Abstract
The absence of standardized molecular profiling to differentiate uterine leiomyosarcomas versus leiomyomas represents a current diagnostic challenge. In this study, we aimed to search for a differential molecular signature for these myometrial tumors based on artificial intelligence. For this purpose, differential exome and transcriptome-wide research was performed on histologically confirmed leiomyomas (n = 52) and leiomyosarcomas (n = 44) to elucidate differences between and within these two entities. We identified a significantly higher tumor mutation burden in leiomyosarcomas vs. leiomyomas in terms of somatic single-nucleotide variants (171,863 vs. 81,152), indels (9491 vs. 4098), and copy number variants (8390 vs. 5376). Further, we discovered alterations in specific copy number variant regions that affect the expression of some tumor suppressor genes. A transcriptomic analysis revealed 489 differentially expressed genes between these two conditions, as well as structural rearrangements targeting ATRX and RAD51B. These results allowed us to develop a machine learning approach based on 19 differentially expressed genes that differentiate both tumor types with high sensitivity and specificity. Our findings provide a novel molecular signature for the diagnosis of leiomyoma and leiomyosarcoma, which could be helpful to complement the current morphological and immunohistochemical diagnosis and may lay the foundation for the future evaluation of malignancy risk.
Highlights
Uterine leiomyomas (LM) are benign tumors arising in the smooth muscle cells of the uterine wall
Since we detected a molecular phenotype with a defect in the DNA mismatch repair system, we evaluated microsatellite instability (MSI) status to predict the outcome in LM and LMS tumors, no differences were found in the number of alleles or the fragment size (Supplementary Figure S2)
The search for molecular criteria to differentiate uterine myometrial tumors represents an important current diagnostic challenge, where molecular profiling could be a powerful complement to current diagnosis based on the clinical presentation, imaging features, and microscopic morphologic characteristics
Summary
Uterine leiomyomas (LM) are benign tumors arising in the smooth muscle cells of the uterine wall. They are the most common pelvic tumors in women, with a prevalence of >80% for African American and ~70% for Caucasian women before 50 years of age [1]. LM are non-malignant tumors, the risk of hidden undiagnosed malignancy, such as leiomyosarcoma (LMS), occurs in one among 498 uterine tumors [2–4]. Histological diagnosis is the gold standard option for myometrial tumors [5,6]. LM and LMS share clinical symptoms and morphological features [7,8], sometimes hindering their differential diagnosis and introducing the risk of the future potential spread of undiagnosed LMS with the use of power morcellators [9]. Alternative invasive approaches, such as laparotomy-based procedures, increase morbidity, mortality, and cost for the patient and healthcare system [10]
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