The IASLC Mesothelioma Staging Project: Proposals for the 'N' descriptors in the forthcoming 9th edition of the TNM classification for Pleural Mesothelioma.

INTRODUCTION: The International Association for the Study of Lung Cancer developed an international database to inform potential revisions in the 9th edition of the Tumor, Node, Metastasis classification of diffuse pleural mesothelioma (PM). This study analyzed the clinical and pathological N categories to determine whether revisions were indicated relative to the 8th edition staging system. METHODS: Of 7,338 PM cases diagnosed 2013 to 2022, 3,598 met all inclusion criteria for planned analyses. Data on 2,836 patients without metastases were included in this study. Overall survival (OS) was measured from date of diagnosis. Patients were included regardless of whether they received neoadjuvant treatment. For the pathological N analysis, patients who underwent resection (extrapleural pneumonectomy or pleurectomy/decortication) were included. N subgroups were analyzed and overall survival (OS) assessed by the Kaplan Meier method. RESULTS: The existing 8th edition N categories performed adequately in the 9th edition dataset. A median OS advantage was noted for clinical and pathological N0 versus N1 patients: 23.2 versus 18.5 and 33.8 vs. 25.0 months, respectively. Patients with resected pN0 had a 3-year OS of 48%. No difference in OS was noted for single versus multiple station nodal metastases. The number of nodal stations sampled at the time of resection was not associated with a difference in OS. CONCLUSIONS: Data regarding clinical and pathological N categories corroborate those used in the 8th edition. No changes in the N categories are recommended in the 9th edition of PM staging system.

The IASLC Pleural Mesothelioma Staging Project: Proposal for Revision of the TNM Stage Groupings in the Forthcoming (Ninth) Edition of the TNM Classification for Pleural Mesothelioma.

INTRODUCTION: The eighth edition of the tumor, node and metastases (TNM) classification of pleural mesothelioma (PM) saw substantial changes in T and N components and stage groupings. The International Association for the Study of Lung Cancer collected data into a multinational database in order to further refine this classification. This 9th edition proposal incorporates changes proposed in the clinical (c)T component, but not the pathologic (p)T component, to include size criteria, and further refines TNM stage groupings for PM. METHODS: Data were submitted via electronic data capture (EDC) or batch transfer from institutional databases. Survival was measured from diagnosis date. Candidate stage groups were developed using a recursive partitioning and amalgamation (RPA) algorithm applied to all cM0 cases for clinical stage and subsequently for pathological stage. Cox models were developed to estimate survival for each stage group. RESULTS: Of 3598 submitted cases, 2192 were analyzable for overall cStage and 445 for overall pStage. RPA generated survival tree on OS outcomes restricted to cM0 with newly proposed (9th edition) cT and cN component-derived optimal stage groupings of: stage I (T1N0), II (T1N1; T2N0), IIIA (T1N2; T2N1/2; any T3), IIIB (any T4), and IV (any M1). Although cT and pT descriptors are different in the 9th edition, aligning pStage groupings with cStage produced better discrimination than retaining 8th edition pStage groupings. CONCLUSIONS: This revision of the clinical TNM classification for PM is the first to incorporate the measurement-based proposed changes in clinical T category. The pathological TNM aligns with clinical TNM.

The International Association for the Study of Lung Cancer Mesothelioma Staging Project: Proposals for Revisions of the "T" Descriptors in the Forthcoming Ninth Edition of the TNM Classification for Pleural Mesothelioma.

INTRODUCTION: The primary tumor (T) component in the eighth edition of pleural mesothelioma (PM) staging system is based on pleural involvement and extent of invasion. Quantitative assessment of pleural tumor has been found to be prognostic. We explored quantitative and qualitative metrics to develop recommendations for T descriptors in the upcoming ninth edition of the PM staging system. METHODS: The International Association for the Study of Lung Cancer prospectively collected data on patients with PM. Sum of maximum pleural thickness (Psum) was recorded. Optimal combinations of Psum and eighth edition cT descriptors were assessed using recursive binary splitting algorithm, with bootstrap resampling to correct for the adaptive nature of the splitting algorithm, and validated in the eighth edition data. Overall survival (OS) was calculated by the Kaplan-Meier method and differences in OS assessed by the log-rank test. RESULTS: Of 7338 patients submitted, 3598 were eligible for cT analysis and 1790 had Psum measurements. Recursive partitioning identified optimal cutpoints of Psum at 12 and 30 mm, which, in combination with extent of invasion, yielded four prognostic groups for OS. Fmax greater than 5 mm indicated poor prognosis. cT4 category (based on invasion) revealed similar performance to eighth edition. Three eighth edition descriptors were eliminated based on low predictive accuracy. Eighth edition pT descriptors remained valid in ninth edition analyses. CONCLUSION: Given reproducible prognostication by Psum, size criteria will be incorporated into cT1 to T3 categories in the ninth edition. Current cT4 category and all pT descriptors will be maintained, with reclassification of fissural invasion as pT2.

The International Association for the Study of Lung Cancer Pleural Mesothelioma Staging Project: Expanded Database to Inform Revisions in the Ninth Edition of the TNM Classification of Pleural Mesothelioma.

The International Association for the Study of Lung Cancer collaborated with the International Mesothelioma Interest Group to propose the first TNM stage classification system for diffuse pleural mesothelioma in 1995, accepted by the Union for International Cancer Control and the American Joint Committee on Cancer for the sixth and seventh edition stage classification manuals. The International Association for the Study of Lung Cancer Staging and Prognostic Factors Committee Mesothelioma Domain developed and analyzed an international registry of patients with pleural mesothelioma and updated TNM descriptors for the eighth edition of the stage classification system. To inform revisions for the forthcoming ninth edition of the TNM stage classification system, data submission was solicited for patients diagnosed between 2013 and 2022 with expanded data elements on the basis of the first project's exploratory analyses, including pleural thickness measurements, updated surgical nomenclature, and molecular markers. The resulting database consisted of a total of 3598 analyzable cases from Europe, Australia, Asia, North America, and South America, with a median age of 71 years (range: 18-99 y), 2775 (77.1%) of whom were men. With only 1310 patients (36.4%) undergoing curative-intent operations, this iteration of the database includes far more patients treated nonsurgically compared with prior. Four separate manuscripts on T, N, M, and stage groupings submitted to this journal will summarize analyses of these data and will serve collectively as the primary source of the proposed changes to the upcoming ninth edition of the pleural mesothelioma stage classification system.

Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier.

Background: Immune microenvironment plays a critical role in cancer from onset to relapse. Machine learning (ML) algorithm can facilitate the analysis of lab and clinical data to predict lung cancer recurrence. Prompt detection and intervention are crucial for long-term survival in lung cancer relapse. Our study aimed to evaluate the clinical and genomic prognosticators for lung cancer recurrence by comparing the predictive accuracy of four ML models. Methods: A total of 41 early-stage lung cancer patients who underwent surgery between June 2007 and October 2014 at New York University Langone Medical Center were included (with recurrence, n=16; without recurrence, n=25). All patients had tumor tissue and buffy coat collected at the time of resection. The CIBERSORT algorithm quantified tumor-infiltrating immune cells (TIICs). Protein-protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to unearth potential molecular drivers of tumor progression. The data was split into training (75%) and validation sets (25%). Ensemble linear kernel support vector machine (SVM) ML models were developed using optimized clinical and genomic features to predict tumor recurrence. Results: Activated natural killer (NK) cells, M0 macrophages, and M1 macrophages showed a positive correlation with progression. Conversely, T CD4+ memory resting cells were negatively correlated. In the PPI network, TNF and IL6 emerged as prominent hub genes. Prediction models integrating clinicopathological prognostic factors, tumor gene expression (45 genes), and buffy coat gene expression (47 genes) yielded varying receiver operating characteristic (ROC)-area under the curves (AUCs): 62.7%, 65.4%, and 59.7% in the training set, 58.3%, 83.3%, and 75.0% in the validation set, respectively. Notably, merging gene expression with clinical data in a linear SVM model led to a significant accuracy boost, with an AUC of 92.0% in training and 91.7% in validation. Conclusions: Using ML algorithm, immune gene expression data from tumor tissue and buffy coat may enhance the precision of lung cancer recurrence prediction.

