Dataset Information

Triple-Negative Breast Cancer: Assessing the Role of Immunohistochemical Biomarkers on Neoadjuvant Treatment.

ABSTRACT:

Objective

This study aimed to investigate the influence of immunohistochemical (IHC) biomarkers in the response to neoadjuvant chemotherapy (NACT) and survival outcomes in the subset of locally advanced triple-negative breast cancer (TNBC).

Materials and methods

The epidermal growth factor receptor (EGFR), androgen receptor (AR), cytokeratins (CK5/6, CK14 and CK17), Ki67 and p53 immunohistochemistry were evaluated on 171 cases of TNBC submitted to NACT and subsequently to surgery. Intensity and percentage of the expression of these biomarkers were combined to formulate a specific score, that was correlated with prognostic features and assessed for survival outcomes.

Results

Most patients had advanced clinical-stage tumors (stage III: 83.6%; cT3/T4: 85.9%; cN1-3: 71.3%). The predominant histological subtype was high-grade (67.3%) and invasive ductal carcinoma (93.6%). The residual cancer burden (RCB) 0-1 corresponded to 28.7% of cases and low-risk lymph node ratio (LNR) represented 77.2%. High Ki67 expression only showed a significant correlation with grade 3 tumors (p = 0.0157). CK5/6 was observed in 16% (27/169), CK14 was positive in 10.1% (17/169), CK17 in 91.1% (153/168), p53 in 52.6% (70/133), EGFR in 92.9% (157/169 cases), AR in 13% (22/169) and Ki67 index was scored ?40% in 57.9% (95/165). No IHC biomarker significantly impacted response or survival. Regarding the analysis of the outcomes of event-free survival (EFS) and overall survival (OS), clinical stage (p = 0.014 and p = 0.042, respectively), RCB (p < 0.0001 and p <0.0001, respectively) and LNR (p <0.0001 and p <0.0001, respectively) showed significant association.

Conclusion

No IHC biomarker evaluated showed a significant association with a response or survival outcomes in TNBC patients. Clinical stage, LNR and RCB stood out for strongly influencing survival.

SUBMITTER: da Silva JL

PROVIDER: S-EPMC7810824 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Triple-Negative Breast Cancer: Assessing the Role of Immunohistochemical Biomarkers on Neoadjuvant Treatment.

da Silva Jesse Lopes JL Rodrigues Fabiana Resende FR de Mesquita Guilherme Gomes GG Fernandes Priscila Valverde PV Thuler Luiz Claudio Santos LCS de Melo Andreia Cristina AC

Breast cancer (Dove Medical Press) 20210111

<h4>Objective</h4>This study aimed to investigate the influence of immunohistochemical (IHC) biomarkers in the response to neoadjuvant chemotherapy (NACT) and survival outcomes in the subset of locally advanced triple-negative breast cancer (TNBC).<h4>Materials and methods</h4>The epidermal growth factor receptor (EGFR), androgen receptor (AR), cytokeratins (CK5/6, CK14 and CK17), Ki67 and p53 immunohistochemistry were evaluated on 171 cases of TNBC submitted to NACT and subsequently to surgery. ...[more]

