Citations

AbstractINTRODUCTIONPhosphorylated ubiquitin (p‐S65‐Ub) is generated during PINK1‐PRKN mitophagy as a specific marker of mitochondrial damage. Despite the widespread deposition of p‐S65‐Ub in aged and diseased human brain, the genetic contribution to its accumulation remains unclear.METHODSTo identify novel mitophagy regulators, we performed a genome‐wide association study using p‐S65‐Ub level as a quantitative trait in 1012 autopsy‐confirmed Lewy body disease (LBD) samples.RESULTSWe identified a significant genome‐wide association with p‐S65‐Ub for rs429358 (apolipoprotein E ε4 [APOE4]) and a suggestive association for rs6480922 (ZMIZ1). APOE4 was associated with higher p‐S65‐Ub levels and greater neuropathological burden. Functional validation in mouse and human induced pluripotent stem cell (iPSC) models confirmed APOE4‐mediated mitophagy alterations. Intriguingly, ZMIZ1 rs6480922 was associated with lower p‐S65‐Ub levels, reduced neuropathological load, and increased brain weight, indicating a potential protective role.DISCUSSIONOur findings underscore the importance of mitochondrial quality control in LBD pathogenesis and nominate regulators that may contribute to disease risk or resilience.Highlights p‐S65‐Ub levels were used as a quantitative marker of mitochondrial damage. A GWAS identified two genetic variants that modify mitophagy in LBD autopsy brain. APOE4 was associated with increased p‐S65‐Ub accumulation and neuropathology. APOE4 altered mitophagy via pathology‐dependent and pathology‐independent mechanisms. ZMIZ1 was linked to reduced p‐S65‐Ub and neuropathology indicative of protection.

Rab32 is a small GTPase and molecular switch implicated in vesicular trafficking. Rab32 is also an A-Kinase Anchoring Protein (AKAP), which anchors cAMP-dependent Protein Kinase (PKA) to specific subcellular locations and specifies PKA phosphorylation of nearby substrates. Surprisingly, we found that a form of Rab32 deficient in PKA binding (Rab32 L188P) relocalized away from the Golgi apparatus and induced a marked disruption in Golgi organization, assembly, and dynamics. Although Rab32 L188P did not cause a global defect in PKA activity, our data indicate that Rab32 facilitates the phosphorylation of a specific PKA substrate. We uncovered a direct interaction between Rab32 and the adaptor protein optineurin (OPTN), which regulates Golgi dynamics. Further, our data indicate that optineurin is phosphorylated by PKA at Ser342 in a Rab32-dependent manner. Critically, blocking phosphorylation at OPTN Ser342 leads to Golgi fragmentation, and a phospho-mimetic version of OPTN rescues Golgi defects induced by Rab32 L188P. Finally, Rab32 AKAP function and OPTN phosphorylation are required for Golgi repositioning during cell migration, contributing to tumor cell invasion. Together, these data reveal a role for Rab32 in regulating Golgi dynamics through PKA-mediated phosphorylation of OPTN.

Pathology remains a labor-intensive discipline, relying on workflows rooted in practices established over a century ago. Despite incremental technological advancements, the diagnostic process—from tissue preparation to interpretation—still heavily depends on thinly sliced tissue sections stained and examined under brightfield microscopy by skilled pathologists. Difficult cases often necessitate iterative staining or complex immunohistochemistry (IHC) analyses. However, the recent emergence of machine learning (ML) presents an opportunity to revolutionize traditional diagnostic workflows. One promising advancement is Digital Chemical Pathology (DCP), a paradigm that combines modern ML algorithms with infrared (IR) imaging to streamline pathology and enhance diagnostic accuracy. Infrared wavelengths inherently align with molecular vibrational frequencies, providing intrinsic contrast based on the chemical composition of samples. Mid-IR spectroscopic microscopes leverage this principle to spatially resolve molecular distributions at the pixel level, creating "chemical fingerprints" that ML algorithms can correlate with cellular identities and disease states. Recent advancements, particularly the development of broadly tunable quantum cascade lasers (QCLs), have enabled faster IR imaging, paving the way for clinical applications of IR spectroscopic microscopes. In this research, we demonstrate the potential of DCP using frozen tissue samples. Our approach focuses on two core tasks: (1) virtual staining, translating IR images into realistic, stained images interpretable by pathologists, and (2) tissue segmentation, enabling the automated detection of cancer and the distinction between normal and cancerous tissues. Furthermore, we incorporate statistical safety algorithms to generate uncertainty maps, ensuring the reliability of predictions. Our findings highlight that the integration of IR imaging and ML can facilitate automated diagnostics, providing pathologists with powerful tools for enhanced healthcare delivery. By leveraging DCP, we aim to modernize pathology workflows, reduce diagnostic turnaround times, and improve patient outcomes through precise and efficient analysis. Citation Format: Kianoush Falahkheirkhah, Kevin Yeh, Ji-Hun Oh, Yen-Ting Liu, Ruo-Jing Ho, Ishaan Sharma, Matthew Confer, Andres Orr, Mykolas Tamonis, John Cheville, Rohit Bhargava. Transforming cancer diagnosis with digital chemical pathology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2470.

