Expanding the spectrum of canine Diffuse Large B-cell Lymphoma (cDLBCL) genetic aberrations through whole genome sequencing (WGS) analysis

Expanding the spectrum of canine Diffuse Large B-cell Lymphoma (cDLBCL) genetic aberrations through whole genome sequencing (WGS) analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Expanding the spectrum of canine Diffuse Large B-cell Lymphoma (cDLBCL) genetic aberrations through whole genome sequencing (WGS) analysis Antonella Fanelli, Eugenio Mazzone, Diana Giannuzzi, Laura Marconato, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5348393/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Diffuse large B-cell lymphoma (DLBCL) is one of the most prevalent hematological malignancies in both humans and dogs, characterized in both species by significant clinical heterogeneity and limited prognostic predictability. With the introduction of next-generation sequencing (NGS) technologies in veterinary medicine over the past decade, researchers have begun to elucidate the molecular basis of canine DLBCL (cDLBCL); however, much of the clinical heterogeneity associated with this tumor remains unexplained. In this study, we performed whole genome sequencing on 10 cDLBCL cases, all treated with chemo-immunotherapy, which exhibited similar clinico-pathological features but markedly different outcomes. Cases were classified as "poor" or "good" responders based on whether their lymphoma-specific survival fell below or above the cohort's median. Protein-coding variants and copy number aberrations unique to poor or good responders revealed novel candidate genes not previously identified in cDLBCL studies, while splicing, untranslated regions, and intronic variants were detected in genes already known to be recurrently mutated. In conclusion, our investigation has broadened the spectrum of potentially pathogenic variants implicated in cDLBCL, though further studies with larger cohorts are necessary to validate these findings. Biological sciences/Cancer/Haematological cancer/Lymphoma/Non hodgkin lymphoma/B cell lymphoma Health sciences/Oncology/Cancer/Cancer genomics dog diffuse large B-cell lymphoma DLBCL whole genome sequencing prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Diffuse large B-cell lymphoma (DLBCL) is the most prevalent and deadly hematologic malignancy in dogs, with clinical manifestations that are highly variable and not reliably predictable based on presentation alone 1 . Recent advances in molecular profiling have shed light on the complex landscape of canine DLBCL (cDLBCL) through comprehensive transcriptomic 2 , 3 , methylome 4 , and genomic analyses, utilizing both whole exome 5 – 7 and targeted sequencing approaches 8 . These investigations have uncovered distinctive gene expression patterns, copy number aberrations (CNAs), and recurrently mutated genes that drive the pathogenesis of cDLBCL and help stratify patients into prognostically distinct subgroups. Notably, the transcriptional profile of cDLBCL closely mirrors the human activated B-cell (ABC) subtype of DLBCL, which is characterized by unique signatures associated with the activation of the B-cell receptor (BCR) and nuclear factor-κB (NF-κB) pathways 3 . Notably, TP53 mutations have emerged as strong independent predictors of poor prognosis, while dogs with wild-type TP53 show significant therapeutic benefit from the addition of immunotherapy to standard CHOP-based protocols 7 . These findings strongly advocate for routine TP53 mutational screening during the initial clinical assessment to aid in both prognostic stratification and informed therapeutic decision-making. Despite advances in understanding the molecular pathogenesis of cDLBCL, substantial clinical and prognostic heterogeneity remains unexplained. While gene expression and mutational profiles have been extensively characterized, the non-coding genome of cDLBCL remains largely unexplored. In human oncology, the historically high costs of whole genome sequencing (WGS) have led to a preference for whole exome sequencing (WES) or targeted gene panels. Consequently, most available data have focused on the coding regions of the genome, concentrating on clinically relevant genes. This focus has left the potential contributions of untranslated, intronic, and intergenic regions to tumor development and progression largely unexplored 9 . The discovery of activating TERT promoter mutations across multiple human cancer histotypes 10 and the identification of numerous cancer susceptibility loci within non-coding regions through genome-wide association studies (GWAS) 11 , 12 have highlighted the potential of non-coding variants as significant drivers of tumorigenesis. These findings, alongside the decreasing costs of WGS, have catalyzed a substantial increase in publication of tumor genomes, culminating in the Pan-Cancer Analysis of Whole Genomes (PCAWG) project. This collaborative effort between The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) has analyzed over 2,500 cancer genomes, uncovering novel mutational signatures, structural variants, and insights into tumor evolution 13 , 14 . In dogs, to date, only a single study has employed WGS to analyze six cases of multicentric B-cell lymphoma (BCL) 15 . However, this investigation was limited in scope, focusing exclusively on protein-coding gene variants and relying on a generic BCL diagnosis without histotype specification or correlation with clinical data. Additionally, the use of potentially contaminated blood samples as matched-normal DNA controls, without flow cytometric confirmation to rule out neoplastic infiltration, may have compromised the comparative analyses, further limiting the study's findings. The present study aims to provide a comprehensive characterization of the genome-wide mutational landscape of cDLBCL to elucidate the complex molecular mechanisms driving its pathogenesis. To facilitate this investigation, we selected ten cases with similar clinico-pathological features but divergent survival outcomes. Results Study population Ten cDLBCL cases with matched-normal tissues were retrieved for WGS analysis. Signalment and clinical data are reported in Supplementary Data 1. The cohort consisted of 2 mixed breed dogs, 2 Dobermann, 2 Golden retrievers, and one each of West Highland White terrier, American Staffordshire, poodle and German shepherd. Six (60%) dogs were males, while 4 (40%) were females. Median age was 7 years (range: 5–11 years) and median weight was 32.9 kg (range: 8.8–37.6 kg). Eight (80%) dogs had stage V disease, while 2 (20%) had stage IV disease. Six (60%) dogs were asymptomatic (substage a), while 4 (40%) showed symptoms (substage b). Six (60%) dogs had bone marrow (BM) infiltration (median: 3.5%; range: 0.9–47.4%), while 4 (40%) had peripheral blood (PB) infiltration (median: 1.7%; range: 0.9–6.8%). Five (50%) dogs had elevated serum lactate dehydrogenase (LDH) activity, and 2 (20%) had been pre-treated with steroids. While all dogs had received chemo-immunotherapy, their treatment response varied significantly. The overall median time to progression (TTP) was 237 days, while the overall median lymphoma-specific survival (LSS) was 344 days (Supplementary Fig. 1). Based on these median values, the dogs of the cohort were categorized as either “poor” or “good” responders. Median TTP was significantly shorter (p = 0.014) for poor responders (60 days; range: 49–76 days) compared to good responders (493 days; range: 237–988 days) (Fig. 1 a). Likewise, median LSS was significantly shorter (p = 0.002) for poor responders (77 days; range: 63–103 days) compared to good responders (1,153 days; range: 585–1,484 days) (Fig. 1 b). None of the examined variables showed a significant association with TTP in the overall population or within either group (Supplementary Data 2). Conversely, in the poor responders’ group, sex, weight and pretreatment with steroids significantly affected LSS. Males (p = 0.04), dogs weighting less than 32.9 kg (p = 0.04) and those pretreated with steroids (p = 0.04) experienced shorter LSS (67 days) compared to their counterparts (94 days) (Supplementary Data 2). In the good responders’ group, dogs younger than 7 years exhibited shorter LSS compared to their older counterparts (676 vs 1,153 days, respectively; p = 0.04) (Supplementary Data 2). Considering the whole cohort, LSS was significantly shorter in dogs presenting increased LDH activity (77 vs 1153 days, p = 0.013) and in dogs that had been pretreated with steroids (67 vs 676 days, p < 0.001) (Supplementary Data 2). Genome-wide mutational landscape of cDLBCL The median sequencing depth was 32.4X (range: 25–37X) for tumors and 25.1X (range: 13.5–28.5X) for normal samples. On average, 99.7% of reads aligned to the canine genome (range: 99.5–99.8%), and there was a mean of 18% duplicate reads (range: 16.1–22.0%). Collectively, the total number of single nucleotide variants (SNVs) and insertion/deletion variants (indels) identified across tumors ranged from 29,368 to 131,378 (median: 50,259). Of these variants, 1.0% laid within coding regions (median: 1.2%; range: 0.7–1.8%), while 99% represented non-coding variants (median: 98.8%; range: 98.2–99.2%) (Fig. 2 ). The full list of the nucleotide variants is reported in Supplementary Data 3. cDLBCL variants in coding regions The median number of exonic variants per case was 653.5 (range: 368–1,545), and a total of 5,562 SNVs and 1,279 indels in coding regions were identified. Among the SNVs, 1,492 were synonymous (S), while 5,536 were nonsynonymous (NS), with 256 determining the appearance of premature stop codons. This yielded a nonsynonymous-to-synonymous (NS:S) ratio of 3.7. Protein-coding variants were retrieved in all dogs (Fig. 3 ). Missense mutations were the most frequent variants (53.9%), followed by frameshift substitutions (13.2%). Inframe substitutions, nonsense mutations, stop-loss and start-loss variants were 5%, 3.6%, 0.17% and 0.16%, respectively. In addition, 2.7% of protein-coding variants were classified as “unknown” (see Material and Methods section). Poor and good responders carried protein-coding variants in 1,910 and 2,191 genes, respectively, and 1,539 and 1,820 genes were altered exclusively in poor and good responders, respectively. Among these, 8 genes in the poor responder group ( TAF5L, CEP131, ABCA9, ETV1, PRAG1, LOC102155129, ARHGEF4 and LOC102153036 ) and 9 genes in the good responder group ( ARFGEF3, SIPA1L3, COL12A1, ZBTB40, AMOTL1, MYO9A, DNAH17, ECEL1 and IGSF9B ) were altered in at least 3 samples. TRAF3 and KMT2D were altered in both groups. By using Sorting Intolerant From Tolerant (SIFT) tool, 1,434 missense variants were predicted as deleterious, and 1,685 as tolerated (Supplementary Data 3). The number of deleterious and tolerated missense variants was not significantly different between poor and good responders (p = 0.6). Likewise, no statistically significant difference was retrieved in the number of truncating mutations (i.e. frameshift substitutions, nonsense and start loss variants) between the two groups (p = 0.4). Among genes mutated in at least 3 samples, twelve genes were associated with TTP ( ABCA9, ARHGEF4, CEP131, FNIP1, HMCN2, MAML2, OBSCN, PBK, SALL4, SMARCE1, TAF5L and ZZEF1 ). All these genes, except for OBSCN , were associated with shorter TTP (p < 0.05) (Supplementary Data 2). ABCA9 , ARHGEF4, CEP131 and TAF5L were mutated in poor responders only. Instead, 11 genes were significantly associated with LSS. Specifically, ABCA9, CEP131, ETV1, NBEAL2, PRAG1, TAF5L and TP53 were associated with shorter LSS (p < 0.05) (Supplementary Data 2). All of these genes, except for NBEAL2 and TP53 , were mutated exclusively in poor responders. Conversely, AMOTL1, ECEL1, IGSF9B , and SIPA1L3 were associated with longer LSS (p < 0.05) (Supplementary Data 2). These genes were mutated exclusively in good responders. cDLBCL variants in non-coding genome To broaden the spectrum of mutations in cDLBCL beyond coding regions, we