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma.

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.

Digital spatial profiling to predict recurrence in grade 3 stage I lung adenocarcinoma.

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.

KEAP1 mutation in lung adenocarcinoma promotes immune evasion and immunotherapy resistance.

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.

HMGB1 released by mesothelial cells drives the development of asbestos-induced mesothelioma.

Asbestos is the main cause of malignant mesothelioma. Previous studies have linked asbestos-induced mesothelioma to the release of HMGB1 from the nucleus to the cytoplasm, and from the cytoplasm to the extracellular space. In the cytoplasm, HMGB1 induces autophagy impairing asbestos-induced cell death. Extracellularly, HMGB1 stimulates the secretion of TNFα. Jointly, these two cytokines kick-start a chronic inflammatory process that over time promotes mesothelioma development. Whether the main source of extracellular HMGB1 were the mesothelial cells, the inflammatory cells, or both was unsolved. This information is critical to identify the targets and design preventive/therapeutic strategies to interfere with asbestos-induced mesothelioma. To address this issue, we developed the conditional mesothelial HMGB1-knockout (Hmgb1ΔpMeso) and the conditional myelomonocytic-lineage HMGB1-knockout (Hmgb1ΔMylc) mouse models. We establish here that HMGB1 is mainly produced and released by the mesothelial cells during the early phases of inflammation following asbestos exposure. The release of HMGB1 from mesothelial cells leads to atypical mesothelial hyperplasia, and in some animals, this evolves over the years into mesothelioma. We found that Hmgb1ΔpMeso, whose mesothelial cells cannot produce HMGB1, show a greatly reduced inflammatory response to asbestos, and their mesothelial cells express and secrete significantly reduced levels of TNFα. Moreover, the tissue microenvironment in areas of asbestos deposits displays an increased fraction of M1-polarized macrophages compared to M2 macrophages. Supporting the biological significance of these findings, Hmgb1ΔpMeso mice showed a delayed and reduced incidence of mesothelioma and an increased mesothelioma-specific survival. Altogether, our study provides a biological explanation for HMGB1 as a driver of asbestos-induced mesothelioma.

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive 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.

The International Association for the Study of Lung Cancer Pleural Mesothelioma Staging Project: Updated Modeling of Prognostic Factors in Pleural Mesothelioma.

INTRODUCTION: The International Association for the Study of Lung Cancer developed an international pleural mesothelioma database to improve staging. Data entered from 1995 to 2009 (training data set) were analyzed previously to evaluate supplemental prognostic factors. We evaluated these factors with new clinical data to determine whether the previous models could be improved. METHODS: Patients entered into the database from 2009 to 2019 (validation cohort) were assessed for the association between previous prognosticators and overall survival using Cox proportional hazards regression with bidirectional stepwise selection. Additional variables were analyzed and models were compared using Harrell's C-index. RESULTS: The training data set included 3101 patients and the validation cohort, 1733 patients. For the multivariable pathologic staging model applied to the training cohort, C-index was 0.68 (95% confidence interval [CI]: 0.656-0.705). For the validation data set (n = 497), C-index was 0.650 (95% CI: 0.614-0.685), and pathologic stage, histologic diagnosis, sex, adjuvant therapy, and platelet count were independently associated with survival. Adding anemia to the model increased the C-index to 0.652 (95% CI: 0.618-0.686). A basic presentation model including all parameters before staging yielded a C-index of 0.668 (95% CI: 0.641-0.695). In comparison, the European Organization for Research and Treatment of Cancer model yielded C-indices of 0.550 (95% CI: 0.511-0.589) and 0.577 (95% CI: 0.550-0.604) for pathologic staging and presentation models, respectively. CONCLUSIONS: Although significant predictors differed slightly, the International Association for the Study of Lung Cancer training model performed well in the validation set and better than the model of the European Organization for Research and Treatment of Cancer. International collaboration is critical to improve outcomes in this rare disease.

Tumor-intrinsic LKB1-LIF signaling axis establishes a myeloid niche to promote immune evasion and tumor growth.

