CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase.

CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC's functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.

Large scale genotype- and phenotype-driven machine learning in Von Hippel-Lindau disease.

Von Hippel-Lindau (VHL) disease is a hereditary cancer syndrome where individuals are predisposed to tumor development in the brain, adrenal gland, kidney, and other organs. It is caused by pathogenic variants in the VHL tumor suppressor gene. Standardized disease information has been difficult to collect due to the rarity and diversity of VHL patients. Over 4100 unique articles published until October 2019 were screened for germline genotype-phenotype data. Patient data were translated into standardized descriptions using Human Genome Variation Society gene variant nomenclature and Human Phenotype Ontology terms and has been manually curated into an open-access knowledgebase called Clinical Interpretation of Variants in Cancer. In total, 634 unique VHL variants, 2882 patients, and 1991 families from 427 papers were captured. We identified relationship trends between phenotype and genotype data using classic statistical methods and spectral clustering unsupervised learning. Our analyses reveal earlier onset of pheochromocytoma/paraganglioma and retinal angiomas, phenotype co-occurrences and genotype-phenotype correlations including hotspots. It confirms existing VHL associations and can be used to identify new patterns and associations in VHL disease. Our database serves as an aggregate knowledge translation tool to facilitate sharing information about the pathogenicity of VHL variants.

Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC).

PURPOSE: Several professional societies have published guidelines for the clinical interpretation of somatic variants, which specifically address diagnostic, prognostic, and therapeutic implications. Although these guidelines for the clinical interpretation of variants include data types that may be used to determine the oncogenicity of a variant (eg, population frequency, functional, and in silico data or somatic frequency), they do not provide a direct, systematic, and comprehensive set of standards and rules to classify the oncogenicity of a somatic variant. This insufficient guidance leads to inconsistent classification of rare somatic variants in cancer, generates variability in their clinical interpretation, and, importantly, affects patient care. Therefore, it is essential to address this unmet need. METHODS: Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group and ClinGen Germline/Somatic Variant Subcommittee, the Cancer Genomics Consortium, and the Variant Interpretation for Cancer Consortium used a consensus approach to develop a standard operating procedure (SOP) for the classification of oncogenicity of somatic variants. RESULTS: This comprehensive SOP has been developed to improve consistency in somatic variant classification and has been validated on 94 somatic variants in 10 common cancer-related genes. CONCLUSION: The comprehensive SOP is now available for classification of oncogenicity of somatic variants.

CIViCpy: A Python Software Development and Analysis Toolkit for the CIViC Knowledgebase.

PURPOSE: Precision oncology depends on the matching of tumor variants to relevant knowledge describing the clinical significance of those variants. We recently developed the Clinical Interpretations for Variants in Cancer (CIViC; civicdb.org) crowd-sourced, expert-moderated, and open-access knowledgebase. CIViC provides a structured framework for evaluating genomic variants of various types (eg, fusions, single-nucleotide variants) for their therapeutic, prognostic, predisposing, diagnostic, or functional utility. CIViC has a documented application programming interface for accessing CIViC records: assertions, evidence, variants, and genes. Third-party tools that analyze or access the contents of this knowledgebase programmatically must leverage this application programming interface, often reimplementing redundant functionality in the pursuit of common analysis tasks that are beyond the scope of the CIViC Web application. METHODS: To address this limitation, we developed CIViCpy (civicpy.org), a software development kit for extracting and analyzing the contents of the CIViC knowledgebase. CIViCpy enables users to query CIViC content as dynamic objects in Python. We assess the viability of CIViCpy as a tool for advancing individualized patient care by using it to systematically match CIViC evidence to observed variants in patient cancer samples. RESULTS: We used CIViCpy to evaluate variants from 59,437 sequenced tumors of the American Association for Cancer Research Project GENIE data set. We demonstrate that CIViCpy enables annotation of > 1,200 variants per second, resulting in precise variant matches to CIViC level A (professional guideline) or B (clinical trial) evidence for 38.6% of tumors. CONCLUSION: The clinical interpretation of genomic variants in cancers requires high-throughput tools for interoperability and analysis of variant interpretation knowledge. These needs are met by CIViCpy, a software development kit for downstream applications and rapid analysis. CIViCpy is fully documented, open-source, and available free online.

Text-mining clinically relevant cancer biomarkers for curation into the CIViC database.

