Gene expression analysis of CALGB 9741 patient tumor samples using the nCounter RUO-PAM50 signature
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE74821 | GEO | 2015/11/10
SECONDARY ACCESSION(S): PRJNA301606
REPOSITORIES: GEO
Similar Datasets
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:Five molecular subtypes have been identified according to intrinsic genes transcription. Microarrays have been adopted for molecular subtyping in addition to the original NanoString nCounter assay. This study aimed to evaluate subtyping consistency among Taiwanese breast cancers. Our study showed that fundamental discrepancy exists between distinct gene expression measuring approaches, and cross platform equivalence should not be overemphasized with too much extrapolation.
2021-09-01 | GSE162228 | GEO
Project description:Concordance of PAM50 molecular subtyping between oligonucleotide microarray and NanoString nCounter assay for Taiwanese breast cancer
| PRJNA681161 | ENA
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:Whole genome expression studies were performed on Xenograft panels with multiple passages from 3 different patient tumors. We assessed the global gene expression concordance from passages to passage and from passage to primary tumor when available and performed PAM50 subtype classification. These results were examined within the context of 40 PAM50 prototype experiments.
2012-10-10 | GSE28860 | GEO
Project description:Gene expression data of HER2+ breast tumor samples Increasing evidence indicates that a subset of patients with HER2-positive breast cancer may achieve significant clinical benefit with anti-HER2 targeted therapy without chemotherapy. Thus, identification of biomarkers of long-term benefit from anti-HER2 agents is needed, especially in the metastatic setting. In the HERLAP study (NCT00842998), patients with HER2-positive metastatic breast cancer were randomized to trastuzumab or lapatinib as first-line therapy. Patients showing radiological signs of tumor regression after 8 weeks of treatment were allowed to continue on single agent anti-HER2 therapy until disease progression. Chemotherapy was added to anti-HER-2 therapy in patients failing to achieve tumor regression at the 8-week evaluation and those progressing at any time. Expression analysis of 105 selected genes was performed from formalin-fixed paraffin-embedded in 17 primary tumor samples. The research-based PAM50 intrinsic subtypes were also identified. The association of the expression of each biomarker with clinical outcome endpoints was evaluated. Nineteen patients were enrolled. In 4 patients (21.1%) we observed disease control lasting longer than 12 months with single agent anti-HER2 therapy (range 14.9-38.8 months). High expression of 17q12-21 amplicon genes HER2 and GRB7, and the PAM50 HER2-enriched intrinsic profile, were found significantly associated with 8-week overall response rate and with the duration of disease control during single agent anti-HER2 therapy. Conversely, high expression of luminal-related genes such as PGR, MDM2 or PIK3CA, or the PAM50 luminal intrinsic profile, was found associated with reduced benefit from single agent anti-HER2 therapy. Conclusions: Our data indicate that gene expression-based biomarkers can identify patients with HER2-positive metastatic breast cancer that benefit substantially from single agent chemo-free anti-HER2 targeted therapy. In the study presented here, a well-defined cohort of 21 breast cancer cases from the HERLAP trial, was used to acquire expression profiles of a total of 105 unique genes
2013-10-25 | E-GEOD-46350 | biostudies-arrayexpress
Project description:Metastatic prostate cancers are recognized to exhibit subtypes categorized by underlying genomic alterations and phenotypes largely partitioned by androgen receptor signaling and neuroendocrine activity. In the present study we evaluated a phenotypic classification approach originally developed for subtyping breast carcinomas using the PAM50 gene signature. PAM50 subtypes associated with specific genotypes such as RB1 loss and phenotypes such as small cell/neuroendocrine carcinoma as well as tumor histology including cribriform morphology. In the context of clinical translation, PAM50 classification segregated tumors into groups with distinct druggable targets such as cell surface proteins amenable to antibody-drug-conjugates (ADCs). Classification into Luminal A, Luminal B and Basal tumors associated with time on androgen receptor signaling inhibitors, and responses to taxane chemotherapy. These findings support further clinical investigation of PAM50-based classification for prostate cancer patient stratification in therapeutic studies.
2022-05-03 | GSE199596 | GEO
Project description:Our aims is correlate the data and classification of the subtype breast cancer obtained with the Pam50 assay with the data obtained from the analysis of the same patients RNAseq
2021-08-07 | GSE181548 | GEO