Multiomics Approach in Metastatic Clear Renal Cell Carcnoma

Summary

The choice of the best strategy in treatment-naive metastatic clear-cell renal cell carcinoma (mccRCC) patients is becoming an issue, since no biomarkers are available to guide the treatment allocation strategy. The elucidation of predictive factors to develop tailored strategies of treatment is an urgent unmet clinical need. Recently there has been a great deal of interest in non-invasive liquid biopsy methods for their ability to detect and characterize circulating cell-free DNA (cfDNA), extracellular vescicles associated RNAs and circulating tumor cells and to allow longitudinal evaluation of tumor evolution. An additional field of intense research is also radiomics as a novel approach to develop predictive tools by correlating imaging features to tumor characteristics including histology, tumor grade, genetic patterns and molecular phenotypes, as well as clinical outcomes in patients with renal neoplasms.

The use of computational approaches to integrate informations, obtained from genomic and transcriptomic analysis of neoplastic tissues and of cfDNA) or microvescicle-associated RNA in blood and from radiomics, can be exploited to define an optimal allocation strategy for patients with mccRCC undergoing first-line therapy and to identify novel targets in mccRCC.

Aims of the study are: to identify molecular subtypes, signatures or biomarkers in mccRCC associated with different clinical outcome by applying bioinformatic analysis; to extract descriptive features in mccRCC from radiological imaging data; to integrate omics-driven and clinic-pathological characteristics with radiomic features extracted from the tumor and tumor environment to inform on biological features relevant to therapy outcome.

This multicentric prospective study will evaluate genomics and radiomics in treatment-naïve advanced ccRCC patients. 100 eligible patients will be identified after screening, candidate to receive first-line treatment as investigator choice per clinical practice. Tissue and plasma samples and CT exams will be collected at different intervals to provide a comprehensive molecular profile and radiomic features extrapolation, respectively. Artificial neural networks will be used to build a genomic-radiomic profile of patients to correlate to treatment response. This sample size will allow an exploratory analysis of the prognostic and predictive performance of the multiomic classifier, to be subsequently validated in a larger expansion cohort of patients.

Conditions

• Metastatic Clear Cell Renal Carcinoma

Interventions

RADIATION

CT scan

CT scan at baseline and then every three months as per clinical practice. The standardization of the procedure of images' collection through a CT- acquisition's protocol has been planned to control bias.

BIOLOGICAL

Plasma collection

● Blood samples will be collected at baseline, at 1 month and at the first PD. Sixteen ml of blood will be collected in EDTA tubes and centrifuged at 1900×g for 10 min at 4 °C within 2 h after drawing to collect plasma, which will be stored at -80°C until analysis. Plasma samples will be sent to the Laboratory of Pharmacogenetics - Unit of Clinical Pharmacology and Pharmacogenetics - University Hospital of Pisa. Plasma samples will be used to isolate cell free DNA (cfDNA) and microvesicles-derived RNA for molecular analysis.

Sponsors & Collaborators

• Fondazione IRCCS Istituto Nazionale dei Tumori, Milano

  lead OTHER

Principal Investigators

• Giuseppe Procopio, MD · Fondazione IRCCS istituto Nazionale dei Tumori di Milano

Eligibility

Min Age

18 Years

Sex

MALE

Healthy Volunteers

No

Timeline & Regulatory

Start

2023-02-28

Primary Completion

2026-02-28

Completion

2027-09-30

Countries

• Italy

Study Locations