BACKGROUND: Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing, and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature. METHODS: To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated sentences that discussed biomarkers with their clinical associations and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase. RESULTS: We extracted 121,589 relevant sentences from PubMed abstracts and PubMed Central Open Access full-text papers. CIViCmine contains over 87,412 biomarkers associated with 8035 genes, 337 drugs, and 572 cancer types, representing 25,818 abstracts and 39,795 full-text publications. CONCLUSIONS: Through integration with CIVIC, we provide a prioritized list of curatable clinically relevant cancer biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general. All data is publically available and distributed with a Creative Commons Zero license. The CIViCmine knowledgebase is available at http://bionlp.bcgsc.ca/civicmine/.

Standard operating procedure for curation and clinical interpretation of variants in cancer.

Manually curated variant knowledgebases and their associated knowledge models are serving an increasingly important role in distributing and interpreting variants in cancer. These knowledgebases vary in their level of public accessibility, and the complexity of the models used to capture clinical knowledge. CIViC (Clinical Interpretation of Variants in Cancer - www.civicdb.org) is a fully open, free-to-use cancer variant interpretation knowledgebase that incorporates highly detailed curation of evidence obtained from peer-reviewed publications and meeting abstracts, and currently holds over 6300 Evidence Items for over 2300 variants derived from over 400 genes. CIViC has seen increased adoption by, and also undertaken collaboration with, a wide range of users and organizations involved in research. To enhance CIViC's clinical value, regular submission to the ClinVar database and pursuit of other regulatory approvals is necessary. For this reason, a formal peer reviewed curation guideline and discussion of the underlying principles of curation is needed. We present here the CIViC knowledge model, standard operating procedures (SOP) for variant curation, and detailed examples to support community-driven curation of cancer variants.

Open-Sourced CIViC Annotation Pipeline to Identify and Annotate Clinically Relevant Variants Using Single-Molecule Molecular Inversion Probes.

PURPOSE: Clinical targeted sequencing panels are important for identifying actionable variants for patients with cancer; however, existing approaches do not provide transparent and rationally designed clinical panels to accommodate the rapidly growing knowledge within oncology. MATERIALS AND METHODS: We used the Clinical Interpretations of Variants in Cancer (CIViC) database to develop an Open-Sourced CIViC Annotation Pipeline (OpenCAP). OpenCAP provides methods to identify variants within the CIViC database, build probes for variant capture, use probes on prospective samples, and link somatic variants to CIViC clinical relevance statements. OpenCAP was tested using a single-molecule molecular inversion probe (smMIP) capture design on 27 cancer samples from 5 tumor types. In total, 2,027 smMIPs were designed to target 111 eligible CIViC variants (61.5 kb of genomic space). RESULTS: When compared with orthogonal sequencing, CIViC smMIP sequencing demonstrated a 95% sensitivity for variant detection (n = 61 of 64 variants). Variant allele frequencies for variants identified on both sequencing platforms were highly concordant (Pearson's r = 0.885; n = 61 variants). Moreover, for individuals with paired tumor and normal samples (n = 12), 182 clinically relevant variants missed by orthogonal sequencing were discovered by CIViC smMIP sequencing. CONCLUSION: The OpenCAP design paradigm demonstrates the utility of an open-source and open-access database built on attendant community contributions with peer-reviewed interpretations. Use of a public repository for variant identification, probe development, and variant interpretation provides a transparent approach to build dynamic next-generation sequencing-based oncology panels.

Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples.

PURPOSE: Following automated variant calling, manual review of aligned read sequences is required to identify a high-quality list of somatic variants. Despite widespread use in analyzing sequence data, methods to standardize manual review have not been described, resulting in high inter- and intralab variability. METHODS: This manual review standard operating procedure (SOP) consists of methods to annotate variants with four different calls and 19 tags. The calls indicate a reviewer's confidence in each variant and the tags indicate commonly observed sequencing patterns and artifacts that inform the manual review call. Four individuals were asked to classify variants prior to, and after, reading the SOP and accuracy was assessed by comparing reviewer calls with orthogonal validation sequencing. RESULTS: After reading the SOP, average accuracy in somatic variant identification increased by 16.7% (p value = 0.0298) and average interreviewer agreement increased by 12.7% (p value < 0.001). Manual review conducted after reading the SOP did not significantly increase reviewer time. CONCLUSION: This SOP supports and enhances manual somatic variant detection by improving reviewer accuracy while reducing the interreviewer variability for variant calling and annotation.

Adapting crowdsourced clinical cancer curation in CIViC to the ClinGen minimum variant level data community-driven standards.