focused on variants affecting splicing and the 3’- and 5’-untranslated regions (UTRs). Splicing variants were reported in all samples (median: 10.5; range: 6–30). The median number of 5’UTR and 3’UTR variants was 171.5 (range: 87–411) and 404 (range: 234–1,118), respectively. The splicing and UTR variants impacted 4,291 different genes. Of these, 688 had been reported in one previous canine B-cell lymphoma exome study 7 , and 13 were identified as recurrently mutated genes (≥ 5% of the samples) in cDLBCL (Table 1 ). Additionally, 224 genes were listed in the Catalogue of Somatic Mutations in Cancer (COSMIC), while 3,751 and 2,463 genes were reported in human datasets of lymphoid neoplasms and DLBCL, respectively (Supplementary Data 4). A total of 1,814 genes were exclusively altered in poor responders, compared to 1,843 that were mutated in good responders (Supplementary Data 4). Table 1 Genes reporting splicing and UTRs variants that are mutated in at least 5% of cases of cDLBCL from WES studies Gene n. mutated samples Reported in COSMIC Human lymphoid neoplasms hDLBCL cDLBCL (> 5%) LRRIQ1 4 yes yes yes FBXW7 3 yes yes yes yes HIVEP3 2 yes yes yes DIAPH2 2 yes yes yes TRAF3 2 yes yes yes ATXN1 1 yes yes yes KDM6A 1 yes yes yes yes ETV1 1 yes yes yes yes POT1 1 yes yes yes yes PHC3 1 yes yes PIK3CD 1 yes yes yes GBE1 1 yes yes yes RARA 1 yes yes yes yes The median number of intronic variants per case was 19,638 (range: 11,406–53,023). In total, intronic variants were reported in 14,738 different genes. Among these, 625 were listed in the COSMIC database, while 12,560 and 8,178 were reported in human lymphoid neoplasms and DLBCL datasets, respectively (Supplementary Data 5). Thirty-eight genes carrying intronic variants were recurrently mutated in cDLBCL (Table 2 ). Regarding oncologic outcome, 1,927 genes were exclusively altered in poor responders, compared to 1,965 that were mutated in good responders (Supplementary Data 5). Table 2 Genes reporting intronic variants that are mutated in at least 5% of cases of cDLBCL from WES studies. Gene n. mutated samples Reported in COSMIC Human lymphoid neoplasms hDLBCL cDLBCL (> 5%) HIVEP3 10 yes yes yes LRRIQ1 10 yes yes yes FBXW7 10 yes yes yes yes VWF 10 yes yes yes ATXN1 10 yes yes yes ANKRD11 10 yes yes yes SYNE1 9 yes yes yes LRP1B 9 yes yes yes yes MEF2C 9 yes yes yes GBE1 9 yes yes yes TBL1XR1 9 yes yes yes yes SYNE2 9 yes yes yes MYT1L 9 yes yes yes ABCA13 9 yes yes yes ETV1 8 yes yes yes yes KIF21A 8 yes yes yes TTN 8 yes yes yes TRRAP 8 yes yes yes yes LAMA1 8 yes yes yes RARA 8 yes yes yes yes DIAPH2 8 yes yes yes PHC3 8 yes yes MAP3K14 7 yes yes SUZ12 7 yes yes yes yes TRAF3 7 yes yes yes FSIP2 6 yes yes yes THBS2 5 yes yes PLEC 5 yes yes yes yes PIK3CD 5 yes yes yes SETD2 5 yes yes yes yes KDM6A 5 yes yes yes yes CIC 4 yes yes yes yes POT1 3 yes yes yes yes FAM50A 2 yes yes yes DDX3X 2 yes yes yes yes EHD3 2 yes yes yes GADD45A 1 yes MYC 1 yes yes yes yes Collectively, intergenic, downstream, and upstream variants ranged from 14,620 to 62,858 per sample, with a median of 24,815 variants. Variants in non-coding transcripts (those affecting exons, introns, or splicing sites of non-coding RNAs) were reported in all samples, with a median of 4,682 variants (range: 2,646–12,395) affecting 8,045 different ncRNAs. To identify differentially mutated ncRNAs between poor and good responders, genes of uncertain function (LOC symbols) were not considered for further analyses. Ten ncRNAs were shared between poor and good responders, while 8 and 10 were exclusively mutated in poor and good responders, respectively. None of these was altered in more than one tumor from each group (Supplementary Fig. 2). Tumor mutational burden, microsatellite instability and mutational signatures The tumor mutational burden (TMB) ranged from 12.24 to 54.74 mutations per megabase (mean: 25.10; median: 20.94) and was normally distributed within the cohort. Samples #4 and #10 presented a higher TMB compared to the other tumors, and this may be due to the presence of several mutations affecting genes involved in various DNA repair mechanisms. Sample #4 presented frameshift substitutions in LIG1 and MMS19 , involved in nucleotide excision repair (NER), and in PNKP , involved in base excision repair (BER) mechanism. Sample #10 carried a frameshift substitution in NEIL3 , involved in NER. No significant difference in TMB was retrieved between the poor and good responder groups. All samples presented localized hypermutation compatible with kataegis (Supplementary Data 6). Six and 5 samples showed localized hypermutation in correspondence of the subtelomeric region of chromosome 8, in which maps the immunoglobulin heavy chain (IGH) locus, and on chromosome 26, in which maps the immunoglobulin light chain lambda (IGL) locus, respectively (two representative samples are shown in Fig. 4 ). Microsatellite instability (MSI) was detected across all samples, with a mean of 3,032.2 and 15,847.2 involved loci in good and poor responders, respectively. Anyway, no statistically significant difference was retrieved between the two groups (p = 0.4). In addition, no correlation between MSI and TMB was identified. Mutational signatures analysis was performed using a Bayesian non-negative matrix factorization method to evaluate the trinucleotide context of somatic SNVs. Differential exposure analysis revealed no significant difference between the two groups. The analysis of single base substitutions (SBS) signatures revealed exposure to COSMIC signatures SBS6 (defective DNA mismatch repair), SBS5 (clock-like signature of unknown etiology), SBS40c (of unknown etiology) and SBS3 (defective homologous recombination DNA damage repair) in different proportions (Fig. 5 , Supplementary Data 7). Somatic copy number aberrations (SCNAs) and structural variants (SVs) Absolute copy numbers were extracted from ASCAT segmentation files, revealing somatic copy number alterations (SCNAs) in nine tumors (median: 29; range: 9–217). Sample #5 was excluded from the analysis due to insufficient sequencing depth. The analysis identified 110 deletions (median: 7; range: 2–36), 309 amplifications (median: 16; range: 1–147), and 85 gain/loss events (median: 6; range: 3–39). Chromosome CFA31 exhibited the highest frequency of genomic rearrangements, comprising 25 amplifications, 4 deletions, and 7 gain/loss events, while CFA24 demonstrated minimal alterations with only one deletion and two amplifications (Supplementary Data 8). Within amplified regions, several genes associated with transcriptional regulation were identified, including POU1F1, USP16, PAXBP1, ZNF654, BACH1, CGGBP1 , and VGLL3 (Supplementary Fig. 3). Conversely, genes located in deleted regions showed greater heterogeneity across tumors compared to those in amplified regions. Notable deletions were observed in IGHM, MEF2C , and IGKC , all of which are key components of the B-cell receptor signaling pathway (Supplementary Fig. 4). GISTIC analysis was employed to identify recurrent amplifications and deletions across the genome. In poor responders, two statistically significant deletion peaks were identified: one on CFA8 (73,027,410 − 73,751,733; spanning 724 kb) and another on CFA20 (5,459,601 − 12,452,795; spanning 7 Mb) (Fig. 6 a). Good responders exhibited four statistically significant deletion peaks: two on CFA8 (72,864,243 − 73,365,210; spanning 501 kb, and 73,306,168 − 74,292,901; spanning 987 kb), one on CFA19 (19,934,979 − 25,959,251; spanning 6 Mb), and one on CFA26 (26,833,169 − 27,628,477; spanning 795 kb) (Fig. 6 b). Notably, the CFA8 peaks detected in both groups showed partial overlap, corresponding to the canine IGH locus. Also, the peak on CFA26 corresponds to the IGL locus. Structural variant analysis revealed extensive genomic alterations across all samples (median: 25,104; range: 21,715–36,142) (Supplementary Data 9). The comprehensive analysis identified 68,255 inversions (median: 6,703; range: 4,399–10,280), 15,990 insertions (median: 1,622; range: 1,378–1,742), and 6,447 duplications (median: 656.5; range: 405–854). Discussion In the last decades, the application of genome sequencing technologies to cancer research has revolutionized both diagnostics and therapeutics. These advancements have not only reinforced the understanding that cancer is fundamentally a genetic disease but have also uncovered the existence of multiple molecular subgroups within the same tumor histotype. Each subgroup may have a distinct clinical course, prognosis and response to therapy, highlighting the complexity and heterogeneity of cancer. DLBCL represents the most common histotype among all hematologic malignancies in humans and dogs, and several comparative studies have highlighted both similarities and differences between the two species 16 , 17 . In humans, comprehensive analysis of data from hundreds of DLBCL exomes and genomes has identified numerous distinct molecular subtypes, each characterized by specific genetic aberrations and associated with varying prognosis 18 , 19 . In the present study, 10 dogs diagnosed with DLBCL, exhibiting similar clinico-pathological variables but significantly different clinical outcomes, underwent WGS to determine whether these differences could be attributed to distinct molecular characteristics. Considering protein-coding variants, 8 and 9 genes were identified as being exclusively altered in poor and good responders, respectively. Among the genes specific to poor responders, TAF5L encodes a protein that is a component of the P300/CBP-associated factor (PCAF) histone acetyltransferase complex. It has been demonstrated that TAF5L/TAF6L act as epigenetic regulators, transcriptionally activating the MYC regulatory network, which is crucial for maintaining self-renewal in mouse embryonic stem cells 20 . CEP131 encodes a protein involved in many cellular processes, including protein localization to centrosome. Different studies have reported that CEP131 functions as an oncogene, promoting carcinogenesis in breast cancer 21 and tumor progression in colon cancer 22 . Also, CEP131 overexpression has been reported as a predictor of poor prognosis in hepatocellular carcinoma 23 and neuroblastoma 24 . In our cohort, we reported 3 missense variants in TAF5L and 2 missense variants and an inframe substitution in CEP131 . Since the impact of non-truncating variants is challenging to predict, it will be essential to investigate whether these variants lead to alterations in gene and protein expression. This will help determine if they play a role in contributing to the worse clinical outcome observed in these dogs. ABCA9 encodes a gene belonging to the superfamily of ATP-binding cassette (ABC) transporters. One study reported that in a subset of hepatocellular carcinoma human patients, ABCA8 and ABCA9 downregulation was significantly associated with shorter survival time 25 . In our cohort, 3 dogs in the poor responders group carried ABCA9 mutations, specifically two frameshift substitutions and one nonsense variant, which represent loss-of-function mutations likely associated with reduced protein expression. Additionally, ETV1 is the only gene listed in the COSMIC database, and chromosomal translocations involving this gene have been implicated as causative factors in Ewing Sarcoma 26 and prostate cancer 27 . Among the genes found exclusively in the good responder group, COL12A1 upregulation has been identified as a predictor of poor prognosis in human patients with pancreatic adenocarcinoma 28 and gastric cancer 29 . Meanwhile, AMOTL1 has been reported as recurrently mutated in splenic marginal zone lymphoma 30 . To expand the spectrum of cDLBCL mutations beyond coding regions, we first interrogated splicing and UTR variants, that could affect proper gene expression in different ways. In our cohort, 13 genes previously identified as recurrently mutated in cDLBCL 7 exhibited splicing and/or 5’- and 3’UTR variants. In the poor responder group, FBXW7 exhibited a splicing variant