Interactions in CSF1-Driven Tenosynovial Giant Cell Tumors

Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing

Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.

Active surveillance of diffuse-type tenosynovial giant cell tumors: A retrospective, multicenter cohort study

BACKGROUND

Diffuse-type tenosynovial giant cell tumor (D-TGCT) is a mono-articular, soft-tissue tumor. Although it can behave locally aggressively, D-TGCT is a non-malignant disease. This is the first study describing the natural course of D-TGCT and evaluating active surveillance as possible treatment strategy.

METHODS

This retrospective, multicenter study included therapy naïve patients with D-TGCT from eight sarcoma centers worldwide between 2000 and 2019. Patients initially managed by active surveillance following their first consultation were eligible. Data regarding the radiological and clinical course and subsequent treatments were collected.

RESULTS

Sixty-one patients with primary D-TGCT were initially managed by active surveillance. Fifty-nine patients had an MRI performed around first consultation: D-TGCT was located intra-articular in most patients (n = 56; 95 %) and extra-articular in 14 cases (24 %). At baseline, osteoarthritis was observed in 13 patients (22 %) on MRI. Most of the patients' reported symptoms: pain (n = 43; 70 %), swelling (n = 33; 54 %). Eight patients (13 %) were asymptomatic. Follow-up data were available for 58 patients; the median follow-up was 28 months. Twenty-one patients (36 %) had radiological progression after 21 months (median). Eight of 45 patients (18 %) without osteoarthritis at baseline developed osteoarthritis during follow-up. Thirty-seven patients (64 %) did not clinically deteriorate during follow-up. Finally, eighteen patients (31 %) required a subsequent treatment.

CONCLUSION

Active surveillance can be considered adequate for selected therapy naïve D-TGCT patients. Although follow-up data was limited, almost two-thirds of the patients remained progression-free, and 69 % did not need treatment during the follow-up period. However, one-fifth of patients developed secondary osteoarthritis. Prospective studies on active surveillance are warranted.

Identification of potential diagnostic biomarkers for tenosynovial giant cell tumour by integrating microarray and single-cell RNA sequencing data

PURPOSE

Tenosynovial giant cell tumour (TGCT) is a benign hyperplastic and inflammatory disease of the joint synovium or tendon sheaths, which may be misdiagnosed due to its atypical symptoms and imaging features. We aimed to identify biomarkers with high sensitivity and specificity to aid in diagnosing TGCT.

METHODS

Two scRNA-seq datasets (GSE210750 and GSE152805) and two microarray datasets (GSE3698 and GSE175626) were downloaded from the Gene Expression Omnibus (GEO) database. By integrating the scRNA-seq datasets, we discovered that the osteoclasts are abundant in TGCT in contrast to the control. The single-sample gene set enrichment analysis (ssGSEA) further validated this discovery. Differentially expressed genes (DEGs) of the GSE3698 dataset were screened and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were conducted. Osteoclast-specific up-regulated genes (OCSURGs) were identified by intersecting the osteoclast marker genes in the scRNA-seq and the up-regulated DEGs in the microarray and by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The expression levels of OCSURGs were validated by an external dataset GSE175626. Then, single gene GSEA, protein-protein interaction (PPI) network, and gene-drug network of OCSURGs were performed.

RESULT

22 seurat clusters were acquired and annotated into 10 cell types based on the scRNA-seq data. TGCT had a larger population of osteoclasts compared to the control. A total of 159 osteoclast marker genes and 104 DEGs (including 61 up-regulated genes and 43 down-regulated genes) were screened from the scRNA-seq analysis and the microarray analysis. Three OCSURGs (MMP9, SPP1, and TYROBP) were finally identified. The AUC of the ROC curve in the training and testing datasets suggested a favourable diagnostic capability. The PPI network results illustrated the protein-protein interaction of each OCSURG. Drugs that potentially target the OCSURGs were predicted by the DGIdb database.

CONCLUSION

MMP9, SPP1, and TYROBP were identified as osteoclast-specific up-regulated genes of the tenosynovial giant cell tumour via bioinformatic analysis, which had a reasonable diagnostic efficiency and served as potential drug targets.