NCT05782400

Brief 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.

Trial Health

75
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
100

participants targeted

Target at P50-P75 for all trials

Timeline
17mo left

Started Feb 2023

Longer than P75 for all trials

Geographic Reach
1 country

1 active site

Status
active not recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress70%
Feb 2023Sep 2027

First Submitted

Initial submission to the registry

February 10, 2023

Completed
18 days until next milestone

Study Start

First participant enrolled

February 28, 2023

Completed
23 days until next milestone

First Posted

Study publicly available on registry

March 23, 2023

Completed
2.9 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

February 28, 2026

Completed
1.6 years until next milestone

Study Completion

Last participant's last visit for all outcomes

September 30, 2027

Expected
Last Updated

September 3, 2025

Status Verified

September 1, 2025

Enrollment Period

3 years

First QC Date

February 10, 2023

Last Update Submit

September 2, 2025

Conditions

Outcome Measures

Primary Outcomes (1)

  • Blood and tissue analysis

    Investigation of the predictive role of circulating miRNAs and gene alterations in patients who respond to first-line treatments versus those who do not respond before treatment, after 1 month (4 weeks), after 3 months (12 weeks), and at the time of disease progression. Tissue and blood samples will be studied with Illumina NextSeq 500 platform and analyzed with the GeneGlobe online software. Methods that combine different clustering algorithms and gene variability metrics will be used to identify robust mccRCC molecular subtypes from expression data and to investigate their association with clinical outcomes.

    36 Months

Secondary Outcomes (1)

  • Radiomics analysis

    36 Months

Other Outcomes (1)

  • Computational analysis of mutational, transcriptomic and radiomic data

    48 Months

Interventions

CT scanRADIATION

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.

● 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.

Eligibility Criteria

Age18 Years+
Sexmale(Gender-based eligibility)
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Patients (nr 100) diagnosed with advanced RCC with predominantly clear-cell subtype, candidate to receive first-line systemic treatment as per clinical practice (investigators choice).

You may qualify if:

  • Signed Written Informed Consent
  • Male or female subjects aged ≥18 years old
  • Histologically confirmed advanced/metastatic RCC with predominantly clear-cell subtype
  • Previous nephrectomy is permitted
  • Availability of tumor tissue sample for biomarker analysis
  • Advanced (not amenable to curative surgery or radiation therapy) or metastatic (AJCC Stage IV) RCC, candidate to receive first-line systemic treatment with monotherapy TKI or IO+TKI or IO+IO
  • No prior systemic therapy for RCC with the following exception: prior adjuvant therapy for completely resectable RCC (concluded at least 6 months before study entry)
  • All IMDC risk (good, intermediate, poor)
  • TC scan performed with and without contrast medium, at baseline (according to protocol guidelines as reported below in Table 1)
  • At least one measurable lesion as defined by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1
  • Eastern Cooperative Oncology Group performance status 0 or 1
  • Capable of understanding and complying with the protocol requirements.

You may not qualify if:

  • Any prior systemic treatment for RCC in the advanced/metastatic settings
  • Prior treatment with an anti-PD-1, anti-PD-L1, anti-PD-L2, anti-CD137, or anti-CTLA-4 antibody, or any other antibody or drug specifically targeting T-cell co-stimulation or checkpoint pathways
  • Previous exposure to tyrosine kinase inhibitors in the advanced/metastatic settings
  • Active seizure disorder or evidence of brain metastases, spinal cord compression, or carcinomatous meningitis
  • Diagnosis of any non-RCC malignancy occurring within 2 years prior to the date of the start of treatment except for adequately treated basal cell or squamous cell skin cancer, or carcinoma in situ of the breast or of the cervix or low-grade prostate cancer (≤pT2, N0; Gleason 6) with no plans for treatment intervention
  • Radiation therapy for bone metastasis within 2 weeks, any other external radiation therapy within 4 weeks before the start of treatment. Subjects with clinically relevant ongoing complications from prior radiation therapy are not eligible.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Istituto Tumori

Milan, Mi, 20156, Italy

Location

Related Publications (26)

  • Choueiri TK, Motzer RJ. Systemic Therapy for Metastatic Renal-Cell Carcinoma. N Engl J Med. 2017 Jan 26;376(4):354-366. doi: 10.1056/NEJMra1601333. No abstract available.