and two 3’UTR variants, while DIAPH2 had both a 3’- and a 5’UTR variant. Additionally, KDM6A contained two 3’UTR variants in the same dog (sample #8). In the good responder group, TRAF3 carried two 3’UTR variants in sample #2 and a splicing variant in sample #4. Moreover, RARA presented a 5’UTR variant, while POT1 , PIK3CD and GBE1 presented a 3’UTR variant each. Biologically, splicing variants can disrupt an existing splice site or introduce a new one, leading to various outcomes such as exon skipping, intron retention, or the utilization of alternative splicing sites located within introns 31 . UTR variants can influence gene expression with different mechanisms, including alternative splicing, reduced translational efficiency, and altered interactions with proteins and miRNAs 32 . Although these results suggest a potential role for these types of genetic aberrations in the pathogenesis and prognosis of cDLBCL, further genetic screening involving a larger number of cases, along with in vitro functional studies, are necessary to validate this hypothesis. Non-coding mutations can contribute to tumorigenesis through the alteration of cis -regulatory elements, including promoters and distal enhancers, the disruption of chromatin 3D structure and altered function of regulatory non-coding RNAs (ncRNAs) 33 . In humans, the analysis of 2,658 cancer genomes across 38 different tumor types identified somatic non-coding driver mutations 34 . In DLBCL, hypermutation of active super-enhancers linked to known proto-oncogenes has been identified as a novel mutational mechanism involved in the pathogenesis of this tumor 35 . In our cohort, we identified variants in intronic, intergenic, and ncRNAs regions. However, the limited number of cases prevented us from identifying recurrent mutations that may affect regulatory regions or from distinguishing driver events from passenger mutations. Comparative studies have shown that many regulatory elements are located within evolutionarily conserved regions known as conserved non-coding elements (CNEs). Disruption of these CNEs has been implicated in developmental disorders and cancer 36 . TMB, mutational signatures and MSI were also examined. Although TMB did not differ significantly between poor and good responders, two samples (sample #4 and sample #10) exhibited markedly higher TMB compared to the others. This is likely due to the presence of loss-of-function mutations in genes involved in NER and BER pathways. Likewise, there was no significant difference in MSI, nor was MSI correlated with TMB in the present cohort. In human oncology, TMB and MSI have emerged as valuable biomarkers for identifying patients who may benefit from immunotherapy 37 . A recent study analyzed MSI in 692 canine tumors across 10 different histotypes, revealing that B-cell lymphomas presented the highest number of loci affected by MSI 38 . These results suggest that a larger cohort of cDLBCL should be analyzed for MSI to test potential correlations with prognosis and explore the possibility that high-MSI dogs could benefit from immunotherapies targeting the PD-1/PDL-1 axis. The assessment of mutational signatures did not show substantial differences between poor and good responders. The signatures that predominantly contributed to the mutation spectra were identified as COSMIC SBS6 (associated with defective DNA mismatch repair), SBS5 (a clock-like signature of unknown etiology), SBS40c (also of unknown etiology) and SBS3 (linked to defective homologous recombination DNA damage repair). This contrasts with the findings of Giannuzzi et al. (2022) 7 , where the analysis of 77 cDLBCL through WES identified SBS1 (age-related spontaneous deamination of 5’-methylcytosine) as the predominant signature. A lack of correspondence in mutational signatures between WGS and WES has been previously noted in canine osteosarcoma by Gardner et al. (2019) 39 , who suggested that this discrepancy may be due to the higher sequencing depth typically achieved with WES. SCNAs represent a common event in cancer, and recurrent alterations associated with specific histotypes have been identified 40 . In dogs, SCNAs and SVs have been described in osteosarcoma 39 , 41 , melanoma 42 , 43 , hemangiosarcoma 44 and mammary tumors 45 . In our cohort, a recurrent deletion peak on CFA20 was observed in poor responders, while two recurrent deletion peaks on CFA19 and CFA26 were identified in good responders. Additionally, recurrent deletions affecting the sub-telomeric region of CFA8, which corresponds to the canine IGH locus 46 , were reported in both groups, likely due to V(D)J genes rearrangement. Among the 56 genes located within the CFA20 deletion, germline variants in MBD4 , a gene encoding for a mismatch-specific DNA N-glycosylase, have been reported as a predisposing factor for acute myeloid leukemia 46 . Additionally, MBD4 deficiency has been identified as a predictive marker for response to immune checkpoints inhibitors in uveal melanoma 47 . VGLL4 loss was correlated with the suppression of PDL-1 expression 48 . Inactivating mutations in the tumor suppressor gene VHL have been associated with several diseases, including pheochromocytoma, erythrocytosis, renal cell carcinoma, and cerebellar hemangioblastoma 49 . Additionally, FANCD2 deletions are implicated in Fanconi anemia, a genetic disease characterized by chromosomal instability and defective DNA repair 50 . In conclusion, our study provides a comprehensive view of the genomic landscape of cDLBCL at a genome-wide resolution. Despite the main limitations being the small sample size and relatively low coverage, we extended the analysis beyond coding regions, uncovering a broader spectrum of potentially pathogenic variants in genes already known to be recurrently mutated in cDLBCL. Additionally, we identified novel candidate genes that may play a role in the disease's pathogenesis. Further studies with larger cohorts are needed to validate these findings. Methods Samples Ten cDLBCL were selected from the archive of the Canine Lymphoma Biobank 51 . The study and all methods are reported in accordance with ARRIVE guidelines ( https://arriveguidelines.org ), where applicable. The study did not fall within the application areas of Italian Legislative Decree 26/2014 which governs the protection of animals used for scientific or educational purposes; therefore, ethical approval was waived for this study by Department of Veterinary Sciences at University of Turin. Dogs were treated according to the current standards, and dog owners gave written informed consent. All methods were carried out in accordance with relevant guidelines and regulations. To be included in the analysis, dogs had to be treated with chemoimmunotherapy consisting of the administration of the APAVAC vaccine in addition to a standardized CHOP-based protocol as previously described 52 . The following patient demographics and clinico-pathological features were available: clinical stage, substage, immunophenotype determined by flow cytometry (FC) on a neoplastic lymph node (LN) aspirate, infiltration of peripheral blood (PB) and bone marrow (BM) determined by flow cytometry, serum LDH activity and whether the dogs had been pre-treated with steroids. All dogs underwent lymphadenectomy for routine histologic analysis and immunohistochemistry (CD3 and CD20), vaccine preparation, and DNA extraction. Prior to surgery, dogs were fasted for at least 12 hours. Sedation and anesthesia were achieved through premedication with dexmedetomidine (3–5 µg/kg IM) and methadone (0.2–0.3 mg/kg IM). Induction was performed using propofol (2–4 mg/kg IV, titrated to effect), followed by intubation once the dogs were unconscious. Anesthesia was maintained with isoflurane or sevoflurane in 100% oxygen, and IV fluids were administered at 3–5 mL/kg/h for cardiovascular support. Continuous intraoperative analgesia was provided via a fentanyl CRI (5 µg/kg/h IV). A skin-punch biopsy was also collected from each dog to provide matched-normal tissue. TTP, LSS and cause of death were also available. TTP was measured as the time between the start of treatment and disease progression, while LSS was calculated from the start of treatment to death due to lymphoma. DNA isolation and sequencing Total DNA was extracted from tumor samples and matched-normal tissues, using the AllPrep DNA/RNA Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. DNA concentration and quality were assessed by Qubit fluorometer (Life Technologies Ltd, Paisley, UK) and agarose gel electrophoresis. Twenty high quality WGS libraries were prepared using the Illumina-compatible KAPA HyperPlus Library Preparation Kit (Roche Sequencing and Life Science, Wilmington, MA). Libraries were quantified through a Qubit 2.0 Fluorometer using the Qubit DNA Assay Kit (Thermo Fischer, Foster City, CA, USA) and quality was assessed using the Bioanalyzer 2100 instrument (Agilent Technologies, Santa Clara, CA, USA). Ten libraries (fragments ranging from 300 to 400 bp) were then pooled and sequenced on an Illumina NovaSeq 6000 platform in a paired-end (150 PE) mode with an average coverage of 30X. Raw Illumina sequencing data are deposited in BioProject database with BioProject ID PRJNA805123. WGS data pre-processing Firstly, all raw sequencing data underwent a quality check using fastqc software to assure that no errors occurred during the preprocessing step. They were then processed according to the Genome Analysis Toolkit (GATK) 53 preprocessing pipeline, which is gold standard in human and veterinary medicine. Briefly, reads were aligned to the reference genome (canFam3.1, UCSC) using Burrow Wheeler Aligner (BWA) 54 , converted to bam format (Samtools), sorted by coordinate (Samtools), read groups were added (Picard) and duplicates were marked (Picard). Finally, all data underwent Base Quality Score Recalibration. Somatic variants calling Somatic variant calling was performed to access SNVs and indels. First, healthy tissue sequencing data were elaborated using Mutect to collect Single Nucleotide Polymorphisms (SNPs) and artifacts for Panel of Normal (PON). The PON, together with DogSD database ( https://ngdc.cncb.ac.cn/idog/index.jsp ), was used to clean artifacts and polymorphisms in the following variants calling. Variants calling was performed using Mutect2 55 from GATK, with standard parameters, and annotated with ANNOVAR 56 . Short variants were classified as “UNKNOWN” if, as reported in ANNOVAR documentation, [a transcript maps to multiple locations, all as "coding transcripts", but none has a complete ORF]. TMB, mutational signatures and MSI TMB was calculated for each sample by dividing the total number of called variants (both coding and non-coding) for the size in Mb of the haploid canine reference genome (CanFam3.1, 2.4 Gb) and reported as number of mutations/Mb ratio. Mutational signatures were extracted using the Sigminer R package. These signatures were then confronted against COSMIC signatures 57 to extract valuable insight. The maftools R package 58 was used to generate rainfall plots and to identify areas of kataegis. MsiSensor-Pro 59 was used to perform MSI analysis as previously described 38 . Somatic copy number aberrations (SCNAs) and structural variants (SVs) Copy Number Aberrations were accessed using ASCAT segmenter 60 (version available in EaCoN R package). Segmented data underwent tumor purity and ploidy evaluation to estimate the major and minor allele number of copies. Additionally, segmented data were also used to run GISTIC2 61 peaks calculation. Both peaks and CNA regions were annotated using GTF file from UCSC and custom R script based on rtracklayer 62 and GenomicRanges 63 packages. Finally, structural variants (SV) were accessed using Delly 64 and annotated with Sansa . Statistical analysis Survival analysis was conducted using survival and survminer R packages. Median TTP and LSS of the whole cohort were used as cutoff values to divide the dogs into “good” and “poor” responders. Survival curves were constructed using the Kaplan-Meier method and compared by means of log-rank test. All the available clinico-pathological variables, mutated genes and TMB were tested for their influence on both TTP and LSS by means of univariate Cox proportional-hazards model. Dogs lost to follow-up or dead for lymphoma-unrelated causes before disease progression were censored for TTP analysis. Dogs dead from lymphoma-unrelated causes were censored for LSS analysis. Co-occurrence and mutual exclusivity were evaluated using the Maftools 58 R package. Conversely, the Shapiro-Wilk test was employed to test MSI burden and TMB for normal distribution. Differences between good and poor responders with respect to MSI burden and TMB were assessed by Wilcoxon rank-sum test. For categorical variables, Fisher’s exact test was conducted to assess possible associations with treatment response. Differences in TMB across treatment response (“good” vs “poor”), breed (pure vs mixed), sex (female vs male), age (< 7 years vs ≥ 7 years), weight (< 32.9 kg vs ≥ 32.9 kg), stage (IV vs V), substage (a vs b), BM infiltration (< 3% vs ≥ 3%), PB infiltration (< 3% vs ≥ 3%), LDH activity (normal vs increased) and pretreatment with steroids (yes vs no) groups were assessed by Student t-test. Declarations Author contributions L.A. and L.M. designed the study; L.M. provided samples and clinical data; D.G. performed library preparation and sequencing experiments and contributed to data analysis; E.M. performed bioinformatic and statistical analyses and data visualization; L.A. and A.F. interpreted the data and wrote the manuscript; all authors contributed to manuscript revision and approved the final draft. Competing interests The authors declare no competing interests. 