FinnGen provides genetic insights from a well-phenotyped isolated population.

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.

High-Resolution Genotyping of Formalin-Fixed Tissue Accurately Estimates Polygenic Risk Scores in Human Diseases.

Formalin-fixed paraffin-embedded (FFPE) tissues stored in biobanks and pathology archives are a vast but underutilized source for molecular studies on different diseases. Beyond being the "gold standard" for preservation of diagnostic human tissues, FFPE samples retain similar genetic information as matching blood samples, which could make FFPE samples an ideal resource for genomic analysis. However, research on this resource has been hindered by the perception that DNA extracted from FFPE samples is of poor quality. Here, we show that germline disease-predisposing variants and polygenic risk scores (PRS) can be identified from FFPE normal tissue (FFPE-NT) DNA with high accuracy. We optimized the performance of FFPE-NT DNA on a genome-wide array containing 657,675 variants. Via a series of testing and validation phases, we established a protocol for FFPE-NT genotyping with results comparable with blood genotyping. The median call rate of FFPE-NT samples in the validation phase was 99.85% (range 98.26%-99.94%) and median concordance with matching blood samples was 99.79% (range 98.85%-99.9%). We also demonstrated that a rare pathogenic PALB2 genetic variant predisposing to cancer can be correctly identified in FFPE-NT samples. We further imputed the FFPE-NT genotype data and calculated the FFPE-NT genome-wide PRS in 3 diseases and 4 disease risk variables. In all cases, FFPE-NT and matching blood PRS were highly concordant (all Pearson's r > 0.95). The ability to precisely genotype FFPE-NT on a genome-wide array enables translational genomics applications of archived FFPE-NT samples with the possibility to link to corresponding phenotypes and longitudinal health data.

Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma.

Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. Here, we applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy. We examined formalin-fixed paraffin-embedded tissue from (1) regions identified by the AI model as highly associated with short or extended chemotherapy response, and (2) background tumor regions (not identified by the AI model as highly associated with outcome status) from the same tumors. We show that the transcriptomic profiles of AI-identified regions are more distinct than background regions from the same tumors, are superior in predicting outcome, and differ in several pathways including those associated with chemoresistance in HGSC. Further, we find that poor outcome and good outcome regions are enriched by different tumor subpopulations, suggesting distinctive interaction patterns. In summary, our work presents proof of concept that AI-guided spatial transcriptomic analysis improves recognition of biologic features relevant to patient outcomes.

Serum FGF19 predicts outcomes of Kasai portoenterostomy in biliary atresia.

BACKGROUND AND AIMS: Outcomes after Kasai portoenterostomy (KPE) for biliary atresia remain highly variable for unclear reasons. As reliable early biomarkers predicting KPE outcomes are lacking, we studied the prognostic value of FGF19. APPROACH AND RESULTS: Serum and liver specimens, obtained from biliary atresia patients (N=87) at KPE or age-matched cholestatic controls (N=26) were included. Serum concentration of FGF19 and bile acids, liver mRNA expression of FGF19 , and key regulators of bile acid synthesis were related to KPE outcomes and liver histopathology. Immunohistochemistry and in situ hybridization were used for the localization of liver FGF19 expression. Serum levels (223 vs. 61 pg/mL, p <0.001) and liver mRNA expression of FGF19 were significantly increased in biliary atresia. Patients with unsuccessful KPE (419 vs. 145 pg/mL, p =0.047), and those subsequently underwent liver transplantation (410 vs. 99 pg/mL, p =0.007) had significantly increased serum, but not liver, FGF19, which localized mainly in hepatocytes. In Cox hazard modeling serum FGF19 <109 pg/mL predicted native liver survival (HR: 4.31, p <0.001) also among patients operated <60 days of age (HR: 8.77, p =0.004) or after successful KPE (HR: 6.76, p =0.01). Serum FGF19 correlated positively with increased serum primary bile acids ( R =0.41, p =0.004) and ductular reaction ( R =0.39, p =0.004). CONCLUSIONS: Increased serum FGF19 at KPE predicted inferior long-term native liver survival in biliary atresia and was associated with unsuccessful KPE, elevated serum primary bile acids, and ductular reaction.

