Abstract PD5-08: Expression of LAG-3 in breast cancer, and its association with subtype and outcome
Bibliographic record
Abstract
Abstract Aim: To investigate the expression and clinical value of the immune checkpoint marker LAG-3 in breast cancer patients Background: Lymphocyte-activation gene 3 (LAG-3) is a recently discovered immune checkpoint biomarker that is targeted by agents currently being evaluated in early phase clinical trials. LAG-3 functions as a cell surface receptor expressed following T cell activation and negatively impacts T cell functions. This biomarker has not yet been evaluated in large series of breast cancers with long term treatment and outcome data, in the context of subtype and other immune biomarkers. Methods: Two tissue microarray series (a training set with N=330 and a validation set with N = 2203 patients) were constructed from breast carcinoma primary excision specimens from University of British Columbia hospitals, linked to detailed clinical and pathological data. None of these patients had received neoadjuvant treatment. 4µm sections were stained with an antibody to LAG-3 (clone 17B4) by immunohistochemistry using a Ventana Discovery Ultra automated slide stainer. LAG-3+ stromal and intra-epithelial tumor infiltrating lymphocytes (TILs) were reported as absolute counts per tissue microarray core. Stromal TILs (sTIL) were defined as lymphocytes present in the stroma not in direct contact with tumor nest whereas intra-epithelial TIL (iTIL) were lymphocytes in direct contact with carcinoma cells. All descriptive and survival analyses were conducted using SPSS software. Results: LAG-3+ sTILs were found in 16% of breast cancer cases in both the training set and the validation set; LAG-3+iTILs were present in 14 and 11%, respectively. In both the training set and the validation set, the presence of LAG-3 (iTILs or sTILs) was significantly (p<0.001) associated with high grade tumors, estrogen and progesterone receptor negativity, high Ki67 index and with the HER2+ and basal-like subtypes. In survival analyses of ER negative patients, in both sets patients with LAG-3 T cells (iTILs or sTILs) had a significantly improved disease-specific survival (p<0.05). As with other lymphocyte biomarkers, this association was not observed among ER+ patients. Conclusions: LAG-3+TILs are present in breast cancer and are associated with major risk factors and hormone receptor negative subtypes. ER negative breast cancer patients have a better outcome if they contain LAG-3+ TILs, consistent with published data showing better survival among ER- breast cancer patients with immune infiltrates. More than a quarter of ER negative breast cancers contain TILs expressing LAG3, and may represent the most relevant subset to target with emerging checkpoint inhibitors targeting this T cell surface receptor. Citation Format: Burugu S, Gao D, Nielsen TO. Expression of LAG-3 in breast cancer, and its association with subtype and outcome [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 PD5-08.
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 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 | 0.001 |
| 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 | 0.000 |
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".