Abstract PR005: An atlas of cellular heterogeneity in primary and metastatic renal cell carcinomas
Bibliographic record
Abstract
Abstract Renal cell carcinoma (RCC) tumors harbor various layers of intra-tumor heterogeneity at the genetic, transcriptomic, and cellular levels. A series of recent studies have underlined the importance of charting this cellular variability to understand the cell of origin of various RCCs and the mechanisms that underlie their response to therapy. However, the regulatory factors driving intra-tumor heterogeneity in RCCs, the role of this heterogeneity in driving metastasis, and its association with clinical outcomes are mostly unknown. To address these questions, we have generated a cellular map of transcriptional heterogeneity in RCCs, which consists of >100,000 single-cell expression profiles from six patient-derived xenografts (PDX) models, including models derived from primary and brain metastatic tumors, as well as ten primary and six metastatic patient tumor tissues. We also developed a computational method for Gene Expression Decomposition and Integration (GEDI) that enables seamless integration of single-cell transcriptomic data across heterogeneous cancer samples while providing interpretable axes of variation. Using GEDI, we performed a fine-grained analysis of the heterogeneity of cancer cells and found that, despite patient-specific differences, common sources of intra-tumor heterogeneity exist across samples, driven by variable activity of pathways such as TNF-α/NF-κB signaling and oxidative phosphorylation (OxPhos). Analysis of data from our PDX models confirmed that this heterogeneity was stable and reproducible. A major source of intra-tumor heterogeneity was hypoxia signaling, even in VHL-deficient clear cell RCC samples (ccRCC), challenging the traditional view that ccRCC cells have a uniform pseudo-hypoxic status due to VHL inactivation. Regulatory network activity projection allowed us to disentangle the contributions of Hypoxia Inducible Factors HIF1A and HIF2A, revealing their divergent regulatory programs: while HIF1A is associated with cell-cycle and proliferation signatures, the activity of HIF2A correlates with epithelial-mesenchymal transition (EMT) within tumors, which we validated by RNA sequencing of HIF1A and EPAS1 knockdown in VHL-negative cells. Next, we used GEDI to study the cell state changes that occur during metastasis in both neoplastic and non-neoplastic tumor-infiltrating cells. By examining the differences in gene expression patterns between primary and metastatic samples, we identified various pathways that are transcriptionally activated during this transition, including EMT and Oxphos. We also uncovered distinct subpopulations of cancer cells with high gene expression changes associated with a metastatic profile. Analysis of the ligand-receptor interactions in the tumor microenvironment revealed that interactions from malignant cells to immune cells were over-represented in metastatic tumors. Overall, our cellular atlas has uncovered a complex continuum of cell states in RCCs, highlighting various drivers of the intra-tumor heterogeneity and establishing various cell states associated with metastasis. Citation Format: Ariel Madrigal, Minjun Kim, Adrien Osakwe, Tianyuan Lu, Zohreh Mehrjoo, Elham Moslemi, Rick Farouni, Larisa Morales-Soto, Yu Chang Wang, Matthew Dankner, Haig Djambazian, Kevin Petrecca, Jonathan Spicer, Fadi Brimo, Peter Siegel, Morag Park, Jiannis Ragoussis, Simon Tanguay, Yasser Riazalhosseini, Hamed S. Najafabadi. An atlas of cellular heterogeneity in primary and metastatic renal cell carcinomas [abstract]. In: Proceedings of the AACR Special Conference: Advances in Kidney Cancer Research; 2023 Jun 24-27; Austin, Texas. Philadelphia (PA): AACR; Cancer Res 2023;83(16 Suppl):Abstract nr PR005.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".