Citations

Abstract Cerebrospinal fluid (CSF) cell-free DNA (cfDNA) is under evaluation as a liquid biopsy tool across multiple neuro-oncologic indications, including diagnosing lesions of uncertain etiology, delineating progression from pseudoprogression, and confirming suspected leptomeningeal disease (LMD). We evaluated the real-world performance of CSF cfDNA sequencing for identification of variant alleles (VA) using targeted panel sequencing (PredicineCARE) and/or copy number alterations (CNA) using low-pass whole-genome sequencing (LPWGS; PredicineSCORE) in a diverse cohort across three aforementioned indications. From 82 adult patients, 93 samples (92.4% from lumbar puncture and 7.6% from ventriculoperitoneal shunt taps) were analyzed. For CNS progression, gliomas were most frequently evaluated (60%); for LMD, metastatic disease was most common (86.7%). Median CSF volume utilized for analysis was 4.60 mL (range: 0.40-7.50 mL). Median cfDNA yield was 0.29 ng/mL (range: 0.00–1037.74 ng/mL). For lesions of uncertain etiology (n=40 patients, 1 sample/patient), VAs were identified in 10 samples (25%); an additional 10 samples had sufficient cfDNA for LPWGS (25%). 50% of samples in this indication had insufficient cfDNA for testing. For suspected progression (n=26 patients, 35 samples), VAs were identified in 6 samples (17.1%); an additional 14 samples underwent LPWGS (40%); 15 had insufficient cfDNA for further testing (42.9%). For suspected LMD (n=16 patients, 18 samples), VAs were quantifiable in 4 samples (22.2%); LPWGS could be performed in an additional 9 (50%); 5 had insufficient cfDNA (27.8%). Across indications, identification of known tumor-associated alterations in CSF, including those confirmed previously by tissue, served as a non-surgical diagnostic adjunct. In some patients, targetable mutations could be identified via CSF. Conversely, absence of such alterations and/or low cfDNA yields may lower suspicion for active neuro-oncologic disease, when evaluated alongside clinical history. Despite modest cell-free DNA yield, CSF cfDNA evaluation provides valuable information to aid clinical decision-making across neuro-oncologic indications.

Abstract Senile osteoporosis (SOP) is a systemic bone disease characterized by increased susceptibility to fractures. In this study, it is found that senescent bone marrow mesenchymal stem cells (BMSCs) exhibit increased sensitivity to ferroptosis, a phenomenon associated with Steap4, a nicotinamide adenine dinucleotide phosphate hydrogen (NADPH)‐dependent metalloreductase that reduces Fe 3+ to Fe 2+ . Therefore, it is aimed to innovatively elucidate how Steap4 affects ferroptosis in senescent BMSCs. These findings indicate that Steap4 promotes intracellular Fe 2+ accumulation and elevates reactive oxygen species (ROS) levels, collectively driving the upregulation of CCAAT/enhancer binding protein beta (C/EBPβ) expression. Interestingly, a functional C/EBPβ binding site is identified within the Steap4 promoter region. Mechanistically, knockdown studies demonstrated that C/EBPβ depletion attenuated Steap4 expression, whereas C/EBPβ overexpression conversely upregulated Steap4 levels. These regulatory processes establish a self‐amplifying Steap4/Fe 2+ ‐ROS/C/EBPβ positive feedback loop. Notably, a large number of adipocytes are also observed in the bone marrow of aged mice. Knockdown of Steap4 and C/EBPβ suppressed the differentiation of BMSCs into adipocytes. Knockdown of Steap4 or deferoxamine (DFO) treatment in animal experiments effectively relieves SOP. In conclusion, Steap4 accelerates the onset of ferroptosis in senescent BMSCs and promotes their differentiation to adipocytes through the Steap4/Fe 2+ ‐ROS/C/EBPβ axis, ultimately impairing their osteogenic capacity.

Abstract Understanding tumor cell dynamics can improve prognosis and treatment but remains limited for lung adenocarcinoma in people who have never smoked (NS-LUAD). With RNA sequencing data from 684 NS-LUAD cases and validation in an independent dataset, we identified three subtypes with distinct phenotypic traits and cell compositions. Additional genomic and histologic data further characterized the subtypes. “Steady,” marked by low proliferation, high alveolar cell fraction, moderate-to-well differentiation, and fewer driver gene alterations, is linked to prolonged survival and low immune evasion. “Proliferative” shows high proliferation markers, TP53 mutations, and gene fusions. “Chaotic,” with high epithelial-to-mesenchymal transition markers, has the worst prognosis, even within stage I tumors. Lacking known molecular or histologic characteristics, this aggressive subtype is solely identified by transcriptomic data. A 60-gene signature recapitulates the classification and predicts survival even within subgroups based on tumor stage or known genomic features, emphasizing its potential for improving early-stage NS-LUAD prognostication in clinical settings. Significance: The transcriptome of 684 NS-LUAD identifies three subtypes with different cellular dynamics and genomic and morphologic features. A 60-gene signature accurately stratifies subjects for mortality risk, even in stage I, offering a potential clinically applicable tool for treatment decision-making in patients with NS-LUAD. See related commentary by Azizi et al., p. 423

