Citations

ABSTRACT Understanding lung cancer evolution can identify tools for intercepting its growth. In a landscape analysis of 1024 lung adenocarcinomas (LUAD) with deep whole-genome sequencing integrated with multiomic data, we identified 542 LUAD that displayed diverse clonal architecture. In this group, we observed an interplay between mobile elements, endogenous and exogenous mutational processes, distinct driver genes, and epidemiological features. Our results revealed divergent evolutionary trajectories based on tobacco smoking exposure, ancestry, and sex. LUAD from smokers showed an abundance of tobacco-related C:G>A:T driver mutations in KRAS plus short subclonal diversification. LUAD in never smokers showed early occurrence of copy number alterations and EGFR mutations associated with SBS5 and SBS40a mutational signatures. Tumors harboring EGFR mutations exhibited long latency, particularly in females of European-ancestry (EU_N). In EU_N, EGFR mutations preceded the occurrence of other driver genes, including TP53 and RBM10. Tumors from Asian never smokers showed a short clonal evolution and presented with heterogeneous repetitive patterns for the inferred mutational order. Importantly, we found that the mutational signature ID2 is a marker of a previously unrecognized mechanism for LUAD evolution. Tumors with ID2 showed short latency and high L1 retrotransposon activity linked to L1 promoter demethylation. These tumors exhibited an aggressive phenotype, characterized by increased genomic instability, elevated hypoxia scores, low burden of neoantigens, propensity to develop metastasis, and poor overall survival. Re-activated L1 retrotransposition-induced mutagenesis can contribute to the origin of the mutational signature ID2, including through the regulation of the transcriptional factor ZNF695 , a member of the KZFP family. The complex nature of LUAD evolution creates both challenges and opportunities for screening and treatment plans.

Introduction: Primary diffuse large B-cell lymphoma (DLBCL) of the central nervous system (CNS), traditionally referred to as primary CNS lymphoma (PCNSL), is a rare type of primary large B-cell lymphoma of immune-privileged sites with significantly poorer prognosis than systemic DLBCL. Genetic profiles of PCNSL were widely studied, whereas the tumor microenvironment has not been well characterized. Patients and Methods: A series of molecular analyses were performed using DLBCL FFPE tissues collected by the International Lymphoma Consortium. First, multiplex fluorescent immunohistochemistry (mfIHC) using antibodies for CD20, PAX5, CD3, CD4, CD8, FOXP3, CD45RO, CD68, CD56, PD-1, PD-L1, PD-L2, and CTLA-4 was performed on PCNSL tissues from 121 immunocompetent patients and de novo systemic DLBCL tissues from 405 patients. Digital quantification results for 405 patients with EBV-negative systemic DLBCL or primary testicular (PT) DLBCL have been reported previously. The preliminary mfIHC results of PCNSL will be presented. Second, mfIHC for multiple B cell biomarkers, CD3, CD4, CD8, CD68, CD163, CD11c, and CD31 were performed for 10 PCNSL validation cases, 24 PT-DLBCL and 451 systemic DLBCL tissues (unpublished). Third, RNA samples were extracted and sequenced for the targeted 1408 cancer-associated genes, successfully in 142 PCNSL cases, including 102 cases evaluated by either of these two mfIHC panels and additional 40 PCNSL cases, Results: First, in the first large mfIHC cohort, PCNSLs compared with de novo systemic nodal or extranodal DLBCL had significantly higher mean/median CD56+ cell densities whereas lower T cell densities (consistent with the immune privilege), as well as lower CD45RO+, PD-1+, and FOXP3+ percentages in T cells whereas higher CD4¯CD8¯ double-negative percentages in T cells. Prognostic analysis in the PCNSL cohort showed that high CD56+ cell densities and high CD45RO+CD8+ T cell densities were associated with significantly poorer and better patient survival, respectively, which are opposite to their prognostic effects in systemic DLBCL as previously reported. Twelve patients had very high PD-L1 expression in B cells and significantly poorer survival in the PCNSL cohort. Second, as