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Patient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

SUMMARYWe previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.HIGHLIGHTSA new gene expression profiling method, L1000, dramatically lowers costThe Connectivity Map database now includes 1.3 million publicly accessible L1000 perturbational profilesThis expanded Connectivity Map facilitates discovery of small molecule mechanism of action and functional annotation of genetic variantsThe work establishes feasibility and utility of a truly comprehensive Connectivity Map

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMapcan be used to discover mechanism of action of small molecules, functionally annotate genetic variants ofdisease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

Patient-Derived Xenografts (PDX) are proven models to study novel drugs or drug combinations and test hypothesis in preclinical studies. The overarching goal of the PDXNet is to coordinate the development of appropriate PDX models and methods for preclinical drug testing to advance CTEP clinical development of new cancer agents. The PDXNet is an NCI-funded consortium of six PDX Development and Trial Centers (PDTCs) and one PDCCC. Four PDTCs are responsible for developing PDXs and executing specific preclinical trials focused on cancer types including breast cancer, melanoma, and lung cancer. The other two recently awarded centers are specifically focused on minority PDX models and preclinical trials. Besides the PDTCs, the NCI Patient-Derived Models Repository (PDMR) at the Frederick National Laboratory for Cancer Research (FNLCR) is also providing models and data to the PDXNet. The PDCCC is responsible for coordination and developing standards for PDX generation as well as data analysis and metadata harmonization. The PDX Data Commons is built on top of existing NCI resources, leveraging the Cancer Genomics Cloud maintained by Seven Bridges Genomics, where PDXNet data is co-located with TCGA and other large-scale datasets. The PDCCC is co-led by experts from the Jackson Laboratory, providing scientific leadership in xenograft methods and cancer biology to ensure the promulgation of standards that are well-suited for the PDX community. A new portal has been set up at https://www.pdxnetwork.org/ to serve as the point of access to PDXNet resources. In addition, we established ongoing network-wide meetings to facilitate knowledge exchange, held PDXNet portal trainings, and set up working groups to tackle specific challenges. For instance, the Data Ontology working group has been working towards building a common data ontology model specifically for PDX datasets. We are in the process of annotating the very first dataset using this new ontology on the PDXNet portal. Also, the Workflows working group has been working on building and benchmarking various RNA-seq and whole exome sequencing analysis workflows to standardize data processing between PDXNet grantees and create a harmonized PDXNet dataset. These PDX models and the accompanying data will be opened to the community for data mining and/or preclinical research. The PDXNet is a strong step toward building a consensus around PDX models, so that the power for discovery can be expanded by making multi-institutional PDX cohorts a reality. As the coordination center, we are also working closely with the EuroPDX project to exchange standards and knowledge to support the PDX community with a set of standards going forward. The PDCCC is a central part of this process to systematically capture and analyze the variables most influential to PDX models and share protocols and tools to make PDXs an interchangeable research currency for preclinical discovery. Citation Format: Jacqueline Rosains, Anuj Srivastava, Wingyi Woo, Vishal Sarsani, ZiMing Zhao, Javad Noorbakhsh, Ogan D. Abaan, Christian Frech, Jack DiGiovanna, Ryan Jeon, Steve Neuhauser, Peter Robinson, Yvonne A. Evrard, Carol Bult, Jeffrey A. Moscow, Brandi Davis-Dusenbery, Jeffrey H. Chuang. The PDX Data Commons and Coordinating Center (PDCCC) for PDXNet in support of preclinical research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1074.

