High Expression of Three-Gene Signature Improves Prediction of Relapse-Free Survival in Estrogen Receptor-Positive and Node-Positive Breast Tumors.
Ontology highlight
ABSTRACT:
SUBMITTER: Thakkar A
PROVIDER: S-EPMC4666521 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Publications
High Expression of Three-Gene Signature Improves Prediction of Relapse-Free Survival in Estrogen Receptor-Positive and Node-Positive Breast Tumors.
Thakkar Arvind A Raj Hemanth H Ravishankar Muthuvelan Bhaskaran B Balakrishnan Arun A Padigaru Muralidhara M
Biomarker insights 20151130
The objective of the present study was to validate prognostic gene signature for estrogen receptor alpha-positive (ER03B1+) and lymph node (+) breast cancer for improved selection of patients for adjuvant therapy. In our previous study, we identified a group of seven genes (GATA3, NTN4, SLC7A8, ENPP1, MLPH, LAMB2, and PLAT) that show elevated messenger RNA (mRNA) expression levels in ERα (+) breast cancer patient samples. The prognostic values of these genes were evaluated using gene expression ...[more]
Similar Datasets
Project description:Background:Colon cancer (CC) patients with early relapse usually have a poor prognosis. In this study, we aimed to identify a novel signature to improve the prediction of relapse-free survival (RFS) in CC. Methods:Four microarray datasets were merged into a training set (n=1,045), and one RNA-sequencing dataset was used as a validation set (n=384). In the training set, microarray meta-analysis screened out 596 common RFS-related genes across datasets, which were used to construct 177,310 gene pairs. Then, the LASSO penalized generalized linear model identified 16 RFS-related gene pairs, and a risk score was calculated for each sample according to the model coefficients. Results:The risk score demonstrated a good ability in predicting RFS (area under the curve [AUC] at 5 years: 0.724; concordance index [C-index]: 0.642, 95% CI: 0.615-0.669). High-risk patients showed a poorer prognosis than low-risk patients (HR: 3.519, 95% CI: 2.870-4.314). Subgroup analysis reached consistent results when considering multiple confounders. In the validation set, the risk score had a similar performance (AUC at 5 years: 0.697; C-index: 0.696, 95% CI: 0.627-0.766; HR: 2.926, 95% CI: 1.892-4.527). When compared with a 13-gene signature, a 15-gene signature, and TNM stage, the score showed a better performance (P<0.0001; P=0.0004; P=0.0125), especially for the patients with a longer follow-up (R2=0.988, P<0.0001). When the follow-up was >5 years (n=314), the score demonstrated an excellent performance (C-index: 0.869, 95% CI: 0.816-0.922; HR: 13.55, 95% CI: 7.409-24.78). Conclusion:Our study identified a novel gene-pair signature for prediction of RFS in CC.
| S-EPMC6175542 | biostudies-literature
Project description:Fibroblast growth factor receptor 1 (FGFR1) amplification drives poor prognosis and is an emerging therapeutic target. We sought to construct a multigene mRNA expression signature to efficiently identify FGFR1-amplified estrogen receptor-positive (ER+) breast tumors. Five independent breast tumor series were analyzed. Genes discriminative for FGFR1 amplification were screened transcriptome-wide by receiver operating characteristic analyses. The METABRIC series was leveraged to construct/evaluate four approaches to signature composition. A locked-down signature was validated with 651 ER+ formalin-fixed, paraffin-embedded tissues (the University of British Columbia-tamoxifen cohort). A NanoString nCounter assay was designed to profile selected genes. For a gold standard, FGFR1 amplification was determined by fluorescent in situ hybridization (FISH). Prognostic effects of FGFR1 amplification were assessed by survival analyses. Eight 8p11-12 genes (ASH2L, BAG4, BRF2, DDHD2, LSM1, PROSC, RAB11FIP1, and WHSC1L1) together with the a priori selected FGFR1 gene, highly discriminated FGFR1 amplification (area under the receiver operating characteristic curve ?0.82, all genes and all cohorts). The nine-gene signature Call-FGFR1-amp accurately identified FGFR1 FISH-amplified ER+ tumors in the University of British Columbia-tamoxifen cohort (specificity, 0.94; sensitivity, 0.96) and exhibited prognostic effects (disease-specific survival hazard ratio, 1.57; 95% CI, 1.14-2.16; P = 0.005). Call-FGFR1-amp includes several understudied 8p11-12 amplicon-driven oncogenes and accurately identifies FGFR1-amplified ER+ breast tumors. Our study demonstrates an efficient approach to diagnosing rare amplified therapeutic targets with FISH as a confirmatory assay.
