Project description:Background: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%-60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profi les of breast cancers and whether such profi les could be used to improve histologic grading. Methods: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)-positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan-Meier analysis. All statistical tests were two-sided. Results: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1-3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P<.001, log-rank test). Conclusions: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value. NB: The patients coming from Uppsala Hospital have been also used in other studies as in GSE3494. You can find the common set of patients in removing the abbreviation "UPP_" from the sample names and compare the results with the "INDEX (ID)" from the GSE3494 series. Experiment Overall Design: 64 microarray experiments from primary breast tumors used as training set to identify genes differentially expressed in grade 1 and 3. Experiment Overall Design: 129 microarray experiments from primary breast tumors of untreated patients used as validation set to validate the list of genes and its correlation with survival. Experiment Overall Design: No replicate, no reference sample. **NOTE** There are some inconsistencies between the sample annotation provided by GEO for this experiment in the GSE2990_family.soft.gz file and the supplementary data file GSE2990_suppl_info.txt. ***

Project description:Background: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%-60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profi les of breast cancers and whether such profi les could be used to improve histologic grading. Methods: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)-positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan-Meier analysis. All statistical tests were two-sided. Results: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1-3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P<.001, log-rank test). Conclusions: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value. NB: The patients coming from Uppsala Hospital have been also used in other studies as in GSE3494. You can find the common set of patients in removing the abbreviation "UPP_" from the sample names and compare the results with the "INDEX (ID)" from the GSE3494 series. Keywords: disease state analysis

Project description:Background The dramatic increase in incidence of ductal carcinoma in situ (DCIS) associated with mammographic screening for breast cancer has given emphasis to the challenges of managing this important clinical entity. Unlike invasive breast cancer, there is no established histopathologic grading system for DCIS, nor are there biological markers of prognosis to guide clinical management. The aim of this study is to use molecular profiling to identify robust and clinically applicable indicators of DCIS malignant potential. Methods Areas of intraduct carcinoma, atypical ductal hyperplasia and benign epithelium were isolated from 46 well-characterised invasive breast cancers by laser capture microdissection. Microarray based gene expression profiling was used to identify genes differentially expressed between DCIS associated with grade 1 and grade 3 invasive carcinoma (‘grade associated genes’). The expression profile of these genes was then determined in all samples and used to define ‘molecular grade’ categories. The genomic basis of gene expression derived categories was examined by array-based comparative genomic hybridisation (CGH). Results DCIS samples could be divided into two subgroups, designated low and high molecular grade (MG) on the basis of expression at 173 grade associated oligonucleotide probes. The low MG subgroup included 21 DCIS samples of low (n=10) and intermediate (n=11) nuclear grade as well as all samples of ADH (n=4) and benign epithelium (n=7). The high MG subgroup included 27 DCIS samples of intermediate (n=7) and high (n=19) nuclear grade. Array CGH revealed distinct differences in the character and degree of genomic aberration associated with MG and the clinical significance of MG was verified by a strong correlation with survival in two independent invasive breast cancer gene expression datasets (n=295 and n=186). MG categories were strongly associated with histopathologic and biomarker features of DCIS. Using a classification tree model, DCIS MG could be accurately predicted in 44/46 (95.7%) of samples using a combination of nuclear grade and Ki67 score. Conclusions Molecular profiling indicates a binary grading scheme for DCIS that is both biologically relevant and clinically informative. The low and high MG DCIS classification could be recapitulated by a novel combination of routinely accessible features. This practical approach has potential to immediately improve clinical evaluation and management of DCIS. Keywords: Paired gene expression and CGH Paired CGH and Gene Expression on DCIS of the breast

Project description:Histological grading of breast cancer defines morphological subtypes informative of metastatic potential, although not without considerable inter-observer disagreement and clinical heterogeneity particularly among the moderately differentiated grade II (G2) tumors. We posited that a gene expression signature capable of discerning tumors of grade I (G1) and grade III (G3) histology might provide a more objective measure of grade with prognostic benefit for patients with moderately differentiated disease. To this end, we studied the expression profiles of 347 primary invasive breast tumors analyzed on Affymetrix microarrays. Using class prediction algorithms, we identified 264 robust grade-associated markers, six of which could accurately classify G1 and G3 tumors, and separate G2 tumors into two highly discriminant classes (termed G2a and G2b genetic grades) with patient survival outcomes highly similar to those with G1 and G3 histology, respectively. Statistical analysis of conventional clinical variables further distinguished G2a and G2b subtypes from each other, but also from histologic G1 and G3 tumors. In multivariate analyses, genetic grade was consistently found to be an independent prognostic indicator of disease recurrence comparable to that of lymph node status and tumor size. When incorporated into the Nottingham Prognostic Index, genetic grade enhanced detection of patients with less harmful tumors, likely to benefit little from adjuvant therapy. Our findings show that a genetic grade signature can improve prognosis and therapeutic planning for breast cancer patients, and support the view that low and high grade disease, as defined genetically, reflect independent pathobiological entities rather than a continuum of cancer progression. Three separate breast cancer cohorts were analyzed: 1) Uppsala (n=249), 2) Stockholm (n=58), 3) Singapore (n=40). The Uppsala and Singapore data can be accessed here. The Stockholm cohort data can be accessed at GEO Series GSE1456. Experiment Overall Design: All tumor specimens were assessed on U133 A and B arrays.

