas in a tumor is unnecessary N Engl J Med. 2012 Mar 8;366(10):883-92

16. Minimum Tumor Content • Absolute and relative amounts of tumor • Each lab must determine during validation • Pathologist must review each section • Enrichment: Macrodissection is recommended • Laser capture, WGA are error-prone

17. Tumor Sample Heterogeneity • Clinical sample characteristics: size/amount, matrix, preservation • Blood: whole, buffy coat, Ficoll, FACS, CTCs • Tissue: fresh, fixed (FFPE), decal bone, biopsies, resections • Cytology: aspirates (FNA), buccal swabs, smears • Clinical sample composition: Various cell and tissue types • tumor cells, stromal cells, vascular cells, immune/inflammatory cells, normal tissue • Viable tissue (dense, fatty), necrosis, mucin, hemorrhage • Tumor genotypic and phenotypic composition • Mono- vsPolyclonality; tumor evolution; variable differentiation, EMT, stem cells

18. Bioinformatics NGS diagnostics is highly dependent on data analysis and management Clinical Issues: Evaluation of the variant positions “called” involves queries of all known relevant databases Requires bioinformatics and statistical expertise and computational hardware Lack of databases curated to accept clinical standards is significant challenge in managing and reporting genome sequencing data Unprecedented amounts of data and processing algorithms necessitate adequate tools (Alignment and assembly QC of image processing, base calling, filtering, variant calling, SNP finding, archiving) EHR considerations – test ordering, archiving of NGS reports, patient consent, data (reinterpretation?)

19. Clinical Utility - Challenges Which variants are clinically actionable? How to establish significance (Structural, functional, preclinical, clinical)? What are necessary levels of evidence? NGS yields many variants of unknown significance Risk of over interpretation unnecessary medical action unwarranted psychological stress

20. Headline Specimen Issues Predictive Model Development, Specification, and Preliminary Performance Evaluation Clinical Trial Design Assay Issues Ethical, Legal and Regulatory Issues

21. LCCC 1108: Development of a Tumor Molecular Analyses Program and Its Use to Support Treatment Decisions (UNCseqTM) • Primary Objectives of LCCC1108 • To provide a mechanism for association of known molecular alterations with clinical outcome in oncology via genetic profiling of patient specimens • To support treatment decisions by providing rapid genetic profiling of patient specimens and sharing reportable results with treating physicians • Prospective patients are consented such that biopsied tissue may be used for both research and clinical purposes • Executive, Technology, Clinical, and Pathology committees formed to cover all aspects of the study protocol

22. LCCC 1108 (UNCseqTM) Process

23. Targeted Exome Sequencing Normal DNA Libraries Tumor UNCseq 6.0: 247 cancer genes, 10 viruses Computational processing to call somatic mutations pool Illumina HiSeq or MiSeq barcode

24. Sequence Alignment Read ATGCCATTACACAGCGA Human Genome (hg19) … CGATCTAACGTAGCTAGCTAGCTAGCTAGCATGCCATTACACAGCGAACAGGGAGCTTAGGCGC… GTAGCTAGCTAGCTAGC GAACAGGGAGCTAAGG CTAGCTAGCTAGCTAGC ACAGGGAGCTAAGGCGC CGATCTAACGTAGCTAGC ATTACACAGCGAACAGG

25. Tumor Somatic Mutation Calling Normal 526 reads of ‘T’ 416 reads of ‘T’ 98 reads of ‘C’ Tumor

26. Glioblastoma: Tx resection, chemoradiation. Progressed with transformation to gliosarcoma Approved drug linked to gene, SOC (1) or Non-SOC (2A) Headline Potential clinical action, e.g. drug in clinical trials Trametinib: nearing approval (BRAF+ melanoma) CDKN2A (p16Ink4A): 9p21

27. Glioblastoma: Tx resection, chemoradiation. Progressed with transformation to gliosarcoma Approved drug linked to gene, SOC (1) or Non-SOC (2A) Headline Potential clinical action, e.g. drug in clinical trials

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29. UNCseq Gliosarcoma: PTEN IHC Glioma component Sarcoma component Did PTEN mutation accompany evolution to sarcoma?

30. Mol/Genomic Path Education: UNC • Molecular Diagnostics course for residents and fellows includes 3.5 hrs on NGS • 2 Mol Path fellows with focus on genomics: 1 on UNCseq (oncology), 1 on NCGenes (germline) • LabCorp / UNC interactions provides reference lab exposure for Mol Path fellows (diagnostics and clinical trials) • Translational Pathology course for PhD and MD/PhD students includes sections on Mol Path, Genomics and Translational Pathology Ex-Academia

31. Mol/Genomic Path Education: Pharma and Dx Industry • Molecular Pathology and Cancer Genomics integrates with targeted drug development – provides tremendous opportunity for cooperation between pathology centers and industry • Providing clinical-grade assays and laboratories to support trials and high-quality research • Providing expertise to integrate practical pathology with cutting-edge science • Expanding educational and career opportunities for pathologists

32. UNCseqTM Team Investigators • Shelley Earp • Juneko Grilley-Olson • Neil Hayes • Ned Sharpless • Ben Calvo • Matthew Ewend • Matthew Nielson • Linda Van Le • Robert Esther • Nirmal Veeramachaneni • Cary Anders • Peter Voorhees • Keith Amos • Robert Dixon • Stergios Moschos • Young Whang • David Eberhard Operations Group • Juneko Grilley-Olson • Claire Dees • Lisa Carey • Ned Sharpless • David Eberhard • Ian Davis • Jeanne Noe • Wasi Khan Research Team • Bes Baldwin • Ashley Salazar • Michele Hayward • Todd Hoffert Technical Group • Neil Hayes • Ned Sharpless • David Eberhard • Joel Parker • Xiaoying Yin • Will Jeck • Piotr Mieczkowski • Todd Auman • Billy Kim • Chuck Perou • Gary Rosson • Bryan Yonish Pathology Group • David Eberhard • Karen Weck-Taylor • Nirali Patel • Yuri Fedoriw • Ryan Miller • Yuri Trembath • Bill Funkhouser CCGR • Claire Dees • Lisa Carey • Jim Evans • Jonathan Berg • Bert O’Neill • Billy Kim • Vicky Bae-Jump • Carol Shores • Kristy Richards • Carrie Lee • Jing Wu • Andrew Want • HJ Kim • David Ollila Marketing • Ellen de Graffenreid Bioinformatics/Computing • Joel Parker • Alan Hoyle • Lisle Mose • Stuart Jeffries • Sai Balu • Matthew Soloway • Janae Simons • Jeff Roach • Vonn Walter