ATM Inhibition-Induced ISG15/IFI27/OASL Is Correlated with Immunotherapy Response and Inflamed Immunophenotype.

Immune checkpoint blockade (ICB) therapy can improve the survival of cancer patients with a high tumor mutation burden (TMB-H) or deficiency in DNA mismatch repair (dMMR) in their tumors. However, most cancer patients without TMB-H and dMMR do not benefit from ICB therapy. The inhibition of ATM can increase DNA damage and activate the interferon response, thus modulating the tumor immune microenvironment (TIME) and the efficacy of ICB therapy. In this study, we showed that ATM inhibition activated interferon signaling and induced interferon-stimulated genes (ISGs) in cisplatin-resistant and parent cancer cells. The ISGs induced by ATM inhibition were correlated with survival in cancer patients who received ICB therapy. In oral cancer, high expressions of ISG15, IFI27, and OASL were associated with low expressions of ATM, the activation of inflamed immune pathways, and increased tumor-infiltrating scores of CD8+ T, natural killer, and dendritic cells. The high expressions of ISG15, IFI27, and OASL were also correlated with complete remission in patients with cervical cancer treated with cisplatin. These results suggest that ATM inhibition can induce the interferon response and inflamed TIME, which may benefit ICB therapy.

High Expression of Interferon Pathway Genes CXCL10 and STAT2 Is Associated with Activated T-Cell Signature and Better Outcome of Oral Cancer Patients.

To improve the survival rate of cancer patients, biomarkers for both early diagnosis and patient stratification for appropriate therapeutics play crucial roles in precision oncology. Investigation of altered gene expression and the relevant molecular pathways in cancer cells are helpful for discovering such biomarkers. In this study, we explore the potential prognostic biomarkers for oral cancer patients through systematically analyzing five oral cancer transcriptomic data sets (TCGA, GSE23558, GSE30784, GSE37991, and GSE138206). Gene Set Enrichment Analysis (GSEA) was individually applied to each data set and the upregulated Hallmark molecular pathways of each data set were intersected to generate 13 common pathways including interferon-α/γ pathways. Among the 5 oral cancer data sets, 43 interferon pathway genes were commonly upregulated and 17 genes exhibited prognostic values in TCGA cohort. After validating in another oral cancer cohort (GSE65858), high expressions of C-X-C motif chemokine ligand 10 (CXCL10) and Signal transducer and activator of transcription 2 (STAT2) were confirmed to be good prognostic biomarkers. GSEA of oral cancers stratified by CXCL10/STAT2 expression showed that activation of T-cell pathways and increased tumor infiltration scores of Type 1 T helper (Th1) and CD8+ T cells were associated with high CXCL10/STAT2 expression. These results suggest that high CXCL10/STAT2 expression can predict a favorable outcome in oral cancer patients.

Neurocarta: aggregating and sharing disease-gene relations for the neurosciences.

BACKGROUND: Understanding the genetic basis of diseases is key to the development of better diagnoses and treatments. Unfortunately, only a small fraction of the existing data linking genes to phenotypes is available through online public resources and, when available, it is scattered across multiple access tools. DESCRIPTION: Neurocarta is a knowledgebase that consolidates information on genes and phenotypes across multiple resources and allows tracking and exploring of the associations. The system enables automatic and manual curation of evidence supporting each association, as well as user-enabled entry of their own annotations. Phenotypes are recorded using controlled vocabularies such as the Disease Ontology to facilitate computational inference and linking to external data sources. The gene-to-phenotype associations are filtered by stringent criteria to focus on the annotations most likely to be relevant. Neurocarta is constantly growing and currently holds more than 30,000 lines of evidence linking over 7,000 genes to 2,000 different phenotypes. CONCLUSIONS: Neurocarta is a one-stop shop for researchers looking for candidate genes for any disorder of interest. In Neurocarta, they can review the evidence linking genes to phenotypes and filter out the evidence they're not interested in. In addition, researchers can enter their own annotations from their experiments and analyze them in the context of existing public annotations. Neurocarta's in-depth annotation of neurodevelopmental disorders makes it a unique resource for neuroscientists working on brain development.