cBioPortal for Cancer Genomics is a widely used platform for exploratory, interactive visualization and analysis of large-scale clinico-genomic datasets. cBioPortal provides a range of visualizations and analyses including interactive cohort exploration, OncoPrints, mutation “lollipop” plots, survival analysis, alteration enrichment analysis, and detailed patient-level visualizations. cBioPortal also integrates variant annotations from a variety of sources to facilitate interpretation. The public cBioPortal (https://www.cbioportal.org) is accessed by >40,000 unique visitors each month and hosts data from >460 studies. All data is also available in the cBioPortal Datahub: https://github.com/cBioPortal/datahub. In 2024 we added 76 new studies (∼30,000 samples), including data from the NCI Genomic Data Commons. In addition, >94 instances of cBioPortal are installed at academic institutions and companies worldwide. cBioPortal partners with AACR Project GENIE to provide access to the GENIE cohort in a dedicated instance (https://genie.cbioportal.org). Users can explore the full GENIE cohort of >229,000 clinically sequenced samples from 19 institutions, as well as cohorts with comprehensive clinical annotations including response, outcome, and treatment history, from the GENIE Biopharma Collaborative (BPC). BPC cohorts for NSCLC (∼2,000 samples) and colorectal cancer (∼1,500 samples) are available, with more to come. The past year has brought a variety of enhancements to cBioPortal. A new data type selector on the home page enables users to find studies with specific types of data. The interactive cohort exploration has new ways to explore data with the addition of gene-specific charts to summarize the types of mutations in a gene and the integration of the Plots tab for customizable graphs of any two data attributes. The OncoPrint can now display per group alteration frequency based on any categorical attribute. Variant interpretation is enhanced with the integration of AlphaMissense as a novel annotation source and an update to the latest MutationAssessor data. The patient page also has new visualizations, including mutational signatures and the integration of Chromoscope to visualize structural variations. We also made significant changes to the backend code to improve both the developer and user experience. The backend code was repackaged and upgraded to simplify and improve the development process. In addition, we are working on switching to an Online Analytical Processing (OLAP) database which will bring significant performance improvements. cBioPortal is open source: https://github.com/cBioPortal. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, Caris Life Sciences, Bilkent University, SE4BIO and The Hyve. We welcome open source contributions from others in the cancer research community. Ino de Bruijn,Tali Mazor,Rima AlHamad,Calla Chennault,Corey Dubin,Jeremy Easton-Marks,Zhaoyuan Fu,Benjamin Gross,Charles Haynes,David M. Higgins,Jason Hwee,Prasanna K. Jagannathan,Mirella Kalafati,Karthik Kalletla,Zeynep Karagöz,James Ko,Tim Kuijpers,Sowmiyaa Kumar,Priti Kumari,Ritika Kundra,Bryan Lai,Xiang Li,James Lindsay,Aaron Lisman,Qi-Xuan Lu,Ramyasree Madupuri,Zain-ul-Abideen Nasir,Angelica Ochoa,Yusuf Ziya Özgül,Oleguer Plantalech,Matthijs N. Pon,Baby A. Satravada,Jessica Singh,Selcuk Onur Sumer,Pim van Nierop,Floris Vleugels,Avery Wang,Manda Wilson,Hongxin Zhang,Gaofei Zhao,Ugur Dogrusoz,Allison Heath,Adam Resnick,Trevor J. Pugh,Chris Sander,Ethan Cerami,JianJiong Gao,Nikolaus Schultz. cBioPortal for cancer genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1117.
Abstract Introduction: In RRMM, increasing rates of pt attrition and decreasing durability of responses with each line of therapy (LOT) necessitate early treatment (tx) with the most effective therapies. Immunotherapies that are widely accessible across different MM tx settings have the potential to change the trajectory of RRMM. Teclistamab (Tec), the first approved BCMA×CD3 bispecific antibody (BsAb) for heavily pretreated RRMM, provided deep, durable responses in MajesTEC-1, with improved efficacy and safety in earlier LOTs. Daratumumab (Dara), a standard-of-care (SoC) foundational CD38 targeted therapy with direct on-tumor activity, has been shown to deplete immunosuppressive T-cells and expand cytotoxic T-cells, creating an immune-permissive microenvironment for synergistic Tec-mediated killing of MM cells. MajesTEC-3 (NCT05083169) evaluates Tec-Dara vs SoC DPd/DVd in RRMM. We report initial results for this first phase 3 study of BsAb therapy in MM. Methods: Eligible pts had 1-3 prior LOTs including a PI and lenalidomide (Len; pts with 1 prior LOT must have been Len-refractory) with progressive disease (PD) on or after the last LOT. Pts with prior BCMA-directed therapy or refractory to anti-CD38 were excluded; prior anti-CD38 exposure was permitted. Pts were randomized 1:1 to Tec-Dara or DPd/DVd. The Tec-Dara group received 28-day cycles (C) of Tec (1.5 mg/kg QW in C1-2 [C1 preceded by the approved step-up dose schedule]; 3 mg/kg Q2W in C3-6; and 3 mg/kg Q4W in C7+) with Dara; steroids were not required after C1 Day 8. Tec and Dara dosing were aligned with the approved Dara schedule. DPd/DVd were administered per approved schedules. Progression-free survival (PFS) by IRC was the primary endpoint; secondary endpoints included complete response or better (≥CR), overall response, minimal residual disease (MRD) negativity (10–5; next-generation sequencing), overall survival (OS), time to worsening of symptoms (MySIm-Q), and safety. Results: 587 pts were randomized (Tec-Dara, n=291; DPd/DVd, n=296). Median (range) age was 64 (25-88) yrs, median number of prior LOTs was 2 (1-3). With 34.5-mo median follow-up, Tec-Dara significantly improved PFS vs DPd/DVd (HR, 0.17; 