Leukemia

Despite considerable advances, the emergence of treatment resistance to tyrosine kinase inhibitors (TKIs) therapy remains a significant challenge in chronic myeloid leukemia (CML). Here, we report the first clinical case of resistance to combined ponatinib and asciminib therapy in a CML patient who relapsed with B lymphoblastic blast crisis. While at presentation the patient harbored the canonical e13a2 BCR::ABL1 fusion, at relapse his disease harbored the T315I mutation together with a novel e6a3 BCR::ABL1 fusion, arisen by internal deletion in the original translocated allele. Structural modeling and biochemical analyses demonstrated that deletion of exon 2-encoded residues of ABL1 destabilizes the autoinhibited conformation, resulting in a hyperactive kinase with increased propensity for B-cell differentiation. Functional studies revealed that both BCR::ABL1e6a3 and BCR::ABL1e6a3/T315I conferred resistance to ponatinib and asciminib, alone or in combination. BCR::ABL1e6a3 demonstrated enhanced sensitivity to active-state selective inhibitors dasatinib and bosutinib, whereas BCR::ABL1e6a3/T315I remained resistant. Combined drug sensitivity assays showed that axitinib restored inhibitory activity when combined with ponatinib or asciminib. Strikingly, a combination of axitinib and asciminib with low dose ponatinib fully suppressed enzymatic activity of BCR::ABL1e6a3/T315I and cellular proliferation. These data show that treatment with asciminib and ponatinib can select for mutations with notably elevated enzymatic activity, effectively targeted by an axitinib-based triple combination. These data highlight the remarkable mutability of the BCR::ABL1 kinase, including through novel isoforms and provides a strong rationale for the clinical assessment of a triple inhibitor combination as a strategy to overcome resistance to dual ponatinib and asciminib therapy.

Diffuse Large B-cell lymphoma (DLBCL) is the most common aggressive lymphoma in the Western world. First-line immunochemotherapy fails in approximately 30-40% of patients, with refractory and relapse patients presenting a dismal prognosis. Currently, these high-risk patients cannot be accurately identified at diagnosis. Using statistical modeling and machine learning approaches applied to large public DLBCL datasets, we identified a novel predictive signature based on the reactivation of eight normally silent tissue-dependent genes associated with survival. We then developed a multiplex RT-MLPseq based assay, compatible with formalin-fixed paraffin-embedded (FFPE) samples and transferable into routine clinical practice, enabling analysis of expression of these eight genes and validated their prognosis impact in an independent real-life cohort. This signature could be integrated with current prognostic indices and molecular classifications to improve patient stratification and guide treatment selection toward a personalized theragnostic approach, thereby enhancing management of non-responder patients.

Multiple myeloma (MM) is a clonal plasma cell malignancy characterized by bone marrow infiltration, monoclonal immunoglobulin production, and microenvironmental dysregulation that leads to systemic organ damage. The advent of B-cell maturation antigen (BCMA)-directed chimeric antigen receptor (CAR) T-cell therapy has induced unprecedented responses and durability for patients with relapsed/refractory MM. These outcomes are rarely observed with prior salvage strategies, although relapse remains the predominant long-term challenge for most patients. The two currently approved BCMA CAR-T cell products use viral vectors to semi-randomly insert the CAR gene, which results in heterogeneous genomic composition and variability in efficacy, safety, and product consistency. To address these challenges, we integrated targeted CRISPR genome engineering with precise CAR transgene insertion at the T-cell receptor alpha constant (TRAC) locus, 1XX CAR signaling architecture to enhance potency and durability, and non-viral manufacturing with a single-stranded DNA repair template to improve efficiency and yield. This approach confers physiological CAR expression, reduces insertional mutagenesis, and improves persistence by mitigating tonic signaling and exhaustion. Our GMP manufacturing process consistently achieved high CAR integration (37.7-72.7%) and yields across all full-scale runs and met predefined release criteria for identity, purity, safety, and quality. In NSG mouse models of MM, the UCCT-BCMA-1 product exhibited exceptionally potent tumor control, CAR-T cell expansion 100-1000-fold greater than that of lentiviral constructs, and durable clearance of myeloma cells after multiple rechallenges. These findings establish a CRISPR-edited, fully non-viral manufacturing platform for next-generation 1XX-BCMA CAR-T therapies with enhanced persistence, safety, and efficacy. One Sentence SummaryCRISPR-engineered, TRAC-targeted 1XX-BCMA CAR-T therapy with improved safety, potency, and persistence in relapsed and refractory multiple myeloma.

