scholarly journals Genome-scale Pathway Flux Analysis Predicts Efficacy of anti-PD-1 Therapy in Melanoma

2020 ◽  
Author(s):  
Qi Mei ◽  
Shangming Du ◽  
Kathrin Halfter ◽  
Xiaoyu Li ◽  
Guokun Zhang ◽  
...  
Keyword(s):  
Immune Cells ◽  
Biomarker Discovery ◽  
Objective Response ◽  
Predictive Ability ◽  
Flux Analysis ◽  
Cellular Functions ◽  
A Genome ◽  
Metabolic Remodeling ◽  
Genome Scale ◽  
Pathway Flux

Abstract Background: Currently, predicting treatment efficacy to immunotherapy has been under extensive investigation. However, a putative biomarker for immunotherapy in melanoma remains to be found.Methods: Utilizing genetic data from two independent melanoma patient cohorts treated with anti-PD-1 therapy, the study described herein conducted a genome-scale pathway flux analysis (GPFA) and related statistical methods to determine whether specific pathways could be identified that are relevant to immunotherapy efficacy.Results: The analysis results highlighted three mechanisms responsible for the efficacy of immunotherapy in melanoma including 1) proper cellular functions in immune cells; 2) angiogenesis for the development and differentiation of immune cells; 3) energy metabolic remodeling to meet the activation of immune cells. Based on these discoveries, a pathway flux-based biomarker (IM.Index) was developed and assessed to validate its predictive ability with odds ratio (OR) of 3.14 (95%CI: 1.16-8.45; p=3.10E-3), sensitivity 76% and specificity 89%. The IM.Index achieved an objective response rate (ORR) of 70%. Comparison to other four putative biomarkers (TMB, NAL, neo-peptide load and cytolytic score) showed a comparative outcome with an hazard ratio (HR) of 1.83 (95%CI: 1.26-2.67; p=1.62E-3) and area under curve (AUC) of 0.82.Conclusion: These results indicate a translational potential of IM.Index, as a biomarker, for anti-PD-1 therapy in melanoma and the GPFA might pave a new path for biomarker discovery in immunotherapy.

Pathway-flux based biomarker for immunotherapy response in melanoma.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e22064-e22064
Author(s):  
Jian Li ◽  
Qi Mei ◽  
Weiting Cheng ◽  
Guangyuan Hu ◽  
Xianglin Yuan ◽  
...  
Keyword(s):  
Immune System ◽  
Metabolic Flux ◽  
Expression Profiles ◽  
Objective Response ◽  
Superior Performance ◽  
Flux Analysis ◽  
Signaling Transduction ◽  
A Genome ◽  
Genome Scale ◽  
Pathway Flux

e22064 Background: Immune checkpoint therapy (ICT) refers to therapeutic interventions that specifically target immune evasion mechanisms to restore the host immunity with anti-tumor ability. The ICT has revolutionized the immune-based treatment across > 30 different cancers including solid tumors and hematopoietic malignancies, with an ORR of 30% and a 7%-12% grades 3-5 irAEs in average. However, a substantial unmet point is the development of a biomarker with which response of ICT can be predicted before treatment for individual patients. Methods: In order to face this challenge this study developed an advanced genome-scale pathway flux analysis (GPFA) to evaluate the strength of signaling transduction and metabolic flux in immune system. The input of GPFA is the gene expression profiles of individual objects and the output of GPFA can be summarized in a index system, IM.Index, with following definition: IM.Index = 1.78E-4 * Σ flux(P) + 2.37E-4 * Σ flux(P) p ∈ signaling transduction p ∈ energy metabolism. Subsequently, the IM.Index was applied to analyze genetic data of two independent cohorts of melanoma patients treated with anti-PD-1 therapy (nivolumab a. pembrolizumab). Results: The IM.Index predicted the response of anti-PD-1 therapy (nivolumab) in the first cohort with an odds ratio (OR) of 3.14 (95%CI: 1.16-8.45; p = 3.10E-3; AUC = 0.82) and with a sensitivity 89% and specificity 76%. The prediction on overall survival (OS) of this cohort achieved an hazard ratio (HR) of 1.53 (95%CI: 1.22-1.92; p = 7.8E-3). Subsequently, the prediction result for the anti-PD-1 therapy (pembrolizumab) in the second cohort achieved an OR of 2.12 (95%CI: 1.22-3.66; p = 4.50E-4; AUC = 0.87) and the OS prediction in this cohort reached an HR of 1.24 (95%CI: 1.04-1.47; p = 1.40E-2). Comparison with other four potential biomarkers (TMB, TNB, neo-peptide load and cytolytic score) related to immunotherapy showed a comparative outcome of the IM.Index regarding diagnosis and prognosis in melanoma. For instance, IM.Index showed a superior performance on objective response rate (ORR) of 70% and AUC of 0.83. Conclusions: In conclusion this study demonstrated that a pathway flux analysis at a genome-scale may be explorative in biomarker research in immunotherapy, since this type of analysis could reflect the strength or functional status of the immune system. The IM.Index developed in this study may also be applied to investigation the treatment response of immunotherapy in other types of cancer.

scholarly journals Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Javad Aminian-Dehkordi ◽  
Seyyed Mohammad Mousavi ◽  
Arezou Jafari ◽  
Ivan Mijakovic ◽  
Sayed-Amir Marashi
Keyword(s):  
Experimental Data ◽  
Metabolic Network ◽  
Bacillus Megaterium ◽  
In Silico ◽  
Metabolic Model ◽  
Growth Behavior ◽  
Industrial Biotechnology ◽  
Flux Analysis ◽  
A Genome ◽  
Genome Scale

AbstractBacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature (growth behavior patterns, metabolite production capabilities, metabolic flux analysis using 13C glucose and formaldehyde inhibitory effect) were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium.

scholarly journals Advances in metabolic flux analysis toward genome-scale profiling of higher organisms

2018 ◽  
Vol 38 (6) ◽  
Author(s):  
Georg Basler ◽  
Alisdair R. Fernie ◽  
Zoran Nikoloski
Keyword(s):  
Large Scale ◽  
Metabolic Flux ◽  
Cell Types ◽  
Flux Analysis ◽  
Metabolic Labeling ◽  
Modeling Framework ◽  
Metabolomics Data ◽  
Technological Advances ◽  
A Genome ◽  
Genome Scale

Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.

scholarly journals 13C metabolic flux analysis at a genome-scale

2015 ◽  
Vol 32 ◽  
pp. 12-22 ◽  
Author(s):  
Saratram Gopalakrishnan ◽  
Costas D. Maranