Molecular stratification of early breast cancer identifies drug targets to drive stratified medicine.
ABSTRACT: Many women with hormone receptor-positive early breast cancer can be managed effectively with endocrine therapies alone. However, additional systemic chemotherapy treatment is necessary for others. The clinical challenges in managing high-risk women are to identify existing and novel druggable targets, and to identify those who would benefit from these therapies. Therefore, we performed mRNA abundance analysis using the Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial pathology cohort to identify a signature of residual risk following endocrine therapy and pathways that are potentially druggable. A panel of genes compiled from academic and commercial multiparametric tests as well as genes of importance to breast cancer pathogenesis was used to profile 3825 patients. A signature of 95 genes, including nodal status, was validated to stratify endocrine-treated patients into high-risk and low-risk groups based on distant relapse-free survival (DRFS; Hazard Ratio?=?5.05, 95% CI 3.53-7.22, p?=?7.51?×?10-19). This risk signature was also found to perform better than current multiparametric tests. When the 95-gene prognostic signature was applied to all patients in the validation cohort, including patients who received adjuvant chemotherapy, the signature remained prognostic (HR?=?4.76, 95% CI 3.61-6.28, p?=?2.53×?10-28). Functional gene interaction analyses identified six significant modules representing pathways involved in cell cycle control, mitosis and receptor tyrosine signaling; containing a number of genes with existing targeted therapies for use in breast or other malignancies. Thus the identification of high-risk patients using this prognostic signature has the potential to also prioritize patients for treatment with these targeted therapies.
Project description:Quite a few estrogen receptor (ER)-positive breast cancer patients receiving endocrine therapy are at risk of disease recurrence and death. ER-related genes are involved in the progression and chemoresistance of breast cancer. In this study, we identified an ER-related gene signature that can predict the prognosis of ER-positive breast cancer patient receiving endocrine therapy. We collected RNA expression profiling from Gene Expression Omnibus database. An ER-related signature was developed to separate patients into high-risk and low-risk groups. Patients in the low-risk group had significantly better survival than those in the high-risk group. ROC analysis indicated that this signature exhibited good diagnostic efficiency for the 1-, 3- and 5-year disease-relapse events. Moreover, multivariate Cox regression analysis demonstrated that the ER-related signature was an independent risk factor when adjusting for several clinical signatures. The prognostic value of this signature was validated in the validation sets. In addition, a nomogram was built and the calibration plots analysis indicated the good performance of this nomogram. In conclusion, combining with ER status, our results demonstrated that the ER-related prognostic signature is a promising method for predicting the prognosis of ER-positive breast cancer patients receiving endocrine therapy.
Project description:In fully-automatic radiomics model for predicting overall survival (OS) of glioblastoma multiforme (GBM) patients, the effect of image standardization parameters such as voxel size, quantization method and gray level on model reproducibility and prognostic performance are still unclear. In this study, 45792 multiregional radiomics features were automatically extracted from multi-modality MR images with different voxel sizes, quantization methods, and gray levels. The feature reproducibility and prognostic performance were assessed. Multiparametric and fixed-parameter radiomics signatures were constructed based on a training cohort (60 patients). In an independent validation cohort (32 patients), the multiparametric signature achieved better performance for OS prediction (C-Index?=?0.705, 95% CI: 0.672, 0.738) and significant stratification of patients into high- and low-risk groups (P?=?0.0040, HR?=?3.29, 95% CI: 1.40, 7.70), which outperformed the fixed-parameter signatures and conventional factors such as age, Karnofsky Performance Score and tumor volume. This study demonstrated that voxel size, quantization method and gray level had influence on reproducibility and prognosis of radiomics features for GBM OS prediction. An automatic method to determine the optimal parameter settings was provided. It indicated that multiparametric radiomics signature had the potential of offering better prognostic performance than fixed-parameter signatures.
