Predicting response and survival in chemotherapy-treated triple-negative breast cancer

BACKGROUND: In this study, we evaluated the ability of gene expression profiles to predict chemotherapy response and survival in triple-negative breast cancer (TNBC). METHODS: Gene expression and clinical-pathological data were evaluated in five independent cohorts, including three randomised clinical trials for a total of 1055 patients with TNBC, basal-like disease (BLBC) or both. Previously defined intrinsic molecular subtype and a proliferation signature were determined and tested. Each signature was tested using multivariable logistic regression models (for pCR (pathological complete response)) and Cox models (for survival). Within TNBC, interactions between each signature and the basal-like subtype (vs other subtypes) for predicting either pCR or survival were investigated. RESULTS: Within TNBC, all intrinsic subtypes were identified but BLBC predominated (55-81%). Significant associations between genomic signatures and response and survival after chemotherapy were only identified within BLBC and not within TNBC as a whole. In particular, high expression of a previously identified proliferation signature, or low expression of the luminal A signature, was found independently associated with pCR and improved survival following chemotherapy across different cohorts. Significant interaction tests were only obtained between each signature and the BLBC subtype for prediction of chemotherapy response or survival. CONCLUSIONS: The proliferation signature predicts response and improved survival after chemotherapy, but only within BLBC. This highlights the clinical implications of TNBC heterogeneity, and suggests that future clinical trials focused on this phenotypic subtype should consider stratifying patients as having BLBC or not

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Basal breast cancer molecular subtype predicts for lower incidence of axillary lymph node metastases in primary breast cancer

Background: Axillary lymph node involvement remains the most important prognostic factor in early-stage breast cancer. We hypothesized that molecular classification based on breast cancer biology would predict the presence of nodal involvement at diagnosis, which might aid treatment decisions regarding the axilla. Patients and Methods: From a clinically annotated tissue microarray of 4444 early-stage breast cancers, expression of estrogen receptor (ER), progesterone receptor (PgR), HER2, epidermal growth factor receptor, and cytokeratin 5/6 was determined by immunohistochemistry. Cases were classified by published criteria into molecular subtypes of luminal, luminal/HER2 positive, HER2 positive/ER negative/PgR negative, and basal. Risk of axillary nodal involvement at diagnosis was determined in 2 multivariable logistic regression models: a "core biopsy model" including molecular subtype, age, grade, and tumor size and a "lumpectomy model," which also included lymphovascular invasion. Luminal was used as the reference group. After internal validation of findings in 2 independent sets, we conducted combined analysis of both. Results: In the core biopsy model, the molecular subtypes had a predictive effect for nodal involvement (P = .000001), with the basal subtype having an odds ratio for axillary lymph node involvement of 0.53 (95% CI, 0.41-0.69). Tumor grade (P = 5.43 × 10–12) and size (P = 8.52 × 10–35) were also predictive for nodal involvement. Similar results were found in the lumpectomy model, where lymphovascular invasion was also predictive (P = 2.74 × 10–115). Conclusion: These results indicate that the basal breast cancer molecular subtype predicts a lower incidence of axillary nodal involvement, and including biomarker profiles to predict nodal status at diagnosis could help stratification for decisions regarding axillary surgery and locoregional radiation

Triple-negative breast cancer: Present challenges and new perspectives

Contains fulltext : 83751.pdf (publisher's version ) (Open Access)21 p

Predicting response and survival in chemotherapy-treated triple-negative breast cancer

BACKGROUND: In this study, we evaluated the ability of gene expression profiles to predict chemotherapy response and survival in triple-negative breast cancer (TNBC). METHODS: Gene expression and clinical-pathological data were evaluated in five independent cohorts, including three randomised clinical trials for a total of 1055 patients with TNBC, basal-like disease (BLBC) or both. Previously defined intrinsic molecular subtype and a proliferation signature were determined and tested. Each signature was tested using multivariable logistic regression models (for pCR (pathological complete response)) and Cox models (for survival). Within TNBC, interactions between each signature and the basal-like subtype (vs other subtypes) for predicting either pCR or survival were investigated. RESULTS: Within TNBC, all intrinsic subtypes were identified but BLBC predominated (55-81%). Significant associations between genomic signatures and response and survival after chemotherapy were only identified within BLBC and not within TNBC as a whole. In particular, high expression of a previously identified proliferation signature, or low expression of the luminal A signature, was found independently associated with pCR and improved survival following chemotherapy across different cohorts. Significant interaction tests were only obtained between each signature and the BLBC subtype for prediction of chemotherapy response or survival. CONCLUSIONS: The proliferation signature predicts response and improved survival after chemotherapy, but only within BLBC. This highlights the clinical implications of TNBC heterogeneity, and suggests that future clinical trials focused on this phenotypic subtype should consider stratifying patients as having BLBC or not