Citations

Abstract 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.

ABSTRACT Background Evaluation and interpretation of the literature on obstructive sleep apnea (OSA) allows for consolidation and determination of the key factors important for clinical management of the adult OSA patient. Toward this goal, an international collaborative of multidisciplinary experts in sleep apnea evaluation and treatment have produced the International Consensus statement on Obstructive Sleep Apnea (ICS:OSA). Methods Using previously defined methodology, focal topics in OSA were assigned as literature review (LR), evidence‐based review (EBR), or evidence‐based review with recommendations (EBR‐R) formats. Each topic incorporated the available and relevant evidence which was summarized and graded on study quality. Each topic and section underwent iterative review and the ICS:OSA was created and reviewed by all authors for consensus. Results The ICS:OSA addresses OSA syndrome definitions, pathophysiology, epidemiology, risk factors for disease, screening methods, diagnostic testing types, multiple treatment modalities, and effects of OSA treatment on multiple OSA‐associated comorbidities. Specific focus on outcomes with positive airway pressure (PAP) and surgical treatments were evaluated. Conclusion This review of the literature consolidates the available knowledge and identifies the limitations of the current evidence on OSA. This effort aims to create a resource for OSA evidence‐based practice and identify future research needs. Knowledge gaps and research opportunities include improving the metrics of OSA disease, determining the optimal OSA screening paradigms, developing strategies for PAP adherence and longitudinal care, enhancing selection of PAP alternatives and surgery, understanding health risk outcomes, and translating evidence into individualized approaches to therapy.

Abstract Background: We compared the predictive and prognostic value of ctDNA dynamics in high-risk hormone receptor-positive/HER2-negative (HR+/HER2-) and triple negative breast cancer (TNBC) receiving neoadjuvant chemotherapy (NAC) enrolled in the I-SPY 2 trial (NCT01042379). To our knowledge, this is the largest ctDNA study in breast cancer in the neoadjuvant setting. Methods: Blood samples were collected at pre-treatment (T0), during treatment (T1 at 3 weeks, and T2 at 12 weeks) and after NAC (T3 at 24 weeks) from 106 HR+/HER2- and 97 TNBC patients. Plasma samples (n=734) were analyzed using a personalized and tumor-informed mPCR NGS-based ctDNA test (SignateraTM). Patients, all high risk for recurrence by MammaPrint, received paclitaxel-based treatment +/- experimental therapy followed by anthracycline. The median follow-up was 3.0 years (0.5 to 6.5). The predictive and prognostic value of ctDNA dynamics and status at different timepoints were examined. Our analysis is exploratory and does not adjust for other biomarkers. Results: Pretreatment ctDNA positivity (Fisher p<0.0001) and levels (mean tumor molecules/mL, MTM/mL, t test p=0.0062) were significantly higher in TNBC (90.7%, 14.7 MTM/mL) than in high risk HR+/HER2- (66.0%, 5.5 MTM/mL). Early and late ctDNA clearance during treatment (3 and 12 weeks of NAC) was predictive of pathologic complete response (pCR) and residual cancer burden (RCB), class 0-III, in TNBC but not HR+/HER2- (Table). In both subtypes: (1) ctDNA was a significant negative prognostic factor for distant recurrence-free survival (DRFS) at all timepoints (p<0.05) except at pretreatment; (2) all patients who achieved pCR were ctDNA-negative after NAC; (3) among non-responding patients, ctDNA-negativity after NAC was associated with improved DRFS (Table). Conclusions: The predictive value of