Recent developments in immuno-oncology demonstrate that not only cancer cells, but also the tumor microenvironment can guide precision medicine. A comprehensive and in-depth characterization of the tumor microenvironment is challenging since its cell populations are diverse and can be important even if scarce. To identify clinically relevant microenvironmental and cancer features, we applied single-cell RNA sequencing to ten human lung adenocarcinomas and ten normal control tissues. Our analyses revealed heterogeneous carcinoma cell transcriptomes reflecting histological grade and oncogenic pathway activities, and two distinct microenvironmental patterns. The immune-activated CP²E microenvironment was composed of cancer-associated myofibroblasts, proinflammatory monocyte-derived macrophages, plasmacytoid dendritic cells and exhausted CD8+ T cells, and was prognostically unfavorable. In contrast, the inert N³MC microenvironment was characterized by normal-like myofibroblasts, non-inflammatory monocyte-derived macrophages, NK cells, myeloid dendritic cells and conventional T cells, and was associated with a favorable prognosis. Microenvironmental marker genes and signatures identified in single-cell profiles had progonostic value in bulk tumor profiles. In summary, single-cell RNA profiling of lung adenocarcinoma provides additional prognostic information based on the microenvironment, and may help to predict therapy response and to reveal possible target cell populations for future therapeutic approaches.

Recent developments in immuno-oncology demonstrate that not only cancer cells, but also features of the tumor microenvironment guide precision medicine. Still, the relationship between tumor and microenvironment remains poorly understood. To overcome this limitation and identify clinically relevant microenvironmental and cancer features, we applied single-cell RNA sequencing to lung adenocarcinomas. While the highly heterogeneous carcinoma cell transcriptomes reflected histological grade and activity of relevant oncogenic pathways, our analysis revealed two distinct microenvironmental patterns. We identified a prognostically unfavorable group of tumors with a microenvironment composed of cancer-associated myofibroblasts, exhausted CD8+ T cells, proinflammatory monocyte-derived macrophages and plasmacytoid dendritic cells (CEP2 pattern) and a prognostically favorable group characterized by myeloid dendritic cells, anti-inflammatory monocyte-derived macrophages, normal-like myofibroblasts, NK cells and conventional T cells (MAN2C pattern). Our results show that single-cell gene expression profiling allows to identify patient subgroups based on the tumor microenvironment beyond cancer cell-centric profiling.

OBJECTIVES This study aimed to compare the outcomes of patients with ocular myasthenia gravis (OMG) who underwent thymectomy before generalization with the outcomes of those who underwent thymectomy after generalization. METHODS We retrospectively reviewed patients who underwent robotic thymectomy for myasthenia gravis between January 2003 and February 2018. Patients who presented with purely ocular symptoms at myasthenia gravis onset were eligible for inclusion. Exclusion criteria were patients who were lost to follow-up and patients who underwent re-thymectomy. Patients with OMG who developed generalization before thymectomy were categorized into gOMG group and those who did not were categorized into OMG group. The primary outcome was complete stable remission according to the Myasthenia Gravis Foundation of America Post-Intervention Status (MGFA-PIS). RESULTS One hundred and sixty-five (66 males and 99 females) out of 596 patients with myasthenia gravis were eligible for inclusion. Of these, there were 73 and 92 patients undergoing thymectomy before and after the generalization of OMG, respectively. After propensity score matching, a data set of 130 patients (65 per group) was formed and evaluating results showed no statistical differences between the 2 groups. The estimated cumulative probabilities of complete stable remission at 5 years were 49.5% [95% confidence interval (CI) 0.345–0.611] in the OMG group and 33.4% (95% CI 0.176–0.462) in the gOMG group (P = 0.0053). Similar results were also found in patients with non-thymomatous subgroup [55 patients per group, OMG vs gOMG, 53.5% (95% CI 0.370–0.656) vs 28.9% (95% CI 0.131–0.419), P = 0.0041]. CONCLUSIONS Thymectomy in OMG before generalization might result in a higher rate of complete stable remission than thymectomy after generalization.