Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors

Structural and molecular correlates of cognitive aging in the rat

Aging is associated with cognitive decline. Herein, we studied a large cohort of old age and young adult male rats and confirmed that, as a group, old  rats display poorer spatial learning and behavioral flexibility than younger adults. Surprisingly, when animals were clustered as good and bad performers, our data revealed that while in younger animals better cognitive performance was associated with longer dendritic trees and increased levels of synaptic markers in the hippocampus and prefrontal cortex, the opposite was found in the older group, in which better performance was associated with shorter dendrites and lower levels of synaptic markers. Additionally, in old, but not young individuals, worse performance correlated with increased levels of BDNF and the autophagy substrate p62, but decreased levels of the autophagy complex protein LC3. In summary, while for younger individuals "bigger is better", "smaller is better" is a more appropriate aphorism for older subjects.Portuguese Foundation for Science and Technology (FCT) with fellowships granted to: Cristina Mota (SFRH/BD/81881/2011), Susana Monteiro (SFRH/BD/69311/2010), Sofia Pereira das Neves and Sara Monteiro-Martins (PIC/IC/83213/2007); and by the European Commission within the 7th framework program, under the grant agreement: Health-F2-2010-259772 (Switchbox). In addition, this work was co-funded by the Northern Portugal Regional Operational Programme (ON.2 SR&TD Integrated Program – NORTE-07-0124-FEDER-000021), through the European Regional Development Fund (FEDER) and by national funds granted by FCT (PEst-C/SAU/LA0026/2013), and FEDER through the COMPETE (FCOMP-01-0124-FEDER-037298)

A novel single-cell based method for breast cancer prognosis

Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.Xiaomei Li, Lin Liu, Gregory J. Goodall, Andreas Schreiber, Taosheng Xu, Jiuyong Li, Thuc D. L