Dataset Information

Autophagy is associated with a robust specific transcriptional signature in breast cancer subtypes.

ABSTRACT: Previous works have described that autophagy could be associated to both pro- and anti-cancer properties according to numerous factors, such as the gene considered, the step of autophagy involved or the cancer model used. These data might be explained by the fact that some autophagy-related genes may be involved in other cellular processes and therefore differently regulated according to the type or the grade of the tumor. Indeed, using different approaches of transcriptome analysis in breast cancers, and further confirmation using digital PCR, we identified a specific signature of autophagy gene expression associated to Luminal A or Triple Negative Breast Cancers (TNBC). Moreover, we confirmed that ATG5, an autophagy gene specifically expressed in TNBC, favored cell migration, whereas BECN1, an autophagy gene specifically associated with ER-positive breast cancers, induced opposite effects. We also showed that overall inhibition of autophagy promoted cell migration suggesting that the role of individual ATG genes in cancer phenotypes was not strictly dependent of their function during autophagy. Finally, our work led to the identification of TXNIP1 as a potential biomarker associated to autophagy induction in breast cancers. This gene could become an essential tool to quantify autophagy levels in fixed biopsies, sort tumors according to their autophagy levels and determine the best therapeutic treatment.

SUBMITTER: Grandvallet C

PROVIDER: S-EPMC7805539 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Autophagy is associated with a robust specific transcriptional signature in breast cancer subtypes.

Grandvallet Céline C Feugeas Jean Paul JP Monnien Franck F Despouy Gilles G Valérie Perez P Michaël Guittaut G Hervouet Eric E Peixoto Paul P

Genes & cancer 20201006 3-4

Previous works have described that autophagy could be associated to both pro- and anti-cancer properties according to numerous factors, such as the gene considered, the step of autophagy involved or the cancer model used. These data might be explained by the fact that some autophagy-related genes may be involved in other cellular processes and therefore differently regulated according to the type or the grade of the tumor. Indeed, using different approaches of transcriptome analysis in breast ca ...[more]

PMID: 33488952

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Project description:Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs).We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates.Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients.RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment.

Dataset Information

Autophagy is associated with a robust specific transcriptional signature in breast cancer subtypes.

Publications

Autophagy is associated with a robust specific transcriptional signature in breast cancer subtypes.

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