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A novel protein expression signature differentiates benign lipomas from well-differentiated liposarcomas.

ABSTRACT: Benign lipomas and well-differentiated liposarcomas share many histological and molecular features. Due to their similarities, patients with these lipomatous tumors are misdiagnosed up to 40% of the time following radiological detection, up to 17% of the time following histological examination, and in as many as 15% of cases following fluorescent in situ hybridization for chromosomal anomalies. Incorrect classification of these two tumor types leads to increased costs to the patient and delayed accurate diagnoses. In this study, we used genomics analysis to identify several genes whose mRNA expression patterns were significantly altered between lipomas and well-differentiated liposarcomas. We confirmed our findings at the protein level using a panel of 30 human lipomatous tumors, revealing that C4BPB, class II, major histocompatibility complex, CIITA, EPHB2, HOXB7, GLS2, RBBP5, and regulator of RGS2 protein levels were increased in well-differentiated liposarcomas compared to lipomas. We developed a multi-protein model of these markers to increase discriminatory ability, finding the combined expression model with CIITA and RGS2 provided a high ability (AUC=0.93) to differentiate between lipomas and well-differentiated liposarcomas with sensitivity at 83.3% and specificity at 90.9%.

SUBMITTER: Mather Q

PROVIDER: S-EPMC5530308 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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A novel protein expression signature differentiates benign lipomas from well-differentiated liposarcomas.

Mather Quang Q Priego Jonathon J Ward Kristi K Kundan Verma V Tran Dat D Dwivedi Alok A Bryan Brad A BA

Molecular and clinical oncology 20170713 3

Benign lipomas and well-differentiated liposarcomas share many histological and molecular features. Due to their similarities, patients with these lipomatous tumors are misdiagnosed up to 40% of the time following radiological detection, up to 17% of the time following histological examination, and in as many as 15% of cases following fluorescent <i>in situ</i> hybridization for chromosomal anomalies. Incorrect classification of these two tumor types leads to increased costs to the patient and d ...[more]

PMID: 28781809

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Project description:Background To develop a machine learning model based on tumor-to-bone distance and radiomic features derived from preoperative MRI images to distinguish intramuscular (IM) lipomas and atypical lipomatous tumors/well-differentiated liposarcomas (ALTs/WDLSs) and compared with radiologists. Methods The study included patients with IM lipomas and ALTs/WDLSs diagnosed between 2010 and 2022, and with MRI scans (sequence/field strength: T1-weighted (T1W) imaging at 1.5 or 3.0 Tesla MRI). Manual segmentation of tumors based on the three-dimensional T1W images was performed by two observers to appraise the intra- and interobserver variability. After radiomic features and tumor-to-bone distance were extracted, it was used to train a machine learning model to distinguish IM lipomas and ALTs/WDLSs. Both feature selection and classification steps were performed using Least Absolute Shrinkage and Selection Operator logistic regression. The performance of the classification model was assessed using a tenfold cross-validation strategy and subsequently evaluated using the receiver operating characteristic curve (ROC) analysis. The classification agreement of two experienced musculoskeletal (MSK) radiologists was assessed using the kappa statistics. The diagnosis accuracy of each radiologist was evaluated using the final pathological results as the gold standard. Additionally, we compared the performance of the model and two radiologists in terms of the area under the receiver operator characteristic curves (AUCs) using the Delong’s test. Results There were 68 tumors (38 IM lipomas and 30 ALTs/WDLSs). The AUC of the machine learning model was 0.88 [95% CI 0.72–1] (sensitivity, 91.6%; specificity, 85.7%; and accuracy, 89.0%). For Radiologist 1, the AUC was 0.94 [95% CI 0.87–1] (sensitivity, 97.4%; specificity, 90.9%; and accuracy, 95.0%), and as to Radiologist 2, the AUC was 0.91 [95% CI 0.83–0.99] (sensitivity, 100%; specificity, 81.8%; and accuracy, 93.3%). The classification agreement of the radiologists was 0.89 of kappa value (95% CI 0.76–1). Although the AUC of the model was lower than of two experienced MSK radiologists, there was no statistically significant difference between the model and two radiologists (all P > 0.05). Conclusions The novel machine learning model based on tumor-to-bone distance and radiomic features is a noninvasive procedure that has the potential for distinguishing IM lipomas from ALTs/WDLSs. The predictive features that suggested malignancy were size, shape, depth, texture, histogram, and tumor-to-bone distance. Supplementary Information The online version contains supplementary material available at 10.1186/s13018-023-03718-4.

Project description:Myxoid liposarcoma (LS), the most common subtype of LS, is known to be characterized by the specific t(12;16) resulting in a TLS-CHOP fusion in almost all cases. We wished to address the following questions: (i) Is this genetic hallmark also present in other types of LS with predominant myxoid change? (ii) What is the proportion of cases with the variant EWS-CHOP fusion? (iii) What is the optimal approach for Southern blot detection of TLS breakpoints? We identified 59 LS characterized histologically by >90% myxoid component, in which frozen tissue tumor was available for DNA extraction. These 59 LS with myxoid features were divided into 2 groups: 42 LS with classic myxoid/round cell appearance (myxoid LS) and 17 well-differentiated LS (WDLS) with a predominant (>90%) myxoid component. Within the myxoid LS group, 29 tumors were low grade and 13 high grade (>20% round cell component). Among the 17 predominantly myxoid WDLS, there were 15 low grade and 2 focally high grade tumors. In addition, we selected as control group, 20 LS of other histological types with minimal or no myxoid change (17 WDLS and 3 pleomorphic LS) and 13 myxofibrosarcomas. Southern blot analysis was performed in all cases using a CHOP cDNA probe, and in all CHOP rearranged cases using a TLS cDNA probe. Probe/enzyme combinations for Southern blot analysis were CHOP exon 3-4 cDNA probe with BamHI or SacI, TLS exon 3-6 cDNA probe with BclI. All 42 cases of myxoid LS showed a CHOP rearrangement and 38 of them also had a TLS rearrangement. Among the 4 myxoid LS without Southern blot evidence of TLS rearrangement, 1 showed an EWS-CHOP fusion by Southern blotting and reverse transcriptase-polymerase chain reaction and in another case, reverse transcriptase-polymerase chain reaction detected a TLS-CHOP fusion transcript. None of the predominantly myxoid WDLS and none of the tumors included in the control group showed rearranegements with CHOP probe. In addition, 12 predominantly myxoid WDLS, 10 other LS, and 5 myxofibrosarcoma from the control group were also tested for TLS rearrangement; all were negative. The TLS-CHOP fusion is highly sensitive and specific for the entity of classic myxoid/round cell LS. Other types of LS, even with a predominant myxoid component, lack the TLS-CHOP rearrangement, confirming that they represent a genetically distinct group of LS. The prevalence of the EWS-CHOP variant fusion was approximately 2% in this series. The optimal enzyme for TLS genomic breakpoint detection is BclI.

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Dataset Information

A novel protein expression signature differentiates benign lipomas from well-differentiated liposarcomas.

Publications

A novel protein expression signature differentiates benign lipomas from well-differentiated liposarcomas.

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