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      Transient paraproteinemia after allogeneic hematopoietic stem cell transplantation is an underexplored phenomenon associated with graft versus host disease

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          The clinical and biological relevance of a paraprotein that newly arises after allogeneic hematopoietic stem cell transplantation (allo-HSCT) in non-myeloma patients is unknown. In this study, the incidence, the course, and the clinical impact of paraproteins found after allo-HSCT were investigated in a cohort of 383 non-myeloma patients. Paraproteinemia after allo-HSCT was more frequent (52/383 patients, 14%) than the reported incidence of monoclonal gammopathy of unknown significance (MGUS) in age-matched healthy subjects and, in contrast to MGUS, did not correlate with age. In most patients (32/52, 62%), the paraprotein appeared transiently within the first year after allo-HSCT with a median duration of 6.0 months. Post-allo-HSCT paraproteinemia was significantly associated with graft versus host disease (GvHD) and correlated with a survival benefit within the first year, but not after five years following allo-HSCT. Importantly, patients with post-allo-HSCT paraproteinemia did not progress into a plasma cell myeloma as observed for MGUS inferring a distinct pathogenic mechanism. Skewing of lymphocyte subpopulations and alterations in cytokine levels in GvHD may explain the expansion of a specific plasma cell subset in non-myeloma patients undergoing allo-HSCT. Our data suggests that paraproteinemia after allo-HSCT is a reactive phenomenon rather than the consequence of clonal plasma cell transformation.

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          Heterogeneity of genomic evolution and mutational profiles in multiple myeloma

          Multiple myeloma (MM) is a malignant monoclonal plasma cell disorder whose pathogenesis is only partially understood. MM is classically subdivided into subtypes with rearrangements involving the immunoglobulin heavy (IGH) locus and a hyperdiploid subtype, which harbours multiple trisomies1. These chromosomal abnormalities are, however, insufficient for malignant transformation, as they are also observed in monoclonal gammopathy of unknown significance, a premalignant syndrome that precedes virtually every MM case2 3. MM undergoes a multistep transformation process and its genetic landscape changes over time due to additional events such as somatic mutations, epigenetic and chromosomal copy-number changes driving its progression from monoclonal gammopathy of unknown significance to symptomatic MM and ultimately to aggressive extramedullary disease in some patients4. Sequencing efforts in relatively small cohorts of MM samples have already been undertaken, suggesting that MM show a heterogeneous subclonal structure at diagnosis and only few recurrent mutated genes of likely pathogenetic significance, including KRAS, NRAS, TP53, BRAF and FAM46C 5 6 7 8. Interestingly, single nucleotide polymorphism (SNP) arrays on serial samples showed that molecular events necessary for MM progression are not attained in a linear fashion but rather through branching, nonlinear pathways9 10, a pattern typical of a complex ecosystem of clones competing for evolution. The quantitative nature of next generation sequencing (NGS) data allows for higher resolution of the subclonal architecture of cancers11 and its monitoring over time with implication for prognostic stratification, tumour monitoring and emergence of chemoresistance12 13. Nevertheless, initial reports of genomic evolution in MM using NGS were conducted again on small cohorts6 8, thus limiting their relevance to the broadly heterogeneous spectrum of myeloma samples. Importantly, NGS data can also be used to decipher mutational signatures, a combination of mutated nucleotides in a specific context that can inform about mutational processes operative in each cancer and thus offer a mechanistic explanation for the mutations found in the sample14. Recent reports suggested that different processes are operative in MM15 16 17, but their relative representation in each sample, the dynamics of their contribution over time and their relation to clinical features are unknown. Here we show the largest cohort to date of MM patients where we combine cytogenetic, copy number and whole-exome-sequencing analysis. We show that most patients have a complex subclonal structure at diagnosis, which evolves further after treatment in the majority of them. We identify mutational processes leading to the generation of MM heterogeneous mutational repertoire, and how they dynamically evolve over time. We describe new candidate driver gene mutations that could inform about disease pathogenesis and prognosis. Results Landscape of genetic alterations in MM We analysed 84 samples from 67 patients with MM using exome sequencing, high-resolution copy-number arrays and cytogenetics (Supplementary Tables S1 and S2). For 52 patients, one sample was available (pretreatment in 51 cases). We collected serial samples in 15 patients, starting at disease progression