scholarly journals A Machine Learning Analysis of TCGA Expression Data to Finding Signatures for “Normal-Like” IDH-WT Diffuse Gliomas with a Longer Survival

2021 ◽  
Vol 48 (s1) ◽  
pp. S2-S2
Author(s):  
HD Nguyen ◽  
A Allaire ◽  
P Diamandis ◽  
M Bisaillon ◽  
MS Scott ◽  
...  
Keyword(s):  
Diagnostic Algorithm ◽  
The Cancer Genome Atlas ◽  
List Type ◽  
Gamma Aminobutyric Acid ◽  
K Nearest Neighbors ◽  
Molecular Alterations ◽  
Non Coding Rna ◽  
Cancer Transcriptome ◽  
Diffuse Gliomas

Classification of primary CNS tumours is currently achieved by complementing histologic analysis with molecular information, in accordance with the WHO guidelines, and aims at providing accurate prognosis and optimal patient management. cIMPACT-NOW update 3 now recommends grading diffuse IDH-wild type astrocytomas as grade IV glioblastomas if they bear one or more of the following molecular alterations: EGFR amplification, TERT promoter mutation, and whole-chromosome 7 gain combined with chromosome 10 loss. In this reanalysis of the Cancer Genome Atlas (TCGA) glioma expression datasets, we identified 14 IDH-wt infiltrating astrocytic gliomas displaying a “normal-like (NL)” transcriptomic profile associated with a longer survival rate. Some of these tumours would be considered as GBM-equivalents with the current diagnostic algorithm. A k-nearest neighbors model was used to identify 3-gene signatures able to identify NL IDH-WT gliomas. Genes such as C5AR1 (complement receptor) SLC32A1 (vesicular gamma-aminobutyric acid transporter), and SMIM10L2A (long non-coding RNA) were overrepresented in these signatures which were validated further using the Chinese Glioma Genome and Ivy Glioblastoma Atlases. They showed high discriminative power and correlation with survival. This finding could lead to the validation of an immunohistochemical or PCR test which would facilitate classification of IDH-WT astrocytomas with unclear histological grading. Furthermore, associated signaling pathways might represent novel treatment targets for aggressive tumours.LEARNING OBJECTIVESThis presentation will enable the learner to: 1.Reconsider recent updates in the WHO classification of infiltrating gliomas.2.Discuss advanced bioinformatics profiling of the brain cancer transcriptome.

scholarly journals A machine learning analysis of a “normal-like” IDH-WT diffuse glioma transcriptomic subgroup associated with prolonged survival reveals novel immune and neurotransmitter-related actionable targets

BMC Medicine ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
H. D. Nguyen ◽  
A. Allaire ◽  
P. Diamandis ◽  
M. Bisaillon ◽  
M. S. Scott ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Diagnostic Algorithm ◽  
The Cancer Genome Atlas ◽  
Supervised Machine Learning ◽  
World Health ◽  
Diffuse Glioma ◽  
Gamma Aminobutyric Acid ◽  
Gene Signatures ◽  
Genome Atlas

Abstract Background Classification of primary central nervous system tumors according to the World Health Organization guidelines follows the integration of histologic interpretation with molecular information and aims at providing the most precise prognosis and optimal patient management. According to the cIMPACT-NOW update 3, diffuse isocitrate dehydrogenase-wild type (IDH-WT) gliomas should be graded as grade IV glioblastomas (GBM) if they possess one or more of the following molecular markers that predict aggressive clinical course: EGFR amplification, TERT promoter mutation, and whole-chromosome 7 gain combined with chromosome 10 loss. Methods The Cancer Genome Atlas (TCGA) glioma expression datasets were reanalyzed in order to identify novel tumor subcategories which would be considered as GBM-equivalents with the current diagnostic algorithm. Unsupervised clustering allowed the identification of previously unrecognized transcriptomic subcategories. A supervised machine learning algorithm (k-nearest neighbor model) was also used to identify gene signatures specific to some of these subcategories. Results We identified 14 IDH-WT infiltrating gliomas displaying a “normal-like” (NL) transcriptomic profile associated with a longer survival. Genes such as C5AR1 (complement receptor), SLC32A1 (vesicular gamma-aminobutyric acid transporter), MSR1 (or CD204, scavenger receptor A), and SYT5 (synaptotagmin 5) were differentially expressed and comprised in gene signatures specific to NL IDH-WT gliomas which were validated further using the Chinese Glioma Genome Atlas datasets. These gene signatures showed high discriminative power and correlation with survival. Conclusion NL IDH-WT gliomas represent an infiltrating glioma subcategory with a superior prognosis which can only be detected using genome-wide analysis. Differential expression of genes potentially involved in immune checkpoint and amino acid signaling pathways is providing insight into mechanisms of gliomagenesis and could pave the way to novel treatment targets for infiltrating gliomas.