ctDNA for prediction of pCR and RCB differed between subtypes (HR+/HER2- vs. TNBC), while similar prognostic value was observed. In TNBC, early clearance of ctDNA at 3 weeks was a significant predictor of favorable response to NAC. Compared to patients who were ctDNA-positive after NAC, ctDNA-negative status in both subtypes was associated with improved DRFS even in patients with residual cancer (no pCR or RCB-II/III). These findings could inform on the design of future studies that seek to demonstrate the utility of ctDNA in the curative setting. Predictive and prognostic significance of ctDNA in early breast cancer in the neoadjuvant setting HR+HER2- (n=106) TNBC (n=97) Predictive value for prediction of pCR and RCB Fisher p-value Fisher p-value Early ctDNA clearance (between T0 and T1) and pCR 0.4521 <0.0001 Late ctDNA clearance (between T0 and T2) and pCR 0.8071 0.0004 Early ctDNA clearance (between T0 and T1) and RCB (0-III) 0.1360 <0.0001 Late ctDNA clearance (between T0 and T2) and RCB (0-III) 0.4869 0.0004 Early ctDNA clearance at T1 and pCR rates pCR rate pCR rate ctDNA clearance (ctDNA+ at T0/ctDNA- at T1) 21% 67% Late ctDNA clearance (betweeNo early clearance (ctDNA+ at T0/ctDNA+ at T1) 13% 14% Prognostic value for prediction of DRFS Log rank p-value Log rank p-value ctDNA at T3 and pCR vs no PCR 0.0002 <0.0001 ctDNA at T3 and RCB (0-I vs II-III) 0.0110 <0.0001 Timepoints: T0 - pretreatment; T1 - three weeks after treatment initiation; T2 - at 12 weeks, between paclitaxel-based and anthracycline regimens; T3- after neoadjuvant chemotherapy prior to surgery Citation Format: Mark Jesus Mendoza Magbanua, Lamorna Brown Swigart, Derrick Renner, Svetlana Shchegrova, Gillian L. Hirst, Christina Yau, Denise M. Wolf, Hsin-Ta Wu, Ekaterina Kalashnikova, Amy L. Delson, A. Jo Chien, Debu Tripathy, Smita Asare, Raheleh Salari, Angel Rodriguez, Bernhard Zimmermann, Himanshu Sethi, Alexey Aleshin, Paul Billings, Rita Nanda, Hope S. Rugo, Laura J. Esserman, Minetta C. Liu, Angela DeMichele, Laura van 't Veer. Comparison of the predictive and prognostic significance of circulating tumor DNA in patients with high risk HER2-negative breast cancer receiving neoadjuvant chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB111.

Abstract Interpretation of the clinical significance of somatic gene variants in cancer remains a major challenge in cancer diagnosis, prognosis and treatment response prediction. We will report on progress and plans of the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG). The CDWG membership consists of over 150 multi-disciplinary experts in cancer biology, oncology, pathology, genetics, genomics and informatics. The mission of the ClinGen Somatic Cancer CDWG is to facilitate the development of data curation guidelines and standards to determine the clinical significance of somatic alterations in cancer, thereby enhancing the usability, dissemination and implementation of cancer somatic changes in the ClinGen resource and other knowledgebases including CIViC, ClinVar, and the Variant Interpretation for Cancer Consortium (VICC) MetaKB. Our goal is to create high-quality assertions of the clinical significance of specific somatic variants in cancer by leveraging the CIViC curation interface, adapting the germline procedures of ClinGen to somatic variant interpretation, and implementing the interoperability standards of the Global Alliance for Genomics and Health (GA4GH). The ClinGen Somatic effort is overseen by the Somatic CDWG and reports progress to the overall ClinGen consortium. There are Somatic Cancer subdomains focused on particular clinically important domains of cancer variant interpretation including three Task Forces (covering Pediatric Cancer, Hematologic