Abstract 777: Revealing the transcriptional and proteomic spatial neighborhoods of early prostate cancer and the tumor microenvironment
Bibliographic record
Abstract
Intermediate-risk prostate cancer (PCa) poses a challenge for treatment decisions, made more complex by its heterogeneous nature. Growing evidence highlights the critical role of the tumor microenvironment (TME), in cancer initiation, progression, and response to treatment, underscoring the importance of profiling cancer cells but also the TME within their spatial context. Here, we present single-cell in situ transcriptomic and spatial proteomics results from 300 intermediate-risk PCa prostatectomy samples. Multiple regions of the prostate, including regions of the index lesion, secondary lesions, regions of benign and normal were sampled then analyzed using Bruker’s CosMx SMI platform for a 6000-plex RNA assay in a subset of patients; in addition to proteomic profiling using the GeoMx DSP platform. Clustering assigned cells into 15 cell types based on their transcriptional profiles. Each cell type was tested for significantly different proportions in tumor versus non-tumor samples. The cell type with the greatest bias had a 9-fold greater proportion in tumor samples (adjusted P < 0.01). ERG, largely associated with the TMPRSS2-ERG fusion in PCa, was most specifically expressed in cells of this type. Conversely, the cell type with the greatest bias towards non-tumor samples (3-fold greater proportion, adjusted P < 0.01) is characterized most specifically by the expression of MSMB, which encodes an immunoglobulin binding factor previously found to have decreased expression in PCa. Other evidence of intratumoral heterogeneity was found in samples that are not dominated by these stereotypical cell types. For example, a relatively higher-grade sample from an index tumor possessed high proportion of the tumor-dominant cell type whereas a relatively lower-grade sample from the same tumor did not. Similarly, the tumor and non-tumor samples arising from the same patient showed similar cell type composition, highlighting inter-patient heterogeneity. Moreover, we identified proteins with significantly different expression in tumor versus non-tumor samples. In the TME, EpCAM and CD56 were significantly overexpressed and underexpressed in tumor versus non-tumor regions, respectively (adjusted P < 0.01). In the epithelium, cleaved caspase-9, which initiates the caspase cascade leading to apoptosis, was the most significantly underexpressed protein in tumor samples (adjusted P < 0.01). However, the expression of the mRNA encoding caspase-9 is not highly correlated with the expression of cleaved caspase-9, highlighting the added benefit of protein expression profiling. Taken together, this multi-modal spatial analysis of multiple samples from the same PCa patients facilitates a detailed understanding of early PCa for biomarker discovery and pathways of therapeutic intervention. Citation Format: Anna Y. Lee, Megan Hopkins, Linda Liao, Vida Talebian, Tamara Jamaspishvili, David M. Berman, Melanie Spears, Jane Bayani. Revealing the transcriptional and proteomic spatial neighborhoods of early prostate cancer and the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 777.
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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 | 0.001 |
| 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) | 0.004 | 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".