Citations

Abstract Patients with atypical variants of Alzheimer's disease (AD) often present at a younger age with predominantly non‐amnestic impairments and a more aggressive disease course. Historically, individuals with atypical presentations have not been included in large‐scale clinical trials, which typically focus on late‐onset, sporadic amnestic‐predominant AD. Consequently, treatment options and research efforts specific to atypical AD remain limited. The emergence of amyloid‐targeting therapies that slow disease progression underscores these challenges, as evidence supporting their efficacy in early‐onset amnestic and non‐amnestic AD variants is scarce. This perspective article argues that atypical AD represents an excellent disease model for clinical trials and proposes strategies to address critical gaps in clinical trial design for this population. Key considerations include optimizing participant selection approaches, establishing syndrome‐specific or surrogate biological and clinical endpoints, and fostering advocacy to enhance early and accurate diagnosis, equitable representation, and outcomes for these populations.

Abstract The Tau Global Conference 2025, hosted by the Alzheimer's Association, CurePSP, and the Rainwater Charitable Foundation, convened international experts from academia, industry, government, and philanthropy to explore advances and challenges in tauopathy research. The meeting highlighted progress across tau biology, including emerging models of tau regulation, degradation, and propagation; advances in biomarker development for the diagnosis and staging of tauopathies; and evolving therapeutic strategies targeting diverse aspects of tau pathophysiology. Discussions also emphasized the importance of cross‐sector collaboration, and global initiatives to address disparities in tau research. This report synthesizes key insights from the conference and underscores the critical role of interdisciplinary, biomarker‐driven, and globally inclusive approaches in accelerating the translation of tau research into effective clinical applications.

Abstract INTRODUCTION Women are increasingly entering the dementia research workforce, but they frequently fail to attain senior leadership positions in academia. Discrepancies in academic research outputs were investigated to guide equity‐focused policy recommendations. METHODS Bibliometrics was conducted on 400,482 original research articles (2003–2022) followed by surveying the AD/ADRD research community, to quantitatively and qualitatively evaluate discrepancies in publication and authorship trends. In addition, an unsupervised learning algorithm was developed to analyze underlying relationships between author groups. RESULTS Male author gender was consistently predictive of better publication and citation metrics across all bibliometrics outcomes, with senior male authors outperforming all other subgroups and fewer senior female authors in most research areas. Female survey respondents reported more barriers, but comparable productivity and citation strategies as male authors. DISCUSSION Multi‐pronged approaches by stakeholders across institutions, funders, and journals are necessary to provide career support for early‐career scientists working to transition to senior research roles.

Abstract Since 2016, the Alzheimer's Association and the Fondation Alzheimer have hosted Global Alzheimer's Leadership Series (GoALS) global think tanks, with world‐leading experts for innovative discussions to advance Alzheimer's disease (AD) research and care. The second GoALS think tank, held in June 2024 in Paris, focused on the relationship between biological changes and clinical manifestations of AD in the context of the evolving therapeutic landscape. Discussions spanned real‐world experiences of providers, patients, and their families, theoretical considerations, and health system challenges. The lived experience perspective was central to these discussions. The importance of shared decision‐making, clear and transparent communication, and the need for real‐world data to holistically support patients during their experiences were highlighted. This manuscript shares key insights from both the think tank meeting in Paris and a featured research session at the 2024 Alzheimer's Association International Conference that expanded the discussion themes for broader dissemination with the community.

ABSTRACT One quarter of patients with diffuse large B‐cell lymphoma (DLBCL) who achieve a complete response following front‐line therapy will eventually experience disease relapse. Existing prognostic models use baseline factors to predict outcomes from the start of initial therapy and are not intended for use following completion of treatment. We developed a conditional event‐free survival (cEFS) tool that predicts a patient's risk of lymphoma recurrence after completing front‐line therapy taking into consideration baseline risk characteristics plus the time the patient has been in remission. Model development used pooled and harmonized data from two observational cohorts and four randomized trials of patients with newly diagnosed DLBCL treated with immunochemotherapy and either achieving a complete response or not experiencing an event within 12 weeks of end of therapy. Cox proportional‐hazards regression modeling with backwards stepwise selection was utilized for model development. External validation was performed using a large independent data registry. In total, data from 2757 patients were used for model development. The risk of experiencing an event at a time point after having survived a period of time could be computed parsimoniously using four basic predictors: age, ECOG performance status grade, Ann Arbor stage, and lactate dehydrogenase level. C‐index in the model development cohort was 0.65 (95% CI, 0.63–0.68), compared to 0.64 (95% CI, 0.62–0.66) in the external validation cohort. We believe that this dynamic risk prediction tool will not only aid clinicians in providing data‐driven recommendations for surveillance, but also assist patients themselves with key decisions, such as family planning.

