BACKGROUNDMyeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment. METHODSWe sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copynumber changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort. RESULTSA total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy. CONCLUSIONSComprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms. (Funded by the Wellcome Trust and others.

The composition and structure of the pregnancy vaginal microbiome may influence susceptibility to adverse pregnancy outcomes. Studies on the pregnant vaginal microbiome have largely been limited to Northern American populations. Using MiSeq sequencing of 16S rRNA gene amplicons, we characterised the vaginal microbiota of a mixed British cohort of women (n = 42) who experienced uncomplicated term delivery and who were sampled longitudinally throughout pregnancy (8–12, 20–22, 28–30 and 34–36 weeks gestation) and 6 weeks postpartum. We show that vaginal microbiome composition dramatically changes postpartum to become less Lactobacillus spp. dominant with increased alpha-diversity irrespective of the community structure during pregnancy and independent of ethnicity. While the pregnancy vaginal microbiome was characteristically dominated by Lactobacillus spp. and low alpha-diversity, unlike Northern American populations, a significant number of pregnant women this British population had a L. jensenii-dominated microbiome characterised by low alpha-diversity. L. jensenii was predominantly observed in women of Asian and Caucasian ethnicity whereas L. gasseri was absent in samples from Black women. This study reveals new insights into biogeographical and ethnic effects upon the pregnancy and postpartum vaginal microbiome and has important implications for future studies exploring relationships between the vaginal microbiome, host health and pregnancy outcomes.

SummaryClear cell renal cell carcinoma (ccRCC) is characterized by near-universal loss of the short arm of chromosome 3, deleting several tumor suppressor genes. We analyzed whole genomes from 95 biopsies across 33 patients with clear cell renal cell carcinoma. We find hotspots of point mutations in the 5′ UTR of TERT, targeting a MYC-MAX-MAD1 repressor associated with telomere lengthening. The most common structural abnormality generates simultaneous 3p loss and 5q gain (36% patients), typically through chromothripsis. This event occurs in childhood or adolescence, generally as the initiating event that precedes emergence of the tumor’s most recent common ancestor by years to decades. Similar genomic changes drive inherited ccRCC. Modeling differences in age incidence between inherited and sporadic cancers suggests that the number of cells with 3p loss capable of initiating sporadic tumors is no more than a few hundred. Early development of ccRCC follows well-defined evolutionary trajectories, offering opportunity for early intervention.

The multiple myeloma (MM) genome is heterogeneous and evolves through preclinical and post-diagnosis phases. Here we report a catalog and hierarchy of driver lesions using sequences from 67 MM genomes serially collected from 30 patients together with public exome datasets. Bayesian clustering defines at least 7 genomic subgroups with distinct sets of co-operating events. Focusing on whole genome sequencing data, complex structural events emerge as major drivers, including chromothripsis and a novel replication-based mechanism of templated insertions, which typically occur early. Hyperdiploidy also occurs early, with individual trisomies often acquired in different chronological windows during evolution, and with a preferred order of acquisition. Conversely, positively selected point mutations, whole genome duplication and chromoplexy events occur in later disease phases. Thus, initiating driver events, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of evolution that are biologically relevant but heterogeneous across patients.

The evolution and progression of multiple myeloma and its precursors over time is poorly understood. Here, we investigate the landscape and timing of mutational processes shaping multiple myeloma evolution in a large cohort of 89 whole genomes and 973 exomes. We identify eight processes, including a mutational signature caused by exposure to melphalan. Reconstructing the chronological activity of each mutational signature, we estimate that the initial transformation of a germinal center B-cell usually occurred during the first 2 nd -3 rd decades of life. We define four main patterns of activation-induced deaminase (AID) and apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) mutagenesis over time, including a subset of patients with evidence of prolonged AID activity during the premalignant phase, indicating antigen-responsiveness and germinal center reentry. Our findings provide a framework to study the etiology of multiple myeloma and explore strategies for prevention and early detection.

Multiple myeloma (MM) has a heterogeneous genome, evolving through both pre-clinical and post-diagnosis phases. Here, using sequences from 67 MM genomes serially collected from 30 patients together with public datasets, we establish a hierarchy of driver lesions. Point mutations, structural variants and copy number aberrations define at least 7 genomic subgroups of MM, each with distinct sets of co-operating driver mutations. Complex structural events are major drivers of MM, including chromothripsis, chromoplexy and a replication-based mechanism of templated insertions: these typically occur early. Hyperdiploidy also occurs early, with individual chromosomes often gained in more than one chronological epoch of MM evolution, showing a preferred order of acquisition. Positively selected point mutations frequently occur in later phases of disease development, as do structural variants involving MYC. Thus, initiating driver events of MM, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of subsequent evolution. MFAG (n.17658).

The landscape of structural variants (SV) in multiple myeloma remains poorly understood. Here, we performed comprehensive analysis of SVs in a large cohort of 752 patients with multiple myeloma by low-coverage long-insert whole-genome sequencing. We identified 68 SV hotspots involving 17 new candidate driver genes, including the therapeutic targets BCMA (TNFRSF17), SLAM7, and MCL1. Catastrophic complex rearrangements termed chromothripsis were present in 24% of patients and independently associated with poor clinical outcomes. Templated insertions were the second most frequent complex event (19%), mostly involved in super-enhancer hijacking and activation of oncogenes such as CCND1 and MYC. Importantly, in 31% of patients, two or more seemingly independent putative driver events were caused by a single structural event, demonstrating that the complex genomic landscape of multiple myeloma can be acquired through few key events during tumor evolutionary history. Overall, this study reveals the critical role of SVs in multiple myeloma pathogenesis. Significance: Previous genomic studies in multiple myeloma have largely focused on single-nucleotide variants, recurrent copy-number alterations, and recurrent translocations. Here, we demonstrate the crucial role of SVs and complex events in the development of multiple myeloma and highlight the importance of whole-genome sequencing to decipher its genomic complexity. See related commentary by Bergsagel and Kuehl, p. 221. This article is highlighted in the In This Issue feature, p. 215

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