Next Generation Sequencing for Gene Fusion Detection: A Complementary Tool for Cytogenetics

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1 Next Generation Sequencing for Gene Fusion Detection: A Complementary Tool for Cytogenetics Ravindra Kolhe, MD, PhD Assistant Professor of Pathology Medical Director, AUMC Cytogenetics laboratory Laboratory Director, Georgia Esoteric & Molecular Labs Augusta University, Augusta, GA rkolhe@augusta.edu CGC-2016

2 Disclosures Employment or Leadership Position: Medical College of Georgia at Augusta University. Honoraria & Travel Funding : Illumina.

3 Disclosures Research Use only. The experiment designing and performance evaluation was independent of the study sponsor (Illumina).

4 Disclosures One week ago..

5 Disclosures One week ago..

6 Outline Background Content. Study Design. Performance. Results. Future plans.

7 Currently NGS techniques are being widely used as a tool in routine oncology diagnostics. Identification of gene fusions in malignancies (heme, sarcoma s etc) can provide substantial tumor-specific information for research, and shows potential for diagnostic and targeted treatment purposes. Even though florescence in situ hybridization (FISH) technique is the current gold standard in fusion detection, it is limited in the number of genes it can detect in parallel.

8 Also one has to know the fusion targets for probe design in FISH, and it lacks high-resolution molecular characterization which is crucial for understanding the identity of these fusion partners. In contrast, RNA sequencing (RNA-Seq) is a powerful approach for simultaneous discovery of all possible fusion junctions in a single reaction. As oncogenes can fuse to multiple partners, the major advantage of using RNA-Seq technology is it s the ability to accurately identify fusions of all genes in the panel in a single sequencing assay, even without prior knowledge of fusion partners or breakpoints.

9 RNAseq : Current challenges in Lab medicine Challenges in clinical adoption: Specimen stability/quality (FFPE). Relatively longer prep times High bioinformatics burden: Large number of gene fusions of unknown significance (GFUS). Limited annotation for gene fusions Need clinical grade pipeline integrating QC and evidence based annotation to enable precision oncology

10 Advantage of RNA-seq in discovery Gene expression. Expressed mutation. mirna expression. Gene-fusion.

11 CLIA setting for NGS Robust library prep solution on real clinical samples (FFPE, FNA, Core biopsies etc). Cost-effective run. Tech time, chemistry, instrument etc. Local BI solution for analysis and data interpretation. Reporting solution.

12 Sample to report in a semi-closed system. Sample QC Reporting Lib prep Analysis Seq Platform

13 Study Design and Plan For Research Use Only. Not for use in diagnostic procedures.

14 A 2 site Evaluation of the RNA-FUSION NGS panel Training On-site technical support by Illumina at GEM lab Proficiency Laboratory operator testing of provided samples by Illumina FAS. ILUM GEM 250 Accuracy Repeatability Multiple well characterized FFPE, Bone marrow, Lymph node and Peripheral blood samples from Augusta University and Mayo Clinic-Pathology Archived samples prepared in separate library preps 48 Matched Samples Tested Across Both Sites representing unique FFPE samples From Sample To Sequencer in 2-3 Days on both Sites

15 Plan Illumina TruSight RNA Panels 1 TruSight RNA Pan-Cancer: 1385 genes 2 TruSight RNA Fusion: 507 genes Sample source Samples Analyzed Lib prep and sequencing Fusion call & Analysis. GEM lab 60 ILUM BaseSapce Sequence Hub RNA-Seq Alignment App GEM lab 8/250 (48 common) GEM & ILUM Local Run Manager RNA Fusion module

16 Clinically-Relevant Evaluation Samples from Multiple Sources FFPE ( Core Biopsy, Surgical resection, FNA blocks) Bone marrow pellets ( alcohol fixed). Lymph node. Peripheral blood. Cell lines. Future plan: Clinical Residual Samples FNA: Thyroid, Lymph node. Cell Line Mixture Analytical FFPE Titrations

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18 Workflow For Research Use Only. Not for use in diagnostic procedures.

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20 TruSight RNA Pan-Cancer Panel Library Prep Work flow (Not required for FFPE-RNA)

21 TruSight RNA Pan-Cancer Panel Library Prep Work flow Work Flow

22 FFPE RNA Input Recommendation: ng of total RNA extracted from FFPE RNA QC checked by Nanodrop and Agilent Bio analyzer FFPE RNA Input Recommendations DV200: > 200 nt fragment distribution value Example: High Quality FFPE (DV200 = 77%) Example: Medium Quality FFPE (DV200 = 55%) Example: Low Quality FFPE (DV200 = 30%) Example: FFPE Quality Not Recommended for Use (DV200 = 8%)

23 We used 48 FFPE sample for Library prep For Research Use Only. Not for use in diagnostic procedures.

24 Kappa Bioscience Library quantification kit used for Library quantification For Research Use Only. Not for use in diagnostic procedures.

