Explore the vast range of titles available.

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. Multiple myeloma evolves continuously. Here the authors chronologically reconstruct driver events in multiple myeloma, noting a limited repertoire of initiating driver events that shape the evolutionary trajectory of the disease.

Myeloid malignancies exhibit considerable heterogeneity with overlapping clinical and genetic features among subtypes. We present a data-driven approach that integrates mutational features and clinical covariates at diagnosis within networks of their probabilistic relationships, enabling the discovery of patient subgroups. A key strength is its ability to include presumed causal directions in the edges linking clinical and mutational features, and account for them aptly in the clustering. In a cohort of 1323 patients, we identify subgroups that outperform established risk classifications in prognostic accuracy. Our approach generalises well to unseen cohorts with classification based on our subgroups similarly offering advantages in predicting prognosis. Our findings suggest that mutational patterns are often shared across myeloid malignancies, with distinct subtypes potentially representing evolutionary stages en route to leukemia. With pancancer TCGA data, we observe that our modelling framework extends naturally to other cancer types while still offering improvements in subgroup discovery. Myeloid malignancies vary significantly in their clinical outcomes and their genetic background. Here, the authors develop a network-based clustering method to predict subgroups of malignancies across disease subtypes.

Somatic mutations in splicing factor genes frequently occur in myeloid neoplasms. While SF3B1 mutations are associated with myelodysplastic syndromes (MDS) with ring sideroblasts, SRSF2 P95 mutations are found in different disease categories, including MDS, myeloproliferative neoplasms (MPN), myelodysplastic/myeloproliferative neoplasms (MDS/MPN), and acute myeloid leukemia (AML). To identify molecular determinants of this phenotypic heterogeneity, we explored molecular and clinical features of a prospective cohort of 279 SRSF2 P95 -mutated cases selected from a population of 2663 patients with myeloid neoplasms. Median number of somatic mutations per subject was 3. Multivariate regression analysis showed associations between co-mutated genes and clinical phenotype, including JAK2 or MPL with myelofibrosis (OR = 26.9); TET2 with monocytosis (OR = 5.2); RAS-pathway genes with leukocytosis (OR = 5.1); and STAG2 , RUNX1 , or IDH1/2 with blast phenotype (MDS or AML) (OR = 3.4, 1.9, and 2.1, respectively). Within patients with SRSF2–JAK2 co-mutation , JAK2 dominance was invariably associated with clinical feature of MPN, whereas SRSF2 mutation was dominant in MDS/MPN. Within patients with SRSF2–TET2 co-mutation, clinical expressivity of monocytosis was positively associated with co-mutated clone size. This study provides evidence that co-mutation pattern, clone size, and hierarchy concur to determine clinical phenotype, tracing relevant genotype–phenotype associations across disease entities and giving insight on unaccountable clinical heterogeneity within current WHO classification categories.

Of the many types of DNA damage, DNA double-strand breaks (DSBs) are probably the most deleterious. Mounting evidence points to an intricate relationship between DSBs and transcription. A cell system in which the impact on transcription can be investigated at precisely mapped genomic DSBs is essential to study this relationship. Here in a human cell line, we map genome-wide and at high resolution the DSBs induced by a restriction enzyme, and we characterize their impact on gene expression by four independent approaches by monitoring steady-state RNA levels, rates of RNA synthesis, transcription initiation and RNA polymerase II elongation. We consistently observe transcriptional repression in proximity to DSBs. Downregulation of transcription depends on ATM kinase activity and on the distance from the DSB. Our study couples for the first time, to the best of our knowledge, high-resolution mapping of DSBs with multilayered transcriptomics to dissect the events shaping gene expression after DSB induction at multiple endogenous sites. DNA double strand breaks (DSBs) are among the most deleterious types of damage and there is strong evidence indicating a relationship between breaks and transcription. Here the authors provide a high-resolution, genome-wide map of induced DSBs and observe ATM-dependent transcriptional repression.

