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From www.bloodjournal.org by guest on August 3, 2017. For personal use only. Regular Article LYMPHOID NEOPLASIA Integrated genomic DNA/RNA profiling of hematologic malignancies in the clinical setting Jie He,1,* Omar Abdel-Wahab,2,3,* Michelle K. Nahas,1,* Kai Wang,1,* Raajit K. Rampal,2,3 Andrew M. Intlekofer,4,5 Jay Patel,3 Andrei Krivstov,4,6,7 Garrett M. Frampton,1 Lauren E. Young,1 Shan Zhong,1 Mark Bailey,1 Jared R. White,1 Steven Roels,1 Jason Deffenbaugh,1 Alex Fichtenholtz,1 Timothy Brennan,1 Mark Rosenzweig,1 Kimberly Pelak,1 Kristina M. Knapp,6 Kristina W. Brennan,1 Amy L. Donahue,1 Geneva Young,1 Lazaro Garcia,1 Selmira T. Beckstrom,1 Mandy Zhao,1 Emily White,1 Vera Banning,1 Jamie Buell,1 Kiel Iwanik,1 Jeffrey S. Ross,1 Deborah Morosini,1 Anas Younes,5 Alan M. Hanash,8 Elisabeth Paietta,9 Kathryn Roberts,10 Charles Mullighan,10 Ahmet Dogan,11 Scott A. Armstrong,4,6,7 Tariq Mughal,1,12 Jo-Anne Vergilio,1 Elaine Labrecque,1 Rachel Erlich,1 Christine Vietz,1 Roman Yelensky,1 Philip J. Stephens,1 Vincent A. Miller,1 Marcel R. M. van den Brink,8,13 Geoff A. Otto,1 Doron Lipson,1 and Ross L. Levine2,3,6 1 Foundation Medicine, Cambridge, MA; 2Leukemia Service, Department of Medicine, 3Human Oncology and Pathogenesis Program, 4Cancer Biology and Genetics Program, 5Lymphoma Service, Department of Medicine, 6Leukemia Center, 7Department of Pediatrics, and 8Bone Marrow Transplant Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY; 9Department of Medicine (Oncology), Albert Einstein College of Medicine, Yeshiva University, New York, NY; 10Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN; 11Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY; 12Division of Hematology and Oncology, Tufts University Medical Center, Boston, MA; and 13Division of Hematologic Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY Key Points • Novel clinically available comprehensive genomic profiling of both DNA and RNA in hematologic malignancies. • Profiling of 3696 clinical hematologic tumors identified somatic alterations that impact diagnosis, prognosis, and therapeutic selection. The spectrum of somatic alterations in hematologic malignancies includes substitutions, insertions/deletions (indels), copy number alterations (CNAs), and a wide range of gene fusions; no current clinically available single assay captures the different types of alterations. We developed a novel next-generation sequencing-based assay to identify all classes of genomic alterations using archived formalin-fixed paraffin-embedded blood and bone marrow samples with high accuracy in a clinically relevant time frame, which is performed in our Clinical Laboratory Improvement Amendments–certified College of American Pathologists–accredited laboratory. Targeted capture of DNA/RNA and nextgeneration sequencing reliably identifies substitutions, indels, CNAs, and gene fusions, with similar accuracy to lower-throughput assays that focus on specific genes and types of genomic alterations. Profiling of 3696 samples identified recurrent somatic alterations that impact diagnosis, prognosis, and therapy selection. This comprehensive genomic profiling approach has proved effective in detecting all types of genomic alterations, including fusion transcripts, which increases the ability to identify clinically relevant genomic alterations with therapeutic relevance. (Blood. 2016;127(24):3004-3014) Introduction In the last decade, our understanding of the somatic cancer genome has been greatly advanced through gene discovery studies.1-6 These studies delineated the genomic complexity in different types of human cancer, in different patients with the same tumor type, and within an individual’s tumor. These efforts have identified recurrent somatic alterations that in some cases are specific to a particular tumor type but in many cases are shared across different malignancies. These discoveries have had clinical impact in the diagnosis of specific malignancies defined by recurrent somatic alterations3 and in the development of more precise prognostic schema.1 Most importantly, these studies have identified disease alleles that have guided the use of molecularly targeted therapy.2 These data underscore the importance of genomic profiling in clinical oncology and led to the development of DNA-based genomic tests for cancer patients.7 Cytogenetic studies identified recurrent chromosomal translocations in a spectrum of hematologic malignancies, including acute myeloid leukemia (AML),8 acute lymphoblastic leukemia (ALL),9 chronic myeloid leukemia (CML),10 and non-Hodgkin lymphomas (NHLs),11 which impact clinical outcome and can guide therapeutic decisions.12 More recent studies have identified recurrent mutations and amplifications/deletions in a spectrum of hematologic malignancies, including AML,13 ALL, multiple myeloma (MM), and lymphoma, creating a pressing need to develop comprehensive genomic assays to identify somatic alterations in hematologic malignancies. Submitted August 14, 2015; accepted February 28, 2016. Prepublished online as Blood First Edition paper, March 10, 2016; DOI 10.1182/blood-2015-08664649. The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734. *J.H., O.A.-W., M.K.N., and K.W. contributed equally to this work. The online version of this article contains a data supplement. 3004 © 2016 by The American Society of Hematology BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24 From www.bloodjournal.org by guest on August 3, 2017. For personal use only. BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24 INTEGRATED DNA AND RNA GENOMIC PROFILING 3005 A A TUMOR SAMPLE (FFPE, Blood, BMA) B SEQUENCING LIBRARY PREPARATION & HYBRID CAPTURE Genomic DNA FFPE Sequencing Library C Total RNA Biotinylated DNA Baits Sequencing Library Biotinylated DNA Baits BASE SUBSTITUTIONS Bayesian algorithm SHORT INSERTIONS/DELETIONS Local assembly Hybridization Capture Blood/BMA (3ml) PAIRED-END SEQUENCING INTERPRETATION & REPORTING Analysis methods highlights • Sensitivity to variants present at >5% mutant allele frequency • Detection of long (40bp+) indel variants using deBruijn graph-based local assembly • CGH-like analysis of readdepth for CNAs assessment • Genomic rearrangements and gene fusions are identified by analyzing chimeric read pairs from DNA-seq and RNA-seq Reporting approach Interpretation without a matched normal • Germline variants from 1000 Genomes project (dbSNP135) removed • Known driver alterations (COSMIC v62, internal CNA and fusion list) identified as biologically significant • A concise summary of the biomedical literature and current clinical trials is provided per alteration >600x 550–600x 500–550x 450–500x Frequency Laboratory process highlights • Requires ≥50ng of dsDNA (quantified by PicoGreen®) • Requires ≥300ng of RNA (quantified by RiboGreen®) • RNA is converted to cDNA • Fragmentation by sonication (Covaris®) and ‘with-bead’ library construction • Hybridization capture with biotinylated DNA oligonucleotides • 49 × 49 paired-end sequencing on the illumina HiSeq platform • DNA to ~500× average unique coverage • RNA to >3M unique pairs 400–450x 350–400x 300–350x <300x Sample requirements • Tumor content: ≥20% of nucleated cellularity; ≥30,000 nucleated cells • FFPE: Fifteen 5 micron sections of a 5 × 5mm2 piece of tissue • Peripheral blood/bone marrow aspirate: each collected in an EDTA tube GENOMIC REARRANGEMENTS Chimera pair analysis of DNA-seq and RNA-seq data Hybridization Capture NUCLEIC ACID EXTRACTION 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% CLINICAL REPORT COPY NUMBER ALTERATIONS Comparison with processmatched normal control OR B D ANALYSIS PIPELINE Figure 1. Workflow of comprehensive combined DNA and RNA genomic analysis in clinical specimens and coverage distribution of all exons. (A) DNA and RNA are extracted from fresh blood and bone marrow aspirate (BMA) specimens procured in EDTA or FFPE biopsy/surgical specimens; cDNA from 300 to 500 ng RNA and 50 to 200 ng DNA undergoes whole-genome shotgun library construction. cDNA libraries are hybrid capture selected for 265 genes known to be rearranged in RNA and DNA libraries are hybrid capture selected for 405 genes known to be altered in DNA. Hybrid-capture selected libraries are sequenced to high depth using the Illumina HiSeq2500 platform; sequence data are processed using a customized analysis pipeline designed to accurately detect multiple classes of genomic alterations: base substitutions, short insertions/deletions, CNAs, and gene fusions; detected mutations are annotated according to clinical significance and reported. (B) Coverage distribution in DNAseq of all genes in 108 validation samples including 4 Hapmap controls and 104 hematologic tumor specimens. Hematologic malignancies have a high frequency of rearrangements that lead to aberrant expression of oncogenes or to the expression of fusion transcripts that contribute to malignant transformation and maintenance. These include classical chromosomal translocations, inversions/duplications, interstitial deletions, and episomal fusions/amplifications that can give rise to disordered expression of full-length genes or of fusion transcripts. In many cases, these discoveries have led to the use of targeted therapies in specific disease subsets. However, current diagnostic assays, including fluorescence in situ hybridization (FISH) and real-time polymerase chain reaction (PCR), are designed ad hoc to identify specific genomic alterations, and in some cases, there are no assays that can reliably identify specific rearrangements. We sought to develop an integrated DNA/RNA profiling platform using targeted next-generation sequencing (NGS) that could reliably identify single nucleotide substitutions, insertions/deletions, copy number alterations (CNAs), and rearrangements. Methods Description of workflow DNA and RNA from each patient are extracted and made into barcoded libraries through separate workflow streams. The DNA and cDNA undergo library construction and hybrid selection on independent plates. DNA and RNA samples from the same patient then converge in an analysis pipeline using the plate names and shared specimen ID. Detailed protocols for DNA and RNA extraction, cDNA synthesis, library construction, and hybrid selection are described in supplemental Information, available on the Blood Web site. Sequencing The selected libraries are pooled and sequenced on the Illumina HiSeq2500 to ;5003 unique coverage for DNA and to .3 M unique on-target pairs for cDNA. From www.bloodjournal.org by guest on August 3, 2017. For personal use only. B Number of rearrangements A BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24 HE et al MAF of short variants called by previously validated assay 3006 100 R2 = 0.929 80 60 40 20 0 20 40 60 80 90 80 70 60 50 40 30 20 10 0 DNA only RNA only DNA+RNA 100% 50% 33% 25% 20% 10% 100 Tumor content MAF of short variants called by the new assay C D 60 1 FLT3 ITD positive by PCR, negative by NGS (<5%) 1 <1% 211 (99%) 100 positive 111 negative 2 <1% 2 IDH2 exon 4 negative by sanger, positive by NGS (4%, 26%) # of samples 50 40 30 ref test–/NGS test+ ref test+/NGS test– ref test–/NGS test– ref test+/NGS test+ 20 10 FL T3 N ITD C PM +D E 1 BC BP ex 835 R A fu on -A l 12 BL l g e KI 1 fu ne T si ID ex on H on ID 2 e 17 H x M JA 1 e on4 PL K x PM S 2 V on 4 L- 505 61 R + 7F AR W 5 M A f 15 LL us fu ion si on s 0 Figure 2. Alteration calling accuracy and concordance with reference platforms. (A) Correlation of short variant MAFs for substitutions and indels called by new vs previously validated assays. MAFs of 102 substitutions and 59 indels were compared in pairs and showed significant correlation (R2 5 0.929). (B) Summary of sensitivity of fusion calling in cell line mixes, separated by detection method (DNA, RNA, or both). (C) High concordance (99%) observed between NGS and Sanger sequencing or PCR validation results. (D) Concordance of alterations between the new assay and other Clinical Laboratory Improvement Amendments (CLIA)–certified reference platforms. The count of positive call and negative call from reference test and our NGS-base assay from the 11 genes/regions are highlighted by different colors: red (alteration was called negative by reference test but positive by our NGS test), steel blue (alteration was called positive in reference test and negative in our NGS test), peach (alteration was called negative in both reference test and negative test), and lime green (alteration was called positive in both reference test and negative test. Base substitutions, indels, and copy number analysis Samples with median exon coverage in the range 150 to 2503 are considered qualified, whereas those with coverage ,1503 are considered failed. For base substitutions, the mutant allele frequency (MAF) cutoff is 1% for known somatic variants (based on COSMIC v62) and 5% for novel somatic variants. For indels, the MAF cutoff is 3% for known somatic variants and 10% for