Neoplasms

All Conditions

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Rare Diseases

Carcinoma

Carcinoma, Renal Cell

Kidney Neoplasms

Adenocarcinoma

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Urologic Neoplasms

Urogenital Neoplasms

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Kidney Diseases

Urologic Diseases

Male Urogenital Diseases

Renal Cell Carcinoma

Clear Cell Renal Cell Carcinoma

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.

CT scan

● 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.

Plasma collection

Giuseppe Procopio, MD

Marco Stellato, MD

Giuseppe Dr Procopio, MD

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.

IMPACT In the last ten years the systemic treatment of metastatic renal cell carcinoma has been revolutioned with the introduction of at least ten active drugs. With the advent of novel immuno-based and tyrosine kinase inhibitors (TKIs)-based combinations, the choice of the best strategy in treatment-naive metastatic clear-cell renal cell carcinoma (mccRCC) patients (pts) is becoming an issue, since no biomarkers are available to guide the treatment allocation strategy . In recent clinical trials, combination therapies including nivolumab plus ipilimumab, pembrolizumab plus axitinib, atezolizumab plus bevacizumab, avelumab plus axitinib, pembrolizumab plus lenvatinib and nivolumab plus cabozantinib exhibited significant benefits in terms of overall survival (OS) and/or progression-free survival (PFS) for mRCC compared with sunitinib as a standard first-line treatment for mRCC . However, there is a clear need for clinical predictive biomarkers to guide optimal treatment decisions. Through the above research the investigators are confident to provide proof of concept that combine the informations from genomics and radiomics using computational approaches such as machine learning, will provide an opportunity for a molecularly driven patient's stratification.

RATIONALE AND FEASIBILITY

BIOMARKERS Risk stratification models based on gene expression pattern (both messenger and long non-coding RNA) in ccRCC have proven to have strong prognostic values. Hence, there is an interest in the identification and development of treatment predictive biomarkers to enable precision oncology increasing drug response. Multiple candidates for predictive biomarkers from plasma, tumor, and host tissues have been explored in patients with metastatic renal-cell carcinoma who are receiving systemic therapies, but, as yet, none have entered clinical practice and all require prospective validation in clinical trials.

In the era of VEGF inhibitors, the investigators counted on IMDC (International Metastatic RCC Database Consortium) model, considering Karnofsky performance status \<80, time to initiation of therapy \<1 year, hemoglobin \< lower level of normal, serum calcium, neutrophil count, and platelet count \> upper limit of normal. CheckMate 214 study showed that OS and ORR were significantly higher with nivolumab plus ipilimumab than with sunitinib among intermediate- and poor-risk pts. Extended study follow-up of KEYNOTE-426 study demonstrated that the benefit in OS and PFS is consistent in this class of patients's risk also with the IO/TKI combination therapy. The IMDC score is confirmed to be prognostic in every combos study.

PD-L1 has also been demonstrated to be a prognostic marker for poor prognosis in RCC, regardless of the type of treatment used. More recently PD-L1 expression has also been evaluated for its predictive role that is only partially confirmed in the CheckMate-214 population that received IO-IO combo and considered a poor marker for targeted therapies. Gene signatures from ImMotion 150 and 151 evidenced that two different signatures (angiogenesis versus immune signature) in RCC patients could predict the response to combo treatment.

Metabolomics have also been assessed as potential biomarkers for RCC giving new insights into the understanding of RCC clinical behavior and for the development of new therapeutic strategies. Both tumor tissue and blood are interesting source for the study of potential biomarkers. Plasma samples in particular have been analyzed to better understand the role of components of the proangiogenic and cellular proliferation pathways. Another topic is the study of cytokines, circulating endothelial cells, and gene expression controlling mechanisms. Moreover, germline genetic variations in important genes related to drug mechanism-of-action and metabolism have been under investigation as well as factors implicated in gene expression regulation by epigenetic mechanisms or by post-transcriptional regulation.

RADIOMICS Computed tomography (CT) is widely available, routinely used in the care of patients with metastatic tumors treated with antiangiogenic therapy and yields quantitative digital data. Many studies have used CT noninvasive imaging-based methods to assess the pathologic grade of renal tumor before surgery. Various radiological features such as tumor size and pattern enhancement have been shown to correlate with tumor grade. However, it is difficult to predict the pathologic grade of renal tumor with only information obtained from traditional radiologic features. Conversely, radiomics analysis involves the automatic extraction of data not recognizable to the human eye resulting in highly detailed imaging features regarding tumor structure, shape and image intensity. Radiomics may provide 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. Extracting data from imaging the aim is to provide information beyond what can be achieved from human imaging interpretation alone.