Abstract Background Carbon ion radiotherapy (CIRT), characterized by its Bragg peak and a reduced lateral scattering compared to proton radiotherapy (PT), offers excellent dose conformity in radiation treatment. Meanwhile, compared to PT, CIRT causes more complex and severe DNA damage in tumor cells, which results in a higher relative biological effectiveness (RBE), making it particularly advantageous in treating radioresistant and hypoxia tissues than conventional radiotherapy. However, CIRT's high RBE and the nuclear fragmentation dose tail outside the target presents challenges in sparing organ at risk (OARs). Combining PT and CIRT offers a potential hybrid modality that leverages the biological effectiveness of carbon ions while minimizing normal tissue toxicity with high biological damage. Purpose Despite the potential of combining PT and CIRT, balancing their respective advantages while mitigating disadvantages remains a significant challenge. This study aims to develop an optimized treatment approach that automatically adjusts the relative contributions of protons and carbon ions, improving target dose conformity while minimizing biological impact on OARs. Methods We propose a biological optimization framework for multi‐modality combining PT and CIRT (CCIPRT). The biological dose is modeled as the product of the physical dose and RBE. Through iterative Jacobian‐based linearization, the biological dose of carbon ion is expressed in a linear model, enabling the formulation of a linear least‐square objective. The biological dose of proton is formulated as a linear model using the physical dose and dose‐averaged linear energy transfer (LET). The joint optimization problem is then solved using iterative convex relaxation and the alternating direction method of multipliers. For comparison, single‐modality PT and CIRT were also implemented. Results When the target dose of CCIPRT is prescribed in terms of the physical dose, compared to PT, CCIPRT increased the mean biological dose in the target by 37.7%, 29.6%, and 31.1% for brain, lung, and abdomen, respectively; compared to CIRT, CCIPRT reduced the biological dose in CTV1cm (ring surrounding CTV with 1 cm width) by 47.2%, 32.3%, and 40.4% for brain, lung, and abdomen, respectively. When the target dose of CCIPRT is prescribed in terms of the biological dose, compared to PT, CCIPRT achieved higher conformity index (CCIPRT: 0.96 and 0.97; PT: 0.93 and 0.96 for brain and liver cases, respectively) and reduced the dose in CTV1cm by 9.5% and 7.0% for brain and liver cases, respectively; compared to CIRT, CCIPRT enhanced healthy tissue sparing, reducing the dose to the body by 7.5% and 9.2% for brain and liver cases, respectively. Conclusions A novel multi‐modality CCIPRT treatment planning is developed with demonstrated superior performance over single‐modality PT or CIRT, with enhanced biological effectiveness and dose conformity compared to PT and reduced OAR dose compared to CIRT.

Abstract Background Few studies have assessed the burden of chronic medical conditions in adolescent and young adult (AYA) cancer survivors. This study estimated the risk of these conditions among AYA survivors compared to a matched cohort without cancer. Methods This retrospective cohort study included 2‐year survivors ( n = 14,917) of 11 common AYA (15–39 years) cancers diagnosed at the integrated health care organizations of Kaiser Permanente (KP) Southern and Northern California during 2006–2020. A comparison cohort ( n = 149,164) without cancer (matched 10:1 by age, sex, calendar year, and KP site) was included. Cumulative incidence (CMI) accounting for death as a competing risk was calculated. Poisson regression estimated the incidence rate ratio (IRR) of each condition in cancer survivors versus the matched cohort, adjusting for age, sex, and race/ethnicity. Results The 5‐year CMI was highest for thyroid (17.4%), respiratory (6.6%), cardiovascular (5.0%), and liver (4.8%) diseases. At 10 years, the CMI of any condition was 39% in survivors versus 26% in the matched cohort. Survivors had a 2‐fold increased risk of being diagnosed with any medical condition (IRR, 2.0; 95% confidence interval [CI], 1.9–2.0) as well as two or more conditions (IRR, 2.3; 95% CI, 2.2–2.5). Risk was highest among survivors of hematologic cancers and those diagnosed with distant stage disease. Elevated risks were observed within all sociodemographic groups of this insured population. Conclusion AYAs with cancer had a higher risk of chronic medical conditions compared to those without cancer. Long‐term surveillance, risk mitigation through lifestyle modifications and effective disease management are crucial to reduce premature mortality.

