BloodSpot is a gene-centric database of mRNA expression of haematopoietic cells. The web-based interface to the database includes three concomitant levels of visualization for a gene query; foremost is the expression across hematopoietic cell types, second is analysis of survival of Acute Myeloid Leukaemia patients based on gene expression, and lastly, the expression visualized in an interactive developmental tree. With the introduction of single cell data we have now also included an unbiased dimensionality reduction method to show gene expression over the continuum of haematopoiesis. The webserver includes a few select analysis functionalities, like Student's t-test, identification of correlating genes and lookup of whole genetic signatures, with the aim of making generation and testing of hypotheses quick and intuitive. The visualizations have been updated to accommodate new datatypes and the database has been largely expanded with RNA-sequencing datasets, both purified in bulk and at single cell resolution, increasing the number of single samples more than 10 fold, while keeping simplicity in presentation. The database should be of interest for any researcher within leukaemia, haematopoiesis, cellular development, or stem cells. The database is freely available at www.bloodspot.eu

Copy-number variations (CNVs) have important clinical implications for several diseases and cancers. Relevant CNVs are hard to detect because common structural variations define large parts of the human genome. CNV calling from short-read sequencing would allow single protocol full genomic profiling. We reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a reference cohort encompassing 39 whole genome sequencing (WGS) samples paired current clinical standard—SNP-array based CNV calling. Additionally, for nine samples we also performed whole exome sequencing (WES), to address the effect of sequencing protocol on CNV calling. Furthermore, we included Gold Standard reference sample NA12878, and tested 12 samples with CNVs confirmed by multiplex ligation-dependent probe amplification (MLPA). Tool performance varied greatly in the number of called CNVs and bias for CNV lengths. Some tools had near-perfect recall of CNVs from arrays for some samples, but poor precision. Several tools had better performance for NA12878, which could be a result of overfitting. We suggest combining the best tools also based on different methodologies: GATK gCNV, Lumpy, DELLY, and cn.MOPS. Reducing the total number of called variants could potentially be assisted by the use of background panels for filtering of frequently called variants.

Mutations in the TP53 tumor suppressor gene are common in many cancer types, including the acute myeloid leukemia (AML) subtype known as complex karyotype AML (CK-AML). Here, we identify a gain-of-function (GOF) Trp53 mutation that accelerates CK-AML initiation beyond p53 loss and, surprisingly, is required for disease maintenance. The Trp53^R172H mutation (TP53^R175H in humans) exhibits a neomorphic function by promoting aberrant self-renewal in leukemic cells, a phenotype that is present in hematopoietic stem and progenitor cells (HSPC) even prior to their transformation. We identify FOXH1 as a critical mediator of mutant p53 function that binds to and regulates stem cell–associated genes and transcriptional programs. Our results identify a context where mutant p53 acts as a bona fide oncogene that contributes to the pathogenesis of CK-AML and suggests a common biological theme for TP53 GOF in cancer. Significance: Our study demonstrates how a GOF p53 mutant can hijack an embryonic transcription factor to promote aberrant self-renewal. In this context, mutant Trp53 functions as an oncogene to both initiate and sustain myeloid leukemia and suggests a potential convergent activity of mutant Trp53 across cancer types. This article is highlighted in the In This Issue feature, p. 813

Summary: Rare inherited bleeding disorders (IBD) are a common cause of bleeding tendency. To ensure a correct diagnosis, specialized laboratory analyses are necessary. This study reports the results of an upfront diagnostic strategy using targeted whole exome sequencing. In total, 156 patients with a significant bleeding assessment tool score participated in the study, of which a third had thrombocytopenia. Eighty‐seven genes specifically associated with genetic predisposition to bleeding were analysed by whole exome sequencing. Variants were classified according to the five‐tier scheme. We identified 353 germline variants. Eight patients (5%) harboured a known pathogenic variant. Of the 345 previously unknown variants, computational analyses predicted 99 to be significant. Further filtration according to the Mendelian inheritance pattern, resulted in 59 variants being predicted to be clinically significant. Moreover, 34% (20/59) were assigned as novel class 4 or 5 variants upon targeted functional testing. A class 4 or 5 variant was identified in 30% of patients with thrombocytopenia (14/47) versus 11% of patients with a normal platelet count (12/109) (P < 0·01). An IBD diagnosis has a major clinical impact. The genetic investigations detailed here extricated our patients from a diagnostic conundrum, thus demonstrating that continuous optimization of the diagnostic work‐up of IBD is of great benefit.

In chronic lymphocytic leukemia, TP53 mutations and deletion of chromosome 17p are well-characterized biomarkers associated with poor progression-free and overall survival following chemoimmunotherapy. Patients harboring low burden TP53 mutations with variant allele frequencies of 0.3-15% have been shown to have similar dismal outcome as those with high burden mutations. We here describe a highly sensitive deep targeted Next Generation Sequencing assay allowing for the detection of TP53 mutations as low as 0.2% variant allele frequency. Within a consecutive, single center cohort of 290 newly diagnosed patients with chronic lymphocytic leukemia, deletion of chromosome 17p was the only TP53 aberration significantly associated with shorter overall survival and treatment-free survival. We were unable to demonstrate any impact of TP53 mutations, whether high burden (variant allele frequency >10%) or low burden (variant allele frequency ≤10%), in the absence of deletion of chromosome 17p. In addition, the impact of high burden TP53 aberration (deletion of chromosome 17p and/or TP53 mutation with variant allele frequency >10%) was only evident for patients with IGHV unmutated status; no impact of TP53 aberrations on outcome was seen for patients with IGHV mutated status. In 61 patients at time of treatment, the prognostic impact of TP53 mutations above 1% variant allele frequency could be confirmed. This study furthers the identification of a clinical significant limit of detection for robust TP53 mutation analysis in chronic lymphocytic leukemia. Multicenter studies are needed for validation of ultra-sensitive TP53 mutation assays in order to define and implement a technical as well as clinical lower limit of detection.

Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the data. A class of neural networks, namely autoencoders, has been useful for denoising of single cell data, imputation of missing values and dimensionality reduction. Here, we present a striking feature with the potential to greatly increase the usability of autoencoders: With specialized training, the autoencoder is not only able to generalize over the data, but also to tease apart biologically meaningful modules, which we found encoded in the representation layer of the network. Our model can, from scRNA-seq data, delineate biological meaningful modules that govern a dataset, as well as give information as to which modules are active in each single cell. Importantly, most of these modules can be explained by known biological functions, as provided by the Hallmark gene sets. We discover that tailored training of an autoencoder makes it possible to deconvolute biological modules inherent in the data, without any assumptions. By comparisons with gene signatures of canonical pathways we see that the modules are directly interpretable. The scope of this discovery has important implications, as it makes it possible to outline the drivers behind a given effect of a cell. In comparison with other dimensionality reduction methods, or supervised models for classification, our approach has the benefit of both handling well the zero-inflated nature of scRNA-seq, and validating that the model captures relevant information, by establishing a link between input and decoded data. In perspective, our model in combination with clustering methods is able to provide information about which subtype a given single cell belongs to, as well as which biological functions determine that membership.