Faculty Bibliography

Aberrant glycosylation is a hallmark of cancer biology, and altered glycosylation influences multiple facets of melanoma progression. To identify glycosyltransferases, glycans, and glycoproteins essential for melanoma maintenance, we conducted an in vivo growth screen with a pooled short hairpin RNA library of glycosyltransferases, lectin microarray profiling of benign nevus and melanoma samples, and mass spectrometry-based glycoproteomics. We found that α-2,3-sialyltransferases ST3GAL1 and ST3GAL2 and corresponding α-2,3-linked sialosides are up-regulated in melanoma compared to nevi and are essential for melanoma growth. Glycoproteomics revealed that glycoprotein targets of ST3GAL1 and ST3GAL2 are enriched in transmembrane proteins involved in growth signaling, including the amino acid transporter SLC3A2/CD98hc. CD98hc suppression mimicked the effect of ST3GAL1 and ST3GAL2 silencing, inhibiting melanoma cell proliferation. We found that both CD98hc protein stability and its prosurvival effect on melanoma are dependent upon α-2,3-sialylation mediated by ST3GAL1 and ST3GAL2. Our studies reveal α-2,3-sialosides functionally contributing to melanoma maintenance, supporting ST3GAL1 and ST3GAL2 as therapeutic targets in melanoma.

IMPORTANCE/UNASSIGNED:Tumor-infiltrating lymphocytes (TILs) are a provocative biomarker in melanoma, influencing diagnosis, prognosis, and immunotherapy outcomes; however, traditional pathologist-read TIL assessment on hematoxylin and eosin-stained slides is prone to interobserver variability, leading to inconsistent clinical decisions. Therefore, development of newer TIL scoring approaches that produce more reliable and consistent readouts is important. OBJECTIVE/UNASSIGNED:To evaluate the analytical and clinical validity of a machine learning algorithm for TIL quantification in melanoma compared with traditional pathologist-read methods. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This multioperator, global, multi-institutional prognostic study compared TIL scoring reproducibility between traditional pathologist-read methods and an artificial intelligence (AI)-driven approach. The study was conducted using retrospective cohorts of patients with melanoma between January 2022 and June 2023 across 45 institutions, with tissue evaluated by participants from academic, clinical, and research institutions. Participants were selected to ensure diverse expertise and professional backgrounds. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Intraclass correlation coefficient (ICC) values were calculated for the manual and AI-assisted arms using log-transformed data. Kendall W values were calculated for Clark scores (brisk = 3, nonbrisk = 2, and sparse = 1). Reliabilities of ICC and W values were classified as moderate (0.40-0.60), good (0.61-0.80), or excellent (>0.80). AI TIL measurements were dichotomized using the 16.6 and median cutoffs. Univariable and multivariable Cox regression analyses assessed the prognostic value of TIL scores adjusted for clinicopathologic variables. RESULTS/UNASSIGNED:There were 111 patients with melanoma in the independent testing cohort (median [range] age at diagnosis, 61.0 [25.0-87.0] years; 56 [50.5%] male) who contributed melanoma whole tissue sections. A total of 98 participants evaluated TILs on 60 hematoxylin and eosin-stained melanoma tissue sections. All 40 participants in the manual arm were pathologists, while the AI-assisted arm included 11 pathologists and 47 nonpathologists (scientists). The AI algorithm demonstrated superior reproducibility, with ICCs higher than 0.90 for all machine learning TIL variables, significantly outperforming manual assessments (ICC, 0.61 for AI-derived stromal TILs vs Kendall W, 0.44 for manual Clark TIL scoring). AI-based TIL scores showed prognostic associations with patient outcomes (n = 111) using the median cutoff approach with a hazard ratio (HR) of 0.45 (95% CI, 0.26-0.80; P = .005), and using the cutoff of 16.6, with an HR of 0.56 (95% CI, 0.32-0.98; P = .04). CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this prognostic study of TIL quantification in melanoma, the AI algorithm demonstrated superior reproducibility and prognostic associations compared with traditional methods. Although the retrospective nature of the cohorts limits demonstration of clinical utility, the publicly available dataset and open-source AI tool offer a foundation for future validation and integration into melanoma management.

