744 Mapping immune cell responses in whole blood and solid tumor tissue biopsies of renal cancer with CyTOF and imaging mass cytometry
Bibliographic record
Abstract
Background Immunotherapy has limited efficacy in treating solid tumor. The main hurdles include T cell exhaustion, an immunosuppressive tumor microenvironment (TME), a lack of tumor-specific antigens and a complex immune landscape. To overcome the limitation of current immunotherapies and develop novel, safe and effective treatment strategies, a comprehensive understanding of both spatially localized and systemic immune responses is essential. Multimodal analysis provides a comprehensive, holistic view of the tumor and immune environment, which is essential for accurately predicting and improving patient responses to treatment.Methods We applied an integrative multimodal approach to map localized and systemic immune responses by employing CyTOF™ and Imaging Mass Cytometry™ (IMC™) technologies in matched peripheral blood mononuclear cells (PBMC), tumor-derived cells (TDCs) and formalin-fixed, paraffin-embedded tumor tissues from clear cell renal carcinoma (ccRCC) . A 40-plus-marker CyTOF panel was used to stain PBMC and TDC samples and acquired using CyTOF XT. Tumor tissues were stained using a 40-plus-marker immuno-oncology IMC panel and acquired using Hyperion™ XTi attached to CyTOF XT. Subsequent pixel-clustering and single-cell segmentation analyses quantified expression patterns of structural and immune markers in IMC data ( figure 1).Results CyTOF profiling of TDC and PBMC revealed distinct functional states. PBMC showed higher frequencies of functional monocytes and naïve/cytotoxic T cells, while TDCs were enriched for memory T cells with increased regulatory and exhausted phenotypes. These findings suggest a shift toward immune suppression within the TME compared to systemic immunity. Using IMC, multiple tertiary lymphoid- structures (TLSs) with mature morphologies were found within the tumor and at tumor margins. Immunosuppressive M2 macrophages surrounded TLSs with a small degree of penetration. Pixel-clustering analysis provided a detailed view of various clusters present in the sample, including a necrotic cluster and a macrophage cluster within the tumor stroma ( figure 2). Interestingly, we identified a region of tumor cells with high TIM-3 expression and low immune infiltration. Finally, functional cell types identified with CyTOF were spatially mapped to ccRCC tissue acquired on IMC, revealing potential involvement of M2 macrophages in immune cell exhaustion in the TME.Conclusions Our integrated, multimodal approach demonstrates the power of CyTOF and IMC technologies for comprehensive functional profiling of immune cells and their spatial dynamics, respectively. Application of this approach can identify predictive biomarkers and promote the development of novel therapeutic strategies against cancer.For Research Use Only. Not for use in diagnostic procedures.Abstract 744 Figure 1Workflow overview. Tumor tissue and whole blood from ccRCC patient were obtained. Tumor tissue was processed in two ways: (1) FFPE tumor sections were stained with IMC antibody panel, run on Hyperion XTi, (2) PBMC and TDC were stained with CyTOF antibody panel run on CyTOFAbstract 744 Figure 2High-resolution visualization of regions of interest (A) and single-cell segmentation analysis of selected phenotypes (B). ROI from Stage 3 ccRCC containing TLSs (Panel A and B, white box). The single-cell segmentation algorithm was trained to recognize functional phenotypes of T cells and M2 macrophages (Panel B)
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".