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ABSTRACT Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to intensive care units (ICUs) of Mayo Clinic Hospitals over 8-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status. Of 19,177 patients, 42% were female with a median age of 65 (interquartile range [IQR], 55–76) years, The Acute Physiology, Age, and Chronic Health Evaluation III score of 70 (IQR, 56–87), hospital length of stay (LOS) of 7 (IQR, 4–12) days, and ICU LOS of 2 (IQR, 1–4) days. Four distinct trajectories were identified: fast recovery (27% with a mortality rate of 3.5% and median hospital LOS of 3 (IQR, 2–15) days), slow recovery (62% with a mortality rate of 3.6% and hospital LOS of 8 (IQR, 6–13) days), fast decline (4% with a mortality rate of 99.7% and hospital LOS of 1 (IQR, 0–1) day), and delayed decline (7% with a mortality rate of 97.9% and hospital LOS of 5 (IQR, 3–8) days). Distinct trajectories remained robust and were distinguished by Charlson Comorbidity Index, The Acute Physiology, Age, and Chronic Health Evaluation III scores, as well as day 1 and day 3 SOFA ( P < 0.001 ANOVA). These findings provide a foundation for developing prediction models and digital twin decision support tools, improving both shared decision making and resource planning.

Introduction This was the first phase 1 study conducted in the United States. It consisted of dose-escalation (part A) and multiple indication-specific cohort expansion (part B), investigating the safety and preliminary efficacy of toripalimab (anti–programmed cell death-1 inhibitor) in patients with advanced malignancies. Methods Patients with advanced malignancies that progressed after treatment with at least one prior line of standard systemic therapy, including the patients with advanced/recurrent cholangiocarcinoma (CCA), received toripalimab 240 mg every 3 weeks in part B. The primary endpoint was safety assessment. Efficacy endpoints included objective response rate (ORR), disease control rate (DCR), duration of response (DoR), progression-free survival (PFS) as assessed by the investigators according to Response Evaluation Criteria in Solid Tumors (version 1.1) and overall survival (OS). Results In part B, 166 patients, including the 42 patients with CCA, were enrolled and received toripalimab. Among the 166 patients, treatment-emergent adverse events (TEAEs) of any grade occurred in 158 (95.2%) patients, and 97 (58.4%) patients experienced TEAEs of Grade 3 or greater. The most common TEAE was fatigue (42.2%). Seven (4.2%) patients experienced TEAEs with a fatal outcome, none of which were identified by investigators as related to toripalimab. Investigator-assessed immune-related adverse events (irAE) of Grade 3 or higher occurred in 7 (4.2%) patients. In the CCA cohort, with the median follow-up of 4.4 months, the ORR and DCR were 4.8% (95% CI: 0.58, 16.16) and 40.5% (95% CI: 25.63, 56.72), respectively; median DoR was 7.8 (range 4.4+ to 7.8) months; median PFS was 2.1 (95% CI: 1.91, 3.88) months; median OS was not estimable. Conclusions Toripalimab had manageable side effects in patients with refractory cholangiocarcinoma and exhibited preliminary evidence of anti-tumor activity. However, further information regarding biomarkers is needed. ClinicalTrials.gov ID: NCT03474640

Introduction: Liver transplantation (LT) provides significant survival benefits to patients with unresectable HCC. In the United States, organ allocation policies for HCCs within the United Network for Organ Sharing criteria do not prioritize patients based on their differences in oncological characteristics. This study assessed whether transplant-associated survival benefits (TASBs) vary among patients with different tumor burden scores (TBS) measured at the time of listing. Methods: We analyzed data from adults applying for HCC MELD exception points between 2002 and 2019, with follow-up until December 2023, using the Scientific Registry of Transplant Recipients. TBS was determined based on the largest tumor diameter and number of HCCs. Patients were categorized into low (≤3), intermediate (3.1–5), and high (>5) TBS groups. TASB was measured as the difference in 5-year survival with and without LT. Results: This study included 36,634 LT candidates. High-TBS patients had higher waitlist dropout rates and marginally lower post-transplant survival, resulting in a significantly greater TASB. The 5-year TASB for the low, intermediate, and high TBS groups were 15.7, 22.1, and 25.0 months, respectively. The adjusted survival benefit expressed in 5-year survival differences was 21.9%, 34.5%, and 39.4% in the low, intermediate, and high TBS groups, respectively ( p <0.001). Conclusions: Higher TBS during listing correlates with greater LT benefits for patients with unresectable HCC within UNOS criteria. We conclude that organ allocation policies in the United States should prioritize patients with high TBS due to their increased risk of dropout and comparable post-transplant survival when compared to patients with less advanced tumors.