Cancer, and Solid Tumors) and a growing number of Somatic Cancer Variant Curation Expert Panels (SC-VCEPs). To improve quality and consistency of clinical interpretations, each candidate somatic cancer VCEP must complete a four step approval process adapted from ClinGen’s work in Germline disease domains. The Somatic CDWG works to ensure that each group is aware of available training materials and detailed standard operating procedures. Each SC-VCEP also coordinates with the ClinGen Cancer Variant Interpretation Committee (CVI) whose goal is to support development of granular specifications for the AMP/ASCO/CAP guidelines for somatic variant interpretation. New SC-VCEPs are anticipated to focus on specific clinically relevant genes, pathways, disease entities, variant classes or treatment modalities. Currently, three SC-VCEPs have begun to work through the four step process (focused on FGFR mutations, NTRK fusions, and FLT3 mutations respectively), and two more SC-VCEPs are in the planning stage (Histone H3 and Ph-like ALL). To date, ClinGen Somatic groups have contributed 619 evidence lines into CIViC from 353 published papers and 21 assertions of clinical significance. Input from the AACR community is critical for the establishment of new SC-VCEPs that address areas of variant interpretation with the greatest need. Citation Format: Jason Saliba, Gordana Raca, Angshumoy Roy, Ian King, Shamini Selvarajah, Xinjie Xu, Rashmi Kanagal-Shamanna, Laveniya Satgunaseelan, David Meredith, Mark Evans, Alanna Church, Panieh Terraf, Yassmine Akkari, Heather E. Williams, Wan-Hsin Lin, Chimene Kesserwan, Deborah I. Ritter, Kilannin Krysiak, Arpad Danos, Alex Wagner, Marilyn M. Li, Dmitriy Sonkin, Jonathan S. Berg, Sharon E. Plon, Heidi L. Rehm, Shashikant Kulkarni, Ramaswamy Govindan, Obi L. Griffith, Malachi Griffith, on behalf of the ClinGen Somatic CDWG. The Clinical Genome Resource (ClinGen) somatic cancer clinical domain working group [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1192.

Abstract Structural discovery of guanine nucleotide exchange factor (GEF) protein complexes is likely to become increasingly relevant with the development of new therapeutics targeting small GTPases and development of new classes of small molecules that inhibit protein‐protein interactions. Syx (also known as PLEKHG5 in humans) is a RhoA GEF implicated in the pathology of glioblastoma (GBM). Here we investigated protein expression and purification of ten different human Syx constructs and performed biophysical characterizations and computational studies that provide insights into why expression of this protein was previously intractable. We show that human Syx can be expressed and isolated and Syx is folded as observed by circular dichroism (CD) spectroscopy and actively binds to RhoA as determined by co‐elution during size exclusion chromatography (SEC). This characterization may provide critical insights into the expression and purification of other recalcitrant members of the large class of oncogenic—Diffuse B‐cell lymphoma (Dbl) homology GEF proteins. In addition, we performed detailed homology modeling and molecular dynamics simulations on the surface of a physiologically realistic membrane. These simulations reveal novel insights into GEF activity and allosteric modulation by the plekstrin homology (PH) domain. These newly revealed interactions between the GEF PH domain and the membrane embedded region of RhoA support previously unexplained experimental findings regarding the allosteric effects of the PH domain from numerous activity studies of Dbl homology GEF proteins. This work establishes new hypotheses for structural interactivity and allosteric signal modulation in Dbl homology RhoGEFs.

