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Gene expression profiles of the standardized uptake value in 18F-fluorodeoxyglucose positron emission tomography in tripple negative breast cancer

ABSTRACT: The standardized uptake value (SUV), an indicator of the glucose uptake degree in 18F-fluorodeoxyglucose positron emission tomography (FDG-PET), has been used as a prognostic factor in malignant tumors. We aimed to identify a signature reflecting prognostic SUV characteristics in triple negative breast cancer (TNBC). Transcriptome profiling was performed to identify a signature associated with the SUV in TNBC patients who underwent preoperative FDG-PET. We defined a signature significantly associated with the SUV (|r| > .35; P < .01). The SUV signature showed independent clinical utility for predicting BRC prognosis. Integrative analysis demonstrated a significance of the signature in predicting the response to immunotherapy and revealed that a signaling axis defined by TP53-FOXM1 and its downstream effectors in glycolysis-gluconeogenesis, including LDHA, might be important mediators in the FDG-PET process. Our results reveal characteristics of glucose uptake captured by FDG-PET, supporting an understanding of glucose metabolism as well as poor prognosis in TNBC patients with a high SUV.

ORGANISM(S): Homo sapiens

PROVIDER: GSE135565 | GEO | 2020/03/18

REPOSITORIES: GEO

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Project description:Fluorine-18-fluoro-2-deoxy-D-glucose (FDG) is widely used as positron-emission-tomography (PET) radiotracer for the detection and staging of human cancer. Tumor uptake of FDG varies substantially between different cancer types and between patients with the same tumor type. The molecular basis for this heterogeneity is unknown. Using cancer cell lines and primary human tumors of distinct histologic origins, we here show that increased FDG uptake is universally associated with coordinate upregulation of genes within the glycolysis, pentose-phosphate, and other related metabolic pathways. In primary human breast cancers, this FDG signature shows significant overlap with established breast cancer signatures for the “basal-like” disease subtype and “poor prognosis”. FDG high breast cancer showed significantly more gene copy number alterations genome wide than FDG low cancers. About 50 % of primary breast cancers with high FDG uptake and FDG gene expression signature show DNA copy gain encompassing the c-myc gene locus and express gene sets regulated by the transcription factor MYC. Our data shows that FDG-PET marks a distinct subset of “basal-like” human breast cancer which is characterized by MYC and prognostically unfavorable gene expression signatures, suggesting that FDG-PET imaging may be useful to risk-stratify patients with locally advanced breast cancer. Our general strategy to identify molecular determinants of FDG-retention through a genome-wide approach consisted of FDG uptake measurements in cancer cell lines and primary human tumors, the selection of samples with particularly high versus low FDG-retention, and subsequent pathway-based analysis of RNA expression data collected from these samples. Cell line RNA was extracted from a 10 cm plate seeded simultaneously and at the same density as the wells for FDG-uptake assays. For primary human tumor samples, RNA was extracted from macrodissected frozen tumor tissue. All cell line RNA samples and the astrocytoma RNA aliquots were hybridized to Affymetrix U133 Plus 2.0 arrays. All primary breast cancer RNA samples were hybridized to Affymetrix U133A arrays.

Project description:Purpose: Building a universal genomic signature predicting the intensity of FDG uptake in diverse metastatic tumors may allow us to understand better the biological processes underlying this phenomenon and their requirements of glucose uptake. Methods: A balanced training set (n=71) of metastatic tumors including some of the most frequent histologies, with matched PET/CT quantification measurements and whole human genome gene expression microarrays, was used to build the signature. Selection of microarray features was carried out exclusively on the basis of their strong association with FDG uptake (as measured by SUVmean35) by means of univariate linear regression. A thorough bioinformatics study of these genes was performed and multivariable models were built by fitting several state of the art regression techniques to the training set for comparison. Results: The 909 probes with the strongest association with the SUVmean35 (comprising 742 identifiable genes and 62 probes not matched to a symbol) were used to build the signature. Partial Least Squares using 3 components (PLS-3) was the best performing model in the training dataset cross-validation (Root Mean Square Error, RMSE=0.443) and was validated further in an independent validation dataset (n=13) obtaining a performance within the 95% CI of that obtained in the training dataset (RMSE=0.645). Significantly overrepresented biological processes correlating with the SUVmean35 were identified beyond glycolysis, such as ribosome biogenesis and DNA replication (correlating with a higher SUVmean35), and cytoskeleton reorganization and autophagy (correlating with a lower SUVmean35), among others. Conclusions: PLS-3 is a signature predicting accurately the intensity of FDG uptake in diverse metastatic tumors. FDG-PET might help in the design of specific targeted therapies directed to counteract the identified malignant biological processes more likely activated in a tumor as inferred from the SUVmean35 and also from its variations in response to antineoplastic treatments.

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Gene expression profiles of the standardized uptake value in 18F-fluorodeoxyglucose positron emission tomography in tripple negative breast cancer

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