or relapse post treatment in 14 of them, with later time points always collected at relapse/progression after further lines of treatment (Table 1). The median interval between samples was 299 days (range 77–618, Supplementary Table S1). Exome sequencing, performed at a total average depth of 236 × (Supplementary Table S3), allowed us to identify and validate 4,417 variants (range 21–488, median 52 per patient) (Fig. 1a, Supplementary Data 1). Non-silent coding variants (that is, missense, nonsense, indel, splice) were enriched over silent ones (that is, synonymous, intronic, untranslated region) (Fig. 1b). Ranking by mutation type showed an excess of C>T transitions (Fig. 1c). We identified 183 homozygous deletions totalling 35.4 Mb (Supplementary Data 2). From copy-number data, 40/67 patients harboured a hyperdiploid karyotype (Supplementary Table S2). Fluorescence in situ hybridization (FISH) analysis showed 15/67 patients were positive for IGH rearrangements: 6/67 for t(4;14), with increased expression of FGFR3 and MM SET domain; 7/67 for t(11;14), with consequent upregulation of cyclin D1; and 2/67 for t(14;16), involving C-maf. Two hyperdiploid samples also carried a subclonal t(11;14) translocation by FISH (Supplementary Table S2). In 12 patients, FISH on IGH rearrangements other than t(4;14) was not available. Other copy-number alterations such as del(1p), (+1q), del(12p), del(13), del(14q), del(16q), del(17p) were found in both hyperdiploid and IGH rearranged cases. The combination of whole-exome sequencing, FISH and copy-number analysis provided an integrated snapshot of the complexity of alterations affecting the myeloma coding genome. Overall, the detection of such a large burden of somatic variants and copy-number alterations, with high variability across the 67 individuals studied, indicates that the genetic landscape of myeloma is remarkably heterogeneous and that largely distinct sets of chromosomal rearrangements and gene mutations are present in individual patients. Modelling clonal and subclonal mutation clusters Cancer evolves through a Darwinian process of clonal expansion, and the population of cancer cells represents an admixture of competing subclones. We explored the clonal structure of our cohort using SNP arrays and whole-exome sequencing data to identify subclonal copy-number changes, simultaneously estimating and adjusting for tumour ploidy and normal cell contamination. A number of cases showed subclonal gains or losses of large-scale genomic regions (examples shown in Supplementary Fig. S1A). A similar analysis was carried out for point mutations by calculating the 95% confidence interval for the fraction of tumour cells carrying each mutation (again adjusting for the percentage of contaminating normal cells of the sample and the copy number of the locus). Each mutation was classified as clonal (that is, present in all tumour cells) if the upper bound of the 95% confidence interval included 1, and subclonal otherwise. We plotted, for each patient, the absolute number and proportion of clonal and subclonal variants and showed that almost every patient carries variants present in a fraction of tumour cells, implying ongoing tumour evolution at the time of sampling (Fig. 2a). We then clustered the fraction of tumour cells carrying each mutation using a Bayesian Dirichlet process, to reflect the clonal composition of the tumour. We showed that all patients carry a cluster of variants that are clonal (that is, at 1.0 on the x axis in Fig. 2b–d, Supplementary Fig. S1B). Rarely, patients had only few subclonal variants and no significant clustering at the subclonal level (Fig. 2b), whereas most patients showed a major cluster of clonal mutations and one or more clusters of subclonal variants (Fig. 2c, Supplementary Fig. S1B). Interestingly, few patients had many more subclonal mutations than clonal, in several clusters (Fig. 2d), indicating a complex dynamic of tumour evolution from the most recent common ancestor—the most recent tumor cell that has the full complement of somatic mutations found in all tumour cells—through acquisition of new variants in different subclones. We next turned our attention to substitutions in known driver genes in myeloma5 6 7. Interestingly, for each of the known driver genes in myeloma (KRAS, NRAS, BRAF), we found both patients in whom the mutations were clonal and patients in whom they were subclonal (Fig. 2e). Therefore, even variants with known driver potential can be acquired late in myeloma evolution, implying striking variability across patients as to which are early molecular events and which arise later during tumour progression. Another layer of complexity arose in 5/67 samples that surprisingly showed overlap of two or more driver substitutions in KRAS, NRAS or BRAF (Fig. 2e, patient IDs in red). In PD5878, it can be concluded that KRAS and BRAF both coexisted in the main clone of the tumour, and were therefore both present in all tumour cells. In PD4289, KRAS was present in all tumour cells, while a subclone of cells containing a BRAF mutation evolved later. In other cases, both variants were present at the subclonal level, making