| S-EPMC5225309 | biostudies-literature
Project description:BACKGROUND: Various biomarkers for prediction of distant metastasis in lymph-node negative breast cancer have been described; however, predictive biomarkers for patients with lymph-node positive (LNP) disease in the context of distinct systemic therapies are still very much needed. DNA methylation is aberrant in breast cancer and is likely to play a major role in disease progression. In this study, the DNA methylation status of 202 candidate loci was screened to identify those loci that may predict outcome in LNP/estrogen receptor-positive (ER+) breast cancer patients with adjuvant anthracycline-based chemotherapy. METHODS: Quantitative bisulfite sequencing was used to analyze DNA methylation biomarker candidates in a retrospective cohort of 162 LNP/ER+ breast cancer patients, who received adjuvant anthracycline-based chemotherapy. First, twelve breast cancer specimens were analyzed for all 202 candidate loci to exclude genes that showed no differential methylation. To identify genes that predict distant metastasis, the remaining loci were analyzed in 84 selected cases, including the 12 initial ones. Significant loci were analyzed in the remaining 78 independent cases. Metastasis-free survival analysis was conducted by using Cox regression, time-dependent ROC analysis, and the Kaplan-Meier method. Pairwise multivariate regression analysis was performed by linear Cox Proportional Hazard models, testing the association between methylation scores and clinical parameters with respect to metastasis-free survival. RESULTS: Of the 202 loci analysed, 37 showed some indication of differential DNA methylation among the initial 12 patient samples tested. Of those, 6 loci were associated with outcome in the initial cohort (n = 84, log rank test, p < 0.05).Promoter DNA methylation of cysteine dioxygenase 1 (CDO1) was confirmed in univariate and in pairwise multivariate analysis adjusting for age at surgery, pathological T stage, progesterone receptor status, grade, and endocrine therapy as a strong and independent biomarker for outcome prediction in the independent validation set (log rank test p-value = 0.0010). CONCLUSIONS: CDO1 methylation was shown to be a strong predictor for distant metastasis in retrospective cohorts of LNP/ER+ breast cancer patients, who had received adjuvant anthracycline-based chemotherapy.
| S-EPMC2893112 | biostudies-literature
Project description:Background: Testicular germ cell tumors (TGCTs) are commonly diagnosed tumors in young men. However, a satisfactory approach to predict relapse of stage I TGCTs is still lacking. Therefore, this study aimed to develop a robust risk score model for stage I TGCTs. Method: RNA-sequence data of stage I TGCTs and normal testis samples were downloaded and analyzed to identify different expression genes. Gene-based prognostic model was constructed in The Cancer Genome Atlas (TCGA) using least absolute shrinkage and selection operator (LASSO) regression analysis and validated in GSE99420 dataset. Potential biological functions of the genes in prognostic model were determined via Gene Set Enrichment Analysis (GSEA) between high-risk and low-risk patients. Results: A total of 9,391 differentially expressed genes and 84 prognosis-related genes were identified. An eight-gene-based risk score model was constructed to divide patients into high or low risk of relapse. The low-risk patients had a significantly better relapse-free survival (RFS) than high-risk patients in both training and validation cohorts (HR = 0.129, 95% CI = 0.059-0.284, P < 0.001; HR = 0.277, 95% CI = 0.116-0.661, P = 0.004, respectively). The area under the receiver operating characteristic curve (AUC) values at 5 years was 0.805 and 0.724 in the training and validation cohorts, respectively. Functional enrichment analyses showed that DNA replication, ribosome, cell cycle, and TGF-beta signaling pathway may contribute to the relapse process. Conclusion: In summary, our analysis provided a novel eight-gene signature that could predict RFS in stage I TGCT patients.