Background: Diagnostic uncertainty caused by the low positive predictive value of HEV-specific IgM antibody (Ab) testing in a low-prevalence setting. We investigated the utility of a two-step HEV IgM Ab testing approach for diagnosing HEV infection. Methods: We retrospectively reviewed all adults who underwent HEV IgM Ab and/or HEV RNA testing from July 2013 through June 2023 at Mayo Clinic. Two-step HEV IgM testing involved initial testing using recomWell HEV IgM ELISA (Mikrogen, Neuried, Germany), with reflex to recomLine HEV IgM Strip (Mikrogen, Neuried, Germany) on all recomWell HEV IgM-reactive or IgM-equivocal specimens, as recomLine HEV IgM has higher specificity than recomWell HEV IgM but is more labor-intensive. Results: A total of 1640 patients had HEV IgM Ab or HEV RNA testing, including 1293 (79%) with only HEV IgM Ab testing, 213 (13%) with only HEV RNA testing, and 134 (8%) with both HEV IgM Ab and HEV RNA testing. Eighteen HEV infections were diagnosed with acute (N=16) and chronic (N=2) infections. Two-step IgM Ab testing did not identify 2 solid organ transplant recipients with chronic HEV infection. In acute HEV infection with HEV viremia, 3 out of 4 patients (2 solid organ transplant recipients and 1 patient with Guillain-Barre syndrome) were treated with ribavirin. Conclusions: A two-step HEV IgM Ab test may accurately diagnose acute HEV infection in immunocompetent persons. However, this approach fails to identify chronic HEV infection in immunocompromised individuals who need HEV RNA testing to establish the diagnosis.

Introduction: In recent years, multiple myeloma (MM) research has increasingly focused on unraveling the complex interactions between malignant plasma cells and their surrounding bone marrow microenvironment (BMME). To further elucidate these interactions, we developed a comprehensive single-cell atlas of the MM BMME, integrating both coding and noncoding (ncRNA) transcriptional profiles. By correlating these profiles with specific cytogenetic abnormalities in myeloma cells, we aim to uncover how ncRNAs regulate the phenotypic and functional states of myeloma cells and, consequently, shape the cellular landscape of the BMME. A detailed understanding of how the noncoding transcriptome affects the cellular components and immune landscape of the BMME is essential for advancing our knowledge of MM pathogenesis and identifying novel biomarkers and therapeutic targets. Methods: Our atlas was constructed using single-cell RNA sequencing data from 481 samples of CD138neg cells sorted from bone marrow aspirates of MM patients enrolled in the MMRF CoMMpass study. This dataset includes accompanying whole-genome sequencing and survival data, enabling correlative analysis between transcriptional profiles, cytogenetic abnormalities, and patient outcomes. To generate a combined coding and non-coding transcriptome, we developed an expanded human reference genome by systematically merging mRNA and ncRNA transcripts from the LncBook2.0 and GENECODEv42 reference genomes. The sequencing data was aligned to this expanded genome, followed by quality control processing, batch correction, clustering, and supervised analysis. Results: The resulting coding and non-coding atlas comprises over 1.9 million cells, spanning immune, plasma, and stromal compartments. Remarkably, ncRNAs constituted 46% of the associated genes, including 22,102 long ncRNAs (lncRNAs) and 3,112 small ncRNAs. Subclustering analysis of the major