Trial Title:
Multiomics Approach in Metastatic Clear Renal Cell Carcnoma
NCT ID:
NCT05782400
Condition:
Metastatic Clear Cell Renal Carcinoma
Conditions: Official terms:
Carcinoma, Renal Cell
Kidney Neoplasms
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Radiation
Intervention name:
CT scan
Description:
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.
Intervention type:
Biological
Intervention name:
Plasma collection
Description:
● 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.
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.
Detailed description:
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.
Criteria for eligibility:
Study pop:
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).
Sampling method:
Non-Probability Sample
Criteria:
INCLUSION CRITERIA:
- 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.
EXCLUSION CRITERIA:
- 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.
Gender:
Male
Gender based:
Yes
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Istituto Tumori
Address:
City:
Milan
Zip:
20156
Country:
Italy
Status:
Recruiting
Contact:
Last name:
Giuseppe Dr Procopio, MD
Phone:
+39223904450
Email:
Giuseppe.Procopio@istitutotumori.mi.it
Start date:
February 28, 2023
Completion date:
September 30, 2027
Lead sponsor:
Agency:
Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Agency class:
Other
Source:
Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05782400
