Impact of Cell Density in Lymphocyte-rich Areas in the Tumor Microenvironment on Prognosis and Gene Expression Landscape in Hepatocellular Carcinoma.
- May. 2021
- by Jeonghyuk Park et. al.
Background: Cellular and non-cellular components in the tumor microenvironment (TME) impact prognosis and treatment in hepatocellular carcinoma (HCC). We previously reported a deep learning-based model of tissue segmentation in pathology images, showing an impact of stromal and malignant cell distribution with respect to gene expression on survival and molecular subtypes of cancer .
Methods: Clinical outcomes data, mRNA-seq, and histopathology images of 351 patients (pts) with HCC were obtained from TCGA. We established a combined algorithm of two deep learning models: ResNet-based model for tissue segmentation; YOLO-based model for cell detection, using published data sets [2, 3]. The tissue segmentation model defines six segments having following predominant components: malignant cells, lymphocytes, adipose, stromal, mucinous, and normal liver tissues. The cell detection model calculates density and mapping of cells in the TME. The immune landscape was analyzed via mRNA-seq of 770 genes enriched in TME. This comprehensive analysis defined parameters including the cell density per lymphocyte segmented area (CDpLA), representing the density of lymphocytes on a lymphocyte-rich area in TME.
Results: Pts were clustered into two groups with high and low CDpLA (212 and 139 pts). High CDpLA was defined as lymphocyte density > 0.5 (13,618 cells/mm2 lymphocyte area). Pts with high CDpLA showed significantly better median overall survival (OS) than those with low CDpLA (82.9 vs 37.8 month, p < 0.005). The hazard ratio of CDpLA in OS was 0.36 (95% CI 0.18-0.72, p < 0.005). Among pts with available clinical data, 29 and 21 pts were with hepatitis C (HCV) and hepatitis B (HBV). Out of 29 HCV pts, 23 and 6 pts were with high and low CDpLA; out of 21 HBV pts, 17 and 4 pts were with high and low CDpLA. Fifty three were with alcoholic abuse, and 26 and 27 pts were with high and low CDpLA. Of note, pts with high CDpLA had significantly better OS in HCV pts (61.7 vs 19.9 months, p < 0.005). Genomic analysis with mRNA-seq shows that HCV pts with high CDpLA have lower expression of genes related to myeloid-derived suppressor cells (TRANK1, MEGF9, HS3ST2, GPNMB) and higher in genes related to immune activation (PLD4, IL3RA, TNFRSF4).
Conclusions: A deep learning-assisted model of TME segmentation and cell detection showed an impact on survival from CDpLA, rather than the total number of lymphocytes in the TME. HCV pts are more likely to have higher CDpLA, and CDpLA was a strong prognostic indicator in HCV pts. Pts with high CDpLA are those with elevated expression of genes related to immune activation and decreased expression of immunosuppressive genes. Retrospective and prospective analysis of clinical response to immunotherapy and tyrosine kinase inhibitors is underway.
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Jeonghyuk Park, Kyungdoc Kim, Hong-Seok Lee, Guhyun Kang, Kyu-Hwan Jung, Ahmed Omar Kaseb, Sunyoung S. Lee