Abstract P5-02-04: Immune Signatures Display Subtype-Specific Activation in Breast Cancer
Bibliographic record
Abstract
Abstract Background. The development of new anti-HER2 therapies for the treatment of HER2-positive breast cancer (BC) is changing the concept of HER2 dichotomization to select and treat this patient population. In addition, different immunotherapies tested in HER2-low BC are gaining continuous interest. However, a comprehensive characterization of HER2 BC subgroups and patients is required to identify the best treatment approach. Materials and Methods. Using 123 BC samples with gene expression and IHC/FISH determined HER2 status we determined cutoff values to identify HER2 positive, HER2 low, and HER2 ultralow cohorts. With the inclusion of hormone receptor (HR) status six clinically relevant cohorts were defined (HR+/HER2+, HR+/HER2-low, HR+/HER2-ultralow, and HR-/HER2+, HR-/HER2-low, HR-/HER2-low). An ingegrated database of 7,624 BC cases were assigned to the six subtypes. Prognosis determination was based on relapse-free survival (RFS), distant-metastasis-free survival (DMFS), and overall survival (OS). Clinical parameters evaluated include MKI67 expression, lymph node status and grade. All together 17 immune signatures resembling immune genes and related activated pathways were tested against the six molecular BC subgroups. Results. We defined a robust cutoff for HER2 expression levels to define six distinct HER2 BC molecular subgroups (>3034 for HER2 positivity and < 1780 for HER2 ultralow). Regardless the HR positivity, the overall distribution of HER2-low, and HER2-ultralow was 23% and 52%, respectively. In the HR+ subgroups the HER2-low showed a better prognosis as compared to the HER2-ultralow and HER2+ (RFS and DMFS P = 0.0048 and 0.0015, respectively) while there was no prognostic effect of HER2 expression in the HR- subgroups. Not surprisingly, an association with higher grade was demonstrated in all HR- as compared to the HR+ subgroups regardless of HER2 status. Overall, all HR- subgroups showed a higher involvement of immune genes as compared to the three HR+ subgroups. Of interest, HER2-low (HR+ and HR-) and HR-/HER2+ showed a significant overlap expression of immune signatures (71%). While the HR+/HER2-ultralow and HR+/HER2+ displayed minimal activation of immune pathways, the HR-/HER2-ultralow was the group most significantly associated with the activation of immune signaling including IFN signaling (67% percent of genes in the panel with altered expression), T cell active cytokines (34% of genes hit), and cytotoxic effector molecules (48% of genes hit). Conclusions. Our study supports a further molecular stratification of breast cancer based on HER2 status. The different tumor-immune background of these BC subgroups highlights that selected patient cohorts may derive benefit from targeted immunotherapy. Citation Format: Gyongyi Munkacsy, Libero Santarpia, Balazs Gyorffy. Immune Signatures Display Subtype-Specific Activation in Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-02-04.
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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) | 0.004 | 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".