Nonparametric Tikhonov Regularized NMF and Its Application in Cancer Clustering

Mirzal, Andri

doi:10.1109/tcbb.2014.2328342

Cited by 35 publications

(16 citation statements)

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“…Identification of molecular types associated with metabolic genes. Nonnegative matrix factorization (NMF) is an unsupervised clustering method widely used in identifying genomics-based tumor molecular subtypes (35,36). To further determine the association between expression levels of energy metabolism genes and phenotypes, the NMF method was used to cluster samples according to the expression profiles of energy metabolism-related genes associated with ovarian cancer prognosis, and standard 'brunet' for 50 iterations was selected by NMF.…”

Section: Methodsmentioning

confidence: 99%

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

Wang

2020

Oncol Rep

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Changes in energy metabolism may be potential biomarkers and therapeutic targets for cancer as they frequently occur within cancer cells. However, basic cancer research has failed to reach a consistent conclusion on the function(s) of mitochondria in energy metabolism. The significance of energy metabolism in the prognosis of ovarian cancer remains unclear; thus, there remains an urgent need to systematically analyze the characteristics and clinical value of energy metabolism in ovarian cancer. Based on gene expression patterns, the present study aimed to analyze energy metabolism-associated characteristics to evaluate the prognosis of patients with ovarian cancer. A total of 39 energy metabolism-related genes significantly associated with prognosis were obtained, and three molecular subtypes were identified by nonnegative matrix factorization clustering, among which the C1 subtype was associated with poor clinical outcomes of ovarian cancer. The immune response was enhanced in the tumor microenvironment. A total of 888 differentially expressed genes were identified in C1 compared with the other subtypes, and the results of the pathway enrichment analysis demonstrated that they were enriched in the 'PI3K-Akt signaling pathway', 'cAMP signaling pathway', 'ECM-receptor interaction' and other pathways associated with the development and progression of tumors. Finally, eight characteristic genes (tolloid-like 1 gene, type XVI collagen, prostaglandin F2α, cartilage intermediate layer protein 2, kinesin family member 26b, interferon inducible protein 27, growth arrest-specific gene 1 and chemokine receptor 7) were obtained through LASSO feature selection; and a number of them have been demonstrated to be associated with ovarian cancer progression. In addition, Cox regression analysis was performed to establish an 8-gene signature, which was determined to be an independent prognostic factor for patients with ovarian cancer and could stratify sample risk in the training, test and external validation datasets (P<0.01; AUC >0.8). Gene Set Enrichment Analysis results revealed that the 8-gene signature was involved in important biological processes and pathways of ovarian cancer. In conclusion, the present study established an 8-gene signature associated with metabolic genes, which may provide new insights into the effects of energy metabolism on ovarian cancer. The 8-gene signature may serve as an independent prognostic factor for ovarian cancer patients.

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Section: Methodsmentioning

confidence: 99%

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

Wang

2020

Oncol Rep

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“…An illustration of this application of PANAPOLY is provided using the 17 BEAUTY patients with chemoresistant TNBC. NMF clustering 25 was performed with the drug priority scores of these 17 cases. Based on the cophenetic and average silhouette scores, two clusters were selected to be optimal.…”

Section: Resultsmentioning

confidence: 99%

PANOPLY:Omics-guided drug prioritization method tailored to an individual patient

Kalari

Sinnwell

Thompson

et al. 2017

Preprint

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The majority of cancer patients receive treatments that are minimally informed by omics data.Our goal was to develop a precision medicine computational framework (PANOPLY: Precision cancer genomic report: single sample inventory) to identify and prioritize drug targets and cancer therapy regimens. The PANOPLY approach integrates clinical data with germline and somatic features obtained from multi-omics platforms, and apply machine learning, and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY employs multiple steps including i) selection of matched controls, ii) preprocessing of data and identification of driver mutations and altered genes in the patient, iii) identification of suitable drugs using the driver-gene network and random forest analysis, and iv) a multi-omics case report of the patient with prioritization of anti-cancer drugs. The PANOPLY framework can. CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/176396 doi: bioRxiv preprint first posted online Aug. 15, 2017; 2 be executed on a stand-alone virtual machine and is also available for download as an R package. We applied PANOPLY to multiple publicly accessible multi-omics tumor datasets with survival data available. We also applied the method to an institutional breast cancer neoadjuvant chemotherapy study which collected clinical and genomic data as well as patientderived xenografts (PDXs) to investigate the prioritization offered by PANOPLY. We found that that the prioritized drug, olaparib, was more effective than placebo at treating the tumor in the chemotherapy resistant PDX model (P < 0.05). Further studies are ongoing to determine the efficacy of PANOPLY using additional PDXs. In summary, PANOPLY prioritizes drugs based on both clinical and multi-omics data, and it can aid oncologists in their decision-making for therapy in the individual patient.

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“…Non-negative matrix factorization (NMF) is an unsupervised clustering method, which is widely used in the discovery of tumor molecular subtypes based on genomics [23][24],The mutation characteristics in CRC were further observed, and the NMF method was used to cluster the samples based on SNV data to identify the mutation characteristics of the samples, in which the NMF method selected the standard "brunet" for 50 iterations. The clustering number k is set to 2 to 10, and the average contour width of the common member matrix is calculated using the R software package NMF [25].…”

Section: Analysis Of Mutation Characteristicsmentioning

confidence: 99%

Clonal architectures predict clinical outcome in gastric adenocarcinoma based genomic variation and tumor evolution and heterogeneity in gastric adenocarcinoma

Ren

Wang

et al. 2020

Preprint

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Background: Gastric cancer is a highly heterogeneous disease. Due to the lack of effective molecular markers and personalized treatment, the prognosis of patients with gastric cancer is still rather poor. This paper attempts to explore the genomic instability and intratumoral heterogeneity of gastric cancer through bioinformatics analysis. Methods: According to the RNA-Seq data, copy number variation data and clinical follow-up information data of TCGA, brunet algorithm in NMF was used to identify SNV signature. ABSOLUTE algorithm was used to screen and identify CNV signature. The relationship between clonal state and survival of genes was analyzed with Kaplan-Meier method. The Spearman method was used to evaluate the correlation between clonal/sub-clonal events and TMB and neoantigen. Results: Mutation analysis of SNV mutation and CNA data in the TCGA gastric cancer dataset divided mutation of TCGA gastric cancer samples into three signatures with significant differences. Furthermore, the clonal/sub-clonal events of SCNA and SNV were analyzed to identify the clonal/sub-clonal status of mutations in STAD samples. It was further found that the number of mutations in TP53, TTN and MUC16 genes was the highest (> 20%) among the samples. The number of CNV (Gain) in MIEN1, GRB7 and PNMT genes was the highest (> 10%) among the samples. A series of early gastric cancer genes, such as TP53, USH2A and GLI3 as well as advanced genes such as CTNNB1, LRP1B and ERBB4 were further identified. Ultimately, the clonal/sub-clonal status of 5 early genes, 12 intermediate genes and 8 advanced genes was significantly correlated with the overall survival rate of patients. In addition, there was a significant correlation between clonal/sub-clonal events and TMB/Neoantigens. Conclusion: In this paper, the relationship between intratumoral heterogeneity and genomic instability was evaluated based on developmental data of gastric cancer cloning system. A series of molecular features determined by screening can be used as a marker of potential gastric cancer heterogeneity, which is of great significance to the personalized treatment of gastric cancer.

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Nonparametric Tikhonov Regularized NMF and Its Application in Cancer Clustering

Cited by 35 publications

References 36 publications

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

PANOPLY:Omics-guided drug prioritization method tailored to an individual patient

Clonal architectures predict clinical outcome in gastric adenocarcinoma based genomic variation and tumor evolution and heterogeneity in gastric adenocarcinoma

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