The BCL-2 inhibitor venetoclax combined with hypomethylating agents or low-dose cytarabine represents an important new therapy for older or unfit patients with acute myeloid leukemia (AML). We analyzed 81 patients receiving these venetoclax-based combinations to identify molecular correlates of durable remission, response followed by relapse (adaptive resistance), or refractory disease (primary resistance). High response rates and durable remissions were typically associated with NPM1 or IDH2 mutations, with prolonged molecular remissions prevalent for NPM1 mutations. Primary and adaptive resistance to venetoclax-based combinations was most commonly characterized by acquisition or enrichment of clones activating signaling pathways such as FLT3 or RAS or biallelically perturbing TP53. Single-cell studies highlighted the polyclonal nature of intratumoral resistance mechanisms in some cases. Among cases that were primary refractory, we identified heterogeneous and sometimes divergent interval changes in leukemic clones within a single cycle of therapy, highlighting the dynamic and rapid occurrence of therapeutic selection in AML. In functional studies, FLT3 internal tandem duplication gain or TP53 loss conferred cross-resistance to both venetoclax and cytotoxic-based therapies. Collectively, we highlight molecular determinants of outcome with clinical relevance to patients with AML receiving venetoclax-based combination therapies.

Idiopathic juxtafoveal retinal telangiectasis type 2 (macular telangiectasia type 2; MacTel) is a rare neurovascular degenerative retinal disease. To identify genetic susceptibility loci for MacTel, we performed a genomewide association study (GWAS) with 476 cases and 1733 controls of European ancestry. Genome-wide significant associations (P < 5 × 10 -8 ) were identified at 3 independent loci (rs73171800 at 5q14.3, P = 7.74 × 10 -17 ; rs715 at 2q34, P = 9.97 × 10 -14 ; rs477992 at 1p12, P = 2.60 × 10 -12 ) and then replicated (P < 0.01) in an independent cohort of 172 cases and 1134 controls. The 5q14.3 locus is known to associate with variation in retinal vascular diameter, and the 2q34 and 1p12 loci have been implicated in the glycine/serine metabolic pathway. We subsequently found significant differences of blood serum levels of glycine (P = 4.04 × 10 -6 ) and serine (P = 2.48 × 10 -4 ) between MacTel cases and controls.MacTel cases typically present at 40-60 years with abnormal right-angled juxtafoveolar capillaries and parafoveal telangiectasias. It is an uncommon disease with a 0.0045-0.1% population prevalence and no obvious sex bias [1][2][3] . Retinal lesions typically co-present with MacTel, including retinal transparency, outer retinal and choroidal neovascularization, lamellar holes or foveal cysts, photoreceptor dysfunction, minimal exudation, yellow-white parafoveal crystals, and retinal pigment epithelial (RPE) pigmentation abnormalities and atrophy. Central vision impairment and decreased visual acuity are the usual clinical outcomes. MacTel is a bilateral disease, but asymmetry of the eyes for disease severity and presence of lesions is possible. The lesions also occur in 0.06-1.18% of the general population 2 .Risk factors for MacTel are largely unknown, however associations have been observed with smoking 2,4 , diabetes 5,6 , high BMI 6 , hypertension 6 and obesity 6 .Observations of MacTel affected monozygotic twins 4,[7][8][9] , and multiplex families with vertical transmissions of MacTel 1,5,[9][10][11][12] , suggest a genetic etiology for the disease. The late-age of onset, low penetrance and variable phenotype as exemplified by asymptomatic affected relatives 9 , and positive and negative misdiagnoses, complicate the discovery of genetic variants predisposing to MacTel. We previously screened 27 candidate genes in 8 unrelated MacTel cases but found no causative mutations 13 . Linkage analysis of 17 families with MacTel individuals identified a 15.3Mb locus on chromosome 1q41-42.2 (LOD=3.45), however sequencing of the underlying genes revealed no causative mutations 14 . Results Discovery GWAS stageThe GWAS discovery stage included genotype data for 6,312,048 single nucleotide polymorphisms (SNPs) after quality control and imputation (including 1,093,805 SNPs genotyped on the Illumina Omni SNP chips) in 476 MacTel cases and 1,733 controls (see Table 1 and Online Methods). This sample size was large enough to achieve power of at least 0.90 for risk variants with allele frequencies of 0.10-...

Large variation between European countries in the prevalence of back and neck/upper-limb pain may be attributable in part to socioeconomic differences between countries, with higher prevalence where there is less poverty and more social support. Future studies should explore this possibility further, perhaps by comparing trends over time in countries where socioeconomic circumstances have changed differentially.