PURPOSE: Bispecific antibodies (BsAbs) represent a major advancement in the management of relapsed/refractory multiple myeloma (RRMM), offering high response rates even in heavily pretreated patients. However, their use presents operational, safety, and supportive care complexities that require coordinated care teams, and evolving infrastructure. This manuscript summarizes best practice recommendations for adverse event (AE) management, outpatient operational models, referral pathways, and emerging strategies to optimize long-term tolerability. METHODS: Medlive, A PlatformQ Health Brand, conducted qualitative interviews of academic and community-based clinicians. Discussions focused on BsAb implementation, patient selection and counseling, and AE management. Experts provided recommendations on team-based protocols, transitions of care, and inpatient versus outpatient considerations. RESULTS: Ten hematologists/oncologists (academic n=4; community n=6) described practice patterns, barriers, and perspectives on BsAb use. BsAbs were consistently regarded as highly effective across multiple lines of therapy, particularly for patients without alternatives. Cytokine release syndrome (CRS) was the most common acute toxicity, generally low grade and managed effectively with early tocilizumab, including prophylactic use in outpatient settings. Immune effector cell-associated neurotoxicity syndrome (ICANS) was rare, mild, and best mitigated through early recognition and caregiver support. Infections, largely from BCMA-associated hypogammaglobulinemia, frequently interrupted therapy, necessitating antiviral prophylaxis, pneumocystis jirovecii pneumonia (PJP) prophylaxis, and intravenous immunoglobulin (IVIG). Outpatient step-up dosing is expanding, supported by prophylactic strategies and academic-community collaboration. Timely referral was emphasized to preserving eligibility. Major outpatient challenges included sequencing, infrastructure readiness, and standardized caregiver and staff education. CONCLUSION: Effective community implementation of BsAbs requires multidisciplinary coordination, standardized AE protocols, infection prevention, and infrastructure to support monitoring, referrals, and equitable access. These measures are critical to ensure safe, sustainable integration of bispecific therapies and to optimize patient outcomes.

Purpose: Limited CNS bioavailability and pharmacodynamics are obstacles to effective systemic therapies for glioblastoma. One strategy to overcome these challenges is drug combinations enhancing CNS penetration and/or tumor chemosensitivity. LP-184, a synthetic acylfulvene class alkylator, induces DNA damage and inhibits glioblastoma cell viability in pre-clinical models. LP-184 is a prodrug converted to active metabolites by intracellular prostaglandin reductase 1 (PTGR1) that is over-expressed in >70% of glioblastoma. DNA damage induced by LP-184 is MGMT agnostic and reversed by transcription-dependent NER. Patients: LP-184 was evaluated in a Phase 1a study (NCT05933265) in 63 adult patients with advanced malignancies including 16 patients with recurrent glioblastoma. All patients with glioblastoma received prior standard-of-care therapy and most had received 1 or more additional therapies before enrollment. Results: Patients with glioblastoma experienced more frequent transaminitis, Grade 1-2 nausea and a trend towards more frequent and severe thrombocytopenia compared to the non-glioblastoma cohort. Otherwise, overall toxicity profiles were similar. Clinical pharmacokinetic analysis combined with published pre-clinical intra-tumoral bioavailability data (~20% penetration) predicted that LP-184 at the recommended dose for expansion (RDE) would achieve cytotoxic levels if combined with spironolactone, a BBB permeable ERCC3 degrader and TC-NER inhibitor that sensitizes glioblastoma cells to LP-184 3-6-fold. We show that three daily doses of spironolactone deplete orthotopic glioblastoma PDX ERCC3 protein by ~ 80% and increases tumor LP-184 cytotoxicity 2-fold. Conclusions: LP-184 is well tolerated at the RDE, and we establish a clinically translatable scheme for dosing spironolactone in combination with LP-184 for a future Phase 1b clinical trial.