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Bioinformatics 28, i333-i339 (2012). https://doi.org/10.1093/bioinformatics/bts378 Additional Declarations No competing interests reported. Supplementary Files SupplementaryData1.xlsx SupplementaryData2.xlsx SupplementaryData3.xlsx SupplementaryData4.xlsx SupplementaryData5.xlsx SupplementaryData6.xlsx SupplementaryData7.xlsx SupplementaryData8.xlsx SupplementaryData9.xlsx SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5348393","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":376466116,"identity":"15662dd8-7046-4262-8162-21c69c1a5cf2","order_by":0,"name":"Antonella Fanelli","email":"","orcid":"","institution":"University of Turin","correspondingAuthor":false,"prefix":"","firstName":"Antonella","middleName":"","lastName":"Fanelli","suffix":""},{"id":376466117,"identity":"06e920b6-3a51-4a97-8150-a1748ffdf7ae","order_by":1,"name":"Eugenio Mazzone","email":"","orcid":"","institution":"University of Turin","correspondingAuthor":false,"prefix":"","firstName":"Eugenio","middleName":"","lastName":"Mazzone","suffix":""},{"id":376466118,"identity":"c4c2007c-d837-4cad-9032-78ba74984cc8","order_by":2,"name":"Diana Giannuzzi","email":"","orcid":"","institution":"University of Padua","correspondingAuthor":false,"prefix":"","firstName":"Diana","middleName":"","lastName":"Giannuzzi","suffix":""},{"id":376466120,"identity":"96484b7e-083f-4d1e-a043-c6457d8d8a70","order_by":3,"name":"Laura Marconato","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Marconato","suffix":""},{"id":376466121,"identity":"0563abc6-bd9e-40c6-8e80-ee257981f028","order_by":4,"name":"Luca Aresu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACPgST+QADgwGIJqCFDYmZANQC1MNMQA+SFh6QFURYwyaR/PADQ809OfkZOd+kCwr+yDGw8x8goCXNWILhWLGxwY3cbdIzDAyMCTtMIodBAuiNxA0SQC08BgaJDURoYf7B8C+hfv6MnGdEa2GTYGxLSGC4kcNGpBaeZ2YWiX0JhhvOPDO25jEwNmZjZjbAq4WfPfnxjQ/fEuTl25Mf3ub5IyfHz3/wAX5rQCABxV7C6kfBKBgFo2AUEAIA7PQy6b5gzk4AAAAASUVORK5CYII=","orcid":"","institution":"University of Turin","correspondingAuthor":true,"prefix":"","firstName":"Luca","middleName":"","lastName":"Aresu","suffix":""}],"badges":[],"createdAt":"2024-10-28 15:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5348393/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5348393/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70404170,"identity":"65b7281d-f40c-4495-853e-388d14965209","added_by":"auto","created_at":"2024-12-02 22:58:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14619,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier (KM) curves for 10 dogs with DLBCL and treated with chemo-immunotherapy according to clinical outcome\u003c/strong\u003e. KM curves of TTP (a) and LSS (b) according to clinical outcome. Dogs classified as “poor responders” show both shorter TTP (p=0.014) and LSS (p=0.002) compared to those classified as “good” responders. KM curves were compared by means of log-rank test, and significance was set at p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/6eeb9dbc3387af8805df2b06.png"},{"id":70404172,"identity":"7201070a-da63-4573-9997-7925b682db80","added_by":"auto","created_at":"2024-12-02 22:58:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariant distribution across 10 cDLBCL analyzed by WGS\u003c/strong\u003e. Distribution of somatic short variants (SNVs and indels) across 10 cDLBCLs. Each bar represents a single dog, while on the y-axis is reported the total number of variants identified in each sample.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/31d9e11e8a8e36b4d61f1a69.png"},{"id":70404902,"identity":"b047d396-8dfd-4a89-ba12-f29ea073207f","added_by":"auto","created_at":"2024-12-02 23:06:56","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":987382,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOncoplot of genes carrying protein-coding variants in cDLBCL. \u003c/strong\u003eTop 30 mutated genes harboring somatic protein-coding SNVs and indels (upper panel) and distribution of nucleotide substitutions (lower panel) identified by WGS in 10 cDLBCLs. Genes are represented in descending order according to the frequency of mutation. Different mutation types are identified with different colors.\u003c/p\u003e","description":"","filename":"Figure3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/acb91a3f848b7263e2d95529.jpeg"},{"id":70404904,"identity":"c03f3d89-9525-4d5f-be67-939f896edcdf","added_by":"auto","created_at":"2024-12-02 23:06:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154917,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSomatic hypermutation consistent with kataegis identified in cDLBCL.\u003c/strong\u003e Rainfall plots show the density and distribution of somatic mutations in two representative WGS samples (a, b). Hypermutation events localized in correspondence of canine IGH locus (CFA8) and IGL locus (CFA26) are highlighted by red boxes. Base-pair distance between events is represented on the y-axis.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/798dec274a46ba806005b840.png"},{"id":70405674,"identity":"4f8d6bfd-f9ce-4791-a74c-223cc216a802","added_by":"auto","created_at":"2024-12-02 23:14:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":19834,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutational signature analysis in cDLBCL. \u003c/strong\u003eMutational signatures were extracted using the Sigminer R package and confronted against COSMIC signatures. The plot shows the distribution of the six types of substitution (in 96 different trinucleotide contexts) defined by the pyrimidine as inferred from the non-negative matrix factorization (NNMF) algorithm. Both in poor and good responders, the analysis of single base substitutions signatures (SBS) revealed that the signatures mainly contributing to mutation spectra corresponded to COSMIC SBS6 (defective DNA mismatch repair), SBS5 (clock-like signature of unknown etiology), SBS40c (of unknown etiology) and SBS3 (defective homologous recombination DNA damage repair) in different proportions.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/ef7057da8ff6f7750f02996f.png"},{"id":70404171,"identity":"5479ca6c-b008-42fd-adad-b95595615c0b","added_by":"auto","created_at":"2024-12-02 22:58:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":25232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRecurrent somatic copy-number aberrations (SCNAs) identified in cDLBCL. \u003c/strong\u003eThe picture shows statistically significant SCNAs peaks identified by GISTIC in poor (a) and good (b) responders. Amplification peaks are depicted in red, while deletion peaks in blue. Significance threshold is also indicated by a green line.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/1a6429b2be7fea8482a19633.png"},{"id":71004668,"identity":"b3d1c054-c89d-4e2e-b756-c9498ce45b3e","added_by":"auto","created_at":"2024-12-10 06:09:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2342079,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/6c8e9230-cc53-4f58-b4e4-16b0284f1e52.pdf"},{"id":70404182,"identity":"4634e025-40bf-431c-b6ab-b6e5be59535f","added_by":"auto","created_at":"2024-12-02 22:58:57","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":12379,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/935668745dd0a4cae58e7916.xlsx"},{"id":70404905,"identity":"b8c0b4a0-a564-4e12-8662-528d097a5fdf","added_by":"auto","created_at":"2024-12-02 23:06:56","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":107761,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/89c81a7ae75a51c3877a7c5a.xlsx"},{"id":70404185,"identity":"18a4f751-6942-4b7a-ba96-06ba6ccc6550","added_by":"auto","created_at":"2024-12-02 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22:58:56","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":11340,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/3da155570f8b3041fc40994e.xlsx"},{"id":70404178,"identity":"7ff2a8c1-cfd7-43f4-88c0-b052de41cef3","added_by":"auto","created_at":"2024-12-02 22:58:56","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":134326,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/8f06eedcf263661d14394eae.xlsx"},{"id":70404181,"identity":"c1daece9-68e4-455d-8ac3-c9815438a99f","added_by":"auto","created_at":"2024-12-02 22:58:57","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":8919097,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/4334cae80ffb1452c1b676a8.xlsx"},{"id":70404180,"identity":"8e474491-9e50-413f-9adb-3b1836e69d06","added_by":"auto","created_at":"2024-12-02 22:58:57","extension":"docx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":710407,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5348393/v1/067ab5357e67e911dbb4e8ac.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expanding the spectrum of canine Diffuse Large B-cell Lymphoma (cDLBCL) genetic aberrations through whole genome sequencing (WGS) analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) is the most prevalent and deadly hematologic malignancy in dogs, with clinical manifestations that are highly variable and not reliably predictable based on presentation alone\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Recent advances in molecular profiling have shed light on the complex landscape of canine DLBCL (cDLBCL) through comprehensive transcriptomic\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, methylome\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, and genomic analyses, utilizing both whole exome\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and targeted sequencing approaches\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. These investigations have uncovered distinctive gene expression patterns, copy number aberrations (CNAs), and recurrently mutated genes that drive the pathogenesis of cDLBCL and help stratify patients into prognostically distinct subgroups. Notably, the transcriptional profile of cDLBCL closely mirrors the human activated B-cell (ABC) subtype of DLBCL, which is characterized by unique signatures associated with the activation of the B-cell receptor (BCR) and nuclear factor-κB (NF-κB) pathways\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, \u003cem\u003eTP53\u003c/em\u003e mutations have emerged as strong independent predictors of poor prognosis, while dogs with wild-type \u003cem\u003eTP53\u003c/em\u003e show significant therapeutic benefit from the addition of immunotherapy to standard CHOP-based protocols\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These findings strongly advocate for routine \u003cem\u003eTP53\u003c/em\u003e mutational screening during the initial clinical assessment to aid in both prognostic stratification and informed therapeutic decision-making. Despite advances in understanding the molecular pathogenesis of cDLBCL, substantial clinical and prognostic heterogeneity remains unexplained. While gene expression and mutational profiles have been extensively characterized, the non-coding genome of cDLBCL remains largely unexplored.