Search

How the genomic features of a patient's cancer relate to individual disease kinetics remains poorly understood. Here we used the indolent growth dynamics of chronic lymphocytic leukaemia (CLL) to analyse the growth rates and corresponding genomic patterns of leukaemia cells from 107 patients with CLL, spanning decades-long disease courses. We found that CLL commonly demonstrates not only exponential expansion but also logistic growth, which is sigmoidal and reaches a certain steady-state level. Each growth pattern was associated with marked differences in genetic composition, the pace of disease progression and the extent of clonal evolution. In a subset of patients, whose serial samples underwent next-generation sequencing, we found that dynamic changes in the disease course of CLL were shaped by the genetic events that were already present in the early slow-growing stages. Finally, by analysing the growth rates of subclones compared with their parental clones, we quantified the growth advantage conferred by putative CLL drivers in vivo.

This phase 2 study was conducted to prospectively evaluate how clinical and biological factors correlate with outcome in patients with treatment-naive (TN) and relapsed (R) chronic lymphocytic leukemia (CLL) treated with lenalidomide and rituximab. Oral lenalidomide 10 mg was administered daily starting on day 9 of cycle 1. IV rituximab 375 mg/m2 was administered weekly during cycle 1 and every 4 weeks for cycles 3 to 12. Sequencing of a custom panel of 295 genes was performed in pretreatment bone marrow samples. The study included 61 patients with TN CLL and 59 with R CLL; the overall response rate (ORR) was 73% and 64%, respectively. A baseline β2-microglobulin level 2 previous therapies (P = .02) was the only factor associated with shorter PFS in R patients. A trend for association between mutations in the NOTCH pathway and shorter PFS was observed in TN CLL (P = .15). Further exploration of the NOTCH pathway may help optimize the efficacy of this combination in patients with CLL. This study protocol was approved by the University of Texas MD Anderson Cancer Center institutional review board and registered at clinicaltrials.gov (#NCT01446133).

Dysregulation of lipid homeostasis is a precipitating event in the pathogenesis and progression of hepatosteatosis and metabolic syndrome. These conditions are highly prevalent in developed societies and currently have limited options for diagnostic and therapeutic intervention. Here, using a proteomic and lipidomic-wide systems genetic approach, we interrogated lipid regulatory networks in 107 genetically distinct mouse strains to reveal key insights into the control and network structure of mammalian lipid metabolism. These include the identification of plasma lipid signatures that predict pathological lipid abundance in the liver of mice and humans, defining subcellular localization and functionality of lipid-related proteins, and revealing functional protein and genetic variants that are predicted to modulate lipid abundance. Trans-omic analyses using these datasets facilitated the identification and validation of PSMD9 as a previously unknown lipid regulatory protein. Collectively, our study serves as a rich resource for probing mammalian lipid metabolism and provides opportunities for the discovery of therapeutic agents and biomarkers in the setting of hepatic lipotoxicity.

Achieving undetectable MRD (U-MRD) status after chemoimmunotherapy predicts longer progression-free and overall survival. The predictive factors and timing of relapse in patients with U-MRD and value of interim MRD analysis are ill-defined. This was a prospective study of 289 patients with CLL treated first-line with FCR. MRD analysis was performed after course 3 (C3) and at end of therapy (EOT) in bone marrow using 4-color flow cytometry (sensitivity 10-4). Eighteen percent of patients had U-MRD after C3 and 48% at EOT. U-MRD status at EOT was associated with longer PFS (median NR vs 38 mo, p 1%) after C3 predicted greater likelihood of U-MRD status at EOT (64% vs 9%, p 1% after C3 (median 73 mo vs 41 mo, p

This article discusses the evolution of the British constitution and challenges the traditional view of the United Kingdom as a homogeneous, unitary nation-state. It explores counter-narratives based on the histories and political consciousness of the component nations, which argue that the UK is an artificial construction. The article also examines the concept of union and its application to the UK, as well as the difficulties in defining what a union is. It highlights the importance of integrating work from constitutional law, political science, and history in understanding the UK's constitutional framework. The article concludes by discussing the issues of sovereignty, devolution, and human rights in the UK, and the challenges of constitutional reform. [Extracted from the article]

Simple Summary: Clinical data sets incorporate continuous data like blood pressure or sodium levels, categorical data like cancer grade or stage, and binary data like sex or marital status. Measurements on an individual patient define a point in a high-dimensional space; data from many patients defines a "point cloud". The "shape" of the point cloud influences experimental design by describing patient variability. Topological data analysis (TDA) is a mathematical technique for understanding the shape of point clouds by finding "holes" that correspond to combinations of patient characteristics that are never observed. TDA results are stratified by dimension. Zero-dimensional features define patient subtypes. One-dimensional features ("loops") are analogs of the inside of a circle or a donut hole. Two-dimensional features ("voids") are analogs of the inside of a balloon. Here, we apply TDA to a clinical data set of previously untreated patients with Chronic Lymphocytic Leukemia to find loops and voids. Patients are complex and heterogeneous; clinical data sets are complicated by noise, missing data, and the presence of mixed-type data. Using such data sets requires understanding the high-dimensional "space of patients", composed of all measurements that define all relevant phenotypes. The current state-of-the-art merely defines spatial groupings of patients using cluster analyses. Our goal is to apply topological data analysis (TDA), a new unsupervised technique, to obtain a more complete understanding of patient space. We applied TDA to a space of 266 previously untreated patients with Chronic Lymphocytic Leukemia (CLL), using the "daisy" metric to compute distances between clinical records. We found clear evidence for both loops and voids in the CLL data. To interpret these structures, we developed novel computational and graphical methods. The most persistent loop and the most persistent void can be explained using three dichotomized, prognostically important factors in CLL: IGHV somatic mutation status, beta-2 microglobulin, and Rai stage. In conclusion, patient space turns out to be richer and more complex than current models suggest. TDA could become a powerful tool in a researcher's arsenal for interpreting high-dimensional data by providing novel insights into biological processes and improving our understanding of clinical and biological data sets. [ABSTRACT FROM AUTHOR]

The degree of somatic hypermutation, determined as percent deviation of immunoglobulin heavy chain gene variable region sequence from the germline (IGHV%), is an important prognostic factor in chronic lymphocytic leukaemia (CLL). Currently, a cut-off of 2% deviation or 98% sequence identity to germline in IGHV sequence is routinely used to dichotomize CLL patients into mutated and unmutated groups. Because dissimilar IGHV% cut-offs of 1-5% were identified in different studies, we wondered whether no cut-off should be applied and IGHV% treated as a continuous variable. We analysed the significance of IGHV% in 203 CLL patients enrolled on the original frontline fludarabine, cyclophosphamide and rituximab (FCR) trial with a median of 10 years follow-up. Using the Cox Proportional Hazard model, IGHV% was identified as a continuous variable that is significantly associated with progression-free (PFS) and overall survival (OS) (P