immune compartments (B lymphoid, myeloid, and NK and T lymphoid) yielded 70 subclusters, with ncRNAs representing 24-38% of the top 100 differentially expressed genes within each compartment. This underscores the significant role of ncRNAs in distinguishing immune subpopulations. Moreover, examining the association between specific cytogenetic abnormalities and the immune composition of the BMME, 30 subclusters were found to be differentially abundant (P &lt; 0.05) between cytogenetic abnormalities, supporting our hypothesis that myeloma cell genotypes significantly shape the immune microenvironment. For instance, patients with CCND1 amplification displayed an increase CD4+ central memory T cells with a decrease in CD8+ cytotoxic T cells. Similarly, patients with 1q21 gain exhibited an increase in TGFβ-stimulated monocytes, while a decrease in cytotoxic CD8+ T and CD56dim NK cells was observed in patients with 17p13 deletion. Next, examining which ncRNAs are associated with specific cytogenetic abnormalities, we identified 14 ncRNAs significantly differentially expressed across cytogenetic abnormalities. Increased expression of six of these ncRNAs were associated with poor overall survival both independently and in combination (P &lt; 0.05, HR = 3.7). Notably, three of these ncRNAs have documented roles in cancer progression but have not been previously studied in the context of MM. Subsequently stratifying patients based on their enrichment of these outcome-associated ncRNAs revealed even stronger associations with immune composition, including an increase in CD4+ regulatory T cells (P &lt; 0.05). This further supports our hypothesis that myeloma cell genotypes shape the BMME. Lastly, by constructing a gene regulatory network to predict the interactions between coding and noncoding RNAs, we illustrate the regulatory networks of ncRNAs modulating the phenotypes of myeloma and immune cell subpopulations. Conclusion: In summary, we present the first high-resolution transcriptomic atlas of the MM BMME, integrating both coding and noncoding RNAs. This study reveals distinct immune subpopulations associated with specific myeloma cell genotypes and highlights the central role of ncRNAs in modulating the phenotypic and functional states of both malignant and non-malignant cells in the MM BMME. Our findings emphasize the potential of ncRNAs as therapeutic targets and prognostic markers for MM, offering new avenues for research and clinical intervention.

Background: Therapy-related myeloid neoplasms (t-MN) occur after exposure to DNA-damaging agentsincluding cytotoxic chemo and radiation therapy and are associated with dismal outcomes. TP53 and PPM1D (protein phosphatase Mn2+/Mg2+-dependent 1D) mutations (MT) disrupt the DNA damage response and repair (DDR) pathway, are often co-mutated (25-35%), and although enriched in t-MN (20%), can also be seen in age-related clonal hematopoiesis (CH) (5-10%). While progressive genomic instability with biallelic inactivation has been described as a mechanism of MN progression with TP53 MT, similar mechanisms remain to be elucidated for PPM1D MT. We conducted this study to assess the clinical phenotype and outcomes of TP53 and PPM1D co-mutated (PPM1DTP53m) versus PPM1D MT and wild type TP53 (PPM1DTP53wt) CH and MN. Methods: After approval from the Mayo Clinic IRB, the CH and MN databases were queried and 119 pts with PPM1D MT (CH= 26, clonal cytopenia of unknown significance [CCUS]= 64, myelodysplastic syndrome [MDS]=16, myeloproliferative neoplasm [MPN]=8, acute myeloid leukemia [AML]= 5) were identified. We analyzed baseline characteristics, karyotypic changes, somatic mutations, and survival outcomes between the PPM1DTP53m (n= 22) and PPM1DTP53wt (n= 97) groups (gp). Results: Baseline characteristics The median age in years (yrs) was 68.5 (range [R], 43-89) and 71 (R,30-92) in the PPM1DTP53m and PPM1DTP53wt gp (p= 0.65). 