In clinical practice, the prediction of RCC aggressiveness through imaging findings remains a challenge. A retrospective study by Shu et al. demonstrated that radiomics features could be used as biomarkers for the preoperative evaluation of the ccRCC Fuhrman grades. In the post-treatment setting, radiomics may assist in predicting a response to systemic therapy, including to antiangiogenic treatment, which may not be adequately assessed with traditional size-based criteria.

Smith and colleagues used a custom post-processing software and algorithm to develop a novel system to quantify changes in the amount of vascularized tumor within specific attenuation thresholds, termed the vascular tumor burden. This semi automated biomarker, in addition to other tumor metrics, such as length, area, and mean attenuation, were used to predict response to antiangiogenic therapy with sunitinib. Changes in the vascular tumor burden metric on initial post-therapy imaging after the initiation of sunitinib showed a better separation of progression free survival between non-responders and responders compared with other commonly used response criteria changes in tumor metrics, including length, area, mean attenuation, RECIST, CHOI, modified CHOI, MASS, and 10% sum long diameter. Extension of radiomic analysis through radiogenomics, radiometabolomics, and correlation with other epidemiologic, clinical, and tissue-based datasets have the potential to improve patient management in the era of personalized medicine. Understanding what these technologies can offer will allow radiologists to play a larger role in the care of patients with RCC.

CT dynamic contrast-enhanced is a capable tool to quantify tumor enhancement and its response to anti-angiogenetic therapies. Han et al found a correlation between tumor renal enhancement at baseline and response and PFS after treatment with sunitinib or sorafenib. In contrast, other studies have demonstrated that although the perfusion parameters at baseline were higher in patients with longer survival times, they were not significantly predictive of outcome, except when a cut-off analysis was established. Other CT-based methods for assessing tumor response to anti-angiogenic therapy and predicting clinical outcome are undergoing further evaluation. Among them, radiomics CT features such as heterogeneity, entropy, and texture uniformity are additional parameters that show promise for assessing the anti-angiogenic response of metastatic renal cell carcinoma. The correlation of these imaging data with genomics (ie, radiogenomics), metabolomics (ie, radiometabolomics) and beyond, offers an opportunity to generate objective, quantitative biomarkers of tumor biology that may be used to predict patient's prognosis and likelihood of response to therapy, overcoming some of the challenges associated with disease heterogeneity.

ARTIFICIAL INTELLIGENCE Artificial intelligence systems, in particular those based on Machine Learning and Deep Learning, are able to autonomously identify salient patterns and complex relationships among data by just looking at sample populations. Their ability to process heterogeneous data, for both classification and prediction purposes, may provide a valid contribution to better stratify RCC patients. Recently, some works have attempted to use Machine and Deep Learning for the differentiation between benign and malignant small renal masses, based on textural analysis of CT scans, for the prediction of the Fuhrman nuclear grade, and gene expression-based molecular signatures.

Overall, the stratification of RCC patients still remains a challenging task, especially when considering the molecular heterogeneity of kidney tumors.

The ability to combine the information from genomics and radiomics using computational approaches based on Machine Learning, provides an opportunity to re-classify patients into subgroups that could better guide treatment strategies. Both supervised and unsupervised techniques can be used to identify a biomarker signature-score as well as to predict response/resistance to therapies.

PRELIMINARY DATA The investigators have preliminary biological data on blood samples of 32 mRCC patients obtained at treatment baseline. The majority of patients received as first line the combination of nivolumab and ipilimumab, 9 patients received sunitinib, 5 were administered with pazopanib, and 6 patients received cabozantinib. The molecular analysis was conducted using the NGS OncoMine Solid Tumor Panel (Thermo Fisher). Twentyfive patients (78,1%) were carriers of a molecular alteration in one or more genes including: TP53, mTOR, PIK3CA, BRAF, EGFR, RET, GNAS, SF3B1, PDGFRA. The reported allele frequency ranged between 3% and 12%. The preliminary analysis shows that patients with multiple mutations have a better response (PR+SD vs PD) from immunotherapy.

