Citations

BACKGROUND Our knowledge of how the genomic landscape of chronic lymphocytic leukemia (CLL) is impacted by the selective pressure imposed by chemotherapy and targeted agents remains incomplete. We hypothesize that the extent of clonal evolution, baseline transcriptional state, and the overall and clone-specific growth kinetics of CLL will vary under native conditions or following exposure to chemotherapy and targeted therapy. METHODS We report the systematic genomic analysis of leukemia-normal whole-exome sequencing (WES) of 420 samples collected from 170 patients who were >65 years of age with treatment-naïve CLL. The treatment-cohort consisted of 83 patients who received ibrutinib (420mg daily) and 40 patients who received chlorambucil (0.5 mg/kg up to maximum 0.8 mg/kg, on days 1 and 15 of a 28-day cycle, up to 12 cycles) as part of the RESONATE-2 trial (NCT01722487). The ‘watch and wait’ (WW) cohort comprised 47 age-matched patients whose samples originated from the CLL Research Consortium and were untreated for their CLL during sampling. Sampling occurred at baseline and at day 300, and/or day 600, following initiation of therapy or following a diagnosis of CLL for those in the WW cohort. The WW cohort had more cases with a mutated IGHV (72% vs 40%), del17p (14% vs 0%), and del13q (72% vs 45%) when compared to the treatment cohort. Baseline CLL RNA-seq data were available for 150 patients. We used PhylogicNDT to reconstruct the clonal architecture from WES. By determining the fraction of cancer cells with each mutation (cancer cell fraction [CCF]) and their multiplicities (number of DNA molecules carrying a mutation per cancer cell), subclones were identified through clustering of CCFs of mutational events across samples from each patient. This allowed us to estimate the phylogenetic relationships and significant clonal shifts (defined as a distribution shift of CCF in a subclone of >95% between two timepoints, corresponding to a median ΔCCF of 18%) for each patient's CLL and to relate these shifts to somatic and clinical features. RESULTS In total, we identified 791 subclonal clusters across the three patient cohorts (ibrutinib = 406, chlorambucil = 189, WW = 196). We observed higher rates of significant clonal shifts between any two time points in patients exposed to chlorambucil (68%, q=0.008) and ibrutinib (77%, q<0.0001) when compared to patients on WW (36%). There was no significant difference in the number of patients exhibiting a clonal shift between those receiving ibrutinib and chlorambucil (q=0.3). More patients treated with ibrutinib (54%, q=0.0002) and chlorambucil (50%, q=0.004) harbored a clone that reduced in size than those on WW (17%). More patients treated with ibrutinib (71%, q<0.0002) and chlorambucil (55%, q=0.12) possessed at least one clone that increased in size during therapy than patients receiving WW (34%). In ibrutinib-treated patients, SF3B1 mutations (q=0.13, 14% vs 5%) and XPO1 mutations (q=0.13, 5% vs 0.3%) were enriched in subclones that significantly reduced over the complete time period. We also observed more BIRC3 mutations in subclones that had a significant shift in CCF when compared to subclones that remained stable over time (q=0.2, 5% vs 2%). Consistent with the overall study, with 8 years of follow-up in this patient subset, long-term progression-free survival (PFS) was significantly higher in the patients treated with ibrutinib when compared to chlorambucil (P<0.0001). This PFS benefit was also seen regardless of subclonal growth patterns. Using baseline RNA-seq data, we identified 7 of the 8 recently defined CLL expression clusters (EC), that were identified in the context of chemoimmunotherapy for CLL (Knisbacher et al. Nat Gen 2022), and could recapitulate the relationship to outcomes in individuals treated with chlorambucil. However, despite the larger cohort, no significant relationship between EC clusters and PFS were observed in ibrutinib-treated patients, suggesting that a distinct predictive baseline transcriptional signature may be present in ibrutinib-treated patients. CONCLUSION Our analyses highlight the need to interrogate the molecular features of CLL in relation to the impact of therapeutic selective pressure imposed by BTK inhibition, which appear distinct from exposure to chemotherapy. Ongoing analyses aim to identify relationships between patterns of growth and subclonal growth rates molecular and clinical features.

