Faculty Bibliography

BACKGROUND AND OBJECTIVE/OBJECTIVE:The diagnosis of interstitial lung diseases (ILDs) often relies on the integration of various clinical, radiological, and histopathological data. Achieving high diagnostic accuracy in ILDs, particularly for distinguishing usual interstitial pneumonia (UIP), is challenging and requires a multidisciplinary approach. Therefore, this study aimed to develop a multimodal artificial intelligence (AI) algorithm that combines computed tomography (CT) and histopathological images to improve the accuracy and consistency of UIP diagnosis. METHODS:A dataset of CT and pathological images from 324 patients with ILD between 2009 and 2021 was collected. The CT component of the model was trained to identify 28 different radiological features. The pathological counterpart was developed in our previous study. A total of 114 samples were selected and used for testing the multimodal AI model. The performance of the multimodal AI was assessed through comparisons with expert pathologists and general pathologists. RESULTS:The developed multimodal AI demonstrated a substantial improvement in distinguishing UIP from non-UIP, achieving an AUC of 0.92. When applied by general pathologists, the diagnostic agreement rate improved significantly, with a post-model κ score of 0.737 compared to 0.273 pre-model integration. Additionally, the diagnostic consensus rate with expert pulmonary pathologists increased from κ scores of 0.278-0.53 to 0.474-0.602 post-model integration. The model also increased diagnostic confidence among general pathologists. CONCLUSION/CONCLUSIONS:Combining CT and histopathological images, the multimodal AI algorithm enhances pathologists' diagnostic accuracy, consistency, and confidence in identifying UIP, even in cases where specialised expertise is limited.

BACKGROUND:Digital papillary adenocarcinoma (DPAC) is a rare but aggressive cutaneous malignant sweat gland neoplasm that occurs on acral sites. Despite its clinical significance, the cellular and genetic characteristics of DPAC remain incompletely understood. METHODS:We conducted a comprehensive genomic and transcriptomic analysis of DPAC (n = 14) using targeted next-generation DNA and RNA sequencing, along with gene expression profiling employing the Nanostring Technologies nCounter IO 360 Panel. Gene expression in DPAC was compared to that in hidradenoma (n = 10). Immunohistochemistry was employed to validate gene expression. RESULTS:Two out of eight DPACs showed fusion gene rearrangements (CRTC3::MAML2 and TRPS1::PLAG1). No uniform mutational signature was detected in DPAC. Comparative gene expression analysis revealed an enrichment of genes related to matrix remodeling, metabolism, and DNA damage repair. Hallmark pathway analysis demonstrated significant upregulation of E2F target genes in DPAC compared to hidradenoma (p = 0.00710). Human papillomavirus-42 was found to be positive in all of our tested DPAC cases. Immunohistochemistry confirmed increased protein expression of CD56, CDC20, and SOX10 in DPAC. Notably, most DPAC tumors also exhibited B-cell infiltration, as indicated by CD20 staining. CONCLUSIONS:Our findings reveal novel fusions and validate altered replication pathways related to HPV42 in DPAC.

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.

Cancer of unknown primary (CUP) constitutes between 2% and 5% of human malignancies and is among the most common causes of cancer death in the United States. Brain metastases are often the first clinical presentation of CUP; despite extensive pathological and imaging studies, 20%-45% of CUP are never assigned a primary site. DNA methylation array profiling is a reliable method for tumor classification but tumor-type-specific classifier development requires many reference samples. This is difficult to accomplish for CUP as many cases are never assigned a specific diagnosis. Recent studies identified subsets of methylation quantitative trait loci (mQTLs) unique to specific organs, which could help increase classifier accuracy while requiring fewer samples. We performed a retrospective genome-wide methylation analysis of 759 carcinoma samples from formalin-fixed paraffin-embedded tissue samples using Illumina EPIC array. Utilizing mQTL specific for breast, lung, ovarian/gynecologic, colon, kidney, or testis (BLOCKT) (185k total probes), we developed a deep learning-based methylation classifier that achieved 93.12% average accuracy and 93.04% average F1-score across a 10-fold validation for BLOCKT organs. Our findings indicate that our organ-based DNA methylation classifier can assist pathologists in identifying the site of origin, providing oncologists insight on a diagnosis to administer appropriate therapy, improving patient outcomes.

Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of YAP1 and TEAD-family genes. Recent works have highlighted the role of YAP1/TEAD activity in several cancers and its potential therapeutic implications. Therefore, identifying patients with a dysregulated Hippo pathway is key to enhancing treatment impact. Although recent studies have derived RNA-seq-based signatures, there remains a need for a reproducible and cost-effective method to measure the pathway activation. In recent years, deep learning applied to histology slides have emerged as an effective way to predict molecular information from a data modality available in clinical routine. Here, we trained models to predict YAP1/TEAD activity from H&E-stained histology slides in multiple cancers. The robustness of our approach was assessed in seven independent validation cohorts. Finally, we showed that histological markers of disease aggressiveness were associated with dysfunctional Hippo signaling.

