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Immunoglobulin Transcript Sequence and Somatic Hypermutation Computation from Unselected RNA-Seq Reads in Chronic Lymphocytic Leukemia

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Immunoglobulin Transcript Sequence and Somatic Hypermutation Computation from Unselected RNA-Seq Reads in Chronic Lymphocytic Leukemia Immunoglobulin transcript sequence and somatic hypermutation computation from unselected RNA-seq reads in chronic lymphocytic leukemia James S. Blachlya, Amy S. Rupperta, Weiqiang Zhaob, Susan Longb, Joseph Flynna, Ian Flinnc, Jeffrey Jonesa, Kami Maddocksa, Leslie Andritsosa, Emanuela M. Ghiad, Laura Z. Rassentid, Thomas J. Kippsd, Albert de la Chapellee,1, and John C. Byrda,f,1 aDivision of Hematology, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210; bDepartment of Pathology, The Ohio State University, Columbus, OH 43210; cHematologic Malignancies Research Program, Sarah Cannon Research Institute, Nashville, TN 37203; dMoores Cancer Center, University of California at San Diego, La Jolla, CA 92092-0820; eDepartment of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210; and fCollege of Pharmacy, The Ohio State University, Columbus, OH 43210 Contributed by Albert de la Chapelle, February 24, 2015 (sent for review January 28, 2015; reviewed by Megan S. Lim and Adrian Wiestner) Immunoglobulins (Ig) are produced by B lymphocytes as secreted experiment could replace many other discrete tests (qPCR, geno- antibodies or as part of the B-cell receptor. There is tremendous typing, microarray, IGHV mutation analysis, etc.), we hypothe- diversity of potential Ig transcripts (>1 × 1012) as a result of hun- sized that in the presence of a clonal B-cell population, patient- dreds of germ-line gene segments, random nucleotide incorpora- specific or consensus degenerate primers and a dedicated tion during joining of gene segments into a complete transcript, sequencing experiment were not necessary to fully characterize the and the process of somatic hypermutation at individual nucleotides. clonal IGH@ transcript. Here, using the “Ig-ID” pipeline we This recombination and mutation process takes place in the developed, we demonstrate that Ig heavy chain transcripts, in- maturing B cell and is responsible for the diversity of potential cluding, critically, the complete V-D-J sequence, can be computed epitope recognition. Cancers arising from mature B cells are char- from unselected (i.e., using standard random hexamer priming acterized by clonal production of Ig heavy (IGH@) and light chain vice IGH@-targeting primers; ref. 5) RNA-sequencing reads transcripts, although whether the sequence has undergone so- from CLL patient tumor cells. These computed transcripts matic hypermutation is dependent on the maturation stage at matched those obtained from a CLIA-approved clinical labo- which the neoplastic clone arose. Chronic lymphocytic leukemia ratory with high concordance, in some cases uncovering possi- (CLL) is the most common leukemia in adults and arises from a ma- ble misamplification in the traditional approach. ture B cell with either mutated or unmutated IGH@ transcripts, the latter having worse prognosis and the assessment of which is Results routinely performed in the clinic. Currently, IGHV mutation status Seventeen CLL patient tumor samples with clinical data avail- is assessed by Sanger sequencing and comparing the transcript to able were subjected to RNA sequencing (Materials and Methods) known germ-line genes. In this paper, we demonstrate that com- in a pilot study. First, to establish that traditional mapping strat- plete IGH@ V-D-J sequences can be computed from unselected egies inadequately handle the complex germ-line rearrangements RNA-seq reads with results equal or superior to the clinical pro- cedure: in the only discordant case, the clinical transcript was out- Significance of-frame. Therefore, a single RNA-seq assay can simultaneously yield gene expression profile, SNP and mutation information, as IGHV well as IGHV mutation status, and may one day be performed as mutation status is a well established prognostic factor in a general test to capture multidimensional clinically relevant data chronic lymphocytic leukemia, and also provides crucial insights in CLL. into tumor cell biology and function. Currently, determination of IGHV transcript sequence, from which mutation status is calculated, requires a specialized laboratory procedure. RNA RNA sequencing | immunoglobulin | somatic hypermutation | B cells | CLL sequencing is a method that provides high resolution, high dynamic range transcriptome data that can be used for dif- mmunoglobulins (Igs) are proteins produced by mature ferential expression, isoform discovery, and variant deter- IB-lymphocytes that recognize foreign antigens, both as soluble mination. In this paper, we demonstrate that unselected next- antibody molecules and as part of the B-cell receptor. The gen- generation RNA sequencing can accurately determine the IGH@ eration of Ig diversity through gene recombination and hyper- sequence, including the complete sequence of the complemen- mutation of the heavy chain (H) variable region (V) is essential tarity-determining