Unraveling transformation of follicular lymphoma to diffuse large B-cell lymphoma

RESEARCH ARTICLE Unraveling transformation of follicular lymphoma to diffuse large B-cell lymphoma Julia Gonza ´lez-Rinco ´n 1,2 , Miriam Me ´ndez 1,3 , Sagrario Go ´mez 1 , Juan F. Garcı ´a 4 , Paloma Martı ´n 2,5 , Carmen Bellas 2,5 , Lucı ´a Pedrosa 1 , Socorro M. Rodrı ´guez-Pinilla 2,6 , Francisca I. Camacho 7 , Cristina Quero 8 , David Pe ´rez-Callejo 1,3 , Antonio Rueda 9 , Marta Llanos 10 , Jose ´Go ´mez-Codina 11 , Miguel A. Piris 2,6 , Santiago Montes-Moreno 2,12 , Carmen Ba ´rcena 13 , Delvys Rodrı ´guez-Abreu 14 , Javier Mena ´rguez 15 , Luis de la Cruz- Merino 16 , Silvia Monsalvo 17 , Consuelo Parejo 18 , Ana Royuela 19 , Ivo Kwee 20,21,22,23 , Luciano Cascione 20,21,23,24 , Alberto Arribas 20,21,23,24 , Francesco Bertoni 20,21,23,24 , Manuela Mollejo 25 , Mariano Provencio 3‡ , Margarita Sa ´nchez-BeatoID 1‡ * 1Lymphoma Research Group, Medical Oncology Department, Instituto de Investigacio ´n Sanitaria Puerta de Hierro-Segovia de Arana, Madrid, Spain, 2Centro de Investigacio ´n Biome ´dica en Red de Ca ´ncer (CIBERONC),Madrid, Spain, 3Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid, Spain, 4Pathology Department, Hospital MD Anderson Cancer Center, Madrid, Spain, 5Pathology Department, Hospital Universitario Puerta de Hierro, Madrid, Spain, 6Pathology Department, Fundacio ´n Jime ´nez Dı ´az, Madrid, Spain, 7Pathology Department, Hospital Universitario de Getafe, Madrid, Spain, 8Medical Oncology Department, Hospital Universitario Virgen de la Victoria, Malaga, Spain, 9Medical Oncology Department, Hospital Costa del Sol, Malaga, Spain, 10 Medical Oncology Department, Hospital Universitario de Canarias, Santa Cruz de Tenerife, Spain, 11 Medical Oncology Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain, 12 Pathology Department/Translational Hematology Group, Hospital Universitario Marque ´s de Valdecilla/IDIVAL, Santander, Spain, 13 Pathology Department, Hospital Universitario 12 de Octubre, Madrid, Spain, 14 Medical Oncology Department, Hospital Universitario Insular de Gran Canaria, Las Palmas de Gran Canarias, Spain, 15 Pathology Department, Hospital General Universitario Gregorio Maraño ´n, Madrid, Spain, 16 Medical Oncology Department Hospital Universitario Virgen Macarena, Seville, Spain, 17 Hematology Department, Fundacio ´n Jime ´nez Dı ´az, Madrid, Spain, 18 TIC Unit- Medical Oncology Department, Instituto de Investigacio ´n Sanitaria Puerta de Hierro- Segovia de Arana, Madrid, Spain, 19 Clinical Biostatistics Unit, Instituto de Investigacio ´n Sanitaria Puerta de Hierro- Segovia de Arana, Madrid, Spain, 20 Institute of Oncology Research (IOR), Belinzona, Switzerland, 21 Universita ´della Svizzera Italiana (USI), Lugano, Switzerland, 22 Dalle Molle Institute for Artificial Intelligence (IDSIA), Belinzona, Switzerland, 23 Swiss Institute of Bioinformatics (SIB), Belinzona, Switzerland, 24 Oncology Institute of Southern Switzerland (IOSI), Belinzona, Switzerland, 25 Pathology Department, Hospital Virgen de la Salud, Toledo, Spain ‡ Senior authors *[email protected] Abstract Follicular lymphoma (FL) is an indolent but largely incurable disease. Some patients suffer histological transformation to a more aggressive subtype with poorer prognosis. This study aimed to improve our understanding of the genetics underlying FL histological transformation, and to identify genetic drivers or promoters of the transformation by elucidating the differences between FL samples from patients who did and did not transform. We conducted targeted massive parallel sequencing of 22 pre-transformed FL/transformed diffuse large B-cell lymphoma pairs and 20 diagnostic samples from non-transformed FL patients. Additionally, 22 matched samples from 11 transformed FL patients (pre-transformed FL and diffuse large B-cell lymphoma) and 9 non-transformed FLs were studied for copy number variation using SNP arrays. We