Targeted NGS in oncology and hemato-oncology using in-house designed gene panels. Joni Van der Meulen Molecular Diagnostics UZ Ghent (MDG) 24/03/2017
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1 Targeted NGS in oncology and hemato-oncology using in-house designed gene panels Joni Van der Meulen Molecular Diagnostics UZ Ghent (MDG) 24/03/2017
2 MDG = Molecular Diagnostics UZ Ghent Center for Medical Genetics Clinical biology MDG Pathology
3 Outline MDG panels in oncology and hematooncology MDG workflows in routine practice Data-analysis and challenges Validation of MDG panels MDG panels in routine practice
4 Outline MDG panels in oncology and hematooncology MDG workflows in routine practice Data-analysis and challenges Validation of MDG panels MDG panels in routine practice
5 MDG panels in oncology and hemato-oncology Therapy response prediction in solid tumors: Metastatic colorectal cancer KRAS, NRAS, BRAF GIST/metastatic melanoma KIT, PDGFRA, BRAF Glioma IDH1, IDH2 Lung cancer EGFR, ERBB2, KRAS, BRAF Diagnostic/prognostic relevance in hematological malignancies: AML & MDS MPN ASXL1, CEBPA, DNMT3A, FLT3, IDH1, IDH2, KIT, NPM1, NRAS, RUNX1, TET2, TP53, SF3B1, SRSF2, U2AF1 CALR, MPL, JAK2, SETBP1, SRSF2, CSF3R
6 MDG panels in oncology and hemato-oncology Therapy response prediction in solid tumors: Metastatic colorectal cancer KRAS, NRAS, BRAF GIST/metastatic melanoma KIT, PDGFRA, BRAF Glioma IDH1, IDH2 Lung cancer EGFR, ERBB2, KRAS, BRAF Diagnostic/prognostic relevance in hematological malignancies: AML & MDS MPN ASXL1, CEBPA, DNMT3A, FLT3, IDH1, IDH2, KIT, NPM1, NRAS, RUNX1, TET2, TP53, SF3B1, SRSF2, U2AF1 CALR, MPL, JAK2, SETBP1, SRSF2, CSF3R
7 Molecular targeted therapy drugs & biomarkers DRUGS Targets Indication Biomarkers Cetuximab, Panitumumab EGFR metastatic CRC, HNC EGFR expression, KRAS-NRAS WT Imatinib BCR-ABL1, ckit, PDGFR CML, GIST Philadelphia chromosome, ckit or PDGFRA mutations Dasatinib multiple tyrosine kinases CML Philadelphia chromosome Nilotinib BCR-ABL1 CML, ALL Philadelphia chromosome Gefitinib/Erlotinib/ Icotinib/Panitumumab EGFR NSCLC EGFR mutations, KRAS WT Lapatinib EGFR, ERBB2 metastatic breast cancer HER2 expression Trastuzumab HER2/neu metastatic breast cancer, gastric cancer HER2 expression Sorafenib multiple tyrosine kinases advanced RCC, unresectable HCC NA Sunitinib multiple tyrosine kinases advanced RCC, GIST NA Pazopanib/Axitinib ckit, PDGFRA, PDGFRB, VEGFRs RCC, soft tissue sarcoma NA Temsirolimus/Everolimus mtor RCC, breast cancer NA Crizotinib ALK, cmet, ROS NSCLC EML4-ALK fusion Vemurafenib BRAF unresectable/metastatic melanoma BRAF V600E mutation Vandetanib EGFR, VEGFR, RET late-stage medullary thryoid cancer NA Cabozantinib RET, cmet, VEGFR2 medullary thryoid cancer NA Bevacizumab VEGF metastatic CRC, NSCLC, glioblastoma, metastatic kidney cancer NA Adapted from Huang et al. Trends in Pharmacological Sciences 2014
8 Colorectal cancer (CRC) Belgium: 9700 novel cases of CRC yearly 3 th most common cancer in males 2 nd most common cancer in females 40-50% new diagnosed CRC cases metastasize Targeted therapy with cetuximab (FDA 2004) or panitumumab (FDA 2006) for metastatic CRC without KRAS and NRAS mutations Van Cutsem et al NEJM 2009, KOTK, NCI, Davies et al. Nat Rev Cancer 2005
