Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes

Background: The heterogeneous biology of breast cancer leads to high diversity in prognosis and response to treatment, even for patients with similar clinical diagnosis, histology, and stage of disease. Identifying mechanisms contributing to this heterogeneity may reveal new cancer targets or clinically relevant subgroups for treatment stratification. In this study, we have merged metabolite, protein, and gene expression data from breast cancer patients to examine the heterogeneity at a molecular level.Methods: The study included primary tumor samples from 228 non-treated breast cancer patients. High-resolution magic-angle spinning magnetic resonance spectroscopy (HR MAS MRS) was performed to extract the tumors metabolic profiles further used for hierarchical cluster analysis resulting in three significantly different metabolic clusters (Mc1, Mc2, and Mc3). The clusters were further combined with gene and protein expression data.Results: Our result revealed distinct differences in the metabolic profile of the three metabolic clusters. Among the most interesting differences, Mc1 had the highest levels of glycerophosphocholine (GPC) and phosphocholine (PCho), Mc2 had the highest levels of glucose, and Mc3 had the highest levels of lactate and alanine. Integrated pathway analysis of metabolite and gene expression data uncovered differences in glycolysis/gluconeogenesis and glycerophospholipid metabolism between the clusters. All three clusters had significant differences in the distribution of protein subtypes classified by the expression of breast cancer-related proteins. Genes related to collagens and extracellular matrix were downregulated in Mc1 and consequently upregulated in Mc2 and Mc3, underpinning the differences in protein subtypes within the metabolic clusters. Genetic subtypes were evenly distributed among the three metabolic clusters and could therefore contribute to additional explanation of breast cancer heterogeneity.Conclusions: Three naturally occurring metabolic clusters of breast cancer were detected among primary tumors from non-treated breast cancer patients. The clusters expressed differences in breast cancer-related protein as well as genes related to extracellular matrix and metabolic pathways known to be aberrant in cancer. Analyses of metabolic activity combined with gene and protein expression provide new information about the heterogeneity of breast tumors and, importantly, the metabolic differences infer that the clusters may be susceptible to different metabolically targeted drugs

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs

Drug-screening and genomic analyses of HER2-positive breast cancer cell lines reveal predictors for treatment response

Sandra Jernström,1,2 Vesa Hongisto,3 Suvi-Katri Leivonen,1,2 Eldri Undlien Due,1 Dagim Shiferaw Tadele,1 Henrik Edgren,4,5 Olli Kallioniemi,4 Merja Perälä,6 Gunhild Mari Mælandsmo,2,7,8 Kristine Kleivi Sahlberg,1,9 1Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 2KG Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway; 3Misvik Biology Oy, Turku, 4Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 5Medisapiens, Helsinki, Finland, 6VTT Technical Research Centre of Finland, Turku, Finland; 7Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; 8Institute of Pharmacy, Faculty of Health Sciences, University of Tromsø, Tromsø, 9Department of Research, Vestre Viken Hospital Trust, Drammen, Norway Background: Approximately 15%–20% of all diagnosed breast cancers are characterized by amplified and overexpressed HER2 (= ErbB2). These breast cancers are aggressive and have a poor prognosis. Although improvements in treatment have been achieved after the introduction of trastuzumab and lapatinib, many patients do not benefit from these drugs. Therefore, in-depth understanding of the mechanisms behind the treatment responses is essential to find alternative therapeutic strategies.  Materials and methods: Thirteen HER2 positive breast cancer cell lines were screened with 22 commercially available compounds, mainly targeting proteins in the ErbB2-signaling pathway, and molecular mechanisms related to treatment sensitivity were sought. Cell viability was measured, and treatment responses between the cell lines were compared. To search for response predictors and genomic and transcriptomic profiling, PIK3CA mutations and PTEN status were explored and molecular features associated with drug sensitivity sought.  Results: The cell lines were divided into three groups according to the growth-retarding effect induced by trastuzumab and lapatinib. Interestingly, two cell lines insensitive to trastuzumab (KPL4 and SUM190PT) showed sensitivity to an Akt1/2 kinase inhibitor. These cell lines had mutation in PIK3CA and loss of PTEN, suggesting an activated and druggable Akt-signaling pathway. Expression levels of five genes (CDC42, MAPK8, PLCG1, PTK6, and PAK6) were suggested as predictors for the Akt1/2 kinase-inhibitor response.  Conclusion: Targeting the Akt-signaling pathway shows promise in cell lines that do not respond to trastuzumab. In addition, our results indicate that several molecular features determine the growth-retarding effects induced by the drugs, suggesting that parameters other than HER2 amplification/expression should be included as markers for therapy decisions. Keywords: ErbB2, drug screening, gene expression, pharmacogenomics, predictor

STARD3: A Lipid Transfer Protein in Breast Cancer and Cholesterol Trafficking

International audienceSTARD3 was isolated in the early 1990s in a study aimed at finding new genes implicated in breast cancer. The function of the STARD3 gene, referred to at that time as Metastatic Lymph Node clone number 64 (MLN64), remained a mystery until the discovery of the steroidogenic acute regulatory protein (StAR/STARD1). Indeed, homology searches showed a region of significant similarity between StAR and the carboxy-terminal half of STARD3. This homology proved to be functionally relevant with both proteins being cholesterol carriers; however, quite early it appeared that they were very distinct in terms of expression, subcellular localization, and function. It was then reported that STARD3 was part of a family of 15 human proteins that shared a conserved StAR-related lipid transfer (START) domain. Structurally, the STARD3 protein distinguishes itself by the presence of an additional conserved domain spanning the amino-terminal half of the protein that we named the MLN64-N-terminal (MENTAL) domain. This domain contains most of the functional properties that have been attributed to STARD3. This chapter will present our current understanding of STARD3 function in cancer, cell biology, and cholesterol trafficking