Biology subjects

Brcic, L.

Publications and source records attributed to Brcic, L..

2 recordsLinked to original sources

bioRxiv2023

Myeloperoxidase promotes a tumorigenic microenvironment in non-small cell lungcancer

Myeloperoxidase (MPO) is a heme peroxidase that is mainly expressed and secreted by neutrophils. MPOs role in inflammatory diseases has been highlighted in recent years, but its role in tumor development remains unclear. Therefore, we investigated the role of MPO in non-small cell lung cancer (NSCLC). In silico analysis revealed a survival benefit in patients with NSCLC and low MPO expression. Furthermore, a syngeneic tumor model using MPO knockout (KO) mice revealed that mice lacking MPO had lower tumor growth than controls. The reduction in tumor size was accompanied by an increase in lymphoid populations, including natural killer cells and CD8+ T cells, suggesting a shift to a more anti-tumorigenic immune environment in MPO-KO mouse tumors. The T cell induced interferon-gamma (IFN-{gamma}) expression was increased in MPO-KO tumors, indicating increased tumoricidal activity. CD8 depletion abolished the previously observed reduction in tumor size in MPO-KO mice, indicating that CD8+ T cells play an important role. In vitro, T cells treated with MPO showed reduced proliferation and IFN-{gamma} expression. Furthermore, MPO could be internalized into T cells. Heparin pretreatment of T cells blocked MPO binding and internalization into T cells and reversed MPO-induced proliferation reduction. Interestingly, MPO+ lymphocytes were found in tumor samples from patients with NSCLC. Our findings suggest that MPO plays an immunosuppressive role in NSCLC. One Sentence SummaryHigh myeloperoxidase (MPO) expression in non-small cell lung cancer patients is a predictor for adverse outcome and mice lacking MPO showed enhanced anti-tumorigenic leukocyte infiltration, suggesting a pro-tumorigenic role of MPO.

cancer biology↗

bioRxiv2022

Deep learning for histopathological subtyping and grading of lung adenocarcinoma

The histopathological distinction of lung adenocarcinoma (LADC) subtypes is subject to high inter-observer variability, which can compromise the optimal assessment of the patient prognosis. Therefore, this study developed convolutional neural networks (CNNs) capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the LADC tumour grades established recently by the International Association for the Study of Lung Cancer pathology committee. Consensus LADC ground truth histopathological images were obtained from seventeen expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained with EfficientNet b3 architecture to predict eight different LADC classes (lepidic, acinar, papillary, micropapillary, solid, invasive mucinous adenocarcinoma, other carcinoma types, and no carcinoma cells). Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1-scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models. Moreover, the grading prediction of one of the trained models was more accurate than those of 14 out of 15 pulmonary pathologists involved in the study (p=0.0003). Both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained CNNs improve the diagnostic accuracy of the pathologist, standardise LADC subtype recognition, and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.

pathology↗