Abstract P1-10-08: Assessing immune biomarkers of response to anthracyclines in breast cancer
Bibliographic record
Abstract
Abstract Background: Pathologists have long recognized that the interaction between immune and tumour cells is critical in the development and progression of breast cancer. Studies have demonstrated the presence of tumour-infiltrating lymphocytes (TILs) correlates with improved clinical outcome in breast cancer especially in the triple negative and HER-2 positive subtypes. TILs predict for improved response to certain therapies including chemotherapy and trastuzumab. The predictive value of TILs in ER positive tumours is less clear. It has been demonstrated the higher presence of the immune microenvironment is associated with a better prognosis and as a result a higher likelihood of benefit from chemotherapy and possibly from immunotherapy, whereas cold immune microenvironment carries greater risk of relapse and lower benefit from chemotherapy and possibly immuno-therapies. In this study, we evaluated whether TILs could be used to predict chemotherapy response and characterize the pre-existing tumour microenvironment (TME) using NanoString’s GeoMx Digital Spatial Profiling (DSP) platform. Methods: We assessed haematoxylin and eosin stained slides from the phase III BR9601 adjuvant breast cancer trial using software used in the international ring study 2 for standardized evaluation of TILs integrated in VMscope slide explorer. Evaluation of stromal TILs was based on international guidelines. NanoString’s DSP platform was used to analysis 256 patient samples from the BR9601 clinical trial. For analysis, region of interest were selected and compared for the TME (CD45+ve) and tumour rich (pan cytokeratin) in tissue microarrays. A panel of 56-antibodies were analysed in each ROI. Results: The mean TIL score in this cohort of patients was 15.58% (ranging from 0 to 66.67%). The presence of higher levels of TILs was significantly associated with ER negativity (p<0.001), high grade (p=0.01) and increased lymph nodal involvement (p=0.002). In multivariate analysis, patients whose tumours had medium/high levels of TILS expression had better DRFS (HR: 0.49, 95%CI 0.24-1.02, p=0.057) when treated with E-CMF than those treated with CMF alone. Highest levels of TILs were found in Basal and HER2-like tumours. A T-cell score was generated using the average expression of CD3, CD4 and CD8. The T-cell score was examined in both the tumour and TME. Using the T cell score it was apparent that the cohort had a range of immune “hot” and immune “cold” tumours. It was demonstrated that immune “hot” TME doesn’t not always correlate with immune “hot” tumour expression. Proteins that were most associated with T-cell exclusion (p<0.01) in the TME were Fibronectin, B7-H3, PTEN, ER-α, TGFB1, FAPα and CD34. This would indicate that these proteins are causal inhibitors of T-cell invasion. Conclusion: In conclusion, this study highlights the significance of assessing the entire tumour since TILs, tumour and stromal cells collectively engage in a complex interplay that contributes to disease development and progression. NanoString’s GeoMx DSP is a promising technology for multiplexed analysis. TILs, whether measured using automated software, or estimation by protein profiling, are predictive of chemotherapy benefit. Citation Format: Melanie Spears, Carsten Denkert, Sonia L Villagas, Nicola Lyttle, Linda Liao, Mary Anne Quintayo, Christopher J Twelves. Assessing immune biomarkers of response to anthracyclines in breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-10-08.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".