Faculty Bibliography

Myelofibrosis (MF) is a chronic, progressive myeloproliferative neoplasm characterized by bone marrow fibrosis, ineffective blood cell production, and neoplastic extramedullary hematopoiesis (EMH) occurring primarily within the spleen. To explore the molecular mechanisms underlying splenic EMH, we performed single-cell transcriptional and chromatin profiling of cells from MF spleens that had been surgically removed. We demonstrate significant expansion of hematopoietic stem and progenitor cells, coupled with aberrant differentiation toward the erythroid and megakaryocytic lineages, associated with a significant enrichment of inflammatory pathways with enhanced NF-κB signaling and IFN responses, as well as dysregulation of the inferred function of differentiation-defining transcription factors. Finally, we report a significant remodeling of the immune microenvironment in MF spleens, characterized by emergence of dysfunctional T cell subsets and inflammatory memory B cells, suggesting the concomitant establishment of a pro-inflammatory and immune-tolerant tumor microenvironment within the spleen that influences hematopoietic cell differentiation and impairs tumor immune surveillance.

Children with Down syndrome (DS) frequently develop transient abnormal myelopoiesis that can evolve to the myeloid leukemia of DS (ML-DS). TAM spontaneously resolves in most cases but progresses to ML-DS with additional mutations, most commonly in the cohesin complex. However, the mechanisms by which these alterations promote leukemia are unknown. We leveraged the RAD21-mutant CMY cell line and RAD21-corrected CMY isogenic clones, as well as patient data, to investigate the effect of cohesin mutations during leukemia progression. RNA-sequencing revealed that HLA-class II genes were significantly down-regulated with cohesin mutations. Furthermore, HLA-DR was found to be lower in ML-DS relative to TAM, and these decreased levels were associated with increased risk of leukemia progression. Multi-omic analyses revealed that haploinsufficiency of RAD21 altered chromatin accessibility and impaired the occupancy of GATA1s and CIITA, the master regulator of HLA-class II gene expression. Chromatin binding of CIITA was increased with RAD21 correction, providing a mechanism by which restoration of cohesin improves HLA-class II expression. Finally, decreased levels of RAD21 or STAG2 expression in other subtypes of AML also exhibited reduced expression of HLA-class II genes. Thus, cohesin may contribute to leukemia by altering HLA-class II gene expression.

The diagnosis of myelodysplastic neoplasms (MDS) requires examination of the bone marrow for morphologic evidence of dysplasia. We sought to determine whether a self-supervised learning (SSL) artificial intelligence-based image analysis approach can be used to reliably distinguish MDS from its clinically relevant mimics using bone marrow biopsies (BMBx). The whole-slide images (WSIs) of hematoxylin and eosin (H&E)- and reticulin-stained BMBx sections from 243 unique patients (89 MDS, 55 non-MDS cytopenic controls [NMCCs], and 99 negative control [NC] cases) were partitioned into image tiles for analysis. These image tiles were then processed using the Barlow Twins SSL model to identify histomorphologic phenotype clusters (HPCs). Review of the HPCs revealed the clusters enriched in MDS cases captured known histopathologic features of the disease, including hypercellularity (characterized by enrichment in hypercellular image tiles), dysplastic and loosely clustered megakaryocytes, increased immature hematopoietic cells, increased vascularity, fibrosis, and cell streaming patterns. For external validation, 95 MDS BMBx WSIs from an independent institution were analyzed using the trained model. The model demonstrated consistent HPC enrichment patterns, supporting its robustness and generalizability. The trained ensemble model using H&E- and reticulin-stained slides distinguished MDS from NCs with an area under the curve (AUC) of 0.82, and from age-matched NMCCs with an AUC of 0.80. These findings demonstrate the potential of SSL approaches to capture diagnostically relevant morphologic patterns and to improve the reproducibility of MDS diagnosis.

Pancreatic ductal adenocarcinoma (PDAC) is a complex disease characterized by high levels of cellular heterogeneity and pronounced microenvironmental remodelling. Dynamic changes during its initiation and progression contribute to resistance to conventional therapies. Building upon key molecular catalogues established by bulk and single-cell profiling studies that have advanced our understanding of PDAC biology, recent advances in spatial biology have provided much-needed insights by elucidating regionally compartmentalized transcriptomic and proteomic programmes within the PDAC microenvironment. In parallel, emerging computational frameworks in digital pathology and artificial intelligence have advanced the field into a high-dimensional, quantitative discipline, particularly for classifying molecular and clinical features from histopathology images. Despite these advancements, integration of these two modalities remains a major challenge. Here, we summarize the convergence of molecular features identified through spatially resolved profiling in PDAC and its precursor lesions, as well as current developments in AI-powered pathology in cancer research. We further propose a multi-modal integration framework that maps molecular states onto morphological and architectural phenotypes, offering a roadmap for spatially informed patient stratification beyond descriptive tissue characterization. We posit that the path forward relies on disciplined cross-scale integration of spatial, histological, and clinical data to ensure meaningful translation into clinical practice.