9 KRAS mutations and cetuximab resistance in mcrc Karapetis NEJM 2008
10 Mutations in KRAS/NRAS and BRAF cause resistance to EGFRi therapy Walther et al. Nat Rev Cancer 2009, Hyman et al. Cell 2017
11 Therapy response prediction in mcrc KRAS/ NRAS mutation resistant to EGFRi therapy WT for KRAS, NRAS and BRAF sensitive to EGFRi therapy Group of metastatic colorectal cancer patients BRAF mutation poor prognosis, combination therapy EGFRi and BRAFi recommended
12 MDG-colon1 panel Gene Exon Codons Amplicons Most frequent variant type KRAS 2, 3, 4 G12, G13, A59, Q61, K117, A146 3 missense NRAS 2, 3, 4 G12, G13, A59, Q61, K117, A146 3 missense BRAF 15 V600 1 missense MDG-GIST1 panel Gene Exon Codons Amplicons Most frequent variant type KIT 9, 11, 13, 17 all indel, missense PDGFRA 12, 14, 18 all indel, missense BRAF 15 V600 1 missense
13 MDG-BRAIN1 panel Gene Exon Codons Amplicons Most frequent variant type IDH1 4 all missense IDH2 4 all missense MDG-LUNG1 panel Gene Exon Codons Amplicons Most frequent variant type EGFR 18, 19, 20, 21 all indels, missense ERBB2 20 all indels KRAS 2, 3, 4 all missense BRAF 11, 15 all missense, indels
14 MDG panels in oncology and hemato-oncology Therapy response prediction in solid tumors: Metastatic colorectal cancer KRAS, NRAS, BRAF GIST/metastatic melanoma KIT, PDGFRA, BRAF Glioma IDH1, IDH2 Lung cancer BRAF, KRAS, EGFR, ERBB2 Diagnostic/prognostic relevance in hematological malignancies: AML & MDS MPN ASXL1, CEBPA, DNMT3A, FLT3, IDH1, IDH2, KIT, NPM1, NRAS, RUNX1, TET2, TP53, SF3B1, SRSF2, U2AF1 CALR, MPL, JAK2, SETBP1, SRSF2, CSF3R
15 Hematological malignancies Leukemias Acute myeloid leukemia (AML) and acute lymphoid leukemia (ALL): fast proliferation of undifferentiated malignant white blood cells (blasts) Chronic myeloid leukemia (CML) and chronic lymphoblastic leukemia (CLL): leukemic cells can mature partly but not completely and do not have the same features as normal mature blood cells Myelodysplastic syndromes (MDS): immature blood cells in bone marrow do not mature or become healthy blood cells, can evolve to AML Myeloproliferative neoplasms (MPN): bone marrow makes too many red blood cells, platelets, or certain white blood cells Lymphomas: any neoplasm of the lymphatic tissues, lymphomas most commonly develops in the lymph nodes
16 Hematological malignancies Leukemias Acute myeloid leukemia (AML) and acute lymphoid leukemia (ALL): fast proliferation of undifferentiated malignant white blood cells (blasts) Chronic myeloid leukemia (CML) and chronic lymphoblastic leukemia (CLL): leukemic cells can mature partly but not completely and do not have the same features as normal mature blood cells Myelodysplastic syndromes (MDS): immature blood cells in bone marrow do not mature or become healthy blood cells, can evolve to AML Myeloproliferative neoplasms (MPN): bone marrow makes too many red blood cells, platelets, or certain white blood cells Lymphomas: any neoplasm of the lymphatic tissues, lymphomas most commonly develops in the lymph nodes
17 AML & MDS Belgium: 575 novel cases of acute leukemia yearly 835 novel cases of myelodysplastic syndrome yearly KOTK, NCI
18 Genetic defects in AML Patel et al. NEJM 2012
19 Mutation status linked to overall survival in AML Patel et al. NEJM 2012
