Abstract 575: PD-L1 positive tumor-infiltrating lymphocytes and mutational load in breast cancer
Bibliographic record
Abstract
Abstract Background: PD-1 blockade has emerged as an effective treatment for a subset of cancer patients. Studies have shown that PD-L1 expression is associated with likelihood of response to PD-1 blockade. In order to select the right breast cancer patient for immunotherapy, characterization of the immune landscape of breast tumors is required. Therefore, we assessed PD-L1 expression and tumor-infiltrating lymphocytes (TILs) in different breast tumor subtypes and the link with prognosis. We also sequenced a panel of genes to assess the mutational load in triple negative tumors (TNBC) and investigate the association with PD-L1 positive TILs. Material and methods: We analyzed 438 tumor samples from breast cancer patients of all ages treated between 1986 and 2007 with surgery, with or without adjuvant therapy. PD-L1 was stained using whole slide specimens (E1L3N® antibody) after methodological validation. Pathologists quantified TILs based on International TILs Working Group recommendations and scored PD-L1 based on the percentage of positive (tumor and/or immune) cells; as negative if 0%, positive if ≥1%, and high if >50%. Mutational load was assessed based on DNA kinome sequencing. Associations were measured by Cox/logistic regression model, including pathological variables. Multiplex imaging of 20 immune-infiltrated areas from four ER negative tumors were performed using the Vectra® system based on immunofluorescence staining panel of: CD4, CD68, CD8, FOXP3 and PD-L1. Results: PD-L1 expression and TILs were higher in ductal (compared with lobular), high grade and estrogen receptor (ER)-negative tumors (p<0.001). TILs (density ≥5%) were significantly associated with worse distant metastasis-free survival (DMFS) only in ER-positive tumors (n=204): HR=2.72; 95%CI: 1.07-6.94. PD-L1 positivity (≥1%) followed the same trend: HR=1.66; 95%CI: 0.87-3.15. However, in ER-negative tumors (n=171), high PD-L1 expression (>50%) was significantly associated with better DMFS: HR=0.51; 95%CI: 0.27-0.98. TNBC with high PD-L1 expression of TILs (>50%) showed an association with increased mutation load (p=0.019) and a trend for better DMFS (HR=0.41; 95%CI: 0.16-1.04) compared with tumors lacking TILs. Further characterization of PD-L1 positivity in the immune-infiltrated cells was conducted by a multiplex imaging analysis. Preliminary results indicated that PD-L1 is expressed in CD68+, CD4+, FOXP3+ and CD8+ immune-cells. Conclusion: Our findings suggest that PD-L1 positive TILs are associated with worse prognosis in ER-positive breast cancer and with better outcome in ER-negative group. In TNBC, high mutational load correlates with high PD-L1 positive TILs. Citation Format: Marcelo Sobral-Leite, Koen Van de Vijver, Magali Michaut, Hugo M. Horlings, Tesa M. Severson, Philip C. Schouten, Rianne van der Linden, Kelly Kersten, Anna Marie Mulligan, Nayana Weerasooriya, Joyce Sanders, Ashley Cimino-Mathews, Dennis Peters, Gerrit K. Hooijer, Erik Hooijberg, Annegien Broeks, Rene Bernards, Sabine Linn, Irene L. Andrulis, Marc J. van de Vijver, Lodewyk F. Wessels, Marleen Kok, Karin E. de Visser, Marjanka K. Schmidt. PD-L1 positive tumor-infiltrating lymphocytes and mutational load in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 575. doi:10.1158/1538-7445.AM2017-575
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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 | 0.000 |
| 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) | 0.004 | 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".