22% vs 18% (p=0.78), 58% vs 32% (p=0.03), 9% vs 32% (p=0.01), 7% vs 4.5% (p=&gt;0.99) and 3% vs 14% (p=0.07) pts met criteria for CH, CCUS, MDS, MPN and AML in the PPM1DTP53wt and PPM1DTP53m gp, respectively. Similarly, 19% vs 18% (p=&gt;0.99), 47% vs 32% (p=0.23), 7% vs 36% (p=0.001), 2% vs 0% (p=&gt;0.99) and 2% vs 14% (p=0.04) of pts met criteria for t-CH, t-CCUS, t-MDS, t-MPN and t-AML in the PPM1DTP53wt and PPM1DTP53m gp, respectively. All PPM1D variants were truncating, located in exon 6 of the gene, with median variant allele frequency (VAF) (8% [R,1-64] vs 5.5% [R,2-24], p= 0.10) and number of PPM1D MT (1 [R,1-5] vs 1 [R, 1-3], p=&gt;0.99) being similar between the two gp. While proportion of pts with t-MN were similar between the two gps (77% vs 86%, p= 0.40), cytogenetic (CG) abnormalities (67% vs 31%, p= 0.006), including monosomies (28% vs 2%, p= &lt;0.001) and complex CG (53% vs 4%, p= &lt;0.001) were more frequent in PPM1DTP53m gp compared to PPM1DTP53wt gp, respectively. The median TP53 MT VAF was 14% (R, 1-14); 50% had multi-hit TP53 MT. Somatic mutations in PPM1D MT myeloid neoplasm with and without TP53 MT Somatic co-mutations excluding TP53 were seen in 71% of pts; 73.5% in PPM1DTP53wt and 59% in PPM1DTP53m gp (p= 0.20). Apart from CBL (0% vs 9%,p=0.03), MT involving DNMT3A (20% vs 14%, p= 0.56), TET2 (15% vs. 14%, p= &gt;0.99), IDH1 (6% vs 19%, p= 0.07), splicing factors (8% vs 14%, p=0.08), ASXL1 (11% vs 4.5%, p=0.46), and JAK2 (9% vs 0%,p=0.20) were comparable between PPM1DTP53wt and PPM1DTP53m gps, respectively. The proportion of pts with ≥ 2 somatic co-mutations excluding TP53 MT were numerically higher in PPM1DTP53wt gp (43%) compared to PPM1DTP53m gp (23%) (p= 0.09). In the PPM1DTP53wt gp, other somatic driver mutations included RUNX1 (n= 7 [7%]; CH [n=1], CCUS [n=2], MDS [n=3], MPN [n=1]), NRAS (n=1 [1%]; CCUS), BCOR (n=1 [1%]; CCUS), and PTPN11 (n=1 [1%]; CCUS). Leukemic transformation and survival With a median follow up of 6.43 months (mo) (R, 0.23-36.4), median overall survival (mOS) in mo among high-risk MDS/AML (16.4 vs 15.0, p= 0.66) and CHIP/CCUS pts (35.79 vs 26.28, p= 0.93) was similar between PPM1DTP53wt and PPM1DTP53m gps, respectively. CHIP/CCUS pts harboring multiple vs single PPM1D MT had an inferior mOS (23.9 vs 36.7 mo, p= 0.05). Three (2.5%) MDS pts underwent AML transformation; 1 (1%) and 2 (10%) in PPM1DTP53wt and PPM1DTP53m gp, respectively (p= 0.07). Conclusions In our study, 18% of pts had concurrent TP53 and PPM1D MT (82% t-MN), with the PPM1DTP53wt genotype being more frequent in CCUS, while the PPM1DTP53m genotype was more frequent in MN. The PPM1DTP53m genotype had a higher frequency of CG abnormalities, including complex CG. Survival outcome was inferior and comparable in PPM1D MT MN with or without TP53 MT. While 73.5 % of the PPM1DTP53wt gp had somatic driver MT and 31% had karyotypic abnormalities, 22% had no additional MT or CG changes (all CH/CCUS), underscoring the need to understand how PPM1D MT contribute to hematopoietic dysfunction and MN.

