scholarly journals Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer

2020 ◽  
Author(s):  
So Yeon Kim ◽  
Eun Kyung Choe ◽  
Manu Shivakumar ◽  
Dokyoon Kim ◽  
Kyung-Ah Sohn
Keyword(s):  
Integrative Approach ◽  
Proof Of Concept ◽  
R Software ◽  
Pathway Activity ◽  
Source Codes ◽  
Prognostic Features ◽  
Molecular Features ◽  
Urologic Cancer ◽  
Inference Methods ◽  
Urologic Cancers

AbstractMotivationTo better understand the molecular features of cancers, a comprehensive analysis using multi-omics data has been conducted. Additionally, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW, and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene-gene graph using pathway information by assigning interactions between genes in multiple layers of networks.ResultsAs a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene-gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets.AvailabilityiDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW.

scholarly journals Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer

2021 ◽  
Author(s):  
So Yeon Kim ◽  
Eun Kyung Choe ◽  
Manu Shivakumar ◽  
Dokyoon Kim ◽  
Kyung-Ah Sohn
Keyword(s):  
Supplementary Information ◽  
Integrative Approach ◽  
R Software ◽  
Pathway Activity ◽  
Source Codes ◽  
Prognostic Features ◽  
Molecular Features ◽  
Urologic Cancer ◽  
Inference Methods ◽  
Urologic Cancers

Abstract Motivation To better understand the molecular features of cancers, a comprehensive analysis using multi-omics data has been conducted. In addition, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene–gene graph using pathway information by assigning interactions between genes in multiple layers of networks. Results As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene–gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets. Availability and implementation iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Beyond IDH-Mutation: Emerging Molecular Diagnostic and Prognostic Features in Adult Diffuse Gliomas

Cancers ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1817 ◽  
Author(s):  
Kanish Mirchia ◽  
Timothy E. Richardson
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Prognostic Factors ◽  
The United States ◽  
Molecular Diagnostic ◽  
Grading System ◽  
Diffuse Glioma ◽  
Prognostic Features ◽  
Molecular Features ◽  
Diffuse Gliomas

Diffuse gliomas are among the most common adult central nervous system tumors with an annual incidence of more than 16,000 cases in the United States. Until very recently, the diagnosis of these tumors was based solely on morphologic features, however, with the publication of the WHO Classification of Tumours of the Central Nervous System, revised 4th edition in 2016, certain molecular features are now included in the official diagnostic and grading system. One of the most significant of these changes has been the division of adult astrocytomas into IDH-wildtype and IDH-mutant categories in addition to histologic grade as part of the main-line diagnosis, although a great deal of heterogeneity in the clinical outcome still remains to be explained within these categories. Since then, numerous groups have been working to identify additional biomarkers and prognostic factors in diffuse gliomas to help further stratify these tumors in hopes of producing a more complete grading system, as well as understanding the underlying biology that results in differing outcomes. The field of neuro-oncology is currently in the midst of a “molecular revolution” in which increasing emphasis is being placed on genetic and epigenetic features driving current diagnostic, prognostic, and predictive considerations. In this review, we focus on recent advances in adult diffuse glioma biomarkers and prognostic factors and summarize the state of the field.

scholarly journals DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays

2019 ◽  
Vol 35 (17) ◽  
pp. 3055-3062 ◽  
Author(s):  
Amrit Singh ◽  
Casey P Shannon ◽  
Benoît Gautier ◽  
Florian Rohart ◽  
Michaël Vacher ◽  
...  
Keyword(s):  
Data Integration ◽  
Biomarker Discovery ◽  
Predictive Performance ◽  
Supplementary Information ◽  
Integrative Approach ◽  
Data Types ◽  
Common Information ◽  
Molecular Features ◽  
Study Designs ◽  
Integration Analysis

Abstract Motivation In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups. Results Using simulations and benchmark multi-omics studies, we show that DIABLO identifies features with superior biological relevance compared with existing unsupervised integrative methods, while achieving predictive performance comparable to state-of-the-art supervised approaches. DIABLO is versatile, allowing for modular-based analyses and cross-over study designs. In two case studies, DIABLO identified both known and novel multi-omics biomarkers consisting of mRNAs, miRNAs, CpGs, proteins and metabolites. Availability and implementation DIABLO is implemented in the mixOmics R Bioconductor package with functions for parameters’ choice and visualization to assist in the interpretation of the integrative analyses, along with tutorials on http://mixomics.org and in our Bioconductor vignette. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Pancreatic Solid Pseudopapillary Neoplasm: Key Pathologic and Genetic Features

