Faculty Bibliography

INTRODUCTION/BACKGROUND:Pathologists are staging thymic epithelial tumors (TET) according to the 8th UICC/AJCC TNM system. Within the French RYTHMIC network, dedicated to TET, agreement on pathologic tumor stage (pT) among the pathology panelists was difficult. The aim of our study was to determine the interobserver reproducibility of pT at an international level, to explore the source of discrepancies and potential interventions to address these. METHODS:An international panel of pathologists was recruited through the International Thymic Malignancy Interest Group (ITMIG). The study focused on invasion of mediastinal pleura, pericardium, and lung. From a cohort of cases identified as challenging within the RYTHMIC network, we chose a series of test and validation cases (n = 5 and 10, respectively). RESULTS:Reproducibility of the pT stage was also challenging at an international level as none of the 15 cases was classified as the same pT stage by all ITMIG pathologists. The agreement rose from slight (κ = 0.13) to moderate (κ = 0.48) between test and validation series. Discussion among the expert pathologists pinpointed two major reasons underlying discrepancies: 1) Thymomas growing with their "capsule" and adhering to the pleurae, pericardium, or lung were often misinterpreted as invading these structures. 2) Recognition of the mediastinal pleura was identified as challenging. CONCLUSION/CONCLUSIONS:Our study underlines that the evaluation of the pT stage of TET is problematic and needs to be addressed in more detail in an upcoming TNM classification. The publication of histopathologic images of landmarks, including ancillary tests could improve reproducibility for future TNM classifications.

Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.

Lung cancer treatment has benefited greatly through advancements in immunotherapies. However, immunotherapy often fails in patients with specific mutations like KEAP1, which are frequently found in lung adenocarcinoma. We established an antigenic lung cancer model and used it to explore how Keap1 mutations remodel the tumor immune microenvironment. Using single-cell technology and depletion studies, we demonstrate that Keap1-mutant tumors diminish dendritic cell and T cell responses driving immunotherapy resistance. This observation was corroborated in patient samples. CRISPR-Cas9-mediated gene targeting revealed that hyperactivation of the NRF2 antioxidant pathway is responsible for diminished immune responses in Keap1-mutant tumors. Importantly, we demonstrate that combining glutaminase inhibition with immune checkpoint blockade can reverse immunosuppression, making Keap1-mutant tumors susceptible to immunotherapy. Our study provides new insight into the role of KEAP1 mutations in immune evasion, paving the way for novel immune-based therapeutic strategies for KEAP1-mutant cancers.

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

OBJECTIVE:Early-stage lung adenocarcinoma is treated with local therapy alone, although patients with grade 3 stage I lung adenocarcinoma have a 50% 5-year recurrence rate. Our objective is to determine if analysis of the tumor microenvironment can create a predictive model for recurrence. METHODS:Thirty-four patients with grade 3 stage I lung adenocarcinoma underwent surgical resection. Digital spatial profiling was used to perform genomic (n = 31) and proteomic (n = 34) analyses of pancytokeratin positive and negative tumor cells. K-means clustering was performed on the top 50 differential genes and top 20 differential proteins, with Kaplan-Meier recurrence curves based on patient clustering. External validation of high-expression genes was performed with Kaplan-Meier plotter. RESULTS:There were no significant clinicopathologic differences between patients who did (n = 14) and did not (n = 20) have recurrence. Median time to recurrence was 806 days; median follow-up with no recurrence was 2897 days. K-means clustering of pancytokeratin positive genes resulted in a model with a Kaplan-Meier curve with concordance index of 0.75. K-means clustering for pancytokeratin negative genes was less successful at differentiating recurrence (concordance index 0.6). Genes upregulated or downregulated for recurrence were externally validated using available public databases. Proteomic data did not reach statistical significance but did internally validate the genomic data described. CONCLUSIONS:Genomic difference in lung adenocarcinoma may be able to predict risk of recurrence. After further validation, stratifying patients by this risk may help guide who will benefit from adjuvant therapy.

With the development of genomic technologies, the isolation of genomic DNA (gDNA) from clinical samples is increasingly required for clinical diagnostics and research studies. In this study, we explored the potential of utilizing various leftover blood samples obtained from routine clinical tests as a viable source of gDNA. Using an automated method with optimized pre-treatments, we obtained gDNA from seven types of clinical leftover blood, with average yields of gDNA ranging from 3.11 ± 0.45 to 22.45 ± 4.83 μg. Additionally, we investigated the impact of storage conditions on gDNA recovery, resulting in yields of 8.62-68.08 μg when extracting gDNA from EDTA leftover blood samples stored at 4 °C for up to 13 weeks or -80 °C for up to 78 weeks. Furthermore, we successfully obtained sequenceable gDNA from both Serum Separator Tube and EDTA Tube using a 96-well format extraction, with yields ranging from 0.61 to 71.29 μg and 3.94-215.98 μg, respectively. Our findings demonstrate the feasibility of using automated high-throughput platforms for gDNA extraction from various clinical leftover blood samples with the proper pre-treatments.

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.

Aspirates of mediastinal neoplasms pose a unique diagnostic challenge due to the overlapping histologic characteristics of mediastinal lesions and the morphologic similarities between mediastinal neoplasms and those originating at other sites. Presented here is the first reported description of the cytomorphologic features of adenocarcinoma NOS of the thymus in aspirate and pleural effusion specimens. The morphologic similarities between thymic and metastatic adenocarcinomas and variable immunohistochemical staining patterns of thymic epithelial neoplasms underscore the importance of pathology-radiology correlation and the careful consideration of the clinical context in the interpretation of cytology specimens.