Harmonization of cancer variant representation, efficient communication, and free distribution of clinical variant-associated knowledge are central problems that arise with increased usage of clinical next-generation sequencing. The Clinical Genome Resource (ClinGen) Somatic Working Group (WG) developed a minimal variant level data (MVLD) representation of cancer variants, and has an ongoing collaboration with Clinical Interpretations of Variants in Cancer (CIViC), an open-source platform supporting crowdsourced and expert-moderated cancer variant curation. Harmonization between MVLD and CIViC variant formats was assessed by formal field-by-field analysis. Adjustments to the CIViC format were made to harmonize with MVLD and support ClinGen Somatic WG curation activities, including four new features in CIViC: (1) introduction of an assertions feature for clinical variant assessment following the Association of Molecular Pathologists (AMP) guidelines, (2) group-level curation tracking for organizations, enabling member transparency, and curation effort summaries, (3) introduction of ClinGen Allele Registry IDs to CIViC, and (4) mapping of CIViC assertions into ClinVar submission with automated submissions. A generalizable workflow utilizing MVLD and new CIViC features is outlined for use by ClinGen Somatic WG task teams for curation and submission to ClinVar, and provides a model for promoting harmonization of cancer variant representation and efficient distribution of this information.

Functional inactivation of Rb sensitizes cancer cells to TSC2 inactivation induced cell death.

We showed previously that inactivation of TSC2 induces death in cancer cells lacking the Retinoblastoma (Rb) tumor suppressor under stress conditions, suggesting that inactivation of TSC2 can potentially be used as an approach to specifically kill cancers that have lost WT Rb. As Rb is often inactivated in cancers by overexpression of cyclin D1, loss of p16(ink4a) cdk inhibitor, or expression of viral oncoproteins, it will be interesting to determine if such functional inactivation of Rb would similarly sensitize cancer cells to TSC2 inactivation induced cell death. In addition, many cancers lack functional Pten, resulting in increased PI3K/Akt signaling that has been shown to modulate E2F-induced cell death. Therefore it will be interesting to test whether loss of Pten will affect TSC2 inactivation induced killing of Rb mutant cancer cells. Here, we show that overexpression of Cyclin D1 or the viral oncogene E1a sensitizes cancer cells to TSC2 knockdown induced cell death and growth inhibition. On the other hand, knockdown of p16(ink4a) sensitizes cancer cells to TSC2 knockdown induced cell death in a manner that is likely dependant on serum induction of Cyclin D1 to inactivate the Rb function. Additionally, we demonstrate that loss of Pten does not interfere with TSC2 knockdown induced cell death in Rb mutant cancer cells. Together, these results suggest that TSC2 is potentially a useful target for a large spectrum of cancer types with an inactivated Rb pathway.

Differential regulation of glycogenolysis by mutant protein phosphatase-1 glycogen-targeting subunits.

PTG and G(L) are hepatic protein phosphatase-1 (PP1) glycogen-targeting subunits, which direct PP1 activity against glycogen synthase (GS) and/or phosphorylase (GP). The C-terminal 16 amino residues of G(L) comprise a high affinity binding site for GP that regulates bound PP1 activity against GS. In this study, a truncated G(L) construct lacking the GP-binding site (G(L)tr) and a chimeric PTG molecule containing the C-terminal site (PTG-G(L)) were generated. As expected, GP binding to glutathione S-transferase (GST)-G(L)tr was reduced, whereas GP binding to GST-PTG-G(L) was increased 2- to 3-fold versus GST-PTG. In contrast, PP1 binding to all proteins was equivalent. Primary mouse hepatocytes were infected with adenoviral constructs for each subunit, and their effects on glycogen metabolism were investigated. G(L)tr expression was more effective at promoting GP inactivation, GS activation, and glycogen accumulation than G(L). Removal of the regulatory GP-binding site from G(L)tr completely blocked the inactivation of GS seen in G(L)-expressing cells following a drop in extracellular glucose. As a result, G(L)tr expression prevented glycogen mobilization under 5 mm glucose conditions. In contrast, equivalent overexpression of PTG or PTG-G(L) caused a similar increase in glycogen-targeted PP1 levels and GS dephosphorylation. Surprisingly, GP dephosphorylation was significantly reduced in PTG-G(L)-overexpressing cells. As a result, PTG-G(L) expression permitted glycogenolysis under 5 mm glucose conditions that was prevented in PTG-expressing cells. Thus, expression of constructs that contained the high affinity GP-binding site (G(L) and PTG-G(L)) displayed reduced glycogen accumulation and enhanced glycogenolysis compared with their respective controls, albeit via different mechanisms.

