Oncogene-driven non-small cell lung cancers in patients with a history of smoking lack smoking-induced mutations.

Non-small cell lung cancers (NSCLCs) in non-smokers are mostly driven by mutations in the oncogenes EGFR, ERBB2, and MET and fusions involving ALK and RET. In addition to occurring in non-smokers, alterations in these "non-smoking-related oncogenes" (NSROs) also occur in smokers. To better understand the clonal architecture and genomic landscape of NSRO-driven tumors in smokers compared to typical-smoking NSCLCs, we investigated genomic and transcriptomic alterations in 173 tumor sectors from 48 NSCLC patients. NSRO-driven NSCLCs in smokers and non-smokers had similar genomic landscapes. Surprisingly, even in patients with prominent smoking histories, the mutational signature caused by tobacco smoking was essentially absent in NSRO-driven NSCLCs, which was confirmed in two large NSCLC datasets from other geographic regions. However, NSRO-driven NSCLCs in smokers had higher transcriptomic activities related to regulation of the cell cycle. These findings suggest that, while the genomic landscape is similar between NSRO-driven NSCLC in smokers and non-smokers, smoking still affects the tumor phenotype independently of genomic alterations.

Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning.

The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobserver variability, which can compromise the optimal assessment of patient prognosis. Therefore, this study developed convolutional neural networks capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathologic images were obtained from 17 expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained to predict eight different LADC classes. Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1 scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models, with both Kaplan-Meier curves showing significance (P = 0.0017 and 0.0003). Moreover, both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained convolutional neural networks improve the diagnostic accuracy of the pathologist and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.

Overcoming the Interobserver Variability in Lung Adenocarcinoma Subtyping: A Clustering Approach to Establish a Ground Truth for Downstream Applications.

CONTEXT.­: The accurate identification of different lung adenocarcinoma histologic subtypes is important for determining prognosis but can be challenging because of overlaps in the diagnostic features, leading to considerable interobserver variability. OBJECTIVE.­: To provide an overview of the diagnostic agreement for lung adenocarcinoma subtypes among pathologists and to create a ground truth using the clustering approach for downstream computational applications. DESIGN.­: Three sets of lung adenocarcinoma histologic images with different evaluation levels (small patches, areas with relatively uniform histology, and whole slide images) were reviewed by 17 international expert lung pathologists and 1 pathologist in training. Each image was classified into one or several lung adenocarcinoma subtypes. RESULTS.­: Among the 4702 patches of the first set, 1742 (37%) had an overall consensus among all pathologists. The overall Fleiss κ score for the agreement of all subtypes was 0.58. Using cluster analysis, pathologists were hierarchically grouped into 2 clusters, with κ scores of 0.588 and 0.563 in clusters 1 and 2, respectively. Similar results were obtained for the second and third sets, with fair-to-moderate agreements. Patches from the first 2 sets that obtained the consensus of the 18 pathologists were retrieved to form consensus patches and were regarded as the ground truth of lung adenocarcinoma subtypes. CONCLUSIONS.­: Our observations highlight discrepancies among experts when assessing lung adenocarcinoma subtypes. However, a subsequent number of consensus patches could be retrieved from each cluster, which can be used as ground truth for the downstream computational pathology applications, with minimal influence from interobserver variability.

Composite glandular and endocrine tumors of the stomach with pancreatic acinar differentiation.

Composite tumors of the stomach consisting of mixed glandular and endocrine components are rare. We report 3 cases of composite glandular and endocrine tumors with pancreatic acinar differentiation in the stomach with their clinicopathologic findings. The patients' presenting symptoms were variable and included abdominal pain, gastrointestinal hemorrhage, and weight loss. One patient with abdominal pain also had an elevated serum lipase level, clinically mimicking acute pancreatitis. The histology of these tumors was similar. They showed admixture of well-differentiated endocrine components with acinar and glandular components. The glandular component consisted of columnar epithelial cells resembling gastric foveolar or intestinal goblet cells, consistent with a well-differentiated adenocarcinoma. A panel of histochemical and immunohistochemical stains was performed, which included PAS, Alcian blue, Mib1, CEA, cytokeratin 7, cytokeratin 20, Muc2, Muc5AC, chromogranin, synaptophysin, trypsin, chymotrypsin, lipase, insulin, gastrin, serotonin, and pancreatic polypeptide. While the immunoreactivity for cytokeratin 7, cytokeratin 20, Muc2, Muc5AC, and CEA was largely restricted to the glandular component, the endocrine and pancreatic acinar markers showed marked variability and overlap. All cases showed immunoreactivity for at least one of the exocrine pancreatic enzymes, and all expressed endocrine differentiation. Some degree of amphicrine differentiation was suggested in all cases. Two cases showed metastases in perigastric lymph nodes, which histologically resembled the primary tumor. In summary, these tumors represent another distinct type of composite glandular and endocrine gastric neoplasm with pancreatic acinar differentiation.