DNA Sequencing of CD138 Cell Population Reveals TP53 and RAS-MAPK Mutations in Multiple Myeloma at Diagnosis.
Mihaela DragomirOnda-Tabita CălugăruBogdan PopescuCerasela JardanDumitru JardanMonica PopescuSilvia AposteanuSorina Nicoleta BădeliţăGabriela NedelcuCătălin ȘerbanCodruța PopaTatiana Vassu-DimovDaniel CoriuPublished in: Cancers (2024)
Multiple myeloma is a hematologic neoplasm caused by abnormal proliferation of plasma cells. Sequencing studies suggest that plasma cell disorders are caused by both cytogenetic abnormalities and oncogene mutations. Therefore, it is necessary to detect molecular abnormalities to improve the diagnosis and management of MM. The main purpose of this study is to determine whether NGS, in addition to cytogenetics, can influence risk stratification and management. Additionally, we aim to establish whether mutational analysis of the CD138 cell population is a suitable option for the characterization of MM compared to the bulk population. Following the separation of the plasma cells harvested from 35 patients newly diagnosed with MM, we performed a FISH analysis to detect the most common chromosomal abnormalities. Consecutively, we used NGS to evaluate NRAS, KRAS, BRAF, and TP53 mutations in plasma cell populations and in bone marrow samples. NGS data showed that sequencing CD138 cells provides a more sensitive approach. We identified several variants in BRAF, KRAS, and TP53 that were not previously associated with MM. Considering that the presence of somatic mutations could influence risk stratification and therapeutic approaches of patients with MM, sensitive detection of these mutations at diagnosis is essential for optimal management of MM.
Keyphrases
- single cell
- induced apoptosis
- newly diagnosed
- bone marrow
- multiple myeloma
- wild type
- cell cycle arrest
- sensitive detection
- cell therapy
- signaling pathway
- end stage renal disease
- oxidative stress
- ejection fraction
- mesenchymal stem cells
- chronic kidney disease
- endoplasmic reticulum stress
- single molecule
- prognostic factors
- machine learning
- cell proliferation
- pi k akt
- gene expression
- cell death
- big data
- artificial intelligence
- genome wide
- cell free
- high grade
- circulating tumor
- circulating tumor cells