OBJECTIVE:The relationship between physical activity and sleep with inflammatory arthritis (IA) is understudied, and existing research has relied largely on self-report or short-term assessments. The NIH All of Us database provides long-term accelerometry data, enabling more precise estimation of the association between lifestyle behaviors and IA. METHODS:Participants from the All of Us database who shared electronic health record and Fitbit data were included. Daily activity and sleep metrics were compared between individuals with and without IA using multiple linear regression. Cox proportional hazards regression was used to examine the association of activity and sleep patterns with incident IA in a 10-year follow-up period. RESULTS:A total of 23,855 participants were included, 200 of whom had IA. Participants with IA took fewer daily steps (P < 0.001) and had greater sleep variability (P < 0.001) compared to those without IA. 122 individuals had incident IA. Every 1,000 extra daily steps were associated with a 7% lower risk of IA (hazard ratio [HR] 0.93 [95% confidence interval (CI) 0.87-0.99], P = 0.02). Compared to those who walked <5,000 steps daily, those who walked 5,000 to 10,000 steps and 10,000+ steps had a 41% (HR 0.59 [95% CI 0.39-0.91], P = 0.02) and 50% (HR 0.50 [95% CI 0.29-0.86], P = 0.01) reduction in risk of IA. CONCLUSION/CONCLUSIONS:Individuals with IA had reduced step counts and more sleep variability compared to those without IA, highlighting how physical activity and sleep contribute to IA. Additionally, increased daily step count was associated with decreased risk of incident IA, suggesting a possible research intervention for those at high risk of IA.

Integrated electronic health record databases provide an unprecedented opportunity to enhance knowledge of disease prediction, prevention, and management in real-world settings. The Precision Medicine (PMED) Registry is a cohort of approximately 2 million patients seen at NYU Langone Health inpatient and outpatient centers, capturing data generated during clinical care from January 1, 2010, to the present, with regular data updates. Data have been used for several research investigations, including international meta-analyses, validation of disease identification algorithms, local evaluation of risk tools, testing analytical pipelines for imaging data, and the investigation of novel correlates of established risk prediction models. Additionally, the assessment of local practice has provided insights into clinical practice patterns and aided quality improvement efforts to assess and promote the uptake of guideline-directed therapies at the system and provider level. This study illustrates how real-world integrated electronic health record data with multi-modal clinical information can be leveraged to support research in prediction, diagnosis, prevention, and treatment optimization across health systems.

Myeloid sarcoma, an aggressive extramedullary subtype of acute myeloid leukemia (AML), occurs in approximately 20% of patients, and remains strikingly understudied in large-scale genomic and multiomic investigations. The key drivers of its tumor evolution are largely unknown, timely detection in asymptomatic patients poses a clinical challenge, and effective treatment options are limited as patients are often excluded from clinical trials, rendering it a largely neglected disease entity. Here, we demonstrate that myeloid sarcoma evolves from medullary AML but exhibits distinct site-specific clonal evolution. This is supported by unique transcriptional signatures of myeloid sarcoma, reflecting adaptation to the extramedullary microenvironment. We establish a proof of concept that circulating tumor DNA sequencing captures the molecular composition of myeloid sarcoma, offering a potential non-invasive approach for molecular profiling of extramedullary AML. Our findings highlight marked differences between medullary AML and myeloid sarcoma, including universal molecular evolution and RAS pathway activation as disease hallmarks.

UNLABELLED:LKB1 mutations in lung cancer promote an immunosuppressive tumor microenvironment, but the underlying mechanisms remain unknown. Using genetically engineered mouse models and human tumor samples, we demonstrate that LKB1 loss leads to high expression of the cytokine leukemia-inhibitory factor (LIF), which through a cancer cell-autonomous autocrine loop, orchestrates the infiltration of immunosuppressive SiglecFHi neutrophils and Arg1+ interstitial macrophages. Genetic deletion of Lifr, the receptor for LIF, on Lkb1-mutant lung tumors revealed that autocrine LIF signaling induces tumor plasticity and the emergence of a Sox17+ dedifferentiated inflammatory cell state. Antibody-mediated LIF neutralization selectively eliminates the Sox17+ tumor cell state, reduces immunosuppressive myeloid cells, and enhances antitumor T-cell responses. Our study uncovers a novel LKB1-LIF axis driving immune evasion and identifies LIF as a potential therapeutic target in LKB1-mutant lung cancer. This work highlights the interplay between tumor genetics, cellular plasticity, and immune regulation in lung cancer progression. SIGNIFICANCE/UNASSIGNED:LKB1-mutant lung cancers express LIF, which induces an immunosuppressive Sox17+ tumor state. Anti-LIF therapy eliminates this state and restores antitumor immunity, revealing a novel vulnerability in this aggressive cancer subtype lacking effective targeted therapies.

The identification of molecular subgroups of pediatric B-cell acute lymphocytic leukemia (B-ALL) has proven to be a powerful tool in understanding disease pathogenesis and treatment stratification. Studies have suggested aberrant transcription factor function and epigenetic regulation can explain differences between B-ALL subtypes, however, the impact of 3D genome re-organization remains unclear. Here we used in situ Hi-C and RNA-seq to profile the chromatin architectural landscape in healthy B-cell progenitors and B-ALL patient samples harboring prognostically relevant structural variations, including ETV6::RUNX1, KMT2A::AFF1, and BCR::ABL. We showed that B-ALLs undergo subtype-specific changes that, in part, reflect the differentiation stage of the disease, and that they acquire aberrant chromatin configurations that allow expression of oncogenic drivers. One such driver, ERG, displayed increased interactivity and expression in ETV6::RUNX1 B-ALL, and evidence suggests it plays a role in regulating survival and differentiation. Overall, these results underscore the essential role of 3D nuclear organization in acute leukemia.