95% CI, 0.12-0.23; P<0.0001); mPFS was NR and 18.1 mo, and 36-mo PFS rate was 83.4% and 29.7%, respectively. PFS benefit was consistent across all prespecified and clinically relevant pt subgroups, including age ≥75 yrs, Len-refractory, high-risk cytogenetics, ≥60% bone marrow plasma cells, soft-tissue plasmacytomas, and anti-CD38 exposed. Significantly higher rates of ≥CR (81.8% vs 32.1%; OR, 9.56; 95% CI, 6.47-14.14), overall response (89.0% vs 75.3%; OR, 2.65; 95% CI, 1.68-4.18), and MRD-negativity (58.4% vs 17.1%; OR, 6.78; 95% CI, 4.53-10.15) were observed with Tec-Dara (P<0.0001). There were 45 deaths with Tec-Dara and 96 with DPd/DVd, primarily due to PD (4.6%; 20.3%). OS significantly favored Tec-Dara (HR, 0.46; 95% CI, 0.32-0.65; P<0.0001), including across all prespecified subgroups. The 36-mo OS rates were 83.3% and 65.0%, respectively and >90% of Tec-Dara pts alive at 6 mo were also alive at 30 mo. Median time to worsening of MM symptoms was NR with Tec-Dara vs 39.9 mo with DPd/DVd (HR, 0.50; 95% CI, 0.34-0.72; P=0.0002). At data cutoff, 49.4% of pts remained on study tx (Tec-Dara, 71.0%; DPd/DVd, 28.3%). Median tx duration was twice as long with Tec-Dara vs DPd/DVd (32.4 vs 16.1 mo). Frequency of grade 3/4 (Tec-Dara, 95.1%; DPd/DVd, 96.6%) and grade 5 (7.8%; 6.2%) treatment-emergent adverse events (TEAEs), were comparable (safety set: Tec-Dara, n=283; DPd/DVd, n=290). Serious TEAEs occurred in 70.7% Tec-Dara and 62.4% DPd/DVd pts; tx discontinuations due to TEAEs were low (4.6% vs 5.5%). Any grade infections occurred in 96.5% and 84.1% of Tec-Dara and DPd/DVd pts, respectively; grade 3/4 infections occurred in 54.1% and 43.4%. New onset grade ≥3 infections decreased over time, coinciding with transition to Q4W dosing and supported by antimicrobial and Ig prophylaxis guidance. CRS rate was 60.1% (grade 1/2: 44.2%/15.9%) and ICANS was 1.1% with Tec-Dara. Conclusion: We demonstrate the clinically remarkable and statistically significant PFS and OS benefits of Tec-Dara vs SoC triplets in RRMM, with 83.4% of Tec-Dara pts alive and progression-free at 3 yrs. Infections with Tec-Dara were well managed with established protocols. This highly effective, off-the-shelf, immunotherapy combination represents a new SoC for RRMM as early as first relapse.
BACKGROUND:Femoral shaft fractures tend to be rare among children; however, these injuries are the most common major paediatric injuries treated by orthopaedic surgeons. The purpose of this study is to characterise the demographics and mechanisms of femoral injury associated with consumer products in the age group treated with spica casting, children 6 months to 6 years, to identify areas for injury prevention. METHODS:Data from 2012 to 2021 were obtained from the National Electronic Injury Surveillance System maintained by the Consumer Products Safety Commission, documenting emergency department visits for unintentional injuries associated with consumer products. Narrative descriptions were analysed to identify common factors in the injury events such as location, products and mechanisms of action. RESULTS:From 2012 to 2021, the estimated incidence of femur fractures was 23.5 cases per 100 000 children with no significant difference in yearly frequency. The most common mechanism of injury was a fall with the most frequent fracture sources being bed/bunk beds (16.1%), floor (slips/falls, 9.7%) and trampolines (9.7%). Most fractures occurred at the patient's home (58.4%). The incidence of injury outside of the home and frequency of fractures involving play structures/trampolines increased with age. CONCLUSIONS:The incidence and demographic characteristics of paediatric femur fractures associated with consumer products have remained consistent over the past 10 years. As home was the most common location of fracture, prevention of femur fractures should focus on caregiver education around high-risk sources of fracture (bed, stairs and trampolines) and manufacturers should consider design alternatives that discourage potential misuse.
In this paper we discuss the analysis of a machine repair queuing model with N machines under a single server. The machines in the environment are subjected to shocks as a result of which the need for repair arises. A Markovian arrival process (MAP) is used to model the shocks that impact the system, whereas the service time follows phase-type distribution. This proposed model generalizes a working vacation machine repair queuing model previously published in the literature. Uniformization techniques and block Gauss Siedel method are, respectively, used to analyze the transient and steady-state of the model under study. Some illustrative numerical examples are also presented.
Hypothyroidism is one of the most common yet underdiagnosed medical conditions in Saudi Arabia. It is more prevalent in aged and pregnant women as well as in patients with diabetes and sleep apnea. Hypothyroidism is characterized by the thyroid gland producing inadequate thyroid hormones, which might result in other chronic illnesses if left untreated. For this reason, this study proposes machine learning techniques to preemptively diagnose this disease using a straightforward clinical dataset from Saudi Arabia. Given the data size, this work serves as proof of concept. Algorithms such as KNN, SVM, Gradient boosting, and soft voting ensemble classifier were chosen for their promising performance in the proactive diagnosis of hypothyroidism and associated diseases compared to other algorithms in literature. The best performing model was the soft voting ensemble classifier, which achieved an accuracy of 94.7%. SVM, KNN, and XGBoost achieved 94%, 93.42%, and 92.1% accuracies, respectively. These results were obtained using 10 fold cross validation and forward sequential feature selection.
