A novel, integrated in vitro carcinogenicity test to identify genotoxic and non-genotoxic carcinogens using human lymphoblastoid cells

Human exposure to carcinogens occurs via a plethora of environmental sources, with 70–90% of cancers caused by extrinsic factors. Aberrant phenotypes induced by such carcinogenic agents may provide universal biomarkers for cancer causation. Both current in vitro genotoxicity tests and the animal-testing paradigm in human cancer risk assessment fail to accurately represent and predict whether a chemical causes human carcinogenesis. The study aimed to establish whether the integrated analysis of multiple cellular endpoints related to the Hallmarks of Cancer could advance in vitro carcinogenicity assessment. Human lymphoblastoid cells (TK6, MCL-5) were treated for either 4 or 23 h with 8 known in vivo carcinogens, with doses up to 50% Relative Population Doubling (maximum 66.6 mM). The adverse effects of carcinogens on wide-ranging aspects of cellular health were quantified using several approaches; these included chromosome damage, cell signalling, cell morphology, cell-cycle dynamics and bioenergetic perturbations. Cell morphology and gene expression alterations proved particularly sensitive for environmental carcinogen identification. Composite scores for the carcinogens’ adverse effects revealed that this approach could identify both DNA-reactive and non-DNA reactive carcinogens in vitro. The richer datasets generated proved that the holistic evaluation of integrated phenotypic alterations is valuable for effective in vitro risk assessment, while also supporting animal test replacement. Crucially, the study offers valuable insights into the mechanisms of human carcinogenesis resulting from exposure to chemicals that humans are likely to encounter in their environment. Such an understanding of cancer induction via environmental agents is essential for cancer prevention

GeneSigDB: a manually curated database and resource for analysis of gene expression signatures

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a ‘basket’ feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org

A Phase 2, Multicenter, Open-Label Study of Anti-Lag-3 Ieramilimab in Combination With Anti-Pd-1 Spartalizumab in Patients With Advanced Solid Malignancies

Ieramilimab, a humanized anti-LAG-3 monoclonal antibody, was well tolerated in combination with the anti-PD-1 antibody spartalizumab in a phase 1 study. This phase 2 study aimed to further investigate the efficacy and safety of combination treatment in patients with selected advanced (locally advanced or metastatic) solid malignancies. Eligible patients with non-small cell lung cancer (NSCLC), melanoma, renal cell carcinoma (RCC), mesothelioma, and triple-negative breast cancer (TNBC) were grouped depending on prior anti-PD-1/L1 therapy (anti-PD-1/L1 naive or anti-PD-1/L1 pretreated). Patients received ieramilimab (400 mg) followed by spartalizumab (300 mg) every 3 weeks. The primary endpoint was objective response rate (ORR), along with safety, pharmacokinetics, and biomarker assessments. Of 235 patients, 142 were naive to anti-PD-1/L1 and 93 were pretreated with anti-PD-1/L1 antibodies. Durable responses (\u3e24 months) were seen across all indications for patients naive to anti-PD-1/L1 and in melanoma and RCC patients pretreated with anti-PD1/L1. The most frequent study drug-related AEs were pruritus (15.5%), fatigue (10.6%), and rash (10.6%) in patients naive to anti-PD-1/L1 and fatigue (18.3%), rash (14.0%), and nausea (10.8%) in anti-PD-1/L1 pretreated patients. Biomarker assessment indicated higher expression of T-cell-inflamed gene signature at baseline among responding patients. Response to treatment was durable (\u3e24 months) in some patients across all enrolled indications, and safety findings were in accordance with previous and current studies exploring LAG-3/PD-1 blockade

Improving Specificity for Ovarian Cancer Screening Using a Novel Extracellular Vesicle–Based Blood Test: Performance in a Training and Verification Cohort

The low incidence of ovarian cancer (OC) dictates that any screening strategy needs to be both highly sensitive and highly specific. This study explored the utility of detecting multiple colocalized proteins or glycosylation epitopes on single tumor-associated extracellular vesicles from blood. The novel Mercy Halo Ovarian Cancer Test (OC Test) uses immunoaffinity capture of tumor-associated extracellular vesicles, followed by proximity-ligation real-time quantitative PCR to detect combinations of up to three biomarkers to maximize specificity and measures multiple combinations to maximize sensitivity. A high-grade serous carcinoma (HGSC) case-control training set of EDTA plasma samples from 397 women was used to lock down the test design, the data interpretation algorithm, and the cutoff between cancer and noncancer. Performance was verified and compared with cancer antigen 125 in an independent blinded case-control set of serum samples from 390 women (132 controls, 66 HGSC, 83 non-HGSC OC, and 109 benign). In the verification study, the OC Test showed a specificity of 97.0% (128/132; 95% CI, 92.4%–99.6%), a HGSC sensitivity of 97.0% (64/66; 95% CI, 87.8%–99.2%), and an area under the curve of 0.97 (95% CI, 0.93–0.99) and detected 73.5% (61/83; 95% CI, 62.7%–82.6%) of the non-HGSC OC cases. This test exhibited fewer false positives in subjects with benign ovarian tumors, nonovarian cancers, and inflammatory conditions when compared with cancer antigen 125. The combined sensitivity and specificity of this new test suggests it may have potential in OC screening

Screening of Chinese Medicinal Plants for Inhibition of COX-2 Gene Expression

Herbal drugs used in Traditional Chinese Medicine (TCM) for anti-inflammatory purposes have been examined regarding their impact on cyclooxygenase-2 (COX-2) in an in vitro COX-2 gene expression assay.[...

RamiGO: an R/Bioconductor package providing an AmiGO Visualize interface

Summary: The R/Bioconductor package RamiGO is an R interface to AmiGO that enables visualization of Gene Ontology (GO) trees. Given a list of GO terms, RamiGO uses the AmiGO visualize API to import Graphviz-DOT format files into R, and export these either as images (SVG, PNG) or into Cytoscape for extended network analyses. RamiGO provides easy customization of annotation, highlighting of specific GO terms, colouring of terms by P-value or export of a simplified summary GO tree. We illustrate RamiGO functionalities in a genome-wide gene set analysis of prognostic genes in breast cancer. Availability and implementation: RamiGO is provided in R/Bioconductor, is open source under the Artistic-2.0 License and is available with a user manual containing installation, operating instructions and tutorials. It requires R version 2.15.0 or higher. URL: http://bioconductor.org/packages/release/bioc/html/RamiGO.html Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online