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AbstractBackgroundThe efficacy of Calorie Restriction (CR) in enhancing cognition, promoting healthy aging, and extending lifespan is well‐established. Yet, it remains unclear whether the apolipoprotein E (APOE) genotype, a known modifier for aging and age‐related disorders, influences the beneficial effects of CR in countering aging.MethodsTo investigate this question, we utilized humanized APOE mouse models, which express APOE2, APOE3, or APOE4 alleles systematically (refer to as E2, E3, and E4 mice). These mice were subjected to either ad libitum (AL) feeding or 30% CR feeding, starting from 12 months and continuing up to 20 months of age (N = 11‐20/ genotype /group, mixed sex). We monitored energy expenditure using the Comprehensive Laboratory Animal Monitoring System (CLAMS). Additionally, we evaluated anxiety levels and memory capabilities through various behavioral tests. Finally, we collected brain, blood, and feces samples for transcriptomic, lipidomic, and microbiomic analyses.ResultsCLAMS measurements showed that CR significantly decreased the metabolic rate in E3 and E4 mice, but not E2 mice, during the non‐feeding period of the CR group, compared to the AL group. Furthermore, CR reduced anxiety and enhanced associative memory in E3 and E4 mice relative to the AL group. Brain transcriptomics revealed that CR upregulated the cholesterol synthesis pathway, potentially improving myelination‐related pathways in E3 and E4 mice. This finding was further validated by the increased staining intensity of myelin basic protein (MBP) observed in E3 and E4 mice from an independent cohort. In contrast, these effects were not pronounced in E2 mice. Additionally, while CR’s impact on the brain lipid profile was minor across all APOE genotypes, it significantly altered blood lipid profile of E2 mice, with minimal changes observed in E3 and E4 mice. Finally, in microbiomics, CR led to notable changes in the abundance of Bifidobacterium species in E3 and E4 mice and Lactobacillus species in E2 mice.ConclusionsOur study highlights the role of APOE genotypes in modulating the diverse effects of CR on cognition, brain transcriptomics, lipid metabolism, and the microbiome, providing a crucial foundation for considering APOE genotypes in developing prevention strategies for aging and age‐related disorders.

Abstract Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significance Integration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.

Background/Objectives: Lung cancer is a devastating disease with the highest mortality rate among cancer types. Over 60% of non-small cell lung cancer (NSCLC) patients, accounting for 87% of lung cancer diagnoses, require radiation therapy. Rapid treatment initiation significantly increases the patient’s survival rate and reduces the mortality rate. Accurate tumor segmentation is a critical step in diagnosing and treating NSCLC. Manual segmentation is time- and labor-consuming and causes delays in treatment initiation. Although many lung nodule detection methods, including deep learning-based models, have been proposed. Most of these methods still have a long-standing problem of high false positives (FPs). Methods: Here, we developed an electronic health record (EHR)-guided lung tumor auto-segmentation called EXACT-Net (EHR-enhanced eXACtitude in Tumor segmentation), where the extracted information from EHRs using a pre-trained large language model (LLM) was used to remove the FPs and keep the TP nodules only. Results: The auto-segmentation model was trained on NSCLC patients’ computed tomography (CT), and the pre-trained LLM was used with the zero-shot learning approach. Our approach resulted in a 250% boost in successful nodule detection using the data from ten NSCLC patients treated in our institution. Conclusions: We demonstrated that combining vision-language information in EXACT-Net multi-modal AI framework greatly enhances the performance of vision only models, paving the road to multimodal AI framework for medical image processing.

