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...

Focal amplifications of chromosome 3p13-3p14 occur in about 10% of melanomas and are associated with a poor prognosis. The melanoma-specific oncogene MITF resides at the epicentre of this amplicon. However, whether other loci present in this amplicon also contribute to melanomagenesis is unknown. Here we show that the recently annotated long non-coding RNA (lncRNA) gene SAMMSON is consistently co-gained with MITF. In addition, SAMMSON is a target of the lineage-specific transcription factor SOX10 and its expression is detectable in more than 90% of human melanomas. Whereas exogenous SAMMSON increases the clonogenic potential in trans, SAMMSON knockdown drastically decreases the viability of melanoma cells irrespective of their transcriptional cell state and BRAF, NRAS or TP53 mutational status. Moreover, SAMMSON targeting sensitizes melanoma to MAPK-targeting therapeutics both in vitro and in patient-derived xenograft models. Mechanistically, SAMMSON interacts with p32, a master regulator of mitochondrial homeostasis and metabolism, to increase its mitochondrial targeting and pro-oncogenic function. Our results indicate that silencing of the lineage addiction oncogene SAMMSON disrupts vital mitochondrial functions in a cancer-cell-specific manner; this silencing is therefore expected to deliver highly effective and tissue-restricted anti-melanoma therapeutic responses.

SummaryHypermethylation of tumor suppressor gene (TSG) promoters confers growth advantages to cancer cells, but how these changes arise is poorly understood. Here, we report that tumor hypoxia reduces the activity of oxygen-dependent TET enzymes, which catalyze DNA de-methylation through 5-methylcytosine oxidation. This occurs independently of hypoxia-associated alterations in TET expression, proliferation, metabolism, HIF activity or reactive oxygen, but directly depends on oxygen shortage. Hypoxia-induced loss of TET activity increases hypermethylation at gene promoters in vitro. Also in patients, TSG promoters are markedly more methylated in hypoxic tumors, independently of proliferation, stromal cell infiltration and tumor characteristics. Our data suggest cellular selection of hypermethylation events, with almost half of them being ascribable to hypoxia across tumor types. Accordingly, increased hypoxia after vessel pruning in murine breast tumors increases hypermethylation, while restored tumor oxygenation by vessel normalization abrogates this effect. Tumor hypoxia thus acts as a novel regulator underlying DNA methylation.

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Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models Minimal Information standard (PDX-MI) for reporting on the generation, quality assurance and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient’s tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models.