Methylation-based Machine Learning Classifiers Discriminate Sellar Tumors By Lobe Origin Using Liquid Biopsy Or Surgical Specimens

Document Type

Conference Proceeding

Publication Date


Publication Title

J Endocr Soc


Background: The differential diagnosis of challenging sellar tumor cases can be inconclusive through imaging features and could benefit from noninvasive diagnostic approaches, such as liquid biopsy (LB). Similar to tissue, LB specimens carry tumor-specific DNA methylation signatures amenable to the construction of accurate machine learning models able to discriminate CNS tumors. We aimed to develop methylation-based classifiers which classify sellar tumors by lobe of origin, using either LB or tumor tissue specimens. Methodology: We analyzed the DNA methylome (EPIC array) of tumor tissue (T) and LB specimens from adult patients with tumors representing each of the three pituitary lobes (Anterior: T=177; LB=37; Intermediate: T= 7; LB: 10 and Posterior: T=44, LB=2 cases). Using the most variably methylated CpG probes derived from the unsupervised variance-based analyses across tumors from different lobes, we applied multi-class linear discriminant analysis to constructmachine learningmodels to classify sellar tumor tissue and/or LB specimens. Results: We generated classifiers based on lobe-specific methylation signatures that were able to discriminate across sellar tumors either using tissue and/or LB specimens (500 and 600 CpGs, respectively) with observed accuracies of ∼99% across independent validation. DISCUSSION/CONCLUSION: Our findings suggest that methylation-based classifiers constitute an accurate diagnostic approach to discriminate sellar tumors according to the lobe origin, either pre-surgically through a blood draw or through surgical tumor specimens. These classifiers are objective approaches that could complement imaging and pathology reports for an accurate diagnosis of inconclusive cases, ultimately leading to optimal management of the patients with these diseases.

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Not assigned.



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