2020 ◽  
Vol 144 (7) ◽  
pp. 829-837 ◽  
Author(s):  
Stefano La Rosa ◽  
Massimo Bongiovanni
Keyword(s):  
Good Prognosis ◽  
Low Grade ◽  
Solid Pseudopapillary Neoplasm ◽  
Prognostic Features ◽  
Molecular Alterations ◽  
Immunohistochemical Markers ◽  
Molecular Features ◽  
Pubmed Database ◽  
Genetic Features ◽  
Solid Pseudopapillary Neoplasms

Context.— Solid pseudopapillary neoplasm of the pancreas is a low-grade malignant tumor generally associated with a good prognosis. Solid pseudopapillary neoplasms show peculiar morphologic features, but sometimes the differential diagnosis with other pancreatic neoplasms (ie, pancreatic neuroendocrine tumors) can be a challenging task, especially in cytologic or biopsy specimens. In these cases immunohistochemistry is a useful tool, but the diagnostic utility of several proposed immunohistochemical markers is questionable. In recent years, despite several attempts to characterize the pathogenetic, molecular, and prognostic features of solid pseudopapillary neoplasms, they still remain unclear. Objective.— To give the reader a comprehensive update on this entity. Data Sources.— The PubMed database (US National Library of Medicine) was searched using the following string: pseudopapillary tumor [AND/OR] neoplasm [AND/OR] pancreas. All articles written in English were included. In addition, because a heterogeneous terminology has been used in the past to define solid pseudopapillary neoplasms, the reference lists of each paper selected in the PubMed database were also reviewed. Conclusions.— This review gives a comprehensive update on the pathologic, clinical, and molecular features of solid pseudopapillary neoplasms, particularly addressing issues and challenges related to diagnosis. In addition, we have tried to correlate the molecular alterations with the morphologic and clinical features.

scholarly journals An integrative analysis identifying transcriptional features and key genes involved in COVID-19

Epigenomics ◽  
2020 ◽  
Vol 12 (22) ◽  
pp. 1969-1981
Author(s):  
Guoping Li ◽  
Junyi Wang ◽  
Xiang He ◽  
Lei Zhang ◽  
Qin Ran ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Immune Cells ◽  
Bronchoalveolar Lavage Fluid ◽  
Lavage Fluid ◽  
Integrative Approach ◽  
Molecular Features ◽  
Infected Cells ◽  
Key Genes ◽  
Upregulated Genes ◽  
Core Genes

Aim: To elucidate the transcriptional characteristics of COVID-19. Materials & methods: We utilized an integrative approach to comprehensively analyze the transcriptional features of both COVID-19 patients and SARS-CoV-2 infected cells. Results: Widespread infiltration of immune cells was observed. We identified 233 genes that were codifferentially expressed in both bronchoalveolar lavage fluid and lung samples of COVID-19 patients. Functional analysis suggested upregulated genes were related to immune response such as neutrophil activation and antivirus response, while downregulated genes were associated with cell adhesion. Finally, we identified LCN2, STAT1 and UBE2L6 as core genes during SARS-CoV-2 infection. Conclusion: The identification of core genes involved in COVID-19 can provide us with more insights into the molecular features of COVID-19.

scholarly journals A Heuristic Local-sensitive Program-Wide Diffing Method for IoT Binary Files

2021 ◽  
Author(s):  
Lu Yu ◽  
Yuliang Lu ◽  
Yi Shen ◽  
Zulie Pan ◽  
Hui Huang
Keyword(s):  
Denial Of Service ◽  
Third Party ◽  
Semantic Features ◽  
Proof Of Concept ◽  
Vulnerability Detection ◽  
Source Codes ◽  
Flow Dependence ◽  
Iot Devices ◽  
The Impact ◽  
Earth Mover

AbstractCode reuse brings vulnerabilities in third-party library to many Internet of Things (IoT) devices, opening them to attacks such as distributed denial of service. Program-wide binary diffing technology can help detect these vulnerabilities in IoT devices whose source codes are not public. Considering the architectures of IoT devices may vary, we propose a data-aware program-wide diffing method across architectures and optimization levels. We rely on the defined anchor functions and call relationship to expand the comparison scope within the target file, reducing the impact of different architectures on the diffing result. To make the diffing result more accurate, we extract the semantic features that can represent the code by data flow dependence analysis. Earth mover distance is used to calculate the similarity of functions in two files based on semantic features. We implemented a proof-of-concept DAPDiff and compared it with baseline BinDiff, TurboDiff and Asm2vec. Experiments showed the availability and effectiveness of our method across optimization levels and architectures. DAPDiff outperformed BinDiff in recall and precision by 41.4% and 9.2% on average when making diffing between standard third-party library and the real-world firmware files. This proves that DAPDiff can be applicable for the vulnerability detection in IoT devices.