| S-EPMC7412879 | biostudies-literature
Project description:Lrig1 is the founding member of the Lrig family and has been implicated in the negative regulation of several oncogenic receptor tyrosine kinases including ErbB2. Lrig1 is expressed at low levels in several cancer types but is overexpressed in some prostate and colorectal tumors. Given this heterogeneity, whether Lrig1 functions to suppress or promote tumor growth remains a critical question. Previously, we found that Lrig1 was poorly expressed in ErbB2-positive breast cancer, suggesting that Lrig1 has a growth-inhibitory role in this tumor type. However, breast cancer is a complex disease, with ErbB2-positive tumors accounting for just 25% of all breast cancers. To gain a better understanding of the role of Lrig1 in breast cancer, we examined its expression in estrogen receptor ? (ER?)-positive disease which accounts for the majority of breast cancers. We find that Lrig1 is expressed at significantly higher levels in ER?-positive disease than in ER?-negative disease. Our study provides a molecular rationale for Lrig1 enrichment in ER?-positive disease by showing that Lrig1 is a target of ER?. Estrogen stimulates Lrig1 accumulation and disruption of this induction enhances estrogen-dependent tumor cell growth, suggesting that Lrig1 functions as an estrogen-regulated growth suppressor. In addition, we find that Lrig1 expression correlates with prolonged relapse-free survival in ER?-positive breast cancer, identifying Lrig1 as a new prognostic marker in this setting. Finally, we show that ErbB2 activation antagonizes ER?-driven Lrig1 expression, providing a mechanistic explanation for Lrig1 loss in ErbB2-positive breast cancer. This work provides strong evidence for a growth-inhibitory role for Lrig1 in breast cancer.
| S-EPMC3196675 | biostudies-literature
Project description:Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs).We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates.Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients.RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment.
| S-EPMC3961800 | biostudies-other
Project description:Purpose: The benefit of postoperative adjuvant chemotherapy in patients with Dukes´ B colorectal cancer is still uncertain and its routine use is not recommended. The five years survival rate is approximately 75% and identification of the patients at high risk of recurrence would represent an important strategy for the use of adjuvant chemotherapy. In this study we identify new prognostic markers for tumor relapse in Dukes´ B colon cancer patients. Patients and Methods: We retrospectively analyzed gene expression profiles in frozen tumor specimens from 16 patients with Dukes´ B colorectal cancer by using high density oligonucleotide microarrays. Data were normalized and subsequently analyzed with two different statistical procedures. The intensity value associated to each spot is the result of subtracting a gaussian function of the noise from the foreground values {Kooperberg, 2002}. After this background subtraction, base 2 logarithms of all data were calculated and genes with more than two missing values were excluded from the analysis. The remaining missing values were replaced by using the KNN imputation method {Troyanskaya, 2001}. The great number of genes present in the microarray allowed us to consider that the overall fluorescence intensity must be the same for all slides, as the vast majority of genes will not change their expression between the two conditions tested. So we used the quantile normalization method {Bolstad, 2003}, which equalizes not only the average intensity but also the range of intensity values between slides. We decided to use two different statistical procedures in the search of significant differences in gene expression between the relapsed and non-relapsed patients. Only the genes selected by both statistical procedures were considered as differentially expressed between relapsed and non-relapsed patients. The first test was a permutation t-test for comparison of two means {Dudoit, 2003} and the second one a variation of the Fisher test based on the work presented by Iizuka et al. {Iizuka, 2003} in which they searched for the optimal number of genes that could differentiate between two groups of samples. Briefly, we used the same algorithms for the calculation of the Fisher criterion, but instead of looking for the optimal subset of genes able to differentiate between groups, what requires a lot of computing capacity, we tried different numbers of candidate genes and selected a number which yielded very good classification results when evaluated by means of Fast ICA {Lee, 2003} and Hierarchical Clustering {Everitt, 1974}. The procedure consisted of selecting the 30 genes with the highest Fisher criterion value in 6 rounds of iteration, each round leaving one sample of the relapsed group and two of the non-relapsed group out of the calculations (a variant of the “leave one out” method). We selected the genes present in at least 3 of the iterations. The intersection of the groups of genes selected by the two statistical procedures was selected as a prognostic signature for relapse in Dukes´stage B colon cancer. Results: Our results show a group of 48 genes differentially expressed between the relapsed and non-relapsed groups with an associated probability below 0,001 in the t test. Another statistical procedure based on the Fisher criterion resulted in 11 genes able to separate both groups. In order to minimize false positives we only considered a good gene signature the 8 genes selected by both statistical procedures. These genes are ribosomal protein S5, chromodomain helicase DNA binding protein 2, lysosomal ATPase V0 subunit A isoform 1, zinc finger protein 148, brain protein I3 (BRI3), hypothetical protein MGC23401 and one unknown clone. In order to verify the obtained results, the differential expression of the first two genes was confirmed by real time PCR. Keywords: repeat sample
2005-05-07 | GSE2630 | GEO
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.
