Abstract P1-09-08: Predictive effect of cytotoxic tumor infiltrating lymphocytes in HER2-positive metastatic breast cancer: A correlative study with CCTG MA.31
Bibliographic record
Abstract
Abstract Background and Objectives: The presence of tumor infiltrating lymphocytes (TILs), particularly CD8+ cytotoxic T-cells, has been associated with improved prognosis in patients with HER2+ breast cancer. Increasing levels of TILs also appear to predict response to adjuvant trastuzumab in early breast cancer, although they did not predict benefit of combined trastuzumab-lapatinib neoadjuvant dual therapy over monotherapy in NeoALLTO. CCTG MA.31 randomized 652 women with HER2+ metastatic breast cancer to treatment with trastuzumab (T) vs. lapatinib (L), in combination with taxane (Tax) chemotherapy for 24 weeks, followed by the same HER2-targeted monotherapy. Final results from MA.31 found trastuzumab was superior to lapatinib for the primary endpoint of progression free survival (PFS): the hazard ratio (HR) for lapatinib to trastuzumab was 1.37 (95% CI, 1.13-1.65). Although both agents block HER2 signaling, trastuzumab has additional mechanisms of action via the immune system. We hypothesized that TIL levels may predict response to HER2-targeted therapy (trastuzumab vs. lapatinib). Methods: MA.31 included HER2+ metastatic breast cancer patients, median age 55 years, and median follow-up 21.5 months. Overall TILs were counted per published guidelines on the original H&E stained sections used for pathology review at study entry. Immunohistochemistry (IHC) was performed on unstained sections from tissue microarrays or individual formalin-fixed paraffin-embedded blocks to test expression of lymphocyte biomarkers CD8, FOXP3, CD56 and PD-1 on stromal and intra-tumoral TILs (sTILs, iTILs). Statistical analysis was conducted by CCTG for a total of 9 prespecified biomarker tests. Associations of TILs with PFS were evaluated by univariate stratified log-rank test with graphical Kaplan-Meier curves, and by stratified multivariate Cox proportional hazards regression analysis. Predictive effect was examined with a test of interaction between treatment allocation and biomarker classification (high vs. low, using pre-established cutpoints). Results: Of the 652 cases, 614 had slides for overall TIL assessment and 427 for IHC biomarker assessments. In this correlative study set, superiority of trastuzumab over lapatinib for PFS was confirmed in multivariate analysis (LTax/T vs. TTax/L: HR = 2.55, 95% CI = 1.43-4.55, p = 0.001). TIL counts by H&E were neither prognostic nor predictive in this set of metastatic HER2+ breast cancers. Lymphocyte IHC markers were not prognostic. However, prespecified stratified univariate analysis detected a significantly higher risk for lapatinib over trastuzumab (HR = 2.94, 95% CI = 1.40-6.17, p = 0.003) in patients with low CD8+ sTIL (< 3) than was observed among those with high CD8+ sTIL (HR = 1.36, 95% CI = 1.05-1.75, p = 0.019). This differential effect was confirmed in multivariate analysis (interaction test p = 0.042). The other tested biomarkers did not demonstrate significant predictive effects. Conclusions: In this correlative study of metastatic HER2+ breast cancer, a low level of pre-existing stromal cytotoxic T cell infiltration predicts women who benefit most from trastuzumab over lapatinib. Overall TIL counts were neither prognostic nor predictive. Citation Format: Liu S, Chen B, Burugu S, Leung S, Gao D, Virk S, Kos Z, Parulekar WR, Shepherd L, Gelmon K, Nielsen TO. Predictive effect of cytotoxic tumor infiltrating lymphocytes in HER2-positive metastatic breast cancer: A correlative study with CCTG MA.31 [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 P1-09-08.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".