Project description:Background:EarlyR gene signature in estrogen receptor-positive (ER+) breast cancer is computed from the expression values of ESPL1, SPAG5, MKI67, PLK1, and PGR. EarlyR has been validated in multiple cohorts profiled using microarrays. This study sought to verify the prognostic features of EarlyR in a case-cohort sample from BIG 1-98, a randomized clinical trial of ER+ postmenopausal breast cancer patients treated with adjuvant endocrine therapy (letrozole or tamoxifen). Methods:Expression of EarlyR gene signature was estimated by Illumina cDNA-mediated Annealing, Selection, and Ligation assay of RNA from formalin-fixed, paraffin-embedded primary breast cancer tissues in a case-cohort subset of ER+ women (N?=?1174; 216 cases of recurrence within 8?years) from BIG 1-98. EarlyR score and prespecified risk strata (?25?=?low, 26-75?=?intermediate, >75?=?high) were "blindly" computed. Analysis endpoints included distant recurrence-free interval and breast cancer-free interval at 8?years after randomization. Hazard ratios (HRs) and test statistics were estimated with weighted analysis methods. Results:The distribution of the EarlyR risk groups was 67% low, 19% intermediate, and 14% high risk in this ER+ cohort. EarlyR was prognostic for distant recurrence-free interval; EarlyR high-risk patients had statistically increased risk of distant recurrence within 8?years (HR?=?1.73, 95% confidence interval = 1.14 to 2.64) compared with EarlyR low-risk patients. EarlyR was also prognostic of breast cancer-free interval (HR?=?1.74, 95% confidence interval = 1.21 to 2.62). Conclusions:This study confirmed the prognostic significance of EarlyR using RNA from formalin-fixed, paraffin-embedded tissues from a case-cohort sample of BIG 1-98. EarlyR identifies a set of high-risk patients with relatively poor prognosis who may be considered for additional treatment. Further studies will focus on analyzing the predictive value of EarlyR signature.
Project description:Several prognostic signatures for early oestrogen receptor-positive (ER+) breast cancer have been established with a 10-year follow-up. We tested the hypothesis that signatures optimised for 0-5-year and 5-10-year follow-up separately are more prognostic than a single signature optimised for 10 years.Genes previously identified as prognostic or associated with endocrine resistance were tested in publicly available microarray data set using Cox regression of 747 ER+/HER2- samples from post-menopausal patients treated with 5 years of endocrine therapy. RNA expression of the selected genes was assayed in primary ER+/HER2- tumours from 948 post-menopausal patients treated with 5 years of anastrozole or tamoxifen in the TransATAC cohort. Prognostic signatures for 0-10, 0-5 and 5-10 years were derived using a penalised Cox regression (elastic net). Signature comparison was performed with likelihood ratio statistics. Validation was done by a case-control (POLAR) study in 422 samples derived from a cohort of 1449.Ninety-three genes were selected by the modelling of microarray data; 63 of these were significantly prognostic in TransATAC, most similarly across each time period. Contrary to our hypothesis, the derived early and late signatures were not significantly more prognostic than the 18-gene 10-year signature. The 18-gene 10-year signature was internally validated in the TransATAC validation set, showing prognostic information similar to that of Oncotype DX Recurrence Score, PAM50 risk of recurrence score, Breast Cancer Index and IHC4 (score based on four IHC markers), as well as in the external POLAR case-control set.The derived 10-year signature predicts risk of metastasis in patients with ER+/HER2- breast cancer similar to commercial signatures. The hypothesis that early and late prognostic signatures are significantly more informative than a single signature was rejected.
Project description:BACKGROUND:Expression of the oestrogen receptor (ER) in breast cancer predicts benefit from endocrine therapy. Minimising the frequency of false negative ER status classification is essential to identify all patients with ER positive breast cancers who should be offered endocrine therapies in order to improve clinical outcome. In routine oncological practice ER status is determined by semi-quantitative methods such as immunohistochemistry (IHC) or other immunoassays in which the ER expression level is compared to an empirical threshold. The clinical relevance of gene expression-based ER subtypes as compared to IHC-based determination has not been systematically evaluated. Here we attempt to reduce the frequency of false negative ER status classification using two gene expression approaches and compare these methods to IHC based ER status in terms of predictive and prognostic concordance with clinical outcome. METHODOLOGY/PRINCIPAL FINDINGS:Firstly, ER status was discriminated by fitting the bimodal expression of ESR1 to a mixed Gaussian model. The discriminative power of ESR1 suggested bimodal expression as an efficient way to stratify breast cancer; therefore we identified a set of genes whose expression was both strongly bimodal, mimicking ESR expression status, and highly expressed in breast epithelial cell lines, to derive a 23-gene ER expression signature-based classifier. We assessed our classifiers in seven published breast cancer cohorts by comparing the gene expression-based ER status to IHC-based ER status as a predictor of clinical outcome in both untreated and tamoxifen treated cohorts. In untreated breast cancer cohorts, the 23 gene signature-based ER status provided significantly improved prognostic power compared to IHC-based ER status (P?=?0.006). In tamoxifen-treated cohorts, the 23 gene ER expression signature predicted clinical outcome (HR?=?2.20, P?=?0.00035). These complementary ER signature-based strategies estimated that between 15.1% and 21.8% patients of IHC-based negative ER status would be classified with ER positive breast cancer. CONCLUSION/SIGNIFICANCE:Expression-based ER status classification may complement IHC to minimise false negative ER status classification and optimise patient stratification for endocrine therapies.
