In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n ¼ 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. Cancer Res; 77(21); e91-100. Ó2017 AACR.
Although several site-specific nucleases (SSNs), such as zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and the clustered regularly interspaced short palindromic repeat (CRISPR)/Cas, have emerged as powerful tools for targeted gene editing in many organisms, to date, gene targeting (GT) in plants remains a formidable challenge. In the present study, we attempted to substitute a single base in situ on the rice OsEPSPS gene by co-transformation of TALEN with chimeric RNA/DNA oligonucleotides (COs), including different strand composition such as RNA/DNA (C1) or DNA/RNA (C2) but contained the same target base to be substituted. In contrast to zero GT event obtained by the co-transformation of TALEN with homologous recombination plasmid (HRP), we obtained one mutant showing target base substitution although accompanied by undesired deletion of 12 bases downstream the target site from the co-transformation of TALEN and C1. In addition to this typical event, we also obtained 16 mutants with different length of base deletions around the target site among 105 calli lines derived from transformation of TALEN alone (4/19) as well as co-transformation of TELAN with either HRP (5/30) or C1 (2/25) or C2 (5/31). Further analysis demonstrated that the homozygous gene-edited mutants without foreign gene insertion could be obtained in one generation. The induced mutations in transgenic generation were also capable to pass to the next generation stably. However, the genotypes of mutants did not segregate normally in T1 population, probably due to lethal mutations. Phenotypic assessments in T1 generation showed that the heterozygous plants with either one or three bases deletion on target sequence, called d1 and d3, were more sensitive to glyphosate and the heterozygous d1 plants had significantly lower seed-setting rate than wild-type.
Background More than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need more accurate stratification to establish an individualized precise treatment plan for the patients to improve the long-term survival rate. Results We focus on extracting features and providing a workflow to improve survival prediction for neuroblastoma patients. With a workflow for gene co-expression network (GCN) mining in microarray and RNA-Seq datasets, we extracted molecular features from each co-expressed module and summarized them into eigengenes. Then we adopted the lasso-regularized Cox proportional hazards model to select the most informative eigengene features regarding association to the risk of metastasis. Nine eigengenes were selected which show strong association with patient survival prognosis. All of the nine corresponding gene modules also have highly enriched biological functions or cytoband locations. Three of them are unique modules to RNA-Seq data, which complement the modules from microarray data in terms of survival prognosis. We then merged all eigengenes from these unique modules and used an integrative method called Similarity Network Fusion to test the prognostic power of these eigengenes for prognosis. The prognostic accuracies are significantly improved as compared to using all eigengenes, and a subgroup of patients with very poor survival rate was identified. Conclusions We first compared GCNs mined from microarray and RNA-seq data. We discovered that each data modality yields unique GCNs, which are enriched with clear biological functions. Then we do module unique analysis and use lasso-cox model to select survival-associated eigengenes. Integration of unique and survival-associated eigengenes from both data types provides complementary information that leads to more accurate survival prognosis. Reviewers Reviewed by Susmita Datta, Marco Chierici and Dimitar Vassilev. Electronic supplementary material The online version of this article (10.1186/s13062-018-0229-2) contains supplementary material, which is available to authorized users.
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