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26 BI solution post-run For Research Use Only. Not for use in diagnostic procedures.

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30 Simple, local analysis designed to enable new to NGS customers* MiSeq and MiniSeq only. Use BaseSpace for NextSeq and HiSeq series.

31 The Local Run Manager RNA Fusion analysis module aligns reads against a specified reference using the STAR aligner, and then detects gene fusions using Manta. This workflow is designed specifically for RNA libraries prepared with the TruSight RNA Fusion Panel kit.

32 TruSight RNA Fusion Analysis Local Run Manager RNA Fusion Module Creating a run Import Samples Either enter manually into the table, or import Defaults to 8 samples (recommended throughput on MiSeq and MiniSeq) Save Run For Research Use Only. Not for use in diagnostic procedures.

33 MiSeq Reporter: Demultiplexes index reads Generates FASTQ files The RNA Fusion analysis module performs the following analysis steps and then writes analysis output files to the Alignment folder. Aligns to the RNA Fusion reference genome The RNA Fusion minimal reference genome is a modified version of the hg19 genome. Detects gene fusions

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35 TruSight RNA Fusion Analysis Local Run Manager RNA Fusion Module View Analysis Results High Confidence Fusion Calls Fusions Passing Filter and meeting a minimum threshold score Table is searchable, sortable and exportable Cytogenetic coordinates are listed as rsa(22;11) rather than t(22;11) because the assay looks at specific regions (genes) rather than the entire chromosome. RSA = Region Specific Assay. For Research Use Only. Not for use in diagnostic procedures.

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38 TruSight RNA Fusion Analysis Local Run Manager RNA Fusion Module View Analysis Results Low Confidence Fusion Calls Fusion calls that are listed as recurrent in the Mitelman database* but do not pass filter or meet the threshold score These are that do not pass the high confidence parameters, but may require additional investigation *

39 TruSight RNA Fusion Analysis Local Run Manager RNA Fusion Module View Analysis Results Recurrent Fusions Not Called Fusions of interest (i.e. Recurrent Fusions from the Mitelman Database) where a fusion was not detected Searchable for example, for a CML sample where a BCR-ABL1 fusion is not identified a customer may want to check expression of BCR and ABL1

40 TruSight RNA Fusion Analysis Local Run Manager RNA Fusion Module View Analysis Results Recurrent Fusions Not Called Fusions of interest (i.e. Recurrent Fusions from the Mitelman Database) where a fusion was not detected Searchable for example, for a CML sample where a BCR-ABL1 fusion is not identified a customer may want to check expression of BCR and ABL1

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42 Results For Research Use Only. Not for use in diagnostic procedures.

43 Run Summary RNA samples from FFPE, BM, PB, LN BSTS tissues. RNA quality of all samples was determined by Bioanalyzer, Nanodrop. QC cut-off for assay (DV200 >30). All samples were processed through initial library preprecommended yield for enrichment (200 ng). All 20 samples were processed through library capture (if <200ng, maximum amount of library was used). Libraries were sequenced on NextSeq & Miseq. Fusion detection was performed on LRM RNA fusion module.

44 Samples with known fusion type Bone marrow Pellet Peripheral blood Lymph node Cells Lines Bone & Soft tissue Chronic Myeloid Leukemia CML BL K-562 (BCR-ABL) Dermatofibrosarco ma protuberans Lymphoma MCL THP-1 (MLL-MLLT3) Alveolar rhabdomyosarcoma APL RT Solid variant of aneurysmal bone cyst Aneurysmal bone cyst. Giant cell fibroblastoma Myxoid lps with focal round cell component For Research Use Only. Not for use in diagnostic procedures. ES, EMC

