Identification of high-risk patients with clear cell renal cell carcinoma based on interphase-FISH

Sanjmyatav, Jimsgene; Matthes, Sophie; Muehr, Martin; Sava, Doriana; Sternal, Maria; Wunderlich, Heiko; Gajda, Mieczysław; Grimm, Marc‐Oliver; Junker, Kerstin

doi:10.1038/bjc.2014.159

Cited by 7 publications

(7 citation statements)

References 23 publications

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“…In the first step, our prior findings were validated in an independent cohort of fresh frozen tumors (cohort 1). Each chromosomal aberration revealed a significant association with the metastatic status at the cut‐offs determined in the development cohort with AUC values ranging from 0.680 to 0.760. The TNSA was a much stronger indicator of metastasis compared to single aberrations, with an AUC of 0.867 ( P ‐value = 1×10 −8 ) in ROC curve analysis (Supporting Information Table ).…”

Section: Resultsmentioning

confidence: 99%

“…However, in both cohorts, the genetic score was the best independent predictor of RFS in tumors of all sizes and in organ confined tumors. The total number of specific aberrations a tumor possessed, but not single alterations, reflected their aggressiveness, although it had been demonstrated earlier that gains of chromosomes 1q, 7q, and 20q as well as loss of 9p are poor prognosis markers, leading to decreased RFS, CSS, and OS . Gains of chromosomes 1q, 7q, and 20q have been linked to tumor aggressiveness in various entities .…”

Section: Discussionmentioning

confidence: 99%

“…Statistical analysis was done using the IBM SPSS statistics program version 24. Cut‐off values for each FISH probe in fresh frozen samples were adopted from a previously published study . As cell nuclei might be partially cropped off in FFPE sections new cut‐off values for cohort 2 had to be determined by receiver‐operator curve (ROC) analysis with metastasis as the endpoint in tumor samples.…”

Section: Methodsmentioning

confidence: 99%

“…Recurrently aberrant chromosomal regions present possible target regions of genes involved in tumor initiation or progression. Our previous studies revealed gains of chromosome bands 1q21.3, 7q36.3, and 20q11.21q13.32 and loss of chromosome bands 9p21.3p24.1 as important prognostic factors . Based on the sum of these specific aberrations, a new genetic prognostic score was established, which achieved excellent results in identifying high‐risk patients in the whole cohort and particularly in organ‐confined tumors.…”

Section: Introductionmentioning

confidence: 99%

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Metastatic risk stratification of clear cell renal cell carcinoma patients based on genomic aberrations

Grimm

Zeuschner

Janssen

et al. 2019

Genes Chromosomes & Cancer

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Prognostic markers for the definition of the individual metastatic risk in renal cell carcinoma are still missing. The aim of our study was to establish a total number of specific aberrations (TNSA) genetic score as a new prognostic test for metastatic risk evaluation. Fluorescence in situ hybridization (FISH) was performed on isolated cell nuclei of 100 ccRCCs (50 M1/50 M0) and 100 FFPE sections (second cohort, 32 M1/68 M0). For each chromosomal region (1q21.3, 7q36.3, 9p21.3p24.1, 20q11.21q13.32) cut-off values were determined by receiver-operator curve (ROC)-curve analysis. TNSA was calculated based on the dichotomized specific CNVs.The prognostic significance of CNVs was proven by Cox and logistic regression. TNSA was the best predictor of metastasis and recurrence free survival in both cohorts. We derived an algorithm for risk stratification by combining TNSA and T-category, which increased the prognostic accuracy to 87% (specificity = 86%, sensitivity = 88%). This model divides patients into two risk groups with significantly different RFS, CSS, and OS (P = 3.8×10 −5 , P = 5×10 −6 and P = 3.57×10 −8 respectively). The genetic risk model was superior to Leibovich score and was able to identify patients with metachronous metastatic spread which were incorrectly classified as "low" or "intermediate risk." We present a new tool for individual risk stratification by combining genetic alterations with clinico-pathologic parameters. Interphase FISH proves to be a dependable method for prognostic evaluation in primary tumor tissue on isolated cell nuclei as well as on FFPE sections. Especially in organ-confined tumors the genetic score seems to be an important tool to identify patients at high risk for metastatic disease.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Metastatic risk stratification of clear cell renal cell carcinoma patients based on genomic aberrations

Grimm

Zeuschner

Janssen

et al. 2019

Genes Chromosomes & Cancer

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“…Es zeigte sich, dass nicht die Kombination einzelner Regionen, sondern die Anzahl der Veränderungen (0 -4) pro Tumor signifikant mit dem Auftreten von Metastasen assoziiert ist, was im sog. TNSA (total number of specific aberrations) Score definiert ist [16,17]. Nach einer ersten Validierung mittels Fluoreszenz-in-situ-Hy-▶ Tab.…”