it impossible to resolve whether they belong to the same or different clones. If they were in different clones, this would be suggestive of convergent evolution, that is, two different subclones independently acquiring mutations activating the same pathway, as reported in ALL18, pancreatic cancer19 and renal cancer20. Clonal evolution We next analysed cases with serial samples. Through analysis of the copy-number patterns, we could track clonal evolution in large-scale genomic aberrations over time (Fig. 3a–d). We also clustered point mutations based on clonality again using a Bayesian Dirichlet process, extended into two dimensions to give insights of the changes in the clonal composition of each tumour over time. We plotted the fraction of tumour cells harbouring each variant on the x axis for the ‘early’ sample, and on the y axis for the ‘late’ sample (Fig. 3ai–di; Supplementary Fig. S2A). We observed four major patterns in these paired samples, instructive of genomic evolution over time and possibly across sampling sites21. In the first pattern we observed, in 5/15 patients, the various clonal and subclonal clusters were found on the leading diagonal of the plots (Fig. 3ai) suggesting there was no change in either the mutational profile or the clonal and subclonal composition between the two time points (Fig. 3aii). This is rather remarkable, given that these five patients were all treated between the early and late samples, and indeed some had a substantial response as measured by paraprotein levels (Supplementary Table S1). The lack of change in mutational composition suggests that various subclones repopulating the myeloma at relapse were equivalently affected (or unaffected) by treatment. Second, we observed ‘differential clonal response’ in the tumour, in which each subclone was identified at the two time points, but their relative proportions changed over time (Fig. 3bi,ii). These changes might reflect random drift of subclones over time, differential response among subclones to chemotherapy or clonal expansion due to selective advantage of one subclone over the others. This pattern was found in 4/15 patients. Interestingly in PD4292, for which three samples were available, we observed differential clonal response in ‘early’ versus ‘late’ and ‘late versus later’ samples. Nevertheless, the drift of subclones at the two intervals was in opposite direction, so that there was actually no change between the first and third sample (Supplementary Fig. S2A). This illustrates how different subclones can show alternating dominance over time, likely in this case influenced by treatment. Third, we observed linear evolution in 2/15 patients, in which a new subclone emerged in the late sample that was not evident despite the deep sequencing in the earlier sample (Fig 3ci,ii). Fourth, we found evidence of branching evolution in 4/15 patients (Fig. 3di,ii). Here, in the time between the early and late time points, one or more new clones have emerged, whereas others have declined in frequency or disappeared. We found striking concordance between point mutations and copy-number changes (Fig. 3a–d, Supplementary Fig. S2B), so that karyotypic changes of prognostic value were only observed between paired samples showing linear or branching evolution (P=0.0002, Fisher exact test). This concordance suggests that the mutational processes acting at the level of point mutations and at the level of aneuploidy in myeloma are broadly in concert over time and across subclones. Surprisingly, the pattern of genomic evolution could not be predicted by response to treatment, interval between sampling or treatment type. We noted that 4/5 cases with the t(11;14) translocation showed no change, as compared with only 1/9 in the hyperdiploid group (P-value from Fisher test =0.023, Fig. 3f), suggesting that cytogenetic subtypes can show different evolution in response to treatment. Cases showing either branching evolution or differential clonal response highlight how current anti-myeloma treatment can have a differential effect across subclones, suppressing some and leaving others untouched. While this could have implications for the choice of treatment modalities and for the definition of disease response, larger sample sizes would be important to explore these points in more detail. In our cohort of 15 patients with serial samples, we found no differences in the survival based on the type of genomic evolution shown at relapse. Interestingly, the two most distinct examples of branching evolution (PD4283 and PD4301) came from patients where the late samples represented an extramedullary relapse of myeloma (secondary plasma cell leukaemia and malignant ascites, respectively). Not surprisingly, acquired variants in the late samples included mutations in genes with known oncogenic potential in MM. In the plasma cell leukemia sample, we found a TP53 mutation and a homozygous deletion of CDKN2C (Fig. 3di,ii). In the ascites relapse, there were new clonal mutations in NRAS, NFKBIA and FAM46C (Supplementary Fig. S2). Finally, we looked at mutations in five known driver myeloma genes (KRAS, NRAS, TP53, BRAF, FAM46C) across all