Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes (original) (raw)
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Defining breast cancer prognosis based on molecular phenotypes: results from a large cohort study
Breast cancer research and treatment, 2011
The objective of this study is to define the survival outcomes associated with distinct molecular phenotypes defined by immunohistochemical staining of paraffin-embedded tissues among invasive breast cancer cases identified from the Nurses' Health Study (NHS). Tissue microarrays were constructed from archived tissue blocks of women diagnosed with breast cancer in the NHS . Invasive non-metastatic breast cancer tumors (n = 1,945) were classified into 1 of 5 molecular phenotypes based on immunohistochemistry assays for estrogen receptor (ER), progesterone receptor (PR), HER2, cytokeratin (CK) 5/6, epidermal growth factor receptor (EGFR) and grade. Survival outcomes were estimated using the Kaplan-Meier product limit method. Cox-proportional hazards models were fitted to determine the association of molecular phenotype with survival outcomes after adjusting for covariates. 1,279 (65.8%) tumors were classified as luminal A, 279 (14.3%) as luminal B, 95 (4.9%) as HER2 type, 203 (10.4%) as basal-like and 89 (4.6%) tumors were unclassified. The 5-year breast cancer-specific survival estimates for women with luminal A, luminal B, HER2-type, basal-like and unclassified tumors were 96, 88, 81, 89 and 85%, respectively. In the multivariable model, compared to cases with luminal A tumors, cases with luminal B (HR 1.90, 95% CI 1.33-2.71), HER2-type (HR 1.36, 95% CI 0.87-2.12), basal-like (HR 1.58, 95% CI 1.05-2.39) and unclassified (HR 1.38, 95% CI 0.87-2.20) tumors had higher hazard of breast cancer death. Similar trends were observed for both overall and recurrence-free survival. In conclusion, compared to women who have luminal A tumors those with luminal B, HER2-type, basallike and unclassified tumors had a worse prognosis, when tumor subtype was defined by immunohistochemistry. This method may provide a cost-effective means of determining prognosis in the clinical setting.
Association between Molecular Subtypes and Survival in Patients with Breast Cancer
Journal of Analytical Oncology, 2017
Background: Aim of this study is to classify intrinsic subtypes and evaluate the differences in clinical/pathological characteristics and survival outcomes among the molecular types. Patients and Methods: Breast cancer subtypes were classified according to the 2013 St. Gallen Consensus. Five molecular subtypes were determined, Luminal A, Luminal B-like HER2 negative, Luminal B-like HER2 positive, HER2 positive, and triple negative. Data was obtained from the records of patients with invasive breast cancer retrospectively. The differences in clinical/pathological parameters, overall survival and disease-free survival among the molecular subtypes were analyzed. The Kaplan-Meier method, log-rank test and Cox regression tests were used to compare groups. Results: The median follow-up period is 48 months. The Luminal B-HER2 negative was the most prevalent type (26.6%). Patient demographics, tumor characteristics and survival data were analyzed. The Luminal A and Luminal B-HER2 negative ...
Breast Cancer Research and Treatment, 2012
Classification of breast cancer into molecular subtypes maybe important for the proper selection of therapy, as tumors with seemingly similar histopathological features can have strikingly different clinical outcomes. Herein, we report the development of a molecular subtyping profile (BluePrint), that enables rationalization in patient selection for either chemotherapy or endocrine therapy prescription. An 80-Gene Molecular Subtyping Profile (BluePrint) was developed using 200 breast cancer patient specimens and confirmed on four independent validation cohorts (n = 784). Additionally, the profile was tested as a predictor of chemotherapy response in 133 breast cancer patients, treated with T/FAC neoadjuvant chemotherapy. BluePrint classification of a patient cohort that was treated with neoadjuvant chemotherapy (n = 133) shows improved distribution of pathological Complete Response (pCR), among molecular subgroups compared with local pathology: 56% of the patients had a pCR in the Basal-type subgroup, 3% in the MammaPrint Low-risk, Luminal-type subgroup, 11% in the MammaPrint Highrisk, Luminal-type subgroup, and 50% in the HER2-type subgroup. The group of genes identifying Luminal-type breast cancer is highly enriched for genes having an Estrogen Receptor binding site proximal to the promoterregion, suggesting that these genes are direct targets of the Estrogen Receptor. Implementation of this profile may improve the clinical management of breast cancer patients, by enabling the selection of patients who are most likely to benefit from either chemotherapy or from endocrine therapy.
