Single-cell spatial landscapes of the lung tumour immune microenvironment.

Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution1-9. Most clinical strategies rely on histopathological stratification of tumour subtypes, yet the spatial context of single-cell phenotypes within these stratified subgroups is poorly understood. Here we apply imaging mass cytometry to characterize the tumour and immunological landscape of samples from 416 patients with lung adenocarcinoma across five histological patterns. We resolve more than 1.6 million cells, enabling spatial analysis of immune lineages and activation states with distinct clinical correlates, including survival. Using deep learning, we can predict with high accuracy those patients who will progress after surgery using a single 1-mm2 tumour core, which could be informative for clinical management following surgical resection. Our dataset represents a valuable resource for the non-small cell lung cancer research community and exemplifies the utility of spatial resolution within single-cell analyses. This study also highlights how artificial intelligence can improve our understanding of microenvironmental features that underlie cancer progression and may influence future clinical practice.

Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provide both common and discordant biological insights.

Whether single-cell RNA-sequencing (scRNA-seq) captures the same biological information as single-nucleus RNA-sequencing (snRNA-seq) remains uncertain and likely to be context-dependent. Herein, a head-to-head comparison was performed in matched normal-adenocarcinoma human lung samples to assess biological insights derived from scRNA-seq versus snRNA-seq and better understand the cellular transition that occurs from normal to tumoral tissue. Here, the transcriptome of 160,621 cells/nuclei was obtained. In non-tumor lung, cell type proportions varied widely between scRNA-seq and snRNA-seq with a predominance of immune cells in the former (81.5%) and epithelial cells (69.9%) in the later. Similar results were observed in adenocarcinomas, in addition to an overall increase in cell type heterogeneity and a greater prevalence of copy number variants in cells of epithelial origin, which suggests malignant assignment. The cell type transition that occurs from normal lung tissue to adenocarcinoma was not always concordant whether cells or nuclei were examined. As expected, large differential expression of the whole-cell and nuclear transcriptome was observed, but cell-type specific changes of paired normal and tumor lung samples revealed a set of common genes in the cells and nuclei involved in cancer-related pathways. In addition, we showed that the ligand-receptor interactome landscape of lung adenocarcinoma was largely different whether cells or nuclei were evaluated. Immune cell depletion in fresh specimens partly mitigated the difference in cell type composition observed between cells and nuclei. However, the extra manipulations affected cell viability and amplified the transcriptional signatures associated with stress responses. In conclusion, research applications focussing on mapping the immune landscape of lung adenocarcinoma benefit from scRNA-seq in fresh samples, whereas snRNA-seq of frozen samples provide a low-cost alternative to profile more epithelial and cancer cells, and yield cell type proportions that more closely match tissue content.

Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy. METHODS: Advanced NSCLC patients (n = 149) were retrospectively identified from two institutions who were treated with first-line ICIs. Radiomics features extracted from pretreatment imaging scans were used to build the predictive models for progression-free survival (PFS) and overall survival (OS). A compendium of five feature selection methods and seven machine learning approaches were utilized to build the survival risk models. The concordance index (C-index) was used to evaluate model performance. RESULTS: From our results, we found several combinations of machine learning algorithms and feature selection methods to achieve similar performance. K-nearest neighbourhood (KNN) with ReliefF (RL) feature selection was the best-performing model to predict PFS (C-index = 0.61 and 0.604 in discovery and validation cohorts), while XGBoost with Mutual Information (MI) feature selection was the best-performing model for OS (C-index = 0.7 and 0.655 in discovery and validation cohorts). CONCLUSION: The results of this study highlight the importance of implementing an appropriate feature selection method coupled with a machine learning strategy to develop robust survival models. With further validation of these models on external cohorts when available, this can have the potential to improve clinical decisions by systematically analyzing routine medical images.

Optimizing tissue stewardship in non-small cell lung cancer to support molecular characterization and treatment selection: statement from a working group of thoracic pathologists.

