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Due to its dynamic nature, the evolution of cancer cell-extracellular matrix (ECM) crosstalk, critically affecting metastasis and treatment resistance, remains elusive. Our results show that platinum-chemotherapy itself enhances resistance by progressively changing the cancer cell-intrinsic adhesion signaling and cell-surrounding ECM. Examining ovarian high-grade serous carcinoma (HGSC) transcriptome and histology, we describe the fibrotic ECM heterogeneity at primary tumors and distinct metastatic sites, prior and after chemotherapy. Using cell models from systematic ECM screen to collagen-based 2D and 3D cultures, we demonstrate that both specific ECM substrates and stiffness increase resistance to platinum-mediated, apoptosis-inducing DNA damage via FAK and β1 integrin-pMLC-YAP signaling. Among such substrates around metastatic HGSCs, COL6 was upregulated by chemotherapy and enhanced the resistance of relapse, but not treatment-naïve, HGSC organoids. These results identify matrix adhesion as an adaptive response, driving HGSC aggressiveness via co-evolving ECM composition and sensing, suggesting stromal and tumor strategies for ECM pathway targeting., Platinum chemotherapy is standard of care in ovarian cancers but treatment resistance commonly develops. Here, the authors show that the extracellular microenvironment is modulated following chemotherapy and the changes in matrix proteins and stiffness alter the cell death response of tumour cells.

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.

Autosomal recessive Gaucher disease (GD) is likely underdiagnosed in many countries. Because the number of diagnosed GD patients in Finland is relatively low, and the true prevalence is currently not known, it was hypothesized that undiagnosed GD patients may exist in Finland. Our previous study demonstrated the applicability of Gaucher Earlier Diagnosis Consensus point-scoring system (GED-C PSS; Mehta et al., 2019) and Finnish biobank data and specimens in the automated point scoring of large populations. An indicative point-score range for Finnish GD patients was determined, but undiagnosed patients were not identified partly due to high number of high-score subjects in combination with a lack of suitable samples for diagnostics in the assessed biobank population. The current study extended the screening to another biobank and evaluated the feasibility of utilising the automated GED-C PSS in conjunction with small nucleotide polymorphism (SNP) chip genotype data from the FinnGen study of biobank sample donors in the identification of undiagnosed GD patients in Finland. Furthermore, the applicability of FFPE tissues and DNA restoration in the next-generation sequencing (NGS) of thePreviously diagnosed Finnish GD patients eligible to the study, and up to 45,100 sample donors in Helsinki Biobank (HBB) were point scored. The GED-C point scoring, adjusted to local data, was automated, but also partly manually verified for GD patients. The SNP chip genotype data for rareThree previously diagnosed GD patients and one patient previously treated for GD-related features were included. A genetic diagnosis was confirmed for the patient treated for GD-related features. The GED-C point score of the GD patients was 12.5-22.5 in the current study. The score in eight Finnish GD patients of the previous and the current study is thus 6-22.5 points per patient. In the automated point scoring of the HBB subpopulation (N ≈ 45,100), the overall scores ranged from 0 to 17.5, with 0.77% (346/45,100) of the subjects having ≥10 points. The analysis of SNP chip genotype data was able to identify the diagnosed GD patients, but potential undiagnosed patients with the GED-C score and/or theThese findings imply that the prevalence of diagnosed patients (~1:325,000) may indeed correspond the true prevalence of GD in Finland. The SNP chip genotype data is a valuable tool that complements the screening with the GED-C PSS, especially if the genotyping pipeline is tuned for rare variants. These proof-of-concept biobank tools can be adapted to other rare genetic diseases.