Introduction Plasmablastic lymphoma (PBL) is a rare aggressive subtype of lymphoma. It is more common in HIV positive patients, and is often associated with Epstein-Barr virus (EBV) and MYC rearrangements. PBL is characterized by markers of plasmacytic differentiation including CD138 and MUM1/IRF4, and loss of B-cell markers such as CD20, which suggests a cell of origin similar to that of multiple myeloma (MM) or activated B-cell-like (ABC) diffuse large B-cell lymphoma (DLBCL). The standard of care for PBL is CHOP-based chemotherapy but this is frequently not curative. Other than EBV-negative status, which defines a high-risk group, no other biological features have been discovered that are associated with outcomes. Methods Here, we provide a comprehensive analysis of the genomic and transcriptomic landscape of PBL using a collection of 176 exomes and genomes (n=55 new, contributed by Lymphoma/Leukemia Molecular Profiling Project [LLMPP] sites and centrally reviewed by the LLMPP pathology panel [WHO 2016]; n=121 previously published [PMID: 33225311, 33951889, 34465776, 34714908]), 65 transcriptomes (n=44 new; n=21 published) and 37 in situ single cell transcriptomic profiles (CosMx Spatial Molecular Imager) from archival diagnostic tissue biopsies. Somatic mutations (SNVs/Indels) were identified using an ensemble of four variant callers (Strelka2, Lofreq, Mutect2, SAGE). Salmon and DESeq2 were used to identify differentially expressed genes. This multi-omics dataset was compared to other known B-cell malignancies including 92 MM, 238 Burkitt lymphomas (BL), and 208 DLBCLs. Cell segmentation of the CosMx data was performed using Baysor and downstream analyses were conducted using the Seurat workflow. For functional validation, cell viability assays (CellTiter-Glo Luminescent Assay) were conducted in the PBL cell line, PBL-1, and 4 control DLBCL cell lines. Results Within the cohort, 63% (106/169) of primary PBL cases were EBV-positive (EBV+) and 57% (82/144) harbored MYC rearrangements (MYC+). When stratifying patients by EBV and MYC rearrangement status, mutations in different genes were enriched in each group: STAT3 occurred in 55% (29/53) of EBV+/MYC+ PBLs; NOTCH1 in 19% (7/36) of EBV+/MYC-; MYC in 24% (6/25) and TP53 in 40% (10/25) of EBV-/MYC+ tumors; TET2 was mutated in 38% (9/24) and KRAS in 25% (6/24) of EBV-/MYC- PBLs. EBV-/MYC+ tumors represented a subgroup of patients with dismal outcomes. The 2-year overall survival (OS) of these patients was 22% compared to 61%, 78%, and 54% of EBV+/MYC+, EBV+/MYC-, and EBV-/MYC- PBL patients, respectively (log-rank test p-adj. < 0.05). Consistent with previous studies, we observed recurrent mutations affecting JAK-STAT (STAT3, SOCS1, SOCS3, DUSP2) in 41% of cases and RAS-RAF (NRAS, KRAS, BRAF) signaling in 38% of all PBLs. In contrast to ABC-DLBCL, very few mutations occurred in the NF-κB signalling pathway. Accordingly, PBL showed marked downregulation of genes involved in B-cell receptor and NF-κB signaling. Consistent with the lack of reliance on these pathways, functional analyses in PBL-1 showed resistance to treatment to ibrutinib as well as PI3K- and MALT1-inhibitors. Moreover, constitutive expression of the IκBα super-repressor did not affect viability of PBL-1 cells. In situ single cell transcriptomic data analyses showed a sparse tumor microenvironment (TME) in PBL, with the primary non-malignant cell types being, on average, 10% macrophages, 7% NK/T cells, and 4% stromal cells of all represented cell types. EBV+/MYC+ tumors displayed a more abundant immune cell population with higher levels of macrophages (median 12%) and NK/T cells (median 9%), whereas EBV-/MYC- tumors were composed of the lowest levels of immune cells (median 4% macrophages and 1% NK/T cells). With respect to malignant cell phenotypes, we noted a novel population of SPP1-expressing cells that were enriched in EBV+/MYC+ tumors and a population of CD44-expressing cells that were enriched in EBV+/MYC- tumors. Conclusion Here we present novel subgroups of PBL with unique biology and clinical outcomes. We show that PBL does not rely on B-cell receptor signaling, instead relying on potentially targetable vulnerabilities in JAK-STAT, NOTCH, and RAS-RAF signaling according to EBV positivity and MYC rearrangement status. Lastly, we provide insight into the TME of PBL and demonstrate a novel population of SPP1 and CD44 expressing malignant cells in EBV+ tumors.

Introduction: Recent efforts have shed light on the clinical and laboratory factors associated with toxicity and response to BCMA-directed CAR-T therapy in patients with relapsed/refractory multiple myeloma (RRMM). However, a major limitation of current scoring systems lies in their assessment at time of lymphodepletion. Because they are not determined before apheresis, they are ill-equipped to guide patient selection, anticipate level-of-care, or plan pre-emptive toxicity management strategies. We therefore aimed to characterize early predictors of CAR-T toxicity and response. Study Design: In this multicenter observational study, we examined demographic, clinical, and laboratory data across three distinct timepoints: indication (e.g., treatment decision), apheresis, and pre-lymphodepletion (LD). CRS and ICANS were graded according to ASTCT criteria. Hematotoxicity (ICAHT) was graded using EHA/EBMT criteria. Early infections (day 0-100) were defined based on microbiologic data or as a clinical infection syndrome, and graded per Hill et al, Blood 2018. Kaplan-Meier estimates of progression-free (PFS) and overall survival (OS) were compared by log-rank test. Cox proportional hazard models were applied to explore associations between pre-apheresis risk factors and key toxicity and survival outcomes. The CAR-HEMATOTOX (HT) score - encompassing markers of inflammation (e.g., CRP, ferritin) and marrow reserve (e.g., hemoglobin, ANC, platelet count) - was determined at each timepoint (Rejeski et al, Blood 2021). Results: We included 530 RRMM patients treated with standard-of-care cilta-cel (n=184) or ide-cel (n=346) across nine centers. Median age was 65 (range 31-88), median ECOG was 1 (IQR 0-1), and patients received a median of 5 prior lines of therapy (IQR 4-7). The median time from indication to apheresis was 16 days (IQR 7-52), while the median time from apheresis to infusion was 56 days (IQR 46-69). The proportion of high-risk patients by HT score did not change between the pre-apheresis and LD timepoints (19% vs. 20%, p=0.82). Most patients stayed either low-risk (n=388, 73.2%) or high-risk (n=68, 12.8%). However, 35 patients (6.6%) shifted from high-to-low risk, while 39 patients (7.4%) progressed from low-to-high risk - reflecting their response to bridging therapies. We noted considerable differences in morbidity and mortality based on the pre-apheresis HT score. Compared to their low-risk counterparts (score 0-2, n=427), high-risk patients (score ≥3, n=103) were hospitalized longer (12 vs. 15 days, p=0.009) and required more frequent ICU admissions (6% vs. 12%, p=0.05). They exhibited more severe early cytopenias (G3+: 8% vs. 25%, p<0.001) and late cytopenias (G3+: 7% vs. 21%, p<0.001), resulting in increased use of TPO agonists and stem cell boosts. Grade 3 or higher ICANS was also more common in HThigh patients (3% vs. 11%, p=0.006), as were severe infections (14% vs. 27%, p=0.007). We found increased non-relapsed mortality in HThigh patients (1-yr NRM 4.6% vs. 16.1%, p<0.001), but no significant difference in the occurrence of secondary malignancies (p>0.9). With a median follow-up of 12.4 months, HThigh patients showed inferior PFS (1-yr PFS 29% vs. 63%, p<0.0001) and OS (1-yr OS 49% vs. 83%, p<0.0001). Transitioning from a low-to-high score was linked to poor survival (1-yr PFS 26%), while the shift from high-to-low risk was associated with favorable outcomes (1-yr PFS 51%). In a multivariable Cox regression model of pre-apheresis features, a high HT score (aHR 2.52, p<0.0001), prior anti-BCMA therapy (aHR 1.55, p=0.04), history of extramedullary disease (aHR 1.42, p=0.02), and penta-refractoriness (aHR 1.45, p=0.02) were independently associated with PFS. Notably, the presence of more than 3 of these high-risk features defined an ultra-high-risk cohort of 28 patients (5.3%) with particularly dismal survival (1-year PFS 15%). Conclusions: Leveraging granular pre-apheresis data from a large international cohort, we identify early markers of toxicity and response in CAR-T-treated RRMM patients. The HT score - reflecting baseline inflammation and hematopoietic reserve - identified high-risk patients likely to benefit from pre-emptive toxicity mitigation strategies such as prophylactic stem cell collection. Most importantly, we characterize a small yet ultra-high-risk group of RRMM patients in need of more effective bridging and therapeutic options.

BACKGROUND AND OBJECTIVES: Stereotactic procedures are used to manage a diverse set of patients across a variety of clinical contexts. The stereotactic devices and software used in these procedures vary between surgeons, but the fundamental principles that constitute safe and accurate execution do not. The aim of this work is to describe these principles to equip readers with a generalizable knowledge base to execute and understand stereotactic procedures. METHODS: A combination of a review of the literature and empirical experience from several experienced surgeons led to the creation of this work. Thus, this work is descriptive and qualitative by nature, and the literature is used to support instead of generate the ideas of this framework. RESULTS: The principles detailed in this work are categorized based on 5 clinical domains: imaging, registration, mechanical accuracy, target planning and adjustment, and trajectory planning and adjustment. Illustrations and tables are used throughout to convey the concepts in an efficient manner. CONCLUSION: Stereotactic procedures are complex, requiring a thorough understanding of each step of the workflow. The concepts described in this work enable functional neurosurgeons with the fundamental knowledge necessary to provide optimal patient care.

BACKGROUND AND OBJECTIVES: Identifying and characterizing sources of targeting error in stereotactic procedures is essential to maximizing accuracy, potentially improving surgical outcomes. We aim to describe a generic framework which characterizes sources of stereotactic inaccuracy. METHODS: We assembled a list of stereotactic systems: ROSA, Neuromate, Mazor Renaissance, ExcelsiusGPS, Cirq, STarFix (FHC), Nexframe, ClearPoint, CRW, and Leksell. We searched the literature for qualitative and quantitative work identifying and quantifying potential sources of inaccuracy and describing each system's implementation using Standards for Reporting Qualitative Research guidelines. Our literature search spanned 1969 to 2024, and various studies were included, with formats ranging from phantom studies to systematic reviews. Keyword searches were conducted, and the details about each system were used to create a framework for identifying and describing the unique targeting error profile of each system. RESULTS: We describe and illustrate the details of various sources of stereotactic inaccuracies and generate a framework to unify these sources into a single framework. This framework entails 5 domains: imaging, registration, mechanical accuracy, target planning and adjustment, and trajectory planning and adjustment. This framework was applied to 10 stereotactic systems. CONCLUSION: This framework provides a rubric to analyze the sources of error for any stereotactic system. Illustrations allow the reader to understand sources of error conceptually so that they may apply them to their practice.

We studied movement-associated spectral changes in human motor thalamus and observed focal increases in broadband power with movement. This biomarker may be used as a tool for intraoperative functional mapping, DBS targeting, and closed-loop device control.