scholarly journals Inferring perturbation profiles of cancer samples

2021 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Indirect Evidence ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Patient Specific ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Incomplete Coverage ◽  
Gene Perturbations

Abstract Motivation Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations. Results We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas. Availability and implementation The method is available as the R-package nempi at https://github.com/cbg-ethz/nempi and http://bioconductor.org/packages/nempi. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The local structure of the free boundary in the fractional obstacle problem

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Matteo Focardi ◽  
Emanuele Spadaro
Keyword(s):  
Hausdorff Dimension ◽  
Free Boundary ◽  
Hausdorff Measure ◽  
Local Structure ◽  
Obstacle Problem ◽  
List Type ◽  
Minkowski Content ◽  
Local Finiteness ◽  
Lower Dimensional

AbstractBuilding upon the recent results in [M. Focardi and E. Spadaro, On the measure and the structure of the free boundary of the lower-dimensional obstacle problem, Arch. Ration. Mech. Anal. 230 2018, 1, 125–184] we provide a thorough description of the free boundary for the solutions to the fractional obstacle problem in {\mathbb{R}^{n+1}} with obstacle function φ (suitably smooth and decaying fast at infinity) up to sets of null {{\mathcal{H}}^{n-1}} measure. In particular, if φ is analytic, the problem reduces to the zero obstacle case dealt with in [M. Focardi and E. Spadaro, On the measure and the structure of the free boundary of the lower-dimensional obstacle problem, Arch. Ration. Mech. Anal. 230 2018, 1, 125–184] and therefore we retrieve the same results:(i)local finiteness of the {(n-1)}-dimensional Minkowski content of the free boundary (and thus of its Hausdorff measure),(ii){{\mathcal{H}}^{n-1}}-rectifiability of the free boundary,(iii)classification of the frequencies and of the blowups up to a set of Hausdorff dimension at most {(n-2)} in the free boundary.Instead, if {\varphi\in C^{k+1}(\mathbb{R}^{n})}, {k\geq 2}, similar results hold only for distinguished subsets of points in the free boundary where the order of contact of the solution with the obstacle function φ is less than {k+1}.

Histomolecular classification of adult diffuse gliomas: The diagnostic value of immunohistochemical markers

2011 ◽  
Vol 167 (10) ◽  
pp. 683-690 ◽  
Author(s):  
D. Figarella-Branger ◽  
A. Maues de Paula ◽  
C. Colin ◽  
C. Bouvier
Keyword(s):  
Diagnostic Value ◽  
Immunohistochemical Markers ◽  
Diffuse Gliomas

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods

2017 ◽  
Vol 100 (2) ◽  
pp. 345-350 ◽  
Author(s):  
Ana M Jiménez-Carvelo ◽  
Antonio González-Casado ◽  
Estefanía Pérez-Castaño ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Olive Oil ◽  
Vegetable Oils ◽  
Edible Oils ◽  
Support Vector ◽  
Classification Methods ◽  
Multivariate Classification ◽  
Chromatographic Fingerprint ◽  
K Nearest Neighbors ◽  
Pls Discriminant Analysis

Abstract A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phaseLC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis tookonly 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil wereused: one input-class, two input-class, and pseudo two input-class.

scholarly journals Competing endogenous RNA (ceRNA) hypothetic model based on comprehensive analysis of long non-coding RNA expression in lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8024 ◽  
Author(s):  
Xiwen Wang ◽  
Rui Su ◽  
Qiqiang Guo ◽  
Jia Liu ◽  
Banlai Ruan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Inverse Correlation ◽  
The Cancer Genome Atlas ◽  
Competing Endogenous Rna ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Pathway Analyses ◽  
Lung Adenocarcinomas ◽  
Cancer Tissues

