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The treatment options for central chondrosarcoma are limited, and prognoses are generally unreliable. The presence and absence of mutations in IDH1, and IDH2 are defining events, and TERT mutations have been recently been associated with poor outcome. Despite this, molecular biomarkers are lacking. Here, analysing data from 356 patients, comprising results from whole genome sequencing (n=68), digital droplet PCR (n=346), and methylation arrays (n=57), we present a comprehensive genetic analysis of chondrosarcoma and suggest its clinical utility. Methylation profiles, TERT promoter mutations, genome doubling with prior haploidisation, and age at diagnosis of high grade, distinguish IDH1-mutant, IDH2-mutant and IDH wildtype tumours. The majority of IDH2-mutant tumours harbour TERT mutations, though a significant reduction in survival is only found in the less common mutational combination of IDH1 and TERT. We suggest that diagnostic testing for IDH1, IDH2 and TERT mutations could guide clinical monitoring and prognostication.

Central conventional chondrosarcoma (CS) is the most common subtype of primary malignant bone tumour in adults. Treatment options are usually limited to surgery, and prognosis is challenging. These tumours are characterised by the presence and absence of IDH1 and IDH2 mutations, and recently, TERT promoter alterations have been reported in around 20% of cases. The effect of these mutations on clinical outcome remains unclear. The purpose of this study was to determine if prognostic accuracy can be improved by the addition of genomic data, and specifically by examination of IDH1, IDH2, and TERT mutations.In this study, we combined both archival samples and data sourced from the Genomics England 100,000 Genomes Project (n = 356). Mutations in IDH1, IDH2, and TERT were profiled using digital droplet PCR (n = 346), whole genome sequencing (n=68), or both (n = 64). Complex events and other genetic features were also examined, along with methylation array data (n = 84). We correlated clinical features and patient outcomes with our genetic findings.IDH2-mutant tumours occur in older patients and commonly present with high-grade or dedifferentiated disease. Notably, TERT mutations occur most frequently in IDH2-mutant tumours, although have no effect on survival in this group. In contrast, TERT mutations are rarer in IDH1-mutant tumours, yet they are associated with a less favourable outcome in this group. We also found that methylation profiles distinguish IDH1- from IDH2-mutant tumours. IDH wild-type tumours rarely exhibit TERT mutations and tend to be diagnosed in a younger population than those with tumours harbouring IDH1 and IDH2 mutations. A major genetic feature of this group is haploidisation and subsequent genome doubling. These tumours evolve less frequently to dedifferentiated disease and therefore constitute a lower risk group.Tumours with IDH1 or IDH2 mutations or those that are IDHwt have significantly different genetic pathways and outcomes in relation to TERT mutation. Diagnostic testing for IDH1, IDH2, and TERT mutations could therefore help to guide clinical monitoring and prognostication.