Project description:Background The dramatic increase in incidence of ductal carcinoma in situ (DCIS) associated with mammographic screening for breast cancer has given emphasis to the challenges of managing this important clinical entity. Unlike invasive breast cancer, there is no established histopathologic grading system for DCIS, nor are there biological markers of prognosis to guide clinical management. The aim of this study is to use molecular profiling to identify robust and clinically applicable indicators of DCIS malignant potential. Methods Areas of intraduct carcinoma, atypical ductal hyperplasia and benign epithelium were isolated from 46 well-characterised invasive breast cancers by laser capture microdissection. Microarray based gene expression profiling was used to identify genes differentially expressed between DCIS associated with grade 1 and grade 3 invasive carcinoma (‘grade associated genes’). The expression profile of these genes was then determined in all samples and used to define ‘molecular grade’ categories. The genomic basis of gene expression derived categories was examined by array-based comparative genomic hybridisation (CGH). Results DCIS samples could be divided into two subgroups, designated low and high molecular grade (MG) on the basis of expression at 173 grade associated oligonucleotide probes. The low MG subgroup included 21 DCIS samples of low (n=10) and intermediate (n=11) nuclear grade as well as all samples of ADH (n=4) and benign epithelium (n=7). The high MG subgroup included 27 DCIS samples of intermediate (n=7) and high (n=19) nuclear grade. Array CGH revealed distinct differences in the character and degree of genomic aberration associated with MG and the clinical significance of MG was verified by a strong correlation with survival in two independent invasive breast cancer gene expression datasets (n=295 and n=186). MG categories were strongly associated with histopathologic and biomarker features of DCIS. Using a classification tree model, DCIS MG could be accurately predicted in 44/46 (95.7%) of samples using a combination of nuclear grade and Ki67 score. Conclusions Molecular profiling indicates a binary grading scheme for DCIS that is both biologically relevant and clinically informative. The low and high MG DCIS classification could be recapitulated by a novel combination of routinely accessible features. This practical approach has potential to immediately improve clinical evaluation and management of DCIS. Keywords: Paired gene expression and CGH

Project description:Histological grading of breast cancer defines morphological subtypes informative of metastatic potential, although not without considerable inter-observer disagreement and clinical heterogeneity particularly among the moderately differentiated grade II (G2) tumors. We posited that a gene expression signature capable of discerning tumors of grade I (G1) and grade III (G3) histology might provide a more objective measure of grade with prognostic benefit for patients with moderately differentiated disease. To this end, we studied the expression profiles of 347 primary invasive breast tumors analyzed on Affymetrix microarrays. Using class prediction algorithms, we identified 264 robust grade-associated markers, six of which could accurately classify G1 and G3 tumors, and separate G2 tumors into two highly discriminant classes (termed G2a and G2b genetic grades) with patient survival outcomes highly similar to those with G1 and G3 histology, respectively. Statistical analysis of conventional clinical variables further distinguished G2a and G2b subtypes from each other, but also from histologic G1 and G3 tumors. In multivariate analyses, genetic grade was consistently found to be an independent prognostic indicator of disease recurrence comparable to that of lymph node status and tumor size. When incorporated into the Nottingham Prognostic Index, genetic grade enhanced detection of patients with less harmful tumors, likely to benefit little from adjuvant therapy. Our findings show that a genetic grade signature can improve prognosis and therapeutic planning for breast cancer patients, and support the view that low and high grade disease, as defined genetically, reflect independent pathobiological entities rather than a continuum of cancer progression. Three separate breast cancer cohorts were analyzed: 1) Uppsala (n=249), 2) Stockholm (n=58), 3) Singapore (n=40). The Uppsala and Singapore data can be accessed here. The Stockholm cohort data can be accessed at GEO Series GSE1456. Keywords: Tumor sample comparisons

Dataset Information

Homo sapiens

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets