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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<.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P<.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<.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P<.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 < .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 < .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 < .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.

Importance Typical cysteine-altering NOTCH3 ( NOTCH3 cys ) variants are highly prevalent (approximately 1 in 300 individuals) and are associated with a broad spectrum of small vessel disease (SVD), ranging from early-onset stroke and dementia (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy [CADASIL]) to nonpenetrance. A staging system that captures the full NOTCH3 -SVD severity spectrum is needed and currently lacking. Objective To design a simple disease severity staging system that captures the broad clinicoradiological NOTCH3 -SVD severity spectrum. Design, Setting, and Participants A cohort study was performed in which the NOTCH3 -SVD severity staging system was developed using a discovery cohort (2019-2020) and validated in independent international CADASIL cohorts (1999-2023) and the UK Biobank. Clinical and imaging data were collected from participants originating from 23 international CADASIL cohorts and from the UK Biobank. Eligibility criteria were presence of a NOTCH3 cys variant, availability of brain magnetic resonance imaging, and modified Rankin Scale score. The discovery cohort consisted of 195 NOTCH3 cys -positive cases from families with CADASIL; the validation set included 1713 NOTCH3 cys -positive cases from 15 countries. The UK Biobank cohort consisted of 101 NOTCH3 cys -positive individuals. Data from 2-year (2019-2023) and 18-year (1999-2017) follow-up studies were also analyzed. Data analysis was performed from July 2023 to August 2024. Main Outcomes and Measures Percentage of cases following the sequence of events of the NOTCH3 -SVD stages, and the association between the stages and ischemic stroke, intracerebral hemorrhage, global cognition, processing speed, brain volume, brain microstructural damage, and serum neurofilament light chain (NfL) level. Results The NOTCH3 -SVD staging system encompasses 9 disease stages or substages, ranging from stage 0 (premanifest stage) to stage 4B (end stage). Of all 1908 cases, which included 195 in the discovery cohort (mean [SD] age, 52.4 [12.2] years) and 1713 in the validation cohorts (mean [SD] age, 53.1 [13.0] years), 1789 (94%) followed the sequence of events defined by the NOTCH3 -SVD staging system. The NOTCH3 -SVD stages were associated with neuroimaging outcomes in the NOTCH3 cys -positive cases in the CADASIL cohorts and in the UK Biobank and with cognitive outcomes and serum NfL level in cases from the CADASIL cohorts. The NOTCH3 -SVD staging system captured disease progression and was associated with 18-year survival. Conclusions and Relevance The NOTCH3 -SVD staging system captures the full disease spectrum, from asymptomatic individuals with a NOTCH3 cys variant to patients with end-stage disease. The NOTCH3 -SVD staging system is a simple but effective tool for uniform disease staging in the clinic and in research.

Abstract Systematic multiomic analysis has revealed ancestry-dependent molecular alterations, but their impact on the efficacy of anticancer treatment is yet largely unknown. In this study, we analyzed clinical trials from ClinicalTrials.gov and found that only 8,779/102,721 (8.5%) oncology clinical trials posted information on enrollment by race/ethnicity. The underrepresentation of non-White populations suggests that it remains challenging to determine differences in the efficacy of antitumor treatments among different racial groups. Through a comprehensive analysis of clinically actionable genes, imputed drug responses, and immune features, we identified potential differences in treatment responses to targeted therapy, chemotherapy, and immunotherapy between different ancestral populations. Further analysis of multiple independent cohorts confirmed some of our key findings. Such potential ancestral effects are also identified in response to emerging new treatments like chimeric antigen receptor T-cell therapy and proteolysis-targeting chimeras. These findings are made publicly available in a comprehensive web portal, Ancestral Differences of Efficacy in Cancers (https://hanlaboratory.com/ADEC), to facilitate their further investigation. Significance: Our study charts a global landscape of ancestry-associated differences in therapeutic efficacy, highlighting the importance of considering ancestry in anticancer therapies.