Introduction Multiple Myeloma (MM) precursors Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM) have variable risk of progression to active MM, and identifying which patients may progress remains a clinical challenge. Deep proteome profiling of peripheral blood (PB) plasma may advance non-invasive precursor disease staging, monitoring and characterization. Here, we performed plasma proteomic profiling on patients across the MM disease continuum as well as progressive and stable disease to provide insights into MM disease biology and identify protein-based high-risk disease features that may improve prognostication. Methods We performed high-throughput profiling of >5400 proteins using the Olink® Explore HT library and Proximity Extension Assay (PEA) technology. We profiled 529 PB plasma samples from 485 individuals, including MGUS (n=100), SMM (n=203), MM (n=100), and healthy donors (HDs) (n=82). Sequential samples from SMM-MM progressors (n=32) and stable SMM-SMM non-progressors (n=32) were also profiled, where precursor samples ranged 1.04-6.91 years (median 2.33 years) prior to MM progression. T-tests, ANOVAs, and linear mixed effect (LME) models were used to identify significant proteins across disease stages, progression status, and time. Results were adjusted for multiple testing using Benjamini-Hochberg procedure. A subset of 86 individuals (13 HDs, 14 MGUS, 41 SMM, 18 MM) also had single-cell RNA sequencing (scRNA-seq) performed on tumor and immune cells from paired PB/BM collected at the same timepoint to enable cellular mapping of signals detected in plasma. Results We successfully captured significantly dysregulated proteins including proteins highly expressed on the surface of plasma cells, such as BCMA, SLAMF7 and CD38, highlighting the utility of PEA technology to monitor soluble levels of clinically relevant targets. Given SMM patients are clinically heterogeneous and can resemble MGUS or MM, we aimed to improve discrimination of disease states using biological features obtained from the plasma proteome. We developed a classifier using an Elastic Net model for each stage, where plasma proteins such as SLAMF7, BCMA, TACI, FCRL5 and others had the highest importance scores for the model. We demonstrated 97% SMM/MM samples could be identified from healthy samples (AUC=0.82), indicating our classifier could reliably screen disease-related cases. Moreover, 84% of SMM cases were correctly classified as clinically defined SMM, while misclassified samples were labelled as MM cases. Interestingly, the misclassified patients had consistently rising M-spike levels during clinical follow-up but without a shift in clinical stage or risk status, suggesting the plasma proteome may provide earlier indication of evolving disease. Since proteomic information may also hold value for improved risk stratification, we evaluated protein levels in precursor stage timepoint samples from SMM-MM progressors and stable SMM non-progressors. We identified a prognostic five-protein signature that was significantly elevated in SMM-MM progressors, of which BCMA and TACI were top proteins, as well as proteins vital for calcium homeostasis and integrin-mediated cell adhesion. BCMA and TACI levels also had a strong positive correlation with BM plasma cell infiltration, which suggests these proteins may be useful surrogates of BM tumor burden during routine blood-based assessment of precursor patients. Lastly, integrative analysis of scRNA-seq of tumor and immune cells was used to elucidate cell origin level information of the prognostic signature proteins. Four proteins were highly expressed in malignant versus non-malignant plasma cells, suggesting our prognostic signature partially provides a readout of plasma cell biology. Meanwhile, one protein was constitutively expressed on most leukocytes, suggesting that the interplay of other cell types in the immune microenvironment involved in progression may also be reflected in the plasma. Conclusion We performed the first comprehensive analysis of the plasma proteome of MM and its precursor conditions. Overall, we developed a classifier that utilizes plasma proteins alone to accurately classify disease stages and identified a prognostic protein signature associated with progressive disease.

