Atypical spindle cell lipomatous tumor/atypical pleomorphic lipomatous tumor and atypical mammary-type myofibroblastoma: clinicopathological, immunohistochemical, and multiplex fluorescence in situ hybridization analyses.
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ABSTRACT:
SUBMITTER: Zhang Z
PROVIDER: S-EPMC8509905 | biostudies-literature | 2021 Sep
REPOSITORIES: biostudies-literature
Publications
Atypical spindle cell lipomatous tumor/atypical pleomorphic lipomatous tumor and atypical mammary-type myofibroblastoma: clinicopathological, immunohistochemical, and multiplex fluorescence in situ hybridization analyses.
Zhang Zhang Z Liu Nan N Chen Min M Peng Ran R Chen Hui-Jiao HJ Zhang Hong-Ying HY
Chinese medical journal 20210916 19
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Project description:AimsWell-differentiated lipomatous neoplasms encompass a broad spectrum of benign and malignant tumours, many of which are characterised by recurrent genetic abnormalities. Although a key regulator of p53 signalling, MDM2, is characteristically amplified in well-differentiated liposarcoma, recurrent abnormalities of p53 itself have not been reported in well-differentiated adipocytic neoplasms. Here, we present a series of well-differentiated lipomatous tumours characterised by p53 alterations and histological features in keeping with atypical pleomorphic lipomatous tumour (APLT).Methods and resultsWe reviewed the morphological, immunohistochemical and molecular genetic features of eight lipomatous tumours with p53 alterations. Four tumours arose in the thigh, and one case each arose in the shoulder, calf, upper back, and subclavicular regions; six tumours were deep/subfascial and two were subcutaneous. Relevant clinical history included two patients with Li-Fraumeni syndrome. Morphologically, all cases showed well-differentiated adipocytes with prominent nuclear pleomorphism, limited mitotic activity, and no tumour cell necrosis. All cases were negative for MDM2 overexpression and amplification as determined with immunohistochemistry and fluorescence in-situ hybridisation, respectively. Immunohistochemically, p16 was diffusely overexpressed in all cases; seven tumours (88%) showed abnormal loss of Rb and p53. TP53 mutation or deletion was identified in four of six tumours evaluated with exon-targeted hybrid capture-based massively parallel sequencing; RB1 mutation or deletion was present in five of six cases.ConclusionsWe present a series of eight well-differentiated lipomatous neoplasms characterised by p53 alterations in addition to Rb loss and histological features of APLT. These findings suggest that impaired p53 signalling may contribute to the pathogenesis of APLT in a subset of cases.
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Project description:IntroductionAtypical spindle cell lipomatous tumor (ASPLT), a separate entity for the group of benign/low grade adipocytic neoplasm that is characterized by adipocyte adequacy and the presence of lipoblast-like cells and spindle cells at varying degrees.Case presentationHere, we report a rare case of 60 years old male with atypical spindle cell lipomatous tumor in the anterior abdominal wall.DiscussionHistopathology is the gold standard for establishing the diagnosis and grade of soft tissue tumor and consistent radiology-pathology correlation is essential to avoid any diagnostic pitfalls [1]. Ultrasound is preferred as an initial investigation for superficially located lesions.ConclusionASPLT show a wide variety of microscopic features, and differential diagnosis is important and difficult. Recognition of morphologic clues and immunohistochemistry/molecular tests to confirm the diagnosis.
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Project description:IntroductionPleomorphic adenoma is the most common benign neoplasm of the salivary glands. Extensive lipomatous involvement of the tumor is, however, a very rare finding.Case reportHerein, a rare case of lipomatous pleomorphic adenoma arising in the parotid gland of a 14-year-old Japanese woman is presented.ConclusionThis is the sixth case of lipomatous pleomorphic adenoma in the English literature. Recognition of this rare subtype of pleomorphic adenoma is important for clinical diagnosis and management. On CT scan, it may not be detected possibly due to the extensive fatty component.
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Project description:Atypical fibroxanthoma (AFX) and pleomorphic dermal sarcoma (PDS) are rare tumors developing in chronically sun-exposed skin. Clinicopathological features are similar, but they differ in prognosis, while PDS has a more aggressive course with a higher risk for local recurrence and metastases. In current clinical practice, they are diagnosed by exclusion using immunohistochemistry. Thus, stringent diagnostic criteria and correct differentiation are critical in management and treatment for optimal outcomes. This retrospective single-center study collected clinicopathological data and tumor samples of 10 AFX and 18 PDS. Extracted genomic DNA from tumor specimens was analyzed by a next-generation sequencing (NGS) platform (FoundationOne-CDx™). Among 65 identified mutations, TP53 inactivating mutations were observed in all tumor specimens. In both AFX and PDS, the known pathogenic gene alterations in CDKN2A, TERT promoter, and NOTCH1 were frequently present, along with high mutational burden and stable Micro-Satellite Instability status. The mutational profiles differed only in ASXL1, which was only present in AFX. Further differences were identified in likely pathogenic and unknown gene alterations. Similarities in their genomic signatures could help to distinguish them from other malignancies, but they are not distinguishable between each other using the FoundationOne-CDx™ NGS panel. Therefore, histological criteria to determine diagnosis remain valid. For further insight, performing deep tumor profiling may be necessary.