novel somatic variants. Additional details of the methods were described previously.7 Rearrangement calling methods A customized alignment workflow was developed for fusion detection from RNA-seq. Raw sequence reads were aligned to whole transcriptome (refSeq) first, and reads with suboptimal mapping were aligned to whole genome references. Alignments to the 2 different references were then merged and calibrated based on the full genome reference (hg19) for fusion detection. Gene rearrangements were detected by identifying clusters of chimeric read pairs from both DNA (pairs mapping .10 kbp apart or on different chromosomes) and RNA (pairs mapping to refSeq sequences corresponding to different genes or to genomic loci .10 kbp apart). Chimera clusters were filtered for repetitive sequence (average mapq .30) and by distribution of mapped positions (SD .10). Identified rearrangements were then annotated according to the genomic loci of both clusters and categorized as gene fusions (eg, BCR-ABL1), gene rearrangements (eg, IGH-BCL2), or truncating events (eg, TP53 rearrangement). Rearrangement candidates were then filtered based on number of chimera reads supporting the rearrangement events (for documented fusions, a minimum 10 chimera reads are required; for putative somatic driver rearrangements, 50 chimera reads are required). In addition to the de novo rearrangement detection method described above, reads were also separately aligned to a custom reference library generated based on common fusions and rearrangements. Fusions were detected based on the observation of reads aligned across the junction of rearrangement breakpoints. This method includes the detection of 6 common isoforms of MLL-PTD14 (supplemental Table 3a). Immunoglobulin heavy locus (IGH) rearrangements were detected by targeting rearrangement hotspots of both common immunoglobulin fusion partner genes (major and minor translocations involving MYC, BCL2, and CCND1), as well as IGH breakpoint regions. Expression measurement We first calculated read counts per million for each gene, and then relative expression level was normalized by the median read counts per million of all 449 MM patients. The statistical significance of expression between the rearrangement positive cohort and rearrangement negative cohort was determined by a 1-sided Wilcoxon rank-sum test. From www.bloodjournal.org by guest on August 3, 2017. For personal use only. BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24 1 AL EM K L4 4 XO R BC 2 SS PR ERG 1 TM NX RU Y X 22 21 MLL bcr 2 20 TC 18 19 F3 3 RAR 17 A TP5 3 4 2 SLC34A FIP1L1 PDGFRA AFF1 1 MLL (WT) DNA 2 MLL – PTD RNA 15 PML B X648577 16 CBFB MYH11 CIITA 3007 B PB X1 FO A INTEGRATED DNA AND RNA GENOMIC PROFILING 14 5 3 4 5 6 78 910 11 12–16 1 2 3 4 5 6 7 8 2 1 2 3 4 5 6 7 8 9 10 2 1 2 3 4 5 6 7 8 4 3 5 4 3 6 7 8 36 5 6 7 8 4 25% samples 9–36 5 6 7 8 9 10 11–36 25% samples 50% samples 9–36 13 NP 6 M1 12 RO S1 1O ET P2 V6 M PIC LL AL M CCDC6 RET MLLT10 R1 X1T1 RUN FGF MLLT3 C 8 9 NUP214 ABL1 SET FG FR 10 T4 LL M 7 11 BCL6 MYC 22% samples 9% samples 187,439K 128,753.4M 128,750.9M 128,748.4M 128,745.9M 187,445K 187,451K 187,457K 187,457K CCND1 22% samples BCL2 48% samples 69,470K 69,430K 69,390K 69,350K 69,310K 106,042,434 60,760K 60,800K 106,337,406 106,632,378 106,927,350 60,840K 60,880K 60,920K 60,960K 107,222,322 IGH Figure 3. Combined DNA and RNA sequencing for detection of complex rearrangements. (A) Circos plot showing broad range of fusions in cell line mixes used for validation of fusion calling. (B) Three MLL-PTD isoforms that were tested (from top to bottom): duplication of MLL exons 2 to 8, exons 2 to 10, and exons 4 to 8. (C) Breakpoints on the IGH and its partner genes for the 13 clinical concordance samples including 5 IGH-MYC, 2 IGH-BCL6, 5 IGH-CCND1, and 11 IGH-BCL2 rearrangements. Results NGS-based test for detecting genomic alterations in DNA and RNA The assay we developed uses next-generation DNA and RNA sequencing and builds on the workflow that has been optimized for genomic profiling of DNA from patients with solid tumors.7 Genomic profiling is accomplished by integration of data from targeted DNA and RNA profiling with up-to-date interpretation of the clinical significance, thereby achieving increased breadth and improved sensitivity to identify rearrangements that result in aberrant expression of fusion transcripts. The genes targeted by DNAseq and RNAseq are listed in supplemental Table 1a-b, and the intervals of each exons/introns targeted by this assay are listed in supplemental Table 1c-d. The clinical implications of targeted genes in ALL, AML, myelodysplastic syndromes (MDS)/ myeloproliferative neoplasms, NHL, and MM are annotated in supplemental Table 1e. Uniform coverage across all genes are observed from a total of 108 Hapmap and hematologic tumor specimens. The distribution of coverage of 405 genes targeted in DNAseq is shown in Figure 1B, and coverage of each gene is listed in supplemental Table 1f. The distribution of exonic coverage (1244 exons) of all clinical relevant genes is shown in supplemental Figure 1. Eight exons had consistent low coverage ,1003 and were excluded from further analysis (supplemental Table 1a). The read depth of each gene targeted in RNAseq from 61 validation samples is listed in supplemental Table 1g. The current workflow is shown in Figure 1A and described in the supplemental Methods. The coverage of each target of genes involved in therapy, clinical trial, and/or other prognostic and standard of care is summarized in supplemental Table 1h. Detection of DNA sequence variants and CNAs We first established analytic accuracy of detecting substitutions, shorts insertions and deletions (indels), and CNAs by comparing the performance of the new assay with a DNA-only assay that had previously undergone comprehensive validation across a large number of clinical samples. Compared with the previously validated assay, the new assay contained an additional 90 genes relevant to hematologic malignancies. Substitution, indel, and copy number detection were validated by reanalyzing 47 samples previously profiled with a validated test7 in which 169 alterations were identified in 55 genes common to both assays (102 substitutions, 59 indels, and 8 CNAs; 10 low-frequency subclonal variants were excluded from the analysis). The concordance between the 2 sets of results was 99.4%: 168 of 169 variants were concordant with a single discordant low-frequency variant (supplemental Table 2a). Additionally, MAF