Högskolan i Skövde

Induced pluripotent stem cells (iPSCs) have significant potential for disease modeling and cell therapies. However, their wide-spread application has faced challenges, including batch-to-batch variabilities, and notable distinctions when compared to embryonic stem cells (ESCs). Some of these disparities can stem from using undefined culture conditions and the reprogramming procedure, however, the precise mechanisms remain understudied. Here, we compared gene expression data from over 100 iPSC and ESC lines cultivated under undefined and defined conditions. Defined conditions significantly reduced inter-PSC line variability, irrespective of PSC cell type, highlighting the importance of standardization to minimize PSC biases. This variability is concurrent with decreased somatic cell marker and germ layer differentiation gene expression and increased Ca²⁺-binding protein expression. Moreover, SERCA pump inhibition highlighted an important role for intracellular Ca²⁺ activity in maintaining pluripotency gene expression under defined conditions. Further understanding of these processes can help standardize and improve defined hPSC culture conditions.

Making breast cancer prognosis from gene expression profiles of the primary tumor has become a promising application of deep learning. Yet, to be relevant to real world applications in the clinic and for knowledge discovery, these models must be robust to common distribution shifts. In this study, we evaluate recently proposed methods for improving domain and subgroup shifts. We test the in-distribution and out-of-distribution generalization of multiple episode learning, stochastic weight averaging, group distributionally robust optimization, and a subsampling scheme on one training and four external breast cancer prognosis datasets. The evaluation found that the methods can, to various degrees, improve generalization across domains, although there remain, partially high, generalization gaps. Additionally, in-distribution and out-of-distribution generalization differs between clinical subtypes of breast cancer. Thus, we conclude that further research into methods specifically addressing challenges in breast cancer prognosis from gene expression data are warranted. 

STUDY QUESTION: Can the alleged association between ovarian endometriosis and ovarian carcinoma be substantiated by genetic analysis of endometriosis diagnosed prior to the onset of the carcinoma?

SUMMARY ANSWER: The data suggest that ovarian carcinoma does not originate from ovarian endometriosis with a cancer-like genetic profile; however, a common precursor is probable.

WHAT IS KNOWN ALREADY: Endometriosis has been implicated as a precursor of ovarian carcinoma based on epidemiologic studies and the discovery of common driver mutations in synchronous disease at the time of surgery. Endometrioid ovarian carcinoma and clear cell ovarian carcinoma are the most common endometriosis-associated ovarian carcinomas (EAOCs).

STUDY DESIGN, SIZE, DURATION: The pathology biobanks of two university hospitals in Sweden were scrutinized to identify women with surgically removed endometrioma who subsequently developed ovarian carcinoma (1998-2016). Only 45 archival cases with EAOC and previous endometriosis were identified and after a careful pathology review, 25 cases were excluded due to reclassification into non-EAOC (n = 9) or because ovarian endometriosis could not be confirmed (n = 16). Further cases were excluded due to insufficient endometriosis tissue or poor DNA quality in either the endometriosis, carcinoma, or normal tissue (n = 9). Finally 11 cases had satisfactory DNA from all three locations and were eligible for further analysis.

PARTICIPANTS/MATERIALS, SETTING, METHODS: Epithelial cells were collected from formalin-fixed and paraffin-embedded (FFPE) sections by laser capture microdissection (endometrioma n = 11) or macrodissection (carcinoma n = 11) and DNA was extracted. Normal tissue from FFPE sections (n = 5) or blood samples collected at cancer diagnosis (n = 6) were used as the germline controls for each included patient. Whole-exome sequencing was performed (n = 33 samples). Somatic variants (single-nucleotide variants, indels, and copy number alterations) were characterized, and mutational signatures and kataegis were assessed. Microsatellite instability and mismatch repair status were confirmed with PCR and immunohistochemistry, respectively.

MAIN RESULTS AND THE ROLE OF CHANCE: The median age for endometriosis surgery was 42 years, and 54 years for the subsequent ovarian carcinoma diagnosis. The median time between the endometriosis and ovarian carcinoma was 10 (7-30) years. The data showed that all paired samples harbored one or more shared somatic mutations. Non-silent mutations in cancer-associated genes were frequent in endometriosis; however, the same mutations were never observed in subsequent carcinomas. The degree of clonal dominance, demonstrated by variant allele frequency, showed a positive correlation with the time to cancer diagnosis (Spearman's rho 0.853, P < 0.001). Mutations in genes associated with immune escape were the most conserved between paired samples, and regions harboring these genes were frequently affected by copy number alterations in both sample types. Mutational burdens and mutation signatures suggested faulty DNA repair mechanisms in all cases.