Project description:PURPOSE: The identification of a molecular signature predicting the relapse of tamoxifen-treated primary breast cancers should help the therapeutic management of estrogen receptor-positive cancers. EXPERIMENTAL DESIGN: A series of 132 primary tumors from patients who received adjuvant tamoxifen were analyzed for expression profiles at the whole-genome level by 70-mer oligonucleotide microarrays. A supervised analysis was done to identify an expression signature. RESULTS: We defined a 36-gene signature that correctly classified 78% of patients with relapse and 80% of relapse-free patients (79% accuracy). Using 23 independent tumors, we confirmed the accuracy of the signature (78%) whose relevance was further shown by using published microarray data from 60 tamoxifen-treated patients (63% accuracy). Univariate analysis using the validation set of 83 tumors showed that the 36-gene classifier is more efficient in predicting disease-free survival than the traditional histopathologic prognostic factors and is as effective as the Nottingham Prognostic Index or the "Adjuvant!" software. Multivariate analysis showed that the molecular signature is the only independent prognostic factor. A comparison with several already published signatures demonstrated that the 36-gene signature is among the best to classify tumors from both training and validation sets. Kaplan-Meier analyses emphasized its prognostic power both on the whole cohort of patients and on a subgroup with an intermediate risk of recurrence as defined by the St. Gallen criteria. CONCLUSION: This study identifies a molecular signature specifying a subgroup of patients who do not gain benefits from tamoxifen treatment. These patients may therefore be eligible for alternative endocrine therapies and/or chemotherapy.
Project description:PURPOSE:Although most patients with estrogen receptor (ER)-positive breast cancer benefit from endocrine therapies, a significant proportion do not. Our aim was to identify inherited genetic variations that might predict survival among patients receiving adjuvant endocrine therapies. EXPERIMENTAL DESIGN:We performed a meta-analysis of two genome-wide studies; Helsinki Breast Cancer Study, 805 patients, with 240 receiving endocrine therapy and Prospective study of Outcomes in Sporadic versus Hereditary breast cancer, 536 patients, with 155 endocrine therapy patients, evaluating 486,478 single-nucleotide polymorphisms (SNP). The top four associations from the endocrine treatment subgroup were further investigated in two independent datasets totaling 5,011 patients, with 3,485 receiving endocrine therapy. RESULTS:A meta-analysis identified a common SNP rs8113308, mapped to 19q13.41, associating with reduced survival among endocrine-treated patients [hazard ratio (HR), 1.69; 95% confidence interval (CI), 1.37-2.07; P = 6.34 × 10(-7)] and improved survival among ER-negative patients, with a similar trend in ER-positive cases not receiving endocrine therapy. In a multivariate analysis adjusted for conventional prognostic factors, we found a significant interaction between the rs8113308 and endocrine treatment, indicating a predictive, treatment-specific effect of the SNP rs8113308 on breast cancer survival, with the per-allele HR for interaction 2.16 (95% CI, 1.30-3.60; Pinteraction = 0.003) and HR = 7.77 (95% CI, 0.93-64.71) for the homozygous genotype carriers. A biologic rationale is suggested by in silico functional analyses. CONCLUSIONS:Our findings suggest carrying the rs8113308 rare allele may identify patients who will not benefit from adjuvant endocrine treatment.
