Purpose-Defining robust criteria for drug activity in preclinical studies allows for fewer animals per treatment group, and potentially allows for inclusion of additional cancer models that more accurately represent genetic diversity and, potentially, allows for tumor sensitivity biomarker identification.Methods-Using a single mouse design, 32 pediatric xenograft tumor models representing diverse pediatric cancer types (Ewing sarcoma [9], brain [4], rhabdomyosarcoma [10], Wilms tumor [4], and non-CNS rhabdoid tumors [5]) were evaluated for response to a single administration of pegylated-SN38 (PLX038A), a controlled-release PEGylated formulation of SN-38. Endpoints measured were percent tumor regression, and Event Free Survival (EFS). The correlation between response to PLX038A was compared to that for 10 models treated with irinotecan (2.5 mg/kg × 5 days × 2 cycles), using a traditional design (10 mice/group). Correlations between tumor sensitivity, genetic mutations and gene expression were sought. Models showing no disease at week 20 were categorized as 'extreme responders' to PLX038A, whereas those with EFS less than 5 weeks were categorized as 'resistant'.Results-The activity of PLX038A was evaluable in 31/32 models. PLX038A induced >50% volume regressions in 25 models (78%). Initial tumor volume regression correlated only modestly Terms of use and reuse: academic research for non-commercial purposes, see here for full terms. https://www.springer.com/aamterms-v1
Most noncoding RNAs are considered by their expression at low levels and as having a limited phylogenetic distribution in the cytoplasm, indicating that they may be only involved in specific biological processes. However, recent studies showed the protein-coding potential of ncRNAs, indicating that they might be a source of some special proteins. Although there are increasing noncoding RNAs identified to be able to code proteins, it is challenging to distinguish coding RNAs from previously annotated ncRNAs, and to detect the proteins from their translation. In this article, we designed a pipeline to identify these noncoding RNAs in Arabidopsis thaliana from three NCBI GEO data sets with coding potential and predict their translation products. 31 311 noncoding RNAs were predicted to be translated into peptides, and they showed lower conservation rate than common proteins. In addition, we built an interaction network between these peptides and annotated Arabidopsis proteins using BIPS, which included 69 peptides from noncoding RNAs. Peptides in the interaction network showed different characteristics from other noncoding RNA-derived peptides, and they participated in several crucial biological processes, such as photorespiration and stress-responses. All the information of putative ncPEPs and their interaction with proteins predicted above are finally integrated in a database, PncPEPDB ( http://bis.zju.edu.cn/PncPEPDB ). These results showed that peptides derived from noncoding RNAs may play important roles in noncoding RNA regulation, which provided another hypothesis that noncoding RNA may regulate the metabolism via their translation products.
Noncoding RNAs are known to associate with mitotic chromosomes, but the identities and functions of chromosome-associated RNAs in mitosis remain elusive. Here, we show that rRNA species associate with condensed chromosomes during mitosis. In particular, pre-rRNAs such as 45S, 32S, and 30S are highly enriched on mitotic chromosomes. Immediately following nucleolus disassembly in mitotic prophase, rRNAs are released and associate with and coat each condensed chromosome at prometaphase. Using unbiased mass spectrometry analysis, we further demonstrate that chromosome-bound rRNAs are associated with Ki-67. Moreover, the FHA domain and the repeat region of Ki-67 recognize and anchor rRNAs to chromosomes. Finally, suppression of chromosome-bound rRNAs by RNA polymerase I inhibition or by using rRNA-binding-deficient Ki-67 mutants impair mitotic chromosome dispersion during prometaphase. Our study thus reveals an important role of rRNAs in preventing chromosome clustering during mitosis.
Although cancer is the leading cause of disease-related mortality in children, the relative rarity of pediatric cancers poses a significant challenge for developing novel therapeutics to further improve prognosis. Patient-derived xenograft (PDX) models, which are usually developed from high-risk tumors, are a useful platform to study molecular driver events, identify biomarkers and prioritize therapeutic agents. Here, we develop PDX for Childhood Cancer Therapeutics (PCAT), a new integrated portal for pediatric cancer PDX models. Distinct from previously reported PDX portals, PCAT is focused on pediatric cancer models and provides intuitive interfaces for querying and data mining. The current release comprises 324 models and their associated clinical and genomic data, including gene expression, mutation and copy number alteration. Importantly, PCAT curates preclinical testing results for 68 models and 79 therapeutic agents manually collected from individual agent testing studies published since 2008. To facilitate comparisons of patterns between patient tumors and PDX models, PCAT curates clinical and molecular data of patient tumors from the TARGET project. In addition, PCAT provides access to gene fusions identified in nearly 1000 TARGET samples. PCAT was built using R-shiny and MySQL. The portal can be accessed at http://pcat.zhenglab.info or http://www.pedtranscriptome.org.
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