Altering PPARgamma ligand selectivity impairs adipogenesis by thiazolidinediones but not hormonal inducers.

Peroxisome proliferator-activated receptor gamma (PPARgamma) acts as a ligand-dependent transcription factor with a key role in mediating adipocyte differentiation and insulin sensitivity. Recently, we and others have shown that PPARgamma recruits the nuclear corepressors NCoR and silencing mediator for retinoid and thyroid hormone receptors (SMRT) to modulate adipogenesis. While the synthetic ligands for PPARgamma, the thiazolidinediones (TZD), are widely used in the treatment of type 2 diabetes mellitus, the biologically relevant endogenous PPARgamma ligand involved in adipogenesis remains unidentified. To further understand the role of ligand binding and corepressor interaction in PPARgamma-mediated adipogenesis, a mutation was introduced in the ligand-binding domain (LBD) of murine PPARgamma. PPARgammamut was created via two amino acid substitutions known to be major determinants of ligand selectivity among PPAR isotypes, H323Y and R288M. These mutations alter PPARgamma to the corresponding residues of the PPARalpha. Characterizing the in vitro functional properties of this mutant, we show that PPARgammamut preferentially responds to the PPARalpha agonist, WY-14643, over the TZD, pioglitazone. When expressed in 3T3-L1 preadipocytes using recombinant adenovirus, wild-type PPARgamma leads to adipocyte formation with both hormonal and TZD treatment. PPARgammamut blocks the upregulation of adipocyte-specific proteins by TZD, but surprisingly, not by standard hormonal inducers. Our data suggest that TZDs and the purported endogenous ligand do not interact in the same way with the PPARgamma LBD. We propose that the endogenous ligand has distinct properties that allow for promiscuity within the hydrophobic PPAR ligand-binding pocket, yet fosters appropriate cofactor recruitment and release to allow adipogenesis to proceed.

The role of protein translocation in the regulation of glycogen metabolism.

Early biochemical analyses of metabolic pathways assumed that the free diffusion of substrates and enzymes in an evenly mixed cellular space provided the interactions that enabled reactions to proceed. Metabolic complexes have since been shown to assemble and disassemble in response to changes in cellular conditions, and in turn, to channel metabolic intermediates within discreet cellular compartments, allowing for the efficient use or storage of energy. A fundamental component to the formation of metabolic complexes and the channeling of metabolites is the translocation of enzymes in response to specific extra- and intracellular signals. These generalities play an important role in the metabolism of glucose to glycogen within skeletal muscle and liver. In this review, the similarities and differences in skeletal muscle and liver glucose metabolism with regards to glucose transport and intracellular processing will be addressed during the fasted to fed transition. More specifically, the importance of isoform expression and protein translocation in the tissue specific control of glucose homeostasis will be covered.

Transgenic overexpression of protein targeting to glycogen markedly increases adipocytic glycogen storage in mice.

Adipocytes express the rate-limiting enzymes required for glycogen metabolism and increase glycogen synthesis in response to insulin. However, the physiological function of adipocytic glycogen in vivo is unclear, due in part to the low absolute levels and the apparent biophysical constraints of adipocyte morphology on glycogen accumulation. To further study the regulation of glycogen metabolism in adipose tissue, transgenic mice were generated that overexpressed the protein phosphatase-1 (PP1) glycogen-targeting subunit (PTG) driven by the adipocyte fatty acid binding protein (aP2) promoter. Exogenous PTG was detected in gonadal, perirenal, and brown fat depots, but it was not detected in any other tissue examined. PTG overexpression resulted in a modest redistribution of PP1 to glycogen particles, corresponding to a threefold increase in the glycogen synthase activity ratio. Glycogen synthase protein levels were also increased twofold, resulting in a combined greater than sixfold enhancement of basal glycogen synthase specific activity. Adipocytic glycogen levels were increased 200- to 400-fold in transgenic animals, and this increase was maintained to 1 yr of age. In contrast, lipid metabolism in transgenic adipose tissue was not significantly altered, as assessed by lipogenic rates, weight gain on normal or high-fat diets, or circulating free fatty acid levels after a fast. However, circulating and adipocytic leptin levels were doubled in transgenic animals, whereas adiponectin expression was unchanged. Cumulatively, these data indicate that murine adipocytes are capable of storing far higher levels of glycogen than previously reported. Furthermore, these results were obtained by overexpression of an endogenous adipocytic protein, suggesting that mechanisms may exist in vivo to maintain adipocytic glycogen storage at a physiological set point.

