Excessive inflammation is pathogenic in the pneumonitis associated with severe COVID-19. Neutrophils are among the most abundantly present leukocytes in the inflammatory infiltrates and may form neutrophil extracellular traps (NETs) under the local influence of cytokines. NETs constitute a defense mechanism against bacteria, but have also been shown to mediate tissue damage in a number of diseases. Could NETs and their tissue-damaging properties inherent to neutrophil-associated functions play a role in the respiratory failure seen in patients with severe COVID-19, and how does this relate to the SARS-CoV-2 viral loads, IL-8 (CXCL8) chemokine expression, and cytotoxic T-lymphocyte infiltrates? Sixteen lung biopsy samples obtained immediately after death were analyzed methodically as exploratory and validation cohorts. NETs were analyzed quantitatively by multiplexed immunofluorescence and were correlated with local levels of IL-8 messenger RNA (mRNA) and the density of CD8+ T-cell infiltration. SARS-CoV-2 presence in tissue was quantified by reverse-transcriptase polymerase chain reaction and immunohistochemistry analysis. NETs were found in the lung interstitium and surrounding the bronchiolar epithelium with interindividual and spatial heterogeneity. NET density did not correlate with SARS-CoV-2 tissue viral load. NETs were associated with local IL-8 mRNA levels. NETs were also detected in pulmonary thrombi and in only one of eight liver tissues. NET focal presence correlated negatively with CD8+ T-cell infiltration in the lungs. Abundant neutrophils undergoing NETosis are found in the lungs of patients with fatal COVID-19, but no correlation was found with viral loads. The strong association between NETs and IL-8 points to this chemokine as a potentially causative factor. The function of cytotoxic T-lymphocytes in the immune responses against SARS-CoV-2 may be interfered with by the presence of NETs.

Lung cancer continues to be the leading cause of cancer‐related mortality, with cytologic samples often serving as the primary diagnostic tool. However, differentiating between benign and malignant processes in lung cytology can be difficult because various non‐neoplastic conditions closely resemble malignant lesions, which may lead to misdiagnosis. This review offers nine essential tips to help cytopathologists prevent false‐positive diagnoses of malignancy in non‐neoplastic processes. By examining common non‐neoplastic entities that can mimic lung malignancies, this review emphasizes their key cytomorphologic features and distinguishing characteristics. The authors stress the importance of a thorough clinical and radiologic context, slide background evaluation, and identification of cytomorphologic features for an accurate diagnosis of these mimics. The objective of this review was to enhance diagnostic accuracy and prevent misdiagnosis, thereby improving patient management in lung cancer cytopathology.

In the era of personalized medicine, there is an increasing demand for comprehensive and complex diagnosis using minimally invasive techniques. Nowadays, it is mandatory to integrate biomarkers in the diagnostic process, as well as in the treatment and clinical management of many cancer patients. Patients with non-small cell lung cancer (NSCLC), for instance, are frequently diagnosed in advanced stages, at a point when only cytological material or small biopsies can be obtained. This pathology constitutes an interesting challenge for the testing of biomarkers in cytology. Furthermore, there is a growing development of imaging techniques that guide non-invasive approaches to obtain small biopsies or cytological samples. This has allowed fine needle aspiration cytology and fine needle aspiration biopsy (FNAC, FNAB) to become front-line procedures in the management of patients with NSCLC. It is well known that the list of biomarkers to be tested in these patients continues to increase. Nevertheless, there are several of essential biomarkers that should always be analyzed in all patients with NSCLC, not only in non-squamous but also in some squamous carcinomas (SqCC). Some of them, such as PDL1, are tested by immunocytochemistry (ICC), while others, mainly ALK and ROS1, can be tested by ICC and confirmed using other techniques such a Fluorescence In Situ Hybridization (FISH). Other biomarkers, namely EGFR and BRAF mutations, are currently evaluated by polymerase chain reaction (PCR)-based techniques including Next-Generation Sequencing (NGS). In this review, we will address the particularities and challenges that ICC and FISH pose in different types of cytological samples from an eminently practical point of view.

