Clonal evolution during metastatic spread in high-risk neuroblastoma.

Patients with high-risk neuroblastoma generally present with widely metastatic disease and often relapse despite intensive therapy. As most studies to date focused on diagnosis-relapse pairs, our understanding of the genetic and clonal dynamics of metastatic spread and disease progression remain limited. Here, using genomic profiling of 470 sequential and spatially separated samples from 283 patients, we characterize subtype-specific genetic evolutionary trajectories from diagnosis through progression and end-stage metastatic disease. Clonal tracing timed disease initiation to embryogenesis. Continuous acquisition of structural variants at disease-defining loci (MYCN, TERT, MDM2-CDK4) followed by convergent evolution of mutations targeting shared pathways emerged as the predominant feature of progression. At diagnosis metastatic clones were already established at distant sites where they could stay dormant, only to cause relapses years later and spread via metastasis-to-metastasis and polyclonal seeding after therapy.

Patient-specific MDS-RS iPSCs define the mis-spliced transcript repertoire and chromatin landscape of SF3B1-mutant HSPCs.

SF3B1K700E is the most frequent mutation in myelodysplastic syndrome (MDS), but the mechanisms by which it drives MDS pathogenesis remain unclear. We derived a panel of 18 genetically matched SF3B1K700E- and SF3B1WT-induced pluripotent stem cell (iPSC) lines from patients with MDS with ring sideroblasts (MDS-RS) harboring isolated SF3B1K700E mutations and performed RNA and ATAC sequencing in purified CD34+/CD45+ hematopoietic stem/progenitor cells (HSPCs) derived from them. We developed a novel computational framework integrating splicing with transcript usage and gene expression analyses and derived a SF3B1K700E splicing signature consisting of 59 splicing events linked to 34 genes, which associates with the SF3B1 mutational status of primary MDS patient cells. The chromatin landscape of SF3B1K700E HSPCs showed increased priming toward the megakaryocyte- erythroid lineage. Transcription factor motifs enriched in chromatin regions more accessible in SF3B1K700E cells included, unexpectedly, motifs of the TEA domain (TEAD) transcription factor family. TEAD expression and transcriptional activity were upregulated in SF3B1-mutant iPSC-HSPCs, in support of a Hippo pathway-independent role of TEAD as a potential novel transcriptional regulator of SF3B1K700E cells. This study provides a comprehensive characterization of the transcriptional and chromatin landscape of SF3B1K700E HSPCs and nominates novel mis-spliced genes and transcriptional programs with putative roles in MDS-RS disease biology.

Bone Marrow Surveillance of Pediatric Cancer Survivors Identifies Clones that Predict Therapy-Related Leukemia.

PURPOSE: Therapy-related myelodysplastic syndrome and acute leukemias (t-MDS/AL) are a major cause of nonrelapse mortality among pediatric cancer survivors. Although the presence of clonal hematopoiesis (CH) in adult patients at cancer diagnosis has been implicated in t-MDS/AL, there is limited published literature describing t-MDS/AL development in children. EXPERIMENTAL DESIGN: We performed molecular characterization of 199 serial bone marrow samples from 52 patients treated for high-risk neuroblastoma, including 17 with t-MDS/AL (transformation), 14 with transient cytogenetic abnormalities (transient), and 21 without t-MDS/AL or cytogenetic alterations (neuroblastoma-treated control). We also evaluated for CH in a cohort of 657 pediatric patients with solid tumor. RESULTS: We detected at least one disease-defining alteration in all cases at t-MDS/AL diagnosis, most commonly TP53 mutations and KMT2A rearrangements, including involving two novel partner genes (PRDM10 and DDX6). Backtracking studies identified at least one t-MDS/AL-associated mutation in 13 of 17 patients at a median of 15 months before t-MDS/AL diagnosis (range, 1.3-32.4). In comparison, acquired mutations were infrequent in the transient and control groups (4/14 and 1/21, respectively). The relative risk for development of t-MDS/AL in the presence of an oncogenic mutation was 8.8 for transformation patients compared with transient. Unlike CH in adult oncology patients, TP53 mutations were only detectable after initiation of cancer therapy. Last, only 1% of pediatric patients with solid tumor evaluated had CH involving myeloid genes. CONCLUSIONS: These findings demonstrate the clinical relevance of identifying molecular abnormalities in predicting development of t-MDS/AL and should guide the formation of intervention protocols to prevent this complication in high-risk pediatric patients.

Fast likelihood calculation for multivariate Gaussian phylogenetic models with shifts.

Phylogenetic comparative methods (PCMs) have been used to study the evolution of quantitative traits in various groups of organisms, ranging from micro-organisms to animal and plant species. A common approach has been to assume a Gaussian phylogenetic model for the trait evolution along the tree, such as a branching Brownian motion (BM) or an Ornstein-Uhlenbeck (OU) process. Then, the parameters of the process have been inferred based on a given tree and trait data for the sampled species. At the heart of this inference lie multiple calculations of the model likelihood, that is, the probability density of the observed trait data, conditional on the model parameters and the tree. With the increasing availability of big phylogenetic trees, spanning hundreds to several thousand sampled species, this approach is facing a two-fold challenge. First, the assumption of a single Gaussian process governing the entire tree is not adequate in the presence of heterogeneous evolutionary forces acting in different parts of the tree. Second, big trees present a computational challenge, due to the time and memory complexity of the model likelihood calculation. Here, we explore a sub-family, denoted GLInv, of the Gaussian phylogenetic models, with the transition density exhibiting the properties that the expectation depends Linearly on the ancestral trait value and the variance is Invariant with respect to the ancestral value. We show that GLInv contains the vast majority of Gaussian models currently used in PCMs, while supporting an efficient (linear in the number of nodes) algorithm for the likelihood calculation. The algorithm supports scenarios with missing data, as well as different types of trees, including trees with polytomies and non-ultrametric trees. To account for the heterogeneity in the evolutionary forces, the algorithm supports models with "shifts" occurring at specific points in the tree. Such shifts can include changes in some or all parameters, as well as the type of the model, provided that the model remains within the GLInv family. This contrasts with most of the current implementations where, due to slow likelihood calculation, the shifts are restricted to specific parameters in a single type of model, such as the long-term selection optima of an OU process, assuming that all of its other parameters, such as evolutionary rate and selection strength, are global for the entire tree. We provide an implementation of this likelihood calculation algorithm in an accompanying R-package called PCMBase. The package has been designed as a generic library that can be integrated with existing or novel maximum likelihood or Bayesian inference tools.