Microarray-based molecular subtyping of breast cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Kao KJ
PROVIDER: S-ECPF-GEOD-20685 | biostudies-other | 2011
REPOSITORIES: biostudies-other
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Project description:The primary goal of this study is to identify molecular subtypes of breast cancer through gene expression profiles of 327 breast cancer samples and determine molecular and clinical characteristics of different breast cancer subtypes. We studied expression signatures of different cellular functions (e.g., cell proliferation/cell cycle, wound response, tumor stromal response, vascular endothelial normalization, drug esponse genes, etc.) in different breast cancer molecular subtypes and investigated how microarray-based breast cancer molecular subtypes may be used to guide treatment.
2011-04-24 | GSE20685 | GEO
Project description:The primary goal of this study is to identify molecular subtypes of breast cancer through gene expression profiles of 327 breast cancer samples and determine molecular and clinical characteristics of different breast cancer subtypes. We studied expression signatures of different cellular functions (e.g., cell proliferation/cell cycle, wound response, tumor stromal response, vascular endothelial normalization, drug esponse genes, etc.) in different breast cancer molecular subtypes and investigated how microarray-based breast cancer molecular subtypes may be used to guide treatment. Gene expression profiles of 327 breast cancer samples were determined using total RNA and Affymetrix U133 plus 2.0 arrays.
2011-04-24 | E-GEOD-20685 | biostudies-arrayexpress
Project description:Breast cancer intrinsic subtypes have been identified based on the transcription of a predefined gene expression (GE) profiles and algorithm (prediction analysis of microarray 50 gene set, PAM50). The present study compared molecular subtyping with oligonucleotide microarray and NanoString nCounter assay. A total of 109 Taiwanese breast cancers (24 with adjacent normal breast tissues) were assayed with Affymetrix Human Genome U133 plus 2.0 microarrays and 144 were assayed with the NanoString nCounter while 64 patients were assayed for both platforms. Subtyping with the nearest centroid (single sample prediction (SSP)) was performed, and 16 out of 24 (67%) matched normal breasts were categorized as the normal breast-like subtype. For 64 breast cancers assayed for both platforms, 41 (65%, one unclassified by microarray) were predicted with an identical subtype, resulting in a fair κ statistic of 0.60. Taking nCounter subtyping as the gold standard, prediction accuracy was 43% (3/7), 81% (13/16), 25% (5/20), and 100% (20/20) for basal-like, human epidermal growth factor receptor II (HER2)-enriched, luminal A and luminal B subtypes predicted from microarray GE profiles. Microarray identified more luminal B cases from luminal A subtype predicted by nCounter. It is not uncommon to use microarray for breast cancer molecular subtyping for research. Our study showed that fundamental discrepancy existed between distinct GE assays, and cross-platform equivalence should be carefully appraised when molecular subtyping was conducted with oligonucleotide microarray.
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Project description:Molecular subtyping of breast cancer is of considerable interest owing to its potential for personalized therapy and prognosis. However, current methodologies cannot be used for precise subtyping, thereby posing a challenge in clinical practice. The aim of the present study is to develop a cell-specific single-stranded DNA (ssDNA) aptamer-based fluorescence probe for molecular subtyping of breast cancer. Methods: Cell-SELEX method was utilized to select DNA aptamers. Flow cytometry and confocal microscopy were used to study the specificity, binding affinity, temperature effect on the binding ability and target type analysis of the aptamers. In vitro and in vivo fluorescence imaging were used to distinguish the molecular subtypes of breast cancer cells, tissue sections and tumor-bearing mice. Results: Six SK-BR-3 breast cancer cell-specific ssDNA aptamers were evolved after successive in vitro selection over 21 rounds by Cell-SELEX. The Kd values of the selected aptamers were all in the low-nanomolar range, among which aptamer sk6 showed the lowest Kd of 0.61 ± 0.14 nM. Then, a truncated aptamer-based probe, sk6Ea, with only 53 nt and high specificity and binding affinity to the target cells was obtained. This aptamer-based probe was able to 1) differentiate SK-BR-3, MDA-MB-231, and MCF-7 breast cancer cells, as well as distinguish breast cancer cells from MCF-10A normal human mammary epithelial cells; 2) distinguish HER2-enriched breast cancer tissues from Luminal A, Luminal B, triple-negative breast cancer tissues, and adjacent normal breast tissues (ANBTs) in vitro; and 3) distinguish xenografts of SK-BR-3 tumor-bearing mice from those of MDA-MB-231 and MCF-7 tumor-bearing mice within 30 min in vivo. Conclusion: The results suggest that the aptamer-based probe is a powerful tool for fast and highly sensitive subtyping of breast cancer both in vitro and in vivo and is also very promising for the identification, diagnosis, and targeted therapy of breast cancer molecular subtypes.
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Project description:Breast cancer is a highly heterogeneous disease that can be classified into multiple subtypes based on the tumor transcriptome. Most of the subtyping schemes used in clinics today are derived from analyses of microarray data from thousands of different tumors together with clinical data for the patients from which the tumors were isolated. However, RNA sequencing (RNA-Seq) is gradually replacing microarrays as the preferred transcriptomics platform, and although transcript abundances measured by the two different technologies are largely compatible, subtyping methods developed for probe-based microarray data are incompatible with RNA-Seq as input data. Here, we present an RNA-Seq data processing pipeline, which relies on the mapping of sequencing reads to the probe set target sequences instead of the human reference genome, thereby enabling probe-based subtyping of breast cancer tumor tissue using sequencing-based transcriptomics. By analyzing 66 breast cancer tumors for which gene expression was measured using both microarrays and RNA-Seq, we show that RNA-Seq data can be directly compared to microarray data using our pipeline. Additionally, we demonstrate that the established subtyping method CITBCMST (Guedj et al., ), which relies on a 375 probe set-signature to classify samples into the six subtypes basL, lumA, lumB, lumC, mApo, and normL, can be applied without further modifications. This pipeline enables a seamless transition to sequencing-based transcriptomics for future clinical purposes.
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Project description:Breast cancer is commonly classified into intrinsic molecular subtypes. Standard gene centering is routinely done prior to molecular subtyping, but it can produce inaccurate classifications when the distribution of clinicopathological characteristics in the study cohort differs from that of the training cohort used to derive the classifier.We propose a subgroup-specific gene-centering method to perform molecular subtyping on a study cohort that has a skewed distribution of clinicopathological characteristics relative to the training cohort. On such a study cohort, we center each gene on a specified percentile, where the percentile is determined from a subgroup of the training cohort with clinicopathological characteristics similar to the study cohort. We demonstrate our method using the PAM50 classifier and its associated University of North Carolina (UNC) training cohort. We considered study cohorts with skewed clinicopathological characteristics, including subgroups composed of a single prototypic subtype of the UNC-PAM50 training cohort (n = 139), an external estrogen receptor (ER)-positive cohort (n = 48) and an external triple-negative cohort (n = 77).Subgroup-specific gene centering improved prediction performance with the accuracies between 77% and 100%, compared to accuracies between 17% and 33% from standard gene centering, when applied to the prototypic tumor subsets of the PAM50 training cohort. It reduced classification error rates on the ER-positive (11% versus 28%; P = 0.0389), the ER-negative (5% versus 41%; P < 0.0001) and the triple-negative (11% versus 56%; P = 0.1336) subgroups of the PAM50 training cohort. In addition, it produced higher accuracy for subtyping study cohorts composed of varying proportions of ER-positive versus ER-negative cases. Finally, it increased the percentage of assigned luminal subtypes on the external ER-positive cohort and basal-like subtype on the external triple-negative cohort.Gene centering is often necessary to accurately apply a molecular subtype classifier. Compared with standard gene centering, our proposed subgroup-specific gene centering produced more accurate molecular subtype assignments in a study cohort with skewed clinicopathological characteristics relative to the training cohort.
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