64defining cancer initiating cells and their vulnerabilities in renal cell carcinoma
Bibliographic record
Abstract
Abstract Background Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, leading to 179 000 cancer-related deaths annually. ccRCC initiating cells (CICs) are thought to drive tumor initiation, growth, therapy resistance, and metastasis, yet their molecular characteristics remain poorly defined. This study aims to identify putative CICs and their essential genes using a cell marker-agnostic strategy. Methods We performed a comprehensive analysis of ccRCC transcriptomes at single-cell resolution, developed patient-derived xenograft (PDX) and 3D patient-derived organoids (PDO) models of ccRCC, and conducted functional examinations in these models to investigate our findings. Results Computational modeling of tumor formation using single-cell RNA velocity analysis of five primary and metastatic ccRCC-PDXs revealed a minor cell population as the origin of other tumor cells, representing putative CICs. Pathway and network analyses suggested that a core network of proteins, conventionally known to regulate mitosis, are highly active in CICs and may be essential for their function. These proteins were expressed in PDOs and PDX-derived spheroids established following a CIC enrichment protocol. Spheroid cells exhibited higher tumorigenicity and colony formation ability than parental tumor cells, as confirmed by in-vivo injection in nude mice and in-vitro colony formation assays. Successive in vivo passaging confirmed the self-renewal capacity of spheroid-derived tumors. Pharmacological blockade of candidate proteins elicited dose-dependent inhibitory effect on spheroid and colony formation, with in-vivo validation showing that blocking these proteins significantly delayed tumor growth and more efficiently prevented tumor formation in mice. These results highlight the importance of these proteins in the cancer-initiating abilities of malignant cells. Interestingly, our results suggest that the identified target proteins may elicit their CIC-related function independently from their mitosis regulatory roles. Conclusions We identified and validated essential proteins in RCC-CICs, supported by single-cell transcriptome data and RCC spheroids and PDX models. Targeting the vulnerabilities of RCC-CICs, given their role in tumor initiation, progression, and therapy resistance, offers significant potential for developing new anti-cancer therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".