PURPOSE Survival for patients with melanoma has recently improved. The propensity of melanoma to metastasize to the brain remains a common and serious feature of this disease. The purposes of this study were to evaluate prognostic factors for patients with newly diagnosed melanoma brain metastases (MBMs) in a large cohort treated with modern multimodal therapies, compare those results with those in prior eras, and update the Melanoma Graded Prognostic Assessment (GPA). METHODS Univariable and multivariable (MVA) analyses of prognostic factors and treatments associated with survival were performed on 1,796 patients with newly diagnosed MBM treated between January 01, 2015, and December 31, 2021, using a multi-institutional retrospective database. Multiple imputation was used to address missingness of potential predictors. Significant variables in combined MVA were used to update the Melanoma GPA. Comparisons were made with legacy cohorts. RESULTS Median survivals for cohorts A (1985-2007, n = 481), B (2006-2015, n = 823), and C (2015-2021, n = 1,796) were 6.7, 9.8, and 16.6 months and median follow-up times were 40.1, 43.6, and 48.8 months, respectively. In combined MVA, significant prognostic factors for survival were higher Karnofsky Performance Status, fewer MBMs, absence of extracranial metastases, lower serum lactate dehydrogenase, and no immunotherapy before MBM. These factors were incorporated into the updated Melanoma GPA. The combined median and 3-year survivals for patients with GPA 0-1, 1.5-2, and 2.5-4.0 were 5.4, 13.2, and 43.2 months and 12.4%, 28.8%, and 51.6%, respectively. CONCLUSION Prognostic factors have changed and survival has improved for patients with MBM but varies widely by GPA. The updated Melanoma GPA calculator (BrainMetGPA), available free online, can be used to estimate survival, individualize treatment, stratify clinical trials, guide surveillance, and augment clinical trial eligibility. Multidisciplinary treatment is essential. Trials are needed to elucidate the optimal sequencing of various therapeutic modalities.

Diffuse large B cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma, and the activated B cell–like subtype (ABC-DLBCL) is associated with particularly poor outcome. Many ABC-DLBCLs harbor gain-of-function mutations that cause inappropriate assembly of the CARMA1-BCL10-MALT1 (CBM) signalosome, a cytoplasmic complex that drives downstream NF-κB signaling. MALT1 is the effector protein of the CBM signalosome such that its recruitment to the signalosome via interaction with BCL10 allows it to exert both protease and scaffolding activities that together synergize in driving NF-κB. Here, we demonstrate that a molecular groove located between two adjacent immunoglobulin-like domains within MALT1 represents a binding pocket for BCL10. Leveraging this discovery, we performed an in silico screen to identify small molecules that dock within this MALT1 groove and act as BCL10-MALT1 protein-protein interaction (PPI) inhibitors. We report the identification of M1i-124 as a first-in-class compound that blocks BCL10-MALT1 interaction, abrogates MALT1 scaffolding and protease activities, promotes degradation of BCL10 and MALT1 proteins, and specifically targets ABC-DLBCLs characterized by dysregulated MALT1. Our findings demonstrate that small-molecule inhibitors of BCL10-MALT1 interaction can function as potent agents to block MALT1 signaling in selected lymphomas, and provide a road map for clinical development of a new class of precision-medicine therapeutics.

Abstract While tissue-resident memory CD8+ T cells (TRM cells) provide superior frontline defense against pathogens, the molecular mechanisms governing their persistence and functional regulation remain poorly defined. Our study reveals that the costimulatory receptors CD27 and ICOS coordinately sustain PD-1high CD8+ TRM cell populations following resolution of acute influenza infection. These costimulatory signals serve as critical targets for PD-1/PD-L1 blockade, thereby facilitating the rejuvenation of PD-1high TRM cells and influencing the progression of fibrotic sequelae during the memory phase. Mechanistic dissection identifies the nuclear receptor Nur77 (NR4A1) as the convergent transcriptional hub downstream of CD27/ICOS, governing proliferative renewal and maintenance of PD-1high TRM cells. Therapeutic administration of a CD27 agonist not only amplified this TRM cell subset in late-stage memory but also conferred cross-protective immunity against heterosubtypic viral challenges. Clinically, the expression of CD27 and ICOS is enriched in CD8+ T cells within the lung tissues of patients with pulmonary fibrosis. Collectively, our findings establish the "CD27/ICOS-NR4A1-Proliferation" axis as a linchpin of PD-1/PD-L1-mediated TRM cell homeostasis, revealing druggable targets for intercepting infection-associated fibrotic progression.

