A renal cell carcinoma tumorgraft platform to advance precision medicine.
Ontology highlight
ABSTRACT:
SUBMITTER: Elias R
PROVIDER: S-EPMC8762721 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Renal cell carcinoma (RCC) encompasses a heterogenous group of diseases, creating challenges for drug development. Preclinical models which are both translatable and representative of disease heterogeneity are needed. Patient-derived xenograft (PDX) or tumorgraft (TG) models recapitulate the biology and treatment responsiveness of RCC. Through the orthotopic implantation into NOD/SCID mice of tumor samples from >900 ethnically-diverse patients at UT Southwestern Medical Center and the affiliated county hospital, we have generated a resource comprising 172 independently-derived stably-engrafted TG lines from 148 patients. The lines have been characterized histologically and genomically (whole exome sequencing [n = 97] and RNA sequencing [n =102]). They represent a variety of histological and on
cogenotypes including representative TCGA clades further validated through orthogonal metabolomic analyses in a subset. We illustrate how this platform has enabled a deeper understanding of the biology and molecular genetics of RCC, the development of both tissue- and imaging-based biomarkers, as well as advances in drug development. This resource expands the diversity of models available to the scientific community to advance precision diagnosis and therapy in RCC.
| EGAS00001005516 | EGA
Project description:Traditionally, xenograft models have been used to study tumors in vivo. However, their utility is reduced by the use of tumor cell lines for implantation. Tumorgrafts (TGs; also known as patient-derived xenografts (PDXs)), which involve patient-derived tumor samples, are increasingly recognized as more representative models than traditional xenografts. Furthermore, we showed previously that renal cell carcinoma (RCC) TGs retain the histology, gene expression, DNA copy number alterations, mutations and treatment responsiveness of patient tumors. In skilled hands, implantations require ?5 min per mouse, and TGs typically grow to 1 cm in 1-4 months. Here we outline the process of implantation of patient-derived RCC samples into the kidneys of immunodeficient mice, as well as the s.c. implantation for preclinical drug testing, including guidelines for the design and execution of drug trials. TGs have extensive applications besides therapeutic studies and may identify biomarkers and mechanisms of resistance. In addition, they may provide insights into tumor biology.
| S-EPMC4314943 | biostudies-literature
Project description:Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.
| S-EPMC9237320 | biostudies-literature
Project description:Most anticancer drugs entering clinical trials fail to achieve approval from the U.S. Food and Drug Administration. Drug development is hampered by the lack of preclinical models with therapeutic predictive value. Herein, we report the development and validation of a tumorgraft model of renal cell carcinoma (RCC) and its application to the evaluation of an experimental drug. Tumor samples from 94 patients were implanted in the kidneys of mice without additives or disaggregation. Tumors from 35 of these patients formed tumorgrafts, and 16 stable lines were established. Samples from metastatic sites engrafted at higher frequency than those from primary tumors, and stable engraftment of primary tumors in mice correlated with decreased patient survival. Tumorgrafts retained the histology, gene expression, DNA copy number alterations, and more than 90% of the protein-coding gene mutations of the corresponding tumors. As determined by the induction of hypercalcemia in tumorgraft-bearing mice, tumorgrafts retained the ability to induce paraneoplastic syndromes. In studies simulating drug exposures in patients, RCC tumorgraft growth was inhibited by sunitinib and sirolimus (the active metabolite of temsirolimus in humans), but not by erlotinib, which was used as a control. Dovitinib, a drug in clinical development, showed greater activity than sunitinib and sirolimus. The routine incorporation of models recapitulating the molecular genetics and drug sensitivities of human tumors into preclinical programs has the potential to improve oncology drug development.
| S-EPMC3570965 | biostudies-literature
Project description:Here we define precision medicine approaches and discuss how these may be applied to renal stone-formers to optimise diagnosis and a management. Using the gene discovery of monogenic stone disorders as examples, we discuss the benefits of personalising therapies for renal stone-formers to provide improved prevention and treatment of these disorders.
| S-EPMC6373186 | biostudies-literature
Project description:Beginning in the early 2000s, the accumulation of biospecimens linked to electronic health records (EHRs) made possible genome-phenome studies (i.e., comparative analyses of genetic variants and phenotypes) using only data collected as a by-product of typical health care. In addition to disease and trait genetics, EHRs proved a valuable resource for analyzing pharmacogenetic traits and developing reverse genetics approaches such as phenome-wide association studies (PheWASs). PheWASs are designed to survey which of many phenotypes may be associated with a given genetic variant. PheWAS methods have been validated through replication of hundreds of known genotype-phenotype associations, and their use has differentiated between true pleiotropy and clinical comorbidity, added context to genetic discoveries, and helped define disease subtypes, and may also help repurpose medications. PheWAS methods have also proven to be useful with research-collected data. Future efforts that integrate broad, robust collection of phenotype data (e.g., EHR data) with purpose-collected research data in combination with a greater understanding of EHR data will create a rich resource for increasingly more efficient and detailed genome-phenome analysis to usher in new discoveries in precision medicine.
