Graph Perceiver Network for Lung Tumor and Bronchial Premalignant Lesion Stratification from Histopathology.

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. The network was further tested using endobronchial biopsies from two data cohorts, containing normal to carcinoma in situ histology. It demonstrated a unique capability to differentiate carcinoma in situ lung squamous PMLs based on their progression status to invasive carcinoma. The network may have utility in stratifying PMLs for chemoprevention trials or more aggressive follow-up.

Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival.

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. In our approach, pathology images are represented as undirected graphs, and their embeddings are combined with embeddings of gene expression signatures using an attention mechanism to stratify tumors by patient survival. We show that our framework improves the survival prediction of human non-small cell lung cancers, outperforming existing state-of-the-art approaches that leverage multimodal data. Our framework can facilitate spatial molecular profiling to identify tumor heterogeneity using pathology images and gene expression data, complementing results obtained from more expensive spatial transcriptomic and proteomic technologies.

A Lung Cancer Mouse Model Database.

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. Meticulous curation and collaboration with data depositors have produced a robust and comprehensive database, enhancing the fidelity of the genetic landscape it depicts. The LCMMDB aligns 859 tumors from GEMMs with human lung cancer mutations, enabling comparative analysis and revealing a pressing need to broaden the diversity of genetic aberrations modeled in GEMMs. Accompanying this resource, we developed a web application that offers researchers intuitive tools for in-depth gene expression analysis. With standardized reprocessing of gene expression data, the LCMMDB serves as a powerful platform for cross-study comparison and lays the groundwork for future research, aiming to bridge the gap between mouse models and human lung cancer for improved translational relevance.

Single-Cell Characterization of Pulmonary Nodules Implicates Suppression of Immunosurveillance across Early Stages of Lung Adenocarcinoma.

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. This was accompanied by a reduction in effector T cells and an increase of CD4+ regulatory T cells (Treg) in subsolid nodules. An independent set of resected pulmonary nodules consisting of both adenocarcinomas and associated premalignant lesions corroborated the early increment of Tregs in premalignant lesions compared with the associated normal lung tissues by MIF. Gene expression analysis indicated that cancer-associated alveolar type 2 cells and fibroblasts may contribute to the deregulation of the extracellular matrix, potentially affecting immune infiltration in subsolid nodules through ligand-receptor interactions. These findings suggest that there is a suppression of immune surveillance across the spectrum of early-stage lung adenocarcinoma. SIGNIFICANCE: Analysis of a spectrum of subsolid pulmonary nodules by single-cell RNA sequencing provides insights into the immune regulation and cell-cell interactions in the tumor microenvironment during early lung tumor development.

Convergence of YAP/TAZ, TEAD and TP63 activity is associated with bronchial premalignant severity and progression.

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. In this study we examine the functional association of YAP/TAZ, TEADs and TP63 in bronchial epithelial cells and PMLs. METHODS: We performed RNA-seq in primary human bronchial epithelial cells following small interfering RNA (siRNA)-mediated depletion of YAP/TAZ, TEADs or TP63, and combined these data with ChIP-seq analysis of these factors. Directly activated or repressed genes were identified and overlapping genes were profiled across gene expression data obtained from progressive or regressive human PMLs and across lung single cell RNA-seq data sets. RESULTS: Analysis of genes regulated by YAP/TAZ, TEADs, and TP63 in human bronchial epithelial cells revealed a converged transcriptional network that is strongly associated with the pathological progression of bronchial PMLs. Our observations suggest that YAP/TAZ-TEAD-TP63 associate to cooperatively promote basal epithelial cell proliferation and repress signals associated with interferon responses and immune cell communication. Directly repressed targets we identified include the MHC Class II transactivator CIITA, which is repressed in progressive PMLs and associates with adaptive immune responses in the lung. Our findings provide molecular insight into the control of gene expression events driving PML progression, including those contributing to immune evasion, offering potential new avenues for lung cancer interception. CONCLUSIONS: Our study identifies important gene regulatory functions for YAP/TAZ-TEAD-TP63 in the early stages of lung cancer development, which notably includes immune-suppressive roles, and suggest that an assessment of the activity of this transcriptional complex may offer a means to identify immune evasive bronchial PMLs and serve as a potential therapeutic target.

Inactivation of LATS1/2 drives luminal-basal plasticity to initiate basal-like mammary carcinomas.

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. Gene expression analyses of LATS1/2-deleted mammary epithelial cells notably revealed a transcriptional program that associates with human basal-like breast cancers. Our study demonstrates in vivo roles for the LATS1/2 kinases in mammary epithelial homeostasis and luminal-basal fate control and implicates signaling networks induced upon the loss of LATS1/2 activity in the development of basal-like breast cancer.

Smoking modulates different secretory subpopulations expressing SARS-CoV-2 entry genes in the nasal and bronchial airways.