identified recurrently mutated genes that were enriched at transformation, most notably LRP1B,GNA13 and POU2AF1, which have roles in B-cell PLOS ONE | https://doi.org/10.1371/journal.pone.0212813 February 25, 2019 1 / 16 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Gonza ´lez-Rinco ´n J, Me ´ndez M, Go ´mez S, Garcı ´a JF, Martı ´n P, Bellas C, et al. (2019) Unraveling transformation of follicular lymphoma to diffuse large B-cell lymphoma. PLoS ONE 14(2): e0212813. https://doi.org/10.1371/journal. pone.0212813 Editor: Javier Marcelo Di Noia, Institut de recherches cliniques de Montreal, CANADA Received: November 14, 2018 Accepted: February 8, 2019 Published: February 25, 2019 Copyright: ©2019 Gonza ´lez-Rinco ´n et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All BAM files are available from the SRA database (accession number SRP121648). Funding: This work was supported by the Spanish Ministry of Economy and Competence (MINECO): SAF2013-47416-R; ISCIII-MINECO AES-FEDER (Plan Estatal de I+D+I 2008-2011 and 2013-2016) (RD12/0036/0060 (MAP), RD12/0036/0041(MP), PI14/00221(MSB), PIE14/0064(MP), PI16/01294 (MAP), CIBERONC CB16/12/00291(MAP), DTS17/ 00039(MSB)), PT13/0010/0007 (MAP) and by differentiation, GC architecture and migration. Mutations in POU2AF1 might be associated with lower levels of expression, were more frequent in transformed FLs, and seemed to be specific to transformedcompared with de novo-diffuse large B-cell lymphomas. Pre-trans- formed FLs carried more mutations per sample and had greater subclonal heterogeneity than non-transformed FLs. Finally, we identified four mutated genes in FL samples that differed between patients who did and did not transform: NOTCH2,DTX1,UBE2A and HIST1H1E. The presence of mutations in these genes was associated with shorter time to transformation when mutated in the FL biopsies. This information might be useful for identifying patients at higher risk of transformation. Introduction Follicular lymphoma (FL) is an indolent, but typically incurable disease with a long natural history. FL patients respond to a variety of treatments and have a median overall survival (OS) of >10 years in the immunochemotherapy era [1], but the outcome is variable: some patients do not need treatment, while others follow a more aggressive course characterized by interspersed episodes of remission and relapse, associated with lower sensitivity to therapy [2,3]. The reported frequency of histological transformation (HT) into a more aggressive lymphoma (transformed FL or tFL), most commonly diffuse large B-cell lymphoma (DLBCL), varies markedly, from 30% to 60% of patients. HT has been associated with poor prognosis, and in more than half of cases, occurred within the first year of follow-up [3,4]. Transformation has previously been associated with the activation of the MYC oncogene and the inactivation of tumor suppressor genes, such as CDKN2A/B and TP53 [5,6]. More recently, several studies have broadened our knowledge of the genetic alterations associated with HT, revealing, among others, two genes involved in the control of immune recognition by cytotoxic T lymphocytes and NK cells: B2M [5,7] and CD58 [5,7,8]. However, additional studies are needed to clarify the process and to identify biomarkers for predicting transformation. Recent research on FL suggests that two patterns of tumor evolution drive transformation: the linear and branching/divergent modes [7–10]. HT seems more frequently to follow a divergent pattern of evolution. In this study, we aimed to improve our knowledge of the genetic aberrations associated with transformation, and to identify genetic alterations associated with transformation by elucidating the differences between FL samples from patients who did and did not transform. Materials and methods Patients and samples We collected paired formalin-fixed, paraffin-embedded tissue (FFPET) samples, consisting of paired FL (pre-tFL) and FL transformed to DLBCL (tFL) from 22 transformed patients as well as 20 non-transformed FL (ntFL, median follow-up 11.5 years) diagnostic samples. We obtained the germinal DNA of eight patients from oral mucosa or other non-neoplastic biopsies. The research project was approved by the Ethics Committee of Hospital Universitario Puerta de Hierro-Majadahonda (PI-67/14), and was conducted in accordance with the Declaration of Helsinki. Samples and clinical data were collected (Supplementary Information (SI), S1 Table and S1 Fig), processed and stored according to quality protocols, ensuring the safety Genomics of follicular lymphoma transformation PLOS ONE | https://doi.org/10.1371/journal.pone.0212813 February 25, 2019 2 / 16 Direccio ´n General de Universidades e Investigacio ´n Consejerı ´a de Educacio ´n e Investigacio ´n de la Comunidad de Madrid (B2017/BMD-3778); JG-R is a recipient of an iPFIS predoctoral fellowship (IFI14/00003), PM is supported by a Juan Rode ´s contract (JR14/0018) and MSB currently holds a Miguel Servet II contract (CPII16/00024), supported by ISCIII-MINECO AES-FEDER (Plan Estatal I+D+I 2013-2016) and the Fundacio ´n de Investigacio ´n Biome ´dica Puerta de Hierro; DP-C by a SEOM-Rio Hortega 2015 contract and LP by Plan de Empleo Juvenil from Comunidad de Madrid (Madrid I+D+I); FB by a grant from the GELU Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. and confidentiality of donors’ data. Patients agreed to participate in the study and gave written informed consent. Informed consent for samples previous to 2007 was waived by the Ethics Committee. Transfer of material and data from other collaborator biobanks was approved by the corresponding ethical and external scientific committees according to Spanish legislation (Ley 14/2007 de Investigacion Biomedica and Real Decreto 1716/2011) and anonymously transferred to our center. All samples were reviewed by expert hematopathologists (MMo and CB) upon arrival to confirm their diagnosis and to select enriched tumoral areas, as necessary. An additional cohort of 48 samples from patients with de novo DLBCL including GCB and ABC type cases (data not shown) was included to enable comparison. Target resequencing The genes included in the panels (S2 Table) are involved in lymphomagenesis pathways. They were selected based on the results of previous studies [7,8,11]. Genomic DNA was extracted with a truXTRAC FFPE DNA Kit (Covaris, Woburn, MA, USA) or a QIAamp DNA FFPE Tissue Kit (Qiagen, Manchester, UK) in accordance with the manufacturers’ instructions. The HaloPlex Target Enrichment custom panel was designed using the SureDesign webbased tool (earray.chem.agilent.com/suredesign/) (Agilent Technologies, Santa Clara, CA, USA). The design covered all coding exons and 10 flanking bases at the 3´ and 5´ ends of the 167 selected genes (S2 Table). The target regions (according to Human Assembly GRCh37/ hg19) were captured using a HaloPlex Target Enrichment kit (Agilent), following the manufacturer’s instructions. Samples enriched using this custom panel are indicated in S3 Table. Sequencing was performed using the Ion Proton system (Thermo Fisher Scientific, Waltham, MA, USA) at NIMGenetics Geno ´mica y Medicina, S.L. (Madrid, Spain) to achieve a minimum read depth of 500x (S3 Table). Nine tumoral and two non-tumoral samples (from seven patients) were analyzed in this way (S3 Table and S2 Fig), but the system was eventually discarded because the large quantity of DNA needed and the large amount of non-targeted sequencing data generated meant that a substantial percentage of samples could not be used. A SureSelect target enrichment custom panel was then designed using the SureDesign (Agilent) web-based tool. The design covered all coding exons of 186 genes. Captured libraries were diluted to 11 pM for Illumina clustering, and paired-end sequencing was performed on MiSeq (Illumina Inc., San Diego, CA, USA). 110 genes were common to the two designs and are referred to as the common gene set (CGS). Two independent analyses were performed. The first was done with the tools available in the Variant Reporter instrument (Illumina) or with the Ion Reporter Software (Thermo Fisher). Sequencing data were also aligned using a BWA aligner, the duplicates being marked using a Picard tool (Broad Institute, Cambridge, MA, USA). Single-nucleotide variants were predicted using VarScan [12] then annotated using Annovar [13]. The TruSeq Custom Amplicon v1.5 Low Input Library for dual-strand sequencing (Illumina) was used to validate mutations in selected samples. The probes for this custom panel were designed with DesignStudio (Illumina) and consisted of 1399 amplicons with an average size of 175 bp and a cumulative target region of 140 kb. The target genes are listed in S2 Table. Target enrichment was performed in FFPET-extracted DNA in accordance with the manufacturer’s instructions (TruSeq Amplicon—Cancer Panel Library Preparation Guide; March 2016; Illumina). The pooled libraries were sequenced with a MiSeq Reagent Kit V2 (pairedend, 2x151) in a MiSeq instrument (Illumina), as described in the manufacturer’s protocol. The variant lists obtained were analyzed by Excel filtering and visualization using the Integrative Genome viewer (IGV) tool [14]. The Genome Analysis Toolkit annotated the SNPs Genomics of follicular lymphoma transformation PLOS ONE | https://doi.org/10.1371/journal.pone.0212813 February 25, 2019 3 / 16 available at dbSNP 138 (hg19), and those reported by the 1000 Genomes Project were filtered out. Missense, nonsense, frameshift and splicing mutations were selected. Data are available in the SRA (accession number SRP121648). SNP arrays The DNAs were first subjected to the Infinium HD FFPE DNA Restore Protocol (Illumina) and up to 200 ng DNA per sample were hybridized for the SNP analysis. Genotyping was conducted using the Illumina HumanOmni2.5 BeadChip according to the manufacturer’s protocols (Illumina). The BeadChip images were scanned on the iScan system and the data (genotypes, log R ratio (LRR) and B allele frequency (BAF)) were extracted into Illumina’s Genome Studio v2011.1. The software’s default settings, developed by Illumina for genotype calling, were applied with the cluster file. Raw LRR values were normalized probe-wise using the median of each batch. Segmentation was conducted using Fast First Derivative Segmentation after normalizing each profile [15]. Copy number segments overlapping known copy number variants [16] were excluded from further analysis. Segments with LRR values <-0.2 were considered to be ‘lost’; segments with LRR values >0.2 were considered to be ‘gained’. Focal recurrent aberrations were detected using GISTIC 2.0 [17]. Recurrent minimal common regions (MCRs) were defined using the algorithm described by Lenz et al. [18]. Sanger sequencing Sanger sequencing was used to verify the published POU2AF1 mutations in cell lines. Primers (FW-CCAATTCCAGACCAAGCTGT and RV-AGGAAACAGCCTCAGCTCAA) were designed using Primer 3 (http://frodo.wi.mit.edu/prim). PCR products were cleaned using ExoSAP-IT (Affymetrix, Santa Clara, CA, USA) and sequenced using ABI Prism BigDye terminator v3.1 (Life Technologies). Sequencing reactions were run on an ABI-3100 Avant Genetic Analyzer (Life Technologies). Western blot Protein was extracted from Raji, RL, SU-DHL6 and OCI-LY19 using radioimmunoprecipitation assay (RIPA) lysis buffer supplemented with protease and phosphatase inhibitors. Western blot analysis was conducted following standard protocols. The primary antibodies used were BOB1 (SP92) from Ventana Medical Systems, Inc. (Oro Valley, AZ, USA) and α-tubulin (T-6074) from Sigma-Aldrich (San Luis, MO, USA). The secondary antibodies used were labeled with Alexa 680 nm or Alexa 800 nm. Western blots were quantified using the Image Studio program (LI-COR, Lincoln, NE, USA) and graphs generated using GraphPad Prism7 (GraphPad