validation, the 10 PCNSL cases in the second mfIHC study showed significantly less T cells than systemic DLBCL and PT-DLBCL cases, and low CD8 T cell densities and having low percentage of CD8 T cells adjacent to tumor cells were associated with significantly poorer survival. Immunofluorescence landscapeand spatial analysisrevealed significant differences in immune privilege between PCNSL and PT-DLBCL. Third, gene expression profiling analysis by targeted RNA-seq identified 282 significantly differentially expressed genes between PCNSL and systemic DLBCL, referred to as the PCNSL signature. Unsupervised clustering analysis divided the PCNSL cohort into two clusters, which had no significant difference in MYD88 mutation frequencies analyzed at the mRNA level. Importantly, the cluster with higher expression of many neural glial genes in the PCNSL signature had significantly poorer survival, which remained significant in multivariate analysis adjusting for clinical factors and/or treatments, two PCNSL subcohorts (as training and validation cohorts), and methotrexate-treated patients. NCAM1 (Neural Cell Adhesion Molecule 1, encoding CD56) was among the upregulated PCNSL signature genes, explaining the poorer prognosis associated with higher CD56+ cell densities in PCNSL by the mfIHC analysis. Summary: Our studies represent the largest immunophenotypic and gene expression profiling studies currently in PCNSL, a rare type of primary DLBCL of immune-privileged sites. Multiplex immunofluorescence revealed the immune landscape in PCNSL in comparison with systemic and testicular DLBCL, and identified prognostic immune biomarkers. Targeted RNA-seq identified a PCNSL gene signature enriched with neuronal-glial genes and showing significant prognostic effect in PCNSL. These novel findings in the PCNSL microenvironment warrant further studies.

NPM1 mutations (NPM1+) are found in about 30% of adult AML. While NPM1+ are generally associated with favorable risk, prior studies have shown that NPM1+ older patients, defined as ≥ 65 years, have significantly worse outcomes. The updated European Leukemia Net 2022 (ELN22) now classifies NPM1+ AML patients with FLT3-ITD (ITD+) and MDS-associated mutations (MDSm+) as adverse-risk, while NPM1+/ITD-/MDSm+ patients remain favorable-risk unless they have an adverse-risk karyotype. A recent report (Chan et al., Blood Adv 2024) found MDSm+ conferred worse OS only in patients ≥ 60 years; however another report (Othman et al., Blood 2024) did not find MDSm+ were significantly associated with adverse outcomes in a larger cohort of NPM1+ patients. To further assess this question, we examined the mutational landscape in a large cohort of pre-treatment, diagnostic specimens from 456 patients enrolled in SWOG clinical trials (SWOG-9031, SWOG-9333, S0106, and S0112) and 126 patients from the UW/FHCC Hematopoietic Repository (Total N=582). Patients were treated with curative intent as per standard practice or clinical trials. Out of the 582 patients, 191 were NPM1+ (33%), of which 148 patients had cytogenetic and long-term outcome data. Kaplan-Meier and cumulative incidence estimates were estimated. C-statistics and AUC for Cox, cause-specific and logistic regression models for overall survival (OS), event-free survival (EFS), time to relapse (TTR, calculated in n=106 who achieved complete remission [CR], death in remission analyzed as a competing risk) and CR were calculated for age, ELN17, and ELN22, as well as ELN17 and ELN22 adjusting for age. ELN17 favorable-risk patients reclassified as adverse-risk (N=5 of 108) demonstrated significantly shorter EFS (HR=2.50, p=0.05) and CR rate (OR=0.20, p=0.024), although with similar OS (HR 1.30, p=0.66) and TTR (HR=1.20, p=0.86). ELN17 favorable-risk patients reclassified as ELN22 intermediate-risk (N=23 of 108) did not show a significant difference in OS (HR 1.47, p=0.20), EFS (HR 1.35, p=0.12), CR rate (OR 0.75, p=0.46), or TTR (HR=1.64, p=0.11) compared to ELN22 favorable-risk. For ELN17 intermediate-risk (N=40), 5 patients were reclassified as ELN22 adverse-risk (12.5%) and had significantly shorter EFS (HR 2.89, p=0.037) compared to ELN22 intermediate risk, although there was not a