WNT2 activation through proximal germline deletion predisposes to small intestinal neuroendocrine tumors and intestinal adenocarcinomas.

Many hereditary cancer syndromes are associated with an increased risk of small and large intestinal adenocarcinomas. However, conditions bearing a high risk to both adenocarcinomas and neuroendocrine tumors are yet to be described. We studied a family with 16 individuals in four generations affected by a wide spectrum of intestinal tumors, including hyperplastic polyps, adenomas, small intestinal neuroendocrine tumors, and colorectal and small intestinal adenocarcinomas. To assess the genetic susceptibility and understand the novel phenotype, we utilized multiple molecular methods, including whole genome sequencing, RNA sequencing, single cell sequencing, RNA in situ hybridization and organoid culture. We detected a heterozygous deletion at the cystic fibrosis locus (7q31.2) perfectly segregating with the intestinal tumor predisposition in the family. The deletion removes a topologically associating domain border between CFTR and WNT2, aberrantly activating WNT2 in the intestinal epithelium. These consequences suggest that the deletion predisposes to small intestinal neuroendocrine tumors and small and large intestinal adenocarcinomas, and reveals the broad tumorigenic effects of aberrant WNT activation in the human intestine.

Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer.

Each patient's cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.

Single-Cell RNA Sequencing-Based Characterization of Resident Lung Mesenchymal Stromal Cells in Bronchopulmonary Dysplasia.

Late lung development is a period of alveolar and microvascular formation, which is pivotal in ensuring sufficient and effective gas exchange. Defects in late lung development manifest in premature infants as a chronic lung disease named bronchopulmonary dysplasia (BPD). Numerous studies demonstrated the therapeutic properties of exogenous bone marrow and umbilical cord-derived mesenchymal stromal cells (MSCs) in experimental BPD. However, very little is known regarding the regenerative capacity of resident lung MSCs (L-MSCs) during normal development and in BPD. In this study we aimed to characterize the L-MSC population in homeostasis and upon injury. We used single-cell RNA sequencing (scRNA-seq) to profile in situ Ly6a+ L-MSCs in the lungs of normal and O2-exposed neonatal mice (a well-established model to mimic BPD) at 3 developmental timepoints (postnatal days 3, 7, and 14). Hyperoxia exposure increased the number and altered the expression profile of L-MSCs, particularly by increasing the expression of multiple pro-inflammatory, pro-fibrotic, and anti-angiogenic genes. In order to identify potential changes induced in the L-MSCs transcriptome by storage and culture, we profiled 15 000 Ly6a+ L-MSCs after in vitro culture. We observed great differences in expression profiles of in situ and cultured L-MSCs, particularly those derived from healthy lungs. Additionally, we have identified the location of Ly6a+/Col14a1+ L-MSCs in the developing lung and propose Serpinf1 as a novel, culture-stable marker of L-MSCs. Finally, cell communication analysis suggests inflammatory signals from immune and endothelial cells as main drivers of hyperoxia-induced changes in L-MSCs transcriptome.

The impact of implementing current treatment modalities and female sex on gastric cancer outcomes, 2000-2016: a longitudinal nationwide cohort study.

BACKGROUND: The implementation of current treatment modalities and their impact on nationwide gastric cancer outcomes remain poorly understood. Biological differences between females and males could impact survival. We aimed to analyze rates of gastric surgery, chemotherapy, and radiotherapy as well as changes in overall survival among gastric cancer patients diagnosed between 2000-2008 and 2009-2016, respectively, in Finland. MATERIAL AND METHODS: Data on gastric cancer patients were collected from national registries. Cox regression analysis and the Kaplan-Meier method were used to analyze differences in survival. RESULTS: We identified 9223 histologically confirmed gastric cancer patients. The rate of gastric surgery decreased from 44% (n = 2282) to 34% (n = 1368; p < 0.001). The proportion of gastric surgery patients who underwent preoperative oncological treatment increased from 0.5% (n = 12) to 16.2% (n = 222) between the calendar periods (p < 0.001) and stood at 30% in 2016. The median overall survival (OS) improved from 30 months [95% confidence interval (CI) 28-33] to 38 months (95%CI 33-42; p = 0.006) and the period 2009-2016 independently associated with a lower risk of death [hazard ratio (HR) 0.78, 95%CI 0.70-0.87] among patients who underwent gastric surgery. Females exhibited a lower risk of death (HR 0.88, 95%CI 0.81-0.97) among patients who underwent gastric surgery. CONCLUSION: Preoperative oncological treatment was gradually introduced into clinical practice and OS among gastric surgery patients improved. Moreover, female surgical patients exhibited a better survival than male patients.

QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability.

RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology.

Distinct effects on mRNA export factor GANP underlie neurological disease phenotypes and alter gene expression depending on intron content.

Defects in the mRNA export scaffold protein GANP, encoded by the MCM3AP gene, cause autosomal recessive early-onset peripheral neuropathy with or without intellectual disability. We extend here the phenotypic range associated with MCM3AP variants, by describing a severely hypotonic child and a sibling pair with a progressive encephalopathic syndrome. In addition, our analysis of skin fibroblasts from affected individuals from seven unrelated families indicates that disease variants result in depletion of GANP except when they alter critical residues in the Sac3 mRNA binding domain. GANP depletion was associated with more severe phenotypes compared with the Sac3 variants. Patient fibroblasts showed transcriptome alterations that suggested intron content-dependent regulation of gene expression. For example, all differentially expressed intronless genes were downregulated, including ATXN7L3B, which couples mRNA export to transcription activation by association with the TREX-2 and SAGA complexes. Our results provide insight into the molecular basis behind genotype-phenotype correlations in MCM3AP-associated disease and suggest mechanisms by which GANP defects might alter RNA metabolism.

FUNGI: FUsioN Gene Integration toolset.

MOTIVATION: Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules. RESULTS: We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance. AVAILABILITYAND IMPLEMENTATION: FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples.

MOTIVATION: A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. RESULTS: To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. AVAILABILITYAND IMPLEMENTATION: https://bitbucket.org/anthakki/prism. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Agile workflow for interactive analysis of mass cytometry data.

MOTIVATION: Single-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single-cell resolution. These large amounts of data, require dedicated, interactive tools for translating the data into knowledge. RESULTS: We present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automates the use of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood and cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples. AVAILABILITYAND IMPLEMENTATION: The method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cyto. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dubious effects of methadone as an "anticancer" drug on ovarian cancer cell-lines and patient-derived tumor-spheroids.

BACKGROUND: The opioid agonist D,L-methadone exerts analgesic effects via the mu opioid receptor, encoded by OPRM1 and therefore plays a role in chronic pain management. In preclinical tumor-models D,L-methadone shows apoptotic and chemo-sensitizing effects and was therefore hyped as an off-label "anticancer" drug without substantiation from clinical trials. Its effects in ovarian cancer (OC) are completely unexplored. METHODS: We analyzed OPRM1-mRNA expression in six cisplatin-sensitive, two cisplatin-resistant OC cell-lines, 170 OC tissue samples and 12 non-neoplastic control tissues. Pro-angiogenetic, cytotoxic and apoptotic effects of D,L-methadone were evaluated in OC cell-lines and four patient-derived tumor-spheroid models. RESULTS: OPRM1 was transcriptionally expressed in 69% of OC-tissues and in three of eight OC cell-lines. D,L-methadone exposure significantly reduced cell-viability in five OC cell-lines irrespective of OPRM1 expression. D,L-methadone, applied alone or combined with cisplatin, showed no significant effects on apoptosis or VEGF secretion in cell-lines. Notably, in two of the four spheroid models, treatment with D,L-methadone significantly enhanced cell growth (by up to 121%), especially after long-term exposure. This is consistent with the observed attenuation of the inhibitory effects of cisplatin in three spheroid models when adding D,L-methadone. The effect of methadone treatment on VEGF secretion in tumor-spheroids was inconclusive. CONCLUSIONS: Our study demonstrates that certain OC samples express OPRM1, which, however, is not a prerequisite for D,L-methadone function. As such, D,L-methadone may exert also detrimental effects by stimulating the growth of certain OC-cells and abrogating cisplatin's therapeutic effect.

qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets.