patients with serial samples, and identified 12 non-silent variants. Six out of twelve were clonal at both time points. Of the remaining, 4/6 appeared in the late sample with no evidence in the early sample despite high-sequence coverage, and 2/6 were subclonal at the initial time point and increased their clonal fraction at the later time point, consistent with the expected positive selection for the subclones harbouring them (Supplementary Fig. S2C). Different mutational processes active in MM The catalogue of somatic mutations in a cancer is the aggregate outcome of strength and duration of exposure to one or more mutational processes. Each process generates mutations characterized by a specific combination of nucleotide change and nucleotide context, therefore providing a ‘signature’ that can be used for its identification. Because there are six classes of base substitution (that is, C>A, C>T, C>G, T>A, T>C and T>G; all in pyrimidine context) and 16 possible sequence contexts for each, there are 96 possible mutated trinucleotides, whose relative contribution we represented as a heatmap for each case (Supplementary Fig. S3A). We then employed a nonnegative matrix factorization (NMF) and model selection approach to extract mutational signatures from all cases22. Evaluation of NMF decompositions suggested that at least two biologically distinct mutational signatures were present (Fig. 4a). Each signature was characterized by a different profile of the 96 potential trinucleotide mutations and contributed to a different extent to each cancer. The most represented signature, named Signature A (Fig. 4a), is a rather generic mutation signature with enrichment of C>T transitions at CpG dinucleotides, an intrinsic mutational process reflecting spontaneous deamination of methylated cytosine to thymine. A similar signature is dominant in myeloid malignancies23 24, a major contributor to early mutations in breast cancer11 22 and seen at high rates in the germline25. Potentially, other signatures operative in MM could be admixed in Signature A, but larger studies will be required to pursue this observation. A second signature, namely C>T, C>G and C>A mutations in a TpC context (Signature B, Fig. 4b), was the major contributor of mutations in a few samples (Fig. 4c). In PD5863 and PD5874, the two patients with the highest overall number of variants (Fig. 1a), virtually all of the mutational repertoire could be attributed to Signature B. While mutations attributed to Signature B were generally spread across the exome, in two samples we found clusters of 4–6 cytosine mutations at TpC dinucleotides within an interval of few hundred bp that also showed strand specificity, indicative of a process known as ‘kataegis’22 (Supplementary Fig. S3B, arrows). Both the genome-wide and the clustered variants of this mutational signature were first documented in breast cancer11 22 26, and are hypothesized to result from the aberrant activity of APOBECs, a family of proteins that enzymatically modify single-stranded DNA27. While the genome-wide variant of Signature B was recently described in MM15 16, here we report the first evidence of clusters of kataegis in this disease. Kataegis was not the only process leading to regional clustering of mutations in the cohort. We found clusters of mutations in the first exon of CCND1 in two cases (Supplementary Fig. S3C, arrows), both characterized by the t(11;14) translocation, juxtaposing CCND1 with the IGH locus. Interestingly, selection analysis of the mutations in CCND1 in our cohort revealed that the observed mutations reflected a local mutation rate much higher than the genomic average (Supplementary Table S4). The breakpoints on 11q13 are dispersed over 330 kb centromeric to CCND1 (ref. 28), and thus we do not believe that this could arise from the proximity of CCND1 to the site of rearrangement. Rather, the even representation of cytosine and adenine mutations and the high synonymous to non-synonymous ratio suggest CCND1 mutation clusters may result from somatic hypermutation driven by the AID protein29 30, as previously described for CCND1 itself in mantle cell lymphoma31 and for BCL2 in follicular lymphomas32. We found that CCND1 was significantly enriched for mutations in AID recognition motifs (Supplementary Fig. S3C), further reinforcing this hypothesis. Last, we analysed the contribution of each signature, over time or across subclones, in seven patients where the number of variants was high enough to allow a statistical analysis. We observed a significant change in contribution from the two signatures in 5/7 patients. In both cases of extramedullary relapse showing branching evolution, the contribution from Signature B increased significantly at relapse (Fig. 4d), and in PD4301d, clustered in a pattern consistent with kataegis (Supplementary Fig. S3Bi). Conversely, in the five other samples, we found that the contribution of Signature B could either increase or decrease over time or in different subclones (Supplementary Fig. S3D). Overall, the representation of Signature B at relapse was not predictive of the type of genomic evolution shown or of survival. In