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.
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.
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.
This study plans to enroll 10 patients with locally advanced renal cell carcinoma. Participants must be newly diagnosed and treatment-naïve prior to surgery, with primary tumors ≥7 cm exhibiting a multinodular gross appearance, and scheduled to undergo radical nephrectomy.
Case-only
Prospective
Multi-region Tumor Sampling and Integrated Multi-omics Analysis and Microtumor PTC Drug Sensitivity Assay
Diagnostic testThis 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.
Identification of Intra-tumoral Nodule Driving Relationships
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.
Correlation between Ex Vivo Drug Sensitivity and Multi-omics Features
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
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.
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