Abstract Despite the well-established role of recurrent gene fusions as oncogenic drivers, current practices for characterizing and interpreting gene fusion events in clinical testing and in biomedical literature are inconsistent. From the conceptual definition of gene fusions to the salient elements that characterize these alterations, a lack of community-driven standards for the curation of gene fusions has resulted in a disparate landscape of fusion representations and supporting tools. Consequently, the evidence-based clinical evaluation of gene fusions requires extensive expert review for accurate interpretation of observed gene fusions with respect to putative evidence from biomedical literature. Furthermore, the lack of these standards inhibits the interoperability of tools, resources, and pipelines - impeding data sharing and downstream utility.To address these challenges, a cross-consortia initiative between the Variant Interpretation for Cancer Consortium and ClinGen was formed to develop a standard operating procedure (SOP) for the curation of gene fusions. The SOP is under development by an international and diverse set of experts in the representation, detection, and clinical interpretation of gene fusions. Participating stakeholders across academic, government, and industry sectors showcased challenges and solutions, and participated in community surveys and discussions to define and develop the SOP for this diverse class of alterations.An initial result of this effort was the precise molecular definition of genomic events and features constituting gene fusions. We distinguish these from similar but distinct classes of structural alterations through clinically-relevant examples. Next, we discuss our findings on community practices around the description and evaluation of gene fusions. We provide our recommendations for characterization and representation of gene fusions from these practices, and compare these recommendations to existing variant representation standards and formats (e.g. HGVS variant nomenclature). We also discuss the concurrent application of formats for standardized human- and machine-readable representations of gene fusion events.We conclude with discussion of the salient elements to enable rapid, scalable, and consistent evaluation of fusions curated from the biomedical literature. Recommendations are provided for the standardized capture of these elements to enable both intuitive and precise characterization of this diverse class of alterations in clinical reporting and literature. In summary, we provide a clinical-practice driven framework and nomenclature for gene fusions, including recommendations for human readability, computational precision, and data integrity within the SOP. This work is a substantial advancement towards standardized communication, investigation, and sharing of gene fusion data across clinical and research domains and specialties. Citation Format: Alex H. Wagner, Ioannis S. Vlachos, Dmitriy Sonkin, Panieh Terraf, Chimene Kesserwan, Andrea Sboner, Thomas Coard, Christian Reich, Deborah I. Ritter, Peter Horak, Ying S. Zou, Anna Tanska, Aaron M. Berlin, Anna Lu, Daniel Cameron, Heather E. Williams, Wan-Hsin Lin, Gokce Toruner, Arpad Danos, Jason Saliba, Huiling Xu, Xinjie Xu, Georgina Ryland, Michele Ceccarelli, Liying Zhang, Sarah Rapisardo, Catherine Rehder, Xuelu Liu, Aparna Pallavajjala, Nicole Park, Laveniya Satgunaseelan, Kristy Lee, Jie Liu, Obi Griffith, Robert R. Freimuth, Albrecht Stenzinger, Linda B. Baughn, Michael Baudis, Jennifer Lee, Marilyn Li, Angshumoy Roy, Gordana Raca. A standard operating procedure for the curation of gene fusions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 449.

Abstract Childhood cancers are driven by unique profiles of somatic genetic alterations, with a significant contribution from predisposing germline variants. Understanding the genomic landscape of pediatric cancers is complicated by their rarity, the heterogeneity of variation within a given disease, and the complex forms of structural variation they contain. Variants in childhood disease may differ from those in adult versions of the same cancer type, or may have different clinical significance. Currently, pediatric variants are underrepresented in cancer variant databases, and an urgent need exists for their publicly available expert curation. To address this, the Pediatric Cancer Taskforce (PCT) was formed within the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) (https://www.clinicalgenome.org/working-groups/somatic/). The PCT is a multi-institutional group of 39 members with broad experience in childhood cancer and variant curation, whose work consists of standardization and classification of genetic variants in pediatric cancers. The CIViC knowledgebase (www.civicdb.org) is a freely available resource for Clinical Interpretation of Variants in Cancer, which leverages public curation and expert moderation to address the problem of annotating the large volume of clinically actionable cancer variants. PCT curators work together with PCT expert members and the CIViC team on variant curation, and have submitted