Orders of magnitude speed increase in Partial Least Squares feature selection with new simple indexing technique for very tall data sets

Feature selection is a challenging combinatorial optimization problem that tends to require a large number of candidate feature subsets to be evaluated before a satisfying solution is obtained. Because of the computational cost associated with estimating the regression coefficients for each subset, feature selection can be an immensely time-consuming process and is often left inadequately explored. Here, we propose a simple modification to the conventional sequence of calculations involved when fitting a number of feature subsets to the same response data with partial least squares (PLS) model fitting. The modification consists in establishing the covariance matrix for the full set of features by an initial calculation and then deriving the covariance of all subsequent feature subsets solely by indexing into the original covariance matrix. By choosing this approach, which is primarily suitable for tall design matrices with significantly more rows than columns, we avoid redundant (identical) recalculations in the evaluation of different feature subsets. By benchmarking the time required to solve regression problems of various sizes, we demonstrate that the introduced technique outperforms traditional approaches by several orders of magnitude when used in conjunction with PLS modeling. In the supplementary material, we provide code for implementing the concept with kernel PLS regression.acceptedVersio

Applying System Analysis and System Dynamics Modelling In Complex Research Projects - The Case Of VALUMICS

VALUMICS is a Horizon 2020 project funded by the European Commission (2017-2021). The project structure is highly integrated and transdisciplinary, building on the expertise of over 30 specialists in various fields of research including knowledge integration through systems analysis and system dynamics modelling, food science, supply chain management, life cycle assessment, logistics, economics and social science. The aim of the project is to analyze the dynamics of food supply systems using a structural analysis including system analysis and perform system simulations using system dynamics. The VALUMICS research approach and the project design are explained and it is justified why system analysis is needed to obtain an understanding of the complex connections and interactions of the distinct parts of food systems. Patterns will be recognized and thus causes and effects of complex relations within the selected food supply system and networks will be identified. This understanding of the functioning of the system can in turn be used to identify policy interventions

System Dynamics Modelling and System Analysis Applied in Complex Research Projects - the Case of VALUMICS

VALUMICS is a Horizon 2020 project funded by the European Commission (2017-2021). The project structure is highly integrated and transdisciplinary, building on the expertise of over 30 specialists in various fields of research including knowledge integration through systems analysis and system dynamics modelling, food science, supply chain management, life cycle assessment, logistics, economics and social science. The aim of the project is to analyze the dynamics of food supply- and value chain systems using a structural analysis including system analysis and perform system simulations using system dynamics. The VALUMICS research approach and the project design are explained and it is justified why system analysis is needed to obtain an understanding of the complex connections and interactions of the distinct parts of food systems. Patterns will be recognized and thus causes and effects of complex relations within the selected food supply system and networks will be identified. This understanding of the functioning of the system can in turn be used to identify policy interventions

Modeling of Integrated Supply-, Value- and Decision Chains within Food Systems

This paper presents a work in progress on the development of a mental model of a food system using system analysis. The aim is to be able to use this model to create a mathematical simulation model that can be used to identify policy intervention opportunities, specifically focusing on the resilience, integrity and sustainability of food supply networks. The traditional view of food systems as supply chains with a downstream physical flow of products is extended to include the associated upstream flow of money and the decision chains that link these flows. Central to this work is the idea that supply systems are driven by profit and regulated by market dynamics and that these factors generate the underlying feedback structure of the system. Studying the structure of such systems as integrated supply-, value- and decision chains has underscored their complexity and the need for further, more food system specific research

Polygenic risk scores for schizophrenia and bipolar disorder associate with addiction

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked FilesWe use polygenic risk scores (PRSs) for schizophrenia (SCZ) and bipolar disorder (BPD) to predict smoking, and addiction to nicotine, alcohol or drugs in individuals not diagnosed with psychotic disorders. Using PRSs for 144 609 subjects, including 10 036 individuals admitted for in-patient addiction treatment and 35 754 smokers, we find that diagnoses of various substance use disorders and smoking associate strongly with PRSs for SCZ (P = 5.3 × 10-50 -1.4 × 10-6 ) and BPD (P = 1.7 × 10-9 -1.9 × 10-3 ), showing shared genetic etiology between psychosis and addiction. Using standardized scores for SCZ and BPD scaled to a unit increase doubling the risk of the corresponding disorder, the odds ratios for alcohol and substance use disorders range from 1.19 to 1.31 for the SCZ-PRS, and from 1.07 to 1.29 for the BPD-PRS. Furthermore, we show that as regular smoking becomes more stigmatized and less prevalent, these biological risk factors gain importance as determinants of the behavior.National Institute on Drug Abuse (NIDA) European Community's Seventh Framework Programme under the Marie Curie Industry Academia Partnership and Pathways (PsychDPC