Background: Monoclonal B-cell lymphocytosis (MBL) is a common premalignant condition that can be classified into chronic lymphocytic leukemia (CLL)-type (CD5, CD23, and CD20 co-expression), and non-CLL-type (CD5-negative) based on flow cytometry immunophenotypes. CLL-type MBL is relatively well characterized with increased risk of hematologic (especially lymphoid) malignancies, serious infections, and decreased response to vaccinations. However, the prevalence, genetic predispositions, and clinical consequences of non-CLL-type MBL remain to be fully elucidated. Herein, we report findings in non-CLL-type MBL in a large screening cohort. Methods: We screened 10,330 individuals (40 years or older with no prior history of hematologic cancers) for MBL with highly sensitive flow cytometry, in the Mayo Clinic Biobank, with patients recruited from primary care-based clinics. Individuals were genotyped by Regeneron Genetics. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). Among those individuals residing in the 27 counties surrounding Rochester, Minnesota, we identified incident hematologic malignancies and melanomas using ICD codes and the Rochester Epidemiology Project, a population based medical records linkage system. All diagnoses were manually reviewed for confirmation. Cox regression was used to calculate hazard ratios (HR) and 95% CIs, adjusted for age and sex. Results: We identified 1,736 individuals with MBL and 8,341 individuals without MBL. Among those with MBL, 246 (14.2%) had non-CLL-type MBL as the major clone. The median age of non-CLL-type MBL was 77 years (range, 47-95). The prevalence of non-CLL-type MBL significantly increased with age, with 0.5% prevalence among those between 40 to 59 years of age, 2.3% prevalence among those between 60 and 79 years, and 7.8% prevalence in those 80 years of age or older. The prevalence of non-CLL-type MBL was significantly higher (P &lt; .001) among males (3.1%) than females (2.0%). Using the genome-wide complex trait analysis tool, the heritability of non-CLL-type MBL was estimated to be ~26%, suggesting inherited genetics plays a role in etiology. To identify common inherited variants, we first evaluated the known CLL susceptibility variants, many of which also associated with CLL-type MBL. Among the 42 variants, none were statistically associated (all P &gt; .05) with non-CLL-type MBL. We next conducted a genome-wide association study to identify novel variants, and the most significant variant resided in 10q26.3 (minor allele frequency [MAF]control = 0.7%, MAFMBL = 3.5%, OR = 6.4, 95% CI 3.6-11.3, P = 5.7 * 10-8). Among the 6,478 individuals (192 with non-CLL-type MBL and 6,286 without MBL) followed for incident hematologic malignancies, 36 developed lymphoid and 23 developed myeloid malignancies after a median of 4.2 years of follow up. Similar to CLL-type MBL, non-CLL-type MBL was associated with a 5.2-fold risk for lymphoid malignancies (95% CI 2.0-13.0, P &lt; .001). Elevated but not significant risks were observed for myeloid malignancies (HR = 2.3, 95% CI 0.5-10.3, P = 0.27). The lymphoid malignancies developed by individuals with non-CLL-type MBL included diffuse large B-cell lymphoma, multiple myeloma, marginal zone lymphoma, and other low-grade lymphomas. We recently reported that individuals with CLL-type MBL are at higher risk for developing melanoma (HR = 1.9, 95% CI 1.3-2.8). Interestingly, non-CLL-type MBL had no evidence of an association with melanoma risk (HR = 0.4, 95% CI 0.1-1.8). We did not observe a significant difference in overall survival between individuals with non-CLL-type MBL vs those without MBL. Conclusions: In the largest screening cohort to date, we evaluated the etiology and clinical outcomes of non-CLL-type MBL, an understudied yet common condition that occurs in ~4 million adults in the United States. Non-CLL-type MBL is more common in men and older individuals, and evidence supports that inherited genetics plays an etiological role with 10q26.3 locus identified as a novel susceptibility locus. Individuals with non-CLL-type MBL had significantly increased risk of lymphoid malignancies, similar to CLL-type MBL, but, distinct from CLL-type MBL, no evidence of an increased risk of melanoma. Our study provides insights into the prevalence, genetic predispositions and clinical consequences of non-CLL-type MBL and how it differs from the classic CLL-type MBL.

Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P&lt;.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P&lt;.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P&lt;.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P&lt;.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.