The populational heterogeneity of a disease, in part due to comorbidity, poses several complexities. Individual comorbidity profiles, on the other hand, contain useful information to refine phenotyping, prognostication, and risk assessment, and they provide clues to underlying biology. Nevertheless, the spectrum and the implications of the diagnosis profiles remain largely uncharted. Here we mapped comorbidity patterns in 100 common diseases using 4-year retrospective data from 526,779 patients and developed an online tool to visualize the results. Our analysis exposed disease-specific patient subgroups with distinctive diagnosis patterns, survival functions, and laboratory correlates. Computational modeling and real-world data shed light on the structure, variation, and relevance of populational comorbidity patterns, paving the way for improved diagnostics, risk assessment, and individualization of care. Variation in outcomes and biological correlates of a disease emphasizes the importance of evaluating the generalizability of current treatment strategies, as well as considering the limitations that selective inclusion criteria pose on clinical trials.

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 three 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 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.

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.

OBJECTIVE Human epididymis protein 4 (HE4) is a validated, complementary biomarker to cancer antigen 125 (CA125) for high grade serous ovarian carcinoma (HGSC). Currently, there are insufficient data on the utility of longitudinal HE4 measurement during HGSC treatment and follow up. We set to provide a comprehensive analysis on the kinetics and prognostic performance of HE4 with serial measurements during HGSC treatment and follow up. METHODS This prospective study included 143 patients with advanced HGSC (ClinicalTrials.gov identifier: NCT01276574). Serum CA125 and HE4 were measured at baseline, before each cycle of chemotherapy and during follow up until first progression. Baseline biomarker values were compared to the tumor load assessed during surgery and to residual disease. Biomarker nadir values and concentrations at progression were correlated to survival. RESULTS The baseline HE4 concentration distinguished patients with a high tumor load from patients with a low tumor load assessed during surgery (p

Chemotherapy resistance is a critical contributor to cancer mortality and thus an urgent unmet challenge in oncology. To characterize chemotherapy resistance processes in high-grade serous ovarian cancer, we prospectively collected tissue samples before and after chemotherapy and analyzed their transcriptomic profiles at a single-cell resolution. After removing patient-specific signals by a novel analysis approach, PRIMUS, we found a consistent increase in stress-associated cell state during chemotherapy, which was validated by RNA in situ hybridization and bulk RNA sequencing. The stress-associated state exists before chemotherapy, is subclonally enriched during the treatment, and associates with poor progression-free survival. Co-occurrence with an inflammatory cancer–associated fibroblast subtype in tumors implies that chemotherapy is associated with stress response in both cancer cells and stroma, driving a paracrine feed-forward loop. In summary, we have found a resistant state that integrates stromal signaling and subclonal evolution and offers targets to overcome chemotherapy resistance.

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.

Apolipoprotein E ε4 allele (APOE4) has been shown to associate with increased susceptibility to SARS-CoV-2 infection and COVID-19 mortality in some previous genetic studies, but information on the role of APOE4 on the underlying pathology and parallel clinical manifestations is scarce. Here we studied the genetic association between APOE and COVID-19 in Finnish biobank, autopsy and prospective clinical cohort datasets. In line with previous work, our data on 2611 cases showed that APOE4 carriership associates with severe COVID-19 in intensive care patients compared with non-infected population controls after matching for age, sex and cardiovascular disease status. Histopathological examination of brain autopsy material of 21 COVID-19 cases provided evidence that perivascular microhaemorrhages are more prevalent in APOE4 carriers. Finally, our analysis of post-COVID fatigue in a prospective clinical cohort of 156 subjects revealed that APOE4 carriership independently associates with higher mental fatigue compared to non-carriers at six months after initial illness. In conclusion, the present data on Finns suggests that APOE4 is a risk factor for severe COVID-19 and post-COVID mental fatigue and provides the first indication that some of this effect could be mediated via increased cerebrovascular damage. Further studies in larger cohorts and animal models are warranted.

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.