over 230 Evidence Items and over 10 Assertions to CIViC. To further address issues specific to pediatric curation, the PCT is working with CIViC to develop new pediatric-specific CIViC features and modifications of the data model that will aid in pediatric curation. A pediatric user interface, as well as representation of large scale structural and copy number variation are being developed for version two of CIViC, expected to be released in 1-2 years, which will enable curation of a new class of structural variants often encountered in pediatric cancer. A novel standard operating procedure for childhood cancer curation in CIViC is being developed by PCT experts, curators and the CIViC team. This SOP will cover topics including curation of structural variants, as well as pediatric-specific variant tiering guidelines which take into account the sparse nature of evidence in pediatric cases. A companion resource, CIViCmine (http://bionlp.bcgsc.ca/civicmine/), will be further developed to incorporate pediatric data. These and other joint efforts of the PCT and CIViC will significantly enhance pediatric variant representation for public use, to support the care of children with cancer. Citation Format: Arpad Danos, Wan-Hsin Lin, Jason Saliba, Angshumoy Roy, Alanna J. Church, Shruti Rao, Deborah Ritter, Kilannin Krysiak, Alex Wagner, Erica Barnell, Lana Sheta, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Laura Corson, Kevin Fisher, Heather E. Williams, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Kesserwan A. Chimene, Laura Fuqua, Lisa Dyer, Huiling Xu, Jeffrey Jean, Laveniya Satgunaseelan, Liying Zhang, Ted W. Laetsch, Donald W. Parsons, Ryan Schmidt, Lynn M. Schriml, Kristen L. Sund, Shashikant Kulkarni, Subha Madhavan, Xinjie Xu, Rashmi Kanagal-Shamana, Marian Harris, Yasmine Akkari, Nurit Paz Yacov, Panieh Terraf, Malachi Griffith, Obi L. Griffith, Gordana Raca. Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 210.

Abstract The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase (civicdb.org) is an open access, centralized hub for structured, community curated and expertly moderated relationships between genomic variants and cancer. Evidence is curated from peer-reviewed, published literature and is classified into one of five Types: Predisposing, Diagnostic, Prognostic, Predictive (therapeutic), or Functional. The robustness of the Evidence is conveyed through the assignment of Levels with the first three derived from patient studies (Validated, Clinical, Case Study), Preclinical, generated from in vivo or in vitro data, and Inferential, which describes indirect associations. Each Evidence Item requires an Evidence Statement written in the curator's own words summarizing the source's results regarding the variant's clinical impact. Collaborations with groups like ClinGen have generated a significant influx of new curators, increasing the demand for detailed principles regarding data prioritization in the Evidence Statement in order to streamline the curation process. The curation community would benefit from simpler, visual guides through the complex decisions needed to appropriately and consistently curate Evidence Items. We are devoting significant effort to continue the development of straightforward Evidence curation algorithms (decision trees) similar to those used in clinical molecular testing labs to aid CIViC curators. Previously published guidelines on development of these statements are the basis of our Evidence algorithms. Obvious inflection points for curators are clearly identified with specific details noted for each to optimize decision efficiency. As the predominant Evidence Type comprising 57% of all CIViC submissions, 58% of referenced patient trials, and 92% of Preclinical submissions, Predictive Evidence is the initial focus of our pilot guidelines with Diagnostic and Prognostic to follow. Within the Predictive Evidence Type, clinical trials, case studies, and preclinical Levels each require vastly different Evidence Statement details and ultimately the creation of three separate, uniquely modeled algorithms. The implementation of these algorithms will assist in streamlining both curation and the expert review process. Notably, a template is not being created, as the preservation of curator style and voice is important to maintain the community feel of the database. To ensure the highest level of clarity, our team is utilizing specific novice and experienced curators to assist with the development process. As these algorithms pass the pilot phase, they are being tested as curator training tools. Ultimately, these guidelines will be used to encourage independence in curators and to enhance the Evidence already contained in CIViC. Citation Format: Jason Saliba, Lana Sheta, Kilannin Krysiak, Arpad Danos, Alex Marr, Erica Barnell, Shahil Pema, Wan-Hsin Lin, Panieh Terraf, Joshua F. McMichael, Cameron J. Grisdale, Shruti Rao, Susanna Kiwala, Adam Coffman, Alex Wagner, Obi L. Griffith, Malachi Griffith. Development of Evidence Statement curation algorithms to aid cancer variant interpretation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 208.