region 3 (CDR3), and mutation status of CLL to adaptive immunity. The extent of this process is strongly as- cells, potentially replacing the current method which is a spe- sociated with both pathology and prognosis in chronic lympho- cialized, single-purpose Sanger-sequencing based test. cytic leukemia (CLL), wherein CLL that expresses an unmutated IGHV tends to be more aggressive than CLL using unmutated Author contributions: J.S.B. and J.C.B. designed research; J.S.B., W.Z., S.L., E.M.G., and L.Z.R. IGHV (1, 2). The accurate assessment of this IGHV mutation performed research; J.F., I.F., J.J., K.M., L.A., and A.d.l.C. contributed new reagents/analytic status is thus of a high clinical priority. As each patient’s leuke- tools; J.S.B. and A.S.R. analyzed data; J.S.B., T.J.K., A.d.l.C., and J.C.B. wrote the paper; and J.F., I.F., J.J., K.M., L.A., and T.J.K. contributed patient material. mia generally expresses only a single IGH@, the mutation status IGHV Reviewers: M.S.L., University of Michigan; and A.W., Hematology Branch, National Heart, of is determined by amplifying the expressed transcript via Lung, and Blood Institute. RT-PCR, sequencing the gene via the Sanger technique, and then The authors declare no conflict of interest. comparing this sequence with known inherited IGHV sequences. Data deposition: The sequences reported in this paper have been deposited in the Gene However, there are limitations to such methods, including vari- Expression Omnibus database (accession no. GSE66228). ation in technique across institutions. RNA-sequencing is a pow- 1To whom correspondence should be addressed. Email: [email protected] erful technology that can simultaneously yield information about or [email protected]. gene and isoform expression as well as underlying DNA sequence This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. (3, 4). Motivated by the notion that a single RNA sequencing 1073/pnas.1503587112/-/DCSupplemental. 4322–4327 | PNAS | April 7, 2015 | vol. 112 | no. 14 www.pnas.org/cgi/doi/10.1073/pnas.1503587112 Downloaded by guest on October 1, 2021 in the IGH@ locus (Fig. S1), we performed a genome-wide spliced- this step, depending on their homology to heavy chain V genes. mapping and examination of IGH@. On average, ∼1% of all reads Light chain transcripts may also be targeted directly at this se- mapped to this region (Table S1). However, of reads mapping to lection step by altering the homology affinity selector from heavy- this region, 21–48% could not be clearly and unambiguously chain V genes to light-chain V genes. Next, multiply-mapped reads assigned to a single feature in the region; this total also reflects the were disambiguated and the transcript with the highest read count few nonimmunoglobulin features annotated in this region. was determined. Likeliest V, D,andJ alleles as well as percent Using a naïve classifier frequently used for digital gene ex- identity to these references were calculated with IgBLAST (9). pression estimates, we counted the number of unambiguously The V, D, J gene use and percent mutation (1 – identity) were mapped reads at V, D, and J genes. In each case, a V gene clearly then compared with available clinical data (Table 1). emerged with the highest read count. In contrast, the D and J genes could not reliably be determined by simple counting, due The Percent Mutation Is Similar and the Binary Classifier Mutated/ either to the lack of a clear consensus highest mapping or the Unmutated Is Perfectly Concordant. Seventeen sequenced samples complete absence of mappings (Fig. S2). The identity of the with percent IGHV mutation (as determined by clinical labora- V gene with the highest mapping was compared with the clinical tory test) recorded in the medical record were evaluated through (and later computed) V gene reported, and showed 94% and our “Ig-ID” pipeline. The actual percent mutation obtained from 100% concordance, respectively. The D and J genes with highest Ig-ID was identical to the results provided by the clinical labo- counts were not as informative. Likewise, neither mutation sta- ratory in 11 of 17 cases, with percentages within 1% for 5 cases, tus, nor Ig nucleotide or translated peptide sequence could be and within 2% for 1 case. Fig. 1 illustrates the substantial con- obtained directly from these mapped data, indicating the need cordance between the computed and clinical results (Pearson’s for an alternative method to correctly identify these genes. r 0.992, 95% CI 0.976–0.998; concordance index 0.988, 95% CI 0.968–0.996), with differences between the two measures for Ig Transcript Reconstruction. Next, using a genome-free method each case versus their average value depicted in the form of (Materials and Methods), each sample’s transcriptome was a Bland–Altman plot. When samples were classified as mutated reconstructed de novo in the way the transcriptome of a poorly (M-CLL) or unmutated (U-CLL) according to the established characterized or nonmodel organism would be (6), except that 98% identity cutoff (10), unmutated cases comprised 12 of the 17 each sample was processed independently (whereas for tran- cases (71%), whereas mutated cases numbered 5 (29%).
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