20 Genetic defects in MDS Haferlach et al. Leukemia 2014, Malcovati et al. Blood 2013, Papaemmanuil et al. Blood 2013
21 Mutation status linked to leukemia-free survival in MDS Papaemmanuil et al. Blood 2013
22 Design of AML/MDS and MPN gene panel AML/MDS (15 genes) and MPN (6 genes) + in-house design of primers
23 MDG-AML1 panel Gene Exon Codon Amplicons Most frequent variant type ASXL1 13 all indels, missense CEBPA 1 all indels, missense DNMT3A 8-23 all missense FLT (ITD), 20 (TKD) all insertions (ITD), missense (TKD) IDH1 4 all missense IDH2 4 all missense KIT 8,17 all indel (ex8), missense (ex17) NPM1 12 all insertion of 4 bp NRAS 2-3 all missense RUNX1 3-8 all indels, missense SF3B all missense SRSF2 1 L85-R120 1 missense TET2 1-9 all missense TP all missense U2AF1 2,6 all missense
24 MDG-MPN1 panel Gene Exon Codons Amplicons Most frequent variant type Neoplasms CALR 9 all indels ET, PMF CSF3R all missense CNL, acml JAK2 12, 14 all missense, indels PV, ET, PMF MPL 10 all missense ET, PMF SETBP1 4 S800-L missense CMML, acml SRSF2 1 L85-R120 1 missense CMML ET: essential thrombocythaemia, PMF: primary myelofibrosis, CNL: chronic neutrophilic leukemia, acml: atypical CML, PV: polycythemia vera, CMML: chronic myelomonocytic leukemia
25 Outline MDG panels in oncology and hematooncology MDG workflows in routine practice Data-analysis and challenges Validation of MDG panels MDG panels in routine practice
26 MDG workflow solid tumors Pathology Centrum for Medical Geneticas Determination tumor type and tumor % Macrodissection and DNA extraction of FFPE coupes PCR-based enrichment of regions of interest + pooling Reporting to oncologist Data-analysis and reporting to Pathology Library prep en MiSEQ NGS
27 Requirements of FFPE samples of solid tumors and macrodissection Requirement of FFPE samples Minimum 10% tumor cells (if 10% or less tumor cells and wild-type, mention on report that a mutation might have been missed) Ideally fixation within one hour after surgical removal in 10% neutrally buffered formalin 6h -72h Macrodissection and DNA extraction 3-5 FFPE coupes of resections or biopsies macrodissection manual extraction QIAamp DNA FFPE Tissue Kit DNA measurement with Qubit
28 MDG workflow hemato-oncology Clinical biology Center for Medical Genetics Morphology, flowcytometry, DNA extraction PCR-based enrichment of regions of interest + pooling Reporting to oncologist Data-analysis and reporting to Clinical Biology Library prep en MiSEQ NGS
29 PCR and control PCR-based enrichment strategy (singleplex and/or multiplex) selection of genes/regions of interest and primer design using FFPE samples: adapted PCR protocol + PCR in duplicate verification of PCR fragments on labchip or fragment analyser De Leeneer et al. Human Mutation 2015
30 Pooling of PCR assays and tests Individual PCRs x µl of each PCR reaction in a patient pool Each MDG-AML1 sample has unique barcode, other MDG samples are pooled with germline assays Pool per patient sample Pool per barcode De Leeneer et al. Human Mutation 2015
31 Preparation of sequencing library Nextera XT v2 kit (Illumina): fragmentation via transposomes and PCR for adding of barcodes 1 Miseq Pool
32 Massive parallel sequencing on MiSEQ MiSEQ (Illumina) hours, 2*250bp: hybridisation, bridge amplification, cluster generation followed by sequencing-by-synthesis
