Citations

Abstract VEXAS (vacuoles, E1-ubiquitin-like modifier activating enzyme, X-linked, autoinflammatory, somatic) syndrome is a recently described autoinflammatory disorder caused by acquired UBA1 mutations in hematopoietic precursor cells. The prevalence, clinical significance, and genomic landscape of UBA1variants in patients with hematologic malignancies (HM) remains unexplored. We analyzed the genomic profiles of 86 patients carrying 17 different UBA1variants amongst an unbiased cohort of 8,976 HM patients. Specific genetic and clinical features seen more frequently in patients with UBA1 pathogenic variants (PV) versus those with variants of uncertain significance (VUS) included: marked male predominance (98% versus 80%, P=0.002), macrocytic anemia (MCV 105 ± 1.4 fl versus 96 ± 1.4 fl, p=0.01), frequent dominant clones without concurrent somatic variants (53% versus 15%, p=0.004), and association with molecularly distinct low-risk myelodysplastic syndrome (MDS; 13%) with superior overall survival (OS). Interestingly, while the genetic profiles of UBA1 PV patients resemble patients with clonal cytopenia of unknown significance (CCUS), rapid UBA1 clonal expansion predispose patients to convert to low-grade MDS with a notably hastened progression compared to UBA1 wild-type CCUS. This “clone surge to clinical stability” (CS) is a mechanism that emphasizes the unique pathobiology of UBA1mutations in VEXAS and supports it as a distinct entity.

DNA replication is a vulnerable cellular process, and its deregulation leads to genomic instability. Here, we demonstrate that chromobox protein homolog 3 (CBX3) binds replication protein A 32-kDa subunit (RPA2) and regulates RPA2 retention at stalled replication forks. CBX3 is recruited to stalled replication forks by RPA2 and inhibits ring finger and WD repeat domain 3 (RFWD3)–facilitated replication restart. Phosphorylation of CBX3 at serine-95 by casein kinase 2 (CK2) kinase augments cadherin 1 (CDH1)–mediated CBX3 degradation and RPA2 dynamics at stalled replication forks, which permits replication fork restart. Increased expression of CBX3 due to gene amplification or CK2 inhibitor treatment sensitizes prostate cancer cells to poly(ADP-ribose) polymerase (PARP) inhibitors while inducing replication stress and DNA damage. Our work reveals CBX3 as a key regulator of RPA2 function and DNA replication, suggesting that CBX3 could serve as an indicator for targeted therapy of cancer using PARP inhibitors.

Abstract Objectives Precise literature recommendation and summarization are crucial for biomedical professionals. While the latest iteration of generative pretrained transformer (GPT) incorporates 2 distinct modes—real-time search and pretrained model utilization—it encounters challenges in dealing with these tasks. Specifically, the real-time search can pinpoint some relevant articles but occasionally provides fabricated papers, whereas the pretrained model excels in generating well-structured summaries but struggles to cite specific sources. In response, this study introduces RefAI, an innovative retrieval-augmented generative tool designed to synergize the strengths of large language models (LLMs) while overcoming their limitations. Materials and Methods RefAI utilized PubMed for systematic literature retrieval, employed a novel multivariable algorithm for article recommendation, and leveraged GPT-4 turbo for summarization. Ten queries under 2 prevalent topics (“cancer immunotherapy and target therapy” and “LLMs in medicine”) were chosen as use cases and 3 established counterparts (ChatGPT-4, ScholarAI, and Gemini) as our baselines. The evaluation was conducted by 10 domain experts through standard statistical analyses for performance comparison. Results The overall performance of RefAI surpassed that of the baselines across 5 evaluated dimensions—relevance and quality for literature recommendation, accuracy, comprehensiveness, and reference integration for summarization, with the majority exhibiting statistically significant improvements (P-values <.05). Discussion RefAI demonstrated substantial improvements in literature recommendation and summarization over existing tools, addressing issues like fabricated papers, metadata inaccuracies, restricted recommendations, and poor reference integration. Conclusion By augmenting LLM with external resources and a novel ranking algorithm, RefAI is uniquely capable of recommending high-quality literature and generating well-structured summaries, holding the potential to meet the critical needs of biomedical professionals in navigating and synthesizing vast amounts of scientific literature.