Citations

Abstract Extracellular vesicles (EVs) have emerged as critical mediators of intercellular communication and promising biomarkers and therapeutics in the central nervous system (CNS). Human brain‐derived EVs (BDEVs) provide a comprehensive snapshot of physiological changes in the brain's environment, however, the isolation of BDEVs and the comparison of different methods for this purpose have not been fully investigated. In this study, we compared the yield, morphology, subtypes and protein cargo composition of EVs isolated from the temporal cortex of aged human brains using three established separation methods: size‐exclusion chromatography (SEC), phosphatidylserine affinity capture (MagE) and sucrose gradient ultracentrifugation (SG‐UC). Our results showed that SG‐UC method provided the highest yield and collected larger EVs compared to SEC and MagE methods as assessed by transmission electron microscopy and nanoparticle tracking analysis (NTA). Quantitative tandem mass‐tag (TMT) mass spectrometry analysis of EV samples from three different isolation methods identified a total of 1158 proteins, with SG‐UC showing the best enrichment of common EV proteins with less contamination of non‐EV proteins. In addition, SG‐UC samples were enriched in proteins associated with ATP activity and CNS maintenance, and were abundant in neuronal and oligodendrocytic molecules. In contrast, MagE samples were more enriched in molecules related to lipoproteins, cell‐substrate junction and microglia, whereas SEC samples were highly enriched in molecules related to extracellular matrix, Alzheimer's disease and astrocytes. Finally, we validated the proteomic results by performing single‐particle analysis using the super‐resolution microscopy and flow cytometry. Overall, our findings demonstrate the differences in yield, size, enrichment of EV cargo molecules and single EV assay by different isolation methods, suggesting that the choice of isolation method will have significant impact on the downstream analysis and protein discovery.

Abstract Imatinib mesylate (IM) has revolutionized the treatment of gastrointestinal stromal tumor (GIST). However, most patients inevitably acquire IM resistance. Second- and third-line treatments exhibit modest clinical benefits with a median time to disease progression of 4 to 6 months, highlighting the urgency for novel therapeutic approaches. Here, we report that the expression of BCL6, a known oncogenic driver and transcriptional repressor, was significantly induced in GIST cells following IM treatment. Elevated BCL6 levels suppressed apoptosis and contributed to IM resistance. Mechanistically, BCL6 recruited SIRT1 to the TP53 promoter to modulate histone acetylation and transcriptionally repress TP53 expression. The reduction in p53 subsequently attenuated cell apoptosis and promoted tolerance of GIST cells to IM. Concordantly, treatment of GIST cells showing high BCL6 expression with a BCL6 inhibitor, BI-3802, conferred IM sensitivity. Furthermore, BI-3802 showed striking synergy with IM in IM-responsive and IM-resistant GIST cells in vitro and in vivo. Thus, these findings reveal a role for BCL6 in IM resistance and suggest that a combination of BCL6 inhibitors and IM could be a potentially effective treatment for GIST. Significance: BCL6 drives resistance to imatinib by inhibiting p53-mediated apoptosis and can be targeted in combination with imatinib to synergistically suppress tumor growth, providing a therapeutic strategy for treating gastrointestinal stromal tumor.

Context.— Unlike B-cell acute lymphoblastic leukemia/lymphoma (ALL/LBL), there have been few therapeutic advances in T-cell ALL (T-ALL)/LBL, an aggressive ALL/LBL subtype. Objective.— To perform a focused tissue array study to elucidate tumor markers of therapeutic potential in T-ALL/LBL. Design.— Using immunohistochemistry, we evaluated expression of leukemic antigens of interest, specifically CC-chemokine receptor 4 (CCR4), among others, on available remnant diagnostic material, including tumor tissue slides obtained from formalin-fixed, paraffin-embedded preserved tissues. Results.— Our analysis identified, for the first time, expression of CCR4 in T-ALL/LBL in 11 of 27 cases (40.7%) and confirmed common expression of BCL2, CD38, and CD47, as reported previously. We also identified the expression of CD123 in 4 of 26 cases (15.4%), whereas BCL6 and PDL1 were expressed in a small number of T-ALL/LBL cases. The potential novel target CCR4 was significantly more common in the Pre/Pro-T immunophenotypic subtype, 6 of 9 (66.7%, P = .01). No additional differences in clinical and epidemiologic variables were noted among positive or negative CCR4 cases. Conclusions.— These findings support preclinical and clinical testing of therapies targeting CCR4, CD47, BCL2, CD38, and CD123 in T-ALL/LBL, and may help guide the development of targeted clinical trials in T-ALL/LBL, a rare disease in urgent need of novel therapies.