    PMID: 28121507BACKGROUND
  • Motzer RJ, Tannir NM, McDermott DF, Aren Frontera O, Melichar B, Choueiri TK, Plimack ER, Barthelemy P, Porta C, George S, Powles T, Donskov F, Neiman V, Kollmannsberger CK, Salman P, Gurney H, Hawkins R, Ravaud A, Grimm MO, Bracarda S, Barrios CH, Tomita Y, Castellano D, Rini BI, Chen AC, Mekan S, McHenry MB, Wind-Rotolo M, Doan J, Sharma P, Hammers HJ, Escudier B; CheckMate 214 Investigators. Nivolumab plus Ipilimumab versus Sunitinib in Advanced Renal-Cell Carcinoma. N Engl J Med. 2018 Apr 5;378(14):1277-1290. doi: 10.1056/NEJMoa1712126. Epub 2018 Mar 21.

    PMID: 29562145BACKGROUND
  • Rini BI, Powles T, Atkins MB, Escudier B, McDermott DF, Suarez C, Bracarda S, Stadler WM, Donskov F, Lee JL, Hawkins R, Ravaud A, Alekseev B, Staehler M, Uemura M, De Giorgi U, Mellado B, Porta C, Melichar B, Gurney H, Bedke J, Choueiri TK, Parnis F, Khaznadar T, Thobhani A, Li S, Piault-Louis E, Frantz G, Huseni M, Schiff C, Green MC, Motzer RJ; IMmotion151 Study Group. Atezolizumab plus bevacizumab versus sunitinib in patients with previously untreated metastatic renal cell carcinoma (IMmotion151): a multicentre, open-label, phase 3, randomised controlled trial. Lancet. 2019 Jun 15;393(10189):2404-2415. doi: 10.1016/S0140-6736(19)30723-8. Epub 2019 May 9.

    PMID: 31079938BACKGROUND
  • Rini BI, Plimack ER, Stus V, Gafanov R, Hawkins R, Nosov D, Pouliot F, Alekseev B, Soulieres D, Melichar B, Vynnychenko I, Kryzhanivska A, Bondarenko I, Azevedo SJ, Borchiellini D, Szczylik C, Markus M, McDermott RS, Bedke J, Tartas S, Chang YH, Tamada S, Shou Q, Perini RF, Chen M, Atkins MB, Powles T; KEYNOTE-426 Investigators. Pembrolizumab plus Axitinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N Engl J Med. 2019 Mar 21;380(12):1116-1127. doi: 10.1056/NEJMoa1816714. Epub 2019 Feb 16.

    PMID: 30779529BACKGROUND
  • Motzer RJ, Penkov K, Haanen J, Rini B, Albiges L, Campbell MT, Venugopal B, Kollmannsberger C, Negrier S, Uemura M, Lee JL, Vasiliev A, Miller WH Jr, Gurney H, Schmidinger M, Larkin J, Atkins MB, Bedke J, Alekseev B, Wang J, Mariani M, Robbins PB, Chudnovsky A, Fowst C, Hariharan S, Huang B, di Pietro A, Choueiri TK. Avelumab plus Axitinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N Engl J Med. 2019 Mar 21;380(12):1103-1115. doi: 10.1056/NEJMoa1816047. Epub 2019 Feb 16.

    PMID: 30779531BACKGROUND
  • Powles T, Plimack ER, Soulieres D, Waddell T, Stus V, Gafanov R, Nosov D, Pouliot F, Melichar B, Vynnychenko I, Azevedo SJ, Borchiellini D, McDermott RS, Bedke J, Tamada S, Yin L, Chen M, Molife LR, Atkins MB, Rini BI. Pembrolizumab plus axitinib versus sunitinib monotherapy as first-line treatment of advanced renal cell carcinoma (KEYNOTE-426): extended follow-up from a randomised, open-label, phase 3 trial. Lancet Oncol. 2020 Dec;21(12):1563-1573. doi: 10.1016/S1470-2045(20)30436-8. Epub 2020 Oct 23.

    PMID: 33284113BACKGROUND
  • Motzer R, Alekseev B, Rha SY, Porta C, Eto M, Powles T, Grunwald V, Hutson TE, Kopyltsov E, Mendez-Vidal MJ, Kozlov V, Alyasova A, Hong SH, Kapoor A, Alonso Gordoa T, Merchan JR, Winquist E, Maroto P, Goh JC, Kim M, Gurney H, Patel V, Peer A, Procopio G, Takagi T, Melichar B, Rolland F, De Giorgi U, Wong S, Bedke J, Schmidinger M, Dutcus CE, Smith AD, Dutta L, Mody K, Perini RF, Xing D, Choueiri TK; CLEAR Trial Investigators. Lenvatinib plus Pembrolizumab or Everolimus for Advanced Renal Cell Carcinoma. N Engl J Med. 2021 Apr 8;384(14):1289-1300. doi: 10.1056/NEJMoa2035716. Epub 2021 Feb 13.