PATH-08. PROGNOSTIC IMPLICATIONS FROM LONG-TERM SURVIVORS (LTS) OF GLIOBLASTOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi116-vi116
Author(s):  
Nicole Briceno ◽  
Zied Abdullaev ◽  
Elizabeth Vera ◽  
Anna Choi ◽  
Alexa Christ ◽  
...  
Keyword(s):  
Poor Prognosis ◽  
Subtotal Resection ◽  
Specific Gene ◽  
Tumor Biopsy ◽  
Term Survival ◽  
Prognostic Features ◽  
Molecular Features ◽  
Mgmt Promoter ◽  
Long Term Survivors

Abstract Glioblastoma (GBM) is the most aggressive primary brain malignancy with < 45% living a year beyond diagnosis which drops to 7% at five years. However, there have been reports of long-term survivors (LTS) living three to ten years beyond diagnosis. Few studies have reported on molecular factors in tumors from LTS cohorts. We identified GBM (IDH1/2 wildtype) patients living at least 3 years post diagnosis (N=25), including 16 with pre-treatment tumor tissue, from our Natural History Study. Available pre- or post-treatment tumors were analyzed with targeted panel sequencing and methylation analysis for classification, MGMT promoter status and copy number changes. Classical clinical prognostic features such as limited resection or older age did not preclude long-term survival as patients with tumor biopsy (n=1) or subtotal resection (n= 5) and patients > 60 were included in the LTS cohort. Furthermore, tumors with molecular features typically associated with poor prognosis were also in this GBM LTS group. MGMT promoter was unmethylated in 17% of tumors; EGFRvIII mutation in 13%, EGFR amplification in 33%, CDKN2A homozygous loss in 30% and complete chromosome 7 gain with 10 loss in 55%. Additionally, the methylation classifier found a higher-than-expected incidence of mesenchymal tumors (29%) and RTK II (57%). Tumors had a higher percent of TP53 mutations (44%) but lower pTERT (76%) compared to TCGA. These data suggest an individual patient’s prognosis cannot easily be predetermined based on classical clinical and molecular data. This underscores the need for further analyses to discover additional factors leading to their unexpected, prolonged survival and elucidate the role of factors typically associated with poor prognosis. Future work will include RNA-sequencing and germline whole genome sequencing to determine tumor specific gene expression and identify any possible genomic alterations that confer improved survival.

scholarly journals Metabolomics Identifies Perturbations in Human Disorders of Propionate Metabolism

2007 ◽  
Vol 53 (12) ◽  
pp. 2169-2176 ◽  
Author(s):  
William R Wikoff ◽  
Jon A Gangoiti ◽  
Bruce A Barshop ◽  
Gary Siuzdak
Keyword(s):  
Clinical Chemistry ◽  
Propionic Acidemia ◽  
Disease Diagnosis ◽  
Metabolic Effects ◽  
P Value ◽  
Proof Of Concept ◽  
Healthy Individuals ◽  
Molecular Features ◽  
Human Disorders ◽  
Screening Platform

Abstract Background: We applied untargeted mass spectrometry-based metabolomics to the diseases methylmalonic acidemia (MMA) and propionic acidemia (PA). Methods: We used a screening platform that used untargeted, mass-based metabolomics of methanol-extracted plasma to find significantly different molecular features in human plasma samples from MMA and PA patients and from healthy individuals. Capillary reverse phase liquid chromatography (4 μL/min) was interfaced to a TOF mass spectrometer, and data were processed using nonlinear alignment software (XCMS) and an online database (METLIN) to find and identify metabolites differentially regulated in disease. Results: Of the approximately 3500 features measured, propionyl carnitine was easily identified as the best biomarker of disease (P value 1.3 × 10−18), demonstrating the proof-of-concept use of untargeted metabolomics in clinical chemistry discovery. Five additional acylcarnitine metabolites showed significant differentiation between plasma from patients and healthy individuals, and γ-butyrobetaine was highly increased in a subset of patients. Two acylcarnitine metabolites and numerous unidentified species differentiate MMA and PA. Many metabolites that do not appear in any public database, and that remain unidentified, varied significantly between normal, MMA, and PA, underscoring the complex downstream metabolic effects resulting from the defect in a single enzyme. Conclusions: This proof-of-concept study demonstrates that metabolomics can expand the range of metabolites associated with human disease and shows that this method may be useful for disease diagnosis and patient clinical evaluation.

