From biology to personalized medicine: Recent knowledge in osteosarcoma.

High-grade osteosarcoma is the most common paediatric bone cancer. More than one third of patients relapse and die of osteosarcoma using current chemotherapeutic and surgical strategies. To improve outcomes in osteosarcoma, two crucial challenges need to be tackled: 1-the identification of hard-to-treat disease, ideally from diagnosis; 2- choosing the best combined or novel therapies to eradicate tumor cells which are resistant to current therapies leading to disease dissemination and metastasize as well as their favorable microenvironment. Genetic chaos, tumor complexity and heterogeneity render this task difficult. The development of new technologies like next generation sequencing has led to an improvement in osteosarcoma oncogenesis knownledge. This review summarizes recent biological and therapeutical advances in osteosarcoma, as well as the challenges that must be overcome in order to develop personalized medicine and new therapeutic strategies and ultimately improve patient survival.

CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data.

Motivation: Spatial transcriptomics enables the analysis of cell crosstalk in healthy and diseased organs by capturing the transcriptomic profiles of millions of cells within their spatial contexts. However, spatial transcriptomics approaches also raise new computational challenges for the multidimensional data analysis associated with spatial coordinates. Results: In this context, we introduce a novel analytical framework called CellsFromSpace based on independent component analysis (ICA), which allows users to analyze various commercially available technologies without relying on a single-cell reference dataset. The ICA approach deployed in CellsFromSpace decomposes spatial transcriptomics data into interpretable components associated with distinct cell types or activities. ICA also enables noise or artifact reduction and subset analysis of cell types of interest through component selection. We demonstrate the flexibility and performance of CellsFromSpace using real-world samples to demonstrate ICA's ability to successfully identify spatially distributed cells as well as rare diffuse cells, and quantitatively deconvolute datasets from the Visium, Slide-seq, MERSCOPE, and CosMX technologies. Comparative analysis with a current alternative reference-free deconvolution tool also highlights CellsFromSpace's speed, scalability and accuracy in processing complex, even multisample datasets. CellsFromSpace also offers a user-friendly graphical interface enabling non-bioinformaticians to annotate and interpret components based on spatial distribution and contributor genes, and perform full downstream analysis. Availability and implementation: CellsFromSpace (CFS) is distributed as an R package available from github at https://github.com/gustaveroussy/CFS along with tutorials, examples, and detailed documentation.