Abstract 2813: A multiplexed, multispectral approach to analyzing the immune microenvironment of oral potentially malignant lesions
Bibliographic record
Abstract
Abstract Objectives: Tissue sections of oral potentially malignant lesions (OPML) can be used not only to provide pathological assessment (diagnosis) but could also be used to analyze the interactions between cellular populations, signaling molecules, and structural proteins that impact the clinical course of disease. However, much of this information remains unpacked due to methodological limitations. Traditional immunohistochemistry (IHC) limits the number of proteins able to be simultaneously analyzed within a tissue section and is also prone to human error in its analysis. Newer multiplexed IHC (mIHC) methods involving repeated cycles of staining allow for quantification of a greater number of markers; however, tissue integrity may be compromised, and complexity is added to the interpretation of the results. There is a need to develop an immunostaining method that overcomes these barriers. Hypothesis: mIHC and an in-house Hyperspectral Cell Sociology (HCS) platform will allow for robust and detailed investigation of the immune microenvironment of OPML compared to traditional IHC staining and scoring techniques. Methods: Automated mIHC staining with a seven immune marker panel was completed on annotated formalin-fixed paraffin-embedded OPML tissue. A cocktail of three primary antibodies was applied, then antigens and chromogens stripped using SDS-glycine before second round staining with four antibodies and a hematoxylin counterstain was completed. Slides were then digitally imaged, regions of interest selected in conjunction with an oral pathologist and staining quantified computationally using the HCS platform. Traditional IHC was completed on a randomized subset of cases using sequentially cut tissue sections and a double-staining technique. Scoring was completed by two blinded clinicians. Results: One cycle of multiplexed staining and de-staining allowed for the detection of seven markers on one section while minimizing loss of tissue integrity. The HCS platform captures a single tissue section at multiple wavelengths, enabling the unmixing of multiple overlapping, colocalized chromogens. The resulting set of images displayed each stain separately, allowing for nuclei segmentation and the generation of a map of the epithelium and underlying connective tissue. True positive cells for each stain were demarcated on this map, allowing for investigation of marker positivity, co-positivity, cell to cell spatial relationships, and layer-based analysis, compared to cell count and density analyses obtained with traditional IHC. Conclusion: The immune microenvironment contains a wealth of information pertaining to the biology, pathogenesis, and outcome of disease. Multiplexed staining and imaging methods to analyze and unpack this information is of great clinical utility. Citation Format: Iris Lin, Kouther Noureddine, Paul Gallagher, Martial Guillaud, Lewei Zhang, Leigha Rock, Miriam Rosin, Denise Laronde. A multiplexed, multispectral approach to analyzing the immune microenvironment of oral potentially malignant lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2813.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".