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. The expression of ACE2 and TMPRSS2 was higher in smokers in the bronchus but not in the nose. scRNA-seq of nasal brushings indicated that ACE2 co-expressed genes were highly expressed in club and C15orf48+ secretory cells while TMPRSS2 co-expressed genes were highly expressed in keratinizing epithelial cells. In contrast, these ACE2 and TMPRSS2 modules were highly expressed in goblet cells in scRNA-seq from bronchial brushings. Cell-type deconvolution of the gene expression data confirmed that smoking increased the abundance of several secretory cell populations in the bronchus, but only goblet cells in the nose. The association of ACE2 and TMPRSS2 with smoking in the bronchus is due to their high expression in goblet cells which increase in abundance in current smoker airways. In contrast, in the nose, these genes are not predominantly expressed in cell populations modulated by smoking. In individuals with elevated lung cancer risk, smoking-induced VE gene expression changes in the nose likely have minimal impact on SARS-CoV-2 infection, but in the bronchus, smoking may lead to higher viral loads and more severe disease.

A Graph-Transformer for Whole Slide Image Classification.

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. Here we present a Graph-Transformer (GT) that fuses a graph-based representation of an WSI and a vision transformer for processing pathology images, called GTP, to predict disease grade. We selected 4,818 WSIs from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), the National Lung Screening Trial (NLST), and The Cancer Genome Atlas (TCGA), and used GTP to distinguish adenocarcinoma (LUAD) and squamous cell carcinoma (LSCC) from adjacent non-cancerous tissue (normal). First, using NLST data, we developed a contrastive learning framework to generate a feature extractor. This allowed us to compute feature vectors of individual WSI patches, which were used to represent the nodes of the graph followed by construction of the GTP framework. Our model trained on the CPTAC data achieved consistently high performance on three-label classification (normal versus LUAD versus LSCC: mean accuracy = 91.2 ± 2.5%) based on five-fold cross-validation, and mean accuracy = 82.3 ± 1.0% on external test data (TCGA). We also introduced a graph-based saliency mapping technique, called GraphCAM, that can identify regions that are highly associated with the class label. Our findings demonstrate GTP as an interpretable and effective deep learning framework for WSI-level classification.

Effect of Intermittent Versus Continuous Low-Dose Aspirin on Nasal Epithelium Gene Expression in Current Smokers: A Randomized, Double-Blinded Trial.

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. Low-dose ASA suppressed urinary prostaglandin E2 metabolite (PGEM; change of -4.55 ± 11.52 from baseline 15.44 ± 13.79 ng/mg creatinine for arms combined, P = 0.02), a surrogate of COX-mediated ARA metabolism, but had minimal effects on nasal gene expression of nasal or bronchial gene-expression signatures associated with smoking, lung cancer, and chronic obstructive pulmonary disease. Suppression of urinary PGEM correlated with favorable changes in a smoking-associated gene signature (P < 0.01). Gene set enrichment analysis (GSEA) showed that ASA intervention led to 1,079 enriched gene sets from the Canonical Pathways within the Molecular Signatures Database. In conclusion, low-dose ASA had minimal effects on known carcinogenesis gene signatures in nasal epithelium of current smokers but results in wide-ranging genomic changes in the nasal epithelium, demonstrating utility of nasal brushings as a surrogate to measure gene-expression responses to chemoprevention. PGEM may serve as a marker for smoking-associated gene-expression changes and systemic inflammation. Future studies should focus on NSAIDs or agent combinations with broader inhibition of pro-inflammatory ARA metabolism to shift gene signatures in an anti-carcinogenic direction.

Molecular subtyping reveals immune alterations associated with progression of bronchial premalignant lesions.

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. A Proliferative subtype-associated gene signature identifies subjects with Proliferative PMLs from normal-appearing uninvolved large airway brushings with high specificity. In progressive/persistent Proliferative lesions expression of interferon signaling and antigen processing/presentation pathways decrease and immunofluorescence indicates a depletion of innate and adaptive immune cells compared with regressive lesions. Molecular biomarkers measured in PMLs or the uninvolved airway can enhance histopathological grading and suggest immunoprevention strategies for intercepting the progression of PMLs to lung cancer.

Using the EPDS to Identify Anxiety in Mothers of Infants on the Neonatal Intensive Care Unit.

Despite the prevalence of postpartum depression and anxiety, current screening recommendations are limited to depression symptoms. Screening using the Edinburgh Postnatal Depression Scale-Anxiety subscale (EPDS-A) may enhance ability to detect distress in postpartum women. We aimed to replicate the EPDS-A in 200 mothers with infants hospitalized in the neonatal intensive care unit (NICU) and examine its incremental utility in identifying emotional distress. Presence of the EPDS-A was identified using exploratory factor analysis. Women experiencing elevated anxiety were identified using a previously established cutoff score. Results replicated the EPDS-A for the first time in mothers with infants hospitalized in the NICU. In all, 21.9% of these women had elevated anxiety symptoms and nearly one quarter of them would have been missed in routine depression screening. Use of the EPDS-A, in addition to the total EPDS score, is a promising approach to identifying anxious women in need of further evaluation, treatment, or support.

Anxiety Screening During Assessment of Emotional Distress in Mothers of Hospitalized Newborns.

