Abstract A040: Spatially resolved immune cell networks in renal cell carcinoma
Bibliographic record
Abstract
Abstract Interactions between immune cells within the tumor immune microenvironment (TIME) dictate immune cell function and their ability to respond to immunotherapy. Here, we have developed and applied a targeted imaging mass cytometry (IMC) approach to spatially interrogate immune cell interactions at a sufficient scale to associate them with patient outcomes. IMC uses heavy metal-conjugated antibodies and time of flight mass-spectrometry to visualize multiplexed protein targets at single-cell resolution on formaldehyde fixed paraffin embedded tissues. Application of this method to a RCC patient cohort requires the development of a tailored approach. To identify clinically relevant RCC immune cell states, fresh surgical renal cell carcinoma (RCC) samples were procured at the University Health Network through the REnal cancer MicroEnvironment DiscoverY (REMEDY) project and have undergone single cell RNA sequencing (scRNA-seq) and single cell suspension mass cytometry (SMC). Immune cell populations identified through scRNA-seq informed the selection of 43 protein targets sufficient to capture the protein profile and spatial relationships of select cell populations. Heavy metal-conjugated antibodies for each of these protein targets were tested and developed into a novel multiplexed IMC panel to quantify immune cell populations in RCC and applied to the REMEDY cohort. An established analytic pipeline was applied to map clusters of IMC-defined immune cell populations, using both supervised and unsupervised methods of cell identification, and benchmarked to those identified by scRNA-seq and SMC. Going forward, this approach will be repeated on a larger cohort of over 500 RCC patients with known clinical outcomes to identify immune cell networks as signatures associated with disease progression. RCC is amongst the most immune infiltrated solid tumors having varied response to immune checkpoint inhibitors, highlighting the potential for immune cell interactions to effect patient overall response to immunotherapy. With this tailored application of IMC to RCC we aim to characterize cell-cell interactions within the TIME to elucidate mechanisms that enable these interactions to dictate cell function and clinical outcomes. Using this approach, we have characterized over 30 cell populations, including clinically relevant RCC immune cell states, and quantified their spatial relationships within the TIME. We have identified preliminary relationships of immune cell networks within and between patients and determined their association to clinical features. In this preliminary data we observed heterogeneity of immune cell populations between patients as well as intratumoral regional spatial heterogeneity, highlighting the importance of utilizing spatial technologies to characterize immune cell interactions more comprehensively. In summary, we have developed scalable IMC measurements which capture spatially resolved immune cell networks at single-cell resolution within the RCC TIME that will enable the first association of immune cell networks to clinical outcomes. Citation Format: Jennifer Pfeil, Daniel Stueckmann, Shirley Hui, Lisa Martin, Sally Zhang, Maria Komisarenko, Keith Lawson, Gary Bader, Antonio Finelli, Hartland Jackson. Spatially resolved 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 A040.
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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".