Label-free quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis, and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web-based analysis environment that can analyze and visualize LFQ data produced by software such as MaxQuant. Unlike other tools, MD 1.0 utilizes a cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but also annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardization in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range. The MD 1.0 platform is available globally via: https://app.massdynamics.com/.

No abstract

Background RNA sequencing allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort of samples, library size is a fundamental factor affecting both the overall cost and the quality of the results. Here we specifically address how overall library size influences the detection of somatic mutations in RNA-seq data in two acute myeloid leukaemia datasets. Results  We simulated shallower sequencing depths by downsampling 45 acute myeloid leukaemia samples (100 bp PE) that are part of the Leucegene project, which were originally sequenced at high depth. We compared the sensitivity of six methods of recovering validated mutations on the same samples. The methods compared are a combination of three popular callers (MuTect, VarScan, and VarDict) and two filtering strategies. We observed an incremental loss in sensitivity when simulating libraries of 80M, 50M, 40M, 30M and 20M fragments, with the largest loss detected with less than 30M fragments (below 90%, average loss of 7%). The sensitivity in recovering insertions and deletions varied markedly between callers, with VarDict showing the highest sensitivity (60%). Single nucleotide variant sensitivity is relatively consistent across methods, apart from MuTect, whose default filters need adjustment when using RNA-Seq. We also analysed 136 RNA-Seq samples from the TCGA-LAML cohort (50 bp PE) and assessed the change in sensitivity between the initial libraries (average 59M fragments) and after downsampling to 40M fragments. When considering single nucleotide variants in recurrently mutated myeloid genes we found a comparable performance, with a 6% average loss in sensitivity using 40M fragments. Conclusions Between 30M and 40M 100 bp PE reads are needed to recover 90–95% of the initial variants on recurrently mutated myeloid genes. To extend this result to another cancer type, an exploration of the characteristics of its mutations and gene expression patterns is suggested.

Data processing is essential to reliably generate knowledge from proteomics studies. The complexity of the proteomics data, as well as the ability of research teams to adopt complex analysis pipelines, have proven to be an obstacle to effective collaboration and more efficient biological insight generation. Here, we introduce MD 2.0, a cloud- and web-based platform for quantitative proteomics data, which implements a novel analysis workspace where statistical analyses, visualizations, and external knowledge generation are integrated into a modular framework. This modularity enables researchers the flexibility to test different hypotheses, and customize and template complex proteomics analyses, thereby expediting insight generation for complex datasets. The extensible MD 2.0 environment has been built with a scalable architecture to allow rapid development of future analysis modules and enhanced tools for remote collaboration, like experiment sharing and a live chat capability. The new drag-and-drop modules allow researchers to easily and quickly assess different aspects of an experiment, including quality control, differential expression and enrichment analysis. The modularity of MD 2.0 lays the foundation to support broader community-based analytical template generation and optimized sharing and collaboration between proteomics experts and biologists, thereby accelerating research teams' abilities to extract knowledge from complex proteomics datasets.

Background RNA-Seq allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort of samples, library size is a fundamental factor affecting both the overall cost and the quality of the results. While several studies analyse the effect that library size has on differential expression analyses, sensitivity analysis for variant detection has received far less attention. Results We simulated shallower sequencing depths by downsampling 45 AML samples that are part of the Leucegene project, which were originally sequenced at high depth. We compared the sensitivity of six methods of recovering validated mutations on the same samples. The methods compared are a combination of three popular callers (MuTect, VarScan, and VarDict) and two filtering strategies. We observed an incremental loss in sensitivity when simulating libraries of 80M, 50M, 40M, 30M and 20M fragments, with the largest loss detected with less than 30M fragments (below 90%). The sensitivity in recovering indels varied markedly between callers, with VarDict showing the highest sensitivity (60%). Single nucleotide variant sensitivity is relatively consistent across methods, apart from MuTect, whose default filters need adjustment when using RNA-Seq. We also analysed 136 RNA-Seq samples from the TCGA-LAML cohort, assessing the change in sensitivity between the initial libraries (average 59M fragments) and after downsampling to 40M fragments. When considering single nucleotide variants in recurrently mutated myeloid genes we found a comparable performance, with a 3% average loss in sensitivity using 40M fragments. Conclusions Between 30M and 40M fragments are needed to recover 90%-95% of the initial variants on recurrently mutated myeloid genes. To extend this result to another cancer type, an exploration of the characteristics of its mutations and gene expression patterns is suggested.