OBJECTIVES: To examine the incremental identification of emotional distress in mothers of hospitalized newborns by screening for anxiety in addition to depression and to provide practical information about anxiety screening scales to facilitate instrument selection and screening implementation by nurses in the NICU. DESIGN: In this secondary data analysis, screening data from the recruitment phase of a feasibility trial to evaluate a nurse-delivered counseling intervention for emotionally distressed mothers of newborns in the NICU were used to examine the effect of anxiety screening. SETTING: A Level IV NICU at a large academic medical center in the Midwestern United States. PARTICIPANTS: Women 18 years of age and older (N = 190) with newborns in the NICU. METHODS: Participants completed multiple measures of depression and anxiety symptoms. RESULTS: Of participants who had negative screening results on a depression-only screening instrument, 4.7% to 14.7% endorsed clinically significant anxiety symptoms depending on the screening instrument used. CONCLUSION: Screening for anxiety in mothers of newborns in the NICU resulted in identification of distressed mothers who would otherwise have been missed during routine depression-only screening. Multiple options for anxiety screening exist that add incremental information to depression-only screening and require little additional burden on providers and mothers of newborns in the NICU.

Peritraumatic Distress Mediates the Effect of Severity of Disaster Exposure on Perinatal Depression: The Iowa Flood Study.

Disaster exposure during pregnancy has received limited attention. This study examined the impact of the 2008 Iowa Floods on perinatal maternal depression and well-being, and the role of peritraumatic distress as a possible mechanism explaining this link. Perinatal women (N = 171) completed measures of depressive symptoms and general well-being at 5 timepoints from pregnancy to 30 months postpartum. Objectively assessed prenatal flood exposure was associated with greater depression (r = .15). Further, flood-related peritraumatic distress was uniquely associated with greater depression (r = .23), and was a key mechanism through which flood exposure led to depression. Prenatal flood exposure was also associated with general well-being (r = .18); however, a mechanism other than peritraumatic distress appears to have been responsible for the effect of flood exposure on well-being. We discuss the implications of these findings for informing etiological models and enhancing the efficacy of interventions for maternal psychopathology.

Experimental evolution of an RNA virus in wild birds: evidence for host-dependent impacts on population structure and competitive fitness.

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. We allowed West Nile virus (WNV) to replicate in wild-caught American crows, house sparrows and American robins to assess how natural selection shapes RNA virus populations in ecologically relevant hosts that differ in susceptibility to virus-induced mortality. After five sequential passages in each bird species, we examined the phenotype and population diversity of WNV through fitness competition assays and next generation sequencing. We demonstrate that fitness gains occur in a species-specific manner, with the greatest replicative fitness gains in robin-passaged WNV and the least in WNV passaged in crows. Sequencing data revealed that intrahost WNV populations were strongly influenced by purifying selection and the overall complexity of the viral populations was similar among passaged hosts. However, the selective pressures that control WNV populations seem to be bird species-dependent. Specifically, crow-passaged WNV populations contained the most unique mutations (~1.7× more than sparrows, ~3.4× more than robins) and defective genomes (~1.4× greater than sparrows, ~2.7× greater than robins), but the lowest average mutation frequency (about equal to sparrows, ~2.6× lower than robins). Therefore, our data suggest that WNV replication in the most disease-susceptible bird species is positively associated with virus mutational tolerance, likely via complementation, and negatively associated with the strength of selection. These differences in genetic composition most likely have distinct phenotypic consequences for the virus populations. Taken together, these results reveal important insights into how different hosts may contribute to the emergence of RNA viruses.

RNAi targeting of West Nile virus in mosquito midguts promotes virus diversification.

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. The mechanistic basis for this increase in genetic diversity in mosquitoes is poorly understood. To determine whether the high mutational diversity of WNV in mosquitoes is driven by RNA interference (RNAi), we characterized the RNAi response to WNV in the midguts of orally exposed Culex pipiens quinquefasciatus using high-throughput, massively parallel sequencing and estimated viral genetic diversity. Our data demonstrate that WNV infection in orally exposed vector mosquitoes induces the RNAi pathway and that regions of the WNV genome that are more intensely targeted by RNAi are more likely to contain point mutations compared to weakly targeted regions. These results suggest that, under natural conditions, positive selection of WNV within mosquitoes is stronger in regions highly targeted by the host RNAi response. Further, they provide a mechanistic basis for the relative importance of mosquitoes in driving WNV diversification.

Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer.

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. Using a training set (n = 77) and gene-expression profiles from Affymetrix HG-U133A microarrays, we identified an 80-gene biomarker that distinguishes smokers with and without lung cancer. We tested the biomarker on an independent test set (n = 52), with an accuracy of 83% (80% sensitive, 84% specific), and on an additional validation set independently obtained from five medical centers (n = 35). Our biomarker had approximately 90% sensitivity for stage 1 cancer across all subjects. Combining cytopathology of lower airway cells obtained at bronchoscopy with the biomarker yielded 95% sensitivity and a 95% negative predictive value. These findings indicate that gene expression in cytologically normal large-airway epithelial cells can serve as a lung cancer biomarker, potentially owing to a cancer-specific airway-wide response to cigarette smoke.