Toward Integrated Clinical and Gene Expression Profiles For Breast Cancer Prognosis: A Review Paper
Breast cancer patients with the same diagnostic and clinical prognostics profile can have markedly different clinical outcomes. This difference is possibly caused by the limitation of current breast cancer prognostic indices, which group molecularly distinct patients into similar clinical classes based mainly on the morphology of diseases. Traditional clinical-based prognosis models were discovered to contain some restrictions to address the heterogeneity of breast cancer. The invention of microarray technology and its ability to simultaneously interrogate thousands of genes has changed the paradigm of molecular classification of human cancers as well as shifting clinical prognosis models to a broader prospect. Numerous studies have revealed the potential value of geneexpression signatures in examining the risk of disease recurrence. However, most of these studies attempted to implement genetic-marker based prognostic models to replace the traditional clinical markers, yet neglecting the rich information contained in clinical information. Therefore, this research took the effort to integrate both clinical and microarray data in order to obtain accurate breast cancer prognosis, by taking into account that these data complement each other. This article presents a review of the development of breast cancer prognosis models, concentrating precisely on clinical and gene-expression profiles. The literature is reviewed in an explicit machine-learning framework, which includes the elements of feature selection and classification techniques.
BMC Medical Genomics, 2011
Background Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes. Methods Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were al...
Clinical Cancer Research, 2004
Purpose: Selection of treatment options with the highest likelihood of successful outcome for individual breast cancer patients is based to a large degree on accurate classification into subgroups with poor and good prognosis reflecting a different probability of disease recurrence and survival after therapy. Here we propose a breast cancer classification algorithm taking into account three main prognostic features determined at the time of diagnosis: estrogen receptor (ER) status; lymph node (LN) status; and gene expression signatures associated with distinct therapy outcome. Experimental Design: Using microarray expression profiling and quantitative reverse transcription-PCR analyses, we compared expression profiles of the 70-gene breast cancer survival signature in established breast cancer cell lines and primary breast carcinomas from cancer patients. We classified 295 breast cancer patients using 14-, 13-, 6-, and 4-gene survival predictor signatures into subgroups having statistically distinct probability of therapy failure (P < 0.0001). We evaluated the prognostic power of breast cancer survival predictor signatures alone and in combination with ER and LN status using Kaplan-Meier analysis. Results: The breast cancer survival predictor algorithm allowed highly accurate classification into subgroups with dramatically distinct 5-and 10-year survival after therapy of a large cohort of 295 breast cancer patients with either ER؉ or ER؊ tumors as well as LN؉ or LN؊ disease (P < 0.0001, log-rank test). Conclusions: Our data imply that quantitative laboratory tests measuring expression profiles of a limited set of identified small gene clusters may be useful in stratification of breast cancer patients at the time of diagnosis into subgroups with statistically distinct probability of positive outcome after therapy and assisting in selection of optimal treatment strategies. The estimated increase in survival due to the optimization of treatment protocols may reach many thousands of breast cancer survivors every year at the 10year follow-up check point.
Breast cancer prognostication and prediction in the postgenomic era
Annals of Oncology, 2007
Currently, much effort is being invested in the identification of new, accurate prognostic and predictive factors in breast cancer. Prognostic factors assess the patient's risk of relapse based on indicators such as intrinsic tumor biology and disease stage at diagnosis, and are traditionally used to identify patients who can be spared unnecessary adjuvant therapy based only on the risk of relapse. Lymph node status and tumor size are accepted as well-defined prognostic factors in breast cancer. Predictive factors, in contrast, determine the responsiveness of a particular tumor to a specific treatment. Despite recent advances in the understanding of breast cancer biology and changing practices in disease management, with the exception of hormone receptor status, which predicts responsiveness to endocrine treatment, no predictive factor for response to systemic therapy in breast cancer is widely accepted. While gene expression studies have provided important new information with regard to tumor biology and prognostication, attempts to identify predictive factors have not been successful so far. This article will focus on recent advances in prognostication and prediction, with emphasis on findings from gene expression profiling studies.
Discovery and validation of breast cancer subtypes
BMC …, 2006
Background: Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2 + .
2023
Purpose: Expression-based classifiers to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) are not routinely used in the clinic. We aimed to build and validate a classifier for pCR after NACT. Patients and Methods: We performed a prospective multicenter study (EXPRESSION) including 114 patients treated with anthracycline/taxane-based NACT. Pretreatment core needle biopsies from 91 patients were used for gene expression analysis and classifier construction, followed by validation in five external cohorts (n ¼ 619). Results: A 20-gene classifier established in the EXPRESSION cohort using a Youden index-based cutoff point predicted pCR in the validation cohorts with an accuracy, AUC, negative predictive value (NPV), positive predictive value, sensitivity, and specificity of 0.811, 0.768, 0.829, 0.587, 0.216, and 0.962, respectively. Alternatively , aiming for a high NPV by defining the cutoff point for classification based on the complete responder with the lowest predicted probability of pCR in the EXPRESSION cohort led to an NPV of 0.960 upon external validation. With this extreme-low cutoff point, a recommendation to not treat with anthracycline/ taxane-based NACT would be possible for 121 of 619 unselected patients (19.5%) and 112 of 322 patients with luminal breast cancer (34.8%). The analysis of the molecular subtypes showed that the identification of patients who do not achieve a pCR by the 20-gene classifier was particularly relevant in luminal breast cancer. Conclusions: The novel 20-gene classifier reliably identifies patients who do not achieve a pCR in about one third of luminal breast cancers in both the EXPRESSION and combined validation cohorts.