Abstract The growing availability of large-scale biomarker datasets has allowed data-driven methods to characterize Alzheimer’s disease biological heterogeneity. However, most prior studies have focused on cohorts of late-onset amnestic cases, leaving early-onset Alzheimer’s disease underexplored. We aimed to characterize tau-PET-based subtypes through a robust data-driven approach in the Longitudinal Early-Onset Alzheimer’s Disease Study. Baseline [18F]Flortaucipir PET scans from 365 amyloid-PET-positive participants with sporadic early-onset Alzheimer’s disease were quantified in the left and right medial temporal, lateral temporal, occipital, parietal, and frontal cortices. Tau PET values were z-scored against 85 amyloid-PET-negative cognitively normal age-matched participants and fitted into Subtype and Stage Inference (SuStaIn)—an unsupervised clustering algorithm that simultaneously models subtypes and progression from cross-sectional data. The derived subtypes were subsequently characterized by baseline and longitudinal clinical, cognitive, MRI, tau and amyloid PET features. We identified three tau-PET-based subtypes: on average, Subtype 1/Typical (n = 144, 40%) showed a predominant bilateral temporoparietal pattern typical of Alzheimer’s disease. Subtype 2/Left temporal (n = 111, 31%) showed predominant left temporal binding. Subtype 3/Posterior (n = 104, 29%) showed early and permeating occipitoparietal involvement. Subtypes did not differ in demographics or global amyloid burden, but were relatively more enriched for specific clinical presentations: S1/Typical for amnestic presentations, S2/Left Temporal for primary progressive aphasia, and S3/Posterior for posterior cortical atrophy. Baseline tau PET subtypes aligned with cortical atrophy patterns and domain-specific cognitive impairment. When follow-up tau PET scans were fitted to SuStaIn trained on baseline data, 85.6% (n = 172/201) of participants retained the same subtype classification, indicating subtype temporal stability, and progressed within subtypes by 0.56 ± 0.70 SuStaIn stage/year. Longitudinal voxel-wise linear mixed-effects modelling revealed tau accumulation patterns for each subtype in regions relatively spared at baseline: occipital lobe accumulation predominated in S1/Typical, bilateral frontal and right temporal in S2/Left Temporal, and bilateral frontotemporal lobes in S3/Posterior. All subtypes showed longitudinal increases in Clinical Dementia Rating-Sum of Boxes, but with slower worsening in S3/Posterior compared with the other subtypes. Our findings reveal robust subtypes in sporadic early-onset Alzheimer’s disease characterized by distinct spatiotemporal tau patterns that parallel differences in clinical presentations and trajectories of neurodegeneration. These subtypes extend beyond traditional clinical syndromes and support a more nuanced framework for individualized prognosis and care. Incorporating tau PET subtyping into clinical trial design could enable more targeted therapeutic approaches for this younger population.

Summary Cancer genome sequencing is essential for understanding tumor evolution and advancing precision medicine. 1 However, reference gaps and germline variants obscure detection of small and large somatic variants and methylation in repetitive regions. 1–3 It is common for tumor cells to gain or lose chromosome arms due to somatic structural changes that occur inside highly repetitive satellite DNA sequences in the centromeres. 4 To identify the full spectrum of somatic variants, including complex rearrangements, we construct and curate near-complete, haplotype-resolved assemblies of the most recent common ancestor of an early-passage broadly-consented hypodiploid pancreatic cancer cell line and matched normal tissues. The tumor assembly completely recapitulates all 35 tumor chromosomes observed with karyotyping, with multiple translocation-induced hybrid chromosomes. The hybrid chromosomes contain putative functional dicentric and fused centromeres, nested foldback inversions causing 14 breakpoints with a haplotype switch in a single event, and centromeric satellite tandem duplications up to 136 kbp. Direct comparison of tumor and normal assembly haplotypes uncovers >7,000 variants altering >1 Mbp of sequence in repetitive regions that have been hidden by reference gaps and germline variants. 44 % of somatic small variants change representation because they alter germline variants on GRCh38, impacting mutational signatures and kataegis/omikli clusters. Most somatic LINE insertions originate from two hypomethylated non-reference germline LINE insertions, highlighting their impact on insertion mutation burden. These assemblies demonstrate that centromeric, acrocentric, and telomeric regions conventionally excluded from analysis harbor extensive somatic and epigenetic changes. Resolving complete tumor genomes enables a deeper understanding of cancer structural plasticity and the endpoints of breakage-fusion-bridge cycles. These assembled, curated paired normal-tumor benchmarks will serve as a critical foundation for developing future algorithms to characterize the most intractable regions of cancer genomes.

Abstract Machine learning (ML) enables adaptive immune receptor repertoires (AIRRs) analyses for biomarker identification and therapeutic development. With the majority of AIRR data partially or imperfectly labeled, unsupervised ML is essential for motif discovery, biologically meaningful clustering, and generation of novel receptor sequences. However, no unified framework for unsupervised ML exists in the AIRR field, hindering the assessment of model robustness and generalizability. Here, we present an immuneML release advancing unsupervised ML in the AIRR field through unified clustering workflows, interpretable generative modeling, integration with protein language model embeddings, dimensionality reduction, and visualization. We demonstrate immuneML’s utility in three use cases: (i) benchmarking generative models for epitope-specific sequence generation, assessing specificity and novelty, (ii) systematic evaluation of clustering approaches on experimental receptor sequences against biological properties, such as epitope specificity and MHC, and (iii) unsupervised analysis of an experimental AIRR dataset to examine potential confounding, a practice widespread in related fields but unexplored in AIRR analyses.

Abstract Inherited susceptibility plays a critical role in prostate cancer (PCa) risk. Using large biobank and case-control datasets, we evaluated the contribution of both common variants and rare germline pathogenic variants (PVs) to overall PCa risk. The Prostate Cancer Exome Sequencing Consortium currently includes 427,388 male participants (51,452 PCa cases and 375,936 controls) with whole-exome sequencing data from ten biobanks and studies: UK Biobank (14,669 cases/195,600 controls), All of Us Research Program (7,577/75,226), African Ancestry Prostate Cancer Consortium (7,176/4,675), Mayo Clinic Biobank (6,031/15,084), Mass General Brigham Biobank (3,393/14,095), Geisinger’s MyCode Community Health Initiative (3,026/15,130), UCLA ATLAS Precision Medicine Biobank (2,850/16,904), Penn Medicine Biobank (2,598/16,255), Colorado Center for Personalized Medicine (2,269/13,399), and Malmo Diet and Cancer (1,863/9,568). Based on self-reported race/ethnicity and estimated genetic ancestry, the cases comprise approximately 79% European, 18% African, and 3% other ancestry populations. Single-variant association analyses tested all variants on chromosomes 1-22 and X with a minor allele count ≥ 5. In gene-based analyses, PVs were defined as rare variants (minor allele frequency [MAF] < 1% in controls) that had either a Variant Effect Predictor (VEP) impact score of “high” or a pathogenic or likely pathogenic ClinVar classification. Associations were estimated using Firth logistic regression, adjusting for age and the top ten genetic principal components. Results from individual studies were combined using fixed-effect meta-analysis. In single-variant association analyses, 496 variants reached genome-wide significance (p<5×10-8; MAF>0.02%). Among these, 458 (92%) variants mapped to previously known risk regions, including three rare PVs in HOXB13 (rs138213197), CHEK2 (rs555607708), and FAM111A (rs533676902). Characterization of the remaining 38 variants is ongoing. Gene-based analyses identified significant associations (p<2.4×10-6) for eight genes: HOXB13 (OR=3.7, 95% CI=3.3-4.2), BRCA2 (OR=2.0, 95% CI=1.7-2.3), CHEK2 (OR=1.6, 95% CI=1.5-1.8), ATM (OR=1.6, 95% CI=1.4-1.9), FAM111A (OR=1.4, 95% CI=1.3-1.5), BIK (OR=1.4, 95% CI=1.2-1.6), SAMHD1 (OR=2.1, 95% CI=1.6-2.7), and SMOC2 (OR=3.2, 95% CI=2.0-5.1). All genes except SMOC2 have been previously implicated in PCa susceptibility. Among cancer predisposition and DNA repair genes, nominal associations were also observed for XRCC2 (OR=1.6, 95% CIs=1.2-2.3) and BRCA1 (OR=1.2, 95% CI=1.0-1.4), whereas the association was not significant for PALB2 (OR=1.2, 95% CI=0.9-1.5). These findings reinforce the role of rare germline PVs, particularly in cancer predisposition and DNA repair genes, in PCa susceptibility. As additional studies are incorporated into the Consortium, we expect this work to provide a more comprehensive characterization of the genetic architecture of PCa. Citation Format: Yifan Zhang, Shuyan Cheng, Nicholas Boddicker, Matthew Lebo, Alexander S. Berry, Roni Haas, Ryan Hausler, Tokhir Dadaev, Heena Desai, Alex A. Rodriguez, Ravi K. Madduri, Andrew Hill, Xin Sheng, Susan M. Gundell, Mine Cicek, Penn Medicine Biobank, Olle Melander, Chris R. Gignoux, Isla P. Garraway, Bogdan Pasaniuc, Paul C. Boutros, Matt Oetjens, Adam S. Kibel, Robert J. Klein, Zsofia Kote-Jarai, Fergus J. Couch, Kara N. Maxwell, Burcu F. Darst, David V. Conti, Christopher A. Haiman, Fei Chen. Genetic risk of prostate cancer: Insights from the Prostate Cancer Sequencing Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB390.