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Classical and Novel Prognostic Markers for Breast Cancer and their Clinical Significance.

ABSTRACT: The use of biomarkers ensures breast cancer patients receive optimal treatment. Established biomarkers such as estrogen receptor (ER) and progesterone receptor (PR) have been playing significant roles in the selection and management of patients for endocrine therapy. HER2 is a strong predictor of response to trastuzumab. Recently, the roles of ER as a negative and HER2 as a positive indicator for chemotherapy have been established. Ki67 has traditionally been recognized as a poor prognostic factor, but recent studies suggest that measurement of Ki67-positive cells during treatment will more effectively predict treatment efficacy for both anti-hormonal and chemotherapy. p53 mutations are found in 20-35% of human breast cancers and are associated with aggressive disease with poor clinical outcome when the DNA-binding domain is mutated. The utility of cyclin D1 as a predictor of breast cancer prognosis is controversial, but cyclin D1b overexpression is associated with poor prognosis. Likewise, overexpression of the low molecular weight form of cyclin E1 protein predicts poor prognosis. Breast cancers from BRCA1/2 carriers often show high nuclear grades, negativity to ER/PR/HER2, and p53 mutations, and thus, are associated with poor prognosis. The prognostic values of other molecular markers, such as p14(ARF), TBX2/3, VEGF in breast cancer are also discussed. Careful evaluation of these biomarkers with current treatment modality is required to determine whether their measurement or monitoring offer significant clinical benefits.

SUBMITTER: Taneja P

PROVIDER: S-EPMC2883240 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Classical and Novel Prognostic Markers for Breast Cancer and their Clinical Significance.

Taneja Pankaj P Maglic Dejan D Kai Fumitake F Zhu Sinan S Kendig Robert D RD Fry Elizabeth A EA Inoue Kazushi K

Clinical Medicine Insights. Oncology 20100420

The use of biomarkers ensures breast cancer patients receive optimal treatment. Established biomarkers such as estrogen receptor (ER) and progesterone receptor (PR) have been playing significant roles in the selection and management of patients for endocrine therapy. HER2 is a strong predictor of response to trastuzumab. Recently, the roles of ER as a negative and HER2 as a positive indicator for chemotherapy have been established. Ki67 has traditionally been recognized as a poor prognostic fact ...[more]

PMID: 20567632

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Project description:IntroductionThe cancer stem cell (CSC) hypothesis states that tumours consist of a cellular hierarchy with CSCs at the apex driving tumour recurrence and metastasis. Hence, CSCs are potentially of profound clinical importance. We set out to establish the clinical relevance of breast CSC markers by profiling a large cohort of breast tumours in tissue microarrays (TMAs) using immunohistochemistry (IHC).MethodsWe included 4, 125 patients enrolled in the SEARCH population-based study with tumours represented in TMAs and classified into molecular subtype according to a validated IHC-based five-marker scheme. IHC was used to detect CD44/CD24, ALDH1A1, aldehyde dehydrogenase family 1 member A3 (ALDH1A3) and integrin alpha-6 (ITGA6). A 'Total CSC' score representing expression of all four CSC markers was also investigated. Association with breast cancer specific survival (BCSS) at 10 years was assessed using a Cox proportional-hazards model. This study was complied with REMARK criteria.ResultsIn ER negative cases, multivariate analysis showed that ITGA6 was an independent prognostic factor with a time-dependent effect restricted to the first two years of follow-up (hazard ratio (HR) for 0 to 2 years follow-up, 2.4; 95% confidence interval (95% CI), 1.2 to 4.8; P = 0.009). The composite 'Total CSC' score carried independent prognostic significance in ER negative cases for the first four years of follow-up (HR for 0 to 4 years follow-up, 1.3; 95% CI, 1.1 to 1.6; P = 0.006).ConclusionsBreast CSC markers do not identify identical subpopulations in primary tumours. Both ITGA6 and a composite Total CSC score show independent prognostic significance in ER negative disease. The use of multiple markers to identify tumours enriched for CSCs has the greatest prognostic value. In the absence of more specific markers, we propose that the effective translation of the CSC hypothesis into patient benefit will necessitate the use of a panel of markers to robustly identify tumours enriched for CSCs.

Project description:BackgroundColorectal cancer (CRC) is a common malignant solid tumor with an extremely low survival rate after relapse. Previous investigations have shown that autophagy possesses a crucial function in tumors. However, there is no consensus on the value of autophagy-associated genes in predicting the prognosis of CRC patients. This work screens autophagy-related markers and signaling pathways that may participate in the development of CRC, and establishes a prognostic model of CRC based on autophagy-associated genes.MethodsGene transcripts from the TCGA database and autophagy-associated gene data from the GeneCards database were used to obtain expression levels of autophagy-associated genes, followed by Wilcox tests to screen for autophagy-related differentially expressed genes. Then, 11 key autophagy-associated genes were identified through univariate and multivariate Cox proportional hazard regression analysis and used to establish prognostic models. Additionally, immunohistochemical and CRC cell line data were used to evaluate the results of our three autophagy-associated genes EPHB2, NOL3, and SNAI1 in TCGA. Based on the multivariate Cox analysis, risk scores were calculated and used to classify samples into high-risk and low-risk groups. Kaplan-Meier survival analysis, risk profiling, and independent prognosis analysis were carried out. Receiver operating characteristic analysis was performed to estimate the specificity and sensitivity of the prognostic model. Finally, GSEA, GO, and KEGG analysis were performed to identify the relevant signaling pathways.ResultsA total of 301 autophagy-related genes were differentially expressed in CRC. The areas under the 1-year, 3-year, and 5-year receiver operating characteristic curves of the autophagy-based prognostic model for CRC were 0.764, 0.751, and 0.729, respectively. GSEA analysis of the model showed significant enrichment in several tumor-relevant pathways and cellular protective biological processes. The expression of EPHB2, IL-13, MAP2, RPN2, and TRAF5 was correlated with microsatellite instability (MSI), while the expression of IL-13, RPN2, and TRAF5 was related to tumor mutation burden (TMB). GO analysis showed that the 11 target autophagy genes were chiefly enriched in mRNA processing, RNA splicing, and regulation of the mRNA metabolic process. KEGG analysis showed enrichment mainly in spliceosomes. We constructed a prognostic risk assessment model based on 11 autophagy-related genes in CRC.ConclusionA prognostic risk assessment model based on 11 autophagy-associated genes was constructed in CRC. The new model suggests directions and ideas for evaluating prognosis and provides guidance to choose better treatment strategies for CRC.

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Classical and Novel Prognostic Markers for Breast Cancer and their Clinical Significance.

Publications

Classical and Novel Prognostic Markers for Breast Cancer and their Clinical Significance.

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