values measured using the 2 assays were highly correlated (R2 5 0.929; Figure 2A). These data demonstrate that the current DNA profiling platform can accurately detect DNA sequence variants and CNAs, as established for the reference assay. Detection of genomic rearrangements To determine our ability to detect genomic rearrangements, we pooled 21 cell lines (Figure 3A; supplemental Table 2b) with 28 known genomic rearrangements to create 39 mixtures with ratios From www.bloodjournal.org by guest on August 3, 2017. For personal use only. 3008 BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24 HE et al A B Hodgkin lymphoma 1% Histiocytic/dendritic cell neoplasms/proliferations 1% Plasma cell neoplasm < 1% Anaplastic large cell lymphoma 2% Lymphoblastic leukemia (B or T) 7% Waldenstroms lymphoma 3% PMBCL 1% AITL 2% Skin lymphoma 5% Myelodysplastic &/or myeloproliferative neoplasms 13% Non-hodgkins lymphoma (NOS) 4% Multiple myeloma 39% Acute myeloid leukemia/myeloid sarcoma 17% Marginal zone lymphoma 2% T-cell lymphoma (NOS) 10% Diffusion large B cell lymphoma 38% Follicular lymphoma 14% Non-hodgkin lymphoma 22% B-cell lymphome (NOS) 10% C Mantle cell lymphoma 8% Burkitt lymphoma 1% D 20 Substitution Indel Copy number Rearrangements 12% Rearrangement Percent of patients 15 Copy number alterations 9% 10 Indels 21% Base substitutions 58% 5 TP53 NRAS KRAS ASXL1 TET2 DNMT3A CDKN2A MLL2 RUNX1 CCND1 CREBBP BCL2 SRSF2 CDKN2B RB1 BRAF MLL FLT3 WHSC1 ARID1A IDH2 SF3B1 ATM EZH2 PTPN11 JAK2 CD36 ETV6 WT1 STAG2 MYD88 U2AF1 TRAF3 TNFAIP3 MYC CDKN2C NOTCH1 NF1 BCOR B2M NPM1 IGH ZRSR2 NFRSF14 BCL6 IDH1 LRP1B FGFR3 EP300 BIRC3 0 Figure 4. Overview of clinical cases profiled by combined DNA/RNA NGS assay. (A) Tumor type distribution of all hematologic tumor cases profiled by the NGS-based DNA/RNA test. (B) Disease ontology distribution of 297 NHL tumors profiled by the assay. PMBCL, primary mediastinal (thymic) large B-cell lymphoma; AITL, angioimmunoblastic T-cell lymphoma. (C) Genomic alterations detected from the 1931 clinical specimens and distribution of alteration types. Top 50 most frequent altered genes are displayed. Red: base substitutions, steel blue: indels, peach: CNAs, lime green: rearrangement. (D) Distribution of alteration types including base substitutions, indels, CNAs, and rearrangements in the clinical specimens. of 10%, 20%, 25%, 33%, and 50% representing a range of clinically relevant tumor fractions. Sensitivity and specificity of the assay were determined by comparing the genomic rearrangements detected in the pooled samples relative to the constituent cell lines. The sensitivity for fusion detection at 20% to 100% tumor fraction was 100% (161 of 161), and 98% at 10% tumor fraction (84 of 86) (supplemental Table 2b). Very few falsepositive calls were observed, with a positive predictive value of .98% (245 of 248, with 2 of 3 false calls being at a marginal level of detection). The combined DNA and RNA sequencing approach accurately detects a wide variety of genomic rearrangements and gene fusions. RNA sequencing enables efficient detection of rearrangements and targets all 28 rearrangements with novel breakpoints in a large number of genes, whereas DNA sequencing allows high sensitivity for well-characterized rearrangements affecting specific introns and rearrangements that do not result in expression of a fusion transcript, including those involving promoter regions. The distribution of rearrangements called by both DNA and RNA, RNA only, and DNA only are shown in Figure 2B. RNA sequencing maintains high sensitivity in high tumor content (100% sensitivity in tumor content 25-100%); at low tumor fraction (10-20%), DNA sequencing is more sensitive in selected introns, including, for example, breakpoints involving the MLL and PDGFRA genes. Concordance with reference platforms We performed blinded comparisons with CLIA-certified diagnostic assays, including Sequenom, reverse transcription-PCR, FISH, and From www.bloodjournal.org by guest on August 3, 2017. For personal use only. BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24 A INTEGRATED DNA AND RNA GENOMIC PROFILING B ALL 35 20 15 10 Substitution/Indel Amplification Homozygous deletion Rearrangement Truncation 20 Percent of patients Percent of patients 25 AML 25 Substitution/Indel Amplification Homozygous deletion Rearrangement Truncation 30 3009 15 10 5 5 0 CDKN2A CDKN2B TP53 ETV6 NRAS KRAS CREBBP MLL JAK2 RUNX1 JAK3 NOTCH1 PHF6 ABL1 RB1 SETD2 PTPN11 PAX5 MLL2 IKZF1 FLT3 DNMT3A PIK3CA PTEN ARID1A MLLT10 BCORL1 FANCA FBXW7 TET2 ASXL1 STK11 CD36 RHGAP26 WT1 NF1 IDH2 IDH1 CCND3 SRSF2 U2AF1 JAK1 STAG2 ATM KDM6A EP300 EZH2 MSH3 BRCA2 SMARCA4 RUNX1 ASXL1 DNMT3A FLT3 TP53 IDH2 NRAS MLL SRSF2 TET2 WT1 NPM1 PTPN11 BCOR NF1 U2AF1 IDH1 STAG2 CREBBP CEBPA KRAS SF3B1 JAK2 EZH2 CDKN2A SETBP1 ETV6 PHF6 GATA2 BCORL1 CD36 CDKN2B KIT MLL2 MYH11 IKZF1 ZRS2 ARID1A FOXP1 MLLT10 NSD1 ATM KDM6A NUP214 CCND2 GNAS CBL ABL1 LRP1B STK11 0 C D MDS Substitution/Indel Amplification Homozygous deletion Rearrangement Truncation 20 15 10 20 15 10 0 0 KRAS NRAS TP53 WHSC1 CCND1 BRAF RB1 TRAF3 CDKN2C FGFR3 DNMT3A ATM TET2 CD36 BIRC3 ASXL1 FAF1 CREBBP CDKN1B ARID2 ARID1A PTPN11 IGH PRDM1 CDKN2A LRP1B NF1 ZRSR2 MAP3K14 BRCA2 SETD2 MYC MLL2 SF3B1 MCL1 ICK STAG2 NCOR2 AXIN1 MLL CDH1 CDKN2B ATR TRAF2 U2AF1 PIK3CA IDH2 IDH1 PASK APC 5 ASXL1 TET2 TP53 STAG2 RUNX1 SRSF2 SF3B1 MLL DNMT3A U2AF1 BCOR NRAS EZH2 ZRSR2 IDH2 FLT3 CEBPA JAK2 IDH1 SETBP1 NF1 WT1 GATA2 ETV6 PHF6 STK11 MUTYH MLL2 NPM1 ATM CUX1 KRAS PTPN11 CBL CSF3R BRCA2 CXCR4 RB1 SMAD4 CDKN1B FGFR1 CD36 NTRK1 POLE BRIP1 TYK2 IKZF1 DAXX STAT3 MLH1 5 E Substitution/Indel Amplification Homozygous deletion Rearrangement Truncation 25 Percent of patients 25 Percent of patients MM 30 30 NHL 30 Percent of patients 25 Substitution/Indel Amplification Homozygous deletion Rearrangement Truncation 20 Therapy Clinical trial 15 Diagnostic Prognostic 10 5 MLL2 BCL2 TP53 CDKN2A CREBBP CDKN2B TNFAIP3 MYD88 BCL6 B2M NFRSF14 ARID1A MYC TET2 EZH2 CCND1 ATM CARD11 RB1 CD79B NOTCH1 IGH BCL7A FAS BCL10 CD36 DDX3X KRAS ETV6 CD58 PIM1 SOCS1 CITTA LRP1B NOTCH2 CD70 STAT3 SGK1 CXCR4 PTEN DNMT3A XPO1 ZRSR2 ASXL1 PAX5 PRDM1 ETS1 MALT1 FOXP1 PIK3R1 0 Figure 5. Genomic landscape in ALL, AML, MDS, NHL, and MM. Observed frequencies of most mutated genes in (A) ALL, (B) AML, (C) MDS, (D) NHL, and (E) MM. Mutation frequency is grouped by function effect, substitution/indel (red), focal amplification (steel blue), homozygous deletion (peach), rearrangement/fusion (lime green), and truncation (purple). The numbers of cases included in this analysis are 102 cases in ALL, 274 cases in AML, 130 cases in MDS, 297 cases in NHL, and 753 cases in MM. In The clinical relevance including therapeutic (green), clinical trial (olive), prognostic (lavender), and diagnostic (blue) are highlighted on top of each gene. PCR fragment analysis, for 76 clinical specimens (supplemental Table 2c) previously tested for 214 clinical relevant alterations in 11 genes that are known and routinely tested in clinical practice in AML, ALL, and MDS (165 short variants and 49 gene fusions; Figures 2C-D). DNA and RNA were extracted from new unstained sections from the originally tested formalin-fixed paraffin-embedded (FFPE) block. Coverage of DNA sequencing across 76 specimens is shown in supplemental Figure 2 and Table 2d. Of the 101 genomic alterations identified by Sequenom or gel sizing, 100 were also called by our new assay (1 positive FLT3 internal tandem duplicate [ITD] was not detected due to low mutant allele frequency). Of 113 negative genomic alterations reported previously, our assay confirmed 111 negative calls From www.bloodjournal.org by guest on August 3, 2017. For personal use only. 3010 BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24 HE et al A B SATB1-ALK (Anaplastic large cell lymphoma) E1–6 GTF2I-BRAF (Multiple myeloma) E1–4 E1–25 E15–24 E1–12 E1–5 TSPAN3-ROS1 (Multiple myeloma) ZFP36L1 CRLF2 1% 1% E10–18 NIN-FLT3 (Hodgkin lymphoma) KIAA1432-JAK2 (B cell acute lymphoblastic leukemia) Other (<5 observations) 17% E20–29 MAFB 2% E3–25 CCND1/ MYEOV 26% WWOX 3% BCL6 4% E31–43 BCL2 23% MMSET/ FGFR3 18% MYC 5% C FGFR3 overexpression in t(4:14)+ MM samples (P = 8.70e–22) ii) WHSC1 overexpression in t(4:14)+ MM samples (P = 5.28e–32) 10 4 5 2 0 0 –5 –2 t(4:14)–MM iii) CCND1 overexpression in t(11:14)+ samples (P = 6.00e–38) 5 0 –5 t(11:14)+ D835H D835Y D835V D835F D835E S72* S451F t(11:14)– D t(4:14)+MM Y589H V592D V592I V592A P606L G613E N676K Log ratio of CCND1 t(4:14)–MM t(4:14)+MM V491L Log ratio of FGFR3 i) 15 Copy number gain (6) 2 Exon number 3 Amino acid 0 4 100 5 6 200 7 lg 8 300 9 10 11 12 13 400 500 e10–11–12 Del (1) Tm 14 Tk 15 600 16 17 700 18 19 20 800 Fusion (1) 21 22 900 23 24 994 FLT3 NM_004119 Protein domains Types of mutations Missense SNV Transmembrane(Tm) Nonsense SNV Tyrosine kinase(Tk) Insertion Complicated Deletion (short) I836del F612 > Ins P606 > Ins F605 > Ins E604 > Ins E598 > Ins Y597 > Ins E596 > Ins R595 > Ins D593 > Ins V592 > Ins Y591 > Ins F590 > Ins N587 > Ins D586 > Ins S585 > Ins G583 > Ins V581 > Ins Q580 > Ins L576_Q580 > G L576 > Ins Y572 > Ins Immunoglobulin(lg) Figure 6. Clinical highlights. (A) Novel fusions involving druggable targets such as ALK, BRAF, FLT3, JAK2, and ROS1 (59 partner salmon, 39 partner including kinase domain of target gene gray; ALCL, anaplastic large cell lymphoma). (B) Distribution of IGH rearrangement partners. (C) Overexpression of (i) FGFR3 and (ii) WHSC1 in t(4;14)-positive MM cases (n 5 61) compared with t(4;14)-negative MM cases (n 5 388). The violin plot shows the distribution of the log expression ratios of FGFR3 and From www.bloodjournal.org by guest on August 3, 2017. For personal use only. BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24 and identified 2 IDH2 R140Q mutations that were not detected by the reference method likely due to the greater sensitivity of this approach than the orthogonal platform (Figure 2C; supplemental Table 2e). These 2 IDH2 alterations were later confirmed from an independent validation using AmpliSeq platform. Overall concordance was 99% (211 of 214; Figure 2C). The concordance between the NGS-based test and reference testing, including positive and negative calls, is shown in Figure 2D. In addition to the concordance analysis, genomic profiling of the 76 test samples identified 126 additional somatic alterations that are not covered by available hotspot assays in the given disease type, including clinically relevant genomic alterations in KRAS, TET2, EZH2, and DNMT3A (supplemental Table 2e; supplemental Figure 3). A subset of samples with low-frequency calls (,10%) was selected for an independent validation using either AmpliSeq assay (supplemental Methods) or a CLIA-certified non-NGS mutation detection assay. Overall, 20 of 21 low-frequency variants were confirmed from AmpliSeq assay and other hotspot clinical assays (supplemental Table 2g). INTEGRATED DNA AND RNA GENOMIC PROFILING 3011 Human Reference RNA for RNA, a mixture of 10 HapMap samples for DNA). These samples were sequenced repeatedly over 5 months; 303 single nucleotide polymorphisms (100% 134 of 134) in DNA and BCR-ABL fusion (100% 132 of 132) in RNA were successfully detected over the entire time series. Workflow compatibility with different input materials We next studied the assays performance using clinical FFPE, blood, and bone marrow aspirate samples. Blood and bone marrow were collected in EDTA for both DNA and RNA extractions. DNA and RNA were obtained from 10 different blood, bone marrow, and FFPE specimens, and nucleic acids were extracted according to protocols described in the supplemental Methods. Of these 40 samples, 39 of 40 exceeded performance specifications, with the only failing sample coming from 1 of 10 FFPE blocks analyzed (supplemental Table 5). Clinical experience to date Detection of complex gene rearrangements In addition to rearrangements that result in expression of a fusion transcript containing a known or putative oncogene (Figure 3A), complex gene rearrangements are often identified in patients with hematologic malignancies. These include large intragenic rearrangements such as MLL partial tandem duplications (PTDs),15 extragenic rearrangements that involve the immunoglobulin genes, and smaller internal gene rearrangements, such as FLT3 internal tandem duplications (ITDs)16 that were assessed in the concordance with reference platforms above (Figure 2D). We profiled 12 samples that were MLLPTD positive by PCR, spanning 3 different isoforms (Figure 3B), and 14 that were negative by PCR. The NGS-based assay identified MLLPTDs in 11 of the 12 PCR positive samples and confirmed the negative PCR calls in all 14 samples, for an overall accuracy of 96.5% (25 of 26, the single missed PTD was present below the calling threshold). Rearrangements involving the IGH locus commonly occur in hematologic malignancies, including NHL.17 The IGH rearrangementcalling algorithm enables detection of known IGH