Papillary renal cell carcinoma and clear cell renal cell carcinoma: Differentiation of distinct histological types with contrast – enhanced ultrasonography

4580 Background: Mutation/inactivation of VHL in clear cell renal cell carcinoma (ccRCC) leads to upregulation of hypoxia inducible factors ( HIFs) and angiogenesis. However, ccRCC is characterized by high intra-tumor heterogeneity (ITH). Random small samples such as those in percutaneous biopsies are likely limited for characterization of molecular alterations in heterogeneous ccRCCs. We hypothesize that whole-tumor dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) is useful to noninvasively identify ITH in ccRCC. Methods: This IRB-approved, prospective, HIPAA-compliant study, included 62 ccRCCs. 3T DCE MRI was obtained prior to nephrectomy. Surgical specimens were sectioned to match MRI acquisition plane. 182 snap frozen samples (49 tumors) and adjacent uninvolved renal parenchyma (URP) were collected. RNA isolations, cDNA library preparation and mRNA sequencing were performed using standard protocols. RNA expression in 81 tumor samples were correlated (Spearman ranked) with % enhancement in a region of interest (ROI) drawn in the same location of the tumor on pre- and 3 different post-contrast DCE MRI phases. Gene function overrepresentation (OR) analyses were done on top positively and negatively correlated genes. False discovery rate (FDR) < 0.1 was considered statistically significant. Results: Principal component analysis of > 20,000 genes indicated distinct gene expression in tumors from URP. Unsupervised clustering showed enrichment of ccA samples (better prognosis) compared to ccB samples (worse prognosis). Importantly, ccA and ccB samples coexisted in 25% of tumors. DCE-MRI % enhancement correlated with expression of > 300 genes (p < 0.003, FDR < 0.1). OR analyses placed angiogenic pathway gene processes and the immune/inflammatory response processes within the top 5 positively- and negatively-correlated gene functions, respectively. HIF2 target genes correlated positively with % enhancement. Conclusions: DCE MRI detects specific molecular signatures and may help overcome the challenges of ITH in ccRCC. Further research is needed to explore the potential role of DCE MRI to assess response to antiangiogenic and immune-based therapies.

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Clear Cell Renal Cell Carcinoma With Borderline Features of Clear Cell Papillary Renal Cell Carcinoma

The American Journal of Surgical Pathology ◽

10.1097/pas.0000000000000514 ◽

2015 ◽

Vol 39 (11) ◽

pp. 1502-1510 ◽

Cited By ~ 38

Author(s):

Sean R. Williamson ◽

Nilesh S. Gupta ◽

John N. Eble ◽

Craig G. Rogers ◽

Susan Michalowski ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Papillary Renal Cell Carcinoma ◽

Cell Renal Cell Carcinoma

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MP23-05 CLEAR CELL PAPILLARY RENAL CELL CARCINOMA: MICRORNA EXPRESSION PROFILING AND COMPARISON WITH CLEAR CELL RENAL CELL CARCINOMA AND PAPILLARY RENAL CELL CARCINOMA

The Journal of Urology ◽

10.1016/j.juro.2014.02.873 ◽

2014 ◽

Vol 191 (4S) ◽

Author(s):

Enrico Munari ◽

Luigi Marchionni ◽

Apurva Chitre ◽

Masamichi Hayashi ◽

Guido Martignoni ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Expression Profiling ◽

Renal Cell ◽

Clear Cell ◽

Papillary Renal Cell Carcinoma ◽

Microrna Expression ◽

Cell Renal Cell Carcinoma

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Case Report: Multifocal biphasic squamoid alveolar renal cell carcinoma

F1000Research ◽

10.12688/f1000research.8451.2 ◽

2016 ◽

Vol 5 ◽

pp. 607 ◽

Cited By ~ 2

Author(s):

José I. López

Keyword(s):

Renal Cell Carcinoma ◽

Case Report ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Papillary Renal Cell Carcinoma ◽

Rare Tumor ◽

Tumor Subtype ◽

Cell Renal Cell Carcinoma ◽

Gross Examination

A multifocal biphasic squamoid alveolar renal cell carcinoma in a 68-year-old man is reported. Four different peripheral tumor nodules were identified on gross examination. A fifth central tumor corresponded to a conventional clear cell renal cell carcinoma. Biphasic squamoid alveolar renal cell carcinoma is a rare tumor that has been very recently characterized as a distinct histotype within the spectrum of papillary renal cell carcinoma. Immunostaining with cyclin D1 seems to be specific of this tumor subtype. This is the first reported case with multifocal presentation.

