Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.

Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs), which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA )RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC) method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes) that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.

Background: Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations. Methods: We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score. Results: We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP. Conclusion: The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.

The Wnt/β-catenin signaling pathway dominates numerous cellular processes including cell proliferation, differentiation, and epithelial-mesenchymal transition, which play a crucial role in human cancer malignancies. Regulator of G-protein signaling 3 (RGS3) is a pivotal molecule involved in the Wnt/β-catenin signaling pathway, which is worthy of intensive research as a potential target in cancer treatment. In this study, we found that RGS3 is significantly upregulated in gastric cancer (GC) tumor samples compared with normal samples from the analysis of two independent GC mRNA microarray datasets in the NCBI public database. Further immunohistochemistry assay and western-blot experiments confirmed this finding on the basis of the results of our own 102 paired GC specimens and three GC cell lines. We found that a high expression of RGS3 is associated with advanced TNM stages and more aggressive malignant behaviors. In addition, the association of overexpression of RGS3 and poor overall survival and progression-free survival outcomes suggests that RGS3 has the potential to serve as a molecular therapy target for GC. Interestingly, our pathways analysis and the follow-up dual-luciferase reporter assay showed that there is a direct 3'-untranslated region binding site between RGS3 mRNA and microRNA-126, a GC inhibitor. On the basis of all the above evidences, our findings suggest that overexpressed RGS3 regulated by microRNA-126 through the post-transcriptional modulation is associated significantly with a poor prognosis of GC patients.

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients’ clinical narratives.Methods: To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method.Results: Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders.Conclusion: Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making.

In pancreatic ductal adenocarcinoma (PDAC), the fibroblastic stroma constitutes most of the tumor mass and is remarkably devoid of functional blood vessels. This raises an unresolved question of how PDAC cells obtain essential metabolites and water-insoluble lipids. We have found a critical role for cancer-associated fibroblasts (CAFs) in obtaining and transferring lipids from blood-borne particles to PDAC cells via trogocytosis of CAF plasma membranes. We have also determined that CAF-expressed phospholipid scramblase anoctamin 6 (ANO6) is an essential CAF trogocytosis regulator required to promote PDAC cell survival. During trogocytosis, cancer cells and CAFs form synapse-like plasma membranes contacts that induce cytosolic calcium influx in CAFs via Orai channels. This influx activates ANO6 and results in phosphatidylserine exposure on CAF plasma membrane initiating trogocytosis and transfer of membrane lipids, including cholesterol, to PDAC cells. Importantly, ANO6-dependent trogocytosis also supports the immunosuppressive function of pancreatic CAFs towards cytotoxic T cells by promoting transfer of excessive amounts of cholesterol. Further, blockade of ANO6 antagonizes tumor growth via disruption of delivery of exogenous cholesterol to cancer cells and reverses immune suppression suggesting a potential new strategy for PDAC therapy.