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.

Bodies have continuous reticular networks, comprising collagens and other extracellular matrix components, through all tissues and organs. We recently validated fluid flow through human interstitium and demonstrated that they are filled with hyaluronic acid by staining with biotinylated hyaluronic acid binding protein. Their continuity across tissue boundaries (skin and subcutis), and between organs (colon and mesentery) and along vessels (within adventitia) and nerves (within perineurium) has been demonstrated in this manner. We aim to evaluate the continuity of interstitium within human pancreas and beyond into adjoining tissues. Tissue blocks of histologically normal pancreas from nine pancreatectomy specimens were sectioned in parallel for staining with hematoxylin and eosin, Picrosirius red, and biotinylated hyaluronic acid binding protein. Also, specimens of invasive pancreatic cancer were assessed for interstitial tumor invasion. Picrosirius red ensheathes all microscopic units of the endocrine and exocrine pancreas, including acini, islets, and ducts, adventitia of blood vessels and perineurium, and into adjacent duodenum. Interstitial spaces within the fibrous tissue are filled with hyaluronic acid by staining and are also continuous through all microscopic structures of the pancreas, into adjoining duodenum and along vessels (within adventitia) and nerves (within perineurium). Invasive carcinoma is seen spreading through pre-existing interstitial spaces. Interstitium of the human pancreas is continuous within and beyond the pancreas. This continuity suggests the capacity to be a route of molecular, microbiome, and cellular trafficking and communication. In particular, it is a route of cancer spread.

Targeted therapies and immunotherapy have improved treatment outcomes for many melanoma patients. However, patients whose melanomas harbor driver mutations in the neurofibromin 1 (NF1) tumor suppressor gene often lack effective targeted treatment options when their tumors do not respond to immunotherapy. In this study, we utilized patient-derived short-term cultures (STCs) and multiomics approaches to identify molecular features that could inform the development of therapies for patients with NF1 mutant melanoma. Differential gene expression analysis revealed that the epidermal growth factor receptor (EGFR) is highly expressed and active in NF1 mutant melanoma cells, where it hyper-activates ERK and AKT, leading to increased tumor cell proliferation, survival, and growth. In contrast, genetic or pharmacological inhibition of EGFR hindered cell proliferation and survival and suppressed tumor growth in patient-derived NF1 mutant melanoma models but not in NF1 wild-type models. These results reveal a connection between NF1 loss and increased EGFR expression that is critical for the survival and growth of NF1 mutant melanoma cells in patient-derived culture and xenograft models, irrespective of their BRAF and NRAS mutation status.

Response to immune checkpoint inhibitors (ICIs) in metastatic melanoma (MM) varies among patients, and current baseline biomarkers predicting treatment outcomes are limited. As mitochondrial (MT) metabolism has emerged as an important regulator of host immune function, we explored the association of host MT genetics (MT haplogroups) with ICI efficacy in 1,225 ICI-treated patients with MM from the clinical trial CheckMate-067 and the International Germline Immuno-Oncology Melanoma Consortium. We discovered and validated significant associations of MT haplogroup T (HG-T) with resistance to anti-programmed cell death protein-1-based ICI (both single-agent and combination) and have shown that HG-T is independent from established tumor predictors. We also found that patients belonging to HG-T exhibit a unique nivolumab-resistant baseline peripheral CD8⁺ T cell repertoire compared to other MT haplogroups, providing, to our knowledge, the first link between MT inheritance, host immunity and ICI resistance. The study proposes a host blood-based biomarker with stand-alone clinical value predicting ICI efficacy and points to an ICI-resistance mechanism associated with MT metabolism, with clinical relevance in immuno-oncology.

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.