Concordance of PAM50 molecular subtyping between oligonucleotide microarray and NanoString nCounter assay for Taiwanese breast cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE162228 | GEO | 2021/09/01
REPOSITORIES: GEO
Similar Datasets
Project description:Concordance of PAM50 molecular subtyping between oligonucleotide microarray and NanoString nCounter assay for Taiwanese breast cancer
| PRJNA681161 | ENA
Project description:'Precision medicine' is a concept that by utilizing modern molecular diagnostics, an effective therapy is accurately applied for each cancer patient to improve their survival rates. The aim of this study was to compare the molecular subtypes of triple negative breast cancer (TNBC) between Taiwanese and other datasets.
2017-06-13 | GSE95700 | GEO
Project description:Intrinsic subtyping of breast cancer was performed using an nCounter RUO-PAM50 gene expression assay to determine the ability of instrinsic subtyping to predict what patients may benefit from altered chemotherapy scheduling in the CALGB 9741 clinical trial population. FFPE primary breast tumor samples archived at the CALGB Pathology Coordinating Office (PCO) were used to obtain total RNA for instrinsic subtyping using the nCounter Analysis System. Gene-expression profiles were generated for 1321 of 1471 patient samples (90%) suitable for inclusion in this study.
2015-11-10 | E-GEOD-74821 | biostudies-arrayexpress
Project description:Intrinsic subtyping of breast cancer was performed using an nCounter RUO-PAM50 gene expression assay to determine the ability of instrinsic subtyping to predict what patients may benefit from altered chemotherapy scheduling in the CALGB 9741 clinical trial population.
2015-11-10 | GSE74821 | GEO
Project description:BackgroundMany methodologies have been used in research to identify the "intrinsic" subtypes of breast cancer commonly known as Luminal A, Luminal B, HER2-Enriched (HER2-E) and Basal-like. The PAM50 gene set is often used for gene expression-based subtyping; however, surrogate subtyping using panels of immunohistochemical (IHC) markers are still widely used clinically. Discrepancies between these methods may lead to different treatment decisions.MethodsWe used the PAM50 RT-qPCR assay to expression profile 814 tumors from the GEICAM/9906 phase III clinical trial that enrolled women with locally advanced primary invasive breast cancer. All samples were scored at a single site by IHC for estrogen receptor (ER), progesterone receptor (PR), and Her2/neu (HER2) protein expression. Equivocal HER2 cases were confirmed by chromogenic in situ hybridization (CISH). Single gene scores by IHC/CISH were compared with RT-qPCR continuous gene expression values and "intrinsic" subtype assignment by the PAM50. High, medium, and low expression for ESR1, PGR, ERBB2, and proliferation were selected using quartile cut-points from the continuous RT-qPCR data across the PAM50 subtype assignments.ResultsESR1, PGR, and ERBB2 gene expression had high agreement with established binary IHC cut-points (area under the curve (AUC) ≥ 0.9). Estrogen receptor positivity by IHC was strongly associated with Luminal (A and B) subtypes (92%), but only 75% of ER negative tumors were classified into the HER2-E and Basal-like subtypes. Luminal A tumors more frequently expressed PR than Luminal B (94% vs 74%) and Luminal A tumors were less likely to have high proliferation (11% vs 77%). Seventy-seven percent (30/39) of ER-/HER2+ tumors by IHC were classified as the HER2-E subtype. Triple negative tumors were mainly comprised of Basal-like (57%) and HER2-E (30%) subtypes. Single gene scoring for ESR1, PGR, and ERBB2 was more prognostic than the corresponding IHC markers as shown in a multivariate analysis.ConclusionsThe standard immunohistochemical panel for breast cancer (ER, PR, and HER2) does not adequately identify the PAM50 gene expression subtypes. Although there is high agreement between biomarker scoring by protein immunohistochemistry and gene expression, the gene expression determinations for ESR1 and ERBB2 status was more prognostic.
| S-EPMC3487945 | biostudies-literature
Project description:PAM50 intrinsic subtype classification (Parker JS, Mullins M, Cheang MCU, et al: Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. J Clin Oncol 27:1160-1167, 2009) was used for the Z1031 patient cohort. However, to account for potential technical biases such as differences in platforms and laboratory protocols between the Z1031 data set and the PAM50 training set2, we normalized the Z1301 data set before applying the PAM50 algorithm. This is a critical step that must be taken into consideration for appropriate subtype predictions as our group and others have previously shown3,4. To appropriately normalize the Z1301 microarray data, which is entirely composed of ER-positive tumors, we needed to estimate technical bias from a cohort with equivalent subtype representation as the PAM50 training set. A set including ER-negative, HER2-positive and true normal breast samples were profiled under the same platform, protocol and laboratory (i.e. WashU) as the Z1301 data set. A total of 40 prototypic samples of the various molecular subtypes were selected to match the distribution in the PAM50 training set. The mean expression of each PAM50 gene was calculated in this set of 40. These values obtained from the prototypical sample set were used for calibration purposes and were included as a new WashU_4X44K_Calibration column in the CalibrationParameter file, which has been already provided in the PAM50 algorithm2. This adjustment step assures that each sample is placed in the appropriate ‘prototypic space’ by subtracting the calibration value of each PAM50 gene from the expression value of the corresponding gene in each tested sample. Finally, single sample predictions were performed in every Z1301 sample using the WashU_4X44K_Calibration parameter. The complete data set for the 40 prototypic arrays is submitted under GSE26082. 40 experiments were used as prototypical cases for subtype calibration purposes.
