Subcellular expression of MTA1, HIF1A and p53 in primary tumor predicts aggressive triple negative breast cancers: a meta-analysis based study.

BACKGROUND: The prevalence of TNBC in India is higher compared to western countries. There is a multitude of biomarkers associated with different clinical outcomes of TNBC with contradictory reports. Identification of a set of specific biomarkers from the very many number of proteins reported in the literature to predict prognosis of TNBC is an urgent clinical need. METHODOLOGY: A systematic review of key molecular biomarkers in cohort studies that have been investigated for their role in breast cancer prognosis was conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was followed. A meta-analysis was used to evaluate their pooled hazard ratio (HR) and the corresponding 95% confidence interval (95% CI) statistically. Immunohistochemical characterization of the meta-analyzed markers were performed in a cohort of 200 retrospective TNBC and 100 non TNBC patient tissues. Kaplan-Meier plot were used to evaluate disease free survival (DFS), and overall survival (OS). Cox regression models were used to evaluate predictors of DFS and OS. RESULTS: Using a meta-analytical approach, we consolidated the biomarker signatures associated with survival outcomes in breast cancers. The promising markers that emerged for the prediction of DFS and OS included E-Cadherin, Survivin, p53, MTA1, HIF1A, CD133, Vimentin and CK5/6. Evaluation of these markers in tumor tissue revealed that subcellular localization of p53, MTA1 and HIF1A had a significant association in predicting TNBC prognosis. Kaplan Meier plot revealed that p53 (OS p = 0.007, DFS p = 0.004), HIF 1 A (OS p = 0.054, DFS p = 0.009) and MTA1 (OS p = 0.043, DFS = p = 0.001) expression in the primary tumor tissue were found to be significantly correlated with poor OS and DFS, whereas expression of Survivin (DFS p = 0.024) and E Cadherin (DFS p = 0.027) correlated with DFS alone in TNBC. Univariate analysis revealed that p53, HIF1A and MTA1 could be independent prognostic markers. CONCLUSION: Our study suggests cytoplasmic over expression of HIF1A, nuclear over expression of MTA1 and mutated p53 in the primary tumor tissue of TNBC have significance as markers predicting survival of TNBC patients.

A multiplex immunoprofiling approach for detecting the co-localization of breast cancer biomarkers using a combination of Alexafluor - Quantum dot conjugates and a panel of chromogenic dyes.

There is a plethora of information embedded in a tissue section that the conventional IHC understands only partially. Predictive biomarkers for precision immuno-oncology heavily dependent on the spatial arrangement of cells and the co-expression patterns in the tissue sections. Here we have explored the versatility of indirect multiplex immunofluorescence (mIF) and indirect multiplex immunohistochemistry (mIHC) for the labeling of breast cancer prognostic markers in routinely processed, formalin-fixed paraffin-embedded (FFPE) tissues at high resolution. The multiplex immunohistochemistry protocol utilized sequential staining for the chromogenic immunolabelling of Estrogen Receptor α (ERα) or Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Nucleoside diphosphate kinase 1 (NM23) by multicolor chromogens in different combinations. A feasible workflow for multiplex immunofluorescence was also effectively standardized for ERα, PR, and HER2 using combinations of commercially available Alexa Fluor and Quantum dots semiconductor nanocrystal conjugated secondary antibodies. Multiplex chromogenic immunolabeling revealed differential expression of the markers on the same slide. Kappa statistics revealed perfect agreement with uniplex immunohistochemistry. For multiplex fluorescence approach, surface receptor detection using Quantum dots and Alexa fluor dyes for cytoplasmic or nuclear markers performed well for profiling multiple co-localized biomarkers on a single paraffin tissue section. The technique developed reveals additional information such as co-expression, spatial relationships, and tumor heterogeneity, providing a deeper insight into developing combinatorial therapeutic strategies in clinical care. This high throughput workflow complements the outcomes of traditional IHC while saving tissue, time, labour, and reagents.