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...

Endometrial cancer is the most commonly diagnosed cancer of the female reproductive tract in developed countries. Through genome-wide association studies (GWAS), we have previously identified eight risk loci for endometrial cancer. Here, we present an expanded meta-analysis of 12,906 endometrial cancer cases and 108,979 controls (including new genotype data for 5624 cases) and identify nine novel genome-wide significant loci, including a locus on 12q24.12 previously identified by meta-GWAS of endometrial and colorectal cancer. At five loci, expression quantitative trait locus (eQTL) analyses identify candidate causal genes; risk alleles at two of these loci associate with decreased expression of genes, which encode negative regulators of oncogenic signal transduction proteins (SH2B3 (12q24.12) and NF1 (17q11.2)). In summary, this study has doubled the number of known endometrial cancer risk loci and revealed candidate causal genes for future study.

Gliomas are the most common primary tumours affecting the adult central nervous system and respond poorly to standard therapy. Myc is causally implicated in most human tumours and the majority of glioblastomas have elevated Myc levels. Using the Myc dominant negative Omomyc, we previously showed that Myc inhibition is a promising strategy for cancer therapy. Here, we preclinically validate Myc inhibition as a therapeutic strategy in mouse and human glioma, using a mouse model of spontaneous multifocal invasive astrocytoma and its derived neuroprogenitors, human glioblastoma cell lines, and patient-derived tumours both in vitro and in orthotopic xenografts. Across all these experimental models we find that Myc inhibition reduces proliferation, increases apoptosis and remarkably, elicits the formation of multinucleated cells that then arrest or die by mitotic catastrophe, revealing a new role for Myc in the proficient division of glioma cells.

Recent evidence points to Myc – a multifaceted bHLHZip transcription factor deregulated in the majority of human cancers – as a priority target for therapy. How to target Myc is less clear, given its involvement in a variety of key functions in healthy cells. Here we report on the action mechanism of the Myc interfering molecule termed Omomyc, which demonstrated astounding therapeutic efficacy in transgenic mouse cancer models in vivo. Omomyc action is different from the one that can be obtained by gene knockout or RNA interference, approaches designed to block all functions of a gene product. This molecule – instead – appears to cause an edge-specific perturbation that destroys some protein interactions of the Myc node and keeps others intact, with the result of reshaping the Myc transcriptome. Omomyc selectively targets Myc protein interactions: it binds c- and N-Myc, Max and Miz-1, but does not bind Mad or select HLH proteins. Specifically, it prevents Myc binding to promoter E-boxes and transactivation of target genes while retaining Miz-1 dependent binding to promoters and transrepression. This is accompanied by broad epigenetic changes such as decreased acetylation and increased methylation at H3 lysine 9. In the presence of Omomyc, the Myc interactome is channeled to repression and its activity appears to switch from a pro-oncogenic to a tumor suppressive one. Given the extraordinary therapeutic impact of Omomyc in animal models, these data suggest that successfully targeting Myc for cancer therapy might require a similar twofold action, in order to prevent Myc/Max binding to E-boxes and, at the same time, keep repressing genes that would be repressed by Myc.

Background The strongest known risk factor for endometrial cancer (EC) is obesity. To determine whether single nucleotide polymorphisms (SNPs) associated with increased body mass index (BMI) or waist-hip ratio (WHR) are associated with EC risk, independent of measured BMI, we investigated relationships between 77 BMI and 47 WHR SNPs and EC in 6,609 cases and 37,926 country-matched controls. Methods Logistic regression analysis and fixed-effects meta-analysis were used to test for associations between EC risk and (i) individual BMI or WHR SNPs, (ii) a combined weighted genetic risk score (wGRS) for BMI or WHR. Causality of BMI for EC was assessed using Mendelian randomization, with BMIwGRS as instrumental variable. Results The BMIwGRS was significantly associated with EC risk (P=3.4×10−17). Scaling the effect of the BMIwGRS on EC risk by its effect on BMI, the EC odds ratio (OR) per 5kg/m2 of genetically predicted BMI was 2.06 (95% confidence interval(CI)=1.89–2.21), larger than the observed effect of BMI on EC risk (OR=1.55, 95%CI 1.44–1.68, per 5kg/m2). The association attenuated but remained significant after adjusting for BMI (OR=1.22, 95%CI=1.10–1.39,P=5.3×10−4). There was evidence of directional pleiotropy (P=1.5×10−4). BMI SNP rs2075650 was associated with EC at study-wide significance (P<4.0×10−4), independent of BMI. EC was not significantly associated with individual WHR SNPs or the WHRwGRS. Conclusions BMI, but not WHR, is causally associated with EC risk, with evidence that some BMI-associated SNPs alter EC risk via mechanisms other than measurable BMI. Impact The causal association between BMI SNPs and EC has possible implications for EC risk modeling.