significant difference in CR rate (OR 0.31, p=0.23) or OS (HR 0.81, p=0.67). We next compared the prognostic significance of age relative to ELN risk classification. Age was associated with higher C-statistics (OS=0.65, TTR=0.57 and EFS=0.60) and AUC (CR=0.64) compared to both ELN17 (OS=0.56, TTR=0.54, EFS=0.54, CR=0.54) or ELN22 (OS=0.57, TTR=0.57, EFS=0.57, CR=0.58). The combination of age and ELN22 resulted in the highest C-statistics for OS, TTR, and EFS (OS=0.68, TTR=0.60, EFS=0.64). To further assess the impact of MDSm on risk-stratification of NPM1+ AML, we performed multivariable analyses adjusting for FLT3-ITD, MDSm, and age in patients without adverse-risk disease. These analyses showed MDSm did not have a significant association with OS when controlling for age or FLT3-ITD. We also compared ELN22 favorable-risk patients with and without MDSm (MDSm+ N=18; MDSm- N=61). Compared with the MDSm-, there was a trend toward an inferior OS (HR 1.85, p=0.054) for MDSm+ patients but not EFS (HR 1.38, p=0.3) or TTR (HR 1.2, p=0.64). When stratified by age (<65, N=53 vs. ≥65, N=26), MDSm+ in younger patients did not show a difference for OS (HR 0.96, p=0.96), EFS (HR 0.63, p=0.53) or TTR (HR 0.4, p=0.37). Older ELN22 favorable-risk patients (≥65) had similarly poor OS (MDSm- HR 3.33 and MDSm+ 3.27, respectively, p=0.001 for both) and EFS (MDSm- HR 3.11 and MDSm+ 2.39, p=0.001 and 0.01) compared to younger MDSm- patients. In our study, the ELN22 guidelines incrementally improve risk stratification for EFS in NPM1+ patients. However, age remains the most prognostic factor, and patients ≥ 65 years have similar adverse clinical outcomes, whether classified as favorable risk by ELN17, ELN22, or incorporating MDSm. Moreover, MDSm do not confer a worse prognosis in NPM1+ patients when accounting for age or FLT3-ITD status. These findings differ from a recent report by Chan et al. and are in keeping with the report by Othman et al. Therefore, further understanding of the underlying biology of age and its effect on NPM1+ AML is needed to improve outcomes for older patients.

Introduction: Follicular lymphoma (FL) is the most common indolent subtype of non-Hodgkin lymphoma. Family history is a risk factor. Genome-wide association studies (GWAS) have identified several germline variants, especially within the HLA region, to be associated with an increased risk of FL, but these variants only explain a fraction (16%) of heritability. Here, we present novel findings from GWAS of FL uncovering multiple germline genetic variants contributing to disease susceptibility. Methods: A GWAS was performed comprised of a discovery analysis in 6,508 FL cases and 64,183 controls of European ancestry followed by a replication of the most significant variants (P<5x10-7) in 2,745 FL cases and 782,226 controls from the biobanks of UK, Copenhagen Hospital, FinnGen and Mayo Clinic . At 9,253 FL cases and 846,409 FL free controls imputed genome-wide, this is the largest FL GWAS of European genetic ancestry to date. HLA alleles were imputed, and Class I and Class II signals were analysed at the amino acid level. A transcriptome wide association study (TWAS) was performed using FUSION investigate relationship to expression in GTEx and Young Finns Study in whole blood and cross-tissue . Functional annotations were done using FUMA, F-MAGMA, Open Targets and FORGE. Colocalization was conducted using CoLoC with whole blood gene expression data to understand causal variants. Mendelian randomization (MR) was performed on blood cell traits and leukocyte telomere lengths (LTL) to infer their relationships with FL genetic risk. Results: We replicated the previously published susceptibility loci within and outside the HLA region, finding two additional independent SNPs at two known loci (P < 5 x10-8) and discovered thirteen novel, genome-wide significant loci located at chromosome 2q13, 3p13, 3p24.1,3q13, 3q24.1, 4q31.21, 6p21, 6p22, 7q31, 10p14, 10q22, 10q23, 17q12, 19p13 and 20p11 with P < 5 x10-8. These SNPs were located near genes that show relevance for B-cell biology, such as BCL2L11, FOXP1, CD86, EOMES, IZKF3, PRF1, KLF2 NKX2-2 and HLA Class I and Class II genes. TWAS uncovered a role for H2AX, IGKV2-29, IMP, UBE4A and IKZF3 and provided directionality of effect for associated transcripts. Colocalization analysis corroborated a positive association with IKZF3 expression showing a robust correlation with FL risk variants (posterior probability PP4>0.7). In addition, several genome-wide significant variants in IKZF3 colocalize with eQTLs across tissues. IKZF3 (Aiolos) is part of the Ikaros family of transcription factors, critically important to the regulation of B-cell development and function. Therapeutic strategies leveraging Immunomodulatory imide drugs (IMiDs) to degrade IKZF3 have shown efficacy in multiple myeloma, and we speculate may also have potential benefits in FL by enhancing anti-tumor immunity and disrupting malignant B-cell function. Gene-set analysis showed blood and spleen to be the most relevant tissues for FL risk variants with lymphocyte activation and immune cell type differentiation genes demonstrating enrichment based on gene-ontology terms. Associated traits included allergies, respiratory diseases (e.g., asthma phenotypes), inflammatory bowel diseases, type 1 diabetes (T1D), lipid levels and commonly measured blood cell traits, with some SNPs showing evidence of colocalization with select blood traits. Interestingly, no causal association to blood traits was observed using MR; however, MR analyses did reveal a strong association with LTL. For the HLA loci, FL shows a diverse set of amino acid associations in HLA Class I and Class II genes that extends beyond what is known from autoimmune disease associations, such as rheumatoid arthritis or T1D. Our study also reveals interesting biological insights about FL susceptibility, such as a novel locus intragenic to FOXP1, an activated oncogene preferentially expressed in FL and DLBCL tumors, but rarely observed in Hodgkin's lymphoma. High levels of FOXP1 are known to result in poor survival in FL. Conclusion: This large GWAS of FL has identified multiple susceptibility variants, uncovering interesting gene candidates for further pathophysiological characterization of FL risk and B-cell development. It suggests new biological pathways and genes that could be potential leads for drug development and targeting treatment, in addition to identifying genetically high-risk populations for FL susceptibility.

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a highperformance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring.

Abstract Little is known about risk factors for central nervous system (CNS) relapse in mature T-cell and natural killer cell neoplasms (MTNKNs). We aimed to describe the clinical epidemiology of CNS relapse in patients with MTNKN and developed the CNS relapse In T-cell lymphoma Index (CITI) to predict patients at the highest risk of CNS relapse. We reviewed data from 135 patients with MTNKN and CNS relapse from 19 North American institutions. After exclusion of leukemic and most cutaneous forms of MTNKNs, patients were pooled with non-CNS relapse control patients from a single institution to create a CNS relapse–enriched training set. Using a complete case analysis (n = 182), including 91 with CNS relapse, we applied a least absolute shrinkage and selection operator Cox regression model to select weighted clinicopathologic variables for the CITI score, which we validated in an external cohort from the Swedish Lymphoma Registry (n = 566). CNS relapse was most frequently observed in patients with peripheral T-cell lymphoma, not otherwise specified (25%). Median time to CNS relapse and median overall survival after CNS relapse were 8.0 and 4.7 months, respectively. We calculated unique CITI risk scores for individual training set patients and stratified them into risk terciles. Validation set patients with low-risk (n = 158) and high-risk (n = 188) CITI scores had a 10-year cumulative risk of CNS relapse of 2.2% and 13.4%, respectively (hazard ratio, 5.24; 95% confidence interval, 1.50-18.26; P = .018). We developed an open-access web-based CITI calculator (https://redcap.link/citicalc) to provide an easy tool for clinical practice. The CITI score is a validated model to predict patients with MTNKN at the highest risk of developing CNS relapse.

Platelet activation and the complement system are mutually dependent. Here, we investigated the effects of storage time on complement activation and platelet function in routinely produced platelet concentrates. The platelet concentrates (n = 10) were stored at 22 °C for seven days and assessed daily for complement and platelet activation markers. Additionally, platelet function was analyzed in terms of their responsiveness to protease-activated receptor-1 (PAR-1) and thromboxane A2 receptor (TXA2R) activation and their capacity to adhere to collagen. Complement activation increased over the storage period for all analyzed markers, including the C1rs/C1-INH complex (fold change (FC) = 1.9; p < 0.001), MASP-1/C1-INH complex (FC = 2.0; p < 0.001), C4c (FC = 1.8, p < 0.001), C3bc (FC = 4.0; p < 0.01), and soluble C5b-9 (FC = 1.7, p < 0.001). Furthermore, the levels of soluble platelet activation markers increased in the concentrates over the seven-day period, including neutrophil-activating peptide-2 (FC = 2.5; p < 0.0001), transforming growth factor beta 1 (FC = 1.9; p < 0.001) and platelet factor 4 (FC = 2.1; p < 0.0001). The ability of platelets to respond to activation, as measured by surface expression of CD62P and CD63, decreased by 19% and 24% (p < 0.05) for PAR-1 and 69–72% (p < 0.05) for TXA2R activation, respectively, on Day 7 compared to Day 1. The extent of platelet binding to collagen was not significantly impaired during storage. In conclusion, we demonstrated that complement activation increased during the storage of platelets, and this correlated with increased platelet activation and a reduced ability of the platelets to respond to, primarily, TXA2R activation.

AbstractUnesbulin is being investigated in combination with dacarbazine (DTIC) as a potential therapeutic agent in patients with advanced leiomyosarcoma (LMS). This paper reports the pharmacokinetics (PK) of unesbulin, DTIC, and its unreactive surrogate metabolite 5‐aminoimidazole‐4‐carboxamide (AIC) in 29 patients with advanced LMS. Drug interactions between DTIC (and AIC) and unesbulin were evaluated. DTIC (1000 mg/m2) was administered to patients with LMS via 1‐h intravenous (i.v.) infusion on day 1 of every 21‐day (q21d) cycle. Unesbulin dispersible tablets were administered orally twice weekly (b.i.w.), starting on day 2 of every cycle, except for cycle 2 (C2), where unesbulin was dosed either on day 1 together with DTIC or on day 2, 1 day after DTIC administration. The PK of DTIC, AIC, and unesbulin in cycle 1 (C1) and C2 were estimated using noncompartmental analysis. DTIC and AIC were measurable immediately after the start of infusion and reached maximum plasma concentration (Cmax) immediately or shortly after end of infusion at 1.0 and 1.4 h (time to Cmax), respectively. Co‐administration of unesbulin orally at 200 mg or above with DTIC inhibited cytochrome P450 (CYP)1A2‐mediated DTIC metabolism, resulting in 66.7% reduction of AIC exposures. Such inhibition could be mitigated when unesbulin was dosed the day following DTIC infusion. Repeated unesbulin dosing demonstrated evidence of clinical CYP1A2 induction and increased AIC Cmax by 69.4% and area under concentration‐time curve to infinity by 57.9%. No meaningful difference in unesbulin PK was observed between C2 and C1. The combination therapy of 1000 mg/m2 i.v. DTIC q21d and 300 mg unesbulin b.i.w. in a staggered regimen is well‐tolerated in patients with LMS.