Many patients with non-small cell lung cancer do not receive guideline-recommended, biomarker-directed therapy, despite the potential for improved clinical outcomes. Access to timely, accurate, and comprehensive molecular profiling, including targetable protein overexpression, is essential to allow fully informed treatment decisions to be taken. In turn, this requires optimal tissue management to protect and maximize the use of this precious finite resource. Here, a group of leading thoracic pathologists recommend factors to consider for optimal tissue management. Starting from when lung cancer is first suspected, keeping predictive biomarker testing in the front of the mind should drive the development of practices and procedures that conserve tissue appropriately to support molecular characterization and treatment selection.

Age-associated differences in the human lung extracellular matrix.

Extracellular matrix (ECM) remodeling has been associated with chronic lung diseases. However, information about specific age-associated differences in lung ECM is currently limited. In this study, we aimed to identify and localize age-associated ECM differences in human lungs using comprehensive transcriptomic, proteomic, and immunohistochemical analyses. Our previously identified age-associated gene expression signature of the lung was re-analyzed limiting it to an aging signature based on 270 control patients (37-80 years) and focused on the Matrisome core geneset using geneset enrichment analysis. To validate the age-associated transcriptomic differences on protein level, we compared the age-associated ECM genes (false discovery rate, FDR < 0.05) with a profile of age-associated proteins identified from a lung tissue proteomics dataset from nine control patients (49-76 years) (FDR < 0.05). Extensive immunohistochemical analysis was used to localize and semi-quantify the age-associated ECM differences in lung tissues from 62 control patients (18-82 years). Comparative analysis of transcriptomic and proteomic data identified seven ECM proteins with higher expression with age at both gene and protein levels: COL1A1, COL6A1, COL6A2, COL14A1, FBLN2, LTBP4, and LUM. With immunohistochemistry, we demonstrated higher protein levels with age for COL6A2 in whole tissue, parenchyma, airway wall, and blood vessel, for COL14A1 and LUM in bronchial epithelium, and COL1A1 in lung parenchyma. Our study revealed that higher age is associated with lung ECM remodeling, with specific differences occurring in defined regions within the lung. These differences may affect lung structure and physiology with aging and as such may increase susceptibility to developing chronic lung diseases.NEW & NOTEWORTHY We identified seven age-associated extracellular matrix (ECM) proteins, i.e., COL1A1, COL6A1, COL6A2 COL14A1, FBLN2, LTBP4, and LUM with higher transcript and protein levels in human lung tissue with age. Extensive immunohistochemical analysis revealed significant age-associated differences for COL6A2 in whole tissue, parenchyma, airway wall, and vessel, for COL14A1 and LUM in bronchial epithelium, and COL1A1 in parenchyma. Our findings lay a new foundation for the investigation of ECM differences in age-associated chronic lung diseases.

Targeted prostaglandin E2 inhibition enhances antiviral immunity through induction of type I interferon and apoptosis in macrophages.

Aspirin gained tremendous popularity during the 1918 Spanish Influenza virus pandemic, 50 years prior to the demonstration of their inhibitory action on prostaglandins. Here, we show that during influenza A virus (IAV) infection, prostaglandin E2 (PGE2) was upregulated, which led to the inhibition of type I interferon (IFN) production and apoptosis in macrophages, thereby causing an increase in virus replication. This inhibitory role of PGE2 was not limited to innate immunity, because both antigen presentation and T cell mediated immunity were also suppressed. Targeted PGE2 suppression via genetic ablation of microsomal prostaglandin E-synthase 1 (mPGES-1) or by the pharmacological inhibition of PGE2 receptors EP2 and EP4 substantially improved survival against lethal IAV infection whereas PGE2 administration reversed this phenotype. These data demonstrate that the mPGES-1-PGE2 pathway is targeted by IAV to evade host type I IFN-dependent antiviral immunity. We propose that specific inhibition of PGE2 signaling might serve as a treatment for IAV.

Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome.

The five-year survival rate of lung cancer patients is very low, mainly because most newly diagnosed patients present with locally advanced or metastatic disease. Therefore, early diagnosis is key to the successful treatment and management of lung cancer. Unfortunately, early detection methods of lung cancer are not ideal. In this brief review, we described early detection methods such as chest X-rays followed by bronchoscopy, sputum analysis followed by cytological analysis, and low-dose computed tomography (LDCT). In addition, we discussed the potential of metabolomic fingerprinting, compared to that of other biomarkers, including molecular targets, as a low-cost, high-throughput blood-based test that is both feasible and affordable for early-stage lung cancer screening of at-risk populations. Accordingly, we proposed a paradigm shift to metabolomics as an alternative to molecular and proteomic-based markers in lung cancer screening, which will enable blood-based routine testing and be accessible to those patients at the highest risk for lung cancer.

Multi-omics highlights ABO plasma protein as a causal risk factor for COVID-19.

SARS-CoV-2 is responsible for the coronavirus disease 2019 (COVID-19) and the current health crisis. Despite intensive research efforts, the genes and pathways that contribute to COVID-19 remain poorly understood. We, therefore, used an integrative genomics (IG) approach to identify candidate genes responsible for COVID-19 and its severity. We used Bayesian colocalization (COLOC) and summary-based Mendelian randomization to combine gene expression quantitative trait loci (eQTLs) from the Lung eQTL (n = 1,038) and eQTLGen (n = 31,784) studies with published COVID-19 genome-wide association study (GWAS) data from the COVID-19 Host Genetics Initiative. Additionally, we used COLOC to integrate plasma protein quantitative trait loci (pQTL) from the INTERVAL study (n = 3,301) with COVID-19 loci. Finally, we determined any causal associations between plasma proteins and COVID-19 using multi-variable two-sample Mendelian randomization (MR). The expression of 18 genes in lung and/or blood co-localized with COVID-19 loci. Of these, 12 genes were in suggestive loci (PGWAS < 5 × 10-05). LZTFL1, SLC6A20, ABO, IL10RB and IFNAR2 and OAS1 had been previously associated with a heightened risk of COVID-19 (PGWAS < 5 × 10-08). We identified a causal association between OAS1 and COVID-19 GWAS. Plasma ABO protein, which is associated with blood type in humans, demonstrated a significant causal relationship with COVID-19 in the MR analysis; increased plasma levels were associated with an increased risk of COVID-19 and, in particular, severe COVID-19. In summary, our study identified genes associated with COVID-19 that may be prioritized for future investigations. Importantly, this is the first study to demonstrate a causal association between plasma ABO protein and COVID-19.

Non-invasive diagnostic imaging tests largely underdiagnose cardiac cirrhosis in patients undergoing advanced therapy evaluation: How can we identify the high-risk patient?

BACKGROUND: Patients with liver cirrhosis are generally considered ineligible for isolated cardiac transplantation or left ventricular assist device (LVAD) implantation. The aim of this retrospective study is to explore the diagnostic value of abdominal ultrasound, computed tomography scan (CT scan) and liver-spleen scintigraphy to detect the presence of cirrhosis in patients with advanced heart failure. METHODS: Among 567 consecutive patients who underwent pre-transplantation or LVAD evaluation, 54 had a liver biopsy to rule out cardiac cirrhosis; we compared the biopsy results with the imaging investigations. RESULTS: In about 26% (n = 14) of patients undergoing liver biopsy, histopathological evaluation identified cirrhosis. The respective sensitivity of abdominal ultrasound, CT scan and liver-spleen scintigraphy to detect cirrhosis was 57% [29-82], 50% [16-84], and 25% [3-65]. The specificity was 80% [64-91], 89% [72-98], and 44% [20-70], respectively. CONCLUSION: Ultrasonography has the best-combined sensitivity and specificity for the diagnosis of cirrhosis. However, more than a third of patients with cirrhosis will go undiagnosed by conventional imaging. As liver biopsy is associated with a low rate of complication, it should be considered in patients with a high-risk of cirrhosis or with evidence of portal hypertension to assess their eligibility for heart transplantation or LVAD implantation.

