Colorectal carcinomas (CRC) carry massive genetic and transcriptional alterations that influence multiple cellular pathways. The study of proteins whose loss-of-function (LOF) alters the growth of ...CRC cells can be used to further understand the cellular processes cancer cells depend upon for survival.
A small-scale RNAi screen of ~400 genes conducted in SW480 CRC cells identified several candidate genes as required for the viability of CRC cells, most prominently CASP8AP2/FLASH. To understand the function of this gene in maintaining the viability of CRC cells in an unbiased manner, we generated gene specific expression profiles following RNAi. Silencing of CASP8AP2/FLASH resulted in altered expression of over 2500 genes enriched for genes associated with cellular growth and proliferation. Loss of CASP8AP2/FLASH function was significantly associated with altered transcription of the genes encoding the replication-dependent histone proteins as a result of the expression of the non-canonical polyA variants of these transcripts. Silencing of CASP8AP2/FLASH also mediated enrichment of changes in the expression of targets of the NFκB and MYC transcription factors. These findings were confirmed by whole transcriptome analysis of CASP8AP2/FLASH silenced cells at multiple time points. Finally, we identified and validated that CASP8AP2/FLASH LOF increases the expression of neurofilament heavy polypeptide (NEFH), a protein recently linked to regulation of the AKT1/ß-catenin pathway.
We have used unbiased RNAi based approaches to identify and characterize the function of CASP8AP2/FLASH, a protein not previously reported as required for cell survival. This study further defines the role CASP8AP2/FLASH plays in the regulating expression of the replication-dependent histones and shows that its LOF results in broad and reproducible effects on the transcriptome of colorectal cancer cells including the induction of expression of the recently described tumor suppressor gene NEFH.
Working with Ontologies Kramer, Frank; Beißbarth, Tim
Methods in molecular biology (Clifton, N.J.),
2017, Volume:
1525
Journal Article
Ontologies are powerful and popular tools to encode data in a structured format and manage knowledge. A large variety of existing ontologies offer users access to biomedical knowledge. This chapter ...contains a short theoretical background of ontologies and introduces two notable examples: The Gene Ontology and the ontology for Biological Pathways Exchange. For both ontologies a short overview and working bioinformatic applications, i.e., Gene Ontology enrichment analyses and pathway data visualization, are provided.
In the context of clinical trials and medical research medical text mining can provide broader insights for various research scenarios by tapping additional text data sources and extracting relevant ...information that is often exclusively present in unstructured fashion. Although various works for data like electronic health reports are available for English texts, only limited work on tools for non-English text resources has been published that offers immediate practicality in terms of flexibility and initial setup. We introduce DrNote, an open source text annotation service for medical text processing. Our work provides an entire annotation pipeline with its focus on a fast yet effective and easy to use software implementation. Further, the software allows its users to define a custom annotation scope by filtering only for relevant entities that should be included in its knowledge base. The approach is based on OpenTapioca and combines the publicly available datasets from WikiData and Wikipedia, and thus, performs entity linking tasks. In contrast to other related work our service can easily be built upon any language-specific Wikipedia dataset in order to be trained on a specific target language. We provide a public demo instance of our DrNote annotation service at https://drnote.misit-augsburg.de/. Author summary Since much highly relevant information in healthcare and clinical research is exclusively stored as unstructured text, retrieving and processing such data poses a major challenge. Novel data-driven text processing methods require large amounts of annotated data in order to exceed non data-driven methods’ performance. In the medical domain, such data is not publicly available and restricted access is limited due to federal privacy regulations. We circumvent this issue by developing an annotation pipeline that works on sparse data and retrieves the training data from publicly available data sources. The fully automated pipeline can be easily adapted by third parties for custom use cases or directly applied within minutes for medical use cases. It significantly lowers the barrier for fast analysis of unstructured clinical text data in certain scenarios.
Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed genes. The traditional gene set enrichment ...approach considers a pathway as a simple gene list disregarding any knowledge of gene or protein interactions. In contrast, the new group of so called pathway topology-based methods integrates the topological structure of a pathway into the analysis.
We comparatively investigated gene set and pathway topology-based enrichment approaches, considering three gene set and four topological methods. These methods were compared in two extensive simulation studies and on a benchmark of 36 real datasets, providing the same pathway input data for all methods.
In the benchmark data analysis both types of methods showed a comparable ability to detect enriched pathways. The first simulation study was conducted with KEGG pathways, which showed considerable gene overlaps between each other. In this study with original KEGG pathways, none of the topology-based methods outperformed the gene set approach. Therefore, a second simulation study was performed on non-overlapping pathways created by unique gene IDs. Here, methods accounting for pathway topology reached higher accuracy than the gene set methods, however their sensitivity was lower.
We conducted one of the first comprehensive comparative works on evaluating gene set against pathway topology-based enrichment methods. The topological methods showed better performance in the simulation scenarios with non-overlapping pathways, however, they were not conclusively better in the other scenarios. This suggests that simple gene set approach might be sufficient to detect an enriched pathway under realistic circumstances. Nevertheless, more extensive studies and further benchmark data are needed to systematically evaluate these methods and to assess what gain and cost pathway topology information introduces into enrichment analysis. Both types of methods for enrichment analysis require further improvements in order to deal with the problem of pathway overlaps.