scholarly journals Deciphering heterogeneity of septic shock patients using immune functional assays: a proof of concept study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Chloé Albert Vega ◽  
Guy Oriol ◽  
François Bartolo ◽  
Jonathan Lopez ◽  
Alexandre Pachot ◽  
...  
Keyword(s):  
Septic Shock ◽  
Immune Response ◽  
Nosocomial Infections ◽  
Added Value ◽  
Functional Assay ◽  
Proof Of Concept ◽  
Concept Study ◽  
The Third ◽  
Molecular Features ◽  
Therapeutic Decisions

Abstract The complexity of sepsis pathophysiology hinders patient management and therapeutic decisions. In this proof-of-concept study we characterised the underlying host immune response alterations using a standardised immune functional assay (IFA) in order to stratify a sepsis population. In septic shock patients, ex vivo LPS and SEB stimulations modulated, respectively, 5.3% (1/19) and 57.1% (12/21) of the pathways modulated in healthy volunteers (HV), highlighting deeper alterations induced by LPS than by SEB. SEB-based clustering, identified 3 severity-based groups of septic patients significantly different regarding mHLA-DR expression and TNFα level post-LPS, as well as 28-day mortality, and nosocomial infections. Combining the results from two independent cohorts gathering 20 HV and 60 patients, 1 cluster grouped all HV with 12% of patients. The second cluster grouped 42% of patients and contained all non-survivors. The third cluster grouped 46% of patients, including 78% of those with nosocomial infections. The molecular features of these clusters indicated a distinctive contribution of previously described genes defining a “healthy-immune response” and a “sepsis-related host response”. The third cluster was characterised by potential immune recovery that underlines the possible added value of SEB-based IFA to capture the sepsis immune response and contribute to personalised management.

scholarly journals Salivary Gland Pleomorphic Adenomas Presenting With Extremely Varied Clinical Courses. A Single Institution Case-Control Study†

2021 ◽  
Vol 10 ◽  
Author(s):  
Krzysztof Piwowarczyk ◽  
Ewelina Bartkowiak ◽  
Paweł Kosikowski ◽  
Jadzia Tin-Tsen Chou ◽  
Małgorzata Wierzbicka
Keyword(s):  
Salivary Gland ◽  
Reference Group ◽  
Practical Importance ◽  
Predictor Variable ◽  
National Registry ◽  
Outcome Variable ◽  
Surgical Specimen ◽  
Prognostic Features ◽  
Molecular Features ◽  
Pleomorphic Adenomas

ObjectivePleomorphic adenomas (PAs) with divergent clinical behavior, differing from the vast majority of PAs, were distinguished. “Fast” PAs are characterized by an unexpectedly short medical history and relatively rapid growth. The reference group consisted of “slow” PAs with very stable biology and long-term progression. We divide the PA group as a whole into three subsets: “fast,” “normal,” and “slow” tumors. Our goal is a multifactorial analysis of the “fast” and “slow” PA subgroups.MethodsConsecutive surgeries in a tertiary referral center, the Department of Otolaryngology and Laryngological Surgery, Poznan University of Medical Sciences, Poland, were carried out between 2002 and 2011. Out of 1,154 parotid tumors, 636 (55.1%) were PAs. The data were collected prospectively in collaboration with the Polish National Registry of Benign Salivary Gland Tumors. The main outcome measure was the recurrence rate in “fast” and “slow” PA subgroups. All surgical qualifications and surgeries were performed by two experienced surgeons.ResultsSlow PAs, compared to fast PAs, presented in older patients (53.25 ± 15.29 versus 47.92 ± 13.44 years). Multifactor logistic regression analysis with recurrence (yes/no) as the outcome variable, fast/slow as the predictor variable and age, gender, margin, FN status as covariates showed that fast PAs were significantly predicting recurrence vs. slow PAs (p = 0.035). Fast PAs were increasing the risk of PAs 10-fold vs. slow PAs, exp β = 10.20, CI95 [1.66; 197.87]. The variables impacting relapse were recent accelerated growth of the tumor OR = 3.35 (SE = 0.56), p = 0.030, positive margins OR = 7.18 (SE = 0.57), p < 0.001, incomplete or bare capsule OR = 9.91 (SE = 0.53), p = 0.001 and location III OR = 3.12 (SE = 0.53), p = 0.033. In the multivariate model only positive margin was selected as the best predictor of relapse, OR = 5.01 (SE = 0.60), p = 0.007.ConclusionsThe simple clinical aspect of slow or fast PA progression is of great practical importance and can constitute a surrogate of the final histopathological information that is derived from the surgical specimen. The slow or fast nature of the PA to some extent indicates prognostic features such as recurrence risk. This finding requires correlation with histological and molecular features in further stages of research.

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