Publications by authors named "Jennifer E Beane"

Article Synopsis
  • Lung cancer remains the leading cause of cancer deaths globally, highlighting the need for better understanding of its early development stages to enable timely interventions.
  • An international team of scientists identified knowledge gaps in how premalignant lung lesions progress to lung cancer and developed research questions to fill these gaps and guide future investigations.
  • Addressing these gaps is crucial for improving screening and early detection methods, which could lead to innovative strategies that effectively reduce lung cancer incidence and enhance patient outcomes.
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Bronchial premalignant lesions (PMLs) precede the development of invasive lung squamous cell carcinoma (LUSC), posing a significant challenge in distinguishing those likely to advance to LUSC from those that might regress without intervention. This study followed a novel computational approach, the Graph Perceiver Network, leveraging hematoxylin and eosin-stained whole slide images to stratify endobronchial biopsies of PMLs across a spectrum from normal to tumor lung tissues. The Graph Perceiver Network outperformed existing frameworks in classification accuracy predicting LUSC, lung adenocarcinoma, and nontumor lung tissue on The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium datasets containing lung resection tissues while efficiently generating pathologist-aligned, class-specific heatmaps.

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Multimodal machine learning models are being developed to analyze pathology images and other modalities, such as gene expression, to gain clinical and biological insights. However, most frameworks for multimodal data fusion do not fully account for the interactions between different modalities. Here, we present an attention-based fusion architecture that integrates a graph representation of pathology images with gene expression data and concomitantly learns from the fused information to predict patient-specific survival.

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Lung cancer, the leading cause of cancer mortality, exhibits diverse histological subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models are disparate, siloed, and difficult to compare in a centralized fashion. Here we established the Lung Cancer Mouse Model Database (LCMMDB), an extensive repository of 1,354 samples from 77 transcriptomic datasets covering 974 samples from genetically engineered mouse models (GEMMs), 368 samples from carcinogen-induced models, and 12 samples from a spontaneous model.

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Unlabelled: A greater understanding of molecular, cellular, and immunological changes during the early stages of lung adenocarcinoma development could improve diagnostic and therapeutic approaches in patients with pulmonary nodules at risk for lung cancer. To elucidate the immunopathogenesis of early lung tumorigenesis, we evaluated surgically resected pulmonary nodules representing the spectrum of early lung adenocarcinoma as well as associated normal lung tissues using single-cell RNA sequencing and validated the results by flow cytometry and multiplex immunofluorescence (MIF). Single-cell transcriptomics revealed a significant decrease in gene expression associated with cytolytic activities of tumor-infiltrating natural killer and natural killer T cells.

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Background: Bronchial premalignant lesions (PMLs) are composed primarily of cells resembling basal epithelial cells of the airways, which through poorly understood mechanisms have the potential to progress to lung squamous cell carcinoma (LUSC). Despite ongoing efforts that have mapped gene expression and cell diversity across bronchial PML pathologies, signaling and transcriptional events driving malignancy are poorly understood. Evidence has suggested key roles for the Hippo pathway effectors YAP and TAZ and associated TEAD and TP63 transcription factor families in bronchial basal cell biology and LUSC.

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Lung squamous cell carcinoma (LUSC) is a type of lung cancer with a dismal prognosis that lacks adequate therapies and actionable targets. This disease is characterized by a sequence of low- and high-grade preinvasive stages with increasing probability of malignant progression. Increasing our knowledge about the biology of these premalignant lesions (PMLs) is necessary to design new methods of early detection and prevention, and to identify the molecular processes that are key for malignant progression.

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Basal-like breast cancers, an aggressive breast cancer subtype that has poor treatment options, are thought to arise from luminal mammary epithelial cells that undergo basal plasticity through poorly understood mechanisms. Using genetic mouse models and ex vivo primary organoid cultures, we show that conditional co-deletion of the LATS1 and LATS2 kinases, key effectors of Hippo pathway signaling, in mature mammary luminal epithelial cells promotes the development of Krt14 and Sox9-expressing basal-like carcinomas that metastasize over time. Genetic co-deletion experiments revealed that phenotypes resulting from the loss of LATS1/2 activity are dependent on the transcriptional regulators YAP/TAZ.

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SARS-CoV-2 infection and disease severity are influenced by viral entry (VE) gene expression patterns in the airway epithelium. The similarities and differences of VE gene expression (ACE2, TMPRSS2, and CTSL) across nasal and bronchial compartments have not been fully characterized using matched samples from large cohorts. Gene expression data from 793 nasal and 1673 bronchial brushes obtained from individuals participating in lung cancer screening or diagnostic workup revealed that smoking status (current versus former) was the only clinical factor significantly and reproducibly associated with VE gene expression.

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Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade. However, patch-based methods introduce label noise during training by assuming that each patch is independent with the same label as the WSI and neglect overall WSI-level information that is significant in disease grading.

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A chemopreventive effect of aspirin (ASA) on lung cancer risk is supported by epidemiologic and preclinical studies. We conducted a randomized, double-blinded study in current heavy smokers to compare modulating effects of intermittent versus continuous low-dose ASA on nasal epithelium gene expression and arachidonic acid (ARA) metabolism. Fifty-four participants were randomized to intermittent (ASA 81 mg daily for one week/placebo for one week) or continuous (ASA 81 mg daily) for 12 weeks.

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Bronchial premalignant lesions (PMLs) are precursors of lung squamous cell carcinoma, but have variable outcome, and we lack tools to identify and treat PMLs at risk for progression to cancer. Here we report the identification of four molecular subtypes of PMLs with distinct differences in epithelial and immune processes based on RNA-Seq profiling of endobronchial biopsies from high-risk smokers. The Proliferative subtype is enriched with bronchial dysplasia and exhibits up-regulation of metabolic and cell cycle pathways.

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Within hosts, RNA viruses form populations that are genetically and phenotypically complex. Heterogeneity in RNA virus genomes arises due to error-prone replication and is reduced by stochastic and selective mechanisms that are incompletely understood. Defining how natural selection shapes RNA virus populations is critical because it can inform treatment paradigms and enhance control efforts.

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West Nile virus (WNV) exists in nature as a genetically diverse population of competing genomes. This high genetic diversity and concomitant adaptive plasticity has facilitated the rapid adaptation of WNV to North American transmission cycles and contributed to its explosive spread throughout the New World. WNV is maintained in nature in a transmission cycle between mosquitoes and birds, with intrahost genetic diversity highest in mosquitoes.

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Lung cancer is the leading cause of death from cancer in the US and the world. The high mortality rate (80-85% within 5 years) results, in part, from a lack of effective tools to diagnose the disease at an early stage. Given that cigarette smoke creates a field of injury throughout the airway, we sought to determine if gene expression in histologically normal large-airway epithelial cells obtained at bronchoscopy from smokers with suspicion of lung cancer could be used as a lung cancer biomarker.

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