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| 2. |
- Gruvberger, Sofia, et al.
(författare)
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Estrogen receptor beta expression is associated with tamoxifen response in ER alpha-negative breast carcinoma
- 2007
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Ingår i: Clinical Cancer Research. - 1078-0432. ; 13:7, s. 1987-1994
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: Endocrine therapies, such as tamoxifen, are commonly given to most patients with estrogen receptor (ERalpha)-positive breast carcinoma but are not indicated for persons with ERalpha-negative cancer. The factors responsible for response to tamoxifen in 5% to 10% of patients with ERalpha-negative tumors are not clear. The aim of the present study was to elucidate the biology and prognostic role of the second ER, ERbeta, in patients treated with adjuvant tamoxifen.EXPERIMENTAL DESIGN: We investigated ERbeta by immunohistochemistry in 353 stage II primary breast tumors from patients treated with 2 years adjuvant tamoxifen, and generated gene expression profiles for a representative subset of 88 tumors.RESULTS: ERbeta was associated with increased survival (distant disease-free survival, P = 0.01; overall survival, P = 0.22), and in particular within ERalpha-negative patients (P = 0.003; P = 0.04), but not in the ERalpha-positive subgroup (P = 0.49; P = 0.88). Lack of ERbeta conferred early relapse (hazard ratio, 14; 95% confidence interval, 1.8-106; P = 0.01) within the ERalpha-negative subgroup even after adjustment for other markers. ERalpha was an independent marker only within the ERbeta-negative tumors (hazard ratio, 0.44; 95% confidence interval, 0.21-0.89; P = 0.02). An ERbeta gene expression profile was identified and was markedly different from the ERalpha signature.CONCLUSION: Expression of ERbeta is an independent marker for favorable prognosis after adjuvant tamoxifen treatment in ERalpha-negative breast cancer patients and involves a gene expression program distinct from ERalpha. These results may be highly clinically significant, because in the United States alone, approximately 10,000 women are diagnosed annually with ERalpha-negative/ERbeta-positive breast carcinoma and may benefit from adjuvant tamoxifen.
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- Gruvberger, Sofia, et al.
(författare)
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Expression profiling to predict outcome in breast cancer: the influence of sample selection
- 2003
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Ingår i: Breast Cancer Research. - : Springer Science and Business Media LLC. - 1465-5411 .- 1465-542X. ; 5:1, s. 23-26
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Tidskriftsartikel (refereegranskat)abstract
- Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor- status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor--positive and estrogen receptor--negative tumors.
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- Kalderstam, Jonas, et al.
(författare)
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Training artificial neural networks directly on the concordance index for censored data using genetic algorithms.
- 2013
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 1873-2860 .- 0933-3657. ; 58:2, s. 125-132
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: The concordance index (c-index) is the standard way of evaluating the performance of prognostic models in the presence of censored data. Constructing prognostic models using artificial neural networks (ANNs) is commonly done by training on error functions which are modified versions of the c-index. Our objective was to demonstrate the capability of training directly on the c-index and to evaluate our approach compared to the Cox proportional hazards model. METHOD: We constructed a prognostic model using an ensemble of ANNs which were trained using a genetic algorithm. The individual networks were trained on a non-linear artificial data set divided into a training and test set both of size 2000, where 50% of the data was censored. The ANNs were also trained on a data set consisting of 4042 patients treated for breast cancer spread over five different medical studies, 2/3 used for training and 1/3 used as a test set. A Cox model was also constructed on the same data in both cases. The two models' c-indices on the test sets were then compared. The ranking performance of the models is additionally presented visually using modified scatter plots. RESULTS: Cross validation on the cancer training set did not indicate any non-linear effects between the covariates. An ensemble of 30 ANNs with one hidden neuron was therefore used. The ANN model had almost the same c-index score as the Cox model (c-index=0.70 and 0.71, respectively) on the cancer test set. Both models identified similarly sized low risk groups with at most 10% false positives, 49 for the ANN model and 60 for the Cox model, but repeated bootstrap runs indicate that the difference was not significant. A significant difference could however be seen when applied on the non-linear synthetic data set. In that case the ANN ensemble managed to achieve a c-index score of 0.90 whereas the Cox model failed to distinguish itself from the random case (c-index=0.49). CONCLUSIONS: We have found empirical evidence that ensembles of ANN models can be optimized directly on the c-index. Comparison with a Cox model indicates that near identical performance is achieved on a real cancer data set while on a non-linear data set the ANN model is clearly superior.