rearrangements in addition to novel partners. IGH rearrangement detection was assessed using 10 cell lines and 38 clinical samples harboring IGH rearrangements detected by FISH and/or karyotypic data (Figure 3C; supplemental Table 3a-b). The 10 cell lines were fully concordant, whereas the clinical samples had 2 discordant positive calls and 1 discordant negative call for an overall concordance of 94% (45 of 48) (supplemental Table 3b-c). Assay reproducibility in clinical samples To demonstrate assay reproducibility, we tested 13 clinical FFPE specimens in 5 replicates, each in 3 different batches with 1 batch including 3 intrabatch replicates. In total, the 65 samples contained 82 alterations (42 subs, 13 indels, 15 CNAs, and 12 rearrangements) providing a comprehensive assessment of intra- and interbatch reproducibility. Concordance between replicates was 97% for both interand intrabatch subsets (supplemental Table 4), with discordant calls being ascribed to sensitivity limit of the assay. Long-term reproducibility was assessed through serial assessment of 2 pooled samples (Universal The new assay was used to perform genomic profiling on 3696 hematologic malignancies submitted to our CLIA-certified, College of American Pathologists–accredited, New York State–approved laboratory; 3433 of 3696 (93%) specimens submitted for processing were successfully characterized, including 27% FFPE samples, 21% from bone marrow aspirates, 18% samples derived from blood, and 34% samples from pre-extracted nucleic acids from relevant tumor tissues. This cohort included 39% MM, 22% NHL, 17% AML, and 13% MDS/ myeloproliferative neoplasm specimens (Figure 4A). Different subtypes of NHL in our clinical experiences include DLBCL (41%), follicular lymphoma (13%), B-cell lymphoma (NOS) (8%), and mantle cell lymphoma (8%) (Figure 4B). Extracted DNA was sequenced to an average depth of 5003 and RNA to an average of 6.9 M unique pairs; 263 of 3696 (7%) specimens did not attain quality control specifications including failures in DNA preparation, or failures in RNA preparation, or blood sample older than acceptance criteria (1 day old) and no evidence for tumor, or no somatic driver mutations reported under qualifying conditions such as low coverage (DNA ,2503), low tumor purity (,20%), and coverage bias impairing ability to call CNAs (supplemental Table 6a). Genomic profiling of hematologic malignancies demonstrates that only a relatively small number of genes are commonly mutated, whereas many specimens harbor a wide range of rarer events (Figure 4C), including base substitutions, indels, copy number amplification/losses, and rearrangements (Figure 4D). At least 1 driver alteration was identified in 3246 of 3433 (95%) tumor specimens, and 2650 (77%) cases harbored $1 alteration linked to a commercially available targeted therapy or one that is in clinical development, including NRAS (14% of cases), KRAS (13%), DNMT3A (7%), CDKN2A (7%), IDH1/2 (5%), BRAF (4%), and FLT3 (4%). In addition, 61% of cases harbored $1 alteration with known prognostic relevance in that tumor type, including TP53 (19% of cases), ASXL1 (9%), TET2 (8%), CDKN2B (5%), CREBBP (5%), MLL (4.0%), and NPM1 (2.0%) (Figure 4C). Disease-specific mutational landscapes of MM, NHL, ALL, AML, and MDS are further presented in Figure 5 with associated clinical relevance, and a full listing of observed genes and mutation frequencies are listed in supplemental Table 6b. Figure 6 (continued) WHSC1 in each group, respectively. For both cases, the difference is statistically significant (FGFR3, P 5 8.7e222; WHSC1, P 5 5.28e232, 1-sided Wilcoxon rank-sum test). (Ciii) Overexpression of CCND1 in all t(11;14)-positive cases (n 5 67) compared with t(11;14)-negative cases (n 5 3095). The difference is also statistically significant (P 5 6.00e238, 1-sided Wilcoxon rank-sum test). (D) Overview of short variants (single nucleotide variants [SNVs], ITDs, indels) and large-scale aberrations including long deletions, copy number gains, and fusions detected in FLT3. The FLT3 protein is drawn with its immunoglobulin-like (Ig, red), transmembrane (Tm, steel blue), and tyrosine kinase (Tk, peach) domains illustrated. Positions and protein effect of all short variants are indicated. For ITDs (green), samples with ITD at the same positions are aggregated together regardless of the actual insert sequence. The ranges of large-scale aberrations are indicated with light blue bars. Commonly tested “hotspot” mutations are shaded in gray. From www.bloodjournal.org by guest on August 3, 2017. For personal use only. 3012 BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24 HE et al Table 1. Summary of genomic alterations identified in Ph-like B-ALL cases Specimen ID ALL1 Kinase fusion Kinase CNA/sequence mutation IKZF1 deletion or mutation PAX5 deletion or mutation EBF1 alteration CDKN2A/B deletion PAX5-JAK2 Unknown Deletion No Yes No IGH-CRLF2 CRLF2 F232C* Deletion (single No Yes No No No Yes RAS pathway Other lesion ALL2 ALL3 exon deletion) ALL4 ALL5 SSBP2-JAK2 Unknown ALL7 RSCD1-ABL2 Unknown ALL8 IGH-EPOR Deletion ALL6 KRAS G12D Deletion No No No Deletion Deletion and No Yes No No Yes* Deletion No Yes mutation ALL11 CREBBP R1081fs ETV6 Deletion (single focal deletion exon deletion) ALL12 ALL13 IGH-CRLF2 ALL14 P2RY8-CRLF2 JAK2 T875N IGH-CRLF2 Unknown Deletion ALL15 ALL16 ALL17 PAX5-MEGF9 No FIP1L1-PDGFRA ALL18 Summary of genomic alterations including kinase fusions, kinase mutation, and deletions in IKZF1, PAX5, EBF1, and CDKN2A/B in 16 Ph-like ALL patients. *Mutations that were detected only by the clinical sequencing assay. In total, we identified 1524 genomic rearrangements in 1256 of 3433 (37%) tumor specimens. These rearrangements involved genes implicated in chromatin/histone remodeling (20%), transcriptional regulation (16%), kinase/oncogene activation (15%), apoptosis regulation (13%), cell cycle regulation (13%), and truncation of tumor suppressors (5%), including novel in-frame fusions in kinase drug targets in ALK, BRAF, FLT3, JAK2, and ROS1 (Figure 6A). In addition, we identified a novel translocation involving NOTCH1, as well as several novel NF1 translocations that might confer sensitivity to MEK inhibitors. Rearrangements involving the IGH locus were identified in 665 of 3433 tumor specimens, including rearrangements involving CCND1/ MYEOV (28%), BCL2 (25%), MMSET/FGFR3 (17%), MYC (6%), and BCL6 (4%) (Figure 6B; supplemental Table 6c). Novel IGH rearrangements were observed in 17% of cases. The locations of the most common IGH rearrangement partners are summarized in supplemental Figure 4. In addition, a sizable fraction of IGH rearrangements involved breakpoints in intergenic regions with no known oncogene within 7 MB of the breakpoint. Genomic rearrangements involving IGH are believed to lead to overexpression of the partner oncogene by placing it under the regulatory control of the IGH locus.18 Expression levels of MMSET/FGFR3 (4p16.3) and CCND1 genes were successfully measured for 449 MM cases (Figure 6C). As depicted in Figure 6C, we observed overexpression of both CCND1/FGFR3 and CCND1 genes in samples harboring the respective rearrangements. Identification of different classes of alterations in a single gene using an integrated genomic profiling assay Previous studies have shown that a subset of oncogenes can be activated by different types of genomic alterations in different patients.19 Constitutive activation of the FLT3 receptor tyrosine kinase can occur in a significant proportion of leukemia patients as a result of somatic ITDs, point mutations (D835/I836), or less common chromosomal rearrangements.16,20 Somatic alterations involving the FLT3 locus have been shown to be of prognostic importance in acute leukemia, and there are potent, specific FLT3 kinase inhibitors in late-stage clinical trials.21 We observed 89 alterations targeting FLT3 in 84 specimens. This included 67 (75%) D835 and ITD mutations detected by existing clinical tests. We also identified other base substitutions (n 5 14), in-frame deletion (n 5 1), CNAs (n 5 6), and fusions involving FLT3 (n 5 1) (Figure 6D). For example, 1 AML case that tested negative for D835Y harbored an I836 del, which has previously been associated with poor disease-free survival.22 Four cases positive for FLT3 ITD or D835 were found to also harbor concurrent lowfrequency V592A and S451F mutations, which have been functionally validated as activating the FLT3 kinase.23 These results indicate that the sequencing-based assay can robustly detect a wide range of alterations in driver genes that are not fully evaluated using conventional methods. Use of a single unified test in B-cell ALL Recent genomic studies in B-cell ALL have led to an increased understanding of the molecular pathogenesis of this B-cell malignancy. This includes the identification of recurrent substitutions, indels, and fusion genes that target B-cell differentiation. Most importantly, recent studies have shown that subsets of patients with high-risk, BCRABL–negative B-cell acute lymphoblastic leukemia (B-ALL) have a Philadelphia chromosome-like (Ph-like) gene expression signature and that the majority of cases of Ph-like ALL harbor somatic alterations in known signaling effectors, including in known and putative therapeutic targets.24-26 However, current approaches to identify the substitution mutations, indels, and fusion genes that drive Ph-like ALL require the use of a series of genomic assays. We performed integrated DNA/RNA profiling on 16 cases classified as Ph-like ALL by gene expression profiling. We found deletions in IKZF1 (7 of 16), PAX5 (2 of 16), and CDKN2A (4 of 16) in a subset of these cases, consistent with previous reports.27 We also identified known activating mutations in CRLF2, KRAS, and JAK2 in some cases. Notably, we identified known and novel gene fusions in 9 of 16 cases. This included 2 JAK2 fusions with different partner genes (PAX5 and SSBP2), 6 known fusions of CRLF2 and EPOR, and a novel gene fusion involving ABL2, all of which would From www.bloodjournal.org by guest on August 3, 2017. For personal use only. BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24 INTEGRATED DNA AND RNA GENOMIC PROFILING be expected to activate kinase signaling and to confer sensitivity to approved drugs. In 1 case of B-ALL, we identified a FIP1L1-PDGRA fusion, which has been shown to respond to imatinib in patients with hypereosinophilic syndrome28 (Table 1). These data show that an integrated DNA/RNA profiling platform can identify a spectrum of alterations in kinase signaling pathway in B-ALL patients that can guide the use of molecularly targeted therapies. The result also achieved high concordance with an integrated research profiling approach which included genome sequencing, targeted DNA sequencing, RNA sequencing, and single nucleotide polymorphism array analysis done on the same samples. Specifically, we observed complete concordance between our clinical genomic profiling assay and research-grade genomic analysis, with the exception a CRLF2 mutation and a CDKN2A/B deletion that were detected only by the clinical sequencing assay. Discussion The identification of somatic genomic alterations with clinical relevance has increased the need for robust genomic profiling assays that can identify the different types of genomic alterations in clinically relevant cancer genes. Here we report the development of an integrated DNA/RNA target capture NGS assay, which we optimized to detect different classes of genomic events, including base substitutions, indels, CNAs, and chromosomal rearrangements. This allows the use of a single test to systematically profile clinical samples from patients with hematologic malignancies for actionable genomic lesions, in a clinically relevant time frame and quality standard. As described in results, 93% specimens were successfully profiled in the clinical setting. Seven percent of patients received failed report due to failures in DNA or RNA preparation, process of aged liquid specimen, and no somatic mutations detected under other qualifying conditions. One of the most common qualifying conditions is RNA preparation failure (132 cases), which highlighted the technical challenges of process and sequence RNA samples (supplemental Table 6a). Previously described assays largely focused on DNA-based approaches or used gene-specific RNA tests to detect fusion transcripts in a small set of genes. We show that targeted RNA capture sequencing can be used to identify a wide spectrum of fusion genes with known roles in malignant transformation and therapeutic response and to identify novel fusions with likely diagnostic and therapeutic relevance. This approach can identify fusion transcripts present in as few as 10% to 20% of cells and can be used to show that chromosomal rearrangements result in expression of in-frame, fusion transcripts. Moreover, combining DNA and RNA capture allows for the identification of full-length transcripts driven by promoter rearrangements