summary, the MM genome is shaped by several different mutational processes. These are likely to include spontaneous deamination of methylated cytosine, kataegis and somatic hypermutation. The scale of such mutational processes can vary from genome wide to localized clusters of events, and frequently changes over time. Further studies will be required to ascertain whether the relative contribution of different mutational processes can influence clinical outcome or can be influenced by treatment. Recurrently mutated genes in MM We next analysed our data set to identify genes that were mutated with a significant recurrence rate, and could therefore represent novel candidate driver genes in MM. We applied a frequentist likelihood ratio test to highlight genes with increased numbers of non-synonymous substitutions and indels, relative to that expected for the synonymous mutation rate. Through this algorithm, we found seven genes recurrent with a high level of confidence (false discovery rate (FDR) 0.10, see ‘Statistical analysis of recurrently mutated genes’ under Methods) to identify novel genes involved in MM, we highlighted a number of candidates (Supplementary Table S4). ROBO1, a transmembrane receptor implicated in beta-catenin and MET signalling and recently reported as mutated in pancreatic cancer42, was mutated in 5/67 patients. Strikingly, three of these mutations were truncating, consistent with the copy-number loss seen in pancreatic cancer (Fig. 6). The EGR1 gene, which encodes the early growth response one transcription factor, carried four missense mutations (Fig. 6), as well as being mutated in two additional myeloma patients in the literature5. FAT3, a transmembrane protein belonging to the Cadherin superfamily43 showed a homozygous deletion, one nonsense and three missense mutations (Fig. 6). Looking at genes targeted by both mutations and copy-number variations, we highlighted TGDS, a gene encoding enzyme involved in nucleotide sugars metabolism44 located in chromosome 13, and SNX7, a member of the sorting nexin family involved in intracellular trafficking45 located in 1p21.3. Both genes reside in commonly deleted regions in myeloma, and both showed two truncating and one missense mutations associated with loss of the WT allele (Fig. 5a,b). Finally, we found a cluster of mutations involving two neighbouring paternally imprinted genes that share transcriptional regulation46 in 6/67 patients. PEG3, a gene involved in NF-kB/TNF signalling47, and USP29, a poorly characterized deubiquitinating enzyme involved in p53 stabilization48, showed a total of three nonsense and three missense mutations, suggesting this locus could be a recurrent target of mutations in MM (Fig. 6). Manual inspection of the gene list allowed the identification of several mutations in other genes previously implicated in lymphoid malignancies, although at a non-significant recurrence rate (Fig. 5b). Across our 67 patients, we find a missense variant affecting the ankyrin repeat domain in NFKB1 and two truncating somatic mutations each in NFKBIA, CYLD and TRAF3. These data confirm that the non-canonical NF-kB pathway is an important target for somatic mutation in MM41. We also find somatic mutations in genes involved in B-cell development, such as a nonsense mutation in RAG2 (deleted in ALL) and a frameshift mutation in CD79A (mutated in diffuse large B-cell lymphoma). Rare mutations were also found in other cancer genes, including SF3B1 (three missense mutations in two patients); ARID2 (one frameshift, one missense); PIK3CA (two recurrent missense mutations); PTEN (one nonsense); KDM6A (one homozygous deletion); CDK4 (one missense); CDKN2C (one frameshift, one homozygous deletion); and SETD2 (one nonsense). In four patients, RNA-seq data were available, and we confirmed that most of the known and candidate driver genes in myeloma are expressed in the tumour cells, although at variable levels (Supplementary Fig. S5A). In seven additional patients, RNA was available for RT–PCR. We showed that eight out of the 10 mutations we investigated were expressed, suggesting good correlation between DNA mutational status and expression of mutated driver genes (Supplementary Fig. S5B). Interestingly, a nonsense mutation in the paternally imprinted gene PEG3 showed a homozygous-mutated peak, confirming hemizygous expression of the mutated allele and predicting loss of function of the locus (Supplementary Fig. S5B, panel 9). Relapse-free (Supplementary Fig. S6A–F) and overall survival data (Supplementary Fig. S6Ai–Fi) were available for the 51 patients sampled at diagnosis. We found, as previously reported1, that hyperdiploid cases had longer overall survival than those with IGH translocations (Supplementary Fig. S6Fi). We found no significant difference in overall and relapse-free survival between cases with mutations in KRAS, NRAS or BRAF compared with those without. Similarly, we found no effect of mutations in FAM46C, EGR1, LTB or ROBO1. Presence or absence of a subclonal driver mutation did not influence clinical outcome either. Interestingly, TP53 and SP140 mutations correlated with shorter relapse-free survival, but both had