Abstract PO2-03-07: Immune Landscape in PD-L1-positive and TROP2-positive Triple-Negative Breast Cancer
Bibliographic record
Abstract
Abstract Background: Triple-negative breast cancer (TNBC) tends to be more aggressive, and more likely to recur than other subtypes of breast cancer, and patients with metastatic TNBC have a poor prognosis. The recent approvals and data of pembrolizumab (anti-PD-1 inhibitor) in combination with chemotherapy in advanced-metastatic and early high-risk TNBC, and sacituzumab govitecan (an ADC that targets the tumor cell surface antigen TROP2 coupled with the irinotecan metabolite SN-38) in metastatic TNBC, has shed light on some of the immune and cancer signaling associated with TNBC. Although exciting progress has been made in the treatment of TNBC, there are still important gaps to fill to understand the biology of TNBC and identify additional therapies. Materials and Methods: We identified a total of 1500 patients whose tumors were negative for the expression of ER and HER2 receptors by gene array. PD-L1 and TROP2 expression data were available for 802 early TNBC patients, of whom 657 were treated with adjuvant chemotherapy. A comprehensive identification and analysis of immune-related genes (n= 1169) was conducted. Mann-Whitney test comparing the gene expression for all immune related-genes was evaluated in patients expressing PD-L1 and TROP2 at low and high expression levels. Immune gene modules resembling key immune gene signaling were analyzed in all subgroups identified. Fold changes and Mann-Whitney P values were calculated in each subgroup. Results: Patients were stratified based on high and low expression levels of PD-L1 and TROP-2 mRNA. PD-L1 highly expressing (PD-L1+) tumors showed a marked difference in immune-related genes as compared to PD-L1 low-level tumors (PD-L1-) with PD-L1+ tumors demonstrating a significantly higher proportion of correlated immune genes (CXCL9/10/11, IFN-γ, CCL5 and STAT1). TROP2 highly and low expressing tumors (TROP2+ and TROP2-, respectively) showed a relatively limited difference in terms of immune-related gene expression with key immune-related genes having statistical differential expression in the two subgroups. TROP2+ tumors showed a high presence of several Mucin family gene members (MUC1, -5AC, -4). In addition, CLDN 4, VTCN1, and MET were among the key immune genes significantly expressed in TROP2+ tumors (P = 5.73E-20; P = 6.2E-13, P = 2.1E-11). Overall, PD-L1+ tumors and TROP2+ tumors showed an immune suppressive genomic landscape but were potentially regulated by different signaling. Of note, tumors with very high expression of PD-L1 showed an inverse correlation with VTCN1 gene expression. Conclusions: This preliminary analysis confirmed that only a limited number of early TNBC highly expressed PD-L1 and that a different immune suppressive landscape for PD-L1± and TROP2± tumors exists. In addition, our data suggest that early TNBC cases lacking PD-L1 might escape immune surveillance by virtue of the upregulation of key alternate signaling. Ongoing efforts investigating the expression of such markers on stroma/epithelial tumor components will help to clarify their biological, clinical, and potential therapeutic role in TNBC. Citation Format: Balazs Gyorffy, Libero Santarpia. Immune Landscape in PD-L1-positive and TROP2-positive Triple-Negative Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-03-07.
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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) | 0.002 | 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".