Abstract P4-12-09: The immune response in triple negative breast cancer
Bibliographic record
Abstract
Abstract Background: Triple Negative Breast Cancer (TNBC) is often associated with a poor prognosis. However TNBC is a heterogeneous group of tumors and while some patients have a poor prognosis others appear to do well long-term. There are currently no clinical or pathologic tumor features that distinguish poor from good outcome. Some TNBCs have infiltration with immune cells and the degree of this infiltration correlates with prognosis. Objectives: 1) To comprehensively examine immune factors associated with outcome in a cohort of TNBC patients, and 2) to develop an immune gene signature that can stratify patients into low and high risk groups. Methods: We profiled RNA from 22 TNBCs (10 who had experienced a recurrence) from our institutional cohort using the PanCancer Immune Profiling Panel from NanoString. This panel consists of an extensive list of 770 genes designed to evaluate the immune microenvironment of tumors. The genes fall into a number of functional categories including; 1. Genes that identify specific immune cells, 2. Cytokines that promote an effective immune response and others that are associated with immunosuppression, 3. Chemokines, which attract immune cells into the tumor, 4. Genes that assess both the activation and inhibition of immune cell function, 5. Genes that identify tumor specific antigens. Analysis was performed in the nSolver Advanced Analysis Program. Results: Using unsupervised hierarchical clustering of genes that were highly differentially expressed, the tumors were classified into 3 immune groups with distinct clinical outcomes. Group 1 ('Immune Excluded'), consisted of tumors with the lowest levels of expression of the immune markers assessed, suggesting that these tumors have a low or absent immune infiltrate; 7 of 7 patients in this group recurred. Group 2 ('Immune Activated') contains tumors that had the highest levels of anti-tumoral immune cell genes and their activation markers. This we interpret to represent a tumor group with a robust anti-tumor immune response; 0 of the 6 patients in this group recurred. In comparison Group 3 ('Immune Low') had moderate to low levels of expression of the majority of immune genes assessed. This we interpret to represent a group of tumors with limited immune cells present; 3 of 9 patients in this group recurred. Lastly, when comparing scores for immune cells, patients that recurred had lower scores for cytotoxic cells, CD8 T cells, Th1 cells and B cells. Conclusion: In this pilot study high expression of anti-tumoral immune genes correlated with good outcome, whereas lower/absent expression of these genes correlated with poor outcome. We are currently extending these findings to our entire cohort of 180 TNBC patients. Citation Format: Gillgrass AE, Pond GR, Levine MN, Whelan TJ, Hassell JA, Bane AL. The immune response in triple negative breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-12-09.
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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) | 0.003 | 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".