PMID: 33469357

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Project description:BackgroundThe persistence of residual tumour after neoadjuvant chemotherapy (NAC) in localised triple-negative breast cancer (TNBC) is known to have a negative prognostic value. However, different degrees of expression of some immunohistochemical markers may correlate with different prognoses.MethodsThe expression of biomarkers with a known prognostic value, i.e., cytokeratin 5/6 (CK5/6), androgen receptor (AR), epidermal growth factor receptor (EGFR) proliferation-related nuclear antigen Ki-67, human epidermal growth factor receptor 2 (HER2), protein 53 (p53), forkhead box protein 3 (FOXP3), and cluster differentiation 8 (CD8), was analysed by immunohistochemistry in 111 samples after NAC in non-metastatic TNBC patients addressed to Georges-François Leclerc Cancer Centre Dijon, France. Clinical and pathological variables were retrospectively collected. Cox regression was used to identify immunohistochemical (IHC) and clinicopathological predictors of event-free survival (EFS) (relapse or death).ResultsMedian age was 50.4 years (range 25.6-88.3), 55.9% (n = 62) were non-menopausal, 70 (63.1%) had stage IIA-IIB disease. NAC was mostly sequential anthracycline-taxanes (72.1%), and surgical intervention was principally conservative (51.3%). We found 65.7% ypT1, 47.2% lymph node involvement (ypN+), and 29.4% lymphovascular invasion (LVI). Most residual tumours were EGFR >110 (H-score) (60.5%, n = 66), AR ≥4% (53.2%, n = 58), p53-positive mutated (52.7%, n = 58), CD8 ≥26 (58.1%, n = 61), FOXP3 ≥7 (51.4%, n = 54), more than half in the stroma, and 52.3% (n = 58) HER2 score 0. After a median follow-up of 80.8 months, 48.6% had relapsed. Median EFS was 62.3 months (95% CI, 37.2-not reached (NR)). Factors independently associated with poor EFS were AR-low (p = 0.002), ypN+ (p < 0.001), and LVI (p = 0.001). Factors associated with lower overall survival (OS) were EGFR-low (p = 0.041), Ki-67 high (p = 0.024), and ypN+ (p < 0.001).ConclusionPost-NAC residual disease in TNBC showed biomarkers specific to a basal-like subtype and markers of lymphocyte infiltration mostly present in the stroma. Prognostic markers for EFS were AR, LVI, and ypN and warrant further validation in a prognostic model.

Project description:Objective: To develop and validate a prediction model for the pathological complete response (pCR) to neoadjuvant chemotherapy (NCT) of triple-negative breast cancer (TNBC). Methods: We systematically searched Gene Expression Omnibus, ArrayExpress, and PubMed for the gene expression profiles of operable TNBC accessible to NCT. Molecular heterogeneity was detected with hierarchical clustering method, and the biological profiles of differentially expressed genes were investigated by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analyses, and Gene Set Enrichment Analysis (GSEA). Next, machine-learning algorithms including random-forest analysis and least absolute shrinkage and selection operator (LASSO) analysis were synchronously performed and, then, the intersected proportion of significant genes was undergone binary logistic regression to fulfill variables selection. The predictive response score (pRS) system was built as the product of the gene expression and coefficient obtained from the logistic analysis. Last, the cohorts were randomly divided in a 7:3 ratio into training cohort and validation cohort for the introduction of a robust model, and a nomogram was constructed with the independent predictors for pCR rate. Results: A total of 217 individuals from four cohort datasets (GSE32646, GSE25065, GSE25055, GSE21974) with complete clinicopathological information were included. Based on the microarray data, a six-gene panel (ATP4B, FBXO22, FCN2, RRP8, SMERK2, TET3) was identified. A robust nomogram, adopting pRS and clinical tumor size stage, was established and the performance was successively validated by calibration curves and receiver operating characteristic curves with the area under curve 0.704 and 0.756, respectively. Results of GSEA revealed that the biological processes including apoptosis, hypoxia, mTORC1 signaling and myogenesis, and oncogenic features of EGFR and RAF were in proactivity to attribute to an inferior response. Conclusions: This study provided a robust prediction model for pCR rate and revealed potential mechanisms of distinct response to NCT in TNBC, which were promising and warranted to further validate in the perspective.

Project description:BackgroundNeoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders.MethodsSerial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses.ResultsHighest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level.ConclusionsOverall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Dataset Information

Triple-Negative Breast Cancer: Assessing the Role of Immunohistochemical Biomarkers on Neoadjuvant Treatment.

Objective

Materials and methods

Results

Conclusion

Publications

Triple-Negative Breast Cancer: Assessing the Role of Immunohistochemical Biomarkers on Neoadjuvant Treatment.

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