Pure Seminoma Subtyping Using Computational Approaches

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Medvedev, Kirill E.
Savelyeva, Anna V.
Bagrodia, Aditya
Jia, Liwei
Grishin, Nick V.

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Testicular germ cell tumors (TGCT) being the most common solid malignancy in adolescent and young men, are second in terms of the average life years lost per person dying of cancer. Two major types of TGCTs are seminoma and non-seminoma (NSE). Management of patients with seminoma includes orchiectomy, platinum-based chemotherapy or radiation therapy. Despite a high patient survival rate, current treatments significantly decrease patients’ quality of life and lead to around 40 severe side effects. We conducted a computational study of 64 pure seminomas (the most common subtype of TGCTs) available at TCGA. Consensus clustering approach of seminoma samples based on transcriptomic data identified two distinct subtypes that showed differences in pluripotency stage, activity of double stranded DNA breaks repair mechanisms, rates of loss of heterozygosity, DNA methylation, expression of lncRNA associated with cisplatin resistance and level of lymphocytes infiltration. Seminoma subtype2 shows signs of differentiation into NSE and therefore may have higher resistance to platinum-based chemotherapy. Despite of the high level of lymphocyte infiltration, TGCTs immunotherapy clinical trials were shut down due to lacking clinical efficacy. We identified 20 significantly overexpressed genes in subtype2 that are related to senescence-associated secretory phenotype. This fact and data on altered pathways in subtype2 allow us to hypothesize that senescence of seminoma infiltrating lymphocytes can be one of the reasons for immunotherapy failure. Using all available histopathological slides of pure seminoma at TCGA we developed test version of deep learning (DL) decision making tool for identification of seminoma subtypes using only slide images (accuracy 0.864). As future direction we plan to develop DL tool for identification of seminoma subtypes using whole slide images (WSI). This approach will simplify utilization of this tool by pathologists but also requires significantly more powerful computational resources and we anticipate to use TACC resources for this task.



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