Abstract
Motivation
Seamless exchange of biological network data enables bioinformatic algorithms to integrate networks as prior knowledge input as well as to document resulting network output. ...However, the interoperability between pathway databases and various methods and platforms for analysis is currently lacking. The Network Data Exchange (NDEx) is an open-source data commons that facilitates the user-centered sharing and publication of networks of many types and formats.
Results
Here, we present a software package that allows users to programmatically connect to and interface with NDEx servers from within R. The network repository can be searched and networks can be retrieved and converted into igraph-compatible objects. These networks can be modified and extended within R and uploaded back to the NDEx servers.
Availability and implementation
ndexr is a free and open-source R package, available via GitHub (https://github.com/frankkramer-lab/ndexr) and Bioconductor (http://bioconductor.org/packages/ndexr/).
Supplementary information
Supplementary data are available at Bioinformatics online.
Increased activity of signal transducer and activator of transcription 3 (STAT3) is common in human malignancies, including colorectal cancers (CRCs). We have recently reported that STAT3 gene ...expression correlates with resistance of CRC cell lines to 5‐fluorouracil (5‐FU)‐based chemoradiotherapy (CT/RT). This is of considerable clinical importance, because a large proportion of rectal cancers are resistant to preoperative multimodal treatment. To test whether STAT3 contributes to CT/RT‐resistance, we first confirmed that STAT3 protein expression correlated positively with increasing resistance. While STAT3 was not constitutively active, stimulation with interleukin‐6 (IL‐6) resulted in remarkably higher expression levels of phosphorylated STAT3 in CT/RT‐resistant cell lines. A similar result was observed when we determined IL‐6‐induced expression levels of phosphorylated STAT3 following irradiation. Next, STAT3 was inhibited in SW480 and SW837 using siRNA, shRNA and the small‐molecule inhibitor STATTIC. Successful silencing and inhibition of phosphorylation was confirmed using Western blot analysis and a luciferase reporter assay. RNAi‐mediated silencing as well as STATTIC treatment resulted in significantly decreased clonogenic survival following exposure to 3 µM of 5‐FU and irradiation in a dose‐dependent manner, with dose‐modifying factors of 1.3–2.5 at a surviving fraction of 0.37. Finally, STAT3 inhibition led to a profound CT/RT‐sensitization in a subcutaneous xenograft model, with a significantly delayed tumor regrowth in STATTIC‐treated mice compared with control animals. These results highlight a potential role of STAT3 in mediating treatment resistance and provide first proof of concept that STAT3 represents a promising novel molecular target for sensitizing resistant rectal cancers to CT/RT.
What's new?
A considerable percentage of rectal cancers are resistant to preoperative chemoradiotherapy, which exposes patients to the potential side effects of both irradiation and chemotherapy without clear benefits. In this study, IL‐6‐stimulated expression levels of phosphorylated STAT3 were remarkably higher in chemoradiotherapy‐resistant colorectal cancer cell lines. RNAi‐ and small molecule‐mediated STAT3 inhibition sensitized to chemoradiotherapy in vitro in a dose‐dependent manner, which led to a profound chemoradiotherapy‐sensitization in a subcutaneous xenograft model. These results highlight a potential role of STAT3 in treatment resistance, and provide first proof of concept that STAT3 represents a promising novel molecular target for sensitizing resistant rectal cancers to chemoradiotherapy.
Preoperative (neoadjuvant) chemoradiotherapy (CRT) and total mesorectal excision is the standard treatment for rectal cancer patients (UICC stage II/III). Up to one-third of patients treated with CRT ...achieve a pathological complete response (pCR). These patients could be spared from surgery and its associated morbidity and mortality, and assigned to a "watch and wait" strategy. However, reliably identifying pCR based on clinical or imaging parameters remains challenging.
We generated gene-expression profiles of 175 patients with locally advanced rectal cancer enrolled in the CAO/ARO/AIO-94 and -04 trials. One hundred and sixty-one samples were used for building, training and validating a predictor of pCR using a machine learning algorithm. The performance of the classifier was validated in three independent cohorts, comprising 76 patients from (i) the CAO/ARO/AIO-94 and -04 trials (n = 14), (ii) a publicly available dataset (n = 38) and (iii) in 24 prospectively collected samples from the TransValid A trial.
A 21-transcript signature yielded the best classification of pCR in 161 patients (Sensitivity: 0.31; AUC: 0.81), when not allowing misclassification of non-complete-responders (False-positive rate = 0). The classifier remained robust when applied to three independent datasets (n = 76).
The classifier can identify >1/3 of rectal cancer patients with a pCR while never classifying patients with an incomplete response as having pCR. Importantly, we could validate this finding in three independent datasets, including a prospectively collected cohort. Therefore, this classifier could help select rectal cancer patients for a "watch and wait" strategy.
Forgoing surgery with its associated side effects could be an option for rectal cancer patients if the prediction of a pathological complete response (pCR) after preoperative chemoradiotherapy would be possible. Based on gene-expression profiles of 161 patients a classifier was developed and validated in three independent datasets (n = 76), identifying over 1/3 of patients with pCR, while never misclassifying a non-complete-responder. Therefore, the classifier can identify patients suited for "watch and wait".
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