High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist review of histologic slides provides no prognostic or predictive information, which is in sharp contrast to almost all other carcinoma types. Deep-learning based image analysis has recently been able to predict outcome and/or identify morphology-based representations of underlying molecular alterations in other tumor types, such as colorectal carcinoma, lung carcinoma, breast carcinoma, and melanoma. Using a carefully stratified HGSC patient cohort consisting of women (n = 30) with similar presentations who experienced very different treatment responses (platinum free intervals of either ≤ 6 months or ≥ 18 months), we used whole slide images (WSI, n = 205) to train a convolutional neural network. The neural network was trained, in three steps, to identify morphologic regions (digital biomarkers) that are highly associating with one or the other treatment response group. We tested the classifier using a separate 22 slide test set, and 18/22 slides were correctly classified. We show that a neural network based approach can discriminate extremes in patient response to primary platinum-based chemotherapy with high sensitivity (73%) and specificity (91%). These proof-of-concept results are novel, because for the first time, prospective prognostic information is identified specifically within HGSC tumor morphology.

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.

Finnish hospital-integrated biobanks administer millions of formalin-fixed paraffin-embedded tissue samples collected within the clinical diagnostics. According to the Finnish Biobank Act, these samples can be coupled with patients’ clinical follow-up data and the data retrieved from national health registries. We collected a nationwide pulmonary carcinoid tumour series from Finnish biobanks to study prognostic factors as well as to explore how the number of tumours found in the Finnish biobanks corresponds to the number of tumours registered by the Finnish Cancer Registry (FCR). Finnish biobanks identified 88% of the tumours registered by the FCR and were able to deliver 63%. The main reasons for lacking samples were paucity of resected primary tumour tissue, incompatible primary diagnosis, and the absence of tissue blocks in the archives. The main bottleneck in the sample application process was retrieving patient data. Altogether, we received 224 tumour samples with appropriate patient data and identified six prognostic factors for shorter disease-specific survival: age over 56 years at the time of diagnosis, tumour size over 2.5 cm, atypical histology, Ki-67 proliferation index higher than 2.5%, hilar/mediastinal lymph node involvement at the time of diagnosis, and the presence of metastatic disease. In conclusion, the Finnish biobank infrastructure offers excellent opportunities for tissue-based research. However, to be able to develop the biobank operations further, involving more medical knowledge in the sample and data acquisition process is a necessity. Also, when working with tissue samples collected over decades, histological expertise is essential for re-evaluation and re-classification of the samples.

Publisher Copyright: © 2021 The Authors Whilst treatment modalities for pancreatic cancer patients have evolved in recent years, their impact on outcomes remains relatively unexamined on a national scale. We aimed to analyse changes in overall survival and trends in surgical and oncological treatments in pancreatic cancer patients diagnosed in the periods 2000 through 2008 and 2009 through 2016 in Finland. We collected data for pancreatic cancer patients diagnosed between 2000 and 2016, gathering data from the Finnish national registries on surgeries, oncological treatments and time of death. Follow-up continued through the end of 2018. We compared patients diagnosed between 2000 and 2008 to those diagnosed between 2009 through 2016. Our study comprised 14 712 pancreatic cancer patients. There was no significant change in the national resection rate (8.1% vs 8.0%, p = 0.690). In radical surgery patients, median survival improved from 20 months (95% confidence interval (CI) 18–22) to 28 months (CI 25–31) (p < 0.001), with 1-year survival ranging from 70% to 81%. In the no-surgery group, median survival slightly improved from 3.1 months (CI 3.0–3.3) to 3.3 months (CI 3.1–3.4) (p < 0.001). The proportion of radical surgery patients receiving preoperative oncological treatment increased from 4% to 13% (p < 0.001) and only postoperative treatment from 25% to 47% (p < 0.001). Whilst the resection rate did not increase, the prognosis of pancreatic cancer patients improved, particularly amongst radical surgery patients resulting most likely from the fact that a larger proportion of patients receive more effective oncological treatments.