Abstract Background Alzheimer’s disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population. Objective Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians’ decisions for AD patients based on the longitude data from electronic health records. Methods In this study, we selected 1736 patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases—depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). We modeled the treatment learning into an RL problem by defining states, actions, and rewards. We built a regression model and decision tree to generate multiple states, used six combinations of medications (ie, cholinesterase inhibitors, memantine, memantine-cholinesterase inhibitors, hypertension drugs, supplements, or no drugs) as actions, and Mini-Mental State Exam (MMSE) scores as rewards. Results Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician’s treatment regimen. Optimal policies (ie, policy iteration and Q-learning) had lower rewards than the clinician’s policy (mean −3.03 and −2.93 vs. −2.93, respectively) for smaller datasets but had higher rewards for larger datasets (mean −4.68 and −2.82 vs. −4.57, respectively). Conclusions Our results highlight the potential of using RL to generate the optimal treatment based on the patients’ longitude records. Our work can lead the path towards developing RL-based decision support systems that could help manage AD with comorbidities.

Abstract Background Hearing loss is a risk factor for dementia; whether the association is causal or due to a shared pathology is unknown. We estimated the association of brain β-amyloid with hearing, hypothesizing no association. As a positive control, we quantified the association of hearing loss with neurocognitive test performance. Methods Cross-sectional analysis of Atherosclerosis Risk in Communities-Positron Emission Tomography study data. Amyloid was measured using global cortical and temporal lobe standardized uptake value ratios (SUVRs) calculated from florbetapir-positron emission tomography scans. Composite global and domain-specific cognitive scores were created from 10 neurocognitive tests. Hearing was measured using an average of better-ear air conduction thresholds (0.5–4 kHz). Multivariable-adjusted linear regression estimated mean differences in hearing by amyloid and mean differences in cognitive scores by hearing, stratified by race. Results In 252 dementia-free adults (72–92 years, 37% Black race, and 61% female participants), cortical or temporal lobe SUVR was not associated with hearing (models adjusted for age, sex, education, and APOE ε4). Each 10 dB HL increase in hearing loss was associated with a 0.134 standard deviation lower mean global cognitive factor score (95% CI: −0.248, −0.019), after adjustment for demographic and cardiovascular factors. Observed hearing-cognition associations were stronger in Black versus White participants. Conclusions Amyloid is not associated with hearing, suggesting that pathways linking hearing and cognition are independent of this pathognomonic Alzheimer’s-related brain change. This is the first study to show that the impact of hearing loss on cognition may be stronger in Black versus White adults.

The MN1::ETV6 gene fusion resulting from t(12;22)(p13;q12) has been rarely reported in myeloid neoplasms. We describe a 69-year-old male with newly diagnosed acute myeloid leukemia (AML) with erythroid differentiation and t(12;22)(p13;q12) demonstrated by conventional chromosome studies. Subsequent fluorescence in situ hybridization studies demonstrated a balanced ETV6 gene rearrangement (at 12p13). To further characterize this translocation, whole-genome sequencing was performed which confirmed t(12;22) with breakpoints involving the MN1 and ETV6 genes. Herein, we describe our case and review the literature to summarize the clinical and laboratory findings in patients with this rare but recurrent MN1::ETV6 gene fusion observed in myeloid neoplasms. Importantly, this case expands the clinical spectrum associated with the MN1::ETV6 gene fusion to include AML with erythroid differentiation. Lastly, this case demonstrates the importance of moving toward more comprehensive molecular testing to fully characterize the driver events in neoplastic genomes.

Introduction: In seniors, the prevalence of abdominal aortic aneurysms (AAA) is high (9-10%). We aim to characterize AAA growth rates (slow vs. rapid) to help inform management strategies, including individualized imaging surveillance frequency. Hypothesis: Modeling thrombosis may improve the characterization of AAAs' growth status. Methods: 3D geometrical AAA models with and without thrombosis (vessel lumen only) were generated for 70 human subjects using available contrast-enhanced CTA data. AAA growth rates were categorized as slow (< 5 mm/year) or rapid (≥ 5 mm/year) based on serial imaging. Python scripts were used to calculate geometrical parameters (83) with and without thrombosis. Patient-specific relevant health information was retrieved through a review of medical records. Support vector machine (SVM), a well-established machine learning method, was run with 10-fold cross-validation (100 iterations) to assess the predictive accuracy. Results: Among 70 AAAs studied, the ratio between rapidly-growing and slowly-growing was nearly 1:2. The combination of antihypertensive medication, coronary artery disease, juxta-aneurysm aortic size, and five geometrical parameters quantifying the extent of thrombosis provided the best accuracy for AAA's growth status. The area under receiving operating curve (AUROC) was 0.85, and total accuracy was 0.81, with 57% and 93% of rapidly- and slowly-growing AAAs correctly identified, respectively. Excluding patients with aneurysm thrombus, the AUROC and accuracy in predicting rapidly-growing AAAs decreased to 0.82 and 48%, respectively. Conclusions: machine-learning-based predictive modeling appears feasible for characterizing AAA's growth rate status. Manual aortic image segmentation, required for data analytics, is labor-intensive (on average, 1 hour). Leveraging artificial intelligence-based segmentation methodology may provide an automated solution to improve workflow and feasibility.