High-throughput B-cell receptor sequencing has transformed the analysis of adaptive immunity, but benchmarking clonal grouping and lineage reconstruction methods remains limited by the absence of datasets with known evolutionary histories. Here we present Ancestra, a lineage-explicit simulator of B-cell receptor heavy-chain affinity maturation. Ancestra models stochastic V(D)J recombination, context-dependent somatic hypermutation, affinity-based selection and clonal expansion while recording complete parent-child relationships and mutation events. The framework generates BCR heavy-chain sequence datasets together with their corresponding ground-truth lineage trees, enabling direct benchmarking of lineage-aware analytical methods. Across simulations, Ancestra recapitulates key properties of human repertoires, including complementarity-determining region 3 length distributions, amino-acid usage patterns, junctional mutation patterns consistent with IMGT criteria and heterogeneous branching topologies. Simulated lineages also reveal multi-label lineage trees, in which identical nucleotide sequences can arise independently along distinct evolutionary paths. Ancestra provides a practical foundation for rigorous benchmarking of lineage-aware immune repertoire analysis.

Allogeneic hematopoietic stem cell transplantation (HSCT) is a life-saving treatment for hematologic malignancies, but long-term survivors present with lower muscle mass and functional capacity. In adult HSCT survivors 10-20 years after treatment, single nucleus RNA sequencing uncovered elevated XRRA1 expression levels in all muscle nuclei populations, which was retained in primary muscle stem cell cultures. HSCT survivors were characterized in vivo by impaired neuromuscular innervation that associated with muscle weakness, and lower muscle stem cell neurotrophic action. Despite these impairments, the molecular and physiological responses to heavy resistance training (HReT) were preserved in HSCT survivors, as demonstrated in a pre-registered clinical trial (ClinicalTrials.gov: NCT04922970). After 12 weeks of HReT, gains in muscle mass and strength were similar in HSCT survivors and healthy controls. In addition, we observed that [~]9% of muscle-resident immune cells persist into adulthood and that bone marrow derived cells do not adopt alternative cell fates in muscle tissue, resolving long-standing questions in human muscle biology. Together, these findings uncover molecular mechanisms of HSCT sequelae in muscle nuclei and muscle stem cells, which, importantly, can at least partly be overcome by mechanical loading. Given the growing population of HSCT survivors and the multitude of benefits of HReT for all organ systems, our findings support the importance of HReT in this population to promote healthspan.