Identification of ovarian cancer (OvCa) patient subpopulations with increased sensitivity to targeted therapies could offer significant clinical benefit. We report that 22% of the high grade OvCa tumors at diagnosis express CIP2A oncoprotein at low levels. CIP2Alow OvCa tumors have significantly lower likelihood of disease relapse after standard chemotherapy, but yet a portion of relapsed tumors retain their CIP2Alow phenotype. We further discover that reactive oxygen species (ROS) inducing compound APR-246 (PRIMA-1Met/Eprenetapopt), currently in clinical development, preferentially kill CIP2Alow OvCa cells across multiple chemotherapy resistant cell lines. Consistent with CIP2Alow OvCa subtype in humans, CIP2A is dispensable for development of MISIIR-TAg-driven mouse OvCa tumors. Nevertheless, CIP2A deficient OvCa tumor cells from MISIIR-TAg mice displayed APR-246 hypersensitivity both in vitro and in vivo. Mechanistically, the lack of CIP2A expression hypersensitizes the OvCa cells to APR-246 by inhibition of NF-kB activity. Accordingly, combination of APR-246 and Nf-kB inhibitor compounds strongly synergized in killing of CIP2A positive OvCa cells. Collectively, we discover low CIP2A expression as a vulnerability for APR-246 in OvCa. The results warrant consideration of clinical testing of APR-246 for CIP2Alow OvCa tumor subtype patients, and reveal CIP2A as a candidate APR-246 combination therapy target.

Simple Summary Most ovarian cancer patients initially show a response to primary treatments, but the development of refractory disease is a major problem. Currently, there are no blood-based prognostic biomarkers, and the prognosis of a patient is determined by the International Federation of Gynecology and Obstetrics (FIGO) stage and residual disease after cytoreductive surgery. In this study, we developed and validated a novel test based on the ratio of two circulatory lipids that enables the prognostic stratification of ovarian cancer patients at the time of diagnosis, prior to any oncological treatments. The translational relevance of this test is to find those patients with poor prognosis early on, and to identify patients that are at high risk of recurrence despite complete cytoreduction. Thus, the test enables the early direction of novel targeted therapies to those ovarian cancer patients at greatest risk of recurrence and death. Epithelial ovarian cancer (EOC) generally responds well to oncological treatments, but the eventual development of a refractory disease is a major clinical problem. Presently, there are no prognostic blood-based biomarkers for the stratification of EOC patients at the time of diagnosis. We set out to assess and validate the prognostic utility of a novel two-lipid signature, as the lipidome is known to be markedly aberrant in EOC patients. The study consisted of 499 women with histologically confirmed EOC that were prospectively recruited at the university hospitals in Turku (Finland) and Charite (Berlin, Germany). Lipidomic screening by tandem liquid chromatography-mass spectrometry (LC-MS/MS) was performed for all baseline serum samples of these patients, and additionally for 20 patients of the Turku cohort at various timepoints. A two-lipid signature, based on the ratio of the ceramide Cer(d18:1/18:0) and phosphatidylcholine PC(O-38:4), showed consistent prognostic performance in all investigated study cohorts. In the Turku cohort, the unadjusted hazard ratios (HRs) per standard deviation (SD) (95% confidence interval) were 1.79 (1.40, 2.29) for overall and 1.40 (1.14, 1.71) for progression-free survival. In a Charite cohort incorporating only stage III completely resected patients, the corresponding HRs were 1.59 (1.08, 2.35) and 1.53 (1.02, 2.30). In linear-mixed models predicting progression of the disease, the two-lipid signature showed higher performance (beta per SD increase 1.99 (1.38, 2.97)) than cancer antigen 125 (CA-125, 1.78 (1.13, 2.87)). The two-lipid signature was able to identify EOC patients with an especially poor prognosis at the time of diagnosis, and also showed promise for the detection of disease relapse.

