Abstract 2010: A spatial profiling approach to evaluating the prognostic impact of heterogeneity in the triple-negative breast cancer immune microenvironment
Bibliographic record
Abstract
Abstract Triple negative breast cancer (TNBC) is the most aggressive breast cancer subtype with the poorest patient outcomes. The composition and active immune pathways of the immune microenvironment play a critical role in tumor progression. TNBC is frequently characterized by a high and heterogeneous immune infiltration in the tumor stroma. The degree of immune infiltration impacts current management: neoadjuvant chemotherapy and anti-PD1 agent pembrolizumab.1, 2, 3 Previously, our lab studied a treatment-naive cohort of 192 patients diagnosed with TNBC and found that patients with higher B cell infiltration in the stromal regions had better outcomes (Kanwar and Balde et. al, Cancer Res 2021)4. While previous studies employed low-plex methods of analysis to study archival formalin-fixed paraffin-embedded tissue samples, the emergence of high-plex in situ protein detection methods like the GeoMx Digital Spatial profiler (DSP) has drastically enhanced the resolution of the tumor microenvironment that can be assessed.5 Using the GeoMx DSP, we are quantifying a panel of 40 immune proteins, to expand our understanding of the immune microenvironment of the pre-treatment cohort previously studied. The protein panel enables the detection of multiple immune cell types and subtypes, certain immune pathways like T cell activation and exhaustion, and the expression of immunotherapeutic targets. Preliminary results show that patients with high CD163 expression, associated with immunosuppressive macrophages had poorer outcomes (p= 0.035). The immune microenvironment for larger tumors expressed higher T cell activation markers (p=0.016). While the overall heterogeneity in immune cells did not have an impact on patient outcomes, a high heterogeneity in T cell activation markers was significantly associated with patient outcomes (p=0.029) as well as tumor size (p=0.024). We have characterized the stromal immune cell infiltrates and their spatial heterogeneity, finding immune markers that may significantly impact patient outcomes. Our findings aid in the identification of therapeutic targets prevalent in the TNBC stroma as well as potentially informing therapy decisions based on the tumor immune microenvironment composition. 1. Won KA, Spruck C. (2020). Int J Oncol, 57(6), 1245-1261. DOI:10.3892/ijo.2020.5135 2. Marra A et al. (2020). NPJ Breast Cancer, 6, 54. DOI:10.1038/s41523-020-00197-2 3. Obidiro O et al. (2023). Pharmaceutics, 15(7), 1796. DOI:10.3390/pharmaceutics15071796 4. Kanwar N et al. (2021). Cancer Res, 81(24), 6196-6206. DOI:10.1158/0008-5472.CAN-21-1079 5. Bergholtz H et al. (2021). Cancers, 13(17), 4456. DOI:10.3390/cancers13174456 Citation Format: Prerana Sensharma, Huidan Zuo, Melanie Dawe, Megan Hopkins, Zeynep Baskurt, Osvaldo Espin-Garcia, Philippe L. Bedard, Melanie Spears, Susan J. Done. A spatial profiling approach to evaluating the prognostic impact of heterogeneity in the triple-negative breast cancer immune 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 2010.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".