MOTIVATION: Non-parametric dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE), are the most frequently used methods in the exploratory analysis of single-cell datasets. Current implementations scale poorly to massive datasets and often require downsampling or interpolative approximations, which can leave less-frequent populations undiscovered and much information unexploited. RESULTS: We implemented a fast t-SNE package, qSNE, which uses a quasi-Newton optimizer, allowing quadratic convergence rate and automatic perplexity (level of detail) optimizer. Our results show that these improvements make qSNE significantly faster than regular t-SNE packages and enables full analysis of large datasets, such as mass cytometry data, without downsampling. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are openly available at https://bitbucket.org/anthakki/qsne/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

FHOD1 and FMNL1 formin proteins in intestinal gastric cancer: correlation with tumor-infiltrating T lymphocytes and molecular subtypes.

BACKGROUND: Gastric cancer (GC) is the third most common cause of cancer death. Intestinal type GC is a molecularly diverse disease. Formins control cytoskeletal processes and have been implicated in the progression of many cancers. Their clinical significance in GC remains unclear. Here, we characterize the expression of formin proteins FHOD1 and FMNL1 in intestinal GC tissue samples and investigate their association with clinical parameters, GC molecular subtypes and intratumoral T lymphocytes. METHODS: The prognostic significance of FHOD1 and FMNL1 mRNA expression was studied with Kaplan-Meier analyses in an online database. The expression of FHOD1 and FMNL1 proteins was characterized in GC cells, and in non-neoplastic and malignant tissues utilizing tumor microarrays of intestinal GC representing different molecular subtypes. FHOD1 and FMNL1 expression was correlated with clinical parameters, molecular features and T lymphocyte infiltration. Immunohistochemical expression of neither formin correlated with survival. RESULTS: Kaplan-Meier analysis associated high FHOD1 and FMNL1 mRNA expression with reduced overall survival (OS). Characterization of FHOD1 and FMNL1 in GC cells showed cytoplasmic expression along the actin filaments. Similar pattern was recapitulated in GC tissue samples. Elevated FMNL1 was associated with larger tumor size and higher disease stage. Downregulation of FHOD1 associated with TP53-mutated GC tumors. Tumor cell FHOD1 expression strongly correlated with high numbers of tumor-infiltrating CD8 + lymphocytes. CONCLUSIONS: FHOD1 and FMNL1 proteins are expressed in the tumor cells of intestinal GC and significantly associate with clinical parameters without direct prognostic significance. FHOD1 correlates with high intratumoral CD8 + T lymphocyte infiltration in this cohort.

ALDH1A1-related stemness in high-grade serous ovarian cancer is a negative prognostic indicator but potentially targetable by EGFR/mTOR-PI3K/aurora kinase inhibitors.

Poor chemotherapy response remains a major treatment challenge for high-grade serous ovarian cancer (HGSC). Cancer stem cells are the major contributors to relapse and treatment failure as they can survive conventional therapy. Our objectives were to characterise stemness features in primary patient-derived cell lines, correlate stemness markers with clinical outcome and test the response of our cells to both conventional and exploratory drugs. Tissue and ascites samples, treatment-naive and/or after neoadjuvant chemotherapy, were prospectively collected. Primary cancer cells, cultured under conditions favouring either adherent or spheroid growth, were tested for stemness markers; the same markers were analysed in tissue and correlated with chemotherapy response and survival. Drug sensitivity and resistance testing was performed with 306 oncology compounds. Spheroid growth condition HGSC cells showed increased stemness marker expression (including aldehyde dehydrogenase isoform I; ALDH1A1) as compared with adherent growth condition cells, and increased resistance to platinum and taxane. A set of eight stemness markers separated treatment-naive tumours into two clusters and identified a distinct subgroup of HGSC with enriched stemness features. Expression of ALDH1A1, but not most other stemness markers, was increased after neoadjuvant chemotherapy and its expression in treatment-naive tumours correlated with chemoresistance and reduced survival. In drug sensitivity and resistance testing, five compounds, including two PI3K-mTOR inhibitors, demonstrated significant activity in both cell culture conditions. Thirteen compounds, including EGFR, PI3K-mTOR and aurora kinase inhibitors, were more toxic to spheroid cells than adherent cells. Our results identify stemness markers in HGSC that are associated with a decreased response to conventional chemotherapy and reduced survival if expressed by treatment-naive tumours. EGFR, mTOR-PI3K and aurora kinase inhibitors are candidates for targeting this cell population. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Multiple formin proteins participate in glioblastoma migration.