Software, La Jolla, CA, USA). Quantitative RT-PCR Total RNA was extracted and purified using an RNeasy Mini-Kit (Qiagen, Hilden, GE, USA), and quantified with a Nanodrop instrument (Thermo Fisher Scientific). cDNA was synthesized from 500-ng DNaseI-treated RNA (RNase-Free DNase Set; Qiagen) with Affinity Script Multi-Temp Reverse Transcriptase (Agilent) and random primers. The level of expression of POU2AF1 transcript was determined by real-time quantitative PCR using splice junction-specific primers (the forward primer was designed in exon 2 and the reverse one in exon 1) combined with LightCycler 480 SYBR green (Roche, Basel, Genomics of follicular lymphoma transformation PLOS ONE | https://doi.org/10.1371/journal.pone.0212813 February 25, 2019 4 / 16 Switzerland). Primers were designed with the Primer 3 tool: forward (5’-TTCCTTCACTGG CTGCTTCA-3’) and reverse (5’-AGGAAACAGCCTCAGCTCAA-3’). Values were normalized with respect to the expression of succinate dehydrogenase complex flavoprotein subunit A (SDHA) as an endogenous control. Measurements were taken from three cultures of each cell line. Each of them was performed in triplicate. PCR reactions were performed and analyzed using the LightCycler 480 (Roche), software release 1.5.0, Advance Relative Quantification method. PCR products were checked by melt curve genotyping, and electrophoresis on 2% agarose gels. Statistical analysis Associations between factors were assessed using Fisher’s exact test. Mann-Whitney U-test was used to analyze the differences in POU2AF1 gene expression between mutated and nonmutated cell lines. Overall survival (OS) was calculated from the date of diagnosis to the date of death or last follow-up, whichever occurred first. Time to transformation (TTT) was calculated from the date of FL diagnosis to the date of HT. To evaluate TTT, the survival functions were estimated by the Kaplan–Meier method. The log-rank test was used to compare patient groups and univariate and multivariate Cox regression models were used to assess associations with TTT. Hazard ratios with 95% confidence intervals were estimated for each parameter. The results were shown graphically and by transformation-free survival at several follow-up times. We then computed the points for each predictive variable (mutated gene) as the rounded value of the quotient resulting from dividing the coefficient of each variable by the lowest coefficient. The risk score was calculated for each patient based on the sum of individual points of each predictive variable included in the final model. For all analyses, values of p <0.05 were considered significant. Statistical analyses were performed using STATA v14.2 (StataCorp LLC, College Station, TX, USA), IBM SPSS Statistics v19 (IBM Corp., Armonk, NY, USA) or GraphPad Prism 7 (GraphPad Software, San Diego, CA, USA) Results Recurrent genetic alterations in transformed follicular lymphoma We conducted targeted massive sequencing analysis in paired samples of pre-tFL and transformed DLBCL (tFL) from 22 patients (S1 and S2 Figs, S1 Table). Median coverage was 543x, with a median of 96% of targeted bases covered by >50x. These values allowed us to identify minor mutated clones with confidence down to a variant allele frequency (VAF) of 5% (some of them validated with the TruSeq panel, S4 Table). In pre-tFL samples, the most recurrently mutated genes (considering only missense, non-sense, frameshift and splicing mutations) were CREBBP (55%), KMT2D (45%), IGLL5 (27%), BCL2 (27%) and DTX1 (23%) (Fig 1,S4 and S5 Tables). In the CGS (S2 Table), there was a median frequency of six non-synonymous mutations (range, 2–14). In tFL samples, the most recurrently mutated genes were CREBBP (64% of cases), BCL2 (32%), KMT2D (50%), IGLL5 (37%), POU2AF1 (32%), DTX1 (26%), LRP1B (27%), and B2M, CSMD3,EP300,GNA13,HISTH1E,PCLO,SOCS1 and TNFSRF14 (23% each) (Fig 1,S4 and S5 Tables). In this case, there was a median of eight mutations (range, 4–16). Some genes were more frequently mutated in tFL than in matched pre-tFL samples, although the differences were not statistically significant. The most relevant were SOCS1 (pretFL = 9.1% vs. tFL = 22.7%), GNA13 (9.1% vs. 22.7%), B2M (9.1% vs. 22.7%), LRP1B (18.2% vs. 27.2%) and POU2AF1 (18.2% vs. 31.8%). Genomics of follicular lymphoma transformation PLOS ONE | https://doi.org/10.1371/journal.pone.0212813 February 25, 2019 5 / 16 Multiple mutations were found in several genes known to be targets of aberrant somatic hypermutation (aSHM) (e.g., BCL2,PIM1,SOCS1,IGLL5 and DTX1) in pre-tFL and tFL samples. aSHM occurs in >50% DLBCLs that arise de novo [19,20], but is rare in FL [7,21]. This pattern was clearer in the transformed samples, but was also present earlier in some pre-tFL Fig 1. Distribution of genes recurrently mutated in 22 pre-tFL/tFL and 20 ntFL cases. Columns represent individual patients, and rows denote specific genes. The bar graph shows the frequency of mutations found in each gene. Green: FL-specific; orange: FL & tFL (shared), blue: tFL-specific; grey: not done. https://doi.org/10.1371/journal.pone.0212813.g001 Genomics of follicular lymphoma transformation PLOS ONE | https://doi.org/10.1371/journal.pone.0212813 February 25, 2019 6 / 16 samples (S4 and S7 Tables). The number of mutations in these genes increased slightly at transformation (S7 Table). We also studied copy number variations (CNVs) using SNP arrays in 22 matched samples (pre-tFL and tFL) from 11 transformed patients. As the DNA analyzed came from FFPETs, we could not obtain high-resolution results, but our findings were interesting nevertheless (Table 1,S3 Fig). Loss of chromosomal region 6q16.1-6q23.3 was a recurrent event in transformed FL patients, being found in 3/11 patients following the diagnosis of pre-tFL. Two patients conserved the loss from the pre-transformed to the transformed sample, whereas in the other we did not detect this loss at transformation. Another patient lost this region at transformation. Genes of interest, such as TNFAIP3,MYB,SGK1 and PRDM1, were located in the minimal common deleted region and has been previously described as being associated with FL transformation [22]. The 1p36.23-p36.32 region, which includes PLEKHG5,PRDM16, TNFSRF14 and TP73, was lost in 1/11 pre-tFL and 2/11 tFL samples, all of which were from different patients. Losses of part of this region have also been described in de novo DLBCL [23]. Finally, 17p13.1 (where TP53 is located) was deleted after transformation in three cases. The deletion was also detected in the pre-tFL biopsy of one patient [22]. Another notable finding was the loss of 2q22.1 in 3/11 tFL samples. This region contains the LRP1B gene, which was mutated in 6/22 (27%) tFL samples, and differs from those with the deletion. This finding was further evidence of the potential importance of this gene in transformation. We also analyzed the subclonal composition and patterns of evolution during transformation. Our design took advantage of known recurrently mutated genes (186 genes, analyzed in most of the patients), and the greater depth achievable (median depth >500x) than with WES (20-60x) [7,8,10], which allowed us to detect minor, but informative mutated clones, and thereby determine the evolutionary patterns. All sample pairs shared common mutations, but in several cases, pre-tFL and tFL biopsies both had a fraction of specific mutations. We concluded linear evolution for patients who conserved all the mutations identified in the pre-tFL biopsy that acquired new mutations in the tFL sample and divergent evolution for those who had independent mutations, even though they shared a pool of mutations (Fig 2 and S4 Fig). Recurrent splicing mutations in POU2AF1 The POU2AF1 gene (also known as BOB1/OBF.1) was recurrently mutated in tFL samples (31.8%) at a higher frequency than in pre-tFL biopsies (18.2%) (Fig 1,S4 and S5 Tables). The mutations were