Faculty Bibliography

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

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.

TOPIC IMPORTANCE/UNASSIGNED:Chest CT imaging holds a major role in the diagnosis of lung diseases, many of which affect the peribronchovascular region. Identification and categorization of peribronchovascular abnormalities on CT imaging can assist in formulating a differential diagnosis and directing further diagnostic evaluation. REVIEW FINDINGS/RESULTS:The peribronchovascular region of the lung encompasses the pulmonary arteries, airways, and lung interstitium. Understanding disease processes associated with structures of the peribronchovascular region and their appearances on CT imaging aids in prompt diagnosis. This article reviews current knowledge in anatomic and pathologic features of the lung interstitium composed of intercommunicating prelymphatic spaces, lymphatics, collagen bundles, lymph nodes, and bronchial arteries; diffuse lung diseases that present in a peribronchovascular distribution; and an approach to classifying diseases according to patterns of imaging presentations. Lung peribronchovascular diseases can appear on CT imaging as diffuse thickening, fibrosis, masses or masslike consolidation, ground-glass or air space consolidation, and cysts, acknowledging that some diseases may have multiple presentations. SUMMARY/CONCLUSIONS:A category approach to peribronchovascular diseases on CT imaging can be integrated with clinical features as part of a multidisciplinary approach for disease diagnosis.

KRAS^G12C inhibitors (adagrasib and sotorasib) have shown clinical promise in targeting KRAS^G12C-mutated lung cancers; however, most patients eventually develop resistance. In lung patients with adenocarcinoma with KRAS^G12C and STK11/LKB1 co-mutations, we find an enrichment of the squamous cell carcinoma gene signature in pre-treatment biopsies correlates with a poor response to adagrasib. Studies of Lkb1-deficient KRAS^G12C and Kras^G12D lung cancer mouse models and organoids treated with KRAS inhibitors reveal tumors invoke a lineage plasticity program, adeno-to-squamous transition (AST), that enables resistance to KRAS inhibition. Transcriptomic and epigenomic analyses reveal ΔNp63 drives AST and modulates response to KRAS inhibition. We identify an intermediate high-plastic cell state marked by expression of an AST plasticity signature and Krt6a. Notably, expression of the AST plasticity signature and KRT6A at baseline correlates with poor adagrasib responses. These data indicate the role of AST in KRAS inhibitor resistance and provide predictive biomarkers for KRAS-targeted therapies in lung cancer.

Histologic grading of tumors is associated with prognosis in many organs. In the lung, the most recent grading system proposed by International association for the Study of Lung Cancer (IASLC) and adopted by the World Health Organization (WHO) incorporates the predominant histologic pattern, as well as the presence of high-grade architectural patterns (solid, micropapillary, and complex glandular pattern) in proportions >20% of the tumor surface. This system has shown improved prognostic ability when compared with the prior grading system based on the predominant pattern alone, across different patient populations. Interobserver agreement is moderate to excellent, depending on the study. IASLC/WHO grading system has been shown to correlate with molecular alterations and PD-L1 expression in tumor cells. Recent studies interrogating gene expression has shown correlation with tumor grade and molecular alterations in the tumor microenvironment that can further stratify risk of recurrence. The use of machine learning algorithms to grade nonmucinous adenocarcinoma under this system has shown accuracy comparable to that of expert pulmonary pathologists. Future directions include evaluation of tumor grade in the context of adjuvant and neoadjuvant therapies, as well as the development of better prognostic indicators for mucinous adenocarcinoma.

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.

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study.

Lung adenocarcinoma staging and grading were recently updated to reflect the link between histologic growth patterns and outcomes. The lepidic growth pattern is regarded as "in-situ," whereas all other patterns are regarded as invasive, though with stratification. Solid, micropapillary, and complex glandular patterns are associated with worse prognosis than papillary and acinar patterns. These recent changes have improved prognostic stratification. However, multiple pitfalls exist in measuring invasive size and in classifying lung adenocarcinoma growth patterns. Awareness of these limitations and recommended practices will help the pathology community achieve consistent prognostic performance and potentially contribute to improved patient management.