Recently, there has been an increasing interest in the development and characterization of patient-derived tumor xenograft (PDX) models for cancer research. PDX models mostly retain the principal histologic and genetic characteristics of their donor tumor and remain stable across passages. These models have been shown to be predictive of clinical outcomes and are being used for preclinical drug evaluation, biomarker identification, biologic studies, and personalized medicine strategies. This article summarizes the current state of the art in this field, including methodologic issues, available collections, practical applications, challenges and shortcomings, and future directions, and introduces a European consortium of PDX models.Significance: PDX models are increasingly used in translational cancer research. These models are useful for drug screening, biomarker development, and the preclinical evaluation of personalized medicine strategies. This review provides a timely overview of the key characteristics of PDX models and a detailed discussion of future directions in the field. Cancer Discov; 4(9); 998-1013.

Patient-derived xenografts (PDXs) have emerged as an important platform to elucidate new treatments and biomarkers in oncology. PDX models are used to address clinically relevant questions, including the contribution of tumour heterogeneity to therapeutic responsiveness, the patterns of cancer evolutionary dynamics during tumour progression and under drug pressure, and the mechanisms of resistance to treatment. The ability of PDX models to predict clinical outcomes is being improved through mouse humanization strategies and implementation of co-clinical trials, within which patients and PDXs reciprocally inform therapeutic decisions. This Opinion article discusses aspects of PDX modelling that are relevant to these questions and highlights the merits of shared PDX resources to advance cancer medicine from the 6 perspective of EurOPDX, an international initiative devoted to PDX-based research.Response to anticancer therapies varies owing to the substantial molecular heterogeneity of human tumours and to poorly defined mechanisms of drug efficacy and resistance 1 . Immortalized cancer cell lines, either cultured in vitro or grown as xenografts, cannot interrogate the complexity of human tumours, and only provide determinate insights into human disease, as they are limited in number and diversity, and have been cultured on plastic over decades 2 .This disconnection in scale and biological accuracy contributes considerably to attrition in drug development [3][4][5] .Surgically derived clinical tumour samples that are implanted in mice (known as patient-derived xenografts (PDXs)) are expected to better inform therapeutic development strategies. As intact tissue -in which the tumour architecture and the relative proportion of cancer cells and stromal cells are both maintained -is directly implanted into recipient animals, the alignment with human disease is enhanced. More importantly, PDXs retain the idiosyncratic characteristics of different tumours from different patients; hence, they can effectively recapitulate the intra-tumour and inter-tumour heterogeneity that typifies human cancer 6-9 . 7 Exhaustive information on the key characteristics and the practical applications of PDXs can be found in recent reviews [10][11][12][13] . In this Opinion article, we discuss basic methodological concepts, as well as challenges and opportunities in developing "next-generation" models to improve the reach of PDXs as preclinical tools for in vivo studies (TABLE 1). We also elaborate on the merits of PDXs for exploring the intrinsic heterogeneity and subclonal genetic evolution of individual tumours, and discuss how this may influence therapeutic resistance. Finally, we examine the utility of PDXs in navigating complex variables in clinical decision-making, such as the discovery of predictive and prognostic biomarkers, and the categorization of genotype-drug response correlations in high-throughput formats. Being primarily co-authored by leading members of the EurOPDX Consortium (see Further information), we provide...

Valosin-containing protein (VCP, also called p97) is an essential and highly conserved adenosine triphosphate-dependent chaperone implicated in a wide range of cellular processes in eukaryotes, and mild VCP mutations can cause severe neurodegenerative disease. Here we show that mammalian VCP is trimethylated on Lys315 in a variety of cell lines and tissues, and that the previously uncharacterized protein mETTL21D (denoted here as VCP lysine methyltransferase, VCP-KmT) is the responsible enzyme. VCP methylation was abolished in three human VCPKmT knockout cell lines generated with zinc-finger nucleases. Interestingly, VCP-KmT was recently reported to promote tumour metastasis, and indeed, VCP-KmT-deficient cells displayed reduced growth rate, migration and invasive potential. Finally, we present data indicating that VCP-KmT, calmodulin-lysine methyltransferase and eight uncharacterized proteins together constitute a novel human protein methyltransferase family. The present work provides new insights on protein methylation and its links to human disease.

Helper-dependent adenovirus (HD-Ad

Breast cancers exhibit genome-wide aberrant DNA methylation patterns. To investigate how these affect the transcriptome and which changes are linked to transformation or progression, we apply genome-wide expression–methylation quantitative trait loci (emQTL) analysis between DNA methylation and gene expression. On a whole genome scale, in cis and in trans, DNA methylation and gene expression have remarkably and reproducibly conserved patterns of association in three breast cancer cohorts (n = 104, n = 253 and n = 277). The expression–methylation quantitative trait loci associations form two main clusters; one relates to tumor infiltrating immune cell signatures and the other to estrogen receptor signaling. In the estrogen related cluster, using ChromHMM segmentation and transcription factor chromatin immunoprecipitation sequencing data, we identify transcriptional networks regulated in a cell lineage-specific manner by DNA methylation at enhancers. These networks are strongly dominated by ERα, FOXA1 or GATA3 and their targets were functionally validated using knockdown by small interfering RNA or GRO-seq analysis after transcriptional stimulation with estrogen.