45 Validated Gene Fusions: Comparison of Fusion Detection Methodology No Fusion Panel RT-PCR FISH Gene1 Gene2 Chr1 Chr2 Pass Score 1 Yes Yes Yes COL1A1 PDGFB chr17 chr22 TRUE Yes SDHA SDHAP3 chr5 chr5 TRUE Yes Yes PAX3 FOXO1 chr2 chr13 TRUE Yes Yes Yes COL1A1 PDGFB chr17 chr22 TRUE Yes Yes FUS DDIT3 chr16 chr12 TRUE Yes Yes Yes EWSR1 FLI1 chr22 chr11 TRUE Yes Yes SS18 SSX2;SSX2B chr18 chrx TRUE 1 8 Yes Yes Yes SS18 SSX1 chr18 chrx TRUE Yes Yes Yes SS18 SSX1 chr18 chrx TRUE Yes Yes CIC C15orf55 chr19 chr15 TRUE Yes Yes TFE3 chr17 chrx TRUE Yes Yes TFE3 chr17 chrx TRUE Yes Yes BRAF chr7 chr7 TRUE* Yes Yes BRAF chr7 chr7 TRUE* Yes Yes Yes EWSR1 FLI1 chr22 chr11 TRUE Yes Yes Yes EWSR1 FLI1 chr22 chr11 TRUE Yes Yes Yes EWSR1 FLI1 chr22 chr11 TRUE Yes Yes Yes TCF12 NR4A3 chr15 chr9 TRUE Yes NF1 SCD5 chr17 chr4 TRUE Yes Yes Yes BCR ABL1 chr22 chr9 TRUE Yes Yes RHOH BACH2 chr4 chr6 TRUE Yes Yes KIAA0125 MYC chr14 chr8 TRUE Yes Yes Yes RARA PML chr17 chr15 TRUE Yes Yes Yes RARA PML chr17 chr15 TRUE Yes Yes Yes RARA PML chr17 chr15 TRUE Yes Yes Yes RARA PML chr17 chr15 TRUE 0.925

46 Validated Gene Fusions: Comparison of Fusion Detection Methodology No Fusion Panel RT-PCR FISH Gene1 Gene2 Chr1 1 Yes Yes Yes COL1A1 PDGFB chr17 2 Yes SDHA SDHAP3 chr5 3 Yes Yes PAX3 FOXO1 chr2 4 Yes Yes Yes COL1A1 PDGFB chr17 5 Yes Yes FUS DDIT3 chr16 6 Yes Yes Yes EWSR1 FLI1 chr22 7 Yes Yes SS18 SSX2;SSX2B chr18 8 Yes Yes Yes SS18 SSX1 chr18 9 Yes Yes Yes SS18 SSX1 chr18 10 Yes Yes CIC C15orf55 chr19 11 Yes Yes TFE3 chr17 12 Yes Yes TFE3 chr17 13 Yes Yes BRAF chr7

47 Validated Gene Fusions: Comparison of Fusion Detection Methodology

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52 Cell lines We demonstrate robust detection of wellcharacterized cancer gene fusions using as little as 10 ng of RNA from commercially available cancer cell lines, such as BCR-ABL translocation detected in K-562 cells (myelogenous leukemia) and MLL- MLLT3 detected in THP-1 cells (acute monocytic leukemia). Furthermore, we show quantitative determination of gene expression levels with high reproducibility and high correlation to results obtained by wholetranscriptome RNA-Seq methods (R 2 = 0.95 for ~1300 targeted genes).

53 Results of the Study The Study Design & Its Efficient Execution Demonstrates The Strong Performance of the fusion panel and the RNA Fusion module Simple protocols, integrated reagents, and built-in bioinformatics facilitated rapid implementation of the kit. Strong Operator/Site and run repeatability, sensitivity and call concordance was observed across both sites, runs and operators. Accurate fusion detection was comparable to orthogonal methods and allowed for the detection of variants in challenging specimen types at low sample input

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55 Current status Mayo sarcoma collaboration to characterize 278 sarcoma cases.

56 Here we describe an approach for selective enrichment of disease-associated genes from RNA- Seq libraries that requires only a fraction of the sequencing depth and enables simultaneous fusion detection and expression profiling of over 1300 genes in one assay. We anticipate that this approach of obtaining highresolution RNA-Seq data from an FFPE sample at reduced sequencing cost, will facilitate studies of gene fusion which was not previously possible.

57 Sample to report in a semi-closed system. Sample QC Reporting Lib prep Analysis Seq Platform

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59 Robust library prep solution on real clinical samples (FFPE, FNA, Core biopsies etc). Cost-effective run. Tech time, chemistry, instrument etc. Local BI solution for analysis and data interpretation. Reporting solution.

60 Acknowledgements Claire Attwooll. Noel Lenny. Lisa Watson. Stephen Xanthoudakis. Sreejith Rajasekharan. James Mellott.

61 Georgia Esoteric Molecular Laboratory at Augusta University Dr. Barb DuPont, PhD. FACMG Dr. Alka Chaubey, PhD. FACMG Dr. Ashis K Mondal, PhD. MB(ASCP) Kimya Jones, MS, HT(ASCP) Olabisi Ojo, MS Dr. Chetan Pundkar, DVM, MS

62 Questions.

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