Lei, H. | Search bioRxiv

bioRxiv2019

Targeting USP9x/SOX2 axis contributes to the anti-osteosarcoma effect of neogambogic acid

SOX2 has been viewed as a critical oncoprotein in osteosarcoma. Emerging evidence show that inducing the degradation of transcription factors such as SOX2 is a promising strategy to make them druggable. Here, we show that neogambogic acid (NGA), an active ingredients in garcinia, significantly inhibited the proliferation of osteosarcoma cells with ubiquitin proteasome-mediated degradation of SOX2 in vitro and in vivo. We further identified USP9x as a bona fide deubiquitinase for SOX2 and NGA directly interacts with USP9x in cells. Moreover, knockdown of USP9x inhibited the proliferation and colony formation of osteosarcoma cells, which could be rescued by overexpression of SOX2. Consistent with this, knockdown of USP9x inhibited the proliferation of osteosarcoma cells in a xenograft mouse model. Collectively, we identify USP9x as the first deubiquitinating enzyme for controlling the stability of SOX2 and USP9x is a direct target for NGA. We propose that targeting the USP9x/SOX2 axis represents a novel strategy for the therapeutic of osteosarcoma and other SOX2 related cancers.

cancer biology↗

bioRxiv2019

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data

Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but mixes many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing (SCS) is a promising alternative, but its limitations -- e.g., high noise, difficulty scaling to large populations, technical artifacts, and large data sets -- have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization (NMF) balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming (MILP) model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semi-simulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.

bioinformatics↗