Abstract 2726: Characterization of immune microenvironment and heterogeneity in breast cancer subtypes
Bibliographic record
Abstract
Abstract Despite ongoing advancements in the clinical management of breast cancer (BC), the variability in the response to treatment requires continued understanding of the molecular biology of this disease. Receptor status for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) along with pathological features dictate the course of therapy. Tumor cells in BC are phenotypically diverse between and within patients due to underlying differences in their cellular biology. This heterogeneity in the expression of key molecular targets renders some tumor cells resistant to therapy. While the etiology of intra-tumor heterogeneity is under investigation, it is equally important to characterize the heterogeneous nature of the tumor microenvironment as both of these compartments are engaged in the molecular crosstalk; with the latter known to contribute to tumor progression. Traditionally considered to be immunologically cold, recent advances have demonstrated that some BC subtypes elicit immunological response. However, further research is warranted to broadly illustrate the immune cell contexture in BC in a spatial context. In this study, the immune microenvironment was characterized through proteomic analysis using NanoString's GeoMX Digital Spatial Profiling (DSP) platform, in a cohort of early BC patients. This cohort consists of Luminal A, Luminal B, Basal, and HER2 BC subtypes. Correlation of proteomic and genomic data will reveal the role of genomic alterations in differential immune response. Protein markers were analyzed in both the tumor microenvironment (TME) and tumor compartments with beta-2-microglobulin, CD40, and CD11 significantly expressed in the TME compartment while PanCK, ERalpha, and HER2 were enriched in the tumor. Interestingly, CD127 was also expressed in the tumor compartment, indicating infiltration by memory T cells. Through this study we spatially characterize the immune microenvironment in BC subtypes, providing further evidence against the “immunologically cold” view of this disease. In addition, we demonstrate the potential clinical use of this novel platform in diagnosing and better stratifying BC patients based on spatial heterogeneity in tumor and TME. Citation Format: Drashti Jain, Linda Liao, Megan Hopkins, Mary Anne Quintayo, Vida Talebian, Jane Bayani, Alison Cheung, Martin Yaffe, John Bartlett, Melanie Spears. Characterization of immune microenvironment and heterogeneity in breast cancer subtypes [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 2726.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".