Abstract P3-07-32: Tumour infiltrating lymphocyte (TIL) and chemokine gene signature predicts for benefit of anthracycline-containing chemotherapy in breast cancer patients
Bibliographic record
Abstract
Abstract Background: The contribution of immune cells has long been appreciated in tumour development and disease progression; however, their translational potential as cancer-associated prognostic and predictive markers was only recently recognized. High densities of tumour-infiltrating lymphocytes (TILs) correlate with improved clinical outcome in breast cancer; whether TILs also predict anthracycline benefit in all, or only a particular subgroup, of breast cancer patients remains largely unknown. Furthermore, since identification of TILs is generally based on H&E staining, it has not previously been possible to evaluate relative contribution of distinct T-cell types, and B cells, to patient outcome. Methods: We assessed 290 patient samples from the BR9601 adjuvant breast cancer trial for the capacity of TIL contexture to predict for anthracycline (E-CMF) benefit over CMF. We immunoprofiled patient samples on the Nanostring platform to gain insight into the impact of lymphocyte populations predicting for anthracycline benefit. Our immunoprofiling panel included 38 genes representing TIL-gene signatures and chemokines that may be responsible for recruiting TILs to the tumour site. Results: The analyses revealed two important findings. First, refinement of the 38-gene panel resulted in the generation of a novel 9-gene signature that includes cytotoxic T lymphocytes (CTL) and chemokine genes. Low CTL gene expression correlated with ER+ expression while high expression correlated with ER- expression (p<0.0001), consistent with the notion that high TIL densities are predominantly observed in non-luminal breast cancers. Second, in an univariate Cox regression analysis, this 9-gene signature was a predictive biomarker of anthracycline benefit with respect to breast-cancer specific OS (HR: 0.371, 95%CI 0.158-0.868, p=0.022) and DRFS (HR: 0.395, 95%CI 0.172-0.907, p=0.028); this effect was no longer significant after adjustment for other prognostic factors (OS HR: 0.437, 95%CI 0.166-1.150, p=0.094; DRFS HR: 0.488, 95%CI 0.185-1.287, p=0.147). 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. Importantly, it reveals that not only CTLs but also chemokines may be clinically relevant and should be validated as potential biomarkers of anthracycline benefit and as therapeutic targets. Citation Format: Braunstein M, Yao C, Lyttle N, Liao L, Boutros PC, Twelves CJ, Bartlett JMS, Spears M. Tumour infiltrating lymphocyte (TIL) and chemokine gene signature predicts for benefit of anthracycline-containing chemotherapy in breast cancer patients. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-07-32.
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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 | 0.001 |
| 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".