BACKGROUND: The prognosis of glioblastoma remains poor, related to its diffuse spread within the brain. There is an ongoing search for molecular regulators of this particularly invasive behavior. One approach is to look for actin regulating proteins that might be targeted by future anti-cancer therapy. The formin family of proteins orchestrates rearrangement of the actin cytoskeleton in multiple cellular processes. Recently, the formin proteins mDia1 and mDia2 were shown to be expressed in glioblastoma in vitro, and their function could be modified by small molecule agonists. This finding implies that the formins could be future therapeutic targets in glioblastoma. METHODS: In cell studies, we investigated the changes in expression of the 15 human formins in primary glioblastoma cells and commercially available glioblastoma cell lines during differentiation from spheroids to migrating cells using transcriptomic analysis and qRT-PCR. siRNA mediated knockdown of selected formins was performed to investigate whether their expression affects glioblastoma migration. Using immunohistochemistry, we studied the expression of two formins, FHOD1 and INF2, in tissue samples from 93 IDH-wildtype glioblastomas. Associated clinicopathological parameters and follow-up data were utilized to test whether formin expression correlates with survival or has prognostic value. RESULTS: We found that multiple formins were upregulated during migration. Knockdown of individual formins mDia1, mDia2, FHOD1 and INF2 significantly reduced migration in most studied cell lines. Among the studied formins, knockdown of INF2 generated the greatest reduction in motility in vitro. Using immunohistochemistry, we demonstrated expression of formin proteins FHOD1 and INF2 in glioblastoma tissues. Importantly, we found that moderate/high expression of INF2 was associated with significantly impaired prognosis. CONCLUSIONS: Formins FHOD1 and INF2 participate in glioblastoma cell migration. Moderate/high expression of INF2 in glioblastoma tissue is associated with worse outcome. Taken together, our in vitro and tissue studies suggest a pivotal role for INF2 in glioblastoma. When specific inhibiting compounds become available, INF2 could be a target in the search for novel glioblastoma therapies.

A longitudinal analysis of CA125 glycoforms in the monitoring and follow up of high grade serous ovarian cancer.

OBJECTIVE: Cancer antigen 125 (CA125) is generally considered the gold standard of biomarkers in the diagnosis and monitoring of high grade serous ovarian carcinoma (HGSC). We recently reported, that two CA125 glycoforms (CA125-STn and CA125-MGL) have a high specificity to HGSC and further hypothesized, that these cancer specific glycoforms are feasible candidates as biomarkers in HGSC treatment and follow up. METHODS: Our cohort consisted of 122 patients diagnosed with HGSC. Serum samples were collected longitudinally at the time of diagnosis, during treatment and follow up. Serum levels of CA125, CA125-STn and CA125-MGL were determined and compared or correlated with different end points (tumor load assessed intraoperatively, residual disease, treatment response, progression free survival). RESULTS: Serum CA125-STn levels at diagnosis differentiated patients with low tumor load and high tumor load (p = 0,030), indicating a favorable detection of tumor volume. Similarly, the CA125-STn levels at diagnosis were significantly lower in patients with subsequent complete cytoreduction than in patients with suboptimal cytoreduction (p = 0,025). Conventional CA125 did not differentiate these patients (p = 0,363 and p = 0,154). The CA125-STn nadir value predicted the progression free survival of patients. The detection of disease relapse was improved with CA125-STn, which presented higher fold increase in 80,0% of patients and earlier increase in 37,0% of patients. CONCLUSIONS: CA125-STn showed promise as a useful biomarker in the monitoring and follow up of patients with HGSC utilizing a robust and affordable technique. Our findings are topical as a suitable indicator of tumor load facilitates patient selection in an era of new targeted therapies.