located in the 3-bp exon 1 splice donor site. They were validated as somatic in two patients from whom we had obtained germinal DNA (P9 and P22). Additionally, two of the POU2AF1-mutated tFL cases had mutations in the POU2F2 gene (known as OCT2). When we analyzed the POU2AF1 gene in a group of 48 de novo DLBCL samples, including germinal center B-cell like (GCB) and non-GCB cases classified according to cell of origin, we found Table 1. Recurrent CNV alterations in 11 pre-tFL / tFL and 9 ntFL cases. Region pre-tFL tFL ntFL CNV Genes included in MCR 1p36.23-1p36.32 9.1% 18.2% 22.2% LOSS PRAK7,TNFRSF25,TNRSF9,PLEKHG5,PRDM16,TNFRSF14,TP73 6q16.1-6q23.3 27.3% 27.3% 0.0% LOSS EPHA7,PRDM1,SGK1,TNFAIP3,MYB 8p1.23-8p21.3 0.0% 36.4% 11.1% LOSS FGFR1,UNC5D,TNFRSF10B,ATP6V1B2,TNFRSF10A 15q15.4-15q21.1 0.0% 18.2% 0.0% LOSS PLA2G4B,TGM7,B2M 17p13.1 9.1% 18.2% 0.0% LOSS TP53 2q22.1 0.0% 27.3% 0.0% LOSS LRP1B CNV: copy number variation; FL: follicular lymphoma; tFL: transformed FL; ntFL: non-transformed FL; MCR: minor common region. https://doi.org/10.1371/journal.pone.0212813.t001 Genomics of follicular lymphoma transformation PLOS ONE | https://doi.org/10.1371/journal.pone.0212813 February 25, 2019 7 / 16 only one mutated case in the series (personal unpublished observations), suggesting that this alteration might be specific to DLBCL of transformed patients (Fisher’s exact test p = 0.001). We analyzed the putative effect of these mutations in silico using the Splicing Prediction Module available within the Alamut Visual (Interactive Biosoftware, Rouen, France), reporting that skipping exon 1 is very likely for mutations in positions chr11: 111249885 (A/T) and chr11: 111249886 (C/T) (S6 Table). We then evaluated the effect of these splicing-site mutations on gene expression levels. To this end, we identified two B-cell lymphoma-derived cell lines, SU-DHL6, with a mutation in chr11: 111249884 T/A (found in two patients, S4 Table), and OCI-LY19, with a mutation in chr11: 111249886 C/T (found also in two patients), and two wild type cell lines, Raji and RL. We performed western blot and RT-PCR to analyze POU2AF1 gene expression. For RT-PCR we used primers designed for each of the alternative POU2AF1 transcripts but only the POU2AF1-001 transcript (the canonical one) was detected by RT-PCR. We then performed quantitative RT-PCR (qPCR) using primers designed in exon 1 and exon 2 to evaluate the effect of the exon 1 splice donor site mutations on POU2AF1-mRNA expression. The results showed that the levels of protein (Mann-Whitney U test, P = ns) and mRNA (Mann-Whitney U test, P = 0.009) in the two POU2AF1-mutated cell lines were lower than in the wild-type cell lines (S5 Fig). Genetic lesions associated with transformation When we analyzed the entire cohort of 42 FL samples (22 pre-tFL and 20 ntFL) we found that the most recurrently mutated genes were CREBBP (67% of cases) and KMT2D (55%), followed by IGLL5 (35%), BCL2 (29%), TNFRSF14 (17%), EP300 (17%), DTX1 (17%), FOXO1 (17%) and POU2AF1 (14%) (Fig 1,S4,S5 and S7 Tables). These results were similar to those of other series [8]. The analysis of the impact of mutated genes on FL patients’ clinical outcome showed that IRF8,BCL7A and TP53 mutations were associated with shorter OS (Table 2). Fig 2. Patterns of evolution of follicular lymphoma to transformed follicular lymphoma for two model cases. Transformed follicular lymphoma (tFL) can arise through two evolutionary modes: linear (A) or divergent (B). In the linear model (A & C, patient 16) the tFL-dominant clone originates from the FL (pretFL) after acquiring additional mutations. In the divergent model (B & C, patient 1), the pre-tFL and tFL are derived from a common precursor and acquire independent and distinct mutations. For more examples, see S4 Fig. VAF: variant allele frequency. CPC: common precursor cell. https://doi.org/10.1371/journal.pone.0212813.g002 Genomics of follicular lymphoma transformation PLOS