Red blood cell (RBC) clearance by macrophages maintains blood homeostasis and is dysregulated in the hemolytic disorder sickle cell disease (SCD) and the lysosomal storage disorder Gaucher disease (GD), where biophysical and biochemical alterations promote premature phagocytosis. We develop a multiscale hybrid modeling framework integrating signaling dynamics, biophysical simulations, and machine learning to investigate the mechanisms governing RBC phagocytosis in these diseases. Our approach couples a systems biology model of macrophage-RBC signaling with Dissipative Particle Dynamics (DPD) simulations of molecular diffusion and membrane interactions, and leverages Physics-Informed Neural Networks (PINNs) for robust parameter inference. The DPD framework provides mechanistic insight into antibody diffusion, receptor engagement, and membrane-level interactions during macrophage-RBC contact, generating spatially resolved trajectories of CD47-SIRP signaling and antibody-receptor binding that serve as intermediate observables constraining the signaling model. The model accurately captures differential phagocytic responses between healthy and altered RBCs, revealing diminished inhibitory signaling and changes in SHP1-mediated pathways in both SCD and GD. Identifiability analysis combining Fisher Information Matrix diagnostics and profile likelihood confirms that parameters governing the CD47-SIRP-SHP1 axis are among the most robustly recoverable, and simulations of therapeutic perturbations with anti-SIRP antibodies demonstrate modulation of engulfment outcomes. We further employ Physics-Informed Kolmogorov-Arnold Networks (PIKANs) as an alternative to standard PINNs, demonstrating improved robustness under noise and sampling variability. More broadly, our multiscale platform linking biophysical simulation with systems-level inference is generalizable, offering mechanistic insights and computational tools for therapeutic exploration in diseases involving dysregulated phagocytosis. Significance statementRed blood cells are normally removed from circulation by macrophages through tightly regulated molecular signals. In diseases such as sickle cell disease and Gaucher disease, this clearance process becomes abnormal, contributing to anemia and other complications. However, the mechanisms linking the physical properties of red blood cells to immune signaling remain poorly understood. Here we develop a multiscale computational framework that combines particle-based biophysical simulations, systems biology models, and physics-informed machine learning. This approach provides a quantitative framework to interpret how changes in red blood cell mechanics and surface signaling disrupt the CD47-SIRP inhibitory pathway that normally prevents phagocytosis. The framework provides a predictive platform for studying immune clearance and may help guide therapeutic strategies targeting red blood cell-macrophage interactions.

Human Fc receptor-like 5 (FCRL5) is a low-affinity IgG Fc receptor expressed on various B cell subsets and a potential therapeutic target. We discovered that commonly used Fc-silencing mutations, designed to prevent interactions between the Fc{gamma} receptors on immune cells and the Fc domain of therapeutic IgG, do not prevent binding to FCRL5. As a result, unintended interactions between Fc-silent therapeutic IgG and human B cells may occur. We isolated a well-expressed variant of the Fc-binding portion of human FCRL5 by directed evolution and used structural modeling to guide the engineering of a human IgG1 Fc variant with approximately 100-fold higher affinity for FCRL5, enabling us to produce FCRL5:Fc complexes in solution. Native mass spectrometry, size exclusion chromatography, and the crystal structure of the FCRL5- IgG1 Fc complex solved at 3.4 [A] indicate that the two proteins bind in a 1:1 stoichiometry. Furthermore, the structure revealed that FCRL5 binds to IgG1 Fc in a manner completely distinct from that of previously characterized Fc-binding proteins, such as Fc{gamma} receptors, explaining why most Fc-silencing mutations do not disrupt FCRL5 binding. We demonstrate that selective cross-linking of FCRL5 with the B cell receptor (BCR) in cis, using Fc-engineered antibodies with either physiological or enhanced FCRL5 affinity, inhibits Ca2+ flux in FCRL5-expressing B cells. We compare this effect with the selective co-ligation of Fc{gamma}RIIb with the BCR. Our work demonstrates that FCRL5 interacts with human IgG Fc in a distinctive manner and that engagement of FCRL5 by Fc-silent therapeutic IgG could influence B cell function.

