scholarly journals Intra-tumor heterogeneity and clonal exclusivity in renal cell carcinoma

2018 ◽  
Author(s):  
Ariane L. Moore ◽  
Jack Kuipers ◽  
Jochen Singer ◽  
Elodie Burcklen ◽  
Peter Schraml ◽  
...  

AbstractTumorigenesis is an evolutionary process in which different clones evolve over time. Interactions between clones can affect tumor evolution and hence disease progression and treatment outcome. We analyzed 178 tumor samples in 89 clear cell renal cell carcinoma patients and found high intra-tumor heterogeneity with 62% of mutations detected in only one of two biopsies per patient. We developed a novel statistical test to identify gene pairs that are altered in co-occurring clones of the same tumor, including the pairsTP53andMUC16, as well asBAP1andTP53. The mutations in these gene pairs are clonally exclusive meaning that they occurred in different branches of the tumor phylogeny, suggesting a synergistic effect between the two clones carrying these mutations. Our analysis sheds new light on tumor development and implies that clonal interactions are common within tumors, which may eventually open up novel treatment strategies to improve cancer treatment.

BMC Urology ◽  
2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Jeanette E. Eckel-Passow ◽  
Daniel J. Serie ◽  
John C. Cheville ◽  
Thai H. Ho ◽  
Payal Kapur ◽  
...  

2020 ◽  
Vol 16 (29) ◽  
pp. 2307-2328
Author(s):  
Peter J Goebell ◽  
Philipp Ivanyi ◽  
Jens Bedke ◽  
Lothar Bergmann ◽  
Dominik Berthold ◽  
...  

The therapy of advanced (clear-cell) renal cell carcinoma (RCC) has recently experienced tremendous changes. Several new treatments have been developed, with PD-1 immune-checkpoint inhibition being the backbone of therapy. Diverse immunotherapy combinations change current first-line standards. These changes also require new approaches in subsequent lines of therapy. In an expert panel, we discussed the new treatment options and how they change clinical practice. While first-line immunotherapies introduce a new level of response rates, data on second-line therapies remains poor. This scenario poses a challenge for clinicians as guideline recommendations are based on historical patient cohorts and agents may lack the appropriate label for their in guidelines recommended use. Here, we summarize relevant clinical data and consider appropriate treatment strategies.


2017 ◽  
Vol 71 (6) ◽  
pp. 979-985 ◽  
Author(s):  
Daniel J. Serie ◽  
Richard W. Joseph ◽  
John C. Cheville ◽  
Thai H. Ho ◽  
Mansi Parasramka ◽  
...  

2020 ◽  
Vol 13 (S11) ◽  
Author(s):  
Xiaohui Zhan ◽  
Yusong Liu ◽  
Christina Y. Yu ◽  
Tian-Fu Wang ◽  
Jie Zhang ◽  
...  

Abstract Background Renal cell carcinoma (RCC) is a complex disease and is comprised of several histological subtypes, the most frequent of which are clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (PRCC) and chromophobe renal cell carcinoma (ChRCC). While lots of studies have been performed to investigate the molecular characterizations of different subtypes of RCC, our knowledge regarding the underlying mechanisms are still incomplete. As molecular alterations are eventually reflected on the pathway level to execute certain biological functions, characterizing the pathway perturbations is crucial for understanding tumorigenesis and development of RCC. Methods In this study, we investigated the pathway perturbations of various RCC subtype against normal tissue based on differential expressed genes within a certain pathway. We explored the potential upstream regulators of subtype-specific pathways with Ingenuity Pathway Analysis (IPA). We also evaluated the relationships between subtype-specific pathways and clinical outcome with survival analysis. Results In this study, we carried out a pathway-based analysis to explore the mechanisms of various RCC subtypes with TCGA RNA-seq data. Both commonly altered pathways and subtype-specific pathways were detected. To identify the distinctive characteristics of each subtype, we focused on subtype-specific perturbed pathways. Specifically, we observed that some of the altered pathways were regulated by several recurrent upstream regulators which presenting different expression patterns among distinct RCC subtypes. We also noticed that a large number of perturbed pathways were controlled by the subtype-specific upstream regulators. Moreover, we also evaluated the relationships between perturbed pathways and clinical outcome. Prognostic pathways were identified and their roles in tumor development and progression were inferred. Conclusions In summary, we evaluated the relationships among pathway perturbations, upstream regulators and clinical outcome for differential subtypes in RCC. We hypothesized that the alterations of common upstream regulators as well as subtype-specific upstream regulators work together to affect the downstream pathway perturbations and drive cancer initialization and prognosis. Our findings not only increase our understanding of the mechanisms of various RCC subtypes, but also provide targets for personalized therapeutic intervention.


2019 ◽  
Author(s):  
I. C. Sorribes ◽  
A. Basu ◽  
R. Brady ◽  
P. M. Enriquez-Navas ◽  
X. Feng ◽  
...  

AbstractRenal cell carcinoma (RCC) is one of the ten most common and lethal cancers in the United States. Tumor heterogeneity and development of resistance to treatment suggest that patient-specific evolutionary therapies may hold the key to better patients prognosis. Mathematical models are a powerful tool to help develop such strategies; however, they depend on reliable biomarker information. In this paper, we present a dynamic model of tumor-immune interactions, as well as the treatment effect on tumor cells and the tumor-immune environment. We hypothesize that the neutrophil-to-lymphocyte ratio (NLR) is a powerful biomarker that can be used to predict an individual patient’s response to treatment. Using randomly sampled virtual patients, we show that the model recapitulates patient outcomes from clinical trials in RCC. Finally, we use in silico patient data to recreate realistic tumor behaviors and simulate various treatment strategies to find optimal treatments for each virtual patient.


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