Introduction Genomic alterations are common in multiple myeloma precursor stages, monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, commonly used stratification systems do not include somatic alterations that play a role in the disease. To address this clinical need, we generated a comprehensive genomic map of MM and precursors by harmonizing and analyzing mostly WGS data from 1,030 patients. We used these data to define a simple MM-like score, that is able to discriminate which premalignant patients are at risk to progress. Methods We collected, enriched, and performed WGS of tumor plasma cells and matched normals from 138 participants with MGUS or SMM, including those in low, intermediate, and high-risk groups. We combined our precursor dataset with genomic profiles from MM (n=20) and external sources of 872 individuals with MGUS (n=18), SMM (n=62), and MM (n=792) for a cohort of 1,030 individuals with MM or a precursor condition. In 16 patients, we could perform WGS serially from the bone marrow or circulating tumor cells (n=39) and reassess the presence of mutations. Results To develop a simple score that reflects the similarity of precursor genomes to full-blown multiple myeloma disease, we searched among the 107 common driver events (occurring in >1% of patients) and initiating translocations for ones that are differently altered in precursor conditions versus MM. We found 26 differentially altered events: only MAF and MAFB translocations were enriched in MGUS/SMM whereas 24 events were enriched in MM, including known drivers MYC, KRAS, FAM46C, del(1p) and gain(1q). We then defined a simple “MM-like” score for each tumor as the number of MM-enriched drivers minus the number of MGUS-enriched drivers. As expected, the MM-like score in our cohort increased throughout MM development (MM-like score median of 1 in MGUS, 2 in SMM, and 3 in MM). Even among SMM patients, we found a gradual increase of the MM-like score from low- (median=1), to intermediate (median=2), to high-risk (median=3) groups that match the overall medians in MGUS, SMM and MM (p=2e-5). We tested whether the MM-like score can be used to estimate risk of progression. Among untreated SMM patients characterized with WGS, an MM-like score greater than 2 was associated with a higher likelihood of progression (HR=5.3, P=0.03). Using an independent dataset of 87 patients with SMM, we were able to validate that a MM-like score greater than 2 was indeed associated with a higher risk of progression (HR=3.3, P=5x10-5). 11 patients who did not progress and for whom we performed longitudinal WGS, has stable MM-like scores over time, while three patients clinically progressed and their MM-like score increased at the time of progression (due to newly detected mutations in KRAS and NRAS). For the two remaining patients, their MM-like score increased, and we observed growth of the subclone that acquired the additional MM-like driver. In the first patient with MGUS/low-risk SMM, a novel hyperdiploid clone was detected in addition to a TP53 bi-allelic hit and t(11;14) translocation and was shown to increase in clonal fraction with tumor burden (light chains +104% within three years). In the second non-progressor with an increasing MM-like score, a novel del(1p) was detected, and other subclones in the phylogenetic tree either shrank or were stable over three years. Finally, because the MM-like score can place tumors on a gradual axis of evolution towards a more genomically-mature tumor, we tested whether the MM-like score was also predictive of survival in patients treated with a newly diagnosed MM. To do so, we calculated the MM-like score in patients from the publicly available CoMMpass database. We found that an increased score significantly correlated with shorter progression-free survival (median PFS1: 3 years in high score, versus 5 years in low/intermediate score; p=1e-3) and overall survival (median OS: 4.5 years in high score, versus not reached in low/intermediate score; p=1e-5). Conclusion Overall, we demonstrate that it is possible to stratify precursor stages of MM by discovering drivers, detecting those that are associated with the stage of the disease, and defining a genomically-based score that can be used to assess risk of progression and is easy to implement in the clinic.

ObjectiveDe novo variants in cullin‐3 ubiquitin ligase (CUL3) have been strongly associated with neurodevelopmental disorders (NDDs), but no large case series have been reported so far. Here, we aimed to collect sporadic cases carrying rare variants in CUL3, describe the genotype–phenotype correlation, and investigate the underlying pathogenic mechanism.MethodsGenetic data and detailed clinical records were collected via multicenter collaboration. Dysmorphic facial features were analyzed using GestaltMatcher. Variant effects on CUL3 protein stability were assessed using patient‐derived T‐cells.ResultsWe assembled a cohort of 37 individuals with heterozygous CUL3 variants presenting a syndromic NDD characterized by intellectual disability with or without autistic features. Of these, 35 have loss‐of‐function (LoF) and 2 have missense variants. CUL3 LoF variants in patients may affect protein stability leading to perturbations in protein homeostasis, as evidenced by decreased ubiquitin‐protein conjugates in vitro. Notably, we show that 4E‐BP1 (EIF4EBP1), a prominent substrate of CUL3, fails to be targeted for proteasomal degradation in patient‐derived cells.InterpretationOur study further refines the clinical and mutational spectrum of CUL3‐associated NDDs, expands the spectrum of cullin RING E3 ligase‐associated neuropsychiatric disorders, and suggests haploinsufficiency via LoF variants is the predominant pathogenic mechanism. ANN NEUROL 2025;97:76–89

