Systematic Bias in Genomic Classification Due to Contaminating Normal Tissue in Breast Tumor Samples
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE22384 | GEO | 2011/07/19
SECONDARY ACCESSION(S): PRJNA127631
REPOSITORIES: GEO
Similar Datasets
Project description:Results: Normal tissue contamination caused misclassification of tumors in all predictors, but different breast cancer predictors showed different susceptibility to normal tissue bias. Sensitivity and negative predictive value (NPV) of the PAM50 assay was improved by accounting for normal tissue. Conclusions: Normal tissue sampled concurrently with tumor tissue is an important source of bias in genomic predictors. Adjustments for normal tissue contamination could improve the application of breast cancer genomic predictors in both research and in clinical settings. Reference x breast tumor samples.
2011-07-18 | E-GEOD-22384 | biostudies-arrayexpress
Project description:PURPOSE: To estimate the effect of contamination with normal breast tissue for the development of gene signatures robust to pre-analytical conditions. METHODS: We evaluated the effect of contamination with normal breast tissue on gene signatures by comparing microarray profiles of breast cancer samples contaminated with increasing amounts of normal breast tissue.
2019-05-30 | GSE124646 | GEO
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:Breast cancer and normal breast tissue samples to estimate the effect of contamination of breast cancer samples with normal breast tissue
| PRJNA512930 | 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.
2011-05-10 | GSE26082 | GEO
Project description:Purpose: Despite advances in radical surgery and chemotherapy delivery, ovarian cancer is the most lethal gynecologic malignancy. Most of these patients are treated with platinum-based chemotherapies, but there is no biomarker model to guide their responses to these therapeutic agents. We have developed and independently tested our novel multivariate molecular predictors for forecasting patients' responses to individual drugs on a cohort of 58 ovarian cancer patients. Experimental Design: We adapted and applied the previously-published COXEN algorithm to develop molecular predictors for therapeutic responses of patients' tumors based on expression signatures derived from the NCI-60 in vitro drug activities and genomic expression data. Genome-wide candidate biomarkers were first triaged by examining expression patterns of frozen and formalin-fixed paraffin embedded (FFPE) tissue samples. We then identify initial drug sensitivity biomarkers for carboplatin and paclitaxel, respectively. These biomarkers were further narrowed by examining concordant expression patterns between cell lines and a historical set of ovarian cancer patients. Multivariate predictors were obtained from the NCI-60 cell lines and refined using historical patient cohorts. To independent validate these molecular predictors, we performed genome-wide profiling on FFPE samples of 58 ovarian cancer patients obtained prior to adjuvant chemotherapy. Results: Carboplatin predictor significantly stratified platinum sensitive and resistant patients (p = 0.019) with sensitivity = 93%, specificity = 33%, PPV = 65%, and NPV = 78%. Paclitaxel predictor also significantly stratified patients' responses (p = 0.033) with sensitivity = 96%, specificity = 26%, PPV = 61%, and NPV = 86%. The combination predictor for platinum-taxane combination demonstrated a significant survival difference between the predicted responders and nonresponders with median survival of 12.9 months vs. 8.1 months (p = 0.045). Conclusions: COXEN predictors successfully stratified platinum resistance and taxane response in this retrospective cohort, especially based on their FFPE tumor samples. Accurate prediction of chemotherapeutic response, especially to platinum agents is highly clinically relevant and could alter primary management of ovarian cancer. Gene expression data from 58 stage III-IV ovarian cancer patients treated with Carboplatin and Taxol agents
2012-08-21 | E-GEOD-30161 | biostudies-arrayexpress
Project description:The objective of the study was to characterize hereditary breast tumors based on their miRNA expression profiles. To this end, we performed global miRNA expression analysis of more than 800 human miRNA genes in a large series of 80 FFPE breast tissue samples. The series included 66 hereditary breast primary tumors from 13 BRCA1 mutation carriers, 10 BRCA2 mutation carriers and 43 non-BRCA12 tumors denominated hereafter as BRCAX tumors. In addition we have analyzed 10 sporadic breast carcinomas and 4 normal breast tissues obtained after breast reduction surgery from healthy donors with no family history of breast cancer. To avoid contamination with normal breast tissue, tumoral area on FFPE blocks was marked by a pathologist and macrodissected for subsequent total RNA extraction.
2014-06-21 | GSE44899 | GEO
Project description:Purpose: Despite advances in radical surgery and chemotherapy delivery, ovarian cancer is the most lethal gynecologic malignancy. Most of these patients are treated with platinum-based chemotherapies, but there is no biomarker model to guide their responses to these therapeutic agents. We have developed and independently tested our novel multivariate molecular predictors for forecasting patients' responses to individual drugs on a cohort of 58 ovarian cancer patients. Experimental Design: We adapted and applied the previously-published COXEN algorithm to develop molecular predictors for therapeutic responses of patients' tumors based on expression signatures derived from the NCI-60 in vitro drug activities and genomic expression data. Genome-wide candidate biomarkers were first triaged by examining expression patterns of frozen and formalin-fixed paraffin embedded (FFPE) tissue samples. We then identify initial drug sensitivity biomarkers for carboplatin and paclitaxel, respectively. These biomarkers were further narrowed by examining concordant expression patterns between cell lines and a historical set of ovarian cancer patients. Multivariate predictors were obtained from the NCI-60 cell lines and refined using historical patient cohorts. To independent validate these molecular predictors, we performed genome-wide profiling on FFPE samples of 58 ovarian cancer patients obtained prior to adjuvant chemotherapy. Results: Carboplatin predictor significantly stratified platinum sensitive and resistant patients (p = 0.019) with sensitivity = 93%, specificity = 33%, PPV = 65%, and NPV = 78%. Paclitaxel predictor also significantly stratified patients' responses (p = 0.033) with sensitivity = 96%, specificity = 26%, PPV = 61%, and NPV = 86%. The combination predictor for platinum-taxane combination demonstrated a significant survival difference between the predicted responders and nonresponders with median survival of 12.9 months vs. 8.1 months (p = 0.045). Conclusions: COXEN predictors successfully stratified platinum resistance and taxane response in this retrospective cohort, especially based on their FFPE tumor samples. Accurate prediction of chemotherapeutic response, especially to platinum agents is highly clinically relevant and could alter primary management of ovarian cancer.
2012-08-21 | GSE30161 | GEO
Project description:Systematic Bias in Genomic Classification Due to Contaminating Normal Tissue in Breast Tumor Samples
| PRJNA127631 | ENA
Project description:Despite the advancement of genomic classification of breast cancer, current clinical tests and treatment decisions are commonly based on protein level information. Formalin-fixed paraffin-embedded (FFPE) tissue specimens are widely available and can be directly linked to extended clinical outcomes. We performed comprehensive proteomic profiling of 300 FFPE breast cancer surgical specimens, 75 of each PAM50 subtype, with linked clinical outcomes. Using our highly sensitive SP3-Clinical Tissue Proteomics platform, we quantified 4214 proteins across all 300 samples. The analysis identified a distinct proteomic group characterized by high expression of immune response proteins and favorable survival rates. Four clinically distinct proteomic clusters within samples classified as triple negative breast cancer by immunohistochemistry had features of “basal-immune hot”, “basal-immune cold”, “mesenchymal”, and “luminal androgen receptor”. Our proteomic analysis characterizes the heterogeneity of breast cancer in a clinically-applicable manner, identifies potential biomarkers and therapeutic targets, and provides a resource for clinical breast cancer classification.
2022-02-22 | PXD024322 | Pride