Over the past decade, molecular cytopathology has emerged as a relevant area of modern pathology. Notably, in patients with advanced-stage cancer, cytological samples could be the only material available for diagnosis and molecular biomarker testing to identify patients suitable for targeted therapies. As a result, the contemporary cytopathologist’s role extends beyond morphological assessments to include critical skills such as evaluating the adequacy of the cytological samples and managing these specimens for molecular testing. This case collection can be a valuable source of insight, especially for young pathologists, who should learn to combine the opportunities offered by molecular biology with the basis of morphological evaluation.

Introduction: Low-dose computed tomography (LDCT) lung cancer screening has significantly enhanced early detection and patient survival rates in the population at risk. Current screening methods, that primarily rely on LDCT imaging, will very likely benefit from molecular biomarkers to achieve a more comprehensive, accurate, personalized and non-invasive risk assessment leveraging multimodal tools. We present a novel open access multimodal (imaging, proteomics and demographic) dataset designed to provide an available research resource on LDCT-based early lung cancer detection. The dataset includes annotated screening LDCT scans and plasma proteomics generated by proximity extension assay (Olink) platform. Methods: The dataset integrates data from control screened individuals without nodules or with benign nodules, and LDCT-diagnosed lung cancer individuals, matched by sex, age and time between image and sample collection. Both radiological and molecular signatures were collected within a six month window, providing detailed insights into disease progression. Nodules were considered as lung cancer cases if biopsy-confirmed lung cancer was diagnosed within 5 years after imaging, enabling the study of longitudinal biomarker evolution and its correlation with imaging findings. To complement the dataset, clinical and demographic data are also available in open access, providing a detailed overview of patient characteristics. The informed consent signed by the participants allows for unrestricted open access for requests directy or indirectly related to lung cancer research. Results: The dataset consists of annotated screening LDCT scans and plasma proteomics data measured with most of the Olink Target 96 platforms (1078 individual proteins across 12 panels focused on a specific area of disease or biology) for a total of 211 screening participants. There are 67 lung cancer patients, 68 matched controls with benign pulmonary nodules, 71 matched controls without nodules and 5 surgically excised false positive lesions. Experiments were performed to assess the technical quality and provide a proof-of-concept of usability of the dataset, showing the alignment with findings from previous published studies. Conclusion: This comprehensive dataset aims to facilitate research towards the development of personalized multimodal artificial intelligence models. We also aim to support the investigation of the relationship between imaging and molecular data, paving the way for more accurate understanding of early lung cancer biology. Finally, our open access dataset may help to develop or validate individualized risk prediction models that could significantly advance early lung cancer detection and intervention strategies.

Purpose: Lung adenocarcinomas (LUADs) harboring concurrent mutations in KRAS and STK11 define a clinically aggressive subgroup characterized by primary resistance to immune checkpoint inhibitors (ICIs). In contrast, KRAS/TP53 co-mutant tumors typically exhibit an inflamed tumor microenvironment (TME) and improved responsiveness to immunotherapy. The mechanisms underlying immune evasion in KRAS/STK11-mutant LUADs remain incompletely understood. Experimental Design: We assembled a multicenter cohort of human LUAD specimens and performed integrative genomic and immunophenotypic analyses to dissect the TME of KRAS/STK11 (KS) and KRAS/TP53 (KP) co-mutant tumors. Results: We identify neutrophil extracellular trap (NET) formation as a defining feature of KS tumors, associated with neutrophil enrichment and reduced CD8⁺ T-cell infiltration. Using immunocompetent Kras-mutant murine models, we demonstrate that Stk11 loss promotes NETosis through activation of the complement C5a-C5aR1 axis, resulting in an immunosuppressive TME and resistance to PD-1/PD-L1 blockade. Pharmacologic inhibition of C5a signaling suppresses NETosis, reduces tumor growth, and restores T-cell infiltration in KS tumors. Conclusions: Our findings uncover NETosis as a key mechanism of immune escape in KRAS/STK11-mutant LUADs and identify the C5a-C5aR1 pathway as a therapeutically actionable vulnerability to overcome immunotherapy resistance in this patient population. These findings provide a rationale for biomarker-driven clinical strategies targeting the complement pathway in patients with STK11-mutant lung adenocarcinoma.