Project description:BACKGROUND: Given the large number of genes purported to be prognostic for breast cancer, it would be optimal if the genes identified are not confounded by the continuously changing systemic therapies. The aim of this study was to discover and validate a breast cancer prognostic expression signature for distant metastasis in untreated, early stage, lymph node-negative (N-) estrogen receptor-positive (ER+) patients with extensive follow-up times. METHODS: 197 genes previously associated with metastasis and ER status were profiled from 142 untreated breast cancer subjects. A "metastasis score" (MS) representing fourteen differentially expressed genes was developed and evaluated for its association with distant-metastasis-free survival (DMFS). Categorical risk classification was established from the continuous MS and further evaluated on an independent set of 279 untreated subjects. A third set of 45 subjects was tested to determine the prognostic performance of the MS in tamoxifen-treated women. RESULTS: A 14-gene signature was found to be significantly associated (p < 0.05) with distant metastasis in a training set and subsequently in an independent validation set. In the validation set, the hazard ratios (HR) of the high risk compared to low risk groups were 4.02 (95% CI 1.91-8.44) for the endpoint of DMFS and 1.97 (95% CI 1.28 to 3.04) for overall survival after adjustment for age, tumor size and grade. The low and high MS risk groups had 10-year estimates (95% CI) of 96% (90-99%) and 72% (64-78%) respectively, for DMFS and 91% (84-95%) and 68% (61-75%), respectively for overall survival. Performance characteristics of the signature in the two sets were similar. Ki-67 labeling index (LI) was predictive for recurrent disease in the training set, but lost significance after adjustment for the expression signature. In a study of tamoxifen-treated patients, the HR for DMFS in high compared to low risk groups was 3.61 (95% CI 0.86-15.14). CONCLUSION: The 14-gene signature is significantly associated with risk of distant metastasis. The signature has a predominance of proliferation genes which have prognostic significance above that of Ki-67 LI and may aid in prioritizing future mechanistic studies and therapeutic interventions.
Project description:Despite the benefits of estrogen receptor (ER)-targeted endocrine therapies in breast cancer, many tumors develop resistance. 14-3-3 ?/YWHAZ, a member of the 14-3-3 family of conserved proteins, is over-expressed in several types of cancer, and our previous work showed that high expression of 14-3-3? in ER-positive breast cancers was associated with a poor clinical outcome for women on tamoxifen. Therefore, we now probe the role of 14-3-3? in endocrine resistance, and we examine the functional dimensions and molecular basis that underlie 14-3-3? activities.From analyses of four independent breast cancer microarray datasets from nearly 400 women, we characterized a gene signature that correlated strongly with high expression of 14-3-3? in breast tumors and examined its association with breast cancer molecular subtypes and clinical-pathological features. We investigated the effects of altering 14-3-3? levels in ER-positive, endocrine sensitive and resistant breast cancer cells on the regulation of 14-3-3? signature genes, and on cellular signaling pathways and cell phenotypic properties.The gene signature associated with high 14-3-3? levels in breast tumors encompassed many with functions in mitosis and cytokinesis, including aurora kinase-B, polo-like kinase-1, CDC25B, and BIRC5/survivin. The gene signature correlated with early recurrence and risk of metastasis, and was found predominantly in luminal B breast cancers, the more aggressive ER-positive molecular subtype. The expression of the signature genes was significantly decreased or increased upon reduction or overexpression of 14-3-3? in ER-positive breast cancer cells, indicating their coregulation. 14-3-3? also played a critical role in the regulation of FOXM1, with 14-3-3? acting upstream of FOXM1 to regulate cell division-signature genes. Depletion of 14-3-3? markedly increased apoptosis, reduced proliferation and receptor tyrosine kinase (HER2 and EGFR) signaling, and, importantly, reversed endocrine resistance.This study reveals that 14-3-3? is a key predictive marker for risk of failure on endocrine therapy and serves a pivotal role impacting growth factor signaling, and promoting cell survival and resistance to endocrine therapies. Targeting 14-3-3? and its coregulated proteins, such as FOXM1, should prove valuable in restoring endocrine sensitivity and reducing risk of breast cancer recurrence.
Project description:To identify prognostic signature that could predict the survival of patients with breast cancer (BC).Breast cancer samples and normal breast tissues in the TCGA-BRCA and GSE7390 were included. Differentially expressed genes (DEGs) were identified using the "limma" method. Overall survival (OS) associated with DEGs were obtained using univariate and multivariable Cox proportional-hazards regression analysis, and the corresponding prognostic signature and nomogram were constructed. Calibration analysis and decision curve analysis (DCA) were performed.In all, 742 DEGs were identified, 19 of which were independently correlated with the OS of BC patients. The OS of patients in the 19-gene signature low-risk group was significantly better than that in high-risk group (hazard ratio [HR] 0.3506, 95% confidence interval [CI] 0.2488-0.4939), and the 19-gene based signature was demonstrated to be an independent prognostic factor in patient with BC in the TCGA-BRCA cohort (HR 1.501, 95% CI 1.374-1.640) and validation cohort GSE7392 ((HR 0.3557, 95% CI 0.2155-0.5871, P?<?.0001)). The primary and internally validated C-indexes for the 19-gene signature-based nomogram were 0.817 and 8.013, respectively. The results of calibration analysis and DCA analysis confirmed the robustness and the clinical usability of the nomogram.We constructed a prognostic signature and nomogram for patient with BC, which showed good application prospect.