ONE | https://doi.org/10.1371/journal.pone.0212813 February 25, 2019 8 / 16 We next compared the number of mutated genes per sample in the pre-tFL with ntFL, and found significantly more mutated genes in the pre-tFL than in the ntFL samples (means of 6.4 in ntFL vs. 9.0 in pre-tFL; p = 0.031). Additionally, the heterogeneity in the patterns of mutations was greater in pre-tFL biopsies: 48 genes were recurrently mutated in the pre-tFL samples, compared with 23 genes in the ntFL samples (S4 and S5 Tables and S6 Fig). We also found that the mean VAF of all mutations in pre-tFL samples was significantly lower than in ntFL (VAF = 27.77% ±0.895 in pre-tFL vs. 34.66% ±1.178 in ntFL; p <0.001). In fact, 35% of the mutations in pre-tFL samples have VAF <20% compared with 20% of the mutations in ntFL samples (S6 Fig), suggesting that a notable proportion of somatic mutations in pre-tFL samples are not present in the dominant tumoral clone. The SNP array analysis (Table 1 and S3 Fig) revealed a loss of the chromosomal region 6q16.1-6q23.3 (containing SGK1 and TNFAIP3 genes), which seems to be specific to or, at least, more frequent in transformed patients (ntFL: 0/9; pre-tFL: 3/11). The 1p36.23-p36.32 region was lost in 2/9 ntFL and 1/11 pre-tFL samples, as previously described, and therefore, it does not seem to be associated with the transformation process. M7-FLIPI integrates the mutation status of seven genes (EZH2,ARID1A,MEF2B,EP300, FOXO1,CREBBP and CARD11), the FL International Prognostic Index (FLIPI) and ECOG performance status, and it was developed to identify patients at high risk of poor outcome to first-line therapy [24]. Although it was not established for the purpose of predicting patients with a higher risk of HT, we still calculated the m7-FLIPI score (low vs. high risk) for the patients included in this series (S1 Table) and performed Cox regression analysis to test its impact on transformation. We found a significant association of this score with TTT (HR = 2.692, p = 0.032). Finally, we examined our series for genes that were recurrently mutated in pre-tFL and not in ntFL, or more frequently in pre-tFL than in ntFL samples and performed univariate and multivariate Cox regression analyses of the association of their mutational status with TTT. There were four such genes: UBE2A,DTX,NOTCH2 and HISTIH1E (Table 3). The Kaplan– Meier analyses further support these results. We assigned a risk score according to the regression coefficients of the multivariate analysis for the four genes and categorizing the scores into Table 2. Multivariate Cox regression analysis of overall survival by mutational status. HR p 95% CI BCL7A 2.074 0.051 0.996 4.320 IRF8 5.212 0.017 1.345 20.191 TP53 5.818 0.033 1.148 29.502 Multivariate analyses considered the mutated genes found to be significant in univariate analyses. HR: hazard ratio; CI: confidence interval. https://doi.org/10.1371/journal.pone.0212813.t002 Table 3. Multivariate Cox regression analysis of time to treatment by mutational status. HR p 95% CI Regression coefficient Adjusted score DTX1 3.005 0.026 1.139 7.929 1.100 1.0 HIST1H1E 4.889 0.010 1.464 16.322 1.587 1.5 UBE2A 5.081 0.018 1.329 19.426 1.626 1.5 NOTCH2 7.533 0.002 2.069 27.431 2.019 2.0 Multivariate analyses considered the mutated genes found to be significant in univariate analyses. HR: hazard ratio; CI: confidence interval. https://doi.org/10.1371/journal.pone.0212813.t003 Genomics of follicular lymphoma transformation PLOS ONE | https://doi.org/10.1371/journal.pone.0212813 February 25, 2019 9 / 16 36. Brunner C, Wirth T. Btk expression is controlled by Oct and BOB.1/OBF.1. Nucleic Acids Res. 2006; 34: 1807–1815. https://doi.org/10.1093/nar/gkl131 PMID: 16582106 37. Chapuy B, McKeown MR, Lin CY, Monti S, Roemer MGM, Qi J, et al. Discovery and Characterization of Super-Enhancer-Associated Dependencies in Diffuse Large B Cell Lymphoma. 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