Cancer-associated fibroblasts expressing fibroblast activation protein and podoplanin in non-small cell lung cancer predict poor clinical outcome.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are a dominant cell type in the stroma of non-small cell lung cancer (NSCLC). Fibroblast heterogeneity reflects subpopulations of CAFs, which can influence prognosis and treatment efficacy. We describe the subtypes of CAFs in NSCLC. METHODS: Primary human NSCLC resections were assessed by flow cytometry and multiplex immunofluorescence for markers of fibroblast activation which allowed identification of CAF subsets. Survival data were analysed for our NSCLC cohort consisting of 163 patients to understand prognostic significance of CAF subsets. RESULTS: We identified five CAF populations, termed CAF S1-S5. CAF-S5 represents a previously undescribed population, and express FAP and PDPN but lack the myofibroblast marker αSMA, whereas CAF-S1 populations express all three. CAF-S5 are spatially further from tumour regions then CAF-S1 and scRNA data demonstrate an inflammatory phenotype. The presence of CAF-S1 or CAF-S5 is correlated to worse survival outcome in NSCLC, despite curative resection, highlighting the prognostic importance of CAF subtypes in NSCLC. TCGA data suggest the predominance of CAF-S5 has a poor prognosis across several cancer types. CONCLUSION: This study describes the fibroblast heterogeneity in NSCLC and the prognostic importance of the novel CAF-S5 subset where its presence correlates to worse survival outcome.

Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images.

Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biomedical and clinical sciences, existing methods for analysing FLIM images often struggle to provide rapid and precise interpretations without reliable references, such as histology images, which are usually unavailable alongside FLIM images. To address this issue, we propose a deep learning (DL)-based approach for generating virtual Hematoxylin and Eosin (H&E) staining. By combining an advanced DL model with a contemporary image quality metric, we can generate clinical-grade virtual H&E-stained images from label-free FLIM images acquired on unstained tissue samples. Our experiments also show that the inclusion of lifetime information, an extra dimension beyond intensity, results in more accurate reconstructions of virtual staining when compared to using intensity-only images. This advancement allows for the instant and accurate interpretation of FLIM images at the cellular level without the complexities associated with co-registering FLIM and histology images. Consequently, we are able to identify distinct lifetime signatures of seven different cell types commonly found in the tumour microenvironment, opening up new opportunities towards biomarker-free tissue histology using FLIM across multiple cancer types.

Formylated Peptide Receptor-1-Mediated Gut Inflammation as a Therapeutic Target in Inflammatory Bowel Disease.

Background: Formylated peptide receptor (FPR)-1 is a G-coupled receptor that senses foreign bacterial and host-derived mitochondrial formylated peptides (FPs), leading to innate immune system activation. Aim: We sought to investigate the role of FPR1-mediated inflammation and its potential as a therapeutic target in inflammatory bowel disease (IBD). Methods: We characterized FPR1 gene and protein expression in 8 human IBD (~1000 patients) datasets with analysis on disease subtype, mucosal inflammation, and drug response. We performed in vivo dextran-sulfate sodium (DSS) colitis in C57/BL6 FPR1 knockout mice. In ex vivo studies, we studied the role of mitochondrial FPs and pharmacological blockade of FPR1 using cyclosporin H in human peripheral blood neutrophils. Finally, we assess mitochondrial FPs as a potential mechanistic biomarker in the blood and stools of patients with IBD. Results: Detailed in silico analysis in human intestinal biopsies showed that FPR1 is highly expressed in IBD (n = 207 IBD vs 67 non-IBD controls, P < .001), and highly correlated with gut inflammation in ulcerative colitis (UC) and Crohn's disease (CD) (both P < .001). FPR1 receptor is predominantly expressed in leukocytes, and we showed significantly higher FPR1+ve neutrophils in inflamed gut tissue section in IBD (17 CD and 24 UC; both P < .001). Further analysis in 6 independent IBD (data available under Gene Expression Omnibus accession numbers GSE59071, GSE206285, GSE73661, GSE16879, GSE92415, and GSE235970) showed an association with active gut inflammation and treatment resistance to infliximab, ustekinumab, and vedolizumab. FPR1 gene deletion is protective in murine DSS colitis with lower gut neutrophil inflammation. In the human ex vivo neutrophil system, mitochondrial FP, nicotinamide adenine dinucleotide dehydrogenase subunit-6 (ND6) is a potent activator of neutrophils resulting in higher CD62L shedding, CD63 expression, reactive oxygen species production, and chemotactic capacity; these effects are inhibited by cyclosporin H. We screened for mitochondrial ND6 in IBD (n = 54) using ELISA and detected ND6 in stools with median values of 2.2 gg/mL (interquartile range [IQR] 0.0-4.99; range 0-53.3) but not in blood. Stool ND6 levels, however, were not significantly correlated with paired stool calprotectin, C-reactive protein, and clinical IBD activity. Conclusions: Our data suggest that FPR1-mediated neutrophilic inflammation is a tractable target in IBD; however, further work is required to clarify the clinical utility of mitochondrial FPs as a potential mechanistic marker for future stratification.

Fibre-based fluorescence-lifetime imaging microscopy: a real-time biopsy guidance tool for suspected lung cancer.

Lung cancer is the most common cause of cancer-related deaths worldwide. Early detection improves outcomes, however, existing sampling techniques are associated with suboptimal diagnostic yield and procedure-related complications. Autofluorescence-based fluorescence-lifetime imaging microscopy (FLIM), a technique which measures endogenous fluorophore decay rates, may aid identification of optimal biopsy sites in suspected lung cancer. Our fibre-based fluorescence-lifetime imaging system, utilising 488 nm excitation, which is deliverable via existing diagnostic platforms, enables real-time visualisation and lifetime analysis of distal alveolar lung structure. We evaluated the diagnostic accuracy of the fibre-based fluorescence-lifetime imaging system to detect changes in fluorescence lifetime in freshly resected ex vivo lung cancer and adjacent healthy tissue as a first step towards future translation. The study compares paired non-small cell lung cancer (NSCLC) and non-cancerous tissues with gold standard diagnostic pathology to assess the performance of the technique. Paired NSCLC and non-cancerous lung tissues were obtained from thoracic resection patients (N=21). A clinically compatible 488 nm fluorescence-lifetime endomicroscopy platform was used to acquire simultaneous fluorescence intensity and lifetime images. Fluorescence lifetimes were calculated using a computationally-lightweight, rapid lifetime determination method. Fluorescence lifetime was significantly reduced in ex vivo lung cancer, compared with non-cancerous lung tissue [mean ± standard deviation (SD), 1.79±0.40 vs. 2.15±0.26 ns, P<0.0001], and fluorescence intensity images demonstrated distortion of alveolar elastin autofluorescence structure. Fibre-based fluorescence-lifetime imaging demonstrated good performance characteristics for distinguishing lung cancer, from adjacent non-cancerous tissue, with 81.0% sensitivity and 71.4% specificity. Our novel fibre-based fluorescence-lifetime imaging system, which enables label-free imaging and quantitative lifetime analysis, discriminates ex vivo lung cancer from adjacent healthy tissue. This minimally invasive technique has potential to be translated as a real-time biopsy guidance tool, capable of optimising diagnostic accuracy in lung cancer.

The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification.

Introduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.