The oncogene AAMDC links PI3K-AKT-mTOR signaling with metabolic reprograming in estrogen receptor-positive breast cancer

Adipogenesis associated Mth938 domain containing (AAMDC) represents an uncharacterized oncogene amplified in aggressive estrogen receptor-positive breast cancers. We uncover that AAMDC regulates the expression of several metabolic enzymes involved in the one-carbon folate and methionine cycles, and lipid metabolism. We show that AAMDC controls PI3K-AKT-mTOR signaling, regulating the translation of ATF4 and MYC and modulating the transcriptional activity of AAMDC-dependent promoters. High AAMDC expression is associated with sensitization to dactolisib and everolimus, and these PI3K-mTOR inhibitors exhibit synergistic interactions with anti-estrogens in IntClust2 models. Ectopic AAMDC expression is sufficient to activate AKT signaling, resulting in estrogen-independent tumor growth. Thus, AAMDC-overexpressing tumors may be sensitive to PI3K-mTORC1 blockers in combination with anti-estrogens. Lastly, we provide evidence that AAMDC can interact with the RabGTPase-activating protein RabGAP1L, and that AAMDC, RabGAP1L, and Rab7a colocalize in endolysosomes. The discovery of the RabGAP1L-AAMDC assembly platform provides insights for the design of selective blockers to target malignancies having the AAMDC amplification.


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April 2020

Data analysis
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Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: Reporting for specific materials, systems and methods Image processing and quantification were performed using NIS-Elements AR (Version 4.13) (Nikon Corporation, Tokyo, Japan) and ImageJ (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, https://imagej.nih.gov/ij/, 1997-2018). DAVID (v6.8) was used for gene ontology functional annotation analysis. Structure analysis was performed using PyMOL (v2.2) (Schrödinger) and modelling was performed using Phyre2 (v2.0) (Structural Bioinformatics Group, Imperial College, London). Combination index determination was performed using the median dose effect method proposed by Chou and Talalay with the CompuSyn software (ComboSyn). TMA visualization was performed using Aperio ImageScope Pathology Slide Viewing Software (v12.3.3) (Leica Biosystem, Nussloch, GmbH). Network analysis and KEGG pathways map of differentially regulated targets were performed by STRING database (v11) (http://string-db.org). RNA-sequencing data has been deposited in the Gene Expression Omnibus public database under accession numbers GSE92893 and GSE123740. All repository data has been made publicly available (https://github.com/jcursons/Golden_2021_NatComm). Analysis of somatic alterations for the AAMDC gene in Fig. 1a was performed using cancer genomic data sets and tools from cBioPortal (https://www.cbioportal.org/). Survival analyses of cancer patients with high and low expression levels of AAMDC (Fig. 1b) is performed in the PPISURV portal http://www.bioprofiling.de/GEO/PPISURV/ ppisurvD.html). The survival analyses of breast, ovarian and lung cancer patients were performed by using the GSE11121, GSE13876, and GSE19188 GEO datasets, respectively. Survival of Luminal B patients treated with tamoxifen with high and low expression of AAMDC was compared using Kaplan Meier Plotter server (https://kmplot.com/analysis/) and the GEO datasets: GSE12093, GSE16391, GSE17705, GSE19615, GSE26971, GSE2990, GSE3494, GSE37946, GSE45255, GSE6532, and GSE9195. GraphPad Prism (v8.4) was used for graphing and statistical analysis. For RNA-seq data shown in Fig. 3, sequenced reads were aligned to human (hg19) genome using TopHat (v2.0.14) and expression at the gene level (FPKM values) was estimated and normalized by Cufflinks (v2.2.1), Cuffmerge (v1.0.0) and Cuffnorm (v2.2.1). Differential gene expression analysis was performed using Cuffdiff (v2.2.1), with significant changes in gene expression determined using a qvalue <0.05 in three biological replicates. For RNA-seq data shown in Fig. 5, sequenced reads underwent pseudo-alignment against the GRCh38 (Ensembl) reference genome and quantification using Salmon (v0.8.2). Data were imported into R (v3.5) using the Bioconductor package tximport (v1.12.3) and collapsed to the gene-level for differential expression analysis using DESeq2 (v1.24.0). Results were visualized with python (v3.6) using the matplotlib (v3. For animal studies 8 mice/group was used to determine statistical significance. This is calculated based on the sample size power calculator where the type 1 (alpha) and type 2 (beta) probability are set to 0.05 and 0.2 accordingly.
Data were not excluded except in cases of technical error.
At least three biological replicates were performed with successful outcomes.
The phenotype of the engineered cell lines was visually obvious. Only the mouse xenograft study was randomized. Mice were randomly assigned to the specific groups indicated in the manuscript.
Blinding was not performed for these studies since it required the generation of genetically engineered cell lines. Validation of these genetically engineered cell lines was necessary in order to confirm the downregulation of the gene before injecting in mice in accordance to the Animal Ethics approval. Also, the phenotype of the engineered cell lines was visually obvious.

April 2020
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