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Introduction Lung cancer (LC) is the most common cause of cancer-related deaths worldwide. Its early detection can be achieved with a CT scan. Two large randomised trials proved the efficacy of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk populations. The decrease in specific mortality is 20%–25%.Nonetheless, implementing LCS on a large scale faces obstacles due to the low number of thoracic radiologists and CT scans available for the eligible population and the high frequency of false-positive screening results and the long period of indeterminacy of nodules that can reach up to 24 months, which is a source of prolonged anxiety and multiple costly examinations with possible side effects.Deep learning, an artificial intelligence solution has shown promising results in retrospective trials detecting lung nodules and characterising them. However, until now no prospective studies have demonstrated their importance in a real-life setting.Methods and analysis This open-label randomised controlled study focuses on LCS for patients aged 50–80 years, who smoked more than 20 pack-years, whether active or quit smoking less than 15 years ago. Its objective is to determine whether assisting a multidisciplinary team (MDT) with a 3D convolutional network-based analysis of screening chest CT scans accelerates the definitive classification of nodules into malignant or benign. 2722 patients will be included with the aim to demonstrate a 3-month reduction in the delay between lung nodule detection and its definitive classification into benign or malignant.Ethics and dissemination The sponsor of this study is the University Hospital of Nice. The study was approved for France by the ethical committee CPP (Comités de Protection des Personnes) Sud-Ouest et outre-mer III (No. 2022-A01543-40) and the Agence Nationale du Medicament et des produits de Santé (Ministry of Health) in December 2023. The findings of the trial will be disseminated through peer-reviewed journals and national and international conference presentations.Trial registration number NCT05704920.

Emerging therapies for non-small cell lung cancer targeting c-Met overexpression have recently demonstrated promising results. However, the evaluation of c-Met expression can be challenging. We aimed to study the inter and intraobserver reproducibility of c-Met expression evaluation. One hundred ten cases with non-small cell lung cancer (40 biopsies and 70 surgical specimens) were retrospectively selected in a single laboratory (LPCE) and evaluated for c-Met expression. Six pathologists (4 seniors and 2 juniors) evaluated the H-score and made a 3-tier classification of c-Met expression for all cases, using conventional light microscopy (CLM) and whole slide imaging (WSI). The interobserver reproducibility with CLM gave global Cohen Kappa coefficients (ƙ) ranging from 0.581 (95% CI: 0.364-0.771) to 0.763 (95% CI: 0.58-0.92) using the c-Met 3-tier classification and H-score, respectively. ƙ was higher for senior pathologists and biopsy samples. The interobserver reproducibility with WSI gave a global ƙ ranging from 0.543 (95% CI: 0.33-0.724) to 0.905 (95% CI: 0.618-1) using the c-Met H-score and 2-tier classification (≥25% 3+), respectively. ƙ for intraobserver reproducibility between CLM and WSI ranged from 0.713 to 0.898 for the c-Met H-score and from 0.600 to 0.779 for the c-Met 3-tier classification. We demonstrated a moderate to excellent interobserver agreement for c-Met expression with a substantial to excellent intraobserver agreement between CLM and WSI, thereby supporting the development of digital pathology. However, some factors (scoring method, type of tissue samples, and expertise level) affect reproducibility. Our findings highlight the importance of establishing a consensus definition and providing further training, particularly for inexperienced pathologists, for c-Met immunohistochemistry assessment in clinical practice., Competing Interests: Conflicts of Interest and Source of Funding: The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

The identification of ALK fusions in advanced non-small-cell lung carcinoma (aNSCLC) is mandatory for targeted therapy. The current diagnostic approach employs an algorithm using ALK immunohistochemistry (IHC) screening, followed by confirmation through ALK FISH and/or next-generation sequencing (NGS). Challenges arise due to the infrequency of ALK fusions (3-7% of aNSCLC), the suboptimal specificity of ALK IHC and ALK FISH, and the growing molecular demands placed on small tissue samples, leading to interpretative, tissue availability, and time-related issues. This study investigates the effectiveness of RNA NGS as a reflex test for identifying ALK fusions in NSCLC, with the goal of replacing ALK IHC in the systematic screening process. The evaluation included 1246 NSCLC cases using paired techniques: ALK IHC, ALK FISH, and ALK NGS. ALK IHC identified 51 positive cases (4%), while RNA NGS detected ALK alterations in 59 cases (4.8%). Of the 59 ALK -positive cases identified via NGS, 53 (89.8%) were confirmed to be positive. This included 51 cases detected via both FISH and IHC, and 2 cases detected only via FISH, as they were completely negative according to IHC. The combined reporting time for ALK IHC and ALK FISH averaged 13 days, whereas ALK IHC and RNA NGS reports were obtained in an average of 4 days. These results emphasize the advantage of replacing systematic ALK IHC screening with RNA NGS reflex testing for a more comprehensive and accurate assessment of ALK status.

