Abstract 5561: Whole slide Imaging Mass Cytometry allows the rapid profiling of the immune landscape of histopathologically aggressive prostate tumors
Bibliographic record
Abstract
Abstract Patients with Gleason pattern 4 or 5 prostate tumors have a high risk of biochemical recurrence and worse survival. Affected by their local environment, dynamic immune cell phenotypes can both hinder or drive cancer progression. To understand the proteomic environments that facilitate specific immune cell responses and interactions in advanced prostate cancer, we have spatially resolved the immune cell content and metabolic features of Gleason pattern 4 and 5 and subpatterns of prostate tumors. To scale quantitative multiplexed histopathology measurements to human disease, we have applied a new high-throughput whole slide modality of high-plex Imaging Mass CytometryTM to simultaneously quantify immune cells, metabolic states, prostate specific pathways, and biomarkers in 125 whole slide sections from 42 patients over 100 cm2 of tissue. It is essential to image whole slide sections in order to fully evaluate the complete heterogeneity of these cancer samples. In parallel, multi-region macrodissection shotgun proteomics and both glycan and extracellular matrix MALDI imaging mass spectrometry have quantified the proteome associated with specific immune cell environments. Together, we identify localized proteomic microenvironments associated with specific tumor supportive or inhibitory immune cell content in human tumors that may help differentiate which high-risk patients will have a rapid biochemical recurrence following radical prostatectomy. Citation Format: Jennifer L. Gorman, Lydia Y. Liu, Jordan P. Hartig, Nikesh Parsotam, Amanda Khoo, Vladimir Ignatchenko, Sarah Asbury, Somi Afiuni, Ricardo Gonzalez, Michael J. Geuenich, Caitlin F. Harrigan, Yuju Lee, Jianan Chen, Liang Lim, Qanber Raza, Peggi M. Angel, Kieran Campbell, Stanley K. Liu, Michelle R. Downes, Richard R. Drake, Thomas Kislinger, David King, Hartland W. Jackson. Whole slide Imaging Mass Cytometry allows the rapid profiling of the immune landscape of histopathologically aggressive prostate tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5561.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".