Background Non-small cell lung cancer (NSCLC) is a major subtype of lung cancer with high malignancy and bad prognosis, consisted of lung adenocarcinomas (LUAD) and lung squamous cell carcinomas (LUSC) chiefly. Multiple studies have indicated that competing endogenous RNA (ceRNA) network centered long noncoding RNAs (lncRNAs) can regulate gene expression and the progression of various cancers. However, the research about lncRNAs-mediated ceRNA network in LUAD is still lacking. Methods In this study, we analyzed the RNA-seq database from The Cancer Genome Atlas (TCGA) and obtained dysregulated lncRNAs in NSCLC, then further identified survival associated lncRNAs through Kaplan–Meier analysis. Quantitative real time PCR (qRT-PCR) was performed to confirm their expression in LUAD tissues and cell lines. The ceRNA networks were constructed based on DIANA-TarBase and TargetScan databases and visualized with OmicShare tools. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to investigate the potential function of ceRNA networks. Results In total, 1,437 and 1,699 lncRNAs were found to be up-regulated in LUAD and LUSC respectively with 895 lncRNAs overlapping (|log2FC| > 3, adjusted P value <0.01). Among which, 222 lncRNAs and 46 lncRNAs were associated with the overall survival (OS) of LUAD and LUSC, and 18 out of 222 up-regulated lncRNAs were found to have inverse correlation with LUAD patients’ OS (|log2FC| > 3, adjusted P value < 0.02). We selected 3 lncRNAs (CASC8, LINC01842 and VPS9D1-AS1) out of these 18 lncRNAs and confirmed their overexpression in lung cancer tissues and cells. CeRNA networks were further constructed centered CASC8, LINC01842 and VPS9D1-AS1 with 3 miRNAs and 100 mRNAs included respectively. Conclusion Through comprehensively analyses of TCGA, our study identified specific lncRNAs as candidate diagnostic and prognostic biomarkers for LUAD. The novel ceRNA network we created provided more insights into the regulatory mechanisms underlying LUAD.

scholarly journals Onco-Multi-OMICS Approach: A New Frontier in Cancer Research

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Sajib Chakraborty ◽  
Md. Ismail Hosen ◽  
Musaddeque Ahmed ◽  
Hossain Uddin Shekhar
Keyword(s):  
Cancer Research ◽  
Systems Biology ◽  
Cancer Cells ◽  
Cellular Response ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Resolving Power ◽  
Cancer Hallmarks ◽  
Molecular Alterations ◽  
Integrated Omics

The acquisition of cancer hallmarks requires molecular alterations at multiple levels including genome, epigenome, transcriptome, proteome, and metabolome. In the past decade, numerous attempts have been made to untangle the molecular mechanisms of carcinogenesis involving single OMICS approaches such as scanning the genome for cancer-specific mutations and identifying altered epigenetic-landscapes within cancer cells or by exploring the differential expression of mRNA and protein through transcriptomics and proteomics techniques, respectively. While these single-level OMICS approaches have contributed towards the identification of cancer-specific mutations, epigenetic alterations, and molecular subtyping of tumors based on gene/protein-expression, they lack the resolving-power to establish the casual relationship between molecular signatures and the phenotypic manifestation of cancer hallmarks. In contrast, the multi-OMICS approaches involving the interrogation of the cancer cells/tissues in multiple dimensions have the potential to uncover the intricate molecular mechanism underlying different phenotypic manifestations of cancer hallmarks such as metastasis and angiogenesis. Moreover, multi-OMICS approaches can be used to dissect the cellular response to chemo- or immunotherapy as well as discover molecular candidates with diagnostic/prognostic value. In this review, we focused on the applications of different multi-OMICS approaches in the field of cancer research and discussed how these approaches are shaping the field of personalized oncomedicine. We have highlighted pioneering studies from “The Cancer Genome Atlas (TCGA)” consortium encompassing integrated OMICS analysis of over 11,000 tumors from 33 most prevalent forms of cancer. Accumulation of huge cancer-specific multi-OMICS data in repositories like TCGA provides a unique opportunity for the systems biology approach to tackle the complexity of cancer cells through the unification of experimental data and computational/mathematical models. In future, systems biology based approach is likely to predict the phenotypic changes of cancer cells upon chemo-/immunotherapy treatment. This review is sought to encourage investigators to bring these different approaches together for interrogating cancer at molecular, cellular, and systems levels.

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