Abstract B017: Single-cell proteogenomic profiling reveals immune cell networks in renal cell carcinoma
Bibliographic record
Abstract
Abstract Introduction: Single-cell proteogenomic profiling has enabled the interrogation of complex milieus of cell types and their corresponding states that interact to regulate the antitumor immune response. While several landmark studies employing single-cell RNA sequencing (scRNAseq) have provided a detailed taxonomy of lymphoid and myeloid phenotypic states within the renal cell carcinoma (RCC) microenvironment, these studies have only been performed on small patient cohorts. As such, the ability to infer coordinated interactions between cellular phenotypes remains limited. Here, we leverage single-cell proteogenomic approaches to map immune cell phenotype co-occurrences across a heterogeneous patient cohort. Experimental procedures: We performed 5’ scRNAseq on 75 dissociated tumor samples from 59 patients undergoing partial or radical nephrectomy using the 10X genomics platform. Paired single-cell TCR/BCR sequencing (scTCR/BCRseq) and cytometry by time-of-flight (CyTOF) were additionally performed on 64 and 48 samples, respectively. Results: scRNAseq captured a total of ~350,000 high quality cells of RCC, stromal and immune (myeloid and lymphoid) origin, across a range of histologies (clear cell 69%, papillary 11%, chromophobe 13%, other 8%) and stages (I 53% II 7%, III 31%, IV 9%), with proportions of major cell types being consistent between scRNAseq and CyTOF. Interestingly, paired scTCRseq revealed a subset of patients (~40%) that displayed intratumoral clonally hyperexpanded TCRs (> 100 copies/clone), with CD8+ cells expressing an exhausted phenotype being most dominant. These samples were also co-enriched for CXCL13+ CD8+ T cells as well as proinflammatory CXCL9/10+ macrophages, a relationship we confirmed upon calculating pairwise correlations between all immune cell phenotype proportions across all samples. Notably, RCC cells from these samples expressed an antigen presentation meta-program identified by non-negative matrix factorization and pathway analysis. A “pro-inflammatory” cell network gene signature was derived and employed across the clear cell RCC (KIRC) cohort of TCGA and IMMotion 151 data sets, to reveal pro-inflammatory enriched RCCs displayed worse survival yet improved response to immunotherapy-based regimens, respectively. Conclusions: Our study provides the largest single-cell proteogenomic characterization of RCC to date, detailing co-occurring immune cell phenotypes across heterogenous patient tumor microenvironments. We leveraged this data to characterize a pro-inflammatory immune cell ecotype of RCC with potential prognostic and predictive implications that warrant further validation. Overall, this data should serve as a fundamental resource for future work developing novel therapeutic strategies and biomarkers against RCC. Citation Format: Keith A Lawson, Shirley Hui, Daniel Stueckmann, Xiaoyu Zhang, Jalna Meens, Lisa Martin, Maria Komisarenko, Julia Szusz, Stephane Chevrier, Sujana Sivapatham, Philip Jonsson, Fred Davis, Cristina Penaranda, Ryan Newton, Nicolas Stransky, Piotr Bielecki, Zhihui Liu, Jennifer Pfeil, Sarah Crome, Dominik Deniffel, Masoom Haider, Jason Lee, Neil Fleshner, Nathan Perlis, Robert Hamilton, Girish Kulkarni, Susan Prendeville, Hartland Jackson, Laurie Ailles, Gromoslaw Smolen, Bernd Bodenmiller, Gary Bader, Antonio Finelli. Single-cell proteogenomic profiling reveals immune cell networks in renal cell carcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B017.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".