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Most mesotheliomas originate in the pleura and are due to asbestos exposure. The incidence is decreasing somewhat with asbestos remediation, but mortality remains high in part because of late diagnosis and treatment resistance. Pemetrexed–cisplatin and immune checkpoint inhibitors modestly extend survival.

Tobacco smoking causes lung cancer 1 – 3 , a process that is driven by more than 60 carcinogens in cigarette smoke that directly damage and mutate DNA 4 , 5 . The profound effects of tobacco on the genome of lung cancer cells are well-documented 6 – 10 , but equivalent data for normal bronchial cells are lacking. Here we sequenced whole genomes of 632 colonies derived from single bronchial epithelial cells across 16 subjects. Tobacco smoking was the major influence on mutational burden, typically adding from 1,000 to 10,000 mutations per cell; massively increasing the variance both within and between subjects; and generating several distinct mutational signatures of substitutions and of insertions and deletions. A population of cells in individuals with a history of smoking had mutational burdens that were equivalent to those expected for people who had never smoked: these cells had less damage from tobacco-specific mutational processes, were fourfold more frequent in ex-smokers than current smokers and had considerably longer telomeres than their more-mutated counterparts. Driver mutations increased in frequency with age, affecting 4–14% of cells in middle-aged subjects who had never smoked. In current smokers, at least 25% of cells carried driver mutations and 0–6% of cells had two or even three drivers. Thus, tobacco smoking increases mutational burden, cell-to-cell heterogeneity and driver mutations, but quitting promotes replenishment of the bronchial epithelium from mitotically quiescent cells that have avoided tobacco mutagenesis. Whole-genome sequencing of normal bronchial epithelium from 16 individuals shows that tobacco smoking increases genomic heterogeneity, mutational burden and driver mutations, whereas stopping smoking promotes replenishment of the epithelium with near-normal cells.

Risk-based screening for lung cancer is currently being considered in several countries; however, the optimal approach to determine eligibility remains unclear. Ensemble machine learning could support the development of highly parsimonious prediction models that maintain the performance of more complex models while maximising simplicity and generalisability, supporting the widespread adoption of personalised screening. In this work, we aimed to develop and validate ensemble machine learning models to determine eligibility for risk-based lung cancer screening. For model development, we used data from 216,714 ever-smokers recruited between 2006 and 2010 to the UK Biobank prospective cohort and 26,616 high-risk ever-smokers recruited between 2002 and 2004 to the control arm of the US National Lung Screening (NLST) randomised controlled trial. The NLST trial randomised high-risk smokers from 33 US centres with at least a 30 pack-year smoking history and fewer than 15 quit-years to annual CT or chest radiography screening for lung cancer. We externally validated our models among 49,593 participants in the chest radiography arm and all 80,659 ever-smoking participants in the US Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. The PLCO trial, recruiting from 1993 to 2001, analysed the impact of chest radiography or no chest radiography for lung cancer screening. We primarily validated in the PLCO chest radiography arm such that we could benchmark against comparator models developed within the PLCO control arm. Models were developed to predict the risk of 2 outcomes within 5 years from baseline: diagnosis of lung cancer and death from lung cancer. We assessed model discrimination (area under the receiver operating curve, AUC), calibration (calibration curves and expected/observed ratio), overall performance (Brier scores), and net benefit with decision curve analysis. Models predicting lung cancer death (UCL-D) and incidence (UCL-I) using 3 variables-age, smoking duration, and pack-years-achieved or exceeded parity in discrimination, overall performance, and net benefit with comparators currently in use, despite requiring only one-quarter of the predictors. In external validation in the PLCO trial, UCL-D had an AUC of 0.803 (95% CI: 0.783, 0.824) and was well calibrated with an expected/observed (E/O) ratio of 1.05 (95% CI: 0.95, 1.19). UCL-I had an AUC of 0.787 (95% CI: 0.771, 0.802), an E/O ratio of 1.0 (95% CI: 0.92, 1.07). The sensitivity of UCL-D was 85.5% and UCL-I was 83.9%, at 5-year risk thresholds of 0.68% and 1.17%, respectively, 7.9% and 6.2% higher than the USPSTF-2021 criteria at the same specificity. The main limitation of this study is that the models have not been validated outside of UK and US cohorts. We present parsimonious ensemble machine learning models to predict the risk of lung cancer in ever-smokers, demonstrating a novel approach that could simplify the implementation of risk-based lung cancer screening in multiple settings.

