Faculty Bibliography

Here we used a series of CTCF mutations to explore CTCF's relationship with chromatin and its contribution to gene regulation. CTCF's impact depends on the genomic context of bound sites and the unique binding properties of WT and mutant CTCF proteins. Specifically, CTCF's signal strength is linked to changes in accessibility, and the ability to block cohesin is linked to its binding stability. Multivariate modeling reveals that both CTCF and accessibility contribute independently to cohesin binding and insulation, but CTCF signal strength has a stronger effect. CTCF and chromatin have a bidirectional relationship such that at CTCF sites, accessibility is reduced in a cohesin-dependent, mutant-specific fashion. In addition, each mutant alters TF binding and accessibility in an indirect manner, changes which impart the most influence on rewiring transcriptional networks and the cell's ability to differentiate. Collectively, the mutant perturbations provide a rich resource for determining CTCF's site-specific effects.

High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The Cancer Genome Atlas to extract features from small image patches (tiles). Leiden community detection groups tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival are confirmed in an independent clinical trial (N = 1213 WSIs). This unbiased atlas results in 47 HPCs displaying unique and shared clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analyses of these HPCs, including immune landscape and gene set enrichment analyses, and associations to clinical outcomes, we shine light on the factors influencing survival and responses to treatments of standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil additional insights and aid decision-making and personalized treatments for colon cancer patients.

BACKGROUND:The bone marrow (BM) niche contains non-hematopoietic elements including mesenchymal stromal cells (MSC) and bone marrow endothelial cells (BMEC) which provide mechanical support, and control hematopoietic cell growth and differentiation. Although it is known that multiple myeloma (MM) cells interact closely with the BM microenvironment, little is known about the impact of MM on non-hematopoietic niche-forming cells. METHODS:To address the role of the niche in MM pathogenesis, we utilized the 5TGM1 murine model. During the asymptomatic precursor stage of the model, we isolated the rare non-hematopoietic cells and performed single cell RNA sequencing. Using in-silico methods we characterized the individual cellular components of the niche, their relative abundance and differentiation state before and after exposure to MM cells as well as their intercellular interactions. RESULTS:MM engraftment increased the abundance of MSC-lineage cells, BMECs and enhanced endothelial to mesenchymal transition. An inflammatory and oxidative stress signal was identified together with polarization of MSC differentiation away from osteocyte formation towards adipocytes which provide growth factors that are known to support MM expansion. BMEC differentiation was polarized towards sinusoidal endothelial cells with a pro-angiogenic/pro-inflammatory phenotype. CONCLUSIONS:MM cells impact the BM niche by generating a pro-inflammatory microenvironment with MSC differentiation being changed to generate cell subsets that favor MM growth and survival. In order to induce remission and improve long-term outcome for MM patients these inflammatory and oxidative stress signals need to be reduced and normal niche differentiation trajectories restored.

Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of 0.73 in the development cohort and 0.84 in the Mayo cohort. The model's interpretability revealed that features like poor differentiation and deep invasion were strongly associated with poor prognosis. Furthermore, the model is effective in stratifying risk among BWH T2a and AJCC T2, known for outcome heterogeneity.

The Hhex gene encodes a transcription factor that is important for both embryonic and post-natal development, especially of hematopoietic tissues. Hhex is one of the most common sites of retroviral integration in mouse models. We found the most common integrations in AKXD (recombinant inbred strains) T-ALLs occur 57-61kb 3' of Hhex and activate Hhex gene expression. The genomic region of murine leukemia virus (MLV) integrations has features of a developmental stage-specific cis regulatory element (CRE), as evidenced by ATAC-seq in murine progenitor cells and high H3K27 acetylation at the syntenic CRE in human hematopoietic cell lines. With ChIP-exonuclease, we describe occupancy of LIM domain binding protein 1 (LDB1), the constitutive partner of the LIM Only-2 (LMO2), GATA1, and TAL1 transcription factors at GATA sites and a composite GATA-E box within the CRE. With virtual 4C analysis, we observed looping between this +65kb CRE and the proximal intron 1 enhancer of HHEX in primary human ETP-ALLs and in normal progenitor cells. Our results show that retroviral integrations at intergenic sites can mark and take advantage of CREs. Specifically, in the case of HHEX activation, this newly described +65kb CRE is co-opted in the pathogenesis of ETP-ALL by the LMO2/LDB1 complex.