EXPERIMENTAL DESIGN This is a multicentric prospective translational study evaluating genomics and radiomics in treatment- naïve advanced ccRCC pts candidate to receive first-line systemic therapy. Nine centers in Italy, including: Istituto Nazionale dei Tumori (INT) of Milan, European Institute of Oncology (IEO) of Milan, Istituto Oncologico Veneto (IOV), Policlinico San Martino of Genova, Istituto Nazionale dei Tumori of Napoli, University Hospital of Parma, Humanitas Research Hospital of Milan, Oncological Center of Aviano and Fondazione Policlinico Universitario Agostino Gemelli of Rome will be involved in the accrual and the treatment of patients.

* 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.

Multiomics Approach in Metastatic Clear Renal Cell Carcnoma

Multiomics Approach for Patients Stratification and Novel Target Identification in Metastatic Clear Renal Cell Carcnoma

Radio-genomic analysis to identify a Radiomics and a molecular score. An artificial neural network-based approach to creating a combined Genomic plus Radiomics signature. Integration of high-dimensional data obtained with clinical data. For this task, we will incorporate NGS and Radiomics datasets to develop an ad hoc AI-based classification model able to efficiently merge and evaluate the array of available information. The achieved biotechnological signature of mccRCC and its evolution over time will also be assessed to suggest an easily interpretable predictive tool (nomogram) of clinical outcome.

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.

Characterization of alterations in radiological imaging data in mRCC in radiological imaging data in mRCC through CT segmentation. Through open-source software analysis, we aim to extrapolate from the image patterns of quantitative characteristics (features) not visible to the naked eye, compare different images of the same disease with known diagnosis (ground truth), and develop a predictive computational model of disease. CT scan for Radiomics will be performed at baseline and every three months.

Fondazione IRCCS Istituto Nazionale dei Tumori, Milano

Director of Genitourinary Medical Oncology

Antineoplastic Agents

All Drugs and Chemicals

Anti-Infective Agents

Everolimus

Sirolimus

Temsirolimus

MTOR Inhibitors

Protein Kinase Inhibitors

Enzyme Inhibitors

Immunosuppressive Agents

Immunologic Factors

10 mg/ day everolimus in the form of an oral tablet for 12 weeks leading up to nephrectomy (with a 1-week perioperative washout period) in patients with locally advanced disease and re-initiating the drug postoperatively in patients with metastatic disease for up to 24 months, or until disease progression. Dosage may be reduced at any point to 5 mg/ day in the event of any grade 3/ 4 toxicity.

everolimus

Anil Kapoor, MD, FRCSC

This study will gather data on potential biomarkers in the treatment of advanced renal cell carcinoma (kidney cancer) and investigate their use as indicators of disease response. The results could eventually enable doctors to match levels of therapy to levels of biomarker on an individual basis and to increase the chance of disease response in patients. This study will also test a new paradigm in the treatment approach of advanced kidney cancer by using the drug everolimus in a neo-adjuvant setting, with the overarching goal of decreasing cancer recurrence rates and improving patient outcomes and survival.

Pilot Study of Neo-Adjuvant Everolimus to Treat Advanced Renal Cell Carcinoma - Analysis of Biomarkers

A Pilot Study of Neo-Adjuvant Everolimus in Patients With Advanced Renal Cell Carcinoma Undergoing Definitive Therapy With Radical Nephrectomy, or Cytoreductive Nephrectomy - Analysis of Serum and Tissue Biomarkers

McMaster University

Novartis Pharmaceuticals

St. Joseph's Healthcare Hamilton

MD, FRCSC

Symptoms and General Pathology

Disease Progression

AZD2014- tablets, starting dose 50mg BD everyday, until disease progression or untolerable toxicity

Everolimus- tablets, starting dose 10mg OD everyday until disease progression or untolerable toxicity

AZD2014

comparing PFS in patients treated with AZD2014 vs Everolimus

Thomas Powles

When kidney cancer spreads beyond the kidney, it is known as metastatic kidney cancer. This is very difficult to treat and almost all patients will die of their disease within 2 years of the diagnosis.