Tumor mutations can influence the surrounding microenvironment leading to suppression of anti-tumor immune responses and thereby contributing to tumor progression and failure of cancer therapies. Here we use genetically engineered lung cancer mouse models and patient samples to dissect how LKB1 mutations accelerate tumor growth by reshaping the immune microenvironment. Comprehensive immune profiling of LKB1 -mutant vs wildtype tumors revealed dramatic changes in myeloid cells, specifically enrichment of Arg1 + interstitial macrophages and SiglecF Hi neutrophils. We discovered a novel mechanism whereby autocrine LIF signaling in Lkb1 -mutant tumors drives tumorigenesis by reprogramming myeloid cells in the immune microenvironment. Inhibiting LIF signaling in Lkb1 -mutant tumors, via gene targeting or with a neutralizing antibody, resulted in a striking reduction in Arg1 + interstitial macrophages and SiglecF Hi neutrophils, expansion of antigen specific T cells, and inhibition of tumor progression. Thus, targeting LIF signaling provides a new therapeutic approach to reverse the immunosuppressive microenvironment of LKB1 -mutant tumors.

Extracorporeal Membrane Oxygenation Impact on Host Transcriptomic Response in Severe Coronavirus.

Background: Evidence suggests that patients critically ill with COVID-19 have a dysregulated host immune response that contributes to end-organ damage. Extracorporeal membrane oxygenation (ECMO) has been used in this population with varying degrees of success. This study was performed to evaluate the impact of ECMO on the host immunotranscriptomic response in these patients. Methods: Eleven patients critically ill with COVID-19 requiring ECMO underwent an analysis of cytokines and immunotranscriptomic pathways before ECMO (T1), after ECMO for 24 hours (T2), and 2 hours after ECMO decannulation (T3). A Multiplex Human Cytokine panel was used to identify cytokine changes, and immunotranscriptomic changes in peripheral leukocytes were evaluated by PAXgene and NanoString nCounter. Results: Differential gene expression of 11 host immune genes was noted at T2 compared with T1. The most significant genes were MD2 and MRC1, which code for binding ligands for the activation of toll-like receptors 2 and 4. Reactome analyses of differential gene expression demonstrated an impact on many of the body's most important immune inflammatory pathways. Conclusions: These findings suggest a temporal impact of ECMO on the host immunotranscriptomic response in patients critically ill with COVID-19.

Lobar or Sublobar Resection for Peripheral Stage IA Non-Small-Cell Lung Cancer.

BACKGROUND: The increased detection of small-sized peripheral non-small-cell lung cancer (NSCLC) has renewed interest in sublobar resection in lieu of lobectomy. METHODS: We conducted a multicenter, noninferiority, phase 3 trial in which patients with NSCLC clinically staged as T1aN0 (tumor size, ≤2 cm) were randomly assigned to undergo sublobar resection or lobar resection after intraoperative confirmation of node-negative disease. The primary end point was disease-free survival, defined as the time between randomization and disease recurrence or death from any cause. Secondary end points were overall survival, locoregional and systemic recurrence, and pulmonary functions. RESULTS: From June 2007 through March 2017, a total of 697 patients were assigned to undergo sublobar resection (340 patients) or lobar resection (357 patients). After a median follow-up of 7 years, sublobar resection was noninferior to lobar resection for disease-free survival (hazard ratio for disease recurrence or death, 1.01; 90% confidence interval [CI], 0.83 to 1.24). In addition, overall survival after sublobar resection was similar to that after lobar resection (hazard ratio for death, 0.95; 95% CI, 0.72 to 1.26). The 5-year disease-free survival was 63.6% (95% CI, 57.9 to 68.8) after sublobar resection and 64.1% (95% CI, 58.5 to 69.0) after lobar resection. The 5-year overall survival was 80.3% (95% CI, 75.5 to 84.3) after sublobar resection and 78.9% (95% CI, 74.1 to 82.9) after lobar resection. No substantial difference was seen between the two groups in the incidence of locoregional or distant recurrence. At 6 months postoperatively, a between-group difference of 2 percentage points was measured in the median percentage of predicted forced expiratory volume in 1 second, favoring the sublobar-resection group. CONCLUSIONS: In patients with peripheral NSCLC with a tumor size of 2 cm or less and pathologically confirmed node-negative disease in the hilar and mediastinal lymph nodes, sublobar resection was not inferior to lobectomy with respect to disease-free survival. Overall survival was similar with the two procedures. (Funded by the National Cancer Institute and others; CALGB 140503 ClinicalTrials.gov number, NCT00499330.).