no effect on overall survival (Supplementary Fig. S6B,D,Bi,Di, respectively). We then looked at the influence of the overall mutational spectrum on survival. We found no difference in patients with higher fraction of mutations generated by Signature B, or higher fraction of subclonal mutations (Supplementary Fig. S6A,C,Ai,Ci, respectively). Conversely, increased number of variants correlated with a higher risk of relapse and death (Supplementary Fig. S6E,Ei). Sample sizes here are clearly limited, and defining the prognostic role of such variables will require considerably larger cohorts. Discussion In this study, we report the largest myeloma cohort to date where the coding exome of purified tumour cells was investigated by NGS. The selection of patients in our study was somewhat different to previously published reports, with overrepresentation of advanced disease, hyperdiploid and del(17p) cases and underrepresentation of t(11;14) and t(4:14). This may explain the low frequency of DIS3 mutations (mostly found in IGH rearranged cases so far5 7), and higher prevalence of TP53 and FAM46C variants. The depth of sequencing in our study was higher than the one from other reports, and we validated every variant that was reported by the initial whole-exome sequencing. This allowed for the validation of numerous low burden variants, and provided enough resolution to dissect the subclonal structure of the tumours. We confirm previous reports showing subclonal KRAS, NRAS and BRAF mutations in MM7 that we observe in about a third of patients. This will be relevant in trials of MEK and BRAF inhibitors, as their therapeutic effect should be maximal only in cases where these driver mutations are present in all tumour cells. We show for the first time concurrent BRAF and NRAS or KRAS mutations at diagnosis in the same patient, another finding that has therapeutic implications. BRAF inhibitors can have a paradoxical ERK-activating effect in RAS-mutated cells, therefore inducing secondary tumours driven by oncogenic RAS49 50. This suggests that there may be paradoxical tumour-enhancing effects of BRAF inhibitors in patients with coexistent BRAF and RAS mutations. Furthermore, the presence of mutations of two genes involved in the MAPK pathway in the same patient raises the possibility that MM shows features of convergent evolution and underscores the relevance of this pathway for MM pathogenesis, but also the challenges to exploit it for therapeutic purposes. Our large cohort allowed for a more comprehensive analysis of recurrently mutated genes in MM compared with previous studies, which resulted in novel findings. We confirm that FAM46C is recurrently inactivated in MM in a pattern typical of a tumour-suppressor gene, and associates with the hyperdiploid subgroup of MM. We describe novel candidate tumour-suppressor genes hit by recurrent inactivating mutations at a significant rate, such as SP140 and LTB. Interestingly, SP140 is expressed to high levels in plasma cells and linked to germline susceptibility to CLL51. The germline risk allele is associated with reduced expression of SP140 in lymphocytes51, which would be consistent with the observations of truncating mutations here. Furthermore, the increased risk of relapse associated with SP140 mutations in our cohort suggests this gene may have prognostic features in MM. Another recurrently mutated gene, EGR1, encodes a protein that acts downstream of the JUN pathway, enacting an apoptosis programme in MM cells through downregulation of survivin and upregulation of caspases52. Knockdown of EGR1 in myeloma cells enhanced their resistance to bortezomib52, and the clustered point mutation of key residues that we observed may have similar effects. We show that the heterogeneous subclonal structure and mutational repertoire of MM samples is generated by at least two mutational processes. The relative contribution of each process changed over time or within subclones in most patients, and the contribution of Signature B increased most markedly in the two patients presenting with extramedullary relapse of MM and showing distinct branching evolution. This suggests that the ability to grow independently of the bone marrow microenvironment is gained through substantial changes in the genome of the neoplastic plasma cells. Nevertheless, we fail to demonstrate a correlation between survival and either the prevalence of a specific mutational signature, or the different levels of subclonal complexity of each tumour. Rather, we show that cases with significantly higher number of mutations had worse relapse-free and overall survival, regardless of how mutations were generated or distributed across subclones. In light of recent data showing that the number of variants in MM correlates with stage of disease8, our findings suggest that more advanced disease at diagnosis negatively impacts the clinical outcome. The presence of high variability in genomic architecture across samples highlights the need for therapeutic interventions directed at multiple targets rather than a single genomic anomaly, and underscores the striking success of combination therapies with proteasomal