Machine Learning Analysis to Define Cell Lineage in Leiomyosarcoma

IntroductionCellular differentiation and lineage commitment are known to be associated with differences in DNA methylation. Leiomyosarcoma (LMS) is a tumor thought to originate from smooth muscle cells in the walls of vessels in the soft tissue (STLMS) or from the uterine myometrium (ULMS). Here, we identify the methylation signatures of normal smooth muscle cells from blood vessels and the uterine wall and compare these with those found in STLMS and ULMS. We hypothesized that these methylation signatures could be used to assign a smooth muscle subtype of origin to individual leiomyosarcomas, and that tumors of different origin would show biological differences with potential therapeutic relevance. MethodsTo define methylation profiles for smooth muscle from vessel walls versus those found in myometrium, EPIC methylation profiling was performed on DNA from 49 formalin-fixed paraffin-embedded (FFPE) normal smooth muscle samples. A supervised machine learning algorithm (Random Forest) was used to distinguish the methylation patterns of normal smooth muscle cells in vessel walls from those in the myometrium. The resulting classifier was applied to methylation data on 67 cases of LMS with corresponding bulk RNAseq data to identify which tumors showed a methylation signature most consistent with either blood vessel wall (LMSvessel) or myometrial smooth muscle (LMSwall). A custom signature matrix derived from scRNAseq data from 6 samples of LMS was used in CIBERSORTx analysis to compare the cellular composition of LMS cases with a vessel or uterine wall methylation signature. ResultsA high degree of correlation was found between the known site of origin for LMS (STLMS vs ULMS) and the methylation signature derived from different types of normal smooth muscle. LMSwall tumors compared to LMSvessel tumors had significantly higher activation of the PD-1 checkpoint pathway in RNAseq analysis. Digital flow cytometry by CIBERSORTx analysis showed an increased expression of transcriptomic signatures of several immune cell subtypes in LMSvessel tumors. ConclusionUsing a supervised machine learning approach we classified LMS samples as either showing a high similarity in methylation patterns to normal smooth muscle cells of either the vessel wall or the myometrium. We found a correlation between LMS showing either a "vessel" or "muscle wall" methylation signature and their site of origin, but notably we also identified some exceptions. When classified based on their methylation signature LMSwall and LMSvessel differed in their PD-1 pathway activation and in their predicted immune cell populations, suggesting potential implications for immunotherapeutic approaches.