Summary Background The cleavage-stage mouse embryo is composed of superficially equivalent blastomeres that will generate both the embryonic inner cell mass (ICM) and the supportive trophectoderm (TE). However, it remains unsettled whether the contribution of each blastomere to these two lineages can be accounted for by chance. Addressing the question of blastomere cell fate may be of practical importance, because preimplantation genetic diagnosis requires removal of blastomeres from the early human embryo. To determine whether blastomere allocation to the two earliest lineages is random, we developed and utilized a recombination-mediated, noninvasive combinatorial fluorescent labeling method for embryonic lineage tracing. Results When we induced recombination at cleavage stages, we observed a statistically significant bias in the contribution of the resulting labeled clones to the trophectoderm or the inner cell mass in a subset of embryos. Surprisingly, we did not find a correlation between localization of clones in the embryonic and abembryonic hemispheres of the late blastocyst and their allocation to the TE and ICM, suggesting that TE-ICM bias arises separately from embryonic-abembryonic bias. Rainbow lineage tracing also allowed us to demonstrate that the bias observed in the blastocyst persists into postimplantation stages and therefore has relevance for subsequent development. Conclusions The Rainbow transgenic mice that we describe here have allowed us to detect lineage-dependent bias in early development. They should also enable assessment of the developmental equivalence of mammalian progenitor cells in a variety of tissues.

Until now, animal cloning and the production of embryonic stem cell lines by somatic cell nuclear transfer have relied on introducing nuclei into meiotic oocytes. In contrast, attempts at somatic cell nuclear transfer into fertilized interphase zygotes have failed. As a result, it has generally been assumed that unfertilized human oocytes will be required for the generation of tailored human embryonic stem cell lines from patients by somatic cell nuclear transfer. Here we report, however, that, unlike interphase zygotes, mouse zygotes temporarily arrested in mitosis can support somatic cell reprogramming, the production of embryonic stem cell lines and the full-term development of cloned animals. Thus, human zygotes and perhaps human embryonic blastomeres may be useful supplements to human oocytes for the creation of patient-derived human embryonic stem cells.

Virus infection, double-stranded RNA, and lipopolysaccharide each induce the expression of genes encoding IFN-α and -β and chemokines, such as RANTES (regulated on activation, normal T cell expressed and secreted) and IP-10 (IFN-γ inducible protein 10). This induction requires the coordinate activation of several transcription factors, including IFN-regulatory factor 3 (IRF3). The signaling pathways leading to IRF3 activation are triggered by the binding of pathogen-specific products to Toll-like receptors and culminate in the phosphorylation of specific serine residues in the C terminus of IRF3. Recent studies of human cell lines in culture have implicated two noncanonical IκB kinase (IKK)-related kinases, IKK-ε and Traf family member-associated NF-κB activator (TANK)-binding kinase 1 (TBK1), in the phosphorylation of IRF3. Here, we show that purified recombinant IKK-ε and TBK1 directly phosphorylate the critical serine residues in IRF3. We have also examined the expression of IRF3-dependent genes in mouse embryonic fibroblasts (MEFs) derived from Tbk1 -/- mice, and we show that TBK1 is required for the activation and nuclear translocation of IRF3 in these cells. Moreover, Tbk1 -/- MEFs show marked defects in IFN -α and -β, IP-10 , and RANTES gene expression after infection with either Sendai or Newcastle disease viruses or after engagement of the Toll-like receptors 3 and 4 by double-stranded RNA and lipopolysaccharide, respectively. Finally, TRIF (TIR domain-containing adapter-inducing IFN-β), fails to activate IRF3-dependent genes in Tbk1 -/- MEFs. We conclude that TBK1 is essential for IRF3-dependent antiviral gene expression.

The nonsense mediated decay (NMD) pathway degrades mRNAs bearing premature translation termination codons. In mammals, SMG-8 has been implicated in the NMD pathway, in part by its association with SMG-1 kinase. Here we use four independent assays to show that C. elegans smg-8 is not required to degrade nonsense-containing mRNAs. We examine the genetic requirement for smg-8 to destabilize the endogenous, natural NMD targets produced by alternative splicing of rpl-7a and rpl-12. We test smg-8 for degradation of the endogenous, NMD target generated by unc-54(r293), which lacks a normal polyadenylation site. We probe the effect of smg-8 on the exogenous NMD target produced by myo-3::GFP, which carries a long 3′ untranslated region that destabilizes mRNAs. None of these known NMD targets is influenced by smg-8 mutations. In addition, smg-8 animals lack classical Smg mutant phenotypes such as a reduced brood size or abnormal vulva. We conclude that smg-8 is unlikely to encode a component critical for NMD.