| S-ECPF-GEOD-26082 | biostudies-other
Project description:PAM50 intrinsic subtype classification (Parker JS, Mullins M, Cheang MCU, et al: Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. J Clin Oncol 27:1160-1167, 2009) was used for the Z1031 patient cohort. However, to account for potential technical biases such as differences in platforms and laboratory protocols between the Z1031 data set and the PAM50 training set2, we normalized the Z1301 data set before applying the PAM50 algorithm. This is a critical step that must be taken into consideration for appropriate subtype predictions as our group and others have previously shown3,4. To appropriately normalize the Z1301 microarray data, which is entirely composed of ER-positive tumors, we needed to estimate technical bias from a cohort with equivalent subtype representation as the PAM50 training set. A set including ER-negative, HER2-positive and true normal breast samples were profiled under the same platform, protocol and laboratory (i.e. WashU) as the Z1301 data set. A total of 40 prototypic samples of the various molecular subtypes were selected to match the distribution in the PAM50 training set. The mean expression of each PAM50 gene was calculated in this set of 40. These values obtained from the prototypical sample set were used for calibration purposes and were included as a new WashU_4X44K_Calibration column in the CalibrationParameter file, which has been already provided in the PAM50 algorithm2. This adjustment step assures that each sample is placed in the appropriate ‘prototypic space’ by subtracting the calibration value of each PAM50 gene from the expression value of the corresponding gene in each tested sample. Finally, single sample predictions were performed in every Z1301 sample using the WashU_4X44K_Calibration parameter. The complete data set for the 40 prototypic arrays is submitted under GSE26082. 40 experiments were used as prototypical cases for subtype calibration purposes.
2011-05-10 | E-GEOD-26082 | biostudies-arrayexpress
Project description:PAM50 intrinsic subtype classification (Parker JS, Mullins M, Cheang MCU, et al: Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. J Clin Oncol 27:1160-1167, 2009) was used for the Z1031 patient cohort. However, to account for potential technical biases such as differences in platforms and laboratory protocols between the Z1031 data set and the PAM50 training set2, we normalized the Z1301 data set before applying the PAM50 algorithm. This is a critical step that must be taken into consideration for appropriate subtype predictions as our group and others have previously shown3,4. To appropriately normalize the Z1301 microarray data, which is entirely composed of ER-positive tumors, we needed to estimate technical bias from a cohort with equivalent subtype representation as the PAM50 training set. A set including ER-negative, HER2-positive and true normal breast samples were profiled under the same platform, protocol and laboratory (i.e. WashU) as the Z1301 data set. A total of 40 prototypic samples of the various molecular subtypes were selected to match the distribution in the PAM50 training set. The mean expression of each PAM50 gene was calculated in this set of 40. These values obtained from the prototypical sample set were used for calibration purposes and were included as a new WashU_4X44K_Calibration column in the CalibrationParameter file, which has been already provided in the PAM50 algorithm2. This adjustment step assures that each sample is placed in the appropriate ‘prototypic space’ by subtracting the calibration value of each PAM50 gene from the expression value of the corresponding gene in each tested sample. Finally, single sample predictions were performed in every Z1301 sample using the WashU_4X44K_Calibration parameter. The complete data set for the 40 prototypic arrays is submitted under GSE26082.
2011-05-10 | GSE26082 | GEO
Project description:Taiwanese-style pickle Raw sequence reads
| PRJNA933389 | ENA
Project description:Breast cancer was one of the first cancer types where molecular subtyping led to explanation of interpersonal heterogeneity and resulted in improvement of treatment regimen. Several multigene classifiers have been developed and in particular those defining molecular signatures of early breast cancers possess significant prognostic information. Hence since 2014, molecular subtyping of primary breast cancers was implemented as a part of routine diagnostics with direct impact of therapy assignment. In this study, we evaluate direct and potential benefits of molecular subtyping in low-risk breast cancers as well as present the advantages of a robust molecular signature in regard to patient work-up among high-risk breast cancers.
2017-12-01 | E-MTAB-5724 | biostudies-arrayexpress