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Project description:PurposeTo determine diagnostic performance of MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor (ALT) of the extremities.Material and methodsThis retrospective study was performed at three tertiary sarcoma centers and included 150 patients with surgically treated and histology-proven lesions. The training-validation cohort consisted of 114 patients from centers 1 and 2 (n = 64 lipoma, n = 50 ALT). The external test cohort consisted of 36 patients from center 3 (n = 24 lipoma, n = 12 ALT). 3D segmentation was manually performed on T1- and T2-weighted MRI. After extraction and selection of radiomic features, three machine learning classifiers were trained and validated using nested fivefold cross-validation. The best-performing classifier according to previous analysis was evaluated and compared to an experienced musculoskeletal radiologist in the external test cohort.ResultsEight features passed feature selection and were incorporated into the machine learning models. After training and validation (74% ROC-AUC), the best-performing classifier (Random Forest) showed 92% sensitivity and 33% specificity in the external test cohort with no statistical difference compared to the radiologist (p = 0.474).ConclusionMRI radiomics-based machine learning may classify deep-seated lipoma and ALT of the extremities with high sensitivity and negative predictive value, thus potentially serving as a non-invasive screening tool to reduce unnecessary referral to tertiary tumor centers.
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Project description:BackgroundLipomatous hypertrophy of the interatrial septum (LHIAS) is a common finding on transthoracic echocardiogram (TTE). Occasionally, the appearance of LHIAS is atypical and multimodality imaging is helpful to make the diagnosis. We present a case of atypical LHIAS to highlight the potential aetiologies for an interatrial septal mass and review features on multimodality imaging that help decrease uncertainty and establish a diagnosis.Case summaryA 64-year-old man with a history of hypertension, diabetes mellitus, and coronary artery disease with multiple percutaneous coronary interventions presented to the emergency room with chest pain. Transthoracic echocardiogram showed a homogenous echo-dense, intracardiac mass present within the interatrial septum. Computed tomography (CT) angiogram of the chest showed a homogenous mass similar in radiodensity to extracardiac and pericardial fat. Cardiac magnetic resonance (CMR) confirmed LHIAS by homogenous signal that was nulled on fat suppression images.DiscussionThis case highlights that while most LHIAS has the standard 'dumbbell' appearance on TTE, there are instances where it can appear more like an adherent mass prompting a wider differential. Unenhanced CT of the heart can be used to confirm LHIAS by the presence of low attenuation values for tissue. Alternatively, CMR can be used for tissue characterization and confirmation of LHIAS. Precontrast T2/T1-weighted CMR images with steady-state free precession show high signal intensity in the area of LHIAS and produce a black/hypointense boundary effect between fat and myocardium. A multimodality approach is crucial in arriving at the appropriate diagnosis using the tissue characterization capabilities of CT and CMR.
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Project description:BackgroundThe aim of this study was to develop and validate radiogenomic models to predict the MDM2 gene amplification status and differentiate between ALTs and lipomas on preoperative MR images.MethodsMR images were obtained in 257 patients diagnosed with ALTs (n = 65) or lipomas (n = 192) using histology and the MDM2 gene analysis as a reference standard. The protocols included T2-, T1-, and fat-suppressed contrast-enhanced T1-weighted sequences. Additionally, 50 patients were obtained from a different hospital for external testing. Radiomic features were selected using mRMR. Using repeated nested cross-validation, the machine-learning models were trained on radiomic features and demographic information. For comparison, the external test set was evaluated by three radiology residents and one attending radiologist.ResultsA LASSO classifier trained on radiomic features from all sequences performed best, with an AUC of 0.88, 70% sensitivity, 81% specificity, and 76% accuracy. In comparison, the radiology residents achieved 60-70% accuracy, 55-80% sensitivity, and 63-77% specificity, while the attending radiologist achieved 90% accuracy, 96% sensitivity, and 87% specificity.ConclusionA radiogenomic model combining features from multiple MR sequences showed the best performance in predicting the MDM2 gene amplification status. The model showed a higher accuracy compared to the radiology residents, though lower compared to the attending radiologist.
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Project description: Not available
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Project description:BackgroundDistinguishing 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.PurposeTo identify metabolic biomarkers of the lipomatous tumor spectrum by examining human tissue specimens using high-resolution 1H magnetic resonance spectroscopy (MRS).Materials and methodsIn 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.ResultsSignificant 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.ConclusionMetabolic signatures identified in 1H 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.
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