    PMID: 33616314BACKGROUND
  • Choueiri TK, Powles T, Burotto M, Escudier B, Bourlon MT, Zurawski B, Oyervides Juarez VM, Hsieh JJ, Basso U, Shah AY, Suarez C, Hamzaj A, Goh JC, Barrios C, Richardet M, Porta C, Kowalyszyn R, Feregrino JP, Zolnierek J, Pook D, Kessler ER, Tomita Y, Mizuno R, Bedke J, Zhang J, Maurer MA, Simsek B, Ejzykowicz F, Schwab GM, Apolo AB, Motzer RJ; CheckMate 9ER Investigators. Nivolumab plus Cabozantinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N Engl J Med. 2021 Mar 4;384(9):829-841. doi: 10.1056/NEJMoa2026982.

    PMID: 33657295BACKGROUND
  • Brooks SA, Brannon AR, Parker JS, Fisher JC, Sen O, Kattan MW, Hakimi AA, Hsieh JJ, Choueiri TK, Tamboli P, Maranchie JK, Hinds P, Miller CR, Nielsen ME, Rathmell WK. ClearCode34: A prognostic risk predictor for localized clear cell renal cell carcinoma. Eur Urol. 2014 Jul;66(1):77-84. doi: 10.1016/j.eururo.2014.02.035. Epub 2014 Feb 25.

    PMID: 24613583BACKGROUND
  • Qu L, Wang ZL, Chen Q, Li YM, He HW, Hsieh JJ, Xue S, Wu ZJ, Liu B, Tang H, Xu XF, Xu F, Wang J, Bao Y, Wang AB, Wang D, Yi XM, Zhou ZK, Shi CJ, Zhong K, Sheng ZC, Zhou YL, Jiang J, Chu XY, He J, Ge JP, Zhang ZY, Zhou WQ, Chen C, Yang JH, Sun YH, Wang LH. Prognostic Value of a Long Non-coding RNA Signature in Localized Clear Cell Renal Cell Carcinoma. Eur Urol. 2018 Dec;74(6):756-763. doi: 10.1016/j.eururo.2018.07.032. Epub 2018 Aug 22.

    PMID: 30143382BACKGROUND
  • Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. 2019 Mar;19(3):133-150. doi: 10.1038/s41568-019-0116-x.

    PMID: 30755690BACKGROUND
  • Sonpavde G, Choueiri TK. Biomarkers: the next therapeutic hurdle in metastatic renal cell carcinoma. Br J Cancer. 2012 Sep 25;107(7):1009-16. doi: 10.1038/bjc.2012.399. Epub 2012 Sep 4.

    PMID: 22948724BACKGROUND
  • McDermott DF, Huseni MA, Atkins MB, Motzer RJ, Rini BI, Escudier B, Fong L, Joseph RW, Pal SK, Reeves JA, Sznol M, Hainsworth J, Rathmell WK, Stadler WM, Hutson T, Gore ME, Ravaud A, Bracarda S, Suarez C, Danielli R, Gruenwald V, Choueiri TK, Nickles D, Jhunjhunwala S, Piault-Louis E, Thobhani A, Qiu J, Chen DS, Hegde PS, Schiff C, Fine GD, Powles T. Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nat Med. 2018 Jun;24(6):749-757. doi: 10.1038/s41591-018-0053-3. Epub 2018 Jun 4.

    PMID: 29867230BACKGROUND
  • Jing L, Guigonis JM, Borchiellini D, Durand M, Pourcher T, Ambrosetti D. LC-MS based metabolomic profiling for renal cell carcinoma histologic subtypes. Sci Rep. 2019 Oct 30;9(1):15635. doi: 10.1038/s41598-019-52059-y.

    PMID: 31666664BACKGROUND
  • Lakshminarayanan H, Rutishauser D, Schraml P, Moch H, Bolck HA. Liquid Biopsies in Renal Cell Carcinoma-Recent Advances and Promising New Technologies for the Early Detection of Metastatic Disease. Front Oncol. 2020 Oct 28;10:582843. doi: 10.3389/fonc.2020.582843. eCollection 2020.

    PMID: 33194717BACKGROUND
  • Bex A, Fournier L, Lassau N, Mulders P, Nathan P, Oyen WJ, Powles T. Assessing the response to targeted therapies in renal cell carcinoma: technical insights and practical considerations. Eur Urol. 2014 Apr;65(4):766-77. doi: 10.1016/j.eururo.2013.11.031. Epub 2013 Nov 28.

    PMID: 24341958BACKGROUND
  • Yi X, Xiao Q, Zeng F, Yin H, Li Z, Qian C, Wang C, Lei G, Xu Q, Li C, Li M, Gong G, Zee C, Guan X, Liu L, Chen BT. Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma. Front Oncol. 2021 Jan 27;10:570396. doi: 10.3389/fonc.2020.570396. eCollection 2020.