Mechanistic modeling has long been used as a tool to describe the dynamics of biological systems, especially cancer in response to treatment. Their key advantage lies in interpretability of relationships between input parameters and outcomes of interest. In contrast, machine learning techniques offer strong prediction performance, especially for high dimensional datasets that are common in oncology. Here, we employ a Mechanstic Learning framework that combines the advantages of both approaches by training machine learning models on mechanistic parameters inferred from clinical patient data. The mechanistic model (a Markov chain model) contains sixteen parameters that describe the rate of cell fate transitions that occur in patients with B-cell precursor acute lymphoblastic leukemia. The machine learning (a ridge logistic regression model) is trained on these parameters to predict two clinically-relevant features: BCR::ABL1 fusion gene status (positive or negative) and minimal residual disease status (positive or negative) post-induction chemotherapy. Model training is done in an iterative fashion to assess which (and how many) parameters are critical to maintain high predictive performance. Using machine learning models trained on the clinical flow-cytometry data, we find that the stem-like cell state alone is the most predictive feature for both BCR::ABL1-positive and MRD-positive disease, with combination scores (defined as the average of accuracy, balanced accuracy, and area under the curve) of 0.80 and 0.67, respectively. By comparison, mechanistic learning achieves comparable or improved combination scores for BCR::ABL1-positive and MRD-positive disease, with scores of 0.81 and 0.71, respectively, using only de-differentiation for BCR::ABL1 and primitive-state persistence together with differentiation-directed exit for MRD. Thus, the mechanistic-learning approach not only preserves predictive performance, but also provides a biological hypothesis for why stemness is predictive of these clinically relevant outcomes.

Glioblastoma (GBM) is the most prevalent primary brain cancer, with poor prognosis and limited therapeutic options available. The genetic and cellular heterogeneity characteristic of GBM contributes to poor response rates. Activating mutations of the epidermal growth factor receptor (EGFR) gene are among the most frequent alterations in GBM, occurring in roughly half of cases. Despite the prevalence of EGFR mutations, EGFR inhibition has shown limited success in GBM. The transcription factor C/EBP{beta} is a master regulator of the mesenchymal transformation in GBM, an aggressive state characterized by increased invasiveness and resistance to chemotherapy. Lucicebtide is a C/EBP{beta} antagonist peptide with demonstrated single agent activity in patients with recurrent GBM that is currently being evaluated in a clinical trial in combination with radiation and temozolomide in patients with newly-diagnosed GBM (NCT04478279), with emerging data supporting clinical activity in that setting. Here we show that in the TCGA-GBM dataset, patients with EGFR mutations display significant enrichment of a high C/EBP{beta} activity signature. Functionally, genetic inactivation of EGFR by CRISPR results in synthetic lethality in the presence of lucicebtide in GBM cell lines, and synergistic in vitro cytotoxicity and suppression of C/EBP{beta} target gene expression was observed in combination experiments with lucicebtide and EGFR inhibitors. Finally, enhanced anti-tumor activity was demonstrated in vivo in the combination setting, as combined subpharmacologic dose levels of lucicebtide and the EGFR inhibitor osimertinib potently suppressed GBM xenograft growth. These data identify EGFR and C/EBP{beta} dependencies in GBM and support lucicebtide combination with EGFR inhibitors as a potential therapeutic option for a sizable fraction of GBM patients.

Protein arginine methyltransferase 5 (PRMT5) is a synthetic lethal target in methylthioadenosine phosphorylase-deleted (MTAP-null) cancers. Second-generation MTA-cooperative PRMT5 inhibitors preferentially target MTAP-null cells while largely sparing MTAP-wildtype (MTAP-WT) cells, thereby improving tumor selectivity over first-generation PRMT5 inhibitors. Despite encouraging efficacy and safety signals in early clinical studies, the modest objective response rates (ORRs) observed with these inhibitors suggest that intrinsic or acquired resistance may limit their clinical benefit. Here, we investigated mechanisms of acquired resistance to the MTA-cooperative PRMT5 inhibitor BMS-986504/MRTX1719 in MTAP-null non-small cell lung cancer (NSCLC) cells and sought to identify therapeutic vulnerabilities that emerge upon resistance. Using multiple in vitro-derived resistant models, we found that acquired resistance was not fully explained by alterations in PRMT5 activity or reduced MTA levels. Instead, resistance was associated with collateral sensitivity to MEK inhibition and enrichment of MAPK-related transcriptional programs. Together, these findings identify MEK inhibition as an actionable collateral vulnerability in MTAP-null NSCLC cells that acquire resistance to PRMT5 inhibition.