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- Niméus, Emma, et al.
(författare)
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Gene expression profilers and conventional clinical markers to predict distant recurrences for premenopausal breast cancer patients after adjuvant chemotherapy.
- 2006
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Ingår i: European Journal of Cancer. - : Elsevier BV. - 1879-0852 .- 0959-8049. ; 42:16, s. 2729-2737
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Tidskriftsartikel (refereegranskat)abstract
- A large proportion of breast cancer patients are treated with adjuvant chemotherapy after the primary operation, but some will recur in spite of this treatment. In order to achieve an improved and more individualised therapy, our knowledge in mechanisms for drug resistance needs to be increased. We have investigated to what extent cDNA microarray measurements could distinguish the likelihood of recurrences after adjuvant CMF (cyclophosphamide, methotrexate and 5-fluorouracil) treatment of premenopausal, lymph node positive breast cancer patients, and have also compared this with the corresponding performance when using conventional clinical variables. We tried several gene selection strategies, and built classifiers using the resulting gene lists. The best performing classifier with odds ratio (OR) = 6.5 (95% confidence interval (CI) = 1.4-62) did not outperform corresponding classifiers based on clinical variables. For the clinical variables, calibrated on the samples, either using all the clinical parameters or the Nottingham Prognostic Index (NPI) parameters, the areas under the receiver operating characteristics (ROC) curve were 0.78 and 0.79, respectively. The ORs at 90% sensitivity were 15 (95% CI = 3.1-140) and 10 (95% CI = 2.1-97), respectively. Our data have provided evidence for a comparable prediction of clinical outcome in CMF-treated breast cancer patients using conventional clinical variables and gene expression based markers. (c) 2006 Elsevier Ltd. All rights reserved.
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- Sjöström, Martin, et al.
(författare)
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Identification and validation of single-sample breast cancer radiosensitivity gene expression predictors
- 2018
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Ingår i: Breast Cancer Research. - : Springer Science and Business Media LLC. - 1465-5411 .- 1465-542X. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Adjuvant radiotherapy is the standard of care after breast-conserving surgery for primary breast cancer, despite a majority of patients being over- or under-treated. In contrast to adjuvant endocrine therapy and chemotherapy, no diagnostic tests are in clinical use that can stratify patients for adjuvant radiotherapy. This study presents the development and validation of a targeted gene expression assay to predict the risk of ipsilateral breast tumor recurrence and response to adjuvant radiotherapy after breast-conserving surgery in primary breast cancer. Methods: Fresh-frozen primary tumors from 336 patients radically (clear margins) operated on with breast-conserving surgery with or without radiotherapy were collected. Patients were split into a discovery cohort (N = 172) and a validation cohort (N = 164). Genes predicting ipsilateral breast tumor recurrence in an Illumina HT12 v4 whole transcriptome analysis were combined with genes identified in the literature (248 genes in total) to develop a targeted radiosensitivity assay on the Nanostring nCounter platform. Single-sample predictors for ipsilateral breast tumor recurrence based on a k-top scoring pairs algorithm were trained, stratified for estrogen receptor (ER) status and radiotherapy. Two previously published profiles, the radiosensitivity signature of Speers et al., and the 10-gene signature of Eschrich et al., were also included in the targeted panel. Results: Derived single-sample predictors were prognostic for ipsilateral breast tumor recurrence in radiotherapy-treated ER+ patients (AUC 0.67, p = 0.01), ER+ patients without radiotherapy (AUC = 0.89, p = 0.02), and radiotherapy-treated ER- patients (AUC = 0.78, p < 0.001). Among ER+ patients, radiotherapy had an excellent effect on tumors classified as radiosensitive (p < 0.001), while radiotherapy had no effect on tumors classified as radioresistant (p = 0.36) and there was a high risk of ipsilateral breast tumor recurrence (55% at 10 years). Our single-sample predictors developed in ER+ tumors and the radiosensitivity signature correlated with proliferation, while single-sample predictors developed in ER- tumors correlated with immune response. The 10-gene signature negatively correlated with both proliferation and immune response. Conclusions: Our targeted single-sample predictors were prognostic for ipsilateral breast tumor recurrence and have the potential to stratify patients for adjuvant radiotherapy. The correlation of models with biology may explain the different performance in subgroups of breast cancer.
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