Abstract Lineage switch (LS), defined as the immunophenotypic transformation of acute leukemia, has emerged as a mechanism of relapse after antigen-targeted immunotherapy, which is associated with dismal outcomes. Through an international collaborative effort, we identified cases of LS after a host of antigen-targeted therapies (eg, CD19, CD22, CD38, and CD7), described how LS was diagnosed, reviewed treatment approaches, and analyzed overall outcomes for this form of postimmunotherapy relapse. Collectively, 75 cases of LS were evaluated, including 53 (70.7%) cases of B-cell acute lymphoblastic leukemia (B-ALL) transforming to acute myeloid leukemia (AML), 17 (22.7%) cases of B-ALL transforming to mixed phenotypic acute leukemia (MPAL)/acute leukemias of ambiguous lineage (ALAL), and 5 (6.7%) cases of rare LS presentation (ie, T-cell ALL to AML). An additional 10 cases with incomplete changes in immunophenotype, referred to as “lineage drift” were also described. With a primary focus on the 70 cases of LS from B-ALL to AML or MPAL/ALAL, LS emerged at a median of 1.5 months (range, 0-36.5) after immunotherapy, with 81.4% presenting with LS within the first 6 months from the most proximal immunotherapy. Although most involved KMT2A rearrangements (n = 45, 64.3%), other rare cytogenetic and/or molecular alterations were uniquely observed. Treatment outcomes were generally poor, with remission rates of <40%. The median overall survival after LS diagnosis was 4.8 months. Outcomes were similarly poor for those with rare immunophenotypes of LS or lineage drift. This global initiative robustly categorizes lineage changes after immunotherapy and, through enhanced understanding, establishes a foundation for improving outcomes of LS.

Abstract Background Conventional proton dose calculation methods are either time‐ and resource‐intensive, like Monte Carlo (MC) simulations, or they sacrifice accuracy, as seen with analytical methods. This trade‐off between computational efficiency and accuracy highlights the need for improved dose calculation approaches in clinical settings. Purpose This study aims to develop a deep‐learning‐based model that calculates dose‐to‐water ( D W ) and dose‐to‐medium ( D M ) using patient anatomy and proton spot map (PSM), achieving approaching MC‐level accuracy with significantly reduced computation time. Additionally, the study seeks to generalize the model to different treatment sites using transfer learning. Methods A SwinUNetr model was developed using 259 four‐field prostate proton stereotactic body radiation therapy (SBRT) plans to calculate patient‐specific D W and D M distributions from CT and projected PSM (PPSM). The PPSM was created by projecting PSM into the CT scans using spot coordinates, stopping power ratio, beam divergence, and water‐equivalent thickness. Fine‐tuning was then performed for the central nervous system (CNS) site using 84 CNS plans. The model's accuracy was evaluated against MC simulation benchmarks using mean absolute error (MAE), gamma analysis (2% local dose difference, 2‐mm distance‐to‐agreement, 10% low dose threshold), and relevant clinical indices on the test dataset. Results The trained model achieved a single‐field dose calculation time of 0.07 s on a Nvidia‐A100 GPU, over 100 times faster than MC simulators. For the prostate site, the best‐performing model showed an average MAE of 0.26 ± 0.17 Gy and a gamma index of 92.2% ± 3.1% in dose regions above 10% of the maximum dose for D W calculations, and an MAE of 0.30 ± 0.19 Gy with a gamma index of 89.7% ± 3.9% for D M calculations. After transfer learning for CNS plans, the model achieved an MAE of 0.49 ± 0.24 Gy and a gamma index of 90.1% ± 2.7% for D W computations, and an MAE of 0.47 ± 0.25 Gy with a gamma index of 85.4% ± 7.1% for D M computations. Conclusions The SwinUNetr model provides an efficient and accurate method for computing dose distributions in proton therapy. It also opens the possibility of reverse‐engineering PSM from D W , potentially speeding up treatment planning while maintaining accuracy.