Background: Immune dysfunction is a hallmark of chronic lymphocytic leukemia (CLL), and several studies suggest that hypogammaglobulinemia at the time of CLL diagnosis may predict shorter time to first therapy (TTFT; Parikh et al, 2015). However, it is unclear if hypogammaglobulinemia can predict TTFT independent of the CLL-International Prognostic Index (CLL-IPI) and the tumor mutational load (TML). We sought to determine if serum immunoglobulin (Ig) levels at diagnosis can independently predict outcomes in patients with CLL and its precursor, high-count monoclonal B-cell lymphocytosis (MBL). Methods: We used the Mayo Clinic CLL Database to identify newly diagnosed CLL/MBL consented within 2 years of diagnosis. Next generation sequencing (NGS) was performed using a 59 gene panel (SureSelect) to detect mutated CLL genes. Serum Ig levels were quantitated by radial immunodiffusion (Immunoplates). Serum Ig levels below the lower limit of normal were defined as hypogammaglobulinemia. We used Chi-squared or Fisher's exact tests to compare discrete variables and the Kruskal Wallis test for continuous variables. TTFT was calculated from date of diagnosis to date of first treatment or date last known to be untreated. We used Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for TTFT and overall survival (OS) associations. Results: We identified 895 newly diagnosed patients (588 CLL and 307 MBL) who had undergone NGS for recurrent somatic mutations and had serum Ig levels available at the time of diagnosis. The median absolute B-cell count was 6.7 x109/L; 613 (69%) were male, 375 (42%) had unmutated IGHV genes, and 81 (9%) had TP53 disruption. Based on CLL-IPI, 412 (46%) patients had low risk, 285 (32%) had intermediate risk, and 198 (22%) had high/very high risk. The most commonly mutated genes were NOTCH1 (14%), SF3B1 (11%), TP53 (10%), NFKBIE (8%), ATM (6%), and BIRC3 (6%). Based on the number of genes with high impact or hotspot mutations, we classified 40%, 29%, and 31% patients with TML scores of 0, 1, or 2+, respectively. At CLL/MBL diagnosis, the median serum IgG, IgA, and IgM was 891 mg/dL (111-2620), 121 mg/dL (1-1150), and 44 mg/dL (5-1320), respectively; 284 (32%) patients had low serum IgG, 128 (17%) had low serum IgA, and 322 (42%) had low serum IgM. Low serum IgA was associated with mutations in NOTCH1, SF3B1, ATM, BIRC3 and NFKBIE, low IgM was associated with SF3B1 and BIRC3 mutations (all comparisons, P &lt; .05), and low IgG was associated with BIRC3 mutation (P = .05). The median follow-up for the cohort was 8.4 years. A total of 359 patients progressed requiring therapy (299 CLL and 60 MBL), and 265 patients died (177 CLL and 88 MBL). The TTFT was significantly shorter for patients with low serum IgG compared to normal serum IgG at diagnosis (median 4.4 vs 10.8 years) and similar findings were observed in individuals with low serum IgA (1.7 vs 10.8 years) and those with low serum IgM (4.6 vs 11.1 years). On univariable analysis, the following were associated with a shorter TTFT: low serum IgG (HR: 1.8, 95% CI 1.4-2.2), low serum IgA (HR: 3.5, 95% CI 2.7-4.4), low serum IgM (HR: 1.9, 95% CI 1.5-2.3), TML score of 1 (HR: 1.7, 95% CI 1.3-2.2), TML of 2+ (HR: 4.4, 95% CI 3.4-5.7), CLL-IPI intermediate risk (HR: 3.7, 95% CI 2.8-4.8), and CLL-IPI high/very high risk (HR: 7.6, 95% CI 5.8-10.1) (all comparisons, P &lt; .0001). In multivariable analyses after adjusting for CLL-IPI and TML, low serum levels of IgG (HR: 1.8, 95% CI: 1.5-2.3), IgA (HR: 2.4, 95% CI: 1.8-3.0), and IgM (HR: 1.8, 95% CI: 1.5-2.3) were independently associated with a shorter TTFT (all comparisons, P &lt; .0001). No significant differences in OS were detected between patients with low and normal serum IgG, IgM, and IgA on univariate analysis. Similar results for TTFT and OS were observed after adding clonal B-cell count to the Cox model. Conclusions: Our study shows that low serum Ig levels at the time of initial diagnosis is predictive of TTFT independently of the CLL-IPI and TML, which can further risk stratify individuals with newly diagnosed CLL/MBL. Additionally, low serum IgA, IgM, and IgG levels at diagnosis are significantly associated with specific somatic mutations in individuals with newly diagnosed CLL/MBL.