\u003c/p\u003e \u003cp\u003eIn human oncology, the historically high costs of whole genome sequencing (WGS) have led to a preference for whole exome sequencing (WES) or targeted gene panels. Consequently, most available data have focused on the coding regions of the genome, concentrating on clinically relevant genes. This focus has left the potential contributions of untranslated, intronic, and intergenic regions to tumor development and progression largely unexplored\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The discovery of activating \u003cem\u003eTERT\u003c/em\u003e promoter mutations across multiple human cancer histotypes\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and the identification of numerous cancer susceptibility \u003cem\u003eloci\u003c/em\u003e within non-coding regions through genome-wide association studies (GWAS)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e have highlighted the potential of non-coding variants as significant drivers of tumorigenesis. These findings, alongside the decreasing costs of WGS, have catalyzed a substantial increase in publication of tumor genomes, culminating in the Pan-Cancer Analysis of Whole Genomes (PCAWG) project. This collaborative effort between The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) has analyzed over 2,500 cancer genomes, uncovering novel mutational signatures, structural variants, and insights into tumor evolution\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn dogs, to date, only a single study has employed WGS to analyze six cases of multicentric B-cell lymphoma (BCL)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, this investigation was limited in scope, focusing exclusively on protein-coding gene variants and relying on a generic BCL diagnosis without histotype specification or correlation with clinical data. Additionally, the use of potentially contaminated blood samples as matched-normal DNA controls, without flow cytometric confirmation to rule out neoplastic infiltration, may have compromised the comparative analyses, further limiting the study's findings.\u003c/p\u003e \u003cp\u003eThe present study aims to provide a comprehensive characterization of the genome-wide mutational landscape of cDLBCL to elucidate the complex molecular mechanisms driving its pathogenesis. To facilitate this investigation, we selected ten cases with similar clinico-pathological features but divergent survival outcomes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eTen cDLBCL cases with matched-normal tissues were retrieved for WGS analysis. Signalment and clinical data are reported in Supplementary Data 1. The cohort consisted of 2 mixed breed dogs, 2 Dobermann, 2 Golden retrievers, and one each of West Highland White terrier, American Staffordshire, poodle and German shepherd. Six (60%) dogs were males, while 4 (40%) were females. Median age was 7 years (range: 5\u0026ndash;11 years) and median weight was 32.9 kg (range: 8.8\u0026ndash;37.6 kg). Eight (80%) dogs had stage V disease, while 2 (20%) had stage IV disease. Six (60%) dogs were asymptomatic (substage a), while 4 (40%) showed symptoms (substage b). Six (60%) dogs had bone marrow (BM) infiltration (median: 3.5%; range: 0.9\u0026ndash;47.4%), while 4 (40%) had peripheral blood (PB) infiltration (median: 1.7%; range: 0.9\u0026ndash;6.8%). Five (50%) dogs had elevated serum lactate dehydrogenase (LDH) activity, and 2 (20%) had been pre-treated with steroids. While all dogs had received chemo-immunotherapy, their treatment response varied significantly. The overall median time to progression (TTP) was 237 days, while the overall median lymphoma-specific survival (LSS) was 344 days (Supplementary Fig.\u0026nbsp;1). Based on these median values, the dogs of the cohort were categorized as either \u0026ldquo;poor\u0026rdquo; or \u0026ldquo;good\u0026rdquo; responders. Median TTP was significantly shorter (p\u0026thinsp;=\u0026thinsp;0.014) for poor responders (60 days; range: 49\u0026ndash;76 days) compared to good responders (493 days; range: 237\u0026ndash;988 days) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Likewise, median LSS was significantly shorter (p\u0026thinsp;=\u0026thinsp;0.002) for poor responders (77 days; range: 63\u0026ndash;103 days) compared to good responders (1,153 days; range: 585\u0026ndash;1,484 days) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNone of the examined variables showed a significant association with TTP in the overall population or within either group (Supplementary Data 2). Conversely, in the poor responders\u0026rsquo; group, sex, weight and pretreatment with steroids significantly affected LSS. Males (p\u0026thinsp;=\u0026thinsp;0.04), dogs weighting less than 32.9 kg (p\u0026thinsp;=\u0026thinsp;0.04) and those pretreated with steroids (p\u0026thinsp;=\u0026thinsp;0.04) experienced shorter LSS (67 days) compared to their counterparts (94 days) (Supplementary Data 2).\u003c/p\u003e \u003cp\u003eIn the good responders\u0026rsquo; group, dogs younger than 7 years exhibited shorter LSS compared to their older counterparts (676 vs 1,153 days, respectively; p\u0026thinsp;=\u0026thinsp;0.04) (Supplementary Data 2).\u003c/p\u003e \u003cp\u003eConsidering the whole cohort, LSS was significantly shorter in dogs presenting increased LDH activity (77 vs 1153 days, p\u0026thinsp;=\u0026thinsp;0.013) and in dogs that had been pretreated with steroids (67 vs 676 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Data 2).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenome-wide mutational landscape of cDLBCL\u003c/h3\u003e\n\u003cp\u003eThe median sequencing depth was 32.4X (range: 25\u0026ndash;37X) for tumors and 25.1X (range: 13.5\u0026ndash;28.5X) for normal samples. On average, 99.7% of reads aligned to the canine genome (range: 99.5\u0026ndash;99.8%), and there was a mean of 18% duplicate reads (range: 16.1\u0026ndash;22.0%). Collectively, the total number of single nucleotide variants (SNVs) and insertion/deletion variants (indels) identified across tumors ranged from 29,368 to 131,378 (median: 50,259). Of these variants, 1.0% laid within coding regions (median: 1.2%; range: 0.7\u0026ndash;1.8%), while 99% represented non-coding variants (median: 98.8%; range: 98.2\u0026ndash;99.2%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The full list of the nucleotide variants is reported in Supplementary Data 3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ecDLBCL variants in coding regions\u003c/h3\u003e\n\u003cp\u003eThe median number of exonic variants per case was 653.5 (range: 368\u0026ndash;1,545), and a total of 5,562 SNVs and 1,279 indels in coding regions were identified. Among the SNVs, 1,492 were synonymous (S), while 5,536 were nonsynonymous (NS), with 256 determining the appearance of premature stop codons. This yielded a nonsynonymous-to-synonymous (NS:S) ratio of 3.7. Protein-coding variants were retrieved in all dogs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Missense mutations were the most frequent variants (53.9%), followed by frameshift substitutions (13.2%). Inframe substitutions, nonsense mutations, stop-loss and start-loss variants were 5%, 3.6%, 0.17% and 0.16%, respectively. In addition, 2.7% of protein-coding variants were classified as \u0026ldquo;unknown\u0026rdquo; (see Material and \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePoor and good responders carried protein-coding variants in 1,910 and 2,191 genes, respectively, and 1,539 and 1,820 genes were altered exclusively in poor and good responders, respectively. Among these, 8 genes in the poor responder group (\u003cem\u003eTAF5L, CEP131, ABCA9, ETV1, PRAG1, LOC102155129, ARHGEF4\u003c/em\u003e and \u003cem\u003eLOC102153036\u003c/em\u003e) and 9 genes in the good responder group (\u003cem\u003eARFGEF3, SIPA1L3, COL12A1, ZBTB40, AMOTL1, MYO9A, DNAH17, ECEL1\u003c/em\u003e and \u003cem\u003eIGSF9B\u003c/em\u003e) were altered in at least 3 samples. \u003cem\u003eTRAF3\u003c/em\u003e and \u003cem\u003eKMT2D\u003c/em\u003e were altered in both groups.\u003c/p\u003e \u003cp\u003eBy using Sorting Intolerant From Tolerant (SIFT) tool, 1,434 missense variants were predicted as deleterious, and 1,685 as tolerated (Supplementary Data 3). The number of deleterious and tolerated missense variants was not significantly different between poor and good responders (p\u0026thinsp;=\u0026thinsp;0.6). Likewise, no statistically significant difference was retrieved in the number of truncating mutations (i.e. frameshift substitutions, nonsense and start loss variants) between the two groups (p\u0026thinsp;=\u0026thinsp;0.4).\u003c/p\u003e \u003cp\u003eAmong genes mutated in at least 3 samples, twelve genes were associated with TTP (\u003cem\u003eABCA9, ARHGEF4, CEP131, FNIP1, HMCN2, MAML2, OBSCN, PBK, SALL4, SMARCE1, TAF5L\u003c/em\u003e and \u003cem\u003eZZEF1\u003c/em\u003e). All these genes, except for \u003cem\u003eOBSCN\u003c/em\u003e, were associated with shorter TTP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Data 2). \u003cem\u003eABCA9\u003c/em\u003e, \u003cem\u003eARHGEF4, CEP131\u003c/em\u003e and \u003cem\u003eTAF5L\u003c/em\u003e were mutated in poor responders only. Instead, 11 genes were significantly associated with LSS. Specifically, \u003cem\u003eABCA9, CEP131, ETV1, NBEAL2, PRAG1, TAF5L\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e were associated with shorter LSS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Data 2). All of these genes, except for \u003cem\u003eNBEAL2\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e, were mutated exclusively in poor responders. Conversely, \u003cem\u003eAMOTL1, ECEL1, IGSF9B\u003c/em\u003e, and \u003cem\u003eSIPA1L3\u003c/em\u003e were associated with longer LSS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Data 2). These genes were mutated exclusively in good responders.