Citations

Abstract Proteins in the antiapoptotic B-cell lymphoma 2 (BCL-2) family play a role in the pathophysiology of multiple myeloma (MM). Venetoclax is a highly selective, potent, oral BCL-2 inhibitor that induces apoptosis of MM cells, and its efficacy may be potentiated through combination with agents that increase BCL-2 dependency or have complementary mechanisms of action. The safety, tolerability, pharmacokinetics, and antitumor activity of venetoclax in combination with carfilzomib and dexamethasone (VenKd) in adults with relapsed/refractory MM (RRMM) were investigated in this phase 2 dose-escalation study. Oral venetoclax (400 or 800 mg) was administered daily in combination with intravenous carfilzomib (27, 56, or 70 mg/m2) and oral dexamethasone (20 or 40 mg) in 4 dose-finding cohorts. The expansion cohort received venetoclax 800 mg, carfilzomib 70 mg/m2, and dexamethasone 40 mg. Forty-nine patients received treatment. Median prior lines of therapy was 1 (range, 1-3), and median time in the study was 27 months. The most common treatment-emergent adverse events were diarrhea (65%), fatigue (47%), nausea (47%), and lymphopenia (35%). Serious adverse events occurred in 26 (53%) patients. Of 3 treatment-emergent deaths, 1 was considered treatment related. The overall response rate was 80% in all patients, 92% in patients with t(11;14) (n = 13), and 75% in patients without (n = 36). The rate of complete response or better was 41%. Median progression-free survival was 22.8 months. Treatment with VenKd was well tolerated and showed promising response rates in this RRMM patient population, with greater responses observed in patients with t(11;14). This trial is registered at www.clinicaltrials.gov as #NCT02899052.

Background . There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches. Methods . A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results . Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes. Discussion . The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion . Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.

AbstractMultiple myeloma (MM) treatment options have evolved rapidly, but how new agents are incorporated into treatment decisions in current practice is not well understood. This study examined prescribing trends of physicians treating newly diagnosed MM and treatment outcomes in the United States. Electronic health record data from 6271 adult patients diagnosed with MM and receiving initial treatment between 1 January 2011 and 31 January 2020 were derived from the Flatiron Health electronic‐health record de‐identified database. The number/types of agents included in therapy regimens, time to next treatment (TTNT), and overall survival (OS) were assessed. Subgroups were analyzed by the International Staging System (ISS) disease stage at diagnosis, stem cell transplant eligibility and timing, and practice type. Exploratory prognostic models evaluated the association between baseline covariates and time‐to‐event outcomes. The proportion of patients receiving triplet therapies increased from 2011 (36%) to 2019 (72%) as those receiving initial monotherapy or doublet therapy decreased. Overall, the most prevalent triplet regimen consisted of an immunomodulatory drug (IMiD), a proteasome inhibitor, and a steroid. From 2017 to 2019, median TTNT from front‐line to second‐line was longer in patients with ISS stage I versus stages II/III, and in those receiving IMiD‐containing doublet or triplet therapies versus other combinations. Overall median OS was 56 months and increased from 2011 to 2014, after which median OS was not yet reached. Age, ISS stage, and high‐risk status were prognostic for both OS and TTNT, while sex, practice type, and ECOG status were prognostic for OS only.

Hyperstimulation of the cholecystokinin 1 receptor (CCK1R), a G protein–coupled receptor (GPCR), in pancreatic acinar cells is commonly used to induce pancreatitis in rodents. Human pancreatic acinar cells lack CCK1R but express cholinergic receptor muscarinic 3 (M3R), another GPCR. To test whether M3R activation is involved in pancreatitis, a mutant M3R was conditionally expressed in pancreatic acinar cells in mice. This mutant receptor loses responsiveness to its native ligand, acetylcholine, but can be activated by an inert small molecule, clozapine- N -oxide (CNO). Intracellular calcium and amylase were elicited by CNO in pancreatic acinar cells isolated from mutant M3R mice but not WT mice. Similarly, acute pancreatitis (AP) could be induced by a single injection of CNO in the transgenic mice but not WT mice. Compared with the cerulein-induced AP, CNO caused more widespread acinar cell death and inflammation. Furthermore, chronic pancreatitis developed at 4 weeks after 3 episodes of CNO-induced AP. In contrast, in mice with 3 recurrent episodes of cerulein-included AP, pancreas histology was restored in 4 weeks. Furthermore, the M3R antagonist ameliorated the severity of cerulein-induced AP in WT mice. We conclude that M3R activation can cause the pathogenesis of pancreatitis. This model may provide an alternative approach for pancreatitis research.

Abstract In the present study, acute onset of severe lupus nephritis was successfully treated in mice using a new, benzamide‐linked, small molecule that targets immune modulation and the NLRP3 inflammasome. Specifically, 6‐(2,4‐difluorophenyl)‐3‐(3‐(trifluoromethyl)phenyl)‐2H‐benzo[e][1,3]oxazine‐2,4(3H)‐dione (Cf‐02) (a) reduced serum levels of IgG anti‐dsDNA, IL‐1β, IL‐6, and TNF‐α, (b) inhibited activation of dendritic cells and differentially regulated T cell functions, and (c) suppressed the NF‐κB/NLRP3 inflammasome axis, targeting priming and activating signals of the inflammasome. Moreover, treatment with Cf‐02 significantly inhibited secretion of IL‐1β in lipopolysaccharide‐stimulated macrophages, but this effect was abolished by autophagy induction. These results recommend Cf‐02 as a promising drug candidate for the serious renal conditions associated with systemic lupus erythematosus. Future investigations should examine whether Cf‐02 may also be therapeutic in other types of chronic kidney disease involving NLRP3 inflammasome‐driven signaling.

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.

9089 Background: RET fusions are targetable oncogenic drivers in 1–2% of non-small cell lung cancer (NSCLC). ARROW (NCT03037385) supported the US FDA approval of pralsetinib, a highly potent oral selective RET inhibitor for RET-altered NSCLC and thyroid cancer. Here, we present updated results for a larger population of patients with RET fusion–positive NSCLC enrolled in ARROW. Methods: ARROW is a phase 1/2 open-label study conducted at 84 sites in 13 countries. Phase 2 expansion cohorts included patients with RET fusion–positive NSCLC. Initially, all treatment-naïve patients were not candidates for platinum-based therapy, a requirement removed by protocol amendment in July 2019. Primary objectives are overall response rate (ORR; blinded independent central review [BICR] per RECIST v1.1), assessed for patients with baseline measurable disease, and safety. Results: Updated analyses were completed as of Nov 6, 2020 (data cut-off), for patients who initiated pralsetinib 400 mg QD by May 22, 2020 (enrollment cut-off). Efficacy results, including analyses for treatment-naïve patients enrolled after eligibility criteria were revised to allow candidates for platinum-based therapy, are shown in the Table. Conclusions: Pralsetinib showed rapid, potent, and durable clinical activity in patients with RET fusion-positive NSCLC (regardless of prior therapies), including poor prognosis patients not eligible for platinum-based therapy. Overall, pralsetinib was well-tolerated. These data highlight the need for RET testing early in the course of disease to identify candidates who may benefit from treatment with pralsetinib. Clinical trial information: NCT03037385. [Table: see text]