Background Regulatory T cells (Tregs) are a non-redundant, suppressive population of CD4+ T cells that are essential for the prevention of autoimmune diseases in humans. They may also contribute to overall immune suppression in malignancies and prevent effective immune surveillance against malignant cells. The correlation between the increased number of Tregs and higher-risk MDS has been shown before (Kordasti, Blood 2007). Expansion of effector T cells (Teff) also signifies the lower-risk disease (Kordasti, BJH, 2009). Using deep phenotyping and unbiased automated clustering we have previously identified novel populations of Tregs with distinct functional properties (Kordasti, Blood 2016), including a CD161-expressing subpopulation (Povoleri, Nature Imm, 2018). The aim of this study was to interrogate Treg and Teff subpopulations in more depth and identify transcriptomic profiles of Tregs and Teff in bone marrow (BM) and peripheral blood (PB) of MDS patients, and potential differences between PB and BM residing cells. Single-cell RNA sequencing (scRNAseq) in combination with oligonucleotide-tagged antibodies and an in-house pipeline utilizing Seurat for QC, data integration and analysis were used for this purpose. Methods PB and BM were collected from a low (del(5q)) and a high-risk (monosomy 7) MDS patient. These samples were magnetically enriched for CD4+ T cells, followed by flow sorting of Tregs (CD4+, CD25high, and CD127low) and Teff (CD4+, CD25low, and CD127high). In total, 8 cell fractions were used for downstream scRNAseq and concomitant protein expression profiling using the BD Rhapsody Single-Cell Analysis System (Becton Dickinson, CA, USA). Cell fractions were first labelled by oligo-tagged antibodies (CD95, CD45RO, CD38, CTLA-4, PD-1, HLA-DR) and a sample tag (for sample multiplexing). After labelling, all samples were combined for cartridge-based single-cell capture and molecular indexing of mRNA transcripts on magnetic barcoded beads. Following bead retrieval and on-bead cDNA synthesis, parallel targeted mRNA (259 T cell-related genes) and Abseq sequencing libraries were generated. Deep sequencing was performed using the HiSeq400 platform. Nonlinear dimensionality reduction was carried out using uniform manifold approximation and projection (UMAP), which produced UMAP layouts and heatmaps to identify populations. Tregs were further stratified according to high/low expression of the CD95 protein, CD161, and CD150 mRNA expression. The Wilcoxon Rank Sum test was used for statistics followed by Bonferroni corrections. The Gene Set Variation Analysis (GSVA) package was used for pathway enrichment analysis. Results and discussion Unbiased analysis by UMAP was able to clearly separate the high- and low-risk patients based on the Treg and Teff signature in BM, which was not the case in PB (Figure 1a and 1b). At protein level, BM Tregs from the high-risk patient had a significantly higher expression of CD95 (p = 7.23 x 10-17), CD45RO (p = 1.06 x 10-51), CTLA4 (p = 4.47 x 10-83) PD-1(p = 5.42 x 10-81), and CD38 (p = 4.16 x 10-63) (Figure 1c), suggesting a more functional Treg profile. GSVA analysis of transcriptomes showed significant changes between high- and low-risk patients, for both Tregs (102 pathways p≤0.05) and Teffs (89 pathways p≤0.05) in BM. Specifically, Tregs from high-risk MDS were more enriched with non-TCR-mediated activation pathways, including leukocyte differentiation, Jak-STAT signaling, and positive regulation of leukocyte cell-cell adhesion (Figure 1d). A deeper investigation of Tregs showed that subsets of interest, in particular the IL-17 secreting CD161+ Tregs and the hematopoietic stem cell quiescence-maintaining CD150+ Tregs (Hirata, 2018) were found in low abundance in the BM of both high- and low-risk patients; 16% and 10%; and, 1% and 7%, respectively. In summary, we have developed an analytical pipeline and provide evidence for the ability of simultaneous single-cell transcriptomics and protein expression to identify different BM-specific T cell signatures in high- versus low-risk MDS. Interestingly, Tregs from high risk MDS BM show higher expression of Treg-related proteins and are more enriched in non-TCR-specific activation pathways, which may reflect a myeloid derived inflammatory environment. Overall, these findings suggest a bone marrow-specific Treg signature in MDS, which could distinguish low- and high-risk disease. Disclosures McLornan: Jazz Pharmaceuticals: Honoraria, Speakers Bureau; Novartis: Honoraria. Platzbecker:Celgene: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria. Kordasti:Celgene: Research Funding; Novartis: Research Funding; Boston Biomed: Consultancy; API: Consultancy.

Structural segmentation of musical audio signals is one of many active areas of Music Information Retrieval (MIR) research. One aspect of this important topic which has so far received little attention though is the potential advantage to be gained by utilizing multitrack audio. This paper gives an overview of current segmentation techniques, and demonstrates that by applying a particular segmentation algorithm to multitrack data, rather than the usual case of fully mixed audio, we achieve a significant and quantifiable increase in accuracy when locating segment boundaries. Additionally, we provide details of a structurally annotated multitrack test set available for use by other researchers.

In the transition from traditional to digital musicology, large scale music data are increasingly becoming available which require research methods that work on the collection level and at scale. In the Digital Music Lab (DML) project, a software system has been developed that provides large-scale analysis of music audio with an interactive interface. The DML system includes distributed processing of audio and other music data, remote analysis of copyright-restricted data, logical inference on the extracted information and metadata, and visual web-based interfaces for exploring and querying music collections. A system prototype has been set up in collaboration with the British Library and I Like Music Ltd, which has been used to analyse a diverse corpus of over 250,000 music recordings. In this paper we describe the system requirements, architecture, components, and data sources, explaining their interaction. Use cases and applications with initial evaluations of the proposed system are also reported.

We propose two speech/music discrimination methods using timbre models and measure their performances on a 3 hour long database of radio podcasts from the BBC. In the first method, the machine estimated classifications obtained with an automatic timbre recognition (ATR) model are post-processed using median filtering. The classification system (LSF/K-means) was trained using two different taxonomic levels, a high-level one (speech, music), and a lower-level one (male and female speech, classical, jazz, rock & pop). The second method combines automatic structural segmentation and timbre recognition (ASS/ATR). The ASS evaluates the similarity between feature distributions (MFCC, RMS) using HMM and soft K-means algorithms. Both methods were evaluated at a semantic (relative correct overlap RCO), and temporal (boundary retrieval F-measure) levels. The ASS/ATR method obtained the best results (average RCO of 94.5% and boundary F-measure of 50.1%). These performances were favourably compared with that obtained by a SVM-based technique providing a good benchmark of the state of the art.