Section: Diagnostikunclassified

Personalisierte Medizin bei Nierenzelltumoren

Junker

Zeuschner

2019

Aktuel Urol

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ZusammenfassungDie Entdeckung von immer kleineren Tumoren durch den umfassenden Einsatz von bildgebenden Verfahren, die Erweiterung der operativen Techniken und der systemischen Therapieoptionen fordern auch für Nierentumorpatienten eine Individualisierung der Therapie. Essenzielle Voraussetzung ist dabei die Kenntnis der Tumorbiologie, da sie eine differenzierte Diagnostik, individuelle Prognosebewertung und Therapiewahl, basierend auf Biomarkern, und die Entwicklung von neuen Therapiestrategien ermöglicht.Nierenzelltumore werden aktuell in 16 histologische Subtypen unterschieden, die durch spezifische genetische Veränderungen charakterisiert sind. Aufgrund ihrer unterschiedlichen Aggressivität ist die Kenntnis der Subtypen sowohl für die Therapieentscheidung als auch für die Nachsorge wichtig. Molekulare Marker können bei der Differenzialdiagnose eingesetzt werden. Molekulare Signaturen werden zukünftig aber auch wesentlich zur Prognosedifferenzierung innerhalb der Subtypen beitragen, wie für das klarzellige Nierenzellkarzinom bereits gezeigt wurde. Dies wird zur Entwicklung differenzierter Nachsorgeprotokolle und zu einer genaueren Selektion der Patienten für adjuvante Therapien führen. Für metastasierte Patienten stehen immer mehr medikamentöse Therapien zur Verfügung, die zielgerichtet in bestimmte zelluläre Signal- oder Kommunikationswege eingreifen. Auch wenn bisher noch keine validen prädiktiven Biomarker verfügbar sind, liegen vielversprechende Daten zu molekularen Signaturen in Korrelation mit dem Ansprechen auf Tyrosinkinaseinhibitoren oder Checkpointinhibitoren vor, die in Zukunft wesentlich besser als die klinischen Scores eine individuelle Therapiewahl ermöglichen werden.Zur Überführung der vielversprechenden Biomarker in die klinische Praxis sind nun prospektive multizentrische Studien erforderlich. Die Berücksichtigung von bereits vorhandenen Erkenntnissen aus der Tumorbiologie der verschiedenen NZK-Entitäten sowohl bez. der veränderten Signalwege in den Tumorzellen selbst als auch der Rolle des Mikromilieus einschließlich der Immunzellen ist eine essenzielle Voraussetzung für eine weitere Verbesserung von Diagnostik und Therapie.

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A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma

et al. 2023

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Purpose To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC). Methods Digitised haematoxylin and eosin-stained slides from The Cancer Genome Atlas were used as a training set for a vision transformer (ViT) to extract image features with a self-supervised model called DINO (self-distillation with no labels). Extracted features were used in Cox regression models to prognosticate OS and DSS. Kaplan–Meier for univariable evaluation and Cox regression analyses for multivariable evaluation of the DINO-ViT risk groups were performed for prediction of OS and DSS. For validation, a cohort from a tertiary care centre was used. Results A significant risk stratification was achieved in univariable analysis for OS and DSS in the training (n = 443, log rank test, p < 0.01) and validation set (n = 266, p < 0.01). In multivariable analysis, including age, metastatic status, tumour size and grading, the DINO-ViT risk stratification was a significant predictor for OS (hazard ratio [HR] 3.03; 95%-confidence interval [95%-CI] 2.11–4.35; p < 0.01) and DSS (HR 4.90; 95%-CI 2.78–8.64; p < 0.01) in the training set but only for DSS in the validation set (HR 2.31; 95%-CI 1.15–4.65; p = 0.02). DINO-ViT visualisation showed that features were mainly extracted from nuclei, cytoplasm, and peritumoural stroma, demonstrating good interpretability. Conclusion The DINO-ViT can identify high-risk patients using histological images of ccRCC. This model might improve individual risk-adapted renal cancer therapy in the future.

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Identification of high-risk patients with clear cell renal cell carcinoma based on interphase-FISH

Cited by 7 publications

References 23 publications

Metastatic risk stratification of clear cell renal cell carcinoma patients based on genomic aberrations

Metastatic risk stratification of clear cell renal cell carcinoma patients based on genomic aberrations

Personalisierte Medizin bei Nierenzelltumoren

A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma

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