Abstract 6354: Using imaging mass cytometry to visualize the multiple myeloma tumor microenvironment post immune priming
Bibliographic record
Abstract
Abstract Background: Multiple myeloma (MM) is an incurable cancer characterized by clonal plasma cell proliferation in the bone marrow, accounting for approximately 10% of all hematologic malignancies. Recently, patients with relapsed or refractory disease have been treated with a combination of the oncolytic reovirus Pelareorep, bortezomib, and dexamethasone, which was well-tolerated and led to prolonged progression free survival of over 3 years in a subset of patients. To understand the complex tumor immune microenvironment (TiME) and immune responses in patients before and after this treatment, we used imaging mass cytometry (IMC) to perform single cell, highly multiplexed, analysis of these patients’ bone marrow samples. Methods: We comprehensively characterized the changes in the MM TIME in pre and post bone marrow biopsy specimens taken from patients treated on a Phase 1b study with a combination of Pelareorep, bortezomib, and dexamethasone. For analysis with IMC, a marker panel of 35 antibodies was assembled to interrogate the various immune subsets of the bone marrow biopsies; each of these antibodies were conjugated to a unique metal isotope. After validation, the antibody cocktail was used to stain the biopsies. Pixel-based classification was performed in Ilastik to generate cell probability masks and processed in Cellprofiler. PhenoGraph was run in HistoCAT to identify the unique phenotypes. Rstudio was used for t-stochastic neighborhood embedding (tSNE) plot generation, and nearest neighbor analyses. ImaCytE was used for image visualization and spatial analysis. Results: Initial visualization of the raw, unsegmented data showed increased infiltration of natural killer cells and T cells in the post-treatment samples when compared against the pre-treatment samples. These changes correlated with immunohistochemical findings, clinical response to treatment, and changes in T cell clonality. After segmentation, the marker expression heatmaps for each of the clusters identified by PhenoGraph and the further subphenotyping in Rstudio showed complex ecosystems of cell-cell interactions. Nearest neighbor spatial analysis of the post-treatment samples revealed that NK cells (NKG2D+ and NKG2A+ subsets), monocytes (CD14+), macrophages (CD68+), cytotoxic T cells (CD3+, CD8+), and T helper cells (CD3+, CD4+) were significantly closer to the Pelareorep-primed MM than the non-primed MM. Further analysis in ImaCytE highlighted specific instances of these immune neighborhoods. Conclusions: IMC allows us to analyze the potent immune response and cellular interactions in the tumor microenvironment in multiple myeloma treated with Pelareorep and Bortezomib. Characterization of these complex interactions allows for a deeper understanding of the key mechanisms of action of these treatments and planning of future combination studies. Citation Format: Julian Olea, Kaijin Wu, Anthony Colombo, Claudia Villa Celi, Thomas Heineman, Matt Coffey, Steffan T. Nawrocki, Akil Merchant, Kevin R. Kelly. Using imaging mass cytometry to visualize the multiple myeloma tumor microenvironment post immune priming [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6354.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".