    PMID: 33585193BACKGROUND
  • Shu J, Tang Y, Cui J, Yang R, Meng X, Cai Z, Zhang J, Xu W, Wen D, Yin H. Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade. Eur J Radiol. 2018 Dec;109:8-12. doi: 10.1016/j.ejrad.2018.10.005. Epub 2018 Oct 5.

    PMID: 30527316BACKGROUND
  • Smith AD, Zhang X, Bryan J, Souza F, Roda M, Sirous R, Zhang H, Vasanji A, Griswold M. Vascular Tumor Burden as a New Quantitative CT Biomarker for Predicting Metastatic RCC Response to Antiangiogenic Therapy. Radiology. 2016 Nov;281(2):484-498. doi: 10.1148/radiol.2016160143. Epub 2016 Sep 2.

    PMID: 27603788BACKGROUND
  • Han KS, Jung DC, Choi HJ, Jeong MS, Cho KS, Joung JY, Seo HK, Lee KH, Chung J. Pretreatment assessment of tumor enhancement on contrast-enhanced computed tomography as a potential predictor of treatment outcome in metastatic renal cell carcinoma patients receiving antiangiogenic therapy. Cancer. 2010 May 15;116(10):2332-42. doi: 10.1002/cncr.25019.

    PMID: 20225226BACKGROUND
  • Fournier LS, Oudard S, Thiam R, Trinquart L, Banu E, Medioni J, Balvay D, Chatellier G, Frija G, Cuenod CA. Metastatic renal carcinoma: evaluation of antiangiogenic therapy with dynamic contrast-enhanced CT. Radiology. 2010 Aug;256(2):511-8. doi: 10.1148/radiol.10091362. Epub 2010 Jun 15.

    PMID: 20551183BACKGROUND
  • Hudson JM, Bailey C, Atri M, Stanisz G, Milot L, Williams R, Kiss A, Burns PN, Bjarnason GA. The prognostic and predictive value of vascular response parameters measured by dynamic contrast-enhanced-CT, -MRI and -US in patients with metastatic renal cell carcinoma receiving sunitinib. Eur Radiol. 2018 Jun;28(6):2281-2290. doi: 10.1007/s00330-017-5220-2. Epub 2018 Jan 30.

    PMID: 29383520BACKGROUND
  • Zhou L, Zhang Z, Chen YC, Zhao ZY, Yin XD, Jiang HB. A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors. Transl Oncol. 2019 Feb;12(2):292-300. doi: 10.1016/j.tranon.2018.10.012. Epub 2018 Dec 17.

    PMID: 30448734BACKGROUND
  • Kuusk T, Neves JB, Tran M, Bex A. Radiomics to better characterize small renal masses. World J Urol. 2021 Aug;39(8):2861-2868. doi: 10.1007/s00345-021-03602-y. Epub 2021 Jan 26.

    PMID: 33495866BACKGROUND
  • Lin F, Cui EM, Lei Y, Luo LP. CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma. Abdom Radiol (NY). 2019 Jul;44(7):2528-2534. doi: 10.1007/s00261-019-01992-7.

    PMID: 30919041BACKGROUND
  • Suarez-Ibarrola R, Hein S, Reis G, Gratzke C, Miernik A. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol. 2020 Oct;38(10):2329-2347. doi: 10.1007/s00345-019-03000-5. Epub 2019 Nov 5.

    PMID: 31691082BACKGROUND

Biospecimen

Retention: SAMPLES WITH DNA

* FFPE samples will be collected at baseline and, when feasible, at the first progression of the disease (PD). FFPE specimens will be sent to the coordinator site (INT). A centralized designated expert pathology will review these specimens to confirm the diagnosis and to identify regions of interest. mRNA and DNA sequencing will be used to dissect the molecular and genomic profiles of this cohort. * 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.

MeSH Terms

Conditions

Carcinoma, Renal Cell

Condition Hierarchy (Ancestors)

AdenocarcinomaCarcinomaNeoplasms, Glandular and EpithelialNeoplasms by Histologic TypeNeoplasmsKidney NeoplasmsUrologic NeoplasmsUrogenital NeoplasmsNeoplasms by SiteFemale Urogenital DiseasesFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital DiseasesKidney DiseasesUrologic DiseasesMale Urogenital Diseases

Study Officials

  • Giuseppe Procopio, MD

    Fondazione IRCCS istituto Nazionale dei Tumori di Milano

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
observational
Observational Model
OTHER
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Director of Genitourinary Medical Oncology

Study Record Dates

First Submitted

February 10, 2023

First Posted

March 23, 2023

Study Start

February 28, 2023

Primary Completion

February 28, 2026

Study Completion (Estimated)

September 30, 2027

Last Updated

September 3, 2025

Record last verified: 2025-09

Locations