Bharti Santosh Kumar, Shannon Brett A, Sharma Raj Kumar, Levin Adam S, Morris Carol D, Bhujwalla Zaver M, Fayad Laura M
Division of Cancer Imaging Research, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Department of Orthopaedic Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Front Oncol. 2022 Sep 9;12:920560. doi: 10.3389/fonc.2022.920560. eCollection 2022.
Distinguishing between some benign lipomas (BLs), atypical lipomatous tumors (ALTs), and dedifferentiated liposarcomas (DDLs) can be challenging due to overlapping magnetic resonance imaging characteristics, and poorly understood molecular mechanisms underlying the malignant transformation of liposarcomas.
To identify metabolic biomarkers of the lipomatous tumor spectrum by examining human tissue specimens using high-resolution H magnetic resonance spectroscopy (MRS).
In this prospective study, human tissue specimens were obtained from participants who underwent surgical resection for radiologically-indeterminate lipomatous tumors between November 2016 and May 2019. Tissue specimens were obtained from normal subcutaneous fat (n=9), BLs (n=10), ALTs (n=7) and DDLs (n=8). Extracts from specimens were examined with high-resolution MRS at 17.6T. Computational modeling of pattern recognition-based cluster analysis was utilized to identify significant differences in metabolic signatures between the lipomatous tumor types.
Significant differences between BLs and ALTs were observed for multiple metabolites, including leucine, valine, branched chain amino acids, alanine, acetate, glutamine, and formate. DDLs were distinguished from ALTs by increased glucose and lactate, and increased phosphatidylcholine. Multivariate principal component analysis showed clear clustering identifying distinct metabolic signatures of the tissue types.
Metabolic signatures identified in H MR spectra of lipomatous tumors provide new insights into malignant progression and metabolic targeting. The metabolic patterns identified provide the foundation of developing noninvasive MRS or PET imaging biomarkers to distinguish between BLs, ALTs, and DDLs.
由于磁共振成像特征重叠，以及脂肪肉瘤恶性转化的分子机制尚不明确，区分一些良性脂肪瘤（BL）、非典型脂肪瘤性肿瘤（ALT）和去分化脂肪肉瘤（DDL）具有挑战性。
通过使用高分辨率氢磁共振波谱（MRS）检查人体组织标本，识别脂肪瘤谱系的代谢生物标志物。
在这项前瞻性研究中，人体组织标本取自2016年11月至2019年5月间因放射学上不确定的脂肪瘤性肿瘤接受手术切除的参与者。组织标本取自正常皮下脂肪（n = 9）、BL（n = 10）、ALT（n = 7）和DDL（n = 8）。标本提取物在17.6T下用高分辨率MRS进行检查。利用基于模式识别的聚类分析的计算模型来识别脂肪瘤类型之间代谢特征的显著差异。
在多种代谢物中观察到BL和ALT之间存在显著差异，包括亮氨酸、缬氨酸、支链氨基酸、丙氨酸、乙酸盐、谷氨酰胺和甲酸盐。DDL与ALT的区别在于葡萄糖和乳酸增加，以及磷脂酰胆碱增加。多变量主成分分析显示出清晰的聚类，识别出不同组织类型的独特代谢特征。
在脂肪瘤的氢磁共振波谱中识别出的代谢特征为恶性进展和代谢靶向提供了新的见解。所识别的代谢模式为开发非侵入性MRS或PET成像生物标志物以区分BL、ALT和DDL奠定了基础。