| S-EPMC2631011 | biostudies-literature
Project description:We here describe two proteomic datasets deposited in ProteomeXchange via PRIDE partner repository [1] with dataset identifiers PXD000484 (defined as "training") and PXD000485 (defined as "test") that have been used for the development of a tamoxifen outcome predictive signature [2]. Both datasets comprised 56 fresh frozen estrogen receptor (ER) positive primary breast tumor specimens derived from patients who received tamoxifen as first line therapy for recurrent disease. Patient groups were defined based on time to progression (TTP) after start of tamoxifen therapy (6 months cutoff): 32 good and 24 poor treatment outcome patients were comprised in the training set, respectively. The test set included 41 good and 15 poor treatment outcome patients. All specimens were subjected to laser capture microdissection (LCM) to enrich for epithelial tumor cells prior to high resolution mass spectrometric (MS) analysis. Protein identification and label-free quantification (LFQ) were performed with MaxQuant software package [3]. A total of 3109 and 4061 proteins were identified and quantified in the training and test set, respectively. We here present the first public proteomic dataset analyzing ER positive recurrent breast cancer by LCM coupled to high resolution MS.
| S-EPMC4773412 | biostudies-literature
Project description:Background: Despite low aggressiveness in tumor biology and high responsiveness to endocrine therapy, subgroups of patients with estrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer relapse early in the first two years after initiation of endocrine therapy, indicating potential endocrine resistance. Accordingly, we attempted to establish a scoring system to inform the first-2-year prognosis (F2P Score). Methods: Patients with node-negative ER+/HER2- breast cancer and complete data of gene expressions in a 21-gene panel were retrospectively retrieved from Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB). The F2P Score was established based on the clinical and genomic variables associated with the first-2-year relapse after shrinkage correction and validated using the bootstrap resampling method. Model performance was quantified by Harrell's concordance-index (C-index) and Bayesian information criteria (BIC). Results: The F2P Score was established by integrating the clinical (age and tumor size) and genomic (ESR1, PGR, BCL2, CD68, GSTM1, and BAG1) variables with a C-index of 0.71 and BIC of 397.46. Bootstrap C-index was 0.72 (95% CI, 0.62-0.81) and BIC was 396.75 (95% CI, 252.37-541.13). A higher score indicated an increased likelihood of a first-2-year relapse, when used as continuous (HR, 2.94; 95% CI, 1.87-4.61) or categorical (HR, 3.68; 95% CI, 1.70-8.00) predictors in multivariate analysis. Both continuous and categorical F2P Score also remained prognostic for overall survival and other endpoints. No significant interaction was observed between the F2P Score and treatment subgroups. Additionally, the F2P Score outperformed the IHC4, clinical treatment score and 21-gene test in predicting first-2-year relapse. Conclusion: The F2P Score reported herein, integrating the clinicopathological and genomic variables, may inform prognosis and endocrine responsiveness. After the benefits and risks have been considered, treatment escalation may be an alternative strategy for patients with a higher score.
| S-EPMC7518385 | biostudies-literature