Molecular and phenotypic characteristics influencing the degree of cytoreduction in high-grade serous ovarian carcinomas

Background: High-grade serous ovarian carcinoma (HGSOC) is the deadliest ovarian cancer subtype, and survival relates to initial cytoreductive surgical treatment. The existing tools for surgical outcome prediction remain inadequate for anticipating the outcomes of the complex relationship between tumour biology, clinical phenotypes, co-morbidity and surgical skills. In this genotype–phenotype association study, we combine phenotypic markers with targeted DNA sequencing to discover novel biomarkers to guide the surgical management of primary HGSOC. Methods: Primary tumour tissue samples (n = 97) and matched blood from a phenotypically well-characterised treatment-naïve HGSOC patient cohort were analysed by targeted massive parallel DNA sequencing (next generation sequencing [NGS]) of a panel of 360 cancer-related genes. Association analyses were performed on phenotypic traits related to complete cytoreductive surgery, while logistic regression analysis was applied for the predictive model. Results: The positive influence of complete cytoreductive surgery (R0) on overall survival was confirmed (p = 0.003). Before surgery, low volumes of ascitic fluid, lower CA125 levels, higher platelet counts and relatively lower clinical stage at diagnosis were all indicators, alone and combined, for complete cytoreduction (R0). Mutations in either the chromatin remodelling SWI_SNF (p = 0.036) pathway or the histone H3K4 methylation pathway (p = 0.034) correlated with R0. The R0 group also demonstrated higher tumour mutational burden levels (p = 0.028). A predictive model was developed by combining two phenotypes and the mutational status of five genes and one genetic pathway, enabling the prediction of surgical outcomes in 87.6% of the cases in this cohort. Conclusion: Inclusion of molecular biomarkers adds value to the pre-operative stratification of HGSOC patients. A potential preoperative risk stratification model combining phenotypic traits and single-gene mutational status is suggested, but the set-up needs to be validated in larger cohorts.publishedVersio

MRI-assessed tumor-free distance to serosa predicts deep myometrial invasion and poor outcome in endometrial cancer

Objectives To explore the diagnostic accuracy of preoperative magnetic resonance imaging (MRI)-derived tumor measurements for the prediction of histopathological deep (≥ 50%) myometrial invasion (pDMI) and prognostication in endometrial cancer (EC). Methods Preoperative pelvic MRI of 357 included patients with histologically confirmed EC were read independently by three radiologists blinded to clinical information. The radiologists recorded imaging findings (T1 post-contrast sequence) suggesting deep (≥ 50%) myometrial invasion (iDMI) and measured anteroposterior tumor diameter (APD), depth of myometrial tumor invasion (DOI) and tumor-free distance to serosa (iTFD). Receiver operating characteristic (ROC) curves for the prediction of pDMI were plotted for the different MRI measurements. The predictive and prognostic value of the MRI measurements was analyzed using logistic regression and Cox proportional hazard model. Results iTFD yielded highest area under the ROC curve (AUC) for the prediction of pDMI with an AUC of 0.82, whereas DOI, APD and iDMI yielded AUCs of 0.74, 0.81 and 0.74, respectively. Multivariate analysis for predicting pDMI yielded highest predictive value of iTFD <  6 mm with OR of 5.8 (p < 0.001) and lower figures for DOI ≥ 5 mm (OR = 2.8, p = 0.01), APD ≥ 17 mm (OR = 2.8, p < 0.001) and iDMI (OR = 1.1, p = 0.82). Patients with iTFD < 6 mm also had significantly reduced progression-free survival with hazard ratio of 2.4 (p < 0.001). Conclusion For predicting pDMI, iTFD yielded best diagnostic performance and iTFD < 6 mm outperformed other cutoff-based imaging markers and conventional subjective assessment of deep myometrial invasion (iDMI) for diagnosing pDMI. Thus, iTFD at MRI represents a promising preoperative imaging biomarker that may aid in predicting pDMI and high-risk disease in EC.publishedVersio

Lipocalin 2 expression is associated with aggressive features of endometrial cancer

Background: Increased expression of lipocalin 2 (LCN2) has been observed in several cancers. The aim of the present study was to investigate LCN2 in endometrial cancer in relation to clinico-pathologic phenotype, angiogenesis, markers of epithelial-mesenchymal transition (EMT), and patient survival. Methods: Immunohistochemical staining was performed using a human LCN2 antibody on a population-based series of endometrial cancer patients collected in Hordaland County (Norway) during 1981-1990 (n = 256). Patients were followed from the time of primary surgery until death or last follow-up in 2007. The median follow-up time for survivors was 17 years. Gene expression data from a prospectively collected endometrial cancer series (n = 76) and a publicly available endometrial cancer series (n = 111) was used for gene correlation studies. Results: Expression of LCN2 protein, found in 49% of the cases, was associated with non-endometrioid histologic type (p = 0.001), nuclear grade 3 (p = 0.001), >50% solid tumor growth (p = 0.001), ER and PR negativity (p = 0.028 and 0.006), and positive EZH2 expression (p < 0.001). LCN2 expression was significantly associated with expression of VEGF-A (p = 0.021), although not with other angiogenesis markers examined (vascular proliferation index, glomeruloid microvascular proliferation, VEGF-C, VEGF-D or bFGF2 expression). Further, LCN2 was not associated with several EMT-related markers (E-cadherin, N-cadherin, P-cadherin, β-catenin), nor with vascular invasion (tumor cells invading lymphatic or blood vessels). Notably, LCN2 was significantly associated with distant tumor recurrences, as well as with the S100A family of metastasis related genes. Patients with tumors showing no LCN2 expression had the best outcome with 81% 5-year survival, compared to 73% for intermediate and 38% for the small subgroup with strong LCN2 staining (p = 0.007). In multivariate analysis, LCN2 expression was an independent prognostic factor in addition to histologic grade and FIGO stage. Conclusion: Increased LCN2 expression is associated with aggressive features and poor prognosis in endometrial cancer

Stathmin expression associates with vascular and immune responses in aggressive breast cancer subgroups

Studies indicate that stathmin expression associates with PI3K activation in breast cancer, suggesting stathmin as a marker for targetable patient subgroups. Here we assessed stathmin in relation to tumour proliferation, vascular and immune responses, BRCA1 germline status, basal-like differentiation, clinico-pathologic features, and survival. Immunohistochemical staining was performed on breast cancers from two series (cohort 1, n = 187; cohort 2, n = 198), and mass spectrometry data from 24 cases and 12 breast cancer cell lines was examined for proteomic profiles. Open databases were also explored (TCGA, METABRIC, Oslo2 Landscape cohort, Cancer Cell Line Encyclopedia). High stathmin expression associated with tumour proliferation, p53 status, basal-like differentiation, BRCA1 genotype, and high-grade histology. These patterns were confirmed using mRNA data. Stathmin mRNA further associated with tumour angiogenesis, immune responses and reduced survival. By logistic regression, stathmin protein independently predicted a BRCA1 genotype (OR 10.0, p = 0.015) among ER negative tumours. Cell line analysis (Connectivity Map) implied PI3K inhibition in tumours with high stathmin. Altogether, our findings indicate that stathmin might be involved in the regulation of tumour angiogenesis and immune responses in breast cancer, in addition to tumour proliferation. Cell data point to potential effects of PI3K inhibition in tumours with high stathmin expression.publishedVersio

Hormone receptor loss in endometrial carcinoma curettage predicts lymph node metastasis and poor outcome in prospective multicentre trial

Background: Preoperative histologic examination of tumour tissue is essential when deciding if endometrial cancer surgery should include lymph node sampling. We wanted to investigate if biomarkers could improve prediction of lymph node metastasis and outcome. Patients and methods: Curettage specimens from 832 endometrial carcinoma patients prospectively recruited from 10 centres in the MoMaTEC trial (Molecular Markers in Treatment of Endometrial Cancer) were investigated for hormone receptor and p53 status. Results: Eighteen per cent of tumours were double negative for oestrogen- and progesterone receptors (ER/PR loss), 24% overexpressed p53. Pathologic expression of all markers correlated with nodal metastases, high FIGO (Federation International of Gynecology and Obstetrics) stage, non-endometrioid histology, high grade and poor prognosis (all P < 0.001). ER/PR loss independently predicted lymph node metastasis (odds ratios (OR) 2.0, 95% confidence interval (CI) 1.1–3.7) adjusted for preoperative curettage histology and predicted poor disease-specific survival adjusted for age, FIGO stage, histologic type, grade and myometrial infiltration (hazard ratio (HR) 2.3, 95% CI 1.4–3.9). For lymph node negative endometrioid tumours, ER/PR loss influenced survival independent of grade. Conclusion: Double negative hormone receptor status in endometrial cancer curettage independently predicts lymph node metastasis and poor prognosis in a prospective multicentre setting. Implementing hormone receptor status to improve risk-stratification for selecting patients unlikely to benefit from lymphadenectomy seems justified.publishedVersio