Erythropoietin producing hepatocellular (Eph) receptors and their membrane-bound ligands ephrins are variably expressed in epithelial cancers, with context-dependent implications to both tumor-promoting and -suppressive processes in ways that remain incompletely understood. Using ovarian cancer tissue microarrays and longitudinally collected patient cells, we show here that ephrinA5/EFNA5 is specifically overexpressed in the most aggressive high-grade serous carcinoma (HGSC) subtype, and increased in the HGSC cells upon disease progression. Among all the eight ephrin genes, high EFNA5 expression was most strongly associated with poor overall survival in HGSC patients from multiple independent datasets. In contrast, high EFNA3 predicted improved overall and progression-free survival in The Cancer Genome Atlas HGSC dataset, as expected for a canonical inducer of tumor-suppressive Eph receptor tyrosine kinase signaling. While depletion of either EFNA5 or the more extensively studied, canonically acting EFNA1 in HGSC cells increased the oncogenic EphA2-S897 phosphorylation, EFNA5 depletion left unaltered, or even increased the ligand-dependent EphA2-Y588 phosphorylation. Moreover, treatment with recombinant ephrinA5 led to limited EphA2 tyrosine phosphorylation, internalization and degradation compared to ephrinA1. Altogether, our results suggest a unique function for ephrinA5 in Eph-ephrin signaling and highlight the clinical potential of ephrinA5 as a cell surface biomarker in the most aggressive HGSCs.

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.

Non

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.

Non

Published descriptions of the neuropathological features of COVID-19 patients have been controversial, ranging from only modest or no pathology to severe hypoxic and hemorrhagic phenotypes, thrombotic complications, acute disseminated encephalomyelitis-like changes, and encephalitis and meningitis. Here, we describe the neuropathological findings of four COVID-19-positive patients autopsied at the Helsinki University Hospital during the spring of 2020. While three of the patients (age range 63-90) exhibited merely mild to moderate hypoxia-associated changes, one 38-year-old subject with obesity, diabetes (type 2), Parkinson's disease and a very severe clinical course was found to have severe ischemic injury, abundant microhemorrhages and enlarged perivascular spaces most pronounced in the white matter and deep gray matter. The pattern of ischemic changes suggested a defect in microcirculation. In addition, a few small perivascular white matter lesions, with macrophages engulfing myelin, were found. No signs of encephalitis or meningitis were detected in any of the patients. When conducting RT-PCR and immunohistochemical analyses of brain tissue, we could not demonstrate in any of the patients marked injury or presence of SARS-CoV2 in the olfactory epithelium, olfactory bulbs or brain areas responsible for respiratory control. In conclusion, our small autopsy series demonstrates various hypoxia-associated neuropathological features in COVID-19 patients, but no evidence of neurotropism or meningitis/encephalitis.

Background:Archived formalin-fixed paraffin-embedded (FFPE) specimens from nonmalignant tissues derived from cancer patients are a vast and potentially valuable resource for high-quality genotyping analyses and could have a role in establishing inherited cancer risk. Methods:We systematically searched PubMed, Ovid MEDLINE, and Scopus databases for all articles that compared genotyping performance of DNA from nonmalignant FFPE tissue with blood DNA derived from cancer patients irrespective of tumor type. Two independent researchers screened the retrieved studies, removed duplicates, excluded irrelevant studies, and extracted genotyping data from the eligible studies. These studies included, but were not limited to, genotyping technique, reported call rate, and concordance. Results:Thirteen studies were reviewed, in which DNA from nonmalignant FFPE tissues derived from cancer patients was successfully purified and genotyped. All these studies used different approaches for genotyping of DNA from nonmalignant FFPE tissues to amplify single nucleotide polymorphisms (SNPs) and to estimate of loss of heterozygosity. The concordance between genotypes from nonmalignant FFPE tissues and blood derived from cancer patients was observed to be high, whereas the call rate of the tested SNPs was not reported in all included studies. Conclusion:This review illustrates that DNA from nonmalignant FFPE tissues derived from cancer patients can serve as an alternative and reliable source for assessment of germline DNA for various purposes, including assessment of cancer predisposition.

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.

