Faculty Bibliography

Subsolid nodules (SSNs) are increasingly encountered in chest computed tomography (CT) imaging and clinical practice, as awareness of their significance and CT utilization grows. Either part-solid or solely ground-glass in attenuation, SSNs are shown to correlate with lung adenocarcinomas and their precursors, although a differential diagnosis is to be considered that includes additional neoplastic and inflammatory etiologies. This review discusses the differential diagnosis for SSNs, imaging and clinical features, and pathology that are helpful when making management decisions that may include PET/CT, biopsy, or surgery. Potential pitfalls in nodule characterization and management will be highlighted, to aid in managing SSNs appropriately.

BACKGROUND:The diagnosis of cardiac sarcoidosis (CS) is often challenging, particularly in atypical cases. CASE SUMMARY/METHODS:This case involves a previously healthy 33-year-old woman who was found to have a biatrial mass and evidence of a diffuse inflammatory or neoplastic process on multimodality imaging. Percutaneous biopsy of the cardiac mass was performed, and histopathologic examination revealed granulomas consistent with CS. DISCUSSION/CONCLUSIONS:This case adds to the growing number of reports of CS manifesting as an intracardiac mass. TAKE-HOME MESSAGES/CONCLUSIONS:The clinical presentation of CS is highly variable, and it may rarely manifest as an intracardiac mass. The diagnosis of cardiac sarcoidosis is often challenging, particularly in patients with atypical presentations. Indeterminate cardiac masses often require direct tissue sampling because the changes in treatment and prognosis are substantial.

There is a body-wide network of interstitial spaces that includes three components: a large-scale fascial network made up of fluid-filled spaces containing collagens and other extracellular matrix components like hyaluronic acid (HA), the peri-vascular/capillary interstitium, and intercellular interstitial spaces. Staining for HA within the colon, skin, and liver has demonstrated spatial continuity of the fascial interstitium across tissue layers and between organs, while continuity of HA staining between perineurial and adventitial sheathes beyond organ boundaries confirmed that they also participate in this body-wide network. We asked whether the pulmonary interstitium comprises a continuous organ-wide network that also connects to the body-wide interstitium via routes along nerves and the vasculature. We studied archival lung lobectomy specimens containing normal tissues inclusive of all lung anatomical units from six females and three males (mean age 53+/- 16.5 years). For comparison, we also studied normal mouse lung. Multiplex immunohistochemical cocktails were used to identify: (1) HA, CD34, and vimentin - highlighting interstitium; (2) HA, CD34, and podoplanin (D2-40) - highlighting relationships between the interstitium, vasculature, and lymphatics. Sizes of extracellular APP were measured. Tissues from nine patients (six females, three males, mean age 53+/- 16.5 years) were studied. HA staining was continuous throughout the five major anatomic compartments of the lung: alveolar walls, subpleural connective tissue, centrilobular peribronchovascular compartment, interlobular septal compartment, and axial peribronchovascular of the hilum, with similar findings in murine lung tissue. Continuity with interstitial spaces of the perineurium and adventitia was confirmed. The distribution of APP corresponded to known routes of lymphatic drainage, superficial and deep. APP within perineurium and perivascular adventitia further demonstrated continuity between intra- and extrapulmonary interstitium. To conclude, all segments of the lung interstitium are connected and are linked along nerves and the vascular tree to a body-wide communication network. These findings have significant implications for understanding lung physiology and pathobiology, suggesting routes of passage for inflammatory cells and mediators, malignant cells, and infectious agents. Interstitial spaces may be important in microbiome signaling within and beyond the lung and may be a component of the lung-brain axis.

How tumor microenvironment shapes lung adenocarcinoma (LUAD) precancer evolution remains poorly understood. Spatial immune profiling of 114 human LUAD and LUAD precursors reveals a progressive increase of adaptive response and a relative decrease of innate immune response as LUAD precursors progress. The immune evasion features align the immune response patterns at various stages. TIM-3-high features are enriched in LUAD precancers, which decrease in later stages. Furthermore, single-cell RNA sequencing (scRNA-seq) and spatial immune and transcriptomics profiling of LUAD and LUAD precursor specimens from 5 mouse models validate high TIM-3 features in LUAD precancers. In vivo TIM-3 blockade at precancer stage, but not at advanced cancer stage, decreases tumor burden. Anti-TIM-3 treatment is associated with enhanced antigen presentation, T cell activation, and increased M1/M2 macrophage ratio. These results highlight the coordination of innate and adaptive immune response/evasion during LUAD precancer evolution and suggest TIM-3 as a potential target for LUAD precancer interception.

The suspicious for malignancy category is used by pathologists to indicate a certain degree of uncertainty but is still able to convey to the treating physician a risk stratification of the deferred diagnosis. The category of suspicious for malignancy can be used in a vast possibility of cytomorphological features and clinical scenarios. Suspicious for malignancy is often used when there is an insufficient number of neoplastic cells for the establishment of a final diagnosis, but in many situations, the number of suspicious cells may be abundant but discrepant with the clinical presentation. In addition, the pathologist must be aware of the many mimickers of malignancy that, when present, may prompt the use of the category. Therefore, there is a need for better illustrations of the use of the category, its pitfalls and suggestions on when the category of suspicious for malignancy can be upgraded for a more definite diagnosis using ancillary studies, even in scant material.

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.

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.