Humans with monogenic inborn errors responsible for extreme disease phenotypes can reveal essential physiological pathways. We investigated germline mutations in GNAI2 , which encodes G αi2 , a key component in heterotrimeric G protein signal transduction usually thought to regulate adenylyl cyclase–mediated cyclic adenosine monophosphate (cAMP) production. Patients with activating G αi2 mutations had clinical presentations that included impaired immunity. Mutant G αi2 impaired cell migration and augmented responses to T cell receptor (TCR) stimulation. We found that mutant G αi2 influenced TCR signaling by sequestering the guanosine triphosphatase (GTPase)–activating protein RASA2, thereby promoting RAS activation and increasing downstream extracellular signal–regulated kinase (ERK)/mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase (PI3K)–AKT S6 signaling to drive cellular growth and proliferation.

Abstract Objectives Urine drug testing (UDT) is a critical tool used in medical, forensic, and occupational settings, but interpreting results can be challenging. We performed a study to assess the ability of health care professionals to interpret UDT results accurately. Methods In total, 911 clinical and laboratory professionals in the United States and Canada responded to a survey with questions gauging expertise in UDT interpretation. Responses were analyzed to identify knowledge gaps. Results Toxicologists and laboratory PhD scientists performed well, with means of 4.82 and 4.63 questions answered correctly (out of 6 possible), respectively. Physicians specializing in pathology, emergency medicine, primary care, and internal medicine, however, displayed concerning knowledge gaps, as did laboratorians with nondoctoral degrees. Experience and training correlated with interpretation accuracy. Identification of simulated compliance as well as understanding opioid exposure, metabolism, and immunoassay cross-reactivity were among the most clinically significant knowledge gaps. More than 30% of survey respondents indicated that they would seek UDT information from the internet or peers rather than clinical or laboratory experts. Conclusions The study highlighted the need for targeted education and better collaboration between clinical and laboratory experts and other health care professionals to ensure that when physicians order UDT, they can accurately interpret results and reduce harm.

Regular, long-term aspirin use may act synergistically with genetic variants, particularly those in mechanistically relevant pathways, to confer a protective effect on colorectal cancer (CRC) risk. We leveraged pooled data from 52 clinical trial, cohort, and case-control studies that included 30,806 CRC cases and 41,861 controls of European ancestry to conduct a genome-wide interaction scan between regular aspirin/nonsteroidal anti-inflammatory drug (NSAID) use and imputed genetic variants. After adjusting for multiple comparisons, we identified statistically significant interactions between regular aspirin/NSAID use and variants in 6q24.1 (top hit rs72833769 ), which has evidence of influencing expression of TBC1D7 (a subunit of the TSC1-TSC2 complex, a key regulator of MTOR activity), and variants in 5p13.1 (top hit rs350047 ), which is associated with expression of PTGER4 (codes a cell surface receptor directly involved in the mode of action of aspirin). Genetic variants with functional impact may modulate the chemopreventive effect of regular aspirin use, and our study identifies putative previously unidentified targets for additional mechanistic interrogation.

Summary: While the current approach to precursor hematologic conditions is to “watch and wait,” this may change with the development of therapies that are safe and extend survival or delay the onset of symptomatic disease. The goal of future therapies in precursor hematologic conditions is to improve survival and prevent or delay the development of symptomatic disease while maximizing safety. Clinical trial considerations in this field include identifying an appropriate at-risk population, safety assessments, dose selection, primary and secondary trial endpoints including surrogate endpoints, control arms, and quality-of-life metrics, all of which may enable more precise benefit–risk assessment.

