Dataset Information

Gene expression profiles of breast cancer metastasis according to organ site.

ABSTRACT: In advanced breast cancer, biomarker identification and patient selection using a metastatic tumor biopsy is becoming more necessary. However, the biology of metastasis according to the organ site is largely unknown. Here, we evaluated the expression of 771 genes in 184 metastatic samples across 11 organs, including liver, lung, brain, and bone, and made the following observations. First, all PAM50 molecular intrinsic subtypes were represented across organs and within immunohistochemistry-based groups. Second, HER2-low disease was identified across all organ sites, including bone, and HER2 expression significantly correlated with ERBB2 expression. Third, the majority of expression variation was explained by intrinsic subtype and not organ of metastasis. Fourth, subtypes and individual subtype-related genes/signatures were significantly associated with overall survival. Fifth, we identified 74 genes whose expression was organ-specific and subtype-independent. Finally, immune profiles were found more expressed in lung compared to brain or liver metastasis. Our results suggest that relevant tumor biology can be captured in metastatic tissues across a variety of organ sites; however, unique biological features according to organ site were also identified and future studies should explore their implications in diagnostic and therapeutic interventions.

SUBMITTER: Braso-Maristany F

PROVIDER: S-EPMC8732356 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Gene expression profiles of breast cancer metastasis according to organ site.

Brasó-Maristany Fara F Paré Laia L Chic Nuria N Martínez-Sáez Olga O Pascual Tomás T Mallafré-Larrosa Meritxell M Schettini Francesco F González-Farré Blanca B Sanfeliu Esther E Martínez Débora D Galván Patricia P Barnadas Esther E Salinas Belinda B Tolosa Pablo P Ciruelos Eva E Carcelero Esther E Guillén Cecilia C Adamo Barbara B Moreno Reinaldo R Vidal Maria M Muñoz Montserrat M Prat Aleix A

Molecular oncology 20210623 1

In advanced breast cancer, biomarker identification and patient selection using a metastatic tumor biopsy is becoming more necessary. However, the biology of metastasis according to the organ site is largely unknown. Here, we evaluated the expression of 771 genes in 184 metastatic samples across 11 organs, including liver, lung, brain, and bone, and made the following observations. First, all PAM50 molecular intrinsic subtypes were represented across organs and within immunohistochemistry-based ...[more]

PMID: 34051058

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Project description:BackgroundVisceral organ metastasis is associated with poor survival outcomes in terms of metastasis free- and overall survival in breast carcinomas. Identification of a gene expression profile in tumours that selects a subpopulation of patients that is more likely to develop visceral organ metastases will help elucidate mechanisms for the development of distant metastases and could be of clinical value. With this study we aimed to determine genomic predictors that would help to distinguish breast cancer patients with more likelihood to develop visceral metastasis.MethodsGene expression profiling data of 157 primary tumours from breast cancer patients who developed distant metastases were analyzed and differentially expressed genes between the group of tumours with visceral metastasis and the those without visceral metastases were identified. Published data were used to validate our findings. Multivariate logistic regression tests were applied to further investigate the association between the gene-expression-signature and clinical variables. Survival analyses were performed by the Kaplan-Meier method.ResultsFourteen differentially expressed genes (WDR6, CDYL, ATP6V0A4, CHAD, IDUA, MYL5, PREP, RTN4IP1, BTG2, TPRG1, ABHD14A, KIF18A, S100PBP and BEND3) were identified between the group of tumours with and without visceral metastatic disease. Five of these genes (CDYL, ATP6V0A4, PREP, RTN4IP1 and KIF18A) were up-regulated and the other genes were down-regulated. This gene expression signature was validated in the training and in the independent data set (p 2.13e- 08 and p 9.68e- 06, respectively). Multivariate analyses revealed that the 14-gene-expression-signature was associated with visceral metastatic disease (p 0.001, 95% CI 1.43-4.27), independent of other clinicopathologic features. This signature has been also found to be associated with survival status of the patients (p < .001).ConclusionWe have identified an unique gene expression signature which is specific to visceral metastasis. This 14-gene-expression-signature may play a role in identifying the subgroup of patients with potential to develop visceral metastasis.

Project description:BACKGROUND:The popularity of a large number of microarray applications has in cancer research led to the development of predictive or prognostic gene expression profiles. However, the diversity of microarray platforms has made the full validation of such profiles and their related gene lists across studies difficult and, at the level of classification accuracies, rarely validated in multiple independent datasets. Frequently, while the individual genes between such lists may not match, genes with same function are included across such gene lists. Development of such lists does not take into account the fact that genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location. Such MGs might be used as features in building a predictive model applicable for classifying independent data. It is, therefore, demanding to systematically compare independent validation of gene lists or classifiers based on metagene or individual gene (SG) features. METHODS:In this study we compared the performance of either metagene-or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance within the same dataset, feature set validation performance, and validation performance of entire classifiers in strictly independent datasets were assessed by 10 times repeated 10-fold cross validation, leave-one-out cross validation, and one-fold validation, respectively. To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach. RESULTS:MG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome in strictly independent data sets, both between different and within similar microarray platforms, while classifiers had a poorer performance when validated in strictly independent datasets. The study showed that MG- and SG-feature sets perform equally well in classifying independent data. Furthermore, SG-classifiers significantly outperformed MG-classifier when validation is conducted between datasets using similar platforms, while no significant performance difference was found when validation was performed between different platforms. CONCLUSION:Prediction of metastasis outcome in lymph node-negative patients by MG- and SG-classifiers showed that SG-classifiers performed significantly better than MG-classifiers when validated in independent data based on the same microarray platform as used for developing the classifier. However, the MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform. The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.

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Gene expression profiles of breast cancer metastasis according to organ site.

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Gene expression profiles of breast cancer metastasis according to organ site.

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