Mantle cell lymphoma (MCL) is genetically characterized by the IG :: CCND1 translocation mediated by an aberrant V(D)J rearrangement. CCND1 translocations and overexpression have been identified in occasional aggressive B-cell lymphomas with unusual features for MCL. The mechanism generating CCND1 rearrangements in these tumors and their genomic profile are not known. We have reconstructed the IG :: CCND1 translocations and the genomic profile of 13 SOX11-negative aggressive B-cell lymphomas using whole genome/exome and target sequencing. The mechanism behind the translocation was an aberrant V(D)J rearrangement in three tumors and by an anomalous IGH class-switch recombination (CSR) or somatic hypermutation (SHM) mechanism in ten. The tumors with a V(D)J-mediated translocation were two blastoid MCL and one high-grade B-cell lymphoma. None of them had a mutational profile suggestive of DLBCL. The ten tumors with CSR/SHM-mediated IGH :: CCND1 were mainly large B-cell lymphomas, with mutated genes commonly seen in DLBCL and BCL6 rearrangements in 6. Two cases, which transformed from marginal zone lymphomas, carried mutations in KLF2 , TNFAIP3 and KMT2D . These findings expand the spectrum of tumors carrying CCND1 rearrangement that may occur as a secondary event in DLBCL mediated by aberrant CSR/SHM and associated with a mutational profile different from that of MCL.

INTRODUCTION: In this paper we present Silico, a new Digital Musical Instrument which ideally represents the performer itself. This instrument is composed by two parts: an interface (a sensor glove), which relies on the movements of the performer’s hand, and a computational engine (a set of patches developed in Max 7), which generates sound events based on the genomic data of the performer.OBJECTIVES: We want to propose a new reflection on the relation between the body and musical instruments. Moreover, we aim to investigate the voluntary and involuntary aspects of our body, intended as a starting point for a musical performance. As a metaphor of these two layers, we used here the hand and the genome of the performer.METHODS: We have investigated our objectives through the whole design process of a Digital Musical Instrument, using a practice-based approach.RESULTS: Our system is a multilayered composed instrument which maps its computational part and its interface on the performer’s body. Silico can be used as a standalone musical instrument to generate music in real time.CONCLUSION: Our works shows a new path about the use of genomic data in a musical way, as a new perspective of human-computer interaction in a performative contexts.

Esophageal adenocarcinoma (EAC) is associated with a marked genomic instability, which underlies disease progression and development of resistance to treatment. In this study, we used an integrated genomics approach to identify a genomic instability signature. Here we show that elevated expression of this signature correlates with poor survival in EAC as well as three other cancers. Knockout and overexpression screens establish the relevance of these genes to genomic instability. Indepth evaluation of three genes (TTK, TPX2 and RAD54B) confirms their role in genomic instability and tumor growth. Mutational signatures identified by whole genome sequencing and functional studies demonstrate that DNA damage and homologous recombination are common mechanisms of genomic instability induced by these genes. Our data suggest that the inhibitors of TTK and possibly other genes identified in this study have potential to inhibit/reduce growth and spontaneous as well as chemotherapy-induced genomic instability in EAC and possibly other cancers.Subodh Kumar et al. identify a gene signature correlated with genomic instability and poor survival in esophageal adenocarcinoma (EAC), using a combination of integrative genomic analysis of patient data and laboratory validation in cell line models and mice. They find that inhibitors of some of the identified proteins, including TTK, could be used to reduce genomic evolution as well as inhibit growth of EAC cells.

