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...

The characterization of cells with tumour initiating potential is significant for advancing our understanding of cancer and improving therapy. Aggressive, triple-negative breast cancers (TNBCs) are enriched for tumour-initiating cells (TICs). We investigated that hypothesis that VEGF receptors expressed on TNBC cells mediate autocrine signalling that contributes to tumour initiation. We discovered the VEGF receptor neuropilin-2 (NRP2) is expressed preferentially on TICs, involved in the genesis of TNBCs and necessary for tumour initiation. The mechanism by which NRP2 signalling promotes tumour initiation involves stimulation of the α6β1 integrin, focal adhesion kinase-mediated activation of Ras/MEK signalling and consequent expression of the Hedgehog effector GLI1. GLI1 also induces BMI-1, a key stem cell factor, and it enhances NRP2 expression and the function of α6β1, establishing an autocrine loop. NRP2 can be targeted in vivo to retard tumour initiation. These findings reveal a novel autocrine pathway involving VEGF/NRP2, α6β1 and GLI1 that contributes to the initiation of TNBC. They also support the feasibility of NRP2-based therapy for the treatment of TNBC that targets and impedes the function of TICs.

Summary Although the α6β1 integrin has been implicated in the function of breast and other cancer stem cells (CSCs), little is known about its regulation and relationship to mechanisms involved in the genesis of CSCs. We report that a CD44high/CD24low population, enriched for CSCs, is comprised of distinct epithelial and mesenchymal populations that differ in expression of the two α6 cytoplasmic domain splice variants: α6A and α6B. α6Bβ1 expression defines the mesenchymal population and is necessary for CSC function, a function that cannot be executed by α6A integrins. The generation of α6Bβ1 is tightly controlled and occurs as a consequence of an autocrine VEGF signaling that culminates in the transcriptional repression of a key RNA splicing factor. These data alter our understanding of how α6β1 contributes to breast cancer and they resolve ambiguities regarding the use of total α6 (CD49f) expression as a biomarker for CSCs.

The claudin-low breast cancer subtype is defined by gene expression characteristics and encompasses a remarkably diverse range of breast tumors. Here, we investigate genomic, transcriptomic, and clinical features of claudin-low breast tumors. We show that claudin-low is not simply a subtype analogous to the intrinsic subtypes (basal-like, HER2-enriched, luminal A, luminal B and normal-like) as previously portrayed, but is a complex additional phenotype which may permeate breast tumors of various intrinsic subtypes. Claudin-low tumors are distinguished by low genomic instability, mutational burden and proliferation levels, and high levels of immune and stromal cell infiltration. In other aspects, claudin-low tumors reflect characteristics of their intrinsic subtype. Finally, we explore an alternative method for identifying claudin-low tumors and thereby uncover potential weaknesses in the established claudin-low classifier. In sum, these findings elucidate the heterogeneity in claudin-low breast tumors, and substantiate a re-definition of claudin-low as a cancer phenotype.

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