Pleural fluid microbiota as a biomarker for malignancy and prognosis.

Malignant pleural effusions (MPE) complicate malignancies and portend worse outcomes. MPE is comprised of various components, including immune cells, cancer cells, and cell-free DNA/RNA. There have been investigations into using these components to diagnose and prognosticate MPE. We hypothesize that the microbiome of MPE is unique and may be associated with diagnosis and prognosis. We compared the microbiota of MPE against microbiota of pleural effusions from non-malignant and paramalignant states. We collected a total of 165 pleural fluid samples from 165 subjects; Benign (n = 16), Paramalignant (n = 21), MPE-Lung (n = 57), MPE-Other (n = 22), and Mesothelioma (n = 49). We performed high throughput 16S rRNA gene sequencing on pleural fluid samples and controls. We showed that there are compositional differences among pleural effusions related to non-malignant, paramalignant, and malignant disease. Furthermore, we showed differential enrichment of bacterial taxa within MPE depending on the site of primary malignancy. Pleural fluid of MPE-Lung and Mesothelioma were associated with enrichment with oral and gut bacteria that are commonly thought to be commensals, including Rickettsiella, Ruminococcus, Enterococcus, and Lactobacillales. Mortality in MPE-Lung is associated with enrichment in Methylobacterium, Blattabacterium, and Deinococcus. These observations lay the groundwork for future studies that explore host-microbiome interactions and their influence on carcinogenesis.

BAP1 is a novel regulator of HIF-1α.

BAP1 is a powerful tumor suppressor gene characterized by haplo insufficiency. Individuals carrying germline BAP1 mutations often develop mesothelioma, an aggressive malignancy of the serosal layers covering the lungs, pericardium, and abdominal cavity. Intriguingly, mesotheliomas developing in carriers of germline BAP1 mutations are less aggressive, and these patients have significantly improved survival. We investigated the apparent paradox of a tumor suppressor gene that, when mutated, causes less aggressive mesotheliomas. We discovered that mesothelioma biopsies with biallelic BAP1 mutations showed loss of nuclear HIF-1α staining. We demonstrated that during hypoxia, BAP1 binds, deubiquitylates, and stabilizes HIF-1α, the master regulator of the hypoxia response and tumor cell invasion. Moreover, primary cells from individuals carrying germline BAP1 mutations and primary cells in which BAP1 was silenced using siRNA had reduced HIF-1α protein levels in hypoxia. Computational modeling and co-immunoprecipitation experiments revealed that mutations of BAP1 residues I675, F678, I679, and L691 -encompassing the C-terminal domain-nuclear localization signal- to A, abolished the interaction with HIF-1α. We found that BAP1 binds to the N-terminal region of HIF-1α, where HIF-1α binds DNA and dimerizes with HIF-1ß forming the heterodimeric transactivating complex HIF. Our data identify BAP1 as a key positive regulator of HIF-1α in hypoxia. We propose that the significant reduction of HIF-1α activity in mesothelioma cells carrying biallelic BAP1 mutations, accompanied by the significant reduction of HIF-1α activity in hypoxic tissues containing germline BAP1 mutations, contributes to the reduced aggressiveness and improved survival of mesotheliomas developing in carriers of germline BAP1 mutations.

Surgical results of the Lung Cancer Mutation Consortium 3 trial: A phase II multicenter single-arm study to investigate the efficacy and safety of atezolizumab as neoadjuvant therapy in patients with stages IB-select IIIB resectable non-small cell lung cancer.