Somatic mutations in splicing factor genes frequently occur in myeloid neoplasms. While SF3B1 mutations are associated with myelodysplastic syndromes (MDS) with ring sideroblasts, SRSF2.sup.P95 mutations are found in different disease categories, including MDS, myeloproliferative neoplasms (MPN), myelodysplastic/myeloproliferative neoplasms (MDS/MPN), and acute myeloid leukemia (AML). To identify molecular determinants of this phenotypic heterogeneity, we explored molecular and clinical features of a prospective cohort of 279 SRSF2.sup.P95-mutated cases selected from a population of 2663 patients with myeloid neoplasms. Median number of somatic mutations per subject was 3. Multivariate regression analysis showed associations between co-mutated genes and clinical phenotype, including JAK2 or MPL with myelofibrosis (OR = 26.9); TET2 with monocytosis (OR = 5.2); RAS-pathway genes with leukocytosis (OR = 5.1); and STAG2, RUNX1, or IDH1/2 with blast phenotype (MDS or AML) (OR = 3.4, 1.9, and 2.1, respectively). Within patients with SRSF2-JAK2 co-mutation, JAK2 dominance was invariably associated with clinical feature of MPN, whereas SRSF2 mutation was dominant in MDS/MPN. Within patients with SRSF2-TET2 co-mutation, clinical expressivity of monocytosis was positively associated with co-mutated clone size. This study provides evidence that co-mutation pattern, clone size, and hierarchy concur to determine clinical phenotype, tracing relevant genotype-phenotype associations across disease entities and giving insight on unaccountable clinical heterogeneity within current WHO classification categories.

Somatic mutations in splicing factor genes frequently occur in myeloid neoplasms. While SF3B1 mutations are associated with myelodysplastic syndromes (MDS) with ring sideroblasts, SRSF2 mutations are found in different disease categories, including MDS, myeloproliferative neoplasms (MPN), myelodysplastic/myeloproliferative neoplasms (MDS/MPN), and acute myeloid leukemia (AML). To identify molecular determinants of this phenotypic heterogeneity, we explored molecular and clinical features of a prospective cohort of 279 SRSF2 -mutated cases selected from a population of 2663 patients with myeloid neoplasms. Median number of somatic mutations per subject was 3. Multivariate regression analysis showed associations between co-mutated genes and clinical phenotype, including JAK2 or MPL with myelofibrosis (OR = 26.9); TET2 with monocytosis (OR = 5.2); RAS-pathway genes with leukocytosis (OR = 5.1); and STAG2, RUNX1, or IDH1/2 with blast phenotype (MDS or AML) (OR = 3.4, 1.9, and 2.1, respectively). Within patients with SRSF2-JAK2 co-mutation, JAK2 dominance was invariably associated with clinical feature of MPN, whereas SRSF2 mutation was dominant in MDS/MPN. Within patients with SRSF2-TET2 co-mutation, clinical expressivity of monocytosis was positively associated with co-mutated clone size. This study provides evidence that co-mutation pattern, clone size, and hierarchy concur to determine clinical phenotype, tracing relevant genotype-phenotype associations across disease entities and giving insight on unaccountable clinical heterogeneity within current WHO classification categories.

Myeloid malignancies exhibit considerable heterogeneity with overlapping clinical and genetic features among different subtypes. Current classification schemes, predominantly based on clinical features, fall short of capturing the complex genomic landscapes of these malignancies. Here, we present a data-driven approach that integrates mutational features and clinical covariates within networks of their probabilistic relationships, enabling the discovery of de novo cancer subgroups. In a cohort of 1323 patients across acute myeloid leukemia, myelodysplastic syndromes, chronic myelomonocytic leukemia and myeloproliferative neoplasms, we identified novel subgroups that outperform established risk classifications in prognostic accuracy. Our findings suggest that mutational patterns are often shared across different types of myeloid malignancies, with distinct subtypes potentially representing evolutionary stages en route to leukemia. Within the novel subgroups, our integrative method discerns unique patterns combining genomic and clinical features to provide a comprehensive view of the multifaceted genomic and clinical landscape of myeloid malignancies. This in turn may guide the development of targeted therapeutic strategies and offers a pathway to enhanced patient stratification.