Our previous studies revealed the existence of a Universal Receptive System that regulates interactions between cells and their environment. This system is composed of DNA- and RNA-based Teazeled receptors (TezRs) found on the surface of prokaryotic and eukaryotic cells, as well as integrases and recombinases. In the current study, we aimed to provide further insight into the regulatory role of TezR and its loss in Staphylococcus aureus gene transcription. To this end, transcriptomic analysis of S. aureus MSSA VT209 was performed following the destruction of TezRs. Bacterial RNA samples were extracted from nuclease-treated and untreated S. aureus MSSA VT209. After destruction of the DNA-based-, RNA-, or combined DNA- and RNA-based TezRs of S. aureus, 103, 150, and 93 genes were significantly differently expressed, respectively. The analysis revealed differential clustering of gene expression following the loss of different TezRs, highlighting individual cellular responses following the loss of DNA- and RNA-based TezRs. KEGG pathway gene enrichment analysis revealed that the most upregulated pathways following TezR inactivation included those related to energy metabolism, cell wall metabolism, and secretion systems. Some of the genetic pathways were related to the inhibition of biofilm formation and increased antibiotic resistance, and we confirmed this at the phenotypic level using in vitro studies. The results of this study add another line of evidence that the Universal Receptive System plays an important role in cell regulation, including cell responses to the environmental factors of clinically important pathogens, and that nucleic acid-based TezRs are functionally active parts of the extrabiome.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Classical biomedical data science models are trained on a single modality and aimed at one specific task. However, the exponential increase in the size and capabilities of the foundation models inside and outside medicine shows a shift toward task-agnostic models using large-scale, often internet-based, data. Recent research into smaller foundation models trained on specific literature, such as programming textbooks, demonstrated that they can display capabilities similar to or superior to large generalist models, suggesting a potential middle ground between small task-specific and large foundation models. This study attempts to introduce a domain-specific multimodal model, Congress of Neurological Surgeons (CNS)-Contrastive Language-Image Pretraining (CLIP), developed for neurosurgical applications, leveraging data exclusively from Neurosurgery Publications. METHODS:We constructed a multimodal data set of articles from Neurosurgery Publications through PDF data collection and figure-caption extraction using an artificial intelligence pipeline for quality control. Our final data set included 24 021 figure-caption pairs. We then developed a fine-tuning protocol for the OpenAI CLIP model. The model was evaluated on tasks including neurosurgical information retrieval, computed tomography imaging classification, and zero-shot ImageNet classification. RESULTS:CNS-CLIP demonstrated superior performance in neurosurgical information retrieval with a Top-1 accuracy of 24.56%, compared with 8.61% for the baseline. The average area under receiver operating characteristic across 6 neuroradiology tasks achieved by CNS-CLIP was 0.95, slightly superior to OpenAI's Contrastive Language-Image Pretraining at 0.94 and significantly outperforming a vanilla vision transformer at 0.62. In generalist classification, CNS-CLIP reached a Top-1 accuracy of 47.55%, a decrease from the baseline of 52.37%, demonstrating a catastrophic forgetting phenomenon. CONCLUSION/CONCLUSIONS:This study presents a pioneering effort in building a domain-specific multimodal model using data from a medical society publication. The results indicate that domain-specific models, while less globally versatile, can offer advantages in specialized contexts. This emphasizes the importance of using tailored data and domain-focused development in training foundation models in neurosurgery and general medicine.

Cancer progression involves genetic and epigenetic changes that disrupt chromatin 3D organization, affecting enhancer-promoter interactions and promoting growth. Here, we provide an integrative approach, combining chromatin conformation, accessibility, and transcription analysis, validated by in silico and CRISPR-interference screens, to identify relevant 3D topologies in pediatric T cell leukemia (T-ALL and ETP-ALL). We characterize 3D hubs as regulatory centers for oncogenes and disease markers, linking them to biological processes like cell division, inflammation, and stress response. Single-cell mapping reveals heterogeneous gene activation in discrete epigenetic clones, aiding in patient stratification for relapse risk after chemotherapy. Finally, we identify MYB as a 3D hub regulator in leukemia cells and show that the targeting of key regulators leads to hub dissolution, thereby providing a novel and effective anti-leukemic strategy. Overall, our work demonstrates the relevance of studying oncogenic 3D hubs to better understand cancer biology and tumor heterogeneity and to propose novel therapeutic strategies.