Abstract Chronic widespread pain (CWP) remains challenging due to its heterogeneous causes and complex mechanisms. A total of 2920 plasma proteins are analyzed from 29,254 UK Biobank participants. A total of 256 proteins are identified as cross‐sectionally correlated with CWP. A simple (top 10 proteins) and comprehensive (all significant proteins) proteomic‐based score (ProtS) is created for CWP diagnosis, both outperforming and improving the existing clinical score (area under the curve, AUC: 0.801, 0.723, and 0.791 alone, and 0.856 and 0.880 in combination). In addition, the protein score predicted 13‐years risk of pain‐related traits over the body, including pain onset, progression, and intensity; Moreover, it has stronger associations with nociplastic pain and fibromyalgia compared to nociceptive and neuropathic pain, implying a unique protein signature of different pain mechanisms. Finally, among 434 candidate proteins prioritized in the observational analysis, 18 are corroborated with causal relevance by Mendelian randomization, and importantly, four (CA14, DPEP1, LGALS3, and TNF) showed potential as novel drug targets repurposed for treating CWP.

Abstract With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide‐ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.

Abstract Background Delirium is often underdiagnosed in clinical practice and is not routinely coded for billing. While manual chart review can identify delirium, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) can analyze unstructured text in electronic health records (EHRs) to extract meaningful clinical information. Methods To support national integration of NLP for EHR-based delirium identification across different institutions, we launched the Delirium Interest Group within the national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) NLP Working Group. This paper outlines our initial efforts to standardize, evaluate, and translate an NLP-based delirium detection model into the i2b2/ENACT platform. Results Multisite contextual inquiry identified several key challenges, including variations in local screening practices (eg, tools used, documentation frequency, and quality control), the need for harmonized definitions in the context of EHRs, and the complexity of modeling temporal logic. Multisite NLP evaluation revealed variable performance degradation driven by differences in delirium screening practices, clinical documentation patterns and semantics, and note syntactic structures. Conclusion Our work represents an important first step toward enabling scalable and standardized NLP-based delirium detection across institutions. By engaging diverse institutions through the ENACT NLP Working Group, we identified shared challenges and site-specific variations that impact model implementation and performance. Our collaborative approach enabled the development of a more robust framework for delirium identification across heterogeneous EHR systems. Future efforts will build on this foundation to enhance the validity, usability, and translational impact of delirium detection.

Abstract Background Completing adjuvant chemotherapy and reducing toxicities are critical for maximizing survival after a colon cancer diagnosis. Sex, as a biological variable, may affect colon cancer chemotherapy completion, toxicities, and survival differently. Methods From a National Cancer Institute–sponsored trial conducted among patients with stage III colon cancer (Cancer and Leukemia Group B (Alliance)/Southwest Oncology Group 80702), we included 2201 patients receiving the standard adjuvant chemotherapy regimen of fluorouracil, leucovorin, and oxaliplatin. We calculated relative dose intensity to indicate chemotherapy completion and considered reduced relative dose intensity (values <85%) as a clinically significant deviation from standard fluorouracil, leucovorin, and oxaliplatin. Using National Cancer Institute’s Common Terminology Criteria for Adverse Events, we defined severe adverse events (grade ≥3) as the occurrence of any following event, including neutrophil count decrease, nausea, platelet count decrease, hypertension, peripheral neuropathy, diarrhea, fatigue, gastritis, creatinine level increase, gastric ulcer, myocardial ischemia, and cerebral ischemia. The primary survival outcome was disease-free survival (time from enrollment to colon cancer recurrence or death from any cause), and secondary survival outcomes were recurrence-free and overall survival. Results Compared with men, women were at statistically significantly higher risks of experiencing reduced relative dose intensity (adjusted odds ratio = 1.59, 95% CI = 1.29 to 1.96; P < .001) and severe advance events (adjusted odds ratio = 1.72, 95% CI = 1.41 to 2.11; P < .001). Yet, women had statistically significantly better disease-free survival (adjusted hazard ratio = 0.72, 95% CI = 0.59 to 0.87; P < .001) as well as better recurrence-free and overall survival. Conclusions Our findings suggested that women with colon cancer are more likely to have worse chemotherapy completion rates and more severe adverse events, but they have better survival. Sex as a biological variable warrants further consideration in chemotherapy administration and survivorship management after colon cancer diagnosis.