Faculty Bibliography

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.

PURPOSE/OBJECTIVE:Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep learning strategies on histology samples to predict outcome for OSA in the neoadjuvant setting. EXPERIMENTAL DESIGN/METHODS:Our study relies on a training cohort from New York University (New York, NY) and an external cohort from Charles university (Prague, Czechia). We trained and validated the performance of a supervised approach that integrates neural network predictions of necrosis/tumor content, and compared predicted overall survival (OS) using Kaplan-Meier curves. Furthermore, we explored morphology-based supervised and self-supervised approaches to determine whether intrinsic histomorphological features could serve as a potential marker for OS in the setting of neoadjuvant. RESULTS:Excellent correlation between the trained network and the pathologists was obtained for the quantification of necrosis content (R2=0.899, r=0.949, p < 0.0001). OS prediction cutoffs were consistent between pathologists and the neural network (22% and 30% of necrosis, respectively). Morphology-based supervised approach predicted OS with p-value=0.0028, HR=2.43 [1.10-5.38]. The self-supervised approach corroborated the findings with clusters enriched in necrosis, fibroblastic stroma, and osteoblastic morphology associating with better OS (lg2HR; -2.366; -1.164; -1.175; 95% CI=[-2.996; -0.514]). Viable/partially viable tumor and fat necrosis were associated with worse OS (lg2HR;1.287;0.822;0.828; 95% CI=[0.38-1.974]). CONCLUSIONS:Neural networks can be used to automatically estimate the necrosis to tumor ratio, a quantitative metric predictive of survival. Furthermore, we identified alternate histomorphological biomarkers specific to the necrotic and tumor regions themselves which can be used as predictors.

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.

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.

As efforts to study the mechanisms of melanoma metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate the effects on tumor burden resulting from specific interventions. We show that automated quantification of tumor content from whole slide images is a compelling solution to assess in vivo experiments. In order to increase the outflow of data collection from preclinical studies, we assembled a large dataset with annotations and trained a deep neural network for the quantitative analysis of melanoma tumor content on histopathological sections of murine models. After assessing its performance in segmenting these images, the tool obtained consistent results with an orthogonal method (bioluminescence) of measuring metastasis in an experimental setting. This AI-based algorithm, made freely available to academic laboratories through a web-interface called MetFinder, promises to become an asset for melanoma researchers and pathologists interested in accurate, quantitative assessment of metastasis burden.

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.

BACKGROUND:Risk of early-stage lung adenocarcinoma (LUAD) recurrence after surgical resection is significant, and post-recurrence median survival is approximately two years. Currently there are no commercially available biomarkers that predict recurrence. Here, we investigated whether microbial and host genomic signatures in the lung can predict recurrence. METHODS:In 91 early-stage (Stage IA/IB) LUAD-patients with extensive follow-up, we used 16s rRNA gene sequencing and host RNA-sequencing to map the microbial and host transcriptomic landscape in tumor and adjacent unaffected lung samples. RESULTS:23 out of 91 subjects had tumor recurrence over 5-year period. In tumor samples, LUAD recurrence was associated with enrichment with Dialister, Prevotella, while in unaffected lung, recurrence was associated with enrichment with Sphyngomonas and Alloiococcus. The strengths of the associations between microbial and host genomic signatures with LUAD recurrence were greater in adjacent unaffected lung samples than in the primary tumor. Among microbial-host features in the unaffected lung samples associated with recurrence, enrichment with Stenotrophomonas geniculata and Chryseobacterium were positively correlated with upregulation of IL-2, IL-3, IL-17, EGFR, HIF-1 signaling pathways among the host transcriptome. In tumor samples, enrichment with Veillonellaceae Dialister, Ruminococcacea, Haemophilus Influenza, and Neisseria were positively correlated with upregulation of IL-1, IL-6, IL17, IFN, and Tryptophan metabolism pathways. CONCLUSIONS:Overall, modeling suggested that a combined microbial/transcriptome approach using unaffected lung samples had the best biomarker performance (AUC=0.83). IMPACT/CONCLUSIONS:This study suggests that LUAD recurrence is associated with distinct pathophysiological mechanisms of microbial-host interactions in the unaffected lung rather than those present in the resected tumor.

Diverse cellular insults converge on activation of the heat shock factor 1 (HSF1), which regulates the proteotoxic stress response to maintain protein homoeostasis. HSF1 regulates numerous gene programmes beyond the proteotoxic stress response in a cell-type- and context-specific manner to promote malignancy. However, the role(s) of HSF1 in immune populations of the tumour microenvironment remain elusive. Here, we leverage an in vivo model of HSF1 activation and single-cell transcriptomic tumour profiling to show that augmented HSF1 activity in natural killer (NK) cells impairs cytotoxicity, cytokine production and subsequent anti-tumour immunity. Mechanistically, HSF1 directly binds and regulates the expression of key mediators of NK cell effector function. This work demonstrates that HSF1 regulates the immune response under the stress conditions of the tumour microenvironment. These findings have important implications for enhancing the efficacy of adoptive NK cell therapies and for designing combinatorial strategies including modulators of NK cell-mediated tumour killing.

Partitioning of repressive from actively transcribed chromatin in mammalian cells fosters cell-type-specific gene expression patterns. While this partitioning is reconstructed during differentiation, the chromatin occupancy of the key insulator, CCCTC-binding factor (CTCF), is unchanged at the developmentally important Hox clusters. Thus, dynamic changes in chromatin boundaries must entail other activities. Given its requirement for chromatin loop formation, we examined cohesin-based chromatin occupancy without known insulators, CTCF and Myc-associated zinc-finger protein (MAZ), and identified a family of zinc-finger proteins (ZNFs), some of which exhibit tissue-specific expression. Two such ZNFs foster chromatin boundaries at the Hox clusters that are distinct from each other and from MAZ. PATZ1 was critical to the thoracolumbar boundary in differentiating motor neurons and mouse skeleton, while ZNF263 contributed to cervicothoracic boundaries. We propose that these insulating activities act with cohesin, alone or combinatorially, with or without CTCF, to implement precise positional identity and cell fate during development.

OBJECTIVE:Early-stage lung adenocarcinoma is treated with local therapy alone, although patients with grade 3 stage I lung adenocarcinoma have a 50% 5-year recurrence rate. Our objective is to determine if analysis of the tumor microenvironment can create a predictive model for recurrence. METHODS:Thirty-four patients with grade 3 stage I lung adenocarcinoma underwent surgical resection. Digital spatial profiling was used to perform genomic (n = 31) and proteomic (n = 34) analyses of pancytokeratin positive and negative tumor cells. K-means clustering was performed on the top 50 differential genes and top 20 differential proteins, with Kaplan-Meier recurrence curves based on patient clustering. External validation of high-expression genes was performed with Kaplan-Meier plotter. RESULTS:There were no significant clinicopathologic differences between patients who did (n = 14) and did not (n = 20) have recurrence. Median time to recurrence was 806 days; median follow-up with no recurrence was 2897 days. K-means clustering of pancytokeratin positive genes resulted in a model with a Kaplan-Meier curve with concordance index of 0.75. K-means clustering for pancytokeratin negative genes was less successful at differentiating recurrence (concordance index 0.6). Genes upregulated or downregulated for recurrence were externally validated using available public databases. Proteomic data did not reach statistical significance but did internally validate the genomic data described. CONCLUSIONS:Genomic difference in lung adenocarcinoma may be able to predict risk of recurrence. After further validation, stratifying patients by this risk may help guide who will benefit from adjuvant therapy.