OBJECTIVE: Multimodality treatment for resectable non-small cell lung cancer has long remained at a therapeutic plateau. Immune checkpoint inhibitors are highly effective in advanced non-small cell lung cancer and promising preoperatively in small clinical trials for resectable non-small cell lung cancer. This large multicenter trial tested the safety and efficacy of neoadjuvant atezolizumab and surgery. METHODS: Patients with stage IB to select IIIB resectable non-small cell lung cancer and Eastern Cooperative Oncology Group performance status 0/1 were eligible. Patients received atezolizumab 1200 mg intravenously every 3 weeks for 2 cycles or less followed by resection. The primary end point was major pathological response in patients without EGFR/ALK+ alterations. Pre- and post-treatment computed tomography, positron emission tomography, pulmonary function tests, and biospecimens were obtained. Adverse events were recorded by Common Terminology Criteria for Adverse Events v.4.0. RESULTS: From April 2017 to February 2020, 181 patients were entered in the study. Baseline characteristics were mean age, 65.1 years; female, 93 of 181 (51%); nonsquamous histology, 112 of 181 (62%); and clinical stages IIB to IIIB, 147 of 181 (81%). In patients without EGFR/ALK alterations who underwent surgery, the major pathological response rate was 20% (29/143; 95% confidence interval, 14-28) and the pathological complete response rate was 6% (8/143; 95% confidence interval, 2-11). There were no grade 4/5 treatment-related adverse events preoperatively. Of 159 patients (87.8%) undergoing surgery, 145 (91%) had pathologic complete resection. There were 5 (3%) intraoperative complications, no intraoperative deaths, and 2 postoperative deaths within 90 days, 1 treatment related. Median disease-free and overall survival have not been reached. CONCLUSIONS: Neoadjuvant atezolizumab in resectable stage IB to IIIB non-small cell lung cancer was well tolerated, yielded a 20% major pathological response rate, and allowed safe, complete surgical resection. These results strongly support the further development of immune checkpoint inhibitors as preoperative therapy in locally advanced non-small cell lung cancer.

The lung microbiome, peripheral gene expression, and recurrence-free survival after resection of stage II non-small cell lung cancer.

BACKGROUND: Cancer recurrence after tumor resection in early-stage non-small cell lung cancer (NSCLC) is common, yet difficult to predict. The lung microbiota and systemic immunity may be important modulators of risk for lung cancer recurrence, yet biomarkers from the lung microbiome and peripheral immune environment are understudied. Such markers may hold promise for prediction as well as improved etiologic understanding of lung cancer recurrence. METHODS: In tumor and distant normal lung samples from 46 stage II NSCLC patients with curative resection (39 tumor samples, 41 normal lung samples), we conducted 16S rRNA gene sequencing. We also measured peripheral blood immune gene expression with nanoString®. We examined associations of lung microbiota and peripheral gene expression with recurrence-free survival (RFS) and disease-free survival (DFS) using 500 × 10-fold cross-validated elastic-net penalized Cox regression, and examined predictive accuracy using time-dependent receiver operating characteristic (ROC) curves. RESULTS: Over a median of 4.8 years of follow-up (range 0.2-12.2 years), 43% of patients experienced a recurrence, and 50% died. In normal lung tissue, a higher abundance of classes Bacteroidia and Clostridia, and orders Bacteroidales and Clostridiales, were associated with worse RFS, while a higher abundance of classes Alphaproteobacteria and Betaproteobacteria, and orders Burkholderiales and Neisseriales, were associated with better RFS. In tumor tissue, a higher abundance of orders Actinomycetales and Pseudomonadales were associated with worse DFS. Among these taxa, normal lung Clostridiales and Bacteroidales were also related to worse survival in a previous small pilot study and an additional independent validation cohort. In peripheral blood, higher expression of genes TAP1, TAPBP, CSF2RB, and IFITM2 were associated with better DFS. Analysis of ROC curves revealed that lung microbiome and peripheral gene expression biomarkers provided significant additional recurrence risk discrimination over standard demographic and clinical covariates, with microbiome biomarkers contributing more to short-term (1-year) prediction and gene biomarkers contributing to longer-term (2-5-year) prediction. CONCLUSIONS: We identified compelling biomarkers in under-explored data types, the lung microbiome, and peripheral blood gene expression, which may improve risk prediction of recurrence in early-stage NSCLC patients. These findings will require validation in a larger cohort.