Abstract Time to progression of disease (POD) after first-line (1L) therapy is prognostic in mantle cell lymphoma (MCL), although studies have included a broad range of 1L, second-line (2L), and subsequent lines of therapy. The purpose of this study was to evaluate the factors predicting outcomes in patients with relapsed/refractory (R/R) MCL exclusively initiating 2L Bruton’s tyrosine kinase inhibitors (BTKis) after 1L rituximab-containing therapy. Patients were accrued from 8 international centers (7 main, 1 validation cohort). Multivariable models evaluating the association between time to POD and clinical/pathologic factors were constructed and converted into nomograms and prognostic indexes predicting outcomes in this population. A total of 360 patients were included, including 160 in the main cohort and 200 in the validation cohort. Time to POD, Ki67 ≥ 30%, and MCL International Prognostic Index (MIPI) were associated with progression-free survival (PFS2) and overall survival (OS2) from the start of 2L BTKis. C-indexes were consistently ≥0.68 in both cohorts. Web/application-based calculators based on nomograms and prognostic indexes to estimate PFS2 and OS2 were constructed. The 2L BTKi MIPI identifies 3 groups with distinct 2-year PFS2, including high risk (14%), intermediate risk (50%), and low risk (64%). Time to POD, Ki67, and MIPI are associated with survival outcomes in patients with R/R MCL receiving 2L BTKis. Simple clinical models incorporating these variables may assist in planning for alternative therapies such as chimeric antigen receptor T-cell therapy, allogeneic stem cell transplantation, or novel agents with alternative mechanisms of action.

7575 Background: Extranodal natural killer/T-cell lymphoma (ENKTL), nasal type, is a rare and aggressive Epstein-Barr virus driven lymphoma, which is most prevalent in East Asia and Latin America. There have been very few large studies outside of Asia. Therapeutic and survival outcomes have not been well characterized in the US. Methods: The National Cancer Database was queried for patients who were diagnosed at aged ≥18 years between 2004 and 2019. Patients who were lost to follow-up were excluded. Kaplan-Meier and multivariate Cox regression analyses were performed. Results: The cohort consists of 2,036 evaluable patients with 68% males, 74% Caucasian, 22% Hispanic Caucasian and 9% African American. The median age at diagnosis was 54 (18-90) years. The median follow up was 71 months (95% CI 66.8-75.7). Early-stage disease (I and II) was reported in 64% while advanced stage disease (III and IV) constituted 36%. Treatment groups include chemotherapy alone (29%), radiation alone (10%), combined modality therapy (CMT-chemoradiation) (42%). The median overall survival (OS) for the whole cohort was 27 months (95% CI 20.6-33.7). Median OS was better in patients with early stage compared to advanced stage disease (90 vs. 6 months, P < 0.001). Among therapeutic groups in early-stage disease, CMT (N = 616) had significantly better median OS than radiation alone (N = 152) or chemotherapy alone (N = 168) (194 vs. 34 vs. 14 months, P < 0.001). No significant difference was seen between radiation alone and chemotherapy alone (P = 0.617). Among patients treated with CMT, chemotherapy-first CMT (N = 134) had better OS than radiation-first CMT (N = 347) (median OS not reached vs. 194 months, P = 0.017). In the advanced stage disease, chemotherapy-treated patients (N = 418) had better median OS compared to patients who did not receive chemotherapy (N = 103) (7.6 vs 1.3 months, P < 0.001). Among patients who received chemotherapy, patients consolidated with SCT (N = 59) had better median OS compared to the patients who received chemotherapy only (N = 353) (63 vs 6 months, P < 0.001) In multivariate analysis, factors associated with worse OS were age (HR 1.021 95% CI 1.015-1.026; P < 0.001) and B-symptoms (HR 1.463 95% CI 1.276-1.678; P < 0.001). Whereas factors associated with better OS were early stage (HR 0.450 95% CI 0.387-0.524, P < 0.001), radiation therapy (HR 0.526 95% CI 0.458-0.603, P < 0.001) and chemotherapy (HR 0.546 95% CI 0.473-0.630, P < 0.001). Conclusions: The study represents one of the largest studies on ENKTL in the US. Early-stage disease is highly curable with CMT with chemotherapy-first sequencing being superior to radiation-first sequencing. Advanced stage disease is associated with extremely poor prognosis overall but is associated with a significant improvement in OS with consolidative SCT with possible role of consolidative RT in advanced stage.