Active clinical trials for "Carcinoma, Renal Cell"

The aim of this study was to show additional diagnostic criteria of computed tomography (CT) scan to diagnose and predict the detection and recurrence of cystic renal cell carcinoma in the patients with complicated renal cysts. Furthermore, we would demonstrate the relationship between complicated renal cysts diagnosed by Bosniak system and some parameters of pathological results. The analysis about detection time of renal malignancy would help determine the practical guidelines of follow-up plan for complicated renal cysts.

In this study, a score based on glycosaminoglycan (GAG) profiling in subjects with suspicion of renal cell carcinoma (RCC) is hypothesized to distinguish malignant masses when early actionable clinical decisions are desirable. For example to diagnose early recurrence after surgical treatment; to screen population at risk of RCC; or to distinguish benign masses from RCC before surgical treatment. To this end, plasma and urine GAGs will be measured in a prospective cohort of patients referred to surgical treatment for RCC. The resulting GAG scores are then correlated to post-surgical recurrence, to post-surgical definitive diagnosis and and to tumor size if RCC. In a subset cohort of patients at high risk of RCC recurrence, plasma and urine GAGs will be monitored to observe its correlation with disease recurrence.

All Hepatocellular carcinoma and Renal cell carcinoma patients who receive molecular targeted therapy will be candidates for the study. No additional treatment or intervention will be conducted except for blood sampling that will be limited to one time only. Blood samples (10 cc in volume) will be collected from all study participants once they provided written informed consent form. DNA will be extracted from peripheral blood samples using DNA isolation kit.

Over the last 10 years, technological advances in molecular biology enabled a more accurate genomic characterization of tumors. For each tumor location, this led to the identification of subgroups with similar molecular characteristics. This identification allowed the development of targeted therapies and thus to improve the patient prognosis. This molecular characterization has also revealed the tumor heterogeneity. It may be the cause of treatment resistance and therefore of relapses. Additionally, tumor cells are in constant dialogue with their microenvironment composed of different immune or non immune cells. This microenvironment is now targeted in cancer treatment. To date, there are few studies that combine a deep genomic characterization of both tumor and tumor microenvironment of the patient. Combining the two types of studies on the same tumor should help to define new therapeutic targets and should allow a combination of targeted and immunomodulatory therapies. To this end, our project is to conduct an exhaustive integrated exploratory analysis at genomic, transcriptomic and immunological levels of 3 tumor types (in colon, kidney and liver cancer).

This research study aims to investigate changes inside kidney cancers (also known as Renal Cell Carcinoma or RCC), and in normal kidney surrounding the tumour, when patients are treated with systemic therapy. Samples, radiological images and data from a previous trial (NeoSUN) will be analysed and/or reanalysed, in accordance with the consent of NeoSUN participants.

Data from the American Cancer Society shows a 70% increase in incidence of kidney and renal pelvis cancer between 2000 and 2010. This increase is attributed to small renal masses (SRM) that are incidentally discovered by abdominal radiological imaging. However, 30% of resected SRMs appear benign on histological examination. Conventional biopsy is currently used to provide pathological information prior to resection. However, its non-diagnostic value is high, up to 33% in SRMs, showing the need for diagnostic improvement. The investigators have shown that optical biopsy (OB) can differentiate malignant from benign tissue and tumor subtypes. However, translation to the clinic requires a phase 2 clinical study. The investigators will use an OB probe that can be combined with a needle puncture during classical biopsy procedures, additionally providing real time micro-scale images containing quantitative information about tissue properties. The investigators are convinced that OB will greatly improve the diagnosis of renal tumor pathology.

For the appropriate individualized treatment of T1-stage renal cell carcinoma with heterogeneous biological features, the expression of PBRM1, SETD2, BAP1, KDM5C and the newly proposed FOXC2 and CLIP4, are compared with clinical features. The investigators evaluated the efficacy of FOXC2 and CLIP4 as prognostic biomarkers and developed a high risk prediction model based on these results. In a previous study, the investigators evaluated the efficacy of FOXC2 and CLIP4 as prognostic biomarkers and reported their association with synchronous metastasis in renal cell carcinomas less than 7 cm in size. The investigators analyzed the expression level of renal cell carcinoma according to the size and malignancy (Fuhrman grade) of renal cell carcinoma in T1-stage clear cell type renal cell carcinoma of tumor size less than 7cm. The aim of this study was to analyze the association of tumor recurrence or metastasis, cancer specific survival rate, overall survival rate, tumor size, malignancy and T stage in postoperative biopsy. For expression analysis, PCR amplification and bidirectional Sanger sequencing and mRNA expression analysis (qRT-PCR) were used. For statistical analysis, Fisher exact test, Wilcoxon exact 2-tailed test, Cox proportional hazard regression analysis and competing risk method were used. In this study, the investigators compared the expression of PBRM1, SETD2, BAP1, and KDM5 with newly proposed biomarkers, FOXC2 and CLIP4 and demonstrate the prognostic value of FOXC2 and CLIP4 as new prognostic biomarkers and compared the clinical outcomes with the clinical outcome. Based on these results, the investigators propose a high risk prediction model for individualized treatment of T1-stage renal cell carcinoma. This study is expected to establish a new prediction model and molecular biologic stage for risk stratification of T1-stage renal cell carcinoma patients and apply genetic test for selection of optimal tailored treatment for T1-stage renal cell carcinoma. In addition, it will be an important basic data of the molecular biologic mechanism of metastasis in early renal cell carcinoma and may be used as a basic data for the development and selection of customized therapeutic agents in patients with distant metastasis.

TRACERx Renal: This is a translational study, which, aims to develop prognostic and predictive biomarkers for patients with renal cell carcinoma (RCC). CAPTURE Sub-study: Covid-19 antiviral response in a pan-tumour immune monitoring study

PubMed, ScienceDirect, Cochrane Database of Systematic Reviews will be used to search for articles published from January 1965 to July 2019 using the key words "renal cancer", "lymphocyte to monocyte ratio" and "prognosis". No restrictions to date, language, or article type will be applied. Cohort or observational studies in patients with non-metastatic renal cell carcinoma histopathologically confirmed, with hazard ratios (HR) and corresponding 95% confidence intervals (CI) that assessed association between LMR and overall survival (OS), cancer-specific survival (CSS), recurrence-free survival (RFS), and disease-free survival (DFS) will be analyzed.

The effect of preoperative glycemic control measured by HbA1c on renal cell carcinoma (RCC) outcome remains controversial. Thus, the investigators aim to examine the association of preoperative glycemic control with oncologic outcomes after radical or partial nephrectomy. The investigators will prospectively collect the relevant data including preoperative HbA1c in 238 patients of RCC patients undergoing nephrectomy. The associations between clinical variables and risk of adverse pathological features and disease recurrence will be tested using a multivariate logistic regression and multiple Cox-proportional hazards model, respectively.