Sunitinib and other related drugs (e.g. pazopanib) have become standard therapy for untreated patients with metastatic kidney cancer. They target a growth factor known as VEGF which is important in treating kidney cancer. Although the results with this drug are impressive, patients develop resistance to the drug and stop therapy. It is currently standard practice is to give everolimus when resistance to sunitinib occurs; this is associated with clear clinical benefit.

However the average time to cancer regrowth with everolimus is only 5 months. It is thought this might be because, everolimus only partially inhibits its target (TORC 1 and TORC 2). Therefore further improvement in treating patients is required. AZD2014 is a promising new drug which does inhibit both TORC 1 and TORC 2 and is therefore worthy of investigation in renal cancer as it theoretically could may have advantages over everolimus. Therefore study compares AZD2014 to everolimus in the setting where everolimus is used as standard of care. (e.g. in patients who have failed drug like sunitinib). The study is a randomised trial allowing us to quantify the benefit and potential for further development of AZD2014. Repeat Xrays (CT scans) will be used to assess if the new drug delays tumour growth. Patients will be closely followed up in clinic to ensure safety. A maximum of 122 patients will be recruited into this multi centre national trial. The primary goal of the study is to investigate if AZ2014 delays the time for cancer regrowth (time to progression) compared to everolimus.

Renal cell cancer, also referred to as kidney cancer, is diagnosed in approximately 170,000 people worldwide annually, resulting in 82,000 deaths. Treatment for metastatic kidney cancer is difficult. Almost all of the patients die from their disease.

In 2006 a new drug called sunitinib, a tyrosine kinase inhibitor, transformed treatment options. It targets the development of new blood vessels within the cancer. Although the results with this drug are impressive, patients develop resistance a median after 11 months to the drug, relapse and die of renal cancer. It is currently standard practice to switch to everolimus when resistance to sunitinib occurs; this is associated with clear clinical benefit.

POTENTIAL RISKS FOR PATIENTS RECEIVING AZD2014:

The main risks and burdens to the patients participating in the study are the potential for side effects of the AZD2014 drug. The phase I study using this drug has been completed, therefore we know it is safe to administer to patients and we have a good idea of what side effects the drug causes. But as the drug is given to larger numbers, additional side effects may be discovered. The activity of the drug has not been evaluated in kidney cancer. Therefore we are not sure if AZD2014 will work

POTENTIAL RISKS FOR PATIENTS RECEIVING EVEROLIMUS:

Everolimus is the current standard therapy for these patients so the risks associated with study drug for these patients are the same as standard of care.

POTENTIAL RISKS FOR ALL PATIENTS:

SIDE EFFECTS:

Side effects will be closely monitored during and after the study. Patients are required to attend clinic weekly for the first four weeks and then every 4 weeks whilst they are on study medication where adverse events will be recorded.

The patient information sheet includes details on expected adverse events for patients to look out for and also details that unexpected events may occur. Patients are provided with the research nurse and principal investigator contact details should any adverse events occur during the course of the study.

Other medical professionals are informed that patients are receiving an experimental drug (through GP letter and labelling of hospital records). There will be an independent data monitoring committee for the trial which will closely assess the side effects of the drugs on a regular basis and the trial results to make sure there are no risk excess to patients.

A Study Comparing AZD2014 vs Everolimus in Patients With Metastatic Renal Cancer

An Open Label, Randomised Phase II Study, Comparing AZD2014 Versus Everolimus With Advanced Metastatic Renal Cancer and Progression on VEGF Targeted Therapy

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Multiomics Approach in Metastatic Clear Renal Cell Carcnoma	NCT05782400	Metastatic Clea...	CT scan Plasma collecti...	18 Years -	Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Pilot Study of Neo-Adjuvant Everolimus to Treat Advanced Renal Cell Carcinoma - Analysis of Biomarkers	NCT01107509	Carcinoma, Rena...	everolimus	18 Years -	St. Joseph's Healthcare Hamilton
A Study Comparing AZD2014 vs Everolimus in Patients With Metastatic Renal Cancer	NCT01793636	Metastatic Clea...	AZD2014 Everolimus	18 Years -	Queen Mary University of London
A Study Comparing AZD2014 vs Everolimus in Patients With Metastatic Renal Cancer	NCT01793636	Metastatic Clea...	AZD2014 Everolimus	18 Years -	Queen Mary University of London

Spots Global Cancer Trial Database for metastatic clear cell renal carcinoma