Introduction

Adipocytic tumors present a spectrum of neoplastic disease including benign lipomas, atypical lipomatous tumors (ALTs), and malignant liposarcomas. Liposarcomas are the most common soft tissue sarcoma, accounting for approximately 20% of all adult soft tissue sarcomas [1]. Some liposarcomas are suspected to arise through dedifferentiation of ALTs, a process that is not well understood. Distinguishing high-grade liposarcomas (DDLs) and premalignant ALTs from benign lipomas can be a diagnostic challenge with implications for surgical and clinical management. The rate of misdiagnosis is approximately 30–40% following radiological detection [2], and 7–17% on histological evaluation [3]. A major objective of the present work is to develop metabolic determinants for adipocytic tumor classification and to identify metabolic targets. Our data demonstrate that high-resolution 1H magnetic resonance spectroscopy (MRS) may be used as an additional method for classification and differential diagnosis of DDLs, ALTs and benign lipomas.

Methods

De-identified human surgical samples were collected from the Department of Orthopaedic Surgery, The Johns Hopkins University School of Medicine Baltimore, MD. Tissue specimens from benign lipoma (n=10), ALTs (n=8), dedifferentiated liposarcoma (n=5) and adjacent non-involved fat (n=8, normal fat) were examined using 1H MRS. Tissue samples were snap frozen and stored at -80°C until 1H MRS analysis. Dual phase solvent extraction was performed on approximately 300 mg of tumor tissue. The water phase was separated, freeze-dried, and reconstituted in 600ul D2O PBS for MRS analysis. All MR spectra were acquired on an Avance III 750 MHz (17.6T) Bruker NMR spectrometer

Results & Discussion

Representative spectra obtained from 1H MRS of water phase tumor samples are shown in Figures 1. The metabolic heatmap (Figure 2) clearly identified the different metabolic patterns of ALTs and DDLs compared to normal fat and benign lipoma. These results provide new insights into the metabolic differences between benign, premalignant and malignant tissue that may be exploited for differential diagnosis and for metabolism based therapeutic strategies. Computational modeling of pattern recognition based cluster analysis on large training data set may provide a useful companion diagnostic tool for differentiating ALTs/DDLs from benign lipomas. A significant increase in lactate levels was observed in ALTs/DDLs compared to lipomas and normal fat. Lactate, glucose and formate were found significantly high in DDLs as compared to ALTs, lipomas and normal fat as well. Although lactate is a non-specific biomarker, it has been used to grade tumors subtypes in several cancers [4, 5]. Glutamine, aspartate, taurine, were significantly higher in ATLs/DDLs as compared to normal fat. We are currently analyzing additional samples from lipomas, ALTs, and dedifferentiated liposarcomas (DDls) to expand this study. Our preliminary data support investigating the use of 1H MRS of liposarcomas for differentiation between subtypes, and to understand malignant progression.

Acknowledgements

This work was supported by NIH R35CA209960.

References

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