Neoplasms

All Conditions

Digestive System Diseases

Carcinoma

Carcinoma, Hepatocellular

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

Adenocarcinoma

Liver Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Liver Diseases

Hematinics

All Drugs and Chemicals

Liver Extracts

HCC patients treated with radioembolization. Plasma collection will be performed at 7 timepoints in relation to treatment.

We will be collecting plasma at 7 different timepoints in hepatocellular carcinoma patients that are treated with radioembolization.

Plasma collection

Robert Lewandowski, MD

To analyze specific angiogenic, inflammatory and immune profiles in hepatocellular carcinoma patients who undergo radioembolization.

Patients who have planned lobar radioembolization (TheraSphere (TS)) and consent to this study will have peripheral blood samples collected pre-TS, post-TS, 4 hours post-TS, 24 hours post-TS, 3 days post-TS, 7 days post-TS and 30 days post-TS to analyze specific angiogenic, inflammatory and immune profiles.

Any leftover patient plasma samples collected for this study will be retained for future research which may or may not involve genetic testing.

Biomarker Analyses in Hepatocellular Carcinoma (HCC) Patients Treated With TheraSphere®

Biomarker Analyses in Hepatocellular Carcinoma Patients Treated With Therasphere®

The percent of change at 7 different timepoints for the following biomarkers will be calculated: Ang-2, FGFb, HB-EGF, HGF, PDGF-BB, PIGF, SDF-1, VEGF, VEGFC, IFNg, IL-1a, IL-1b, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, TNFa trimer, CRP, TGFb1, EGFR, PD-1, PD-L1, CD45RA, CD127, HLA-DR, CD62L, CD3, CD8, CD197, CCR7, CD45RO, PD-L2, FOXP3, Perforin, Granzyme, TIA-1, CD14, CD107a, CD25, CD45, CD4, CD20 and CD56+16.

Alphafetoprotein will be measured in ng/mL.

Time-to-progression will be measured in days.

Overall survival will be measured in days.

BTG International Inc.

Northwestern University

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Rare Diseases

Carcinoma, Renal Cell

Kidney Neoplasms

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.

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

Leiomyosarcoma

Neoplasms, Connective and Soft Tissue

Sarcoma

Soft Tissue Sarcoma

Once enrolled subjects will provide Archival Tissue, Optional Fresh tumor from a biopsy and blood collections at baseline, day 8 of cycle 1, day 1 of cycles 2-6, and at progression.

Patients will provide tissue and blood samples. No medical intervention will be completed for study purposes

Plasma Collection

Scott Schuetze

Scott Schuetze, M.D., PhD.

Scott Schuetze, M.D., PhD

Brittany Siontis, MD

Sujana Movva, MD

Elizabeth J Davis, MD

Leiomyosarcoma (LMS) is one of the most prevalent soft tissue sarcomas (STS) and can occur in various sites including soft tissue, uterus and retroperitoneal large vessels. Metastatic disease occurs in approximately 50% of patients diagnosed with leiomyosarcoma and prognosis is poor in setting of metastatic disease. A minority of patients benefit from treatment with chemotherapy and early biomarkers of benefit from treatment are lacking. A biomarker of tumor response and patient survival benefit from chemotherapy early in the course of chemotherapy would be of significant impact in treatment planning. Circulating tumor DNA (ctDNA) is present in blood of patients with advanced/metastatic cancer and may serve as biomarker of tumor response to chemotherapy. Blood samples will be collected prior to and during and chemotherapy, and analyzed for ctDNA and for mutations in genes that are associated with increased risk of developing sarcoma. Tumor tissue will be collected and analyzed for changes in genes. Digital images of the sarcoma from CT or MRI scans obtained during treatment will be obtained for advanced radiomic analysis. Study participants will be asked to complete a questionnaire on attitudes and understanding of genetics and genetic testing.

Archival Tissue, Optional Fresh tumor biopsy, blood collections prior to chemotherapy, day 8 of cycle 1, day 1 of cycles 2-6, and at progression of sarcoma

Metastatic Leiomyosarcoma Biomarker Protocol

Observational Study of Biomarkers of Disease-related Outcomes in Patients With Metastatic Leiomyosarcoma Receiving Chemotherapy

We will share deidentified genetic data on the NIH site, and deidentified imaging data will be shared with the lab at Columbia University. We are not sharing patient identities.

To examine the correlation of change in ctDNA with objective tumor response per RECIST. Analysis will occur at each subsequent early time-point (pre-cycle 1, mid-cycle 1 and pre-cycle 2).

To examine the correlation of change in ctDNA with progression free survival (PFS). Analysis of ctDNA will occur at each subsequent early time-point (pre-cycle 1, mid-cycle 1 and pre-cycle 2). A Cox regression model will determine whether the baseline ctDNA levels are associated with PFS.

Plasma collections for ctDNA analysis will be collected at each subsequent early time-point (pre-cycle 1, mid-cycle 1 and pre-cycle 2).

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Biomarker Analyses in Hepatocellular Carcinoma (HCC) Patients Treated With TheraSphere®	NCT03203837	Hepatocellular ...	Plasma collecti...	18 Years -	Northwestern University
Multiomics Approach in Metastatic Clear Renal Cell Carcnoma	NCT05782400	Metastatic Clea...	CT scan Plasma collecti...	18 Years -	Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Metastatic Leiomyosarcoma Biomarker Protocol	NCT05653388	Leiomyosarcoma	Plasma Collecti...	-	University of Michigan Rogel Cancer Center
Biomarker Analyses in Hepatocellular Carcinoma (HCC) Patients Treated With TheraSphere®	NCT03203837	Hepatocellular ...	Plasma collecti...	18 Years -	Northwestern University

Spots Global Cancer Trial Database for plasma collection