Faculty Bibliography

PURPOSE/OBJECTIVE:Lung adenocarcinoma (LUAD) is a clinically heterogenous disease, which is highlighted by the unpredictable recurrence in low-stage tumors and highly variable responses observed in patients treated with immunotherapies, which cannot be explained by mutational profiles. DNA methylation-based classification and understanding of microenviromental heterogeneity may allow stratification into clinically relevant molecular subtypes of LUADs. EXPERIMENTAL DESIGN/METHODS:We characterize the genome-wide DNA methylation landscape of 88 resected LUAD tumors. Exome sequencing focusing on a panel of cancer-related genes was used to genotype these adenocarcinoma samples. Bioinformatic and statistical tools, the immune cell composition, DNA methylation age (DNAm age), and DNA methylation clustering were used to identify clinically relevant subgroups. RESULTS:Deconvolution of DNA methylation data identified immunologically hot and cold subsets of lung adenocarcinomas. Additionally, concurrent factors were analyzed that could affect the immune microenvironment, such as smoking history, ethnicity, or presence of KRAS or TP53 mutations. When the DNAm age was calculated, a lower DNAm age was correlated with the presence of a set of oncogenic drivers, poor overall survival, and specific immune cell populations. Unsupervised DNA methylation clustering identified 6 molecular subgroups of LUAD tumors with distinct clinical and microenvironmental characteristics. CONCLUSIONS:Our results demonstrate that DNA methylation signatures can stratify lung adenocarcinoma into clinically relevant subtypes, and thus such classification of LUAD at the time of resection may lead to better methods in predicting tumor recurrence and therapy responses.

Intra-tumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5,000 regions, single-cell copy number sequencing of ~2,000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of GD.

OBJECTIVES/OBJECTIVE:Patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may develop end-stage lung disease requiring lung transplantation. We report the clinical course, pulmonary pathology with radiographic correlation, and outcomes after lung transplantation in three patients who developed chronic respiratory failure due to postacute sequelae of SARS-CoV-2 infection. METHODS:A retrospective histologic evaluation of explanted lungs due to coronavirus disease 2019 was performed. RESULTS:None of the patients had known prior pulmonary disease. The major pathologic findings in the lung explants were proliferative and fibrotic phases of diffuse alveolar damage, interstitial capillary neoangiogenesis, and mononuclear inflammation, specifically macrophages, with varying numbers of T and B lymphocytes. The fibrosis varied from early collagen deposition to more pronounced interstitial collagen deposition; however, pulmonary remodeling with honeycomb change was not present. Other findings included peribronchiolar metaplasia, microvascular thrombosis, recanalized thrombi in muscular arteries, and pleural adhesions. No patients had either recurrence of SARS-CoV-2 infection or allograft rejection following transplant at this time. CONCLUSIONS:The major pathologic findings in the lung explants of patients with SARS-CoV-2 infection suggest ongoing fibrosis, prominent macrophage infiltration, neoangiogenesis, and microvascular thrombosis. Characterization of pathologic findings could help develop novel management strategies.

INTRODUCTION/BACKGROUND:Accurate subtyping of non-small cell lung carcinomas (NSCLC) into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is the cornerstone of NSCLC diagnosis. Cytology samples show higher rates of classification failures, i.e., subtyping as non-small cell carcinoma, not otherwise specified (NSCC-NOS), as compared to histology. This study aims to identify specific algorithms based on known cytomorphological features that aid accurate and successful subtyping of NSCLC on cytology. METHODS:Thirteen expert cytopathologists participated anonymously in an online survey to subtype 119 NSCLC cytology cases (gold standard diagnoses being LUAD in 80 and LUSC in 39) enriched for non-keratinising LUSC. They selected from 23 pre-defined cytomorphological features that they used in subtyping. Data were analysed using machine learning algorithms based on Random Forest Method and regression trees. RESULTS:From 1474 responses recorded, concordant cytology typing was achieved in 53.7% (792/1474) responses. NSCC-NOS rates on cytology were similar among gold standard LUAD (36%) and LUSC (38%) cases. Misclassification rates were higher in gold standard LUSC (17.6%) than gold standard LUAD (5.5%; P<0.0001). Keratinisation, when present, recognised LUSC with high accuracy. In its absence, the ML algorithms developed based on experts' choices were unable to reduce cytology NSCC-NOS rates without increasing misclassification rates. CONCLUSION/CONCLUSIONS:Suboptimal recognition of LUSC in the absence of keratinisation remains the major hurdle in improving cytology subtyping accuracy with such cases either failing classification (NSCC-NOS) or misclassifying as LUAD. NSCC-NOS appears to be an inevitable morphological diagnosis emphasizing that ancillary IC is necessary to achieve accurate subtyping on cytology.

