Abstract 2250: Immuno-oncology study to profile the tumor microenvironment in multiple human cancers using high-plex imaging mass cytometry
Bibliographic record
Abstract
Abstract Immune profiling of tumor tissues has become a key tool in understanding the complexity of the tumor microenvironment (TME), for predictive biomarker discovery, and in cancer treatment. The presence of tumor-infiltrating lymphocytes has been associated with benefit from therapy. Furthermore, the TME also contains immunosuppressive elements that can impede immune response. Imaging Mass Cytometry™ (IMC™) enables detailed assessment of cell phenotype and function using 40-plus markers simultaneously at subcellular resolution on a single slide without spectral overlap or background autofluorescence. High-plex IMC has enabled us to evaluate the TME in different cancer histologies including in highly autofluorescent tissue types like lung, hepatocellular carcinoma, and skin melanoma. The Maxpar® Human Immuno-Oncology IMC Panel Kit (201508) was customized using antibodies from the Standard BioTools™ catalog to create panels for tissue-based immuno-oncology research. Data acquisition was performed using a Hyperion™ Imaging System. To facilitate cell segmentation, an IMC Cell Segmentation Kit (TIS-00001) was applied to enhance cell membrane boundaries. We applied a pixel classification approach and CellProfiler™ for single-cell segmentation. We used histoCAT™ for single-cell analysis to visualize protein expression in various cancer types via PhenoGraph clustering and t-SNE maps. Our panels were applied to normal and cancer human tissue microarrays (TMAs) to phenotype and analyze cell populations in these tissues. We provide detailed analysis of the TME by classifying activation state of immune cell populations, epithelial-to-mesenchymal transition (EMT) progression, and composition of the extracellular matrix. In-depth single-cell analysis quantitatively evaluated the cellular makeup and immune cell component in the TME of cancer tissues and identified major tumor, immune, and stromal cell phenotypes. This work demonstrates the capability of IMC for quantitative and spatial identification of multiple immune parameters in the TME on a single slide of cancer patient samples (e.g. tumor microarray). For Research Use Only. Not for use in diagnostic procedures. Citation Format: Thomas D. Pfister, Liang Lim, Shaida Ouladan, Nick Zabinyakov, Qanber Raza, Christina Loh. Immuno-oncology study to profile the tumor microenvironment in multiple human cancers using high-plex imaging mass cytometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2250.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".