| S-EPMC5480096 | biostudies-literature
Project description:PURPOSE:Precision medicine requires an understanding of individual variability, which can only be acquired from large data collections such as those supported by the Cancer Imaging Archive (TCIA). We have undertaken a program to extend the types of data TCIA can support. This, in turn, will enable TCIA to play a key role in precision medicine research by collecting and disseminating high-quality, state-of-the-art, quantitative imaging data that meet the evolving needs of the cancer research community. METHODS:A modular technology platform is presented that would allow existing data resources, such as TCIA, to evolve into a comprehensive data resource that meets the needs of users engaged in translational research for imaging-based precision medicine. This Platform for Imaging in Precision Medicine (PRISM) helps streamline the deployment and improve TCIA's efficiency and sustainability. More importantly, its inherent modular architecture facilitates a piecemeal adoption by other data repositories. RESULTS:PRISM includes services for managing radiology and pathology images and features and associated clinical data. A semantic layer is being built to help users explore diverse collections and pool data sets to create specialized cohorts. PRISM includes tools for image curation and de-identification. It includes image visualization and feature exploration tools. The entire platform is distributed as a series of containerized microservices with representational state transfer interfaces. CONCLUSION:PRISM is helping modernize, scale, and sustain the technology stack that powers TCIA. Repositories can take advantage of individual PRISM services such as de-identification and quality control. PRISM is helping scale image informatics for cancer research at a time when the size, complexity, and demands to integrate image data with other precision medicine data-intensive commons are mounting.
| S-EPMC7328100 | biostudies-literature
Project description:The proliferation of digital technologies and the application of sophisticated data analysis techniques are increasingly viewed as having the potential to transform translational research and precision medicine. While digital technologies are rapidly applied in innovative ways to develop new diagnostics and therapies, the ultimate approval and adoption of these emerging methods presents several scientific and regulatory challenges. To better understand and address these regulatory science gaps, a working group of the Clinical and Translational Science Awards Program convened the Regulatory Science to Advance Precision Medicine Forum focused on digital health, particularly examining gaps in the use, validation, and interpretation of data from sensors that collect and tools that analyze digital biomarkers. The key findings and recommendations provided here emerged from the Forum and include the need to enhance areas related to data standards, data quality and validity, knowledge management, and building trust between all stakeholders.
| S-EPMC7204775 | biostudies-literature
Project description:BackgroundGene expression profiling has benefited medicine by providing clinically relevant insights at the molecular candidate and systems levels. However, to adopt a more 'precision' approach that integrates individual variability including 'omics data into risk assessments, diagnoses, and therapeutic decision making, whole transcriptome expression needs to be interpreted meaningfully for single subjects. We propose an "all-against-one" framework that uses biological replicates in isogenic conditions for testing differentially expressed genes (DEGs) in a single subject (ss) in the absence of an appropriate external reference standard or replicates. To evaluate our proposed "all-against-one" framework, we construct reference standards (RSs) with five conventional replicate-anchored analyses (NOISeq, DEGseq, edgeR, DESeq, DESeq2) and the remainder were treated separately as single-subject sample pairs for ss analyses (without replicates).ResultsEight ss methods (NOISeq, DEGseq, edgeR, mixture model, DESeq, DESeq2, iDEG, and ensemble) for identifying genes with differential expression were compared in Yeast (parental line versus snf2 deletion mutant; n = 42/condition) and a MCF7 breast-cancer cell line (baseline versus stimulated with estradiol; n = 7/condition). Receiver-operator characteristic (ROC) and precision-recall plots were determined for eight ss methods against each of the five RSs in both datasets. Consistent with prior analyses of these data, ~ 50% and ~ 15% DEGs were obtained in Yeast and MCF7 datasets respectively, regardless of the RSs method. NOISeq, edgeR, and DESeq were the most concordant for creating a RS. Single-subject versions of NOISeq, DEGseq, and an ensemble learner achieved the best median ROC-area-under-the-curve to compare two transcriptomes without replicates regardless of the RS method and dataset (> 90% in Yeast, > 0.75 in MCF7). Further, distinct specific single-subject methods perform better according to different proportions of DEGs.ConclusionsThe "all-against-one" framework provides a honest evaluation framework for single-subject DEG studies since these methods are evaluated, by design, against reference standards produced by unrelated DEG methods. The ss-ensemble method was the only one to reliably produce higher accuracies in all conditions tested in this conservative evaluation framework. However, single-subject methods for identifying DEGs from paired samples need improvement, as no method performed with precision> 90% and obtained moderate levels of recall. http://www.lussiergroup.org/publications/EnsembleBiomarker.
| S-EPMC6624180 | biostudies-literature
Project description:Renal cell carcinoma is a highly heterogeneous cancer group, and the complex microenvironment of the tumor provides appropriate immune evasion opportunities. The molecular mechanism of immune escape in renal cell carcinoma is currently a hot issue, focusing primarily on the major complex of histocompatibility, immunosuppressive cells, their secreted immunosuppressive cytokines, and apoptosis molecule signal transduction. Immunotherapy is the best treatment option for patients with metastatic or advanced renal cell carcinoma and combination immunotherapy based on a variety of principles has shown promising prospects. Comprehensive and in-depth knowledge of the molecular mechanism of immune escape in renal cell carcinoma is of vital importance for the clinical implementation of effective therapies. The goal of this review is to address research into the mechanisms of immune escape in renal cell carcinoma and the use of the latest immunotherapy. In addition, we are all looking forward to the latest frontiers of experimental combination immunotherapy.
| S-EPMC7985340 | biostudies-literature