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A pyramidal deep learning pipeline for kidney whole-slide histology images classification

Scientific Reports ◽

10.1038/s41598-021-99735-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hisham Abdeltawab ◽

Fahmi Khalifa ◽

Mohammed Mohammed ◽

Liang Cheng ◽

Dibson Gondim ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Deep Learning ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Correct Diagnosis ◽

Papillary Renal Cell Carcinoma ◽

Automated Classification ◽

Cell Renal Cell Carcinoma ◽

Whole Slide Images

AbstractRenal cell carcinoma is the most common type of kidney cancer. There are several subtypes of renal cell carcinoma with distinct clinicopathologic features. Among the subtypes, clear cell renal cell carcinoma is the most common and tends to portend poor prognosis. In contrast, clear cell papillary renal cell carcinoma has an excellent prognosis. These two subtypes are primarily classified based on the histopathologic features. However, a subset of cases can a have a significant degree of histopathologic overlap. In cases with ambiguous histologic features, the correct diagnosis is dependent on the pathologist’s experience and usage of immunohistochemistry. We propose a new method to address this diagnostic task based on a deep learning pipeline for automated classification. The model can detect tumor and non-tumoral portions of kidney and classify the tumor as either clear cell renal cell carcinoma or clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole slide images of kidney which were divided into patches of three different sizes for input into the networks. Our approach can provide patchwise and pixelwise classification. The kidney histology images consist of 64 whole slide images. Our framework results in an image map that classifies the slide image on the pixel-level. Furthermore, we applied generalized Gauss-Markov random field smoothing to maintain consistency in the map. Our approach classified the four classes accurately and surpassed other state-of-the-art methods, such as ResNet (pixel accuracy: 0.89 Resnet18, 0.92 proposed). We conclude that deep learning has the potential to augment the pathologist’s capabilities by providing automated classification for histopathological images.

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Similarities and Differences between Clear Cell Tubulo-Papillary and Conventional Clear Cell Renal Cell Carcinoma: A Comparative Phenotypical and Mutational Analysis

Diagnostics ◽

10.3390/diagnostics10020123 ◽

2020 ◽

Vol 10 (2) ◽

pp. 123

Author(s):

Francesca Giunchi ◽

Tania Franceschini ◽

Elisa Gruppioni ◽

Annalisa Altimari ◽

Elisa Capizzi ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Differential Diagnosis ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Mutational Analysis ◽

Papillary Renal Cell Carcinoma ◽

Tumor Type ◽

Wild Type ◽

Cell Renal Cell Carcinoma

Background: Clear cell tubulo-papillary renal cell carcinoma (cctpRCC) is characterized by clear cell morphology, but differs from conventional clear cell carcinoma (ccRCC) for its indolent clinical behavior and genetic background. The differential diagnosis between the two is based on histology and immunohistochemistry (IHC). Methods: We performed a comparative case-control histological, IHC, and genetic analysis by next generation sequencing (NGS), to point out the differences in 10 cases of cctpRCC, and six controls of ccRCC with low stage and grade. Results: All 16 cases showed the IHC profile with cytokeratin 7, racemase, and carbonic anhydrase IX expected for the histological features of each tumor type. By contrast, the NGS mutation analysis that covered 207 amplicons of 50 oncogenes or tumor suppressor genes provided conflicting results. Among the 10 cctpRCC cases, eight (80%) were wild type for all of the genes in the panel, while two (20%) harbored VHL mutations typical of ccRCC. Three of the six (50%) ccRCC control cases showed expected VHL mutations; two (33%) harbored pathogenic mutations in the p53 or the CKIT genes; and one (16%) was wild type. Conclusion: We can assume that histology and ICH are not sufficient for a definitive diagnosis of cctpRCC or ccRCC. Although with a panel covering 50 genes, we found that 80% of cctpRCC were genetically silent; thus, suggesting an indolent biology of these tumors. The differential diagnosis between ccptRCC and ccRCC for the choice of the best therapeutic strategy likely requires the comprehensive evaluation of histology, IHC, and at least VHL mutations.

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