Glycogen branches out: new perspectives on the role of glycogen metabolism in the integration of metabolic pathways.

Glycogen is the storage form of carbohydrate for virtually every organism from yeast to primates. Most mammalian tissues store glucose as glycogen, with the major depots located in muscle and liver. The French physiologist Claude Bernard first identified a starch-like substance in liver and muscle and coined the term glycogen, or "sugar former," in the 1850s. During the 150 years since its identification, researchers in the field of glycogen metabolism have made numerous discoveries that are now recognized as significant milestones in biochemistry and cell signaling. Even so, more questions remain, and studies continue to demonstrate the complexity of the regulation of glycogen metabolism. Under classical definitions, the functions of glycogen seem clear: muscle glycogen is degraded to generate ATP during increased energy demand, whereas hepatic glycogen is broken down for release of glucose into the bloodstream to supply other tissues. However, recent findings demonstrate that the roles of glycogen metabolism in energy sensing, integration of metabolic pathways, and coordination of cellular responses to hormonal stimuli are far more complex.

Central role for protein targeting to glycogen in the maintenance of cellular glycogen stores in 3T3-L1 adipocytes.

Overexpression of the protein phosphatase 1 (PP1) subunit protein targeting to glycogen (PTG) markedly enhances cellular glycogen levels. In order to disrupt the endogenous PTG-PP1 complex, small interfering RNA (siRNA) constructs against PTG were identified. Infection of 3T3-L1 adipocytes with PTG siRNA adenovirus decreased PTG mRNA and protein levels by >90%. In parallel, PTG reduction resulted in a >85% decrease in glycogen levels 4 days after infection, supporting a critical role for PTG in glycogen metabolism. Total PP1, glycogen synthase, and GLUT4 levels, as well as insulin-stimulated signaling cascades, were unaffected. However, PTG knockdown reduced glycogen-targeted PP1 protein levels, corresponding to decreased cellular glycogen synthase- and phosphorylase-directed PP1 activity. Interestingly, GLUT1 levels and acute insulin-stimulated glycogen synthesis rates were increased two- to threefold, and glycogen synthase activation in the presence of extracellular glucose was maintained. In contrast, glycogenolysis rates were markedly increased, suggesting that PTG primarily acts to suppress glycogen breakdown. Cumulatively, these data indicate that disruption of PTG expression resulted in the uncoupling of PP1 activity from glycogen metabolizing enzymes, the enhancement of glycogenolysis, and a dramatic decrease in cellular glycogen levels. Further, they suggest that reduction of glycogen stores induced cellular compensation by several mechanisms, but ultimately these changes could not overcome the loss of PTG expression.

Gene expression profiles in HEK-293 cells with low or high store-operated calcium entry: can regulatory as well as regulated genes be identified?

Gene expression profiles were generated using cDNA microarray technology for clones of human embryonic kidney (HEK)-293 cells selected to have either high or low levels of store-operated Ca2+ entry (SOCE). For five high clones, three low clones, and control HEK-293 cells, duplicate Affymetrix U133A human gene arrays were run after extraction of total RNA from cells growing in the presence of serum. Of the approximately 22,000 genes represented on the microarray, 58 genes had readings at least twofold higher, while 32 genes had readings at least twofold lower, in all five high SOCE clones compared with control HEK-293 cells. In the low SOCE clones, 92 genes had readings at least twofold higher, while 58 genes had readings at least twofold lower, than in HEK-293 cells. Microarray results were confirmed for 18 selected genes by real-time RT-PCR analysis; for six of those genes, predicted changes in the low SOCE clone were confirmed by an alternative method, monitoring mRNA levels in HEK-293 with SOCE decreased by expression of small interfering (si)RNA to canonical transient receptor potential protein-1. Genes regulated by SOCE are involved in signal transduction, transcription, apoptosis, metabolism, and membrane transport. These data provide insight into the physiological role of SOCE. In addition, a potential regulator of SOCE, insulin receptor substrate (IRS)-2, has been identified. A reduction of IRS-2 levels by siRNA methods in two high clones dramatically reduced SOCE, whereas overexpression of IRS-2 in a low SOCE clone elevated SOCE.