33 Data-analysis MDG Data-analysis with commercial software package CLCbio Genomics workbench Detection of mutations and indels: Variant allele frequency (VAF) 5% AND Mutation present with > 300x coverage for hemato-oncology and > 500x coverage for oncology panels Excel report
34 Controls Controls in PCR and MiSEQ Internal quality control PCR: negative template control (NTC) = water PCR and MiSEQ: positive controls HORIZON or patient sample MiSEQ: positive control, pool of 10 germline variants MiSEQ: negative control (air or water) External quality controls: EMQN, UK NEQAS, WIV-ISP, KRAS EQA, ringtests
35 Quality checks Quality checks in diagnostics Quality check PCR: labchip/fragment analyzer + NTC Quality check library preparation: bio-analyzer/fragment analyzer Quality check MiSEQ run: positive and negative control, base quality scores Minimum coverage of each region of interest and VAF cut-off Hemato: >300x coverage, VAF 5% Solid tumors: > x coverage, VAF 5% Verification of novel primer stocks and master mix stocks on previous analysed samples (PCR and MiSEQ)
36 Outline MDG panels in oncology and hematooncology MDG workflows in routine practice Data-analysis and challenges Validation of MDG panels MDG panels in routine practice
37 Data-analysis MDG solid tumor panels Adapter trimming (MiSEQ) Pairing of reads (CLC bio 9.0.1) Quality trimming (CLC bio 9.0.1) Mapping against genes (CLC bio 9.0.1) Duplicate read removal (CLC bio 9.0.1) Local realignment with indeltrack (CLC bio 9.0.1) Low frequency variant detector in ROI (CLC bio 9.0.1) CHALLENGE 1 CHALLENGE 2
38 Challenges for correct indel detection CLC bio workflows CLC bio 7.3 adapter trimming pairing of reads quality trimming mapping remove duplicates QBVD CLC bio 7.5 adapter trimming pairing of reads quality trimming mapping remove duplicates local realignment QBVD CLC bio 7.5 / adapter trimming pairing of reads quality trimming mapping remove duplicates local realignment LFVD
39 Challenges for correct indel detection CHALLENGE 1: Local realignment for calling of indels KIT indel: c.1689_1727del (p.(asn564_leu576del)) - 39bp deletion CLC bio 7.3 adapter trimming pairing of reads quality trimming mapping remove duplicates QBVD not detected with quality-based variant detector CLC bio 7.5 adapter trimming pairing of reads quality trimming mapping remove duplicates local realignment QBVD correctly detected with quality-based variant detector
40 Challenges for correct indel detection CHALLENGE 2: Variant caller for calling of complex indels KIT complex indel : c.1652_1659delinstcatca (p.(pro551_tyr553delinsleuile)) CLC bio 7.5 adapter trimming pairing of reads quality trimming mapping remove duplicates local realignment QBVD detection of c.1656_1657del (p.met552ilefs*2)) CLC bio 7.5 / adapter trimming pairing of reads quality trimming mapping remove duplicates local realignment LFVD correctly detected with low-frequency variant detector
41 Data-analysis MDG-MPN1 panel Adapter trimming (MiSEQ) Pairing of reads (CLC bio 9.0.1) Quality trimming (CLC bio 9.0.1) Mapping against genes (CLC bio 9.0.1) Duplicate read removal (CLC bio 9.0.1) Local realignment with indeltrack (CLC bio 9.0.1) Local realignment with indels in CALR gene from COSMIC (CLC bio 9.0.1) Low frequency variant detector in ROI (CLC bio 9.0.1)