INTRODUCTION/BACKGROUND:Neurological complications among hospitalized COVID-19 patients may be associated with elevated neurodegenerative biomarkers. METHODS:Among hospitalized COVID-19 patients without a history of dementia (N = 251), we compared serum total tau (t-tau), phosphorylated tau-181 (p-tau181), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), ubiquitin carboxy-terminal hydrolase L1 (UCHL1), and amyloid beta (Aβ40,42) between patients with or without encephalopathy, in-hospital death versus survival, and discharge home versus other dispositions. COVID-19 patient biomarker levels were also compared to non-COVID cognitively normal, mild cognitive impairment (MCI), and Alzheimer's disease (AD) dementia controls (N = 161). RESULTS:Admission t-tau, p-tau181, GFAP, and NfL were significantly elevated in patients with encephalopathy and in those who died in-hospital, while t-tau, GFAP, and NfL were significantly lower in those discharged home. These markers correlated with severity of COVID illness. NfL, GFAP, and UCHL1 were higher in COVID patients than in non-COVID controls with MCI or AD. DISCUSSION/CONCLUSIONS:Neurodegenerative biomarkers were elevated to levels observed in AD dementia and associated with encephalopathy and worse outcomes among hospitalized COVID-19 patients.

Thymic carcinomas are rare malignancies that in general arise in the prevascular (anterior) mediastinum. These tumors are usually invasive, often present at advanced stages, and typically behave aggressively. Studies are hampered by the paucity of these tumors, the large variety of carcinoma subtypes, and the lack of unique morphologic and immunophenotypic features. Despite these challenges, advances in diagnostic imaging, surgical approaches, systemic therapies, and radiation therapy techniques have been made. The World Health Organization classification of thymic epithelial tumors has been updated in 2021 and the 8^th tumor nodal metastasis staging by the American Joint Committee on Cancer /Union for International Cancer Control included thymic carcinomas in 2017. Molecular alterations that provide more insight into the pathogenesis of these tumors and that potentially permit use of novel targeted therapies are increasingly being identified. New approaches to radiation therapy, chemotherapy, and immunotherapy are under evaluation. International societies including the International Thymic Malignancy Interest Group, European Society of Thoracic Surgeons, and Japanese, Chinese, and Korean thymic associations have been critical in organizing and conducting multi-institutional clinical studies. Herein we review contemporary multidisciplinary perspectives in diagnosis and management of thymic carcinoma.

The efficacy of neoadjuvant treatment for NSCLC can be pathologically assessed in resected tissue. Major pathologic response (MPR) and pathologic complete response (pCR), defined as less than or equal to 10% and 0% viable tumor cells, respectively, are increasingly being used in NSCLC clinical trials to establish them as surrogate end points for efficacy to shorten time to outcome. Nevertheless, sampling and MPR calculation methods vary between studies. The International Association for the Study of Lung Cancer recently published detailed recommendations for pathologic assessment of NSCLC after neoadjuvant treatment, with methodology being critical. To increase methodological rigor further, we developed a novel MPR calculator tool (MPRCT) for standardized, comprehensive collection of percentages of viable tumor, necrosis, and stroma in the tumor bed. In addition, tumor width and length in the tumor bed are measured and unweighted and weighted MPR averages are calculated, the latter to account for the varying proportions of tumor beds on slides. We propose sampling the entire visible tumor bed for tumors having pCR regardless of size, 100% of tumors less than or equal to 3 cm in diameter, and at least 50% of tumors more than 3 cm. We describe the uses of this tool, including potential formal analyses of MPRCT data to determine the optimum sampling strategy that balances sensitivity against excessive use of resources. Solutions to challenging scenarios in pathologic assessment are proposed. This MPRCT will facilitate standardized, systematic, comprehensive collection of pathologic response data with a standardized methodology to validate studies designed to establish MPR and pCR as surrogate end points of neoadjuvant treatment efficacy.