Small cell lung carcinoma (SCLC) is a highly aggressive malignancy that is typically associated with tobacco exposure and inactivation of RB1 and TP53 genes. Here, we performed detailed clinicopathologic, genomic, and transcriptomic profiling of an atypical subset of SCLC that lacked RB1 and TP53 co-inactivation and arose in never/light smokers. We found that most cases were associated with chromothripsis-massive, localized chromosome shattering-recurrently involving chromosome 11 or 12 and resulting in extrachromosomal amplification of CCND1 or co-amplification of CCND2/CDK4/MDM2, respectively. Uniquely, these clinically aggressive tumors exhibited genomic and pathologic links to pulmonary carcinoids, suggesting a previously uncharacterized mode of SCLC pathogenesis via transformation from lower-grade neuroendocrine tumors or their progenitors. Conversely, SCLC in never-smokers harboring inactivated RB1 and TP53 exhibited hallmarks of adenocarcinoma-to-SCLC derivation, supporting two distinct pathways of plasticity-mediated pathogenesis of SCLC in never-smokers. Significance: Here, we provide the first detailed description of a unique SCLC subset lacking RB1/TP53 alterations and identify extensive chromothripsis and pathogenetic links to pulmonary carcinoids as its hallmark features. This work defines atypical SCLC as a novel entity among lung cancers, highlighting its exceptional histogenesis, clinicopathologic characteristics, and therapeutic vulnerabilities. See related commentary by Nadeem and Drapkin, p. 8.

Sjögren's disease (SjD) is an autoimmune disorder characterized by progressive salivary and lacrimal gland dysfunction, inflammation, and destruction, as well as extraglandular manifestations. SjD is associated with autoreactive B and T cells, but its pathophysiology remains incompletely understood. Abnormalities in regulatory T (T_reg) cells occur in several autoimmune diseases, but their role in SjD is ambiguous. We had previously shown that the function and development of T_reg cells depend on store-operated Ca²⁺ entry (SOCE), which is mediated by ORAI1 Ca²⁺ channels and stromal interaction protein 1 (STIM1) and STIM2. Here, we show that mice with a Foxp3⁺ T_reg cell-specific deletion of Stim1 and Stim2 develop a phenotype that fulfills all classification criteria of human SjD. Mutant mice have salivary and lacrimal gland inflammation characterized by strong lymphocyte infiltration and transcriptional signatures dominated by T helper 1 (T_H1) and interferon (IFN) signaling. CD4⁺ T cells from mutant mice are sufficient to induce SjD-like disease in an IFN-γ-dependent manner. Inhibition of IFN signaling with the JAK1/2 inhibitor baricitinib alleviated CD4⁺ T cell-induced SjD in mice. These findings are consistent with the transcriptional profiles of CD4⁺ T cells from patients with SjD, which indicate enhanced T_H1 but reduced memory T_reg cell function. Together, our study provides evidence for a critical role of dysfunctional T_reg cells and IFN-γ-producing T_H1 cells in the pathogenesis of SjD.

CONTEXT.—/UNASSIGNED:Adenocarcinomas are the most common histologic subtype of lung cancer, and exist within a widely divergent clinical, radiologic, molecular, and histologic spectrum. There is a strong association between histologic patterns and prognosis that served as the basis for a recently described grading system. As the study of molecular pathology rapidly evolves, all targetable mutations so far have been found in adenocarcinomas, thus requiring accurate diagnosis and classification for triage of molecular alterations and adequate therapy. OBJECTIVE.—/UNASSIGNED:To discuss the rationale for adenocarcinoma classifications within the 2021 5th edition of the World Health Organization, with a focus on nonmucinous tumors, including tumor grading and biopsy/cytology diagnosis. DATA SOURCES.—/UNASSIGNED:PubMed search. CONCLUSIONS.—/UNASSIGNED:A grading system for adenocarcinoma has improved prognostic impact of the classification of pulmonary adenocarcinoma. An accurate diagnosis of adenocarcinoma in small biopsy material is important for tissue triage for molecular studies and ultimately for patient management and treatment.

Pathogen encounter can result in epigenetic remodeling that shapes disease caused by heterologous pathogens. Here, we examined innate immune memory in the context of commonly circulating respiratory viruses. Single-cell analyses of airway-resident immune cells in a disease-relevant murine model of SARS-CoV-2 recovery revealed epigenetic reprogramming in alveolar macrophages following infection. Post-COVID-19 human monocytes exhibited similar epigenetic signatures. In airway-resident macrophages, past SARS-CoV-2 infection increased activity of type I interferon (IFN-I)-related transcription factors and epigenetic poising of antiviral genes. Viral pattern recognition and canonical IFN-I signaling were required for the establishment of this innate immune memory and augmented secondary antiviral responses. Antiviral innate immune memory mounted by airway-resident macrophages post-SARS-CoV-2 was necessary and sufficient to ameliorate secondary disease caused by influenza A virus and curtailed hyperinflammatory dysregulation and mortality. Our findings provide insights into antiviral innate immune memory in the airway that may facilitate the development of broadly effective therapeutic strategies.