Potential plants for inflammatory dysfunction in the SARS-CoV-2 infection

Inflammopharmacology - The inflammatory process is a biological response of the organism to remove injurious stimuli and initiate homeostasis. It has been recognized as a key player in the most...     

Functional estrogen receptor signal transduction pathway activity and antihormonal therapy response in low-grade ovarian carcinoma

Background

Advanced low-grade ovarian carcinoma (LGOC) is difficult to treat. In several studies, high estrogen receptor (ER) protein expression was observed in patients with LGOC, which suggests that antihormonal therapy (AHT) is a treatment option. However, only a subgroup of patients respond to AHT, and this response cannot be adequately predicted by currently used immunohistochemistry (IHC). A possible explanation is that IHC only takes the ligand, but not the activity, of the whole signal transduction pathway (STP) into account. Therefore, in this study, the authors assessed whether functional STP activity can be an alternative tool to predict response to AHT in LGOC.

Methods

Tumor tissue samples were obtained from patients with primary or recurrent LGOC who subsequently received AHT. Histoscores of ER and progesterone receptor (PR) were determined. In addition, STP activity of the ER STP and of six other STPs known to play a role in ovarian cancer was assessed and compared with the STP activity of healthy postmenopausal fallopian tube epithelium.

Results

Patients who had normal ER STP activity had a progression-free survival (PFS) of 16.1 months. This was significantly shorter in patients who had low and very high ER STP activity, with a median PFS of 6.0 and 2.1 months, respectively (p < .001). Unlike ER histoscores, PR histoscores were strongly correlated to the ER STP activity and thus to PFS.

Conclusions

Aberrant low and very high functional ER STP activity and low PR histoscores in patients with LGOC indicate decreased response to AHT. ER IHC is not representative of functional ER STP activity and is not related to PFS.     

Stereotactic body radiation therapy for primary liver tumors: An effective liver-directed therapy in the toolbox

The use of radiation for primary liver cancers has historically been limited because of the risk of radiation-induced liver disease. Treatment fields have become more conformal because of several technical advances, and this has allowed for dose escalation. Stereotactic body radiation therapy (SBRT), also known as stereotactic ablative radiotherapy, is now able to safely treat liver tumors to ablative doses while sparing functional liver parenchyma by using highly conformal therapy. Several retrospective and small prospective studies have examined the use of SBRT for liver cancers; however, there is a lack of well-powered randomized studies to definitively guide management in these settings. Recent advances in systemic therapy for primary liver cancers have improved outcomes; however, the optimal selection criteria for SBRT as a local therapy remain unclear among other liver-directed options such as radiofrequency ablation, transarterial chemoembolization, and radioembolization.     

Comprehensive interrogation of gene lists from genome-scale cancer screens with oncoEnrichR

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based service hosted by the Galaxy platform ( https://oncotools.elixir.no ), and can also be accessed as a stand-alone R package ( https://github.com/sigven/oncoEnrichR ).