Renal Cell Carcinoma (Kidney Cancer)
Conditions
Keywords
Multi-omics, Single-Cell Sequencing, Spatial Transcriptomics, Whole Exome Sequencing, Microtumors, Patient-Derived Cells, Drug Sensitivity Testing, Tumor Heterogeneity, Intratumoral Nodules
Brief summary
This is a research study aiming to better understand a type of kidney cancer called Renal Cell Carcinoma (RCC). Doctors have observed that inside some larger RCC tumors, there are multiple smaller nodules. This study wants to find out if these nodules are different from each other and how they might be related. To do this, researchers will study tumor tissue samples from 10 patients with RCC who are having surgery. From each tumor, several nodules will be analyzed using advanced laboratory techniques. These techniques will create very detailed maps of the genes and cells within each nodule. At the same time, tiny 3D tumor models (called microtumors) will be grown from these samples in the lab to test how they respond to different cancer drugs. The main goal is to combine these two types of information to see how the differences in genes and cells between nodules might explain why some tumors stop responding to treatment (become resistant). We hope this study will lead to a deeper understanding of how RCC grows and spreads, and help find new ways to diagnose and treat it in the future.
Detailed description
Background and Rationale: Renal Cell Carcinoma (RCC) frequently exhibits intratumoral morphological heterogeneity, often presenting as distinct multiple nodules within a single tumor mass on cross-section. The biological and clinical significance of this multinodular architecture remains poorly understood. It is hypothesized that these nodules may represent clonal subpopulations with unique genomic, transcriptomic, and functional profiles, potentially driving tumor progression and therapy resistance. This study leverages integrated multi-omics and functional drug testing to systematically decipher the inter-nodular heterogeneity and evolutionary relationships within RCC. Primary Objectives: To delineate the cellular and genomic landscape of different intratumoral nodules in RCC using single-cell RNA sequencing (scRNA-seq), whole-exome sequencing (WES), and spatial transcriptomics. To infer the potential clonal evolutionary relationships and driver-subordinate dynamics between coexisting nodules. To characterize the differential drug sensitivity profiles of patient-derived microtumor (PTC) models established from distinct nodules. To integrate multi-omics data with drug response data to explore underlying mechanisms of drug resistance. Study Design and Methods: This is a single-center, prospective, basic science study. We will enroll 10 treatment-naïve patients with locally advanced RCC (tumor diameter ≥7 cm, with regional lymph node metastasis but no distant metastasis) scheduled for radical nephrectomy. Intraoperatively or immediately post-resection, each grossly multinodular tumor will be sectioned. Three dominant nodules (labeled T1, T2, T3 by size) will be identified from each specimen. From each nodule, four matched samples will be collected for: 1) scRNA-seq, 2) WES, 3) spatial transcriptomics, and 4) generation of 3D patient-derived tumor cell (PTC) microtumor models. Analyses: Bioinformatic integration of scRNA-seq, WES, and spatial data will be performed to construct maps of cellular composition, genetic alterations, and their spatial distribution across nodules. Pseudotime trajectory analysis will be applied to infer potential evolutionary sequences. PTC models will undergo ex vivo drug sensitivity testing (e.g., against axitinib, pembrolizumab, and their combination). Differential response data will be correlated with omics-derived features (e.g., specific mutant alleles, cell subtype abundances, pathway activities) to identify candidate resistance mechanisms. Significance: This is the first study to systematically investigate intratumoral nodular heterogeneity in RCC at a multi-omics level coupled with functional validation. Findings are expected to provide novel insights into RCC tumorigenesis and progression, potentially revealing new biomarkers for prognosis and therapeutic targets to overcome resistance.
Interventions
DIAGNOSTIC_TESTMulti-region Tumor Sampling and Integrated Multi-omics Analysis and Microtumor PTC Drug Sensitivity Assay
This integrated intervention involves: 1) Multi-region sampling of intratumoral nodules from resected RCC tumors for multi-omics analysis (single-cell RNA-seq, whole-exome sequencing, spatial transcriptomics) to map molecular and cellular heterogeneity. 2) Parallel generation of patient-derived microtumor (PTC) models from the same nodules for ex vivo drug sensitivity testing against a panel of oncology agents (e.g., Axitinib, Pembrolizumab). The core purpose is to correlate molecular features from omics with functional drug response data to decipher mechanisms of intra-tumoral heterogeneity and resistance.
Sponsors
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Histologically confirmed renal cell carcinoma with regional lymph node metastasis. * Primary tumor with a maximum diameter ≥ 7 cm. * Tumor exhibits a multinodular distribution pattern on cross-section (assessed via intraoperative or postoperative gross specimen). * Age \> 18 years. * Ability to understand the study and voluntarily provide written informed consent.
Exclusion criteria
* Presence of distant metastasis (M1 stage). * Prior receipt of any targeted therapy or immunotherapy for renal cell carcinoma before surgery. * History of other active malignancies besides RCC (except cured basal cell carcinoma of the skin or carcinoma in situ of the cervix). * Any psychiatric, neurological, or legal condition that may compromise the ability to understand the informed consent or to comply with study procedures.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Identification of Intra-tumoral Nodule Driving Relationships | Through study completion, an average of 16 months | The primary outcome is the bioinformatic inference of potential clonal evolutionary "driver-subordinate" relationships between different intratumoral nodules within the same RCC tumor. This is determined by integrating multi-region single-cell RNA sequencing and whole-exome sequencing data. Key analyses include: 1) Comparative analysis of mutational landscapes and copy number variations across nodules to identify shared trunk mutations and private branch mutations. 2) Pseudotime trajectory analysis of single-cell data to reconstruct the potential temporal sequence of nodule emergence. A relationship will be inferred if a consistent pattern of shared ancestral mutations and/or a unidirectional differentiation trajectory is identified. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Correlation between Ex Vivo Drug Sensitivity and Multi-omics Features | Through study completion, an average of 24 months | This outcome measures the statistical correlation between the drug sensitivity profiles (e.g., IC50 values or cell viability percentages) of patient-derived microtumor (PTC) models from different nodules and specific molecular features derived from matched multi-omics data. Features include the abundance of specific cell subtypes (e.g., a malignant subpopulation), the expression level of a candidate gene, or the activity score of a signaling pathway (e.g., hypoxia, mTOR). Correlation will be assessed using methods such as Spearman's rank correlation or linear regression modeling. |
| Characterization of Inter-nodular Heterogeneity at Single-Cell Resolution | Through study completion, an average of 24 months | This outcome is a comprehensive description of the heterogeneity between intratumoral nodules, quantified by: 1) The number and identity of differentially abundant cell clusters (e.g., T cell subsets, macrophage states, malignant cell subtypes) between nodules, as defined by single-cell RNA-seq analysis. 2) The number of spatially variable genes or gene programs identified by spatial transcriptomics analysis that show distinct distribution patterns across nodules. |
Contacts
CONTACTXiongjun Ye
Outcome results
None listed