scholarly journals Subgroup-Independent Mapping of Renal Cell Carcinoma—Machine Learning Reveals Prognostic Mitochondrial Gene Signature Beyond Histopathologic Boundaries

2021 ◽  
Vol 11 ◽  
Author(s):  
André Marquardt ◽  
Antonio Giovanni Solimando ◽  
Alexander Kerscher ◽  
Max Bittrich ◽  
Charis Kalogirou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Mitochondrial Gene ◽  
External Validation ◽  
Gene Signature ◽  
Gene Signatures ◽  
Transcriptomic Data

Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups.Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256).Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients.Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.

External validation of preoperative AST/ALT (De-Ritis) ratio as a prognostic indicator in localized and metastatic renal cell carcinoma.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 589-589
Author(s):  
Dattatraya H Patil ◽  
Rishi Robert Sekar ◽  
Jeff Pearl ◽  
Yoram Baum ◽  
Mehrdad Alemozaffar ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
External Validation ◽  
Prognostic Indicator ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Metastatic Rcc

589 Background: Recently, the De-Ritis ratio, defined as the ratio of preoperative aspartate aminotransferase (AST) to alanine aminotransferase (ALT), was shown to be an independent predictor of overall and recurrence-free survival in a European cohort with localized renal cell carcinoma (RCC). In this study, we perform an external validation of the De-Ritis ratio as a prognostic indicator in a distinct cohort of patients with localized and metastatic RCC. Methods: Patients that underwent nephrectomy for localized and metastatic RCC between 2001 and 2014 with available laboratory values within one week of surgery were queried from the Emory Nephrectomy Database. De-Ritis ratio of 1.2 was used to divide subjects into high and low subgroups. Using clinical follow-up data, prognostic value of the De-Ritis ratio was analyzed using the Kaplan-Meier method and Cox proportional regression models. Results: In a cohort of 451 patients, an elevated De-Ritis ratio (AST/ALT ≥ 1.2) was associated with significantly decreased overall survival (log-rank, p=0.0023) and recurrence-free survival (Log-rank, p=0.0395). On multivariate analysis, De-Ritis ratio was shown to be an independent and significant predictor of overall survival (HR=0.52, p=0.002) and recurrence-free survival (HR=0.47, p=0.014) as seen in Table. Conclusions: Elevated De-Ritis ratio (AST/ALT ≥ 1.2) is an independent and significant predictor of overall and recurrence-free survival and is capable of differentiating high-risk disease in patients with localized and metastatic RCC. These findings are consistent with a previous study investigating the prognostic value of the De-Ritis ratio in a European cohort, and further validates its prognostic ability in a geographically distinct cohort including patients who presented with metastatic disease [Table: see text]

scholarly journals Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Subhanik Purkayastha ◽  
Yijun Zhao ◽  
Jing Wu ◽  
Rong Hu ◽  
Aidan McGirr ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clinical Decision Making ◽  
External Validation ◽  
Automatic Machine ◽  
Low Grade ◽  
High Grade ◽  
Fuhrman Grade

Abstract Pre-treatment determination of renal cell carcinoma aggressiveness may help guide clinical decision-making. We aimed to differentiate low-grade (Fuhrman I–II) from high-grade (Fuhrman III–IV) renal cell carcinoma using radiomics features extracted from routine MRI. 482 pathologically confirmed renal cell carcinoma lesions from 2008 to 2019 in a multicenter cohort were retrospectively identified. 439 lesions with information on Fuhrman grade from 4 institutions were divided into training and test sets with an 8:2 split for model development and internal validation. Another 43 lesions from a separate institution were set aside for independent external validation. The performance of TPOT (Tree-Based Pipeline Optimization Tool), an automatic machine learning pipeline optimizer, was compared to hand-optimized machine learning pipeline. The best-performing hand-optimized pipeline was a Bayesian classifier with Fischer Score feature selection, achieving an external validation ROC AUC of 0.59 (95% CI 0.49–0.68), accuracy of 0.77 (95% CI 0.68–0.84), sensitivity of 0.38 (95% CI 0.29–0.48), and specificity of 0.86 (95% CI 0.78–0.92). The best-performing TPOT pipeline achieved an external validation ROC AUC of 0.60 (95% CI 0.50–0.69), accuracy of 0.81 (95% CI 0.72–0.88), sensitivity of 0.12 (95% CI 0.14–0.30), and specificity of 0.97 (95% CI 0.87–0.97). Automated machine learning pipelines can perform equivalent to or better than hand-optimized pipeline on an external validation test non-invasively predicting Fuhrman grade of renal cell carcinoma using conventional MRI.

scholarly journals A five-gene signature predicts overall survival of patients with papillary renal cell carcinoma

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0211491 ◽  
Author(s):  
Ze Gao ◽  
Dong Zhang ◽  
Yi Duan ◽  
Lei Yan ◽  
Yidong Fan ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Papillary Renal Cell Carcinoma ◽  
Gene Signature

scholarly journals Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information

2021 ◽  
Author(s):  
Zahra Khodabakhshi ◽  
Mehdi Amini ◽  
Shayan Mostafaei ◽  
Atlas Haddadi Avval ◽  
Mostafa Nazari ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Clinical Features ◽  
Renal Cell ◽  
Radical Nephrectomy ◽  
Clinical Information ◽  
Tumor Grade ◽  
Imaging Biomarkers ◽  
Area Density

AbstractThe aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients’ overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.

scholarly journals Nuclear and cytosolic pS727-STAT3 levels correlate with overall survival of patients affected by clear cell renal cell carcinoma (ccRCC)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jazmine Arévalo ◽  
David Lorente ◽  
Enrique Trilla ◽  
María Teresa Salcedo ◽  
Juan Morote ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Independent Manner ◽  
Fuhrman Grade ◽  
Transcription 3 ◽  
Aggressive Subtype

AbstractClear cell renal cell carcinoma (ccRCC) is the most frequent and aggressive subtype of renal carcinoma. So far, the basis of its oncogenesis remains unclear resulting in a deficiency of usable and reliable biomarkers for its clinical management. Previously, we showed that nuclear expression of the signal transducer and activator of transcription 3 (STAT3), phosphorylated at its serine 727 (pS727), was inversely proportional to the overall survival of ccRCC patients. Therefore, in the present study, we validated the value of pS727-STAT3 as a clinically relevant biomarker in ccRCC. This work is a retrospective study on 82 ccRCC patients treated with nephrectomy and followed-up for 10 years. Immunohistochemical expression of pS727-STAT3 was analyzed on a tissue microarray and nuclear and cytosolic levels were correlated with clinical outcome of patients. Our results showed that pS727-STAT3 levels, whether in the nucleus (p = 0.002; 95% CI 1.004–1.026) or the cytosol (p = 0.040; 95% CI 1.003–1.042), significantly correlate with patients’ survival in an independent-manner of clinicopathological features (Fuhrman grade, risk group, and tumor size). Moreover, we report that patients with high pS727-STAT3 levels who undergone adjuvant therapy exhibited a significant stabilization of the disease (~ 20 months), indicating that pS727-STAT3 can pinpoint a subset of patients susceptible to respond well to treatment. In summary, we demonstrated that high pS727-STAT3 levels (regardless of their cellular location) correlate with low overall survival of ccRCC patients, and we suggested the use of pS727-STAT3 as a prognostic biomarker to select patients for adjuvant treatment to increase their survival.

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