42 Data-analysis MDG-AML1 panel Adapter trimming (MiSEQ) Pairing of reads (CLC bio 9.0.1) Quality trimming (CLC bio 9.0.1) Mapping against genes (CLC bio 9.0.1) Duplicate read removal (CLC bio 9.0.1) Local realignment with indeltrack (CLC bio 9.0.1) Low frequency variant detector, indel and SV caller in ROI (CLC bio 9.0.1)
43 MDG-AML1 workflow quality based trimming mapping to genes of interest remove duplicates coverage analysis of regions of interest local realignment
44 variant calling in ROI indel & SV calling in ROI annotation annotation
45 Challenges in data-interpretation Sukhai et al. Genetics in Med 2016
46 Challenges in data-interpretation Solid tumors: small gene panels (2 till 4 genes) -> straight forward datainterpretation Hematological tumors: small gene panels (4 till 6 genes) -> straight forward datainterpretation larger gene panels (MDG-AML1 = 15 genes) -> harder to interpret the data
47 Challenges in data-interpretation RING TEST AML-MDS for data-interpretation Case1: AML with 94% blasts in peripheral blood Datainterpretation between 9 labs: given advice about prognosis ranging from favorable, intermediate to unfavorable BLUE: report RED: do not report
48 Challenges in data-interpretation RING TEST AML-MDS for data-interpretation Case4: Diagnosis of AML with 62 % blasts in BM BLUE: report RED: do not report Data-interpretation between 9 labs: big discrepancy in interpretation of FLT3 variant
49 Outline MDG panels in oncology and hematooncology MDG workflows in routine practice Data-analysis and challenges Validation of MDG panels MDG panels in routine practice
50 Validation Validation of onco and hemato-onco panels initial validation of samples for small oncopanels and 30 samples for big oncopanels: minimum 10 samples / variant type (SNV, indel) verification of variants by independent reference method or different lab determination of minimum coverage determination of sensitivity / variant type (false negatives) determination of specificity / variant type (false positives) determination of specificity / variant type (true negatives) based on Belgian NGS guidelines
51 Validation Validation of onco and hemato-onco panels evaluation of within run imprecision: minimum 3 samples in triplicate in 1 run evaluation of between run imprecision: minimum 2 samples / variant type in 3 different runs evaluation of inter-operator variation limited variation: only validated in first oncopanel limit of detection (min 5 variants, including indels and SNVs): dilution series using HORIZON probes dilution series using a mutated sample with a wild-type sample based on Belgian NGS guidelines
52 minimum coverage minimum coverage MDG-GIST1 and MDG-colon1: coverage MDG-GIST BRAF ex15 KIT ex9 KIT ex11 KIT ex13 KIT ex17 PDGFRA ex12 PDGFRA ex14 PDGFRA ex MDG-colon BRAF ex15 KRAS ex2 KRAS ex3 KRAS ex4 NRAS ex2 NRAS ex3 NRAS ex4
53 MDG-GIST1 and MDG-colon1: variant detection PDGFRA ex12 SNV 2% KIT ex17 SNV 4% PDGFRA ex14 SNV 2% KIT ex11 SNV 15% PDGFRA ex18 indel 6% KIT ex11 indel 33% WT 25% PDGFRA ex18 c.2525a>t 6% KRAS ex3 8% NRAS ex2 6% NRAS ex3 4% KRAS ex2 38% BRAF c.1799t>a 4% WT 40% KIT ex9 indel n=50 7% n=50