The past decade has witnessed a revolution in cancer treatment, shifting from conventional drugs (chemotherapies) towards targeted molecular therapies and immune-based therapies, in particular immune-checkpoint inhibitors (ICIs). These immunotherapies release the host's immune system against the tumor and have shown unprecedented durable remission for patients with cancers that were thought incurable, such as metastatic melanoma, metastatic renal cell carcinoma (RCC), microsatellite instability (MSI) high colorectal cancer and late stages of non-small cell lung cancer (NSCLC). However, about 80% of the patients fail to respond to these immunotherapies and are therefore left with other less effective and potentially toxic treatments. Identifying and understanding the mechanisms that enable cancerous cells to adapt to and eventually overcome therapy can help circumvent resistance and improve treatment. In this review, we describe the recent discoveries on the onco-immunological processes which govern the tumor microenvironment and their impact on the resistance to PD-1/PD-L1 checkpoint blockade., Competing Interests: MI has received honoraria for travel support and consulting/advisory roles for AstraZeneca, Bristol-Myers Squibb, Roche, Boehringer-Ingelheim and Merck & Co. outside the submitted work. PH has received honoraria for travel support and consulting/advisory roles for AstraZeneca, Roche, Bristol-Myers Squibb, Novartis, Pfizer, MSD, Qiagen, Thermo-Fisher Scientist, Janssen, Abbvie, Biocartis, Pierre Fabre, Sanofi, and Merck & Co. outside the submitted work. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Berland, Gabr, Chang, Ilié, Hofman, Rignol, Ghiringhelli, Mograbi, Rashidian and Hofman.)

Micronodular arrangement of epithelial cells and lymphoid B-cell hyperplasia with follicles are both peculiar histological features in thymic tissue. Such features may especially occur in thymic epithelial tumors. The most common form is called micronodular thymoma with lymphoid stroma. We have recently described some characteristics of thymic micronodular carcinoma with lymphoid hyperplasia, highlighting how this carcinomatous counterpart should not be misdiagnosed as a thymoma. In this review, we discuss these two entities but also other mimics, which may occur in the anterior mediastinum. These mimics include various types of cellular micronodules and lymphoid backgrounds encompassing a wide range of mediastinal lesions. Non-neoplastic lesions, such as thymic nodular epithelial hyperplasia, thymic lymphoid hyperplasia, or sarcoidosis, as well as tumors of very varying aggressiveness, such as micronodular thymic epithelial tumors, low-grade lymphoma, seminoma, or lymphoepithelial carcinoma, are discussed. We show how these lesions may be misleading and we describe how a correct diagnostic may be obtained in current practice., (©The Author(s) 2024. Open Access. This article is licensed under a Creative Commons CC-BY International License.)