Chimeric antigen receptor (CAR) T cell therapy utilizes genetically engineered patient-derived T cells to target cancer cells. Despite its clinical successes in multiple cancer types, the underlying molecular mechanisms by which molecules on CAR-T cells and surrounding cells interact with other proteins and collectively determine treatment efficacy remain elusive. Most previous studies have relied on transcriptome profiling, which does not fully reflect protein-level organization and interactions. In this study, we developed an antibody-oligonucleotide conjugate targeting the FMC63 region of CAR and integrated it into molecular pixelation (MPX). This approach enabled profiling of the dynamics of CAR molecules on cell surfaces as well as their colocalization with other proteins at the single-cell level. By applying MPX to longitudinal samples from three patients undergoing CAR-T cell therapy, we characterized the dynamic changes in CAR-associated protein organization in both pre-infusion CAR products and post-infusion peripheral blood. While CAR protein abundance and polarization showed limited variation across clinical courses, remodeling of a CAR-centered co-localization network was observed over time, including different retentions of specific molecular associations between patients with different clinical outcomes. Although derived from a limited cohort, our study identifies insights from this methodological framework beyond those gained by conventional omics analyses and offers results of a systematic investigation to predict and enhance CAR therapeutic outcomes. Key pointsO_LIMolecular pixelation was applied for chimeric antigen receptor (CAR) profiling at single-molecule and single-cell resolutions. C_LIO_LIProtein and transcriptome analyses of the CAR molecule showed dynamic remodeling during CAR-T therapy in patients with non-Hodgkin lymphoma. C_LI

Hereditary hearing loss is highly genetically heterogeneous, with emerging overlap between genes implicated in early-onset and age-related hearing loss. We report a consanguineous family with autosomal recessive, non-syndromic hearing loss in which the proband harbors a homozygous splice-site variant in PALM3 (NM_001145028.2:c.314+1G>A) and a homozygous missense variant in OTOA. A minigene assay for the PALM3 variant demonstrated aberrant splicing with exon skipping, resulting in a frameshift and a large inframe deletion, both consistent with loss of function and impacting all known transcripts. While the organ of Corti from 12-month-old heterozygous Palm3 mice showed preserved overall architecture, published Palm3 knockout mice exhibit auditory dysfunction, supporting an auditory phenotype with loss of function. Although a dual molecular diagnosis cannot be excluded, the combined genetic, functional, and comparative data support PALM3 as a strong candidate gene for autosomal recessive hearing loss.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest solid tumors and continues to face low treatment-trial participation, fragmented evidence workflows, and labor-intensive ab- straction of unstructured clinical text. Existing oncology-focused language models show promise, but many depend on private institutional corpora, limiting reproducibility and practical reuse across centers. We present Onca, an open 9B dense model designed for four PDAC-relevant tasks: trial eligibility screening, case-specific clinical reasoning, structured pathology report extraction, and molecular variant evidence reasoning. Onca is fine-tuned from Qwopus3.5-9B-v3 with a single Un- sloth BF16 LoRA adapter on 37,364 training rows drawn from openly available sources. The evalu- ation spans 11 panels and compares Onca against Woollie-7B, CancerLLM-7B, OpenBioLLM-8B, and the unmodified Qwopus base. Onca achieves the strongest overall results on Trial Screening (81.6 F1), Clinical Reasoning (14.1 composite), Pathology Extraction (30.5 field exact-match), Pub- MedQA Cancer (68.3 macro-F1), and PubMedQA (66.5 macro-F1). The strongest gains appear in tasks closest to routine oncology workflow, especially trial review and pathology structuring. These findings suggest that clinically targeted pancreatic-cancer language models can be built from open data with competitive performance while remaining practical to train on a single workstation-scale GPU setup.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy but can cause serious immune-related adverse events (irAEs), with pneumonitis (ICI-P) being among the most severe. Early identification of high-risk patients before ICI initiation is critical for closer monitoring, timely intervention, and improved outcomes. Purpose: To develop and validate a deep learning foundation model to predict ICI-P from baseline CT scans in patients with lung cancer. Methods: We designed the Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR (CIPHER), a deep learning foundation model that combines contrastive learning with a transformer-based masked autoencoder to predict ICI-P from baseline CT scans in patients with lung cancer. Using self-supervised learning, CIPHER was pre-trained on 590,284 CT slices from 2,500 non-small cell lung cancer (NSCLC) patients to capture heterogeneous lung parenchymal patterns. After pre-training, the model was fine-tuned on an internal NSCLC cohort for ICI-P risk prediction, using images from 254 patients for model development and 93 patients for internal validation. We compared CIPHER with classical radiomic models and further evaluated it on an external NSCLC cohort of 116 patients. Results: In the internal immunotherapy cohort, CIPHER consistently distinguished patients at elevated risk of ICI-P from those without the event, with AUCs ranging from 0.77 to 0.85. In head-to-head benchmarking, CIPHER achieved an AUC of 0.83, outperforming the radiomic models. In the external validation cohort, CIPHER maintained strong performance (AUC = 0.83; balanced accuracy = 81.7%), exceeding the radiomic models (DeLong p = 0.0318) and demonstrating higher specificity without sacrificing sensitivity. By contrast, the radiomic model showed high sensitivity (85.0%) but markedly lower specificity (45.8%). Confusion matrix analysis confirmed the robust classification performance of CIPHER, correctly identifying 80 of 96 non-ICI-P cases and 16 of 20 ICI-P cases. Conclusions: We developed and externally validated CIPHER for predicting future risk of ICI-P from pre-treatment CT scans. With prospective validation, CIPHER may be incorporated into routine patient management to improve outcomes.

