Abstract PD5-06: Digital spatial mapping of the immune landscape of triple negative breast cancer reveals novel features of immune-tumor cell interaction
Bibliographic record
Abstract
Abstract Background: Growing evidence supports the critical role of preexisting immune response in triple negative breast cancer (TNBC). However, there are limitations with current evaluation approaches: inability to functionally assess the type of immune infiltration with traditional pathologic evaluation and loss of spatial distribution in conventional high-plex gene or protein expression analyses from the whole tumor section. Here, we report the initial analysis of immune protein expression as a function of spatial distribution and clinical outcomes in TNBC samples. Methods: NanoString GeoMxTM Digital Spatial Profiling (DSP) was used to quantify 39 immune-related proteins in stromal and tumor segments from 44 TNBC samples from the FinXX trial. Samples were matched for patient characteristics, treatment arm (capecitabine vs. 5-fluorouracil), and outcome based on recurrence-free survival (RFS) with 22 samples from patients who recurred and 22 samples from patients with durable RFS. Regions of interest (ROIs) were selected based on expression of cytokeratin (tumor), CD45 (leukocytes), or CD68 (macrophages). Each ROI was segmented into tumor (pancytokeratin-positive area) and stroma (pancytokeratin-negative/nuclear SYTO13-positive area). The general linear model was used for statistical analysis of differential expression with RFS as a categorical variable (recur yes or no). Results: A total of 950 tumor and stroma segments were included in this initial analysis. In both tumor and stroma segments, over-expression of T cell activation markers (CD137, GITR) was associated with better outcome, whereas T cell markers (CD3, CD4, CD8) were not significantly associated with outcome. In tumor segments alone, improved outcome was significantly associated with increased protein expression [> 2-fold change (FC) at p<0.001] of CD56, PD-L2, HLA-DR, CD137, GITR, and CD40. In CD45-enriched stroma, improved outcome was associated with elevated expression (FC>2.0, p<0.001) of PD-L2; whereas durable RFS was associated with elevated PD-L2 and IDO1 expression in CD68-enriched stroma. In contrast, macrophage/dendritic cell markers CD68 and CD11c were not associated with outcome. In tumor cells adjacent to CD45-enriched stroma, durable RFS was associated with increased abundance (FC>2.0, p<0.001) of PD-L2, CD56, CD27, GITR, CD20, HLA-DR, and IDO1. Similarly, in tumor segment associated with CD68-enriched stroma, proteins associated with RFS included PD-L2, HLA-DR, CD56, GITR, and CD137. Among 39 immune function proteins, only elevated CTLA4 expression in CD68-enriched stroma was associated with recurrence (FC=0.55, p<0.001), whereas tumor-segment CTLA4 was associated with RFS. Conclusions: Using an in-depth analysis to precisely quantify the abundance of multiple immune function proteins in a spatially defined manner, we observed that PD-L2, IDO1, and T cell activation markers were robustly associated with durable RFS in both tumor and stromal segments. In contrast, MHC components (HLA-DR, beta-2-microglobulin), B cell markers (CD20), and NK cell markers (CD56) were strongly associated with favorable outcome in tumor but not in stromal segments. Our study highlights the conclusion that the immune landscape of TNBC is far too complex to be encompassed by any single molecular marker, and more detailed analyses of the DSP data reported here are ongoing with a view towards using quantitative multiplex analyses to refine our understanding of how therapeutic outcome is influenced by interactions among immune cells and between immune and tumor cells. Acknowledgements: Supported by the Breast Cancer Research Foundation (BCRF18-161), Bankhead Coley (6BC05 Florida Department of Health), 26.2 with Donna Foundation, US National Cancer Institute (CA15083), and the Canadian Cancer Center Citation Format: Saranya Chumsri, Douglas Hinerfeld, Jennifer M. Kachergus, Yaohua Ma, Heather A Brauer, Sarah Warren, Xue Wang, Torsten O. Nielsen, Karama Asleh, Heikki Joensuu, Edith A. Perez, E. A. Thompson. Digital spatial mapping of the immune landscape of triple negative breast cancer reveals novel features of immune-tumor cell interaction [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD5-06.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 0.000 |
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".