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Autophagic Markers in Chordomas: Immunohistochemical Analysis and Comparison with the Immune Microenvironment of Chordoma Tissues.

ABSTRACT: Chordomas are notably resistant to chemotherapy. One of the cytoprotective mechanisms implicated in chemoresistance is autophagy. There are indirect data that autophagy could be implicated in chordomas, but its presence has not been studied in chordoma tissues. Sixty-one (61) chordomas were immunohistochemically studied for autophagic markers and their expression was compared with the expression in notochords, clinicopathological data, as well as the tumor immune microenvironment. All chordomas strongly and diffusely expressed cytoplasmic p62 (sequestosome 1, SQSTM1/p62), whereas 16 (26.2%) tumors also showed nuclear p62 expression. LC3B (Microtubule-associated protein 1A/1B-light chain 3B) tumor cell expression was found in 44 (72.1%) tumors. Autophagy-related 16‑like 1 (ATG16L1) was also expressed by most tumors. All tumors expressed mannose-6-phosphate/insulin-like growth factor 2 receptor (M6PR/IGF2R). LC3B tumor cell expression was negatively associated with tumor size, while no other parameters, such as age, sex, localization, or survival, were associated with the immunohistochemical factors studied. LC3B immune cell expression showed a significant positive association with programmed death-ligand 1 (PD-L1)+ immune cells and with a higher vascular density. ATG16L1 expression was also positively associated with higher vascular density. Notochords (n = 5) showed different immunostaining with a very weak LC3B and M6PR expression, and no p62 expression. In contrast to normal notochords, autophagic factors such as LC3B and ATG16L1 are often present in chordomas, associated with a strong and diffuse expression of p62, suggesting a blocked autophagic flow. Furthermore, PD-L1+ immune cells also express LC3B, suggesting the need for further investigations between autophagy and the immune microenvironment.

SUBMITTER: Karpathiou G

PROVIDER: S-EPMC8124629 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are particularly prone to missingness. A variety of methods to deal with missing data are available. However, the validity of the various approaches is dependent on the structure of the missing data and there are few empirical studies dealing with missing data from molecular pathology. The purpose of this study was to investigate the results of four commonly used approaches to handling missing data from a large, multi-centre study of the molecular pathological determinants of prognosis in breast cancer.Patients and methodsWe pooled data from over 11,000 cases of invasive breast cancer from five studies that collected information on seven prognostic indicators together with survival time data. We compared the results of a multi-variate Cox regression using four approaches to handling missing data - complete case analysis (CCA), mean substitution (MS) and multiple imputation without inclusion of the outcome (MI-) and multiple imputation with inclusion of the outcome (MI+). We also performed an analysis in which missing data were simulated under different assumptions and the results of the four methods were compared.ResultsOver half the cases had missing data on at least one of the seven variables and 11 percent had missing data on 4 or more. The multi-variate hazard ratio estimates based on multiple imputation models were very similar to those derived after using MS, with similar standard errors. Hazard ratio estimates based on the CCA were only slightly different, but the estimates were less precise as the standard errors were large. However, in data simulated to be missing completely at random (MCAR) or missing at random (MAR), estimates for MI+ were least biased and most accurate, whereas estimates for CCA were most biased and least accurate.ConclusionIn this study, empirical results from analyses using CCA, MS, MI- and MI+ were similar, although results from CCA were less precise. The results from simulations suggest that in general MI+ is likely to be the best. Given the ease of implementing MI in standard statistical software, the results of MI+ and CCA should be compared in any multi-variate analysis where missing data are a problem.

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Autophagic Markers in Chordomas: Immunohistochemical Analysis and Comparison with the Immune Microenvironment of Chordoma Tissues.

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