inhibitors and immunomodulatory agents53. As we move towards an era of personalized therapy for myeloma and other cancers, we need to build an understanding of the recurrently mutated genes, their effect on drug response and the impact of the admixture of subclones containing specific mutations on initial presentation, therapy and relapse. The well-established practice of serial bone marrow examination to monitor disease status, the complexity of the genomic landscape and the rapidly evolving therapeutic options suggests MM is a powerful testbed for this vision. Methods Sequencing and genomic alignment The study involved the use of human samples, which were collected after written informed consent was obtained. Samples and data were obtained and managed in accordance with the Declaration of Helsinki under protocol 08/H0308/303: somatic molecular genetics of human cancers, Melanoma and Myeloma (Dana Farber Cancer Institute). The same protocol was approved by RES Committee East of England—Cambridge Central. We sequenced the protein-coding exome of 84 tumour samples from 67 patients with MM. DNA was isolated from CD138-positive myeloma cells purified from bone marrow, and constitutional control DNA originated from peripheral blood mononuclear cells. Purity of the CD138+ fraction was assessed by anti-CD138 immunocytochemistry post sorting, and only samples with >90% plasma cells were sequenced. Two (n=13) or three (n=2) serial samples were available for 15 patients. Fifteen patients were sampled at disease relapse, including 14 with serial samples, and 52 were sampled before treatment. Karyotype and/or FISH data were available for all patients, and the Affymetrix Genome-Wide Human SNP Array 6.0 (SNP6) was performed in 19/84 samples (Supplementary Table S1). Genomic libraries enriched for protein-coding exons were generated by hybridization to RNA baits from matched tumour and germline DNA samples, using the Agilent SureSelect Human Exon Kit (Agilent, G3362)54. The libraries, containing an average insert size of 200–300 bp, were analysed on the Illumina HiSeq2000 sequencing platform. Paired 75 bp sequencing reads were generated using the standard protocol. Paired-end sequencing reads were aligned to the human genome (NCBI build37) using the BWA algorithm55. Reads which were unmapped, PCR-derived duplicates or outside the targeted region were excluded from analysis. The remaining uniquely mapping reads provided an average of 75.38% coverage over the targeted exons at a minimum depth of 30 × (Supplementary Table S2). To identify somatically acquired coding point mutations, we used CaVEMan (Cancer Variants through Expectation Maximization)23 54, an algorithm that uses a naïve Bayesian classifier to estimate the posterior probability of each possible genotype (wild type, germline SNP, somatic mutation) at a given base, accounting for the effects of observables such as base quality (measuring signal-to-noise ratio), read position, sequencing lane and read orientation. CaVEMan is configured to incorporate knowledge of copy number and normal cell contamination in the posterior probability calculations. To call insertions and deletions, we used split-read mapping implemented as a modification of the Pindel algorithm56. We include in the search for indels read-pairs in which one or both ends map to the genome, but allow one of the pair to have mismatches, insertions or deletions. Pindel searches for reads where one end is anchored on the genome, and the other end can be mapped with high confidence in two (split) portions, spanning a putative indel. All somatic coding mutations underwent validation regardless of their allelic fraction. For a fraction of the variants (~25%), we used PCR followed by massively parallel pyrosequencing (Roche 454)26. For the remainder, new libraries were produced after whole-genome amplification, and variants were validated by hybridization and pull-down with a custom bait set. The allelic fraction of variants in the original whole-exome study (on native DNA) and that of the targeted pull-down validation (on amplified DNA) were combined since they showed a near-perfect correlation (r=0.89, P= T transitions in each strand. Three selection parameters (w mis, w non and w splice) quantified selection at missense, nonsense and essential splice-site substitutions, respectively. Maximum likelihood was used to estimate these parameters and likelihood ratio tests were used to test deviations from neutrality (w mis=1, w non=1 or w splice=1). Since the limited number of mutations per gene prevents the estimation of different rates per gene, mutation rates were assumed constant among genes and only the three selection parameters were allowed to vary, thus reflecting a ratio between the observed and the expected numbers of missense, nonsense, splice-site substitutions and indels per gene. Nevertheless, an additional likelihood ratio test was performed to identify those genes whose number of synonymous mutations significantly deviates from the assumed uniform mutation rate. This analysis identified CCND1 as the only gene with a significantly higher background mutation rate in the data set. For indels, a P-value was obtained for each gene comparing the observed number of indels to the expected number (estimated as the product of the CDS length and the exome wide average indel rate per bp) using a cumulative Poisson distribution. For each gene, the four P-values for indels, missense, nonsense and splice-site mutations were combined using Fisher method. P-values were adjusted for multiple testing using Benjamini–Hochberg FDR. Author contributions N.B. collected and analysed the data, wrote the manuscript and prepared the figures. D.C.W., P.V.L., L.B.A., G.R.B., S.N.