Tumor-Infiltrating Lymphocytes in Endometrial Cancer

Endometrium Cancer, Tumor Infiltration

endometrial cancer, tumor-infiltrating lymphocytes, response biomarkers, artificial intelligence, immune microenvironment

Endometrial cancer (EC) is a leading cancer among women globally. The tumor microenvironment in EC is characterized by complex interactions between cancer cells and immune components. Among these proteins, CD133, WNT-1, and mTOR have emerged as key molecular markers with potential prognostic and therapeutic implications in EC. Understanding the association between these molecular alterations and the immune contexture of EC can provide valuable insights into EC biology and lead to the identification of novel therapeutic targets. In this study, the spatial organization of tumor-infiltrating lymphocytes (TILs) in EC and their correlations with tumor grade, stage, and subcellular CD133, WNT-1, and mTOR expression were investigated. Artificial intelligence-assisted image analysis was performed to quantify TIL metrics, including TIL percentage, grey level co-occurrence matrix (GLCM M1 and M2) parameters, and fractal dimension (FD).

The study was conducted using properly stored archival formalin-fixed paraffin-embedded tissue blocks. The inclusion criteria required a confirmed diagnosis of EC, adequate quality of archival material, absence of prior neoadjuvant treatment, and complete medical documentation. Tumor staging followed the FIGO classification system based on surgical protocols and pathomorphological examination results. For analytical purposes, patients results were stratified into two groups based on tumor grade: a low-grade group (grade 1 and 2) and a high-grade group (grade 3). Cancer cells and lymphocytes were identified using Hover-Net, a state-of-the-art nucleic segmentation and classification algorithm. Detected cells were categorized into six categories: unlabeled, neoplastic (cancer), inflammatory (TILs, i.e., lymphocytes and plasma cells), connective, necrosis, and non-neoplastic. To estimate cancer areas from cancer cell segmentation masks, a novel block-processing algorithm optimized for large image analysis, was developed. For each tissue sample, the TIL percentage as the area occupied by lymphocytes divided by the cancer area, expressed as a percentage, was calculated. TIL distribution maps were constructed using tissue segmentation masks, cancer region masks, and TIL segmentation masks. Spatial TIL metrics were subsequently calculated based on GLCM analysis and FD. After grey level co-occurrence matrix (GLCM) calculation, different weights were applied to each matrix element to derive two measures: M1 and M2, representing areas with low and high intensities, respectively. Lower M1 and higher M2 values characterized more structured images with distinct TIL patterns. FD provided a statistical index of pattern complexity in geometric structures. A curve with an FD close to 1 resembles an ordinary line (simple structure), while curves with higher FD values exhibit convoluted spatial arrangements resembling spaces. Higher FD values thus indicate more structured and complex TIL distribution patterns. Data were analyzed using Dell Statistica software v13.3 (TIBCO Software Inc., Palo Alto, California, United States) and MedCalc Statistical Software v19.2.6 (MedCalc Software, Ostend, Belgium).

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