\u003c/p\u003e\n\u003ch3\u003ecDLBCL variants in non-coding genome\u003c/h3\u003e\n\u003cp\u003eTo broaden the spectrum of mutations in cDLBCL beyond coding regions, we focused on variants affecting splicing and the 3\u0026rsquo;- and 5\u0026rsquo;-untranslated regions (UTRs). Splicing variants were reported in all samples (median: 10.5; range: 6\u0026ndash;30). The median number of 5\u0026rsquo;UTR and 3\u0026rsquo;UTR variants was 171.5 (range: 87\u0026ndash;411) and 404 (range: 234\u0026ndash;1,118), respectively. The splicing and UTR variants impacted 4,291 different genes. Of these, 688 had been reported in one previous canine B-cell lymphoma exome study\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and 13 were identified as recurrently mutated genes (\u0026ge;\u0026thinsp;5% of the samples) in cDLBCL (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, 224 genes were listed in the Catalogue of Somatic Mutations in Cancer (COSMIC), while 3,751 and 2,463 genes were reported in human datasets of lymphoid neoplasms and DLBCL, respectively (Supplementary Data 4). A total of 1,814 genes were exclusively altered in poor responders, compared to 1,843 that were mutated in good responders (Supplementary Data 4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenes reporting splicing and UTRs variants that are mutated in at least 5% of cases of cDLBCL from WES studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en. mutated samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReported in COSMIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman lymphoid neoplasms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehDLBCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecDLBCL (\u0026gt;\u0026thinsp;5%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLRRIQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBXW7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIVEP3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDIAPH2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTRAF3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eATXN1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKDM6A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eETV1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePOT1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePHC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIK3CD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGBE1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRARA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe median number of intronic variants per case was 19,638 (range: 11,406\u0026ndash;53,023). In total, intronic variants were reported in 14,738 different genes. Among these, 625 were listed in the COSMIC database, while 12,560 and 8,178 were reported in human lymphoid neoplasms and DLBCL datasets, respectively (Supplementary Data 5). Thirty-eight genes carrying intronic variants were recurrently mutated in cDLBCL (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Regarding oncologic outcome, 1,927 genes were exclusively altered in poor responders, compared to 1,965 that were mutated in good responders (Supplementary Data 5).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenes reporting intronic variants that are mutated in at least 5% of cases of cDLBCL from WES studies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en. mutated samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReported in COSMIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman lymphoid neoplasms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehDLBCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecDLBCL (\u0026gt;\u0026thinsp;5%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIVEP3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLRRIQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBXW7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVWF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eATXN1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANKRD11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSYNE1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGBE1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTBL1XR1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSYNE2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMYT1L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKIF21A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e 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align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAP3K14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSUZ12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTRAF3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFSIP2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTHBS2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLEC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIK3CD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSETD2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKDM6A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePOT1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFAM50A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDDX3X\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEHD3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGADD45A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMYC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCollectively, intergenic, downstream, and upstream variants ranged from 14,620 to 62,858 per sample, with a median of 24,815 variants. Variants in non-coding transcripts (those affecting exons, introns, or splicing sites of non-coding RNAs) were reported in all samples, with a median of 4,682 variants (range: 2,646\u0026ndash;12,395) affecting 8,045 different ncRNAs. To identify differentially mutated ncRNAs between poor and good responders, genes of uncertain function (LOC symbols) were not considered for further analyses. Ten ncRNAs were shared between poor and good responders, while 8 and 10 were exclusively mutated in poor and good responders, respectively. None of these was altered in more than one tumor from each group (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003ch3\u003eTumor mutational burden, microsatellite instability and mutational signatures\u003c/h3\u003e\n\u003cp\u003eThe tumor mutational burden (TMB) ranged from 12.24 to 54.74 mutations per megabase (mean: 25.10; median: 20.94) and was normally distributed within the cohort. Samples #4 and #10 presented a higher TMB compared to the other tumors, and this may be due to the presence of several mutations affecting genes involved in various DNA repair mechanisms. Sample #4 presented frameshift substitutions in \u003cem\u003eLIG1\u003c/em\u003e and \u003cem\u003eMMS19\u003c/em\u003e, involved in nucleotide excision repair (NER), and in \u003cem\u003ePNKP\u003c/em\u003e, involved in base excision repair (BER) mechanism. Sample #10 carried a frameshift substitution in \u003cem\u003eNEIL3\u003c/em\u003e, involved in NER. No significant difference in TMB was retrieved between the poor and good responder groups.\u003c/p\u003e \u003cp\u003eAll samples presented localized hypermutation compatible with kataegis (Supplementary Data 6). Six and 5 samples showed localized hypermutation in correspondence of the subtelomeric region of chromosome 8, in which maps the immunoglobulin heavy chain (IGH) locus, and on chromosome 26, in which maps the immunoglobulin light chain lambda (IGL) locus, respectively (two representative samples are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMicrosatellite instability (MSI) was detected across all samples, with a mean of 3,032.2 and 15,847.2 involved \u003cem\u003eloci\u003c/em\u003e in good and poor responders, respectively. Anyway, no statistically significant difference was retrieved between the two groups (p\u0026thinsp;=\u0026thinsp;0.4). In addition, no correlation between MSI and TMB was identified.\u003c/p\u003e \u003cp\u003eMutational signatures analysis was performed using a Bayesian non-negative matrix factorization method to evaluate the trinucleotide context of somatic SNVs. Differential exposure analysis revealed no significant difference between the two groups. The analysis of single base substitutions (SBS) signatures revealed exposure to COSMIC signatures SBS6 (defective DNA mismatch repair), SBS5 (clock-like signature of unknown etiology), SBS40c (of unknown etiology) and SBS3 (defective homologous recombination DNA damage repair) in different proportions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplementary Data 7).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSomatic copy number aberrations (SCNAs) and structural variants (SVs)\u003c/h2\u003e \u003cp\u003eAbsolute copy numbers were extracted from ASCAT segmentation files, revealing somatic copy number alterations (SCNAs) in nine tumors (median: 29; range: 9\u0026ndash;217). Sample #5 was excluded from the analysis due to insufficient sequencing depth. The analysis identified 110 deletions (median: 7; range: 2\u0026ndash;36), 309 amplifications (median: 16; range: 1\u0026ndash;147), and 85 gain/loss events (median: 6; range: 3\u0026ndash;39). Chromosome CFA31 exhibited the highest frequency of genomic rearrangements, comprising 25 amplifications, 4 deletions, and 7 gain/loss events, while CFA24 demonstrated minimal alterations with only one deletion and two amplifications (Supplementary Data 8).\u003c/p\u003e \u003cp\u003eWithin amplified regions, several genes associated with transcriptional regulation were identified, including \u003cem\u003ePOU1F1, USP16, PAXBP1, ZNF654, BACH1, CGGBP1\u003c/em\u003e, and \u003cem\u003eVGLL3\u003c/em\u003e (Supplementary Fig.\u0026nbsp;3). Conversely, genes located in deleted regions showed greater heterogeneity across tumors compared to those in amplified regions. Notable deletions were observed in \u003cem\u003eIGHM, MEF2C\u003c/em\u003e, and \u003cem\u003eIGKC\u003c/em\u003e, all of which are key components of the B-cell receptor signaling pathway (Supplementary Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eGISTIC analysis was employed to identify recurrent amplifications and deletions across the genome. In poor responders, two statistically significant deletion peaks were identified: one on CFA8 (73,027,410\u0026thinsp;\u0026minus;\u0026thinsp;73,751,733; spanning 724 kb) and another on CFA20 (5,459,601\u0026thinsp;\u0026minus;\u0026thinsp;12,452,795; spanning 7 Mb) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Good responders exhibited four statistically significant deletion peaks: two on CFA8 (72,864,243\u0026thinsp;\u0026minus;\u0026thinsp;73,365,210; spanning 501 kb, and 73,306,168\u0026thinsp;\u0026minus;\u0026thinsp;74,292,901; spanning 987 kb), one on CFA19 (19,934,979\u0026thinsp;\u0026minus;\u0026thinsp;25,959,251; spanning 6 Mb), and one on CFA26 (26,833,169\u0026thinsp;\u0026minus;\u0026thinsp;27,628,477; spanning 795 kb) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Notably, the CFA8 peaks detected in both groups showed partial overlap, corresponding to the canine IGH locus. Also, the peak on CFA26 corresponds to the IGL locus.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStructural variant analysis revealed extensive genomic alterations across all samples (median: 25,104; range: 21,715\u0026ndash;36,142) (Supplementary Data 9). The comprehensive analysis identified 68,255 inversions (median: 6,703; range: 4,399\u0026ndash;10,280), 15,990 insertions (median: 1,622; range: 1,378\u0026ndash;1,742), and 6,447 duplications (median: 656.5; range: 405\u0026ndash;854).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the last decades, the application of genome sequencing technologies to cancer research has revolutionized both diagnostics and therapeutics. These advancements have not only reinforced the understanding that cancer is fundamentally a genetic disease but have also uncovered the existence of multiple molecular subgroups within the same tumor histotype. Each subgroup may have a distinct clinical course, prognosis and response to therapy, highlighting the complexity and heterogeneity of cancer.\u003c/p\u003e \u003cp\u003eDLBCL represents the most common histotype among all hematologic malignancies in humans and dogs, and several comparative studies have highlighted both similarities and differences between the two species\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In humans, comprehensive analysis of data from hundreds of DLBCL exomes and genomes has identified numerous distinct molecular subtypes, each characterized by specific genetic aberrations and associated with varying prognosis\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the present study, 10 dogs diagnosed with DLBCL, exhibiting similar clinico-pathological variables but significantly different clinical outcomes, underwent WGS to determine whether these differences could be attributed to distinct molecular characteristics.\u003c/p\u003e \u003cp\u003eConsidering protein-coding variants, 8 and 9 genes were identified as being exclusively altered in poor and good responders, respectively.