Immunohistochemistry for TFE3 lacks specificity and sensitivity in the diagnosis of TFE3-rearranged neoplasms: a comparative, 2-laboratory study

Sharain, Rosalind F.; Gown, Allen M.; Greipp, Patricia T.; Folpe, Andrew L.

doi:10.1016/j.humpath.2019.02.008

Cited by 46 publications

(52 citation statements)

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“…We compared the performance of our method with that from other reported studies using IHC. Sharain et al 31 found in a two-laboratory study that the overall sensitivity and specificity of TFE3 IHC for TFE3-rearranged neoplasms was 85% and 57% at Laboratory A, and 70% and 95% at Laboratory B, leading to Youden indices of 0.42 and 0.65, respectively (Youden index = sensitivity + specificity−1). Their dataset contained 27 TFE3rearranged neoplasms and 98 controls.…”

Section: Discussionmentioning

confidence: 99%

Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma

et al. 2020

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TFE3 Xp11.2 translocation renal cell carcinoma (TFE3-RCC) generally progresses more aggressively compared with other RCC subtypes, but it is challenging to diagnose TFE3-RCC by traditional visual inspection of pathological images. In this study, we collect hematoxylin and eosin-stained histopathology whole-slide images of 74 TFE3-RCC cases (the largest cohort to date) and 74 clear cell RCC cases (ccRCC, the most common RCC subtype) with matched gender and tumor grade. An automatic computational pipeline is implemented to extract image features. Comparative study identifies 52 image features with significant differences between TFE3-RCC and ccRCC. Machine learning models are built to distinguish TFE3-RCC from ccRCC. Tests of the classification models on an external validation set reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894. Our results suggest that automatically derived image features can capture subtle morphological differences between TFE3-RCC and ccRCC and contribute to a potential guideline for TFE3-RCC diagnosis.

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Section: Discussionmentioning

confidence: 99%

Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma

et al. 2020

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“…Immunohistochemical reactivity for TFE3 in ASPS usually stains weak to moderately positive 7 . Sharain et al compared TFE3 rearrangement immunohistochemistry results between two laboratories 8 . The overall sensitivity of TFE3 immunohistochemistry to detect TFE3‐rearranged neoplasms was 85% in laboratory A, and 70% in laboratory B.…”

Section: Discussionmentioning

confidence: 99%

“…The difference in detection rate could be attributed to different fixation or processing techniques used by each laboratory, yet the presence or absence of immunohistochemical reactivity was not sufficient alone to confirm the diagnosis of ASPS since the specificities of IHC in lab A and lab B were 57% and 95% respectively. Also, a discordance between molecular and immunohistochemistry findings was reported in certain neoplasm in term of TFE3 rearrangement 8 …”

Section: Discussionmentioning

confidence: 99%

“…7 Sharain et al compared TFE3 rearrangement immunohistochemistry results between two laboratories. 8 The overall sensitivity of TFE3 immunohistochemistry to detect TFE3-rearranged neoplasms was 85% in laboratory A, and 70% in laboratory B. The difference in detection rate could be attributed to different fixation or processing techniques used by each laboratory, yet the presence or absence of immunohistochemical reactivity was not sufficient alone to confirm the diagnosis of ASPS since the specificities of IHC in lab A and lab B were 57% and 95% respectively.…”

Section: Casementioning

confidence: 95%

“…Also, a discordance between molecular and immunohistochemistry findings was reported in certain neoplasm in term of TFE3 rearrangement. 8 Although FISH analysis remained extremely sensitive and specific to detect Xp11.23 translocation associated with ASPS and Renal cell carcinoma (RCC), the possibility of chromosomal abnormalities could not be ruled out by FISH analysis alone. 9 In fact, a recent clinicalpathological review of 13 ASPS cases failed to show TFE3 rearrangements in four cases using FISH.…”

Section: Casementioning

confidence: 99%

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Lingual alveolar soft part sarcoma with absent TFE3 rearrangement

Alshahrani

Khan

2020

Cancer Reports

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Background Lingual ASPS is extremely rare and aggressive tumor. rearrangement is typically detected in ASPS patients using FISH analysis. Aim To present the clinical, histopathological, and radiological features of lingual Alveolar Soft Part Sarcoma. Method A 30‐year‐old male presented with a painless, slowly growing mass of the tongue. Initial impression was of benign vascular lesion. Later, the patient became symptomatic as the mass progressed in size, which necessitated further investigations. Result A lip‐split, mandibulotomy was performed for the excision of the tumor and revealed an alveolar soft part sarcoma with PAS‐positive, diastase resistant intracytoplasmic granules. However, molecular analysis using FISH was negative for TFE3 rearrangement. Patient underwent partial glossectomy with postoperative radiotherapy. Conclusion Clinical and pathological correlation of ASPS is very useful to reach a proper diagnosis.

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A nomogram based on TFE3 IHC results and clinical factors as a preliminary screening scheme for TFE3‐rearranged renal cell carcinoma

Li,

Xu,

Chen

et al. 2024

Cancer Medicine

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BackgroundTFE3 immunohistochemistry (TFE3‐IHC) is controversial in the diagnosis of TFE3‐rearranged renal cell carcinoma (TFE3‐rearranged RCC). This study is to investigate the accuracy and sensitivity of IHC and establish a predictive model to diagnose TFE3‐rearranged RCC.MethodsRetrospective analysis was performed by collecting IHC and fluorescence in situ hybridization (FISH) results from 228 patients. IHC results were evaluated using three scoring systems. Scoring system 1 is graded based on nuclear staining intensity, scoring system 2 is graded based on the percentage of stained tumor cell nuclei, and scoring system 3 is graded based on both the nuclear staining intensity and the percentage. We collected patients' IHC results and clinical information. Important variables were screened based on univariate logistic regression analysis. Then, independent risk factors were established through multivariate logistic regression, and a nomogram model was constructed. The model was validated in internal test set and external validation set. The receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis (DCA) were generated to assess discriminative ability of the model.ResultsThe accuracy of IHC based on three scoring systems were 0.829, 0.772, and 0.807, respectively. The model included four factors including age, gender, lymph node metastasis and IHC results. Area under the curve (AUC) values were 0.935 for the training set, 0.934 for the internal test set, 0.933 for all 228 patients, and 0.916 for the external validation set.ConclusionsTFE3 IHC has high accuracy in the diagnosis of TFE3‐rearranged RCC. Clinical information such as age and lymph node metastasis are independent risk factors, which can be used as a supplement to the results of TFE3 IHC. This study confirms the value of IHC in the diagnosis of TFE3‐rearranged RCC. The accuracy of the diagnosis can be improved by incorporating IHC with other clinical risk factors.

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Immunohistochemistry for TFE3 lacks specificity and sensitivity in the diagnosis of TFE3-rearranged neoplasms: a comparative, 2-laboratory study

Cited by 46 publications

References 50 publications

Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma

Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma

Lingual alveolar soft part sarcoma with absent TFE3 rearrangement

A nomogram based on TFE3 IHC results and clinical factors as a preliminary screening scheme for TFE3‐rearranged renal cell carcinoma

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