Abstract A69: The immunological environment in triple negative breast cancer; impact on clinical outcomes towards a prognostic clinical test
Bibliographic record
Abstract
Abstract It is now accepted that changes in the normal cells that constitute the tumor microenvironment (TME) play important roles in determining cancer progression and ultimate outcome. We and others have established that an immune signature, and more recently that the ability of CD8+ T cells to infiltrate the tumor bed, are correlated with good outcome in triple negative breast cancer. Importantly, since the cytotoxic effector functions of CD8+ T cells are mainly contact-dependent, the tumor microenvironment may act to inhibit access to the epithelial tumor bed, leading to uncontrolled tumor growth. Therefore, in order to determine the role that the TME plays on mediating the entry of CD8+ T cells into the epithelial tumor bed, our group performed laser-capture microdissection to separate the tumor epithelial compartment from the surrounding tumor-associated stroma from the primary tumor of 56 triple negative breast cancer patients and followed by gene expression profiling. Our aims are to provide clinical validation of predicted CD8+ T cell localisation based on our bioinformatic analysis, test our predictions that the identified stroma signature correlates with CD8+ T cell retention in other cohort of TN breast cancer patients and develop suitable markers of this stroma, and develop a muliplex based assay integrating CD8+ T cell localisation with stromal markers suitable for a retrospective study to validate predictions with outcome. Using gene expression profiling, our data have identified a canonical gene expression signature which correlates with retention of CD8+ T cells in the stroma and poor outcome. Thus, our results demonstrate that gene expression analysis of clinical triple negative breast cancer samples can predict the location of CD8+ T cells within the tumor and in turn the outcome. Citation Format: Mathieu Gigoux, Tina Gruosso, Nicholas Bertos, Sadiq Saleh, Atilla Omeroglu, Hong Zhao, Margarita Souleimanova, Sarkis Meterissian, Michael Hallett, Morag Park. The immunological environment in triple negative breast cancer; impact on clinical outcomes towards a prognostic clinical test. [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy: A New Chapter; December 1-4, 2014; Orlando, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2015;3(10 Suppl):Abstract nr A69.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 teacher head, 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".