\u003c/p\u003e \u003cp\u003eAmong the genes specific to poor responders, \u003cem\u003eTAF5L\u003c/em\u003e encodes a protein that is a component of the P300/CBP-associated factor (PCAF) histone acetyltransferase complex. It has been demonstrated that TAF5L/TAF6L act as epigenetic regulators, transcriptionally activating the \u003cem\u003eMYC\u003c/em\u003e regulatory network, which is crucial for maintaining self-renewal in mouse embryonic stem cells\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCEP131\u003c/em\u003e encodes a protein involved in many cellular processes, including protein localization to centrosome. Different studies have reported that \u003cem\u003eCEP131\u003c/em\u003e functions as an oncogene, promoting carcinogenesis in breast cancer\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and tumor progression in colon cancer\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Also, \u003cem\u003eCEP131\u003c/em\u003e overexpression has been reported as a predictor of poor prognosis in hepatocellular carcinoma\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and neuroblastoma\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In our cohort, we reported 3 missense variants in \u003cem\u003eTAF5L\u003c/em\u003e and 2 missense variants and an inframe substitution in \u003cem\u003eCEP131\u003c/em\u003e. Since the impact of non-truncating variants is challenging to predict, it will be essential to investigate whether these variants lead to alterations in gene and protein expression. This will help determine if they play a role in contributing to the worse clinical outcome observed in these dogs. \u003cem\u003eABCA9\u003c/em\u003e encodes a gene belonging to the superfamily of ATP-binding cassette (ABC) transporters. One study reported that in a subset of hepatocellular carcinoma human patients, \u003cem\u003eABCA8\u003c/em\u003e and \u003cem\u003eABCA9\u003c/em\u003e downregulation was significantly associated with shorter survival time\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In our cohort, 3 dogs in the poor responders group carried \u003cem\u003eABCA9\u003c/em\u003e mutations, specifically two frameshift substitutions and one nonsense variant, which represent loss-of-function mutations likely associated with reduced protein expression. Additionally, \u003cem\u003eETV1\u003c/em\u003e is the only gene listed in the COSMIC database, and chromosomal translocations involving this gene have been implicated as causative factors in Ewing Sarcoma\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and prostate cancer\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong the genes found exclusively in the good responder group, \u003cem\u003eCOL12A1\u003c/em\u003e upregulation has been identified as a predictor of poor prognosis in human patients with pancreatic adenocarcinoma\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and gastric cancer\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Meanwhile, \u003cem\u003eAMOTL1\u003c/em\u003e has been reported as recurrently mutated in splenic marginal zone lymphoma\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo expand the spectrum of cDLBCL mutations beyond coding regions, we first interrogated splicing and UTR variants, that could affect proper gene expression in different ways. In our cohort, 13 genes previously identified as recurrently mutated in cDLBCL\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e exhibited splicing and/or 5\u0026rsquo;- and 3\u0026rsquo;UTR variants.\u003c/p\u003e \u003cp\u003eIn the poor responder group, \u003cem\u003eFBXW7\u003c/em\u003e exhibited a splicing variant and two 3\u0026rsquo;UTR variants, while \u003cem\u003eDIAPH2\u003c/em\u003e had both a 3\u0026rsquo;- and a 5\u0026rsquo;UTR variant. Additionally, \u003cem\u003eKDM6A\u003c/em\u003e contained two 3\u0026rsquo;UTR variants in the same dog (sample #8).\u003c/p\u003e \u003cp\u003eIn the good responder group, \u003cem\u003eTRAF3\u003c/em\u003e carried two 3\u0026rsquo;UTR variants in sample #2 and a splicing variant in sample #4. Moreover, \u003cem\u003eRARA\u003c/em\u003e presented a 5\u0026rsquo;UTR variant, while \u003cem\u003ePOT1\u003c/em\u003e, \u003cem\u003ePIK3CD\u003c/em\u003e and \u003cem\u003eGBE1\u003c/em\u003e presented a 3\u0026rsquo;UTR variant each. Biologically, splicing variants can disrupt an existing splice site or introduce a new one, leading to various outcomes such as exon skipping, intron retention, or the utilization of alternative splicing sites located within introns\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. UTR variants can influence gene expression with different mechanisms, including alternative splicing, reduced translational efficiency, and altered interactions with proteins and miRNAs\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Although these results suggest a potential role for these types of genetic aberrations in the pathogenesis and prognosis of cDLBCL, further genetic screening involving a larger number of cases, along with \u003cem\u003ein vitro\u003c/em\u003e functional studies, are necessary to validate this hypothesis.\u003c/p\u003e \u003cp\u003eNon-coding mutations can contribute to tumorigenesis through the alteration of \u003cem\u003ecis\u003c/em\u003e-regulatory elements, including promoters and distal enhancers, the disruption of chromatin 3D structure and altered function of regulatory non-coding RNAs (ncRNAs)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In humans, the analysis of 2,658 cancer genomes across 38 different tumor types identified somatic non-coding driver mutations\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In DLBCL, hypermutation of active super-enhancers linked to known proto-oncogenes has been identified as a novel mutational mechanism involved in the pathogenesis of this tumor\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our cohort, we identified variants in intronic, intergenic, and ncRNAs regions. However, the limited number of cases prevented us from identifying recurrent mutations that may affect regulatory regions or from distinguishing driver events from passenger mutations. Comparative studies have shown that many regulatory elements are located within evolutionarily conserved regions known as conserved non-coding elements (CNEs). Disruption of these CNEs has been implicated in developmental disorders and cancer\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTMB, mutational signatures and MSI were also examined. Although TMB did not differ significantly between poor and good responders, two samples (sample #4 and sample #10) exhibited markedly higher TMB compared to the others. This is likely due to the presence of loss-of-function mutations in genes involved in NER and BER pathways. Likewise, there was no significant difference in MSI, nor was MSI correlated with TMB in the present cohort. In human oncology, TMB and MSI have emerged as valuable biomarkers for identifying patients who may benefit from immunotherapy\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. A recent study analyzed MSI in 692 canine tumors across 10 different histotypes, revealing that B-cell lymphomas presented the highest number of \u003cem\u003eloci\u003c/em\u003e affected by MSI\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. These results suggest that a larger cohort of cDLBCL should be analyzed for MSI to test potential correlations with prognosis and explore the possibility that high-MSI dogs could benefit from immunotherapies targeting the PD-1/PDL-1 axis.\u003c/p\u003e \u003cp\u003eThe assessment of mutational signatures did not show substantial differences between poor and good responders. The signatures that predominantly contributed to the mutation spectra were identified as COSMIC SBS6 (associated with defective DNA mismatch repair), SBS5 (a clock-like signature of unknown etiology), SBS40c (also of unknown etiology) and SBS3 (linked to defective homologous recombination DNA damage repair). This contrasts with the findings of Giannuzzi et al. (2022)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, where the analysis of 77 cDLBCL through WES identified SBS1 (age-related spontaneous deamination of 5\u0026rsquo;-methylcytosine) as the predominant signature. A lack of correspondence in mutational signatures between WGS and WES has been previously noted in canine osteosarcoma by Gardner et al. (2019)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, who suggested that this discrepancy may be due to the higher sequencing depth typically achieved with WES.\u003c/p\u003e \u003cp\u003eSCNAs represent a common event in cancer, and recurrent alterations associated with specific histotypes have been identified\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In dogs, SCNAs and SVs have been described in osteosarcoma\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, melanoma\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, hemangiosarcoma\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e and mammary tumors\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In our cohort, a recurrent deletion peak on CFA20 was observed in poor responders, while two recurrent deletion peaks on CFA19 and CFA26 were identified in good responders. Additionally, recurrent deletions affecting the sub-telomeric region of CFA8, which corresponds to the canine IGH locus\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, were reported in both groups, likely due to V(D)J genes rearrangement. Among the 56 genes located within the CFA20 deletion, germline variants in \u003cem\u003eMBD4\u003c/em\u003e, a gene encoding for a mismatch-specific DNA N-glycosylase, have been reported as a predisposing factor for acute myeloid leukemia\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Additionally, \u003cem\u003eMBD4\u003c/em\u003e deficiency has been identified as a predictive marker for response to immune checkpoints inhibitors in uveal melanoma\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eVGLL4\u003c/em\u003e loss was correlated with the suppression of PDL-1 expression\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Inactivating mutations in the tumor suppressor gene \u003cem\u003eVHL\u003c/em\u003e have been associated with several diseases, including pheochromocytoma, erythrocytosis, renal cell carcinoma, and cerebellar hemangioblastoma\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Additionally, \u003cem\u003eFANCD2\u003c/em\u003e deletions are implicated in Fanconi anemia, a genetic disease characterized by chromosomal instability and defective DNA repair\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn conclusion, our study provides a comprehensive view of the genomic landscape of cDLBCL at a genome-wide resolution. Despite the main limitations being the small sample size and relatively low coverage, we extended the analysis beyond coding regions, uncovering a broader spectrum of potentially pathogenic variants in genes already known to be recurrently mutated in cDLBCL. Additionally, we identified novel candidate genes that may play a role in the disease's pathogenesis. Further studies with larger cohorts are needed to validate these findings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSamples\u003c/h2\u003e \u003cp\u003eTen cDLBCL were selected from the archive of the Canine Lymphoma Biobank\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The study and all methods are reported in accordance with ARRIVE guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arriveguidelines.org\u003c/span\u003e\u003cspan address=\"https://arriveguidelines.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), where applicable. The study did not fall within the application areas of Italian Legislative Decree 26/2014 which governs the protection of animals used for scientific or educational purposes; therefore, ethical approval was waived for this study by Department of Veterinary Sciences at University of Turin. Dogs were treated according to the current standards, and dog owners gave written informed consent. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003cp\u003eTo be included in the analysis, dogs had to be treated with chemoimmunotherapy consisting of the administration of the APAVAC vaccine in addition to a standardized CHOP-based protocol as previously described\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. The following patient demographics and clinico-pathological features were available: clinical stage, substage, immunophenotype determined by flow cytometry (FC) on a neoplastic lymph node (LN) aspirate, infiltration of peripheral blood (PB) and bone marrow (BM) determined by flow cytometry, serum LDH activity and whether the dogs had been pre-treated with steroids. All dogs underwent lymphadenectomy for routine histologic analysis and immunohistochemistry (CD3 and CD20), vaccine preparation, and DNA extraction. Prior to surgery, dogs were fasted for at least 12 hours. Sedation and anesthesia were achieved through premedication with dexmedetomidine (3\u0026ndash;5 \u0026micro;g/kg IM) and methadone (0.2\u0026ndash;0.3 mg/kg IM). Induction was performed using propofol (2\u0026ndash;4 mg/kg IV, titrated to effect), followed by intubation once the dogs were unconscious. Anesthesia was maintained with isoflurane or sevoflurane in 100% oxygen, and IV fluids were administered at 3\u0026ndash;5 mL/kg/h for cardiovascular support. Continuous intraoperative analgesia was provided via a fentanyl CRI (5 \u0026micro;g/kg/h IV). A skin-punch biopsy was also collected from each dog to provide matched-normal tissue. TTP, LSS and cause of death were also available. TTP was measured as the time between the start of treatment and disease progression, while LSS was calculated from the start of treatment to death due to lymphoma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDNA isolation and sequencing\u003c/h2\u003e \u003cp\u003eTotal DNA was extracted from tumor samples and matched-normal tissues, using the AllPrep DNA/RNA Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s instructions. DNA concentration and quality were assessed by Qubit fluorometer (Life Technologies Ltd, Paisley, UK) and agarose gel electrophoresis. Twenty high quality WGS libraries were prepared using the Illumina-compatible KAPA HyperPlus Library Preparation Kit (Roche Sequencing and Life Science, Wilmington, MA). Libraries were quantified through a Qubit 2.0 Fluorometer using the Qubit DNA Assay Kit (Thermo Fischer, Foster City, CA, USA) and quality was assessed using the Bioanalyzer 2100 instrument (Agilent Technologies, Santa Clara, CA, USA). Ten libraries (fragments ranging from 300 to 400 bp) were then pooled and sequenced on an Illumina NovaSeq 6000 platform in a paired-end (150 PE) mode with an average coverage of 30X. Raw Illumina sequencing data are deposited in BioProject database with BioProject ID PRJNA805123.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWGS data pre-processing\u003c/h2\u003e \u003cp\u003eFirstly, all raw sequencing data underwent a quality check using fastqc software to assure that no errors occurred during the preprocessing step. They were then processed according to the Genome Analysis Toolkit (GATK)\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e preprocessing pipeline, which is gold standard in human and veterinary medicine. Briefly, reads were aligned to the reference genome (canFam3.1, UCSC) using Burrow Wheeler Aligner (BWA)\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, converted to bam format (Samtools), sorted by coordinate (Samtools), read groups were added (Picard) and duplicates were marked (Picard). Finally, all data underwent Base Quality Score Recalibration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSomatic variants calling\u003c/h2\u003e \u003cp\u003eSomatic variant calling was performed to access SNVs and indels. First, healthy tissue sequencing data were elaborated using Mutect to collect Single Nucleotide Polymorphisms (SNPs) and artifacts for Panel of Normal (PON). The PON, together with DogSD database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/idog/index.jsp\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/idog/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), was used to clean artifacts and polymorphisms in the following variants calling. Variants calling was performed using Mutect2\u003csup\u003e55\u003c/sup\u003e from GATK, with standard parameters, and annotated with ANNOVAR\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Short variants were classified as \u0026ldquo;UNKNOWN\u0026rdquo; if, as reported in ANNOVAR documentation, [a transcript maps to multiple locations, all as \"coding transcripts\", but none has a complete ORF].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTMB, mutational signatures and MSI\u003c/h2\u003e \u003cp\u003eTMB was calculated for each sample by dividing the total number of called variants (both coding and non-coding) for the size in Mb of the haploid canine reference genome (CanFam3.1, 2.4 Gb) and reported as number of mutations/Mb ratio. Mutational signatures were extracted using the \u003cem\u003eSigminer\u003c/em\u003e R package. These signatures were then confronted against COSMIC signatures\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e to extract valuable insight. The \u003cem\u003emaftools\u003c/em\u003e R package\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e was used to generate rainfall plots and to identify areas of kataegis. \u003cem\u003eMsiSensor-Pro\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e was used to perform MSI analysis as previously described\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSomatic copy number aberrations (SCNAs) and structural variants (SVs)\u003c/h2\u003e \u003cp\u003eCopy Number Aberrations were accessed using ASCAT segmenter\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e (version available in \u003cem\u003eEaCoN\u003c/em\u003e R package). Segmented data underwent tumor purity and ploidy evaluation to estimate the major and minor allele number of copies. Additionally, segmented data were also used to run GISTIC2\u003csup\u003e61\u003c/sup\u003e peaks calculation. Both peaks and CNA regions were annotated using GTF file from UCSC and custom R script based on \u003cem\u003ertracklayer\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eGenomicRanges\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e packages. Finally, structural variants (SV) were accessed using \u003cem\u003eDelly\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e and annotated with \u003cem\u003eSansa\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSurvival analysis was conducted using \u003cem\u003esurvival\u003c/em\u003e and \u003cem\u003esurvminer\u003c/em\u003e R packages. Median TTP and LSS of the whole cohort were used as cutoff values to divide the dogs into \u0026ldquo;good\u0026rdquo; and \u0026ldquo;poor\u0026rdquo; responders. Survival curves were constructed using the Kaplan-Meier method and compared by means of log-rank test. All the available clinico-pathological variables, mutated genes and TMB were tested for their influence on both TTP and LSS by means of univariate Cox proportional-hazards model. Dogs lost to follow-up or dead for lymphoma-unrelated causes before disease progression were censored for TTP analysis. Dogs dead from lymphoma-unrelated causes were censored for LSS analysis. Co-occurrence and mutual exclusivity were evaluated using the \u003cem\u003eMaftools\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e R package. Conversely, the Shapiro-Wilk test was employed to test MSI burden and TMB for normal distribution. Differences between good and poor responders with respect to MSI burden and TMB were assessed by Wilcoxon rank-sum test. For categorical variables, Fisher\u0026rsquo;s exact test was conducted to assess possible associations with treatment response. Differences in TMB across treatment response (\u0026ldquo;good\u0026rdquo; vs \u0026ldquo;poor\u0026rdquo;), breed (pure vs mixed), sex (female vs male), age (\u0026lt;\u0026thinsp;7 years vs\u0026thinsp;\u0026ge;\u0026thinsp;7 years), weight (\u0026lt;\u0026thinsp;32.9 kg vs\u0026thinsp;\u0026ge;\u0026thinsp;32.9 kg), stage (IV vs V), substage (a vs b), BM infiltration (\u0026lt;\u0026thinsp;3% vs\u0026thinsp;\u0026ge;\u0026thinsp;3%), PB infiltration (\u0026lt;\u0026thinsp;3% vs\u0026thinsp;\u0026ge;\u0026thinsp;3%), LDH activity (normal vs increased) and pretreatment with steroids (yes vs no) groups were assessed by Student t-test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.A. and L.M. designed the study; L.M. provided samples and clinical data; D.G. performed library preparation and sequencing experiments and contributed to data analysis; E.M. performed bioinformatic and statistical analyses and data visualization; L.A. and A.F. interpreted the data and wrote the manuscript; all authors contributed to manuscript revision and approved the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw Illumina reads of whole genome sequencing are publicly available in BioProject database with BioProject ID PRJNA805123.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u0026nbsp;\u003c/strong\u003eaccompanies this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZandvliet, M. 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DELLY: structural variant discovery by integrated paired-end and split-read analysis. \u003cem\u003eBioinformatics\u003c/em\u003e 28, i333-i339 (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/bts378\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/bts378\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"dog, diffuse large B-cell lymphoma, DLBCL, whole genome sequencing, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-5348393/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5348393/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) is one of the most prevalent hematological malignancies in both humans and dogs, characterized in both species by significant clinical heterogeneity and limited prognostic predictability. With the introduction of next-generation sequencing (NGS) technologies in veterinary medicine over the past decade, researchers have begun to elucidate the molecular basis of canine DLBCL (cDLBCL); however, much of the clinical heterogeneity associated with this tumor remains unexplained.\u003c/p\u003e \u003cp\u003eIn this study, we performed whole genome sequencing on 10 cDLBCL cases, all treated with chemo-immunotherapy, which exhibited similar clinico-pathological features but markedly different outcomes. Cases were classified as \"poor\" or \"good\" responders based on whether their lymphoma-specific survival fell below or above the cohort's median. Protein-coding variants and copy number aberrations unique to poor or good responders revealed novel candidate genes not previously identified in cDLBCL studies, while splicing, untranslated regions, and intronic variants were detected in genes already known to be recurrently mutated.\u003c/p\u003e \u003cp\u003eIn conclusion, our investigation has broadened the spectrum of potentially pathogenic variants implicated in cDLBCL, though further studies with larger cohorts are necessary to validate these findings.\u003c/p\u003e","manuscriptTitle":"Expanding the spectrum of canine Diffuse Large B-cell Lymphoma (cDLBCL) genetic aberrations through whole genome sequencing (WGS) analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 22:58:49","doi":"10.21203/rs.3.rs-5348393/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17311a2b-e424-4158-801b-479cd2775c1d","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40071469,"name":"Biological sciences/Cancer/Haematological cancer/Lymphoma/Non hodgkin lymphoma/B cell lymphoma"},{"id":40071470,"name":"Health sciences/Oncology/Cancer/Cancer genomics"}],"tags":[],"updatedAt":"2024-12-10T06:09:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-02 22:58:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5348393","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5348393","identity":"rs-5348393","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}