Abstract A18: Investigating the differential expressions of miRNAs and programmed-death-ligand-1 related to the T-bet positive tumor-infiltrating lymphocytes in axillary-node-negative breast cancer
Bibliographic record
Abstract
Abstract Axillary lymph node-negative (ANN) breast cancer (BC) patients generally have a good prognosis; however, 20-30% of ANN patients will experience recurrence. To identify biomarkers that distinguish ANN patients who may benefit from systemic therapy we have been studying characteristics of the breast tumor cells and the tumor microenvironment. We have found that tumor-infiltrating lymphocytes (TILs) that express T-bet (T-box transcription factor) are associated with poor prognosis features such as high grade, hormone receptor negativity and basal subtype. Despite these adverse clinical-pathologic characteristics, T-bet+ TILs cells are associated with a favorable outcome. T-bet is essential for Th1 development and IFN-γ production. To investigate how T-bet+ TILs influence the BC suppression, we are examining molecular differences in T-bet+/high and T-bet-/low tumors including expression of tumor miRNA expression profiles and expression of Programmed-Death-Ligand-1 (PD-L1). Tumors expressing PD-L1 inhibit Programmed-Death-receptor-1-(PD-1)-expressing TILs and evade immune surveillance. Interestingly, PD-L1 is induced by IFNγ, the hallmark cytokine of the T-bet+ TILs, which suggests a potential relationship between the tumor PD-L1 and T-bet+ TILs. Microdissected, formalin-fixed, paraffin-embedded (FFPE) samples of 35 T-bet+/high and 35 T-bet-/low ANN BC tumors are being used to identify the differentially-expressed miRNAs via miRNA sequencing. The sufficiency of the FFPE samples as the miRNA source was confirmed by quantifying miRNA expressions in the 3-5 paired frozen and FFPE samples, and observing the expressions to be highly correlated with both qPCR (r=0.81) and miRNA sequencing (rs=0.73). After identifying the differentially-expressed miRNAs via miRNA sequencing, these miRNAs will be validated via qPCR in both the original and independent BC tumor set. Based on the genes and molecular pathways the validated miRNA(s) may regulate, appropriate in vitro functional assays will be conducted to examine their roles in cellular proliferation, migration and invasion. Furthermore, PD-L1 mRNA expression has been evaluated in 107 ANN tumors and correlations with clinico-pathological characteristics will be determined. Identifying the tumor miRNA(s) associated with T-bet+ TILs and studying their functions may indicate differences between T-bet+/high and T-bet-/low tumors that can be used to improve the prognosis of ANN BC. Characterizing the tumor PD-L1 expression based on T-bet+ TILs and the clinical data may distinguish a subset of ANN BC patients who may benefit from the PD-L1 blockade therapy. Citation Format: Minji Lee, Anna-Marie Mulligan, Shelley Bull, Dushanti Pinnaduwage, Irene Andrulis. Investigating the differential expressions of miRNAs and programmed-death-ligand-1 related to the T-bet positive tumor-infiltrating lymphocytes in axillary-node-negative breast cancer. [abstract]. In: Proceedings of the AACR Special Conference: Function of Tumor Microenvironment in Cancer Progression; 2016 Jan 7–10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2016;76(15 Suppl):Abstract nr A18.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
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".