Ki67 and LSD1 Expression in Testicular Germ Cell Tumors Is Not Associated with Patient Outcome: Investigation Using a Digital Pathology Algorithm

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Abstract

TGCTs represent a model of curable disease afflicting especially young men. Defining tumor biological characteristics is crucial to increase current knowledge and tailor the best clinical management. Ki67, a potential prognostic marker, still exhibits heterogenous associations with patient outcomes, thus bringing the need of corroboration with larger cohorts in clinical practice. LSD1, an epigenetic enzyme, represents a future target for epigenetic drugs that may lower treatmentassociated morbidity. This study aimed to assess Ki67/LSD1 immunoexpression across all TGCT histological subtypes and correlate it with clinicopathological features. Results were compared with an in silico analysis of the TCGA database. Immunohistochemistry for Ki67 and LSD1 was carried out in a cohort of 157 TGCT tumor samples and assessed using a digital pathology algorithm. LSD1 protein expression was explored in TGCT cell lines, including ATRA-differentiated clones. There was a significant positive correlation between Ki67 and LSD1 H-scores (rs = 0.182, p = 0.037). Ki67 positivity percentage and H-score were significantly higher in non-seminomas (p = 0.0316 and 0.0113, respectively). Expression was not significantly different according to clinicopathological features, including stage, IGCCCG prognosis-based system, or relapse/progression-free survival, which was corroborated by in silico analysis. Our study, making use of digital image analysis, does not confirm the utility of these biomarkers in a daily practice cohort. Although not affecting patient outcome in our cohort, LSD1 is expressed overall in TGCTs, suggesting sensitivity to LSD1 inhibitors.

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Lourenço, B. C., Guimarães-Teixeira, C., Flores, B. C. T., Miranda-Gonçalves, V., Guimarães, R., Cantante, M., … Lobo, J. (2022). Ki67 and LSD1 Expression in Testicular Germ Cell Tumors Is Not Associated with Patient Outcome: Investigation Using a Digital Pathology Algorithm. Life, 12(2). https://doi.org/10.3390/life12020264

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