-Z., I.M., K.J.D., J.M.C.T., Y.L., G.P., N.R. and F.I. analysed the data. Y.-T.T., M.S., A.S.S, F.M and S.M. provided samples and data. J.W.H., A.P.B. and J.W.T. carried out the bioinformatics analysis. S.ML., S.OM., E.A. and L.M. collected the samples and performed experiments. M.A., P.G.R., P.A.F., K.C.A., H.A.-L, P.M., T.F. and M.F. provided samples and contributed to data analysis. P.J.C. and N.C.M. analysed the data and wrote the manuscript. Additional information Accession codes: The whole-exome sequencing data have been deposited in the European Genome-phenome Archive (EGA) repository under the accession code kEGAD00001000339 Data from SNP array hybridization on the SNP6.0 platform have been deposited in EGA under the accession code EGAD00010000395. How to cite this article: Bolli, N. et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat. Commun. 5:2997 doi: 10.1038/ncomms3997 (2014). Supplementary Material Supplementary Figures and Supplementary Tables Supplementary Figures S1-S6 and Supplementary Tables S1-S4 Supplementary Data 1 List of all the validated variants in the study Supplementary Data 2 Homozygous deletions
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            National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: I. The 2014 Diagnosis and Staging Working Group report.

            The 2005 National Institutes of Health (NIH) Consensus Conference proposed new criteria for diagnosing and scoring the severity of chronic graft-versus-host disease (GVHD). The 2014 NIH consensus maintains the framework of the prior consensus with further refinement based on new evidence. Revisions have been made to address areas of controversy or confusion, such as the overlap chronic GVHD subcategory and the distinction between active disease and past tissue damage. Diagnostic criteria for involvement of mouth, eyes, genitalia, and lungs have been revised. Categories of chronic GVHD should be defined in ways that indicate prognosis, guide treatment, and define eligibility for clinical trials. Revisions have been made to focus attention on the causes of organ-specific abnormalities. Attribution of organ-specific abnormalities to chronic GVHD has been addressed. This paradigm shift provides greater specificity and more accurately measures the global burden of disease attributed to GVHD, and it will facilitate biomarker association studies.
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              Altered B-cell homeostasis and excess BAFF in human chronic graft-versus-host disease.

              Chronic graft-versus-host disease (cGVHD) causes significant morbidity and mortality in patients otherwise cured of malignancy after hematopoietic stem cell transplantation (HSCT). The presence of alloantibodies and high plasma B cell-activating factor (BAFF) levels in patients with cGVHD suggest that B cells play a role in disease pathogenesis. We performed detailed phenotypic and functional analyses of peripheral B cells in 82 patients after HSCT. Patients with cGVHD had significantly higher BAFF/B-cell ratios compared with patients without cGVHD or healthy donors. In cGVHD, increasing BAFF concentrations correlated with increased numbers of circulating pre-germinal center (GC) B cells and post-GC "plasmablast-like" cells, suggesting in vivo BAFF dependence of these 2 CD27(+) B-cell subsets. Circulating CD27(+) B cells in cGVHD comprised in vivo activated B cells capable of IgG production without requiring additional antigen stimulation. Serial studies revealed that patients who subsequently developed cGVHD had delayed reconstitution of naive B cells despite persistent BAFF elevation as well as proportional increase in CD27(+) B cells in the first year after HSCT. These studies delineate specific abnormalities of B-cell homeostasis in patients with cGVHD and suggest that BAFF targeting agents may be useful in this disease.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                5 December 2017
                15 November 2017
                : 8
                : 63
                : 106333-106341
                Affiliations
                1 Division of Hematology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
                22462
                10.18632/oncotarget.22462
                5739737
                Copyright: © 2017 Widmer et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Categories
                Research Paper

                Oncology & Radiotherapy

                myeloma, gvhd, allo-hsct, paraprotein

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