Background Advances in the treatment of Parkinson's disease (PD) and changes in general life expectancy may have improved survival in patients with PD. Objective The objective of this study was to investigate recent trends in PD mortality. Methods In total, 1521 patients with PD in local and national registries were followed for 11 years (2006-2016) from diagnosis until exit date or death, and the causes of death were recorded. Results The survival of men with PD improved during the follow-up period, but no change was observed in women (2-year postdiagnosis survival in men, 79.0%-86.3%, P = 0.03; 2-year postdiagnosis survival in women, 82.8%-87.5%, P = 0.42). Pneumonia was the most common immediate cause of death. Discussion The survival of men with PD has improved in Finland without a similar change in women. Because changes in treatment likely affect both sexes similarly, the results may reflect the decreasing sex gap in life expectancy. This phenomenon will likely increase the already high male-to-female prevalence ratio of PD.

Adult-type granulosa cell tumors (AGCTs) are sex-cord derived neoplasms with a propensity for late relapse. Hormonal modulators have been used empirically in the treatment of recurrent AGCT, albeit with limited success. To provide a more rigorous foundation for hormonal therapy in AGCT, we used a multimodal approach to characterize the expressions of key hormone biomarkers in 175 tumor specimens and 51 serum samples using RNA sequencing, immunohistochemistry, RNA in situ hybridization, quantitative PCR, and circulating biomarker analysis, and correlated these results with clinical data. We show that FSH receptor and estrogen receptor beta (ERβ) are highly expressed in the majority of AGCTs, whereas the expressions of estrogen receptor alpha (ERα) and G-protein coupled estrogen receptor 1 are less prominent. ERβ protein expression is further increased in recurrent tumors. Aromatase expression levels show high variability between tumors. None of the markers examined served as prognostic biomarkers for progression-free or overall survival. In functional experiments, we assessed the effects of FSH, estradiol (E2), and the aromatase inhibitor letrozole on AGCT cell viability using 2 in vitro models: KGN cells and primary cultures of AGCT cells. FSH increased cell viability in a subset of primary AGCT cells, whereas E2 had no effect on cell viability at physiological concentrations. Letrozole suppressed E2 production in AGCTs; however, it did not impact cell viability. We did not find preclinical evidence to support the clinical use of aromatase inhibitors in AGCT treatment, and thus randomized, prospective clinical studies are needed to clarify the role of hormonal treatments in AGCTs.

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.

Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P, Cardiovascular diseases (CVD) are associated with plasma lipid levels. Here, Tabassum et al. perform genome-wide association studies for lipidomic profiles with 141 (non-standard) lipid species which highlights shared genetic loci with CVD and that traditional lipids have low genetic correlation with other lipids.

Current risk factors in stage II colorectal carcinoma are insufficient to guide treatment decisions. Loss of CDX2 has been shown to associate with poor clinical outcome and predict benefit for adjuvant chemotherapy in stage II and III colorectal carcinoma. The prognostic relevance of CDX2 in stage II disease has not been sufficiently validated, especially in relation to clinical risk factors, such as microsatellite instability (MSI) status, BRAF mutation status, and tumor budding. In this study, we evaluated the protein expression of CDX2 in tumor center and front areas in a tissue microarrays material of stage II colorectal carcinoma patients (n=232). CDX2 expression showed a partial or total loss in respective areas in 8.6% and 10.9% of patient cases. Patients with loss of CDX2 had shorter disease-specific survival when scored independently either in tumor center or tumor front areas (log rank P=0.012; P=0.012). Loss of CDX2 predicted survival independently of other stage II risk factors, such as MSI status and BRAF mutation status, pT class, and tumor budding (hazard ratio=5.96, 95% confidence interval=1.55-22.95; hazard ratio=3.70, 95% confidence interval=1.30-10.56). Importantly, CDX2 loss predicted inferior survival only in patients with microsatellite stable, but not with MSI-high phenotype. Interestingly, CDX2 loss associated with low E-cadherin expression, tight junction disruption, and high expression of ezrin protein. The work demonstrates that loss of CDX2 is an independent risk factor of poor disease-specific survival in stage II colorectal carcinoma. Furthermore, the study suggests that CDX2 loss is linked with epithelial-to-mesenchymal transition independently of tumor budding.

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.