by DNA sequencing, as well as fusion transcripts that are more easily identified by RNA profiling. This approach has immediate clinical value in hematologic malignancies, and we believe will show significant clinical value in epithelial tumors. Of note, this approach can also be used to reliably measure expression of genes in the target set, which will inform development and testing of genomic predictors 3013 that combine mutational data and gene expression/epigenetic data to inform prognosis and to identify novel therapeutic targets. The clinical relevance of our DNA/RNA profiling5 approach is underscored by our ability to identify genetic lesions with prognostic and therapeutic relevance in specific diseases. In the case of B-cell ALL, recent studies have identified novel genetic markers with prognostic relevance and identified novel therapeutic targets in patients with high-risk disease.24-27,29,30 The challenge has been that the critical genes implicated in B-ALL can be altered by whole gene/intragenic deletions, DNA base pair substitutions, and larger indels, as well as chromosomal, intergenic, and cryptic rearrangements that lead to expression of fusion transcripts. Currently, most centers use an amalgam of DNA, FISH, and gene-specific RNA approaches to identify a subset of the most critical genetic lesions in B-ALL. Our assay provides a single profiling platform that can reliably identify all known actionable disease alleles relevant to B-ALL to improve diagnosis and risk-adapted therapy for B-ALL patients. Most importantly, our profiling platform, allows for the increased use of genomic profiling to improve the diagnosis and treatment of cancer patients and to more precisely match patients with targeted therapies. These efforts are critical to more broadly expand precision medicine by assuring that cancer patients can be offered genomic profiling regardless of whether they are at tertiary care centers or receive care in other settings. Authorship Contribution: J.H., O.A.-W., D.L., G.A.O., and R.L.L. designed the study, wrote the manuscript, and developed and/or performed analysis; P.J.S. and V.A.M. designed the study and wrote the manuscript; O.A.-W., R.K.R., A.M.I., J.P., A.K., A.Y., A.M.H., E.P., K.R., C.M., A.D., S.A.A., M.R.M.v.d.B., and R.L.L. contributed clinical and genomic expertise; J.H., M.K.N., K.W., T.B., G.M.F., L.E.Y., S.Z., M.B., M.R., and K.P. performed analysis; J.H., L.E.Y., J.R.W., S.R., J.D., and A.F. developed computational tools; M.K.N., K.W.B., A.L.D., G.Y., E.W., and G.A.O. developed the assay; L.G., S.T.B., M.Z., V.B., J.B., K.I., K.M.K., and E.L. performed laboratory experiments; and R.Y., T.M., R.E., C.V., J.-A.V., J.S.R., and D.M. evaluated and edited the manuscript. Conflict-of-interest disclosure: J.H., M.K.N., K.W., G.M.F., L.E.Y., S.Z., M.B., J.R.W., S.R., J.D., T.B., M.R., K.P., K.W.B., A.L.D., G.Y., L.G., S.T.B., M.Z., E.W., V.B., J.B., K.I., J.S.S., D.M., T.M., J.-A.V., E.L., R.E., C.V., R.Y., P.J.S., V.A.M., G.A.O., and D.L. are employees and equity holders of Foundation Medicine, Inc. O.A.-W., A.D., S.A.A., M.R.M.v.d.B., and R.L.L. are consultants for Foundation Medicine, Inc. Correspondence: Geoff Otto, Foundation Medicine, 150 Second St, Cambridge, MA 02141; e-mail: [email protected]; Doron Lipson, Foundation Medicine, 150 Second St, Cambridge, MA 02141; e-mail: [email protected]; and Ross L. Levine, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 20, New York, NY 10065; e-mail: leviner@ mskcc.org. References 1. Bejar R, Stevenson K, Abdel-Wahab O, et al. Clinical effect of point mutations in myelodysplastic syndromes. N Engl J Med. 2011; 364(26):2496-2506. 2. Flaherty KT, Puzanov I, Kim KB, et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N Engl J Med. 2010;363(9):809-819. 3. Tiacci E, Trifonov V, Schiavoni G, et al. BRAF mutations in hairy-cell leukemia. N Engl J Med. 2011;364(24):2305-2315. 4. Sjöblom T, Jones S, Wood LD, et al. The consensus coding sequences of human breast and colorectal cancers. Science. 2006;314(5797): 268-274. 5. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455(7216):1061-1068. 6. Greenman C, Stephens P, Smith R, et al. Patterns of somatic mutation in human cancer genomes. Nature. 2007;446(7132):153-158. From www.bloodjournal.org by guest on August 3, 2017. For personal use only. 3014 BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24 HE et al 7. Frampton GM, Fichtenholtz A, Otto GA, et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol. 2013;31(11): 1023-1031. 8. 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For personal use only. 2016 127: 3004-3014 doi:10.1182/blood-2015-08-664649 originally published online March 10, 2016 Integrated genomic DNA/RNA profiling of hematologic malignancies in the clinical setting Jie He, Omar Abdel-Wahab, Michelle K. Nahas, Kai Wang, Raajit K. Rampal, Andrew M. Intlekofer, Jay Patel, Andrei Krivstov, Garrett M. Frampton, Lauren E. Young, Shan Zhong, Mark Bailey, Jared R. White, Steven Roels, Jason Deffenbaugh, Alex Fichtenholtz, Timothy Brennan, Mark Rosenzweig, Kimberly Pelak, Kristina M. Knapp, Kristina W. Brennan, Amy L. Donahue, Geneva Young, Lazaro Garcia, Selmira T. Beckstrom, Mandy Zhao, Emily White, Vera Banning, Jamie Buell, Kiel Iwanik, Jeffrey S. Ross, Deborah Morosini, Anas Younes, Alan M. Hanash, Elisabeth Paietta, Kathryn Roberts, Charles Mullighan, Ahmet Dogan, Scott A. Armstrong, Tariq Mughal, Jo-Anne Vergilio, Elaine Labrecque, Rachel Erlich, Christine Vietz, Roman Yelensky, Philip J. Stephens, Vincent A. Miller, Marcel R. M. van den Brink, Geoff A. Otto, Doron Lipson and Ross L. Levine Updated information and services can be found at: http://www.bloodjournal.org/content/127/24/3004.full.html Articles on similar topics can be found in the following Blood collections Lymphoid Neoplasia (2595 articles) Myeloid Neoplasia (1716 articles) Information about reproducing this article in parts or in its entirety may be found online at: http://www.bloodjournal.org/site/misc/rights.xhtml#repub_requests Information about ordering reprints may be found online at: http://www.bloodjournal.org/site/misc/rights.xhtml#reprints Information about subscriptions and ASH membership may be found online at: http://www.bloodjournal.org/site/subscriptions/index.xhtml Blood (print ISSN 0006-4971, online ISSN 1528-0020), is published weekly by the American Society of Hematology, 2021 L St, NW, Suite 900, Washington DC 20036. Copyright 2011 by The American Society of Hematology; all rights reserved.