54 theoretical AF (%) BRAF c.1799t>a theoretical AF (%) KIT c.1708_1728del sequencing VAF (%) PDGFRA c.2472c>t sequencing VAF (%) KIT c.2458g>t MDG-GIST1 and MDG-colon1: accuracy & LOD R= R= sequencing VAF (%) KIT c.1672_1683del sequencing VAF (%) KIT c.1689_1727del R= R= sequencing VAF (%) BRAF c.1799t>a sequencing VAF (%) KIT c.1708_1728del
55 MDG-GIST1: between run imprecision gene Mutation between run VAF between run ene duplication run1 run2 run3 KIT gene c.1504_1509dup p.ala502_tyr503dup deletion / synonymous mutation 64,6% 61,6% 64,2% 62,1% 62,4% 65,1% run1 run2 run3 average VAF standard deviation CV 63,3% 1,5 2,3% average VAF standard deviation CV KIT c.1672_1683del p.lys558_glu561del 46,5% 42,6% 47,4% 43,8% 45,3% 41,5% 44,5% 2,3 5,1% PDGFRA c.2472c>t p.(=) 49,6% 50% 50% 50,1% 47,9% 50% 49,6% 0,8 1,7% gene complex indel mutation/ synonymous mutation run1 run2 run3 average VAF standard deviation CV KIT c.1652_1659delinstcata p.pro551_tyr553delinsleuile 36,1% 36,6% 35,6% 37,2% 36,1% 38,8% 36,7% 1,1 3,1% PDGFRA c.2472c>t p.(=) 47% 52,1% 47,5% 52,2% 47,6% 53,4% 50% 2,9 5,8% KIT c.2394c>t p.(=) 51,1% 49,9% 50% 49,7% 49,9% 47,8% 49,7% 1,1 2,2%
56 MDG-AML1: coverage Coverage > 300x for all ROI of 31 sequenced AML/MDS samples Median: 2099x (917x 7042x) Max: 3565x (1702x 10808x) Min: 1283x (410x 5515x)
57 NGS MDG-AML1: variant detection Variants detected in 31 AML-MDS samples with 5% VAF Gene # variants Type of variant ASXL1 3 Frameshift, missense CEBPA 6 Indel DNMT3A 17 Missense, splice-site, nonsense, indel FLT3 15 Indel IDH1 11 Silent, missense IDH2 5 Missense KIT 2 Indel, missense NPM1 14 Indel NRAS 7 Missense RUNX1 5 Splice-site, missense, indel SF3B1 0 / SRSF2 2 Missense TET2 11 Nonsense, indel, splice-site, missense TP53 5 Indel, missense U2AF1 2 Indel, missense + TP53 big deletions: no coverage for TP53 amplicons in 2 samples Different alternative methods 5% VAF Positive Negative Positive 48 0 Negative 0 58 *alamut software for HGVS naming of indels
58 MDG-AML1: within and between run imprecision *within run imprecision *between run imprecision NPM1 c.860_863 dup (VAF%) IDH2 c.419g>a (VAF%) FLT3 c.1795_1863 dup (VAF%) intra intra intra intra intra intra average CV DNMT3A c.2645 G>A IDH1 c.395g>a FLT3 c.2516a>g FLT3 c.1834_1835ins69 NPM1 c.861_862instgca RUNX1 c.182c>t VAF% run1 run2 run3 average CV
59 Outline MDG panels in oncology and hematooncology MDG workflows in routine practice Data-analysis and challenges Validation of MDG panels MDG panels in routine practice
60 MDG-colon1 & MDG-GIST1 NRAS ex3 2% NRAS ex2 3% KRAS ex4 3% BRAF c.1799t>a 8% KIT ex13 SNV 4% PDGFRA ex18 SNV 4% BRAF c.1799t>a 5% KRAS ex3 2% KRAS ex2 31% WT 50% KIT ex11 SNV 14% KIT ex17 SNV 14% KIT ex11 indel 32% WT 23% KIT ex9 indel 4% 183 samples 23 samples
61 MDG-AML1 Variant detection in routine practice (45 samples)
62 MDG-AML1 FLT3 internal tandem duplication (FLT3-ITD)
63 Acknowledgements MDG Pathology Clinical Biology Elien De Latter Isabelle Rottiers Lies Vandemaele David Creytens Jo Van Dorpe Karl Vandepoele Barbara Denys Bruno Verhasselt Center for Medical Genetics Kathleen Claes Kim De Leeneer Nadine Van Roy Greta Van der Cruyssen Toon Rosseel Wouter Steyaert Angelique Holvoet Elfride De Baere Frank Speleman Bruce Poppe + MiSEQ team,
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