Genome-Wide Association Study of Susceptibility to Idiopathic Pulmonary Fibrosis.

Rationale: Idiopathic pulmonary fibrosis (IPF) is a complex lung disease characterized by scarring of the lung that is believed to result from an atypical response to injury of the epithelium. Genome-wide association studies have reported signals of association implicating multiple pathways including host defense, telomere maintenance, signaling, and cell-cell adhesion.Objectives: To improve our understanding of factors that increase IPF susceptibility by identifying previously unreported genetic associations.Methods: We conducted genome-wide analyses across three independent studies and meta-analyzed these results to generate the largest genome-wide association study of IPF to date (2,668 IPF cases and 8,591 controls). We performed replication in two independent studies (1,456 IPF cases and 11,874 controls) and functional analyses (including statistical fine-mapping, investigations into gene expression, and testing for enrichment of IPF susceptibility signals in regulatory regions) to determine putatively causal genes. Polygenic risk scores were used to assess the collective effect of variants not reported as associated with IPF.Measurements and Main Results: We identified and replicated three new genome-wide significant (P < 5 × 10-8) signals of association with IPF susceptibility (associated with altered gene expression of KIF15, MAD1L1, and DEPTOR) and confirmed associations at 11 previously reported loci. Polygenic risk score analyses showed that the combined effect of many thousands of as yet unreported IPF susceptibility variants contribute to IPF susceptibility.Conclusions: The observation that decreased DEPTOR expression associates with increased susceptibility to IPF supports recent studies demonstrating the importance of mTOR signaling in lung fibrosis. New signals of association implicating KIF15 and MAD1L1 suggest a possible role of mitotic spindle-assembly genes in IPF susceptibility.

Transcriptome-wide association study reveals candidate causal genes for lung cancer.

We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of our study has been to integrate the complete GWAS results with a large-scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n = 1,038) to identify candidate causal genes for lung cancer. We performed transcriptome-wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma and small cell lung cancer) and smoking subgroups (never- and ever-smokers). We performed replication analysis using lung data from the Genotype-Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever-smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (pTWAS = 1.09E-99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (pTWAS = 3.72E-6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3-adenocarcinoma on 9p13.3 was replicated using GTEx (pTWAS = 6.55E-5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influences lung cancer risk.

Leveraging lung tissue transcriptome to uncover candidate causal genes in COPD genetic associations.

Causal genes of chronic obstructive pulmonary disease (COPD) remain elusive. The current study aims at integrating genome-wide association studies (GWAS) and lung expression quantitative trait loci (eQTL) data to map COPD candidate causal genes and gain biological insights into the recently discovered COPD susceptibility loci. Two complementary genomic datasets on COPD were studied. First, the lung eQTL dataset which included whole-genome gene expression and genotyping data from 1038 individuals. Second, the largest COPD GWAS to date from the International COPD Genetics Consortium (ICGC) with 13 710 cases and 38 062 controls. Methods that integrated GWAS with eQTL signals including transcriptome-wide association study (TWAS), colocalization and Mendelian randomization-based (SMR) approaches were used to map causality genes, i.e. genes with the strongest evidence of being the functional effector at specific loci. These methods were applied at the genome-wide level and at COPD risk loci derived from the GWAS literature. Replication was performed using lung data from GTEx. We collated 129 non-overlapping risk loci for COPD from the GWAS literature. At the genome-wide scale, 12 new COPD candidate genes/loci were revealed and six replicated in GTEx including CAMK2A, DMPK, MYO15A, TNFRSF10A, BTN3A2 and TRBV30. In addition, we mapped candidate causal genes for 60 out of the 129 GWAS-nominated loci and 23 of them were replicated in GTEx. Mapping candidate causal genes in lung tissue represents an important contribution to the genetics of COPD, enriches our biological interpretation of GWAS findings, and brings us closer to clinical translation of genetic associations.

Transcriptomic data helps refining classification of pulmonary carcinoid tumors with increased mitotic counts.

Pulmonary neuroendocrine neoplasms are classified by WHO as either typical or atypical carcinoids, large cell (LCNEC) or small cell (SCLC) neuroendocrine carcinoma based on mitotic count, morphology, and necrosis assessment. LCNEC with low mitotic count and sharing morphologic features with carcinoids are in a gray zone for classification and their rare prevalence and the paucity of studies precludes proper validation of the current grading system. In this study, we aim to investigate their clinicopathological and transcriptomic profiles. Lung resection specimens obtained from 18 patients diagnosed with carcinoids or LCNEC were selected. Four of them were characterized as borderline tumors based on a mitotic rate ranging between 10 and 30 mitoses per 2 mm2. Comprehensive morphological and immunohistochemical (IHC) evaluation was performed and tumor-based transcriptomic profiles were analyzed through unsupervised clustering. Clustering analysis revealed two distinct molecular groups characterized by low (C1) and high (C2) proliferation. C1 was comprised of seven carcinoids and three borderline tumors, while C2 was comprised of seven LCNEC and one borderline tumor. Furthermore, patients in cluster C1 had a better recurrence-free survival compared with patients in cluster C2 (20% vs 75%). Histological features, IHC profile, and molecular analysis showed that three out of four borderline tumors showed features consistent with carcinoids. Therefore, our findings convey that the current diagnostic guidelines are suboptimal for classification of pulmonary neuroendocrine tumors with increased proliferative index and carcinoid-like morphology. These results support the emerging concept that neuroendocrine tumors with carcinoid-like features and mitotic count of <20 mitoses per 2 mm2 should be regarded as pulmonary carcinoids instead of LCNEC.

High FA2H and UGT8 transcript levels predict hydroxylated hexosylceramide accumulation in lung adenocarcinoma.

Lung cancer causes more deaths than any other cancer. Sphingolipids encompass metabolically interconnected species whose balance has pivotal effects on proliferation, migration, and apoptosis. In this study, we paralleled quantification of sphingolipid species with quantitative (q)PCR analyses of metabolic enzymes in order to identify dysregulated routes of sphingolipid metabolism in different subtypes of lung cancers. Lung samples were submitted to histopathological reexamination in order to confirm cancer type/subtype, which included adenocarcinoma histological subtypes and squamous cell and neuroendocrine carcinomas. Compared with benign lesions and tumor-free parenchyma, all cancers featured decreased sphingosine-1-phosphate and SMs. qPCR analyses evidenced differential mechanisms leading to these alterations between cancer types, with neuroendocrine carcinomas upregulating SGPL1, but CERT1 being downregulated in adenocarcinomas and squamous cell carcinomas. 2-Hydroxyhexosylceramides (2-hydroxyHexCers) were specifically increased in adenocarcinomas. While UDP-glycosyltransferase 8 (UGT8) transcript levels were increased in all cancer subtypes, fatty acid 2-hydroxylase (FA2H) levels were higher in adenocarcinomas than in squamous and neuroendocrine carcinomas. As a whole, we report differing mechanisms through which all forms of lung cancer achieve low SM and lysosphingolipids. Our results also demonstrate that FA2H upregulation is required for the accumulation of 2-hydroxyHexCers in lung cancers featuring high levels of UGT8.

Correction: Prognostic and predictive role of CD8 and PD-L1 determination in lung tumor tissue of patients under anti-PD-1 therapy.

Since the publication of this paper, the authors noticed that Corentin Richard was not credited as contributing equally to the paper. He should be considered as a joint first author with Jean-David Fumet.

The pharmacogenomics of inhaled corticosteroids and lung function decline in COPD.

Inhaled corticosteroids (ICS) are widely prescribed for patients with chronic obstructive pulmonary disease (COPD), yet have variable outcomes and adverse reactions, which may be genetically determined. The primary aim of the study was to identify the genetic determinants for forced expiratory volume in 1 s (FEV1) changes related to ICS therapy.In the Lung Health Study (LHS)-2, 1116 COPD patients were randomised to the ICS triamcinolone acetonide (n=559) or placebo (n=557) with spirometry performed every 6 months for 3 years. We performed a pharmacogenomic genome-wide association study for the genotype-by-ICS treatment effect on 3 years of FEV1 changes (estimated as slope) in 802 genotyped LHS-2 participants. Replication was performed in 199 COPD patients randomised to the ICS, fluticasone or placebo.A total of five loci showed genotype-by-ICS interaction at p<5×10-6; of these, single nucleotide polymorphism (SNP) rs111720447 on chromosome 7 was replicated (discovery p=4.8×10-6, replication p=5.9×10-5) with the same direction of interaction effect. ENCODE (Encyclopedia of DNA Elements) data revealed that in glucocorticoid-treated (dexamethasone) A549 alveolar cell line, glucocorticoid receptor binding sites were located near SNP rs111720447. In stratified analyses of LHS-2, genotype at SNP rs111720447 was significantly associated with rate of FEV1 decline in patients taking ICS (C allele ß 56.36 mL·year-1, 95% CI 29.96-82.76 mL·year-1) and in patients who were assigned to placebo, although the relationship was weaker and in the opposite direction to that in the ICS group (C allele ß -27.57 mL·year-1, 95% CI -53.27- -1.87 mL·year-1).The study uncovered genetic factors associated with FEV1 changes related to ICS in COPD patients, which may provide new insight on the potential biology of steroid responsiveness in COPD.

RIPK3 interacts with MAVS to regulate type I IFN-mediated immunity to Influenza A virus infection.

The type I interferon pathway plays a critical role in both host defense and tolerance against viral infection and thus requires refined regulatory mechanisms. RIPK3-mediated necroptosis has been shown to be involved in anti-viral immunity. However, the exact role of RIPK3 in immunity to Influenza A Virus (IAV) is poorly understood. In line with others, we, herein, show that Ripk3-/- mice are highly susceptible to IAV infection, exhibiting elevated pulmonary viral load and heightened morbidity and mortality. Unexpectedly, this susceptibility was linked to an inability of RIKP3-deficient macrophages (Mφ) to produce type I IFN in the lungs of infected mice. In Mφ infected with IAV in vitro, we found that RIPK3 regulates type I IFN both transcriptionally, by interacting with MAVS and limiting RIPK1 interaction with MAVS, and post-transcriptionally, by activating protein kinase R (PKR)-a critical regulator of IFN-ß mRNA stability. Collectively, our findings indicate a novel role for RIPK3 in regulating Mφ-mediated type I IFN anti-viral immunity, independent of its conventional role in necroptosis.

Sex-Related Discordance Between Aortic Valve Calcification and Hemodynamic Severity of Aortic Stenosis: Is Valvular Fibrosis the Explanation?

RATIONALE: Calcific aortic stenosis (AS) is characterized by calcium deposition in valve leaflets. However, women present lower aortic valve calcification loads than men for the same AS hemodynamic severity. OBJECTIVE: We, thus, aimed to assess sex differences in aortic valve fibrocalcific remodeling. METHODS AND RESULTS: One hundred and twenty-five patients underwent Doppler echocardiography and multidetector computed tomography within 3 months before aortic valve replacement. Explanted stenotic tricuspid aortic valves were weighed, and fibrosis degree was determined. Sixty-four men and 39 women were frequency matched for age, body mass index, hypertension, renal disease, diabetes mellitus, and AS severity. Mean age (75±9 years), mean gradient (41±18 mm Hg), and indexed aortic valve area (0.41±0.12 cm2/m2) were similar between men and women (all P≥0.18). Median aortic valve calcification (1973 [1124-3490] Agatston units) and mean valve weight (2.36±0.99 g) were lower in women compared with men (both P<0.0001). Aortic valve calcification density correlated better with valve weight in men (r2=0.57; P<0.0001) than in women (r2=0.26; P=0.0008). After adjustment for age, body mass index, aortic valve calcification density, and aortic annulus diameter, female sex was an independent risk factor for higher fibrosis score in AS valves (P=0.003). Picrosirius red staining of explanted valves showed greater amount of collagen fibers (P=0.01), and Masson trichrome staining revealed a greater proportion of dense connective tissue (P=0.02) in women compared with men. CONCLUSIONS: In this series of patients with tricuspid aortic valve and similar AS severity, women have less valvular calcification but more fibrosis compared with men. These findings suggest that the pathophysiology of AS and thus potential targets for drug development may be different according to sex.

Prognostic and predictive role of CD8 and PD-L1 determination in lung tumor tissue of patients under anti-PD-1 therapy.

BACKGROUND: No study has evaluated the predictive and prognostic role of CD8 and PD-L1 coexpression in non-small-cell lung cancer (NSCLC). METHODS: We analyzed RNA sequencing and/or immunohistochemistry staining in NSCLC patients from The Cancer Genome Atlas (n = 1016), and 34 metastatic NSCLC samples not treated by immunotherapy as prognostic cohorts. As predictive aspect of CD8 and PD-L1, we used 85 NSCLC patients treated with anti-PD-1. Two validation cohorts were used including 44 NSCLC patients treated with anti-PD-1 and an external cohort with different tumor types. RESULTS: In prognostic cohorts, high CD8A expression was associated with longer OS (p = 0.02), while high CD274 mRNA was associated with poor prognosis (p = 0.05). In predictive cohort, high CD8 expression and CD8A mRNA were associated with longer progression-free survival (PFS) (p = 0.0002). There was no significant association between PD-L1 expression and PFS while high CD274 mRNA was associated with longer PFS (p = 0.009). A combination of CD8A and CD274 was highly predictive of outcome. These results were confirmed in the validation cohorts. This two-genes signature demonstrated similar results compared to gold standard signatures. CONCLUSION: CD8 represents both a prognostic and predictive factor of outcomes, while PD-L1 share different prognostic and predictive roles.

Pulmonary large cell neuroendocrine carcinoma with adenocarcinoma-like features: napsin A expression and genomic alterations.

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a highly aggressive malignancy, which was recently found to comprise three major genomic subsets: small cell carcinoma-like, non-small cell carcinoma (predominantly adenocarcinoma)-like, and carcinoid-like. To further characterize adenocarcinoma-like subset, here we analyzed the expression of exocrine marker napsin A, along with TTF-1, in a large series of LCNECs (n=112), and performed detailed clinicopathologic and genomic analysis of napsin A-positive cases. For comparison, we analyzed napsin A expression in other lung neuroendocrine neoplasms (177 carcinoids, 37 small cell carcinomas) and 60 lung adenocarcinomas. We found that napsin A was expressed in 15% of LCNEC (17/112), whereas all carcinoids and small cell carcinomas were consistently negative. Napsin A reactivity in LCNEC was focal in 12/17 cases, and weak or moderate in intensity in all cases, which was significantly lower in the extent and intensity than seen in adenocarcinomas (P<0.0001). The combination of TTF-1-diffuse/napsin A-negative or focal was typical of LCNEC but was rare in adenocarcinoma, and could thus serve as a helpful diagnostic clue. The diagnosis of napsin A-positive LCNECs was confirmed by classic morphology, diffuse labeling for at least one neuroendocrine marker, most consistently synaptophysin, and the lack of distinct adenocarcinoma component. Genomic analysis of 14 napsin A-positive LCNECs revealed the presence of mutations typical of lung adenocarcinoma (KRAS and/or STK11) in 11 cases. In conclusion, LCNECs are unique among lung neuroendocrine neoplasms in that some of these tumors exhibit low-level expression of exocrine marker napsin A, and harbor genomic alterations typical of adenocarcinoma. Despite the apparent close biological relationship, designation of adeno-like LCNEC as a separate entity from adenocarcinoma is supported by their distinctive morphology, typically diffuse expression of neuroendocrine marker(s) and aggressive behavior. Further studies are warranted to assess the clinical utility and optimal method of identifying adenocarcinoma-like and other subsets of LCNEC in routine practice.