Morphometric and cytomorphologic characterization of EGFR-mutated cancer cells-comparison between cultured lung cancer cell lines and lung adenocarcinoma clinical samples

Сell clusters isolation in glioblastomas and their functional and molecular characterization using new morphometric approaches

BACKGROUND

Digital pathology has come a long way in terms of creating tools to improve existing diagnostic approaches. However, several pathology fields, such as neuropathology, are still characterized by low coverage from machine learning tools and neural network analysis, which may be due to the complexity of the internal cellular and molecular structure of the corresponding neoplasms, including glioblastomas.

METHOD

In the framework of this study, using advanced proprietary tools for obtaining images of histological slides and their deep morphometric analysis, we studied samples of 198 patients with glioblastoma with the selection of morphometric cell clusters. Also, cells of each cluster were isolated, and their proliferative, migratory, invasive activity, survival ability, aerobic glycolysis activity, and chemo- and radioresistance were studied.

RESULTS

Four morphometric clusters were identified, including small-cell cluster, paracirculonuclear cluster, hypochromic cluster, and macronuclear cluster, which significantly differed in morphometric parameters and functional parameters. Hypochromic cluster cells demonstrated the highest proliferation activity; macronuclear cluster was the most active glucose consumer; paracirculonuclear cluster had the most prominent migratory and invasive activity and hypoxia resistance; small-cell cluster demonstrated predominantly average values of all parameters. Moreover, additional analysis revealed the presence of a separate subcluster of stem cell elements that correspond in their molecular properties to glioma stem cells and are present in all four clusters. It also turned out that several key molecular parameters of glioblastoma, such as mutational modifications in the EGFR, PDGFRA, and NF1 genes, along with the molecular GBM subtype, are significantly correlated with the identified cell clusters.

CONCLUSIONS

Thus, the results represent an up-and-coming innovation in the practical field of digital pathology and fundamental questions of glioma carcinogenesis.

Cytomorphometric and flow cytometric analyses using liquid-based cytology materials in subtypes of lung adenocarcinoma

BACKGROUND

The histological classifications of invasive lung adenocarcinoma subtypes are considered to predict patient prognosis after surgical treatment. The objectives of this study were to evaluate cytomorphological characteristics and proliferative activities among the histological predominant patterns by performing cytomorphometric and flow cytometric analyses using liquid-based cytology materials.

METHODS

Cytological samples fixed by liquid-based cytology preservatives from 53 surgically-resected lung adenocarcinoma specimens were obtained between August 2018 and November 2019. The Papanicolaou-stained and paired Ki-67-stained slides were analyzed for calculating nuclear morphology (nuclear area, nuclear perimeter and nuclear circularity) and Ki-67 labeling index using software. The cell proliferation index (CPIx) was calculated and cellular information including cell cycle stage of tumor cells was obtained by flow cytometry.

RESULTS

The 53 cases included papillary (n = 29), acinar (n = 8), lepidic (n = 5), and solid (n = 4) subtypes, and invasive mucinous adenocarcinoma (n = 7) were also included. In the lepidic pattern, nuclear area (79.6 ± 28.8 μm² ) and perimeter (34.1 ± 6.1 μm) were relatively larger and longer than those of the other predominant patterns. The Ki-67 labeling index of the solid pattern (27.9 ± 12.5%) was highest compared with those of other predominant patterns. There were statistically significant differences in the lepidic versus solid patterns and the papillary versus solid patterns (p = .013 and p = .039, respectively). The calculated mean CPIx of the lepidic and the acinar patterns were approximately two-fold higher than those of the other predominant patterns.

CONCLUSION

By revealing the differences of cytomorphological characteristics, these methodologies might be used for diagnosing cytopathological materials using digital cytopathology.

Clinical Characteristics and Prognostic Factors in Bone Metastases from Lung Cancer

Background

This study investigated the prognostic factors of bone metastases from lung cancer.

Material/Methods

From March 2014 to March 2015, 168 patients with bone metastases from lung cancer treated at our hospital were included and the clinical data were reviewed. The Kaplan-Meier survival curves were calculated and analyzed using the log-rank univariate test. Multivariate regression analysis was conducted using Cox’s regression model.

Results

The overall median survival of the 168 patients was 13 months. The 1-year survival was 54.3% and the 2-year survival was 12.9%. Univariate regression analysis indicated that the pathologic types, number of bone metastases, clinical stage, ECOG scores, and serum ALP levels were significantly correlated with survival (P<0.05). Multivariate regression analysis indicated that the number of bone metastases, clinical stage, and serum ALP levels were significantly correlated with prognosis (P<0.05). The risk associated with multiple bone metastases was 1.72 times of that of single bone metastasis (P=0.029); the risk associated with advanced clinical stage was 1.49 times of that of early clinical stage (P=0.001); and the risk associated with a high serum ALP level was 1.75 times of that of the low serum ALP level (P=0.006).

Conclusions

Pathologic types, number of bone metastases, clinical stage, ECOG scores, and serum ALP levels were the prognostic factors for bone metastases from lung cancer.