LARGE SCALE DATA: Datasets are available in the supplementary tables.

LIMITATIONS, REASONS FOR CAUTION: Even though we located several thousands of surgically removed endometriomas between 1998 and 2016, only 45 paired samples were identified and even fewer, 11 cases, were eligible for sequencing. The observed high level of intra- and inter-heterogeneity in both groups (endometrioma and carcinoma) argues for further studies of the alleged genetic association.

WIDER IMPLICATIONS OF THE FINDINGS: The observation of shared somatic mutations in all paired samples supports a common cellular origin for ovarian endometriosis and ovarian carcinoma. However, contradicting previous conclusions, our data suggest that cancer-associated mutations in endometriosis years prior to the carcinoma were not directly associated with the malignant transformation. Rather, a resilient ovarian endometriosis may delay tumorigenesis. Furthermore, the data indicate that genetic alterations affecting the immune response are early and significant events.

STUDY FUNDING/COMPETING INTEREST(S): The present work has been funded by the Sjöberg Foundation (2021-01145 to K.S.; 2022-01-11:4 to A.S.), Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement (965552 to K.S.; 40615 to I.H.; 965065 to A.S.), Swedish Cancer Society (21-1848 to K.S.; 21-1684 to I.H.; 22-2080 to A.S.), BioCARE-A Strategic Research Area at Lund University (I.H. and S.W.-F.), Mrs Berta Kamprad's Cancer Foundation (FBKS-2019-28, I.H.), Cancer and Allergy Foundation (10381, I.H.), Region Västra Götaland (A.S.), Sweden's Innovation Agency (2020-04141, A.S.), Swedish Research Council (2021-01008, A.S.), Roche in collaboration with the Swedish Society of Gynecological Oncology (S.W.-F.), Assar Gabrielsson Foundation (FB19-86, C.M.), and the Lena Wäpplings Foundation (C.M.). A.S. declares stock ownership and is also a board member in Tulebovaasta, SiMSen Diagnostics, and Iscaff Pharma. A.S. has also received travel support from EMBL, Precision Medicine Forum, SLAS, and bioMCC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations.

A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant.

Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31–7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429–0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls.

CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.

Vascular diseases are the largest cause of death globally and impose a major global burden on healthcare. The gold standard for treating vascular diseases is the transplantation of autologous veins, if applicable. Alternative treatments still suffer from shortcomings, including low patency, lack of growth potential, the need for repeated intervention, and a substantial risk of developing infections. The use of a vascular ECM scaffold reconditioned with the patient's own cells has shown successful results in preclinical and clinical studies. In this study, we have compared the proteomes of personalized tissue-engineered veins of humans and pigs. By applying tandem mass tag (TMT) labeling LC/MS-MS, we have investigated the proteome of decellularized (DC) veins from humans and pigs and reconditioned (RC) DC veins produced through perfusion with the patient's whole blood in STEEN solution, applying the same technology as used in the preclinical studies. The results revealed high similarity between the proteomes of human and pig DC and RC veins, including the ECM texture after decellularization and reconditioning. In addition, functional enrichment analysis showed similarities in signaling pathways and biological processes involved in the immune system response. Furthermore, the classification of proteins involved in immune response activity that were detected in human and pig RC veins revealed proteins that evoke immunogenic responses, which may lead to graft rejection, thrombosis, and inflammation. However, the results from this study imply the initiation of wound healing rather than an immunogenic response, as both systems share the same processes, and no immunogenic response was reported in the preclinical and clinical studies. Finally, our study assessed the application of STEEN solution in tissue engineering and identified proteins that may be useful for the prediction of successful transplantations. 

Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to e.g., distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation towards hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

In recent years, serum-based tests for early detection and detection of tissue of origin are being developed. Circulating microRNA has been shown to be a potential source of diagnostic information that can be collected non-invasively. In this study, we investigate circulating microRNAs as predictors of cancer stage. Specifically, we predict whether a sample stems from a patient with early stage (0-II) or late stage cancer (III-IV). We trained five machine learning algorithms on a data set of cancers from twelve different primary sites. The results showed that cancer stage can be predicted from circulating microRNA with a sensitivity of 71.73%, specificity of 79.97%, as well as positive and negative predictive value of 54.81% and 89.29%, respectively. Furthermore, we compared the best pan-cancer model with models specialized on individual cancers and found no statistically significant difference. Finally, in the best performing pan-cancer model 185 microRNAs were significant. Comparing the five most relevant circulating microRNAs in the best performing model with the current literature showed some known associations to various cancers. In conclusion, the study showed the potential of circulating microRNA and machine learning algorithms to predict cancer stage and thus suggests that further research into its potential as a non-invasive clinical test is warranted. 

Numerous studies have shown that lifestyle factors, such as regular physical activity and vitamin D intake, may remarkably improve overall health and mental wellbeing. This is especially important in older adults whose vitamin D deficiency occurs with a high prevalence. This study aimed to examine the influence of lifestyle and vitamin D on global DNA methylation patterns in an elderly cohort in Southwest of Sweden. We also sought to examine the methylation levels of specific genes involved in vitamin D's molecular and metabolic activated pathways. We performed a genome wide methylation analysis, using Illumina Infinium DNA Methylation EPIC 850kBeadChip array, on 277 healthy individuals from Southwest Sweden at the age of 70-95. The study participants also answered queries on lifestyle, vitamin intake, heart medication, and estimated health. Vitamin D intake did not in general affect methylation patterns, which is in concert with other studies. However, when comparing the group of individuals taking vitamin supplements, including vitamin D, with those not taking supplements, a difference in methylation in the solute carrier family 25 (SCL25A24) gene was found. This confirms a previous finding, where changes in expression of SLC25A24 were associated with vitamin D treatment in human monocytes. The combination of vitamin D intake and high physical activity increased methylation of genes linked to regulation of vitamin D receptor pathway, the Wnt pathway and general cancer processes. To our knowledge, this is the first study detecting epigenetic markers associated with the combined effects of vitamin D supplementation and high physical activity. These results deserve to be further investigated in an extended, interventional study cohort, where also the levels of 25(OH)D₃ can be monitored.

This study reports the discovery and evaluation of nanoparticle aided sensitive assays for glycovariants of MUC16 and MUC1 in a unique collection of paired ovarian cyst fluids and serum samples obtained at or prior to surgery for ovarian carcinoma suspicion. Selected glycovariants and the immunoassays for CA125, CA15-3 and HE4 were compared and validated in 347 cyst fluid and serum samples. Whereas CA125 and CA15-3 performed poorly in cyst fluid to separate carcinoma and controls, four glycovariants including MUC16^MGL , MUC16^STn , MUC1^STn and MUC1^Tn provided highly improved separations. In serum, the two STn glycovariants outperformed conventional CA125, CA15-3 and HE4 assays in all sub-categories analysed with main benefits obtained at high specificities and at postmenopausal and early-stage disease. Serum MUC16^STn performed best at high specificity (90-99%), but sensitivity was also improved by the other glycovariants and CA15-3. The highly improved specificity, excellent analytical sensitivity, and robustness of the nanoparticle assisted glycovariant assays carry great promise for improved identification and early detection of ovarian carcinoma in routine differential diagnostics. This article is protected by copyright. All rights reserved.

Cardiomyocyte proliferation has emerged as the main source of new cardiomyocytes in the adult. Progenitor cell populations may on the other hand contribute to the renewal of other cell types, including endothelial and smooth muscle cells. The phenotypes of immature cell populations in the adult human heart have not been extensively explored. We therefore investigated whether SSEA4+CD34- cells might constitute immature cycling cardiomyocytes in the adult failing and non-failing human heart. The phenotypes of Side Population (SP) and C-kit+CD45- progenitor cells were also analyzed. Biopsies from the four heart chambers were obtained from patients with end-stage heart failure as well as organ donors without chronic heart failure. Freshly dissociated cells underwent flow cytometric analysis and sorting. SSEA4+CD34- cells expressed high levels of cardiomyocyte, stem cell and proliferation markers. This pattern resembles that of cycling, immature, cardiomyocytes, which may be important in endogenous cardiac regeneration. SSEA4+CD34- cells isolated from failing hearts tended to express lower levels of cardiomyocyte markers as well as higher levels of stem cell markers. C-kit+CD45- and SP CD45- cells expressed high levels of endothelial and stem cell markers-corresponding to endothelial progenitor cells involved in endothelial renewal.