Synthetic data promise privacy-preserving data sharing for healthcare research and development. Compared with other privacy-enhancing approaches—such as federated learning—analyses performed on synthetic data can be applied downstream without modification, such that synthetic data can act in place of real data for a wide range of use cases. However, the role that synthetic data might play in all aspects of clinical model development remains unknown. In this work, we used state-of-the-art generators explicitly designed for privacy preservation to create a synthetic version of ever-smokers in the UK Biobank before building prognostic models for lung cancer under several data release assumptions. We demonstrate that synthetic data can be effectively used throughout the medical prognostic modeling pipeline even without eventual access to the real data. Furthermore, we show the implications of different data release approaches on how synthetic biobank data could be deployed within the healthcare system.

The molecular alterations that occur in cells before cancer is manifest are largely uncharted. Lung carcinoma in situ (CIS) lesions are the pre-invasive precursor to squamous cell carcinoma. Although microscopically identical, their future is in equipoise, with half progressing to invasive cancer and half regressing or remaining static. The cellular basis of this clinical observation is unknown. Here, we profile the genomic, transcriptomic, and epigenomic landscape of CIS in a unique patient cohort with longitudinally monitored pre-invasive disease. Predictive modeling identifies which lesions will progress with remarkable accuracy. We identify progression-specific methylation changes on a background of widespread heterogeneity, alongside a strong chromosomal instability signature. We observed mutations and copy number changes characteristic of cancer and chart their emergence, offering a window into early carcinogenesis. We anticipate that this new understanding of cancer precursor biology will improve early detection, reduce overtreatment, and foster preventative therapies targeting early clonal events in lung cancer. A multi-omics survey of progressive compared to regressive carcinoma in situ lesions provides a molecular map of early lung cancer development.

Image depicting all tumour‐derived components that can be detected in blood. Produced using BioRender. Lung cancer survival statistics are sobering with survival ranking among the poorest of all cancers despite the addition of targeted therapies and immunotherapies. However, improvements in tools for early detection hold promise. The Nederlands–Leuvens Longkanker Screenings Onderzoek (NELSON) trial recently corroborated the findings from the previous National Lung Screening Trial low‐dose Computerised Tomography (NLST) screening trial in reducing lung cancer mortality. Biomarker research and development is increasing at pace as the molecular life histories of lung cancers become further unravelled. Low‐dose CT screening (LDCT) is effective but targets only those at the highest risk and is burdensome on healthcare. An optimally designed CT screening programme at best will only detect a low proportion of overall lung cancers as only those at very high‐risk meet screening criteria. Biomarkers that help risk stratify suitable patients for LDCT screening, and those that assist in determining which LDCT detected nodules are likely to represent malignant disease are needed. Some biomarkers have been proposed as standalone lung cancer diagnosis tools. Bronchoscopy technology is improving, with better capacity to identify and obtain samples from early lung cancers. Clinicians need to be aware of each early lung cancer detection method’s inherent limitations. We anticipate that the future of early lung cancer diagnosis will involve a synergistic, multimodal approach, combining several early detection methods.

The extent of genetic and epigenetic diversity between and within patient tumors is being mapped in ever more detail. It is clear that cancer is an evolutionary process in which tumor cell intrinsic and extrinsic forces shape clonal selection. The pre-clinical oncology pipeline uses model systems of human cancer – including mouse models, cell lines, patient-derived organoids and patient-derived xenografts – to study tumor biology and assess the efficacy of putative therapeutic agents. Model systems cannot completely replicate the environment of human tumors and, even within the same cancer model, data are often irreproducible between laboratories. One hypothesis is that ongoing evolutionary processes remain relevant in laboratory models, leading to divergence over time. In a recent edition of Nature, Ben-David and colleagues showed that different stocks of widely used cancer cell lines – a staple of cancer research over many decades – are highly heterogeneous in terms of their genetics, transcriptomics and responses to therapies. The authors find compelling evidence of positive selection based on ongoing mutational processes and chromosomal instability. Thus, the origin, culture conditions and cumulative number of population doublings of cell lines likely influence experimental outcomes. Here, we summarize the key findings of this important study and discuss the practical implications of this work for researchers using cell lines in the laboratory.

