The sinoatrial node (SAN) is the natural pacemaker of the heart and initiates the rhythmic contractions of this organ. Its unique genetic profile is mediated by a network of transcriptional regulators. Among them is the homeodomain transcription factor SHOX2, which plays a major role in maintaining the phenotypic border between the SAN and the surrounding tissue. Mutations in this gene have been associated with early-onset and familial forms of Atrial Fibrillation (AF). AF is the most common cardiac rhythm disorder, affecting 1-2% of the general population. In the clinical context, it often co-exists with malfunctions of the sinus node (sinus node dysfunction, SND), however, it is unknown if both diseases interact, perpetuate, or initiate each other. In the first part of this project, a candidate gene study was combined with functional analyses to identify a causal relationship between novel SHOX2 gene variants and the development of AF and SND. Screening 98 SND patients and 450 individuals with AF led to the identification of four heterozygous variants in SHOX2 (p.P33R in the SND cohort and p.G77D, p.L129=, p.L130F, p.A293= in the AF cohort). We selected mutations based on their in silico predicted pathogenic potential and overexpressed them in embryonic zebrafish hearts. A dominant-negative effect leading to bradycardia and pericardial edema was detected for p.G77D, while no effect was revealed for the p.P33R and p.A293= variants. A significantly impaired transactivation activity for both missense variants p.P33R and p.G77D was demonstrated by in vitro reporter assays. Moreover, upon overexpression of the p.P33R mutant in zebrafish hearts, a reduced Bmp4 target gene expression was revealed. This study demonstrated for the first time a genetic link between SND and AF involving SHOX2. Patient-specific human induced pluripotent stem cells (iPSCs) harboring putative disease-causing variants offer unprecedented opportunities for the investigation of cardiovascular diseases. We generated and characterized iPSCs from patients with previously identified heterozygous SHOX2 mutations (SHOX2 c.849C>A and SHOX2 c.*28T>C). To establish an isogenic control, we developed a novel strategy for the scarless correction of heterozygous mutations. Patient-derived iPSCs were gene-edited with the CRISPR/Cas system and subdivided into small cell pools (sib-selection). We quantified wildtype and mutant alleles via digital PCR and next generation sequencing to detect shifts in the wildtype/mutant allele ratio that indicated the presence of gene-corrected cells. Using this method, we managed to enrich our target cells 8-10-fold before generating a monoclonal cell population via single-cell cloning. The recharacterization of the new lines confirmed a preserved pluripotency and a normal karyotype. Future electrophysiological and molecular analysis will give further insights into the contribution of SHOX2 to onset and progression of AF.
As two leading female cancers, breast cancer, especially metastatic breast cancer, and ovarian cancer, have brought an increasing health and economic burden globally. Biomarkers could improve patient outcomes and quality of life because they play vital roles in cancer screening, diagnosis, prognosis, and prediction. Metabolites are promising cancer biomarkers, as they represent the ultimate phenotypic alteration of the organism and are closely related to cancer. Plasma metabolites can be accessed with minimally invasive procedures. Using plasma metabolites as biomarkers for cancer and other diseases has been widely explored because of the possibility of repeated sampling and periodic monitoring of blood samples. However, metabolic studies are still in their infancy, and only a few studies with large sample sizes are available so far. In this thesis project, we explored the potential of metabolites as putative diagnostic and prognostic markers in breast and ovarian cancer. Plasma metabolite profiling and subsequent validation in primary breast cancer patients and healthy controls identified 18 metabolites that were significantly differentially represented (FDR < 0.05). Multivariate logistic regression analysis selected a panel of seven metabolites to discriminate primary breast cancer patients from healthy controls with an AUC of 0.80. If this panel of metabolites identified here could be verified in large prospective study cohorts, this panel, including Glu, Orn, Thr, Trp, Met-SO, C2, and C3, might add value to multi-molecular diagnostic marker sets for breast cancer early detection. The association of plasma metabolites with metastatic breast cancer was investigated as well. Metastatic breast cancer patients with high numbers of circulating tumor cells (termed CTC-positive) and those with low numbers or without CTCs (termed CTC-negative) were analyzed and compared to healthy controls as well as primary breast cancer patients. Lists of 19 and 12 metabolites were identified to significantly distinguish CTC-positive and CTC-negative samples from healthy controls, respectively. A panel comprising His, C4:0, C18:1, lysoPC a C18:2, PC aa C40:6, and PC ae C42:3 for CTC-positive patients with AUC = 0.92, and a combination of Asn, Glu, His, Thr, Trp, C16:0, C18:0, C18:1, C18:2, lysoPC a C18:2, and PC aa C40:6 for CTC-negative patients with AUC = 0.89 were selected to distinguish from healthy controls. Significantly different metabolites between CTC-positive/CTC-negative and primary breast cancer patients exhibited significant overlaps with those between CTC-positive/CTC-negative patients and healthy controls. We also investigated the prognostic value of metabolites in metastatic breast cancer patients. After successive analysis of the discovery and validation cohorts, four metabolites were found to be significantly negatively correlated with progression-free survival, while 12 metabolites were negatively correlated with overall survival. Amongst these metabolites associated with survival, LASSO Cox regression analysis selected a combination of PC ae C36:1 and PC ae C38:3 to predict progression-free survival, and a combination of lysoPC a C20:3, lysoPC a C20:4, PC aa C38:5, PC ae C38:3, and SM (OH) C22:2 to predict overall survival. Even though the proposed metabolic signatures showed a lower prognostic power than the CTC status, an FDA-approved prognostic marker, the combination of the Cox selected metabolites with the CTC status displayed a lower integrated prediction error than CTC status alone. Therefore, the identified metabolic markers might add prognostic value in combination with other biomarkers such as CTC status determination. The majority of the here identified metabolites have previously shown functional roles in cancer and metastasis development, thus laying a supposed mechanistic basis for their differential levels observed in plasma. Lastly, comparative profiling of plasma metabolites in ovarian cancer patients and healthy controls were applied to identify metabolites associated with ovarian cancer. Remarkably, 71 significantly differentially expressed metabolites were identified (FDR < 0.05). Most of them were down-regulated in ovarian cancer patients. A combination of seven metabolites, including His, Trp, C18:1, lysoPC a C18:2, PC aa C32:2, PC aa C34:4, PC ae C34:3, were identified to differentiate ovarian cancer cases from healthy controls with an AUC of 0.95. Furthermore, this panel could distinguish ovarian cancer from primary breast cancer patients with an AUC of 0.93. In conclusion, we identified specific signatures of plasma metabolites associated with primary breast cancer, metastatic breast cancer, and ovarian cancer. Further, we identified sets of metabolites correlated with the prognosis of metastatic breast cancer patients. If these identified metabolic marker signatures can be verified in large, multi-centric, prospective studies, they might add value to the development of blood-based diagnostic tests.