Confirming target engagement in tumor experimental models remains a major challenge in oncology drug development. Pharmacodynamic biomarkers can help address this, but few systematic resources link drug targets to candidate biomarkers. We developed TargetTrace, a comprehensive resource to identify and prioritize pharmacodynamic biomarkers across nine key target classes, including transcription factors/cofactors, kinases, phosphatases, ubiquitin ligases, deubiquitinases, acetyltransferases, deacetylases, methyltransferases, and demethylases. Biomarker candidates were gathered from curated molecular interaction resources and refined using external annotations to improve accuracy. For enzyme targets with measurable substrate changes, we applied a two-agent large language model workflow, followed by manual review, to harmonize antibody information from the antibody resources and ensure that the selected biomarkers are measurable with existing laboratory tests. From more than 92,000 input interactions and over 2,300 targets, we compiled 71,323 target-biomarker relationships involving 2,270 potential drug targets, encompassing both transcription factor/cofactor-target gene and enzyme-substrate interactions. Commercial antibodies were available for over 1,400 biomarkers, supporting laboratory validation. This resource provides a structured and reusable resource for systematic identification and prioritization of pharmacodynamic biomarkers in oncology.

During ageing, hematopoietic stem cells (HSCs) have reduced regenerative potential, skewed differentiation toward the myeloid lineage, and heightened susceptibility to clonal expansion and malignancy. While epigenetic alterations are well documented, the impact of aging on higher-order 3D chromatin architecture remains poorly understood. Here, we examined the 3D genome organisation of aged murine HSCs using in-situ Hi-C then integrated this with gene expression and chromatin accessibility data to build HiC-informed gene regulatory networks (GRNs). Aged HSCs display erosion of topologically associating domain (TAD) boundaries, A/B compartment switching, and reorganised enhancer-promoter loops associated with lineage-inappropriate gene expression. Our GRN analysis identifies a hierarchy of transcription factors, including a c-Maf-Lyl1-Mnt axis that orchestrates the transition from a youthful to aged state and a Gfi1-Sox4 axis in young HSCs that regulates Bach1. This study provides a structural blueprint for aging HSCs and defines specific regulatory targets for potential reprogramming interventions to restore hematopoietic youthfulness.

Genome-wide association studies (GWAS) have advanced the understanding of germline susceptibility in common cancers, yet rare malignancies remain underexplored due to limited sample sizes. To address this gap, we conducted large-scale GWAS across 20 rare cancer types and meta-analyzed results from three cohorts: two clinically sequenced cancer center cohorts and an independent population biobank, comprising over 480,000 individuals. We identified nine novel genome-wide significant susceptibility loci with moderate to large effect sizes that replicated across cohorts in eight rare malignancies, including myelodysplastic syndromes (MDS), germ cell tumors, gastrointestinal stromal tumor (GIST), gastrointestinal neuroendocrine tumors, anal cancer (ANSC), non-melanoma skin cancer, mesothelioma, and hepatobiliary cancer. Among the strongest associations were loci in MDS near API5 (OR = 2.21, p = 1.06x10-8), in GIST near SLC6A18 and TERT (OR = 1.91, p = 8.20x10-50), and in ANSC near HLA-DQA2 (OR = 1.58, p = 5.50x10-18). The GIST risk variant was enriched in tumors harboring somatic KIT mutations (OR = 2.21, p = 6.5x10-4) and was associated with worse survival among carriers with KIT-mutant tumors (hazard ratio = 4.06, p = 0.015), implicating germline-somatic interplay in tumor initiation and progression. The ANSC risk variant was associated with HPV infection (OR = 1.44, p = 3.19x10-5), supporting a host-viral interaction in HPV-driven tumorigenesis. The MDS risk variant at the API5 locus was associated with altered neutrophil counts, suggesting a role in hematopoietic dysregulation in disease pathogenesis. We further identified novel, independent associations with mesothelioma, GIST, and hepatobiliary cancer at the 5p15.33 locus encompassing TERT, consistent with pleiotropic genetic effects at a core telomere-maintenance gene. Collectively, these findings demonstrate that integrating clinically ascertained sequencing cohorts with population biobanks substantially enhances germline discovery in rare cancers, enabling identification of high-confidence susceptibility loci and facilitating downstream biological interpretation through linked somatic, viral, and clinical data. This framework provides a scalable approach for characterizing inherited susceptibility across diverse rare malignancies.

Sarcoidosis is a heterogenous disease of unknown etiology characterized by non-caseating granulomas. Disease prevalence and presentation vary significantly by ancestry and ranges from acute, self-resolving disease to severe, chronic disease. Following previous reports suggesting B cells in the development and pathogenesis of sarcoidosis, we present here results of single-cell RNA sequencing, supporting B cell involvement in sarcoidosis through altered immediate early response, rewiring of MAPK signaling, and ancestry-specific preferential expansion of B cell receptors. Peripheral blood mononuclear cells were obtained from individuals of African or European Ancestry (AA and EA, respectively) including 48 healthy controls, 59 sarcoidosis patients, and 28 systemic lupus erythematosus (SLE) patients. SLE samples were used as a disease control. Differential expression analysis highlighted many differentially expressed genes (DEGs) with almost 5x more in the AA sarcoidosis versus AA control group compared to the EA sarcoidosis versus EA control group. B cells had the most DEGs of all cell types and expression patterns were similar between ancestries, however, sarcoidosis had an opposite transcription pattern than SLE, demonstrating an alternative immune response to acute activation than that seen in a prototypical autoinflammatory disease. This trend was maintained when examining specialized B cell subsets, with the most pronounced effect in the AA sarcoidosis versus AA control comparison. Our results strongly support further investigation of the role of humoral immune response in sarcoidosis and the potential to highlight patient groups likely to benefit from existing B cell therapies.