Abstract Rationale Stem cell–based tracheal replacement represents an emerging therapeutic option for patients with otherwise untreatable airway diseases including long-segment congenital tracheal stenosis and upper airway tumors. Clinical experience demonstrates that restoration of mucociliary clearance in the lungs after transplantation of tissue-engineered grafts is critical, with preclinical studies showing that seeding scaffolds with autologous mucosa improves regeneration. High epithelial cell–seeding densities are required in regenerative medicine, and existing techniques are inadequate to achieve coverage of clinically suitable grafts. Objectives To define a scalable cell culture system to deliver airway epithelium to clinical grafts. Methods Human respiratory epithelial cells derived from endobronchial biopsies were cultured using a combination of mitotically inactivated fibroblasts and Rho-associated protein kinase (ROCK) inhibition using Y-27632 (3T3+Y). Cells were analyzed by immunofluorescence, quantitative polymerase chain reaction, and flow cytometry to assess airway stem cell marker expression. Karyotyping and multiplex ligation-dependent probe amplification were performed to assess cell safety. Differentiation capacity was tested in three-dimensional tracheospheres, organotypic cultures, air–liquid interface cultures, and an in vivo tracheal xenograft model. Ciliary function was assessed in air–liquid interface cultures. Measurements and Main Results 3T3-J2 feeder cells and ROCK inhibition allowed rapid expansion of airway basal cells. These cells were capable of multipotent differentiation in vitro, generating both ciliated and goblet cell lineages. Cilia were functional with normal beat frequency and pattern. Cultured cells repopulated tracheal scaffolds in a heterotopic transplantation xenograft model. Conclusions Our method generates large numbers of functional airway basal epithelial cells with the efficiency demanded by clinical transplantation, suggesting its suitability for use in tracheal reconstruction.

More than 40 years ago, Howard Green's laboratory developed a method for long‐term expansion of primary human epidermal keratinocytes by co‐culture with 3T3 mouse embryonic fibroblasts. This was a breakthrough for in vitro cultivation of cells from human skin and later for other epithelia: it led to the first stem cell therapy using cultured cells and has vastly increased our understanding of epithelial stem cell biology. In recent years, new methods to expand epithelial cells as three‐dimensional organoids have provided novel means to investigate the functions of these cells in health and disease. Here, we outline the history of stratified epithelial stem cell culture and the application of cultured epithelial cells in clinical therapies. We further discuss the derivation of organoids from other types of epithelia and the challenges that remain for the translation of novel stem cell therapies toward clinical use. Graphical Abstract In this timely review article, Paola Bonfanti, Robert Hynds and Sam Janes provide an insightful discussion and chronological overview of stem cells and regenerative medicine.

Many common diseases of the gas exchange surface of the lung have no specific treatment but cause serious morbidity and mortality. Idiopathic Pulmonary Fibrosis (IPF) is characterized by alveolar epithelial cell injury, interstitial inflammation, fibroblast proliferation and collagen accumulation within the lung parenchyma. Keratinocyte Growth Factor (KGF, also known as FGF-7) is a critical mediator of pulmonary epithelial repair through stimulation of epithelial cell proliferation. During repair, the lung not only uses resident cells after injury but also recruits circulating bone marrow-derived cells (BMDC). Several groups have used Mesenchymal Stromal Cells (MSCs) as therapeutic vectors, but little is known about the potential of Hematopoietic Stem cells (HSCs). Using an inducible lentiviral vector (Tet-On) expressing KGF, we were able to efficiently transduce both MSCs and HSCs, and demonstrated that KGF expression is induced in a regulated manner both in vitro and in vivo. We used the in vivo bleomycin-induced lung fibrosis model to assess the potential therapeutic effect of MSCs and HSCs. While both populations reduced the collagen accumulation associated with bleomycin-induced lung fibrosis, only transplantation of transduced HSCs greatly attenuated the histological damage. Using double immunohistochemistry, we show that the reduced lung damage likely occurs through endogenous type II pneumocyte proliferation induced by KGF. Taken together, our data indicates that bone marrow transplantation of lentivirus-transduced HSCs can attenuate lung damage, and shows for the first time the potential of using an inducible Tet-On system for cell based gene therapy in the lung.