Small cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, but implementing subtyping in the clinic has remained challenging, particularly due to limited tissue availability. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that subtype-specific patterns of DNA methylation could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts totaling 179 SCLC patients and using machine learning approaches, we report a highly accurate DNA methylation-based classifier (SCLC-DMC) that can distinguish SCLC subtypes. We further adjust the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and might guide precision SCLC therapy., Competing Interests: Declaration of interests S.H., C.M.G., L.A.B., and J.V.H. own intellectual property on the classification of SCLC from DNA methylation and gene expression. D.F., A.W., A.S., and C.A.S. are full time employees of Nucleix and own stocks and stock options of Nucleix. Furthermore, S.H. reports consulting fees from Guardant Health, AstraZeneca, Boehringer Ingelheim, and Qiagen. C.M.G. is a member of the advisory board at Jazz Pharmaceuticals, AstraZeneca, and Bristol Myers Squibb and served as speaker for AstraZeneca and BeiGene. P.R. received travel support from AstraZeneca, BMS, and MSD. E.A. reports consulting fees from Eli Lilly, AstraZeneca, BMS, Boehringer Ingelheim, Takeda, Roche, and MSD, speaker’s fees from AstraZeneca, BMS, Boehringer Ingelheim, Roche, and MSD, research funding from Roche and AstraZeneca and travel support from AstraZeneca and Takeda. P.H. reports research grants from Thermo Fisher Scientific and Biocartis, and speakers’ fees from AstraZeneca, Roche, Novartis, Bristol-Myers Squibb, Pfizer, Bayer, Illumina, Biocartis, Thermo Fisher Scientific, AbbVie, Amgen, Janssen, Eli Lilly, Daiichi Sankyo, Pierre Fabre, and Guardant. V.H. reports speakers’ fees from BMS. C.M.L. reports personal fees from Amgen, Arrivent, AstraZeneca, Blueprints Medicine, Cepheid, D2G Oncology, Daiichi Sankyo, Eli Lilly, EMD Serono, Foundation Medicine, Genentech, Janssen, Medscape, Novartis, Pfizer, Puma, Syros, and Takeda. N.V. receives consulting fees from Sanofi, Regeneron, Oncocyte, and Eli Lilly, and research funding from Mirati. M.B.N. receives royalties and licensing fees from Spectrum Pharmaceuticals. I.H. received personal as well as institutional funding from Nucleix. J.Z. served on advisory board for AstraZeneca and Geneplus and received speaker’s fees from BMS, Geneplus, OrigMed, Innovent and grants from Merck, Johnson and Johnson. L.A.B received consulting fees and research funding from AstraZeneca, GenMab, Sierra Oncology, research funding from ToleroPharmaceuticals and served as advisor or consultant for PharmaMar, AbbVie, Bristol-Myers Squibb, Alethia, Merck, Pfizer, Jazz Pharmaceuticals, Genentech, and Debiopharm Group. J.V.H. served as advisor for AstraZeneca, EMD Serono, Boehringer-Ingelheim, Catalyst, Genentech, GlaxoSmithKline, Guardant Health, Foundation medicine, Hengrui Therapeutics, Eli Lilly, Novartis, Spectrum, Sanofi, Takeda, Mirati Therapeutics, BMS, BrightPath Biotherapeutics, Janssen Global Services, Nexus Health Systems, Pneuma Respiratory, Kairos Venture Investments, Roche, Leads Biolabs, RefleXion, Chugai Pharmaceuticals, received research support from AstraZeneca, GlaxoSmithKline, Spectrum as well as royalties and licensing fees from Spectrum., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease associated with progressive and irreversible deterioration of respiratory functions that lacks curative therapies. Despite IPF being associated with a dysregulated immune response, current antifibrotics aim only at limiting fibroproliferation. Transcriptomic analyses show that the P2RX7/IL18/IFNG axis is downregulated in IPF patients and that P2 R X7 has immunoregulatory functions. Using our positive modulator of P2 R X7, we show that activation of the P2 R X7/IL-18 axis in immune cells limits lung fibrosis progression in a mouse model by favoring an antifibrotic immune environment, with notably an enhanced IL-18-dependent IFN-γ production by lung T cells leading to a decreased production of IL-17 and TGFβ. Overall, we show the ability of the immune system to limit lung fibrosis progression by targeting the immunomodulator P2 R X7. Hence, treatment with a small activator of P2 R X7 may represent a promising strategy to help patients with lung fibrosis., Competing Interests: SJ, TJ, SL, FB, VH, PH No competing interests declared, AG, VV has a patent related to HEI3090 25 (PCT/EP2019/058013), (© 2023, Janho dit Hreich et al.)

The continuing evolution of treatment options in thoracic oncology requires the pathologist to regularly update diagnostic algorithms for management of tumor samples. It is essential to decide on the best way to use tissue biopsies, cytological samples, as well as liquid biopsies to identify the different mandatory predictive biomarkers of lung cancers in a short turnaround time. However, biological resources and laboratory member workforce are limited and may be not sufficient for the increased complexity of molecular pathological analyses and for complementary translational research development. In this context, the surgical pathologist is the only one who makes the decisions whether or not to send specimens to immunohistochemical and molecular pathology platforms. Moreover, the pathologist can rapidly contact the oncologist to obtain a new tissue biopsy and/or a liquid biopsy if he/she considers that the biological material is not sufficient in quantity or quality for assessment of predictive biomarkers. Inadequate control of algorithms and sampling workflow may lead to false negative, inconclusive, and incomplete findings, resulting in inappropriate choice of therapeutic strategy and potentially poor outcome for patients. International guidelines for lung cancer treatment are based on the results of the expression of different proteins and on genomic alterations. These guidelines have been established taking into consideration the best practices to be set up in clinical and molecular pathology laboratories. This review addresses the current predictive biomarkers and algorithms for use in thoracic oncology molecular pathology as well as the central role of the pathologist, notably in the molecular tumor board and her/his participation in the treatment decision-making. The perspectives in this setting will be discussed., (© 2023. The Author(s).)