Abstract Effective immune synapses are a prerequisite for desirable immune surveillance and control of tumour growth. Cell-to-cell interactions form the basis of immune synapses, resulting in either immunoprotected or immunosuppressed manifestations in the tumour brain that could explain glioblastoma (GBM) recurrence. Using state-of-the-art multiplex imaging, spatial statistics, and spatially aware deep learning tools in 14 patient-matching tissues from primary and recurrent GBM, we detected spatial immune networks (SINets). SINets across 105 regions of interest comprising 2.3 million cells show stronger spatial engagement between myeloid and lymphoid lineages at recurrence. SINets’ architecture in primary tumours is associated with relapse timing. Patients, where the SINets are more modular, recur sooner than patients with denser SiNets. In addition, this association presents sexual dimorphism in our cohort (N=3 females, and N=4 males). Phenotype-to-phenotype spatial coexistence also contributes to enhancing phenotype prediction. A spatial-aware deep learning model trained with single-cell data from primary tumours reaches an average balanced accuracy of 0.76 in primary tumour cellular communities unseen during training, compared to a spatial-agnostic model that reached an average balanced accuracy of 0.69. In summary, we detected a distinct spatial engagement in glioblastoma undergoing chemo-radiation treatment. The spatial patterns of coexistence represent ecological niches that contribute to defining cell occurrence and may underpin effective immune synapses. Overall, the spatiotemporal elements provide a newer approach for dissecting glioblastoma’s complexity and discovering opportunities for stratifying patients by harnessing spatial information encoded in multicellular niches.

BackgroundMultiparametric MRI may cause overdiagnosis of clinically significant prostate cancer (csPCa) with the Prostate Imaging Reporting and Data System version 2.1 (PI‐RADS v2.1).ObjectivesTo investigate the diagnostic performance of stiffness as a standalone and complementary marker to PI‐RADS v2.1 for diagnosing csPCa.Study TypeProspective.SubjectsOne hundred forty‐seven participants with pathologically confirmed prostate lesions (≥1 cm), including 71 with csPCa.Field Strength/SequenceT1‐weighted fast spin‐echo, T2‐weighted fast spin‐echo, single‐shot echo‐planar diffusion‐weighted imaging, fast 3D gradient‐echo T1‐weighted dynamic contrast‐enhanced imaging, and 3D single‐shot spin‐echo based echo‐planar MR elastography at 3.0 T.AssessmentThe PI‐RADS v2.1 score was assessed by three radiologists independently. Lesion shear stiffness (SS) values at 60 Hz and 90 Hz were measured. A modified PI‐RADS integrating stiffness with PI‐RADS v2.1 was developed. Diagnostic performance for csPCa was compared between stiffness, PI‐RADS v2.1 and the modified PI‐RADS.Statistical TestSpearman's correlation, Fleiss κ and intraclass correlation, Pearson correlation, one‐way analysis of variance, area under the receiver operating characteristic curve (AUC), and the Delong test. Significance level was P < 0.05.ResultsIn the peripheral zone, csPCa (N = 35) had significantly higher SS than non‐csPCa at 60 Hz (3.22 ± 0.66 kPa vs. 2.56 ± 0.56 kPa) and at 90 Hz (5.64 ± 1.30 kPa vs. 4.48 ± 0.84 kPa). PI‐RADS v2.1 showed 100% sensitivity, 58% specificity, and 0.79 AUC for detecting csPCa. SS achieved 97% sensitivity, 52% specificity, and 0.80 AUC at 60 Hz, while SS had 63% sensitivity, 87% specificity, and 0.78 AUC at 90 Hz. The modified PI‐RADS, combing SS at 60 Hz with PI‐RADS v2.1, resulted in a significantly increased AUC (0.86) compared to that of PI‐RADS v2.1, with a sensitivity of 97% and specificity of 75%.Data ConclusionStiffness can help identifying csPCa in the peripheral zone. Combining stiffness with the PI‐RADS v2.1 improved the diagnostic accuracy and specificity for csPCa.Evidence Level1Technical EfficacyStage 2

Collaboration between military and civilian investigators is necessary to develop strategies for optimizing treatments to diminish the sequelae of non–battle-related musculoskeletal injuries, which are highly prevalent among servicemembers. The 2024 Extremity War Injuries Symposium provided a forum whereby a diverse group of clinicians and researchers could develop strategies for optimizing treatments and diminishing the sequelae of conditions that are highly prevalent among active individuals. Key points were as follows: 1. The value of collaborations between military and civilian investigators cannot be overemphasized. These synergies can leverage the capabilities of established infrastructures in order to answer important questions pertaining to short-term outcomes—for example, warrior readiness of anterior cruciate ligament reconstructions among servicemembers and long-term posttraumatic osteoarthritis (PTOA) outcomes among civilian cohorts. 2. Current and future prospective trials will inform future treatment decisions related to multiligamentous knee injuries and glenohumeral instability. 3. Novel approaches to addressing PTOA in the acute postinjury period will need longitudinal surveillance to measure their impact on improving clinical outcomes and diminishing residual functional deficits.