ImportanceStroke is a leading cause of death and disability in the US. Accurate and updated measures of stroke burden are needed to guide public health policies.ObjectiveTo present burden estimates of ischemic and hemorrhagic stroke in the US in 2019 and describe trends from 1990 to 2019 by age, sex, and geographic location.Design, Setting, and ParticipantsAn in-depth cross-sectional analysis of the 2019 Global Burden of Disease study was conducted. The setting included the time period of 1990 to 2019 in the US. The study encompassed estimates for various types of strokes, including all strokes, ischemic strokes, intracerebral hemorrhages (ICHs), and subarachnoid hemorrhages (SAHs). The 2019 Global Burden of Disease results were released on October 20, 2020.ExposuresIn this study, no particular exposure was specifically targeted.Main Outcomes and MeasuresThe primary focus of this analysis centered on both overall and age-standardized estimates, stroke incidence, prevalence, mortality, and DALYs per 100 000 individuals.ResultsIn 2019, the US recorded 7.09 million prevalent strokes (4.07 million women [57.4%]; 3.02 million men [42.6%]), with 5.87 million being ischemic strokes (82.7%). Prevalence also included 0.66 million ICHs and 0.85 million SAHs. Although the absolute numbers of stroke cases, mortality, and DALYs surged from 1990 to 2019, the age-standardized rates either declined or remained steady. Notably, hemorrhagic strokes manifested a substantial increase, especially in mortality, compared with ischemic strokes (incidence of ischemic stroke increased by 13% [95% uncertainty interval (UI), 14.2%-11.9%]; incidence of ICH increased by 39.8% [95% UI, 38.9%-39.7%]; incidence of SAH increased by 50.9% [95% UI, 49.2%-52.6%]). The downturn in stroke mortality plateaued in the recent decade. There was a discernible heterogeneity in stroke burden trends, with older adults (50-74 years) experiencing a decrease in incidence in coastal areas (decreases up to 3.9% in Vermont), in contrast to an uptick observed in younger demographics (15-49 years) in the South and Midwest US (with increases up to 8.4% in Minnesota).Conclusions and RelevanceIn this cross-sectional study, the declining age-standardized stroke rates over the past 3 decades suggest progress in managing stroke-related outcomes. However, the increasing absolute burden of stroke, coupled with a notable rise in hemorrhagic stroke, suggests an evolving and substantial public health challenge in the US. Moreover, the significant disparities in stroke burden trends across different age groups and geographic locations underscore the necessity for region- and demography-specific interventions and policies to effectively mitigate the multifaceted and escalating burden of stroke in the country.

Abstract Background: Clinical, molecular, and genetic epidemiology studies displayed remarkable differences between ever- and never-smoking lung cancer. Methods: We conducted a stratified multi-population (European, East Asian, and African descent) association study on 44,823 ever-smokers and 20,074 never-smokers to identify novel variants that were missed in the non-stratified analysis. Functional analysis including expression quantitative trait loci (eQTL) colocalization and DNA damage assays, and annotation studies were conducted to evaluate the functional roles of the variants. We further evaluated the impact of smoking quantity on lung cancer risk for the variants associated with ever-smoking lung cancer. Results: Five novel independent loci, GABRA4, intergenic region 12q24.33, LRRC4C, LINC01088, and LCNL1 were identified with the association at two or three populations (P < 5 × 10−8). Further functional analysis provided multiple lines of evidence suggesting the variants affect lung cancer risk through excessive DNA damage (GABRA4) or cis-regulation of gene expression (LCNL1). The risk of variants from 12 independent regions, including the well-known CHRNA5, associated with ever-smoking lung cancer was evaluated for never-smokers, light-smokers (packyear ≤ 20), and moderate-to-heavy-smokers (packyear > 20). Different risk patterns were observed for the variants among the different groups by smoking behavior. Conclusions: We identified novel variants associated with lung cancer in only ever- or never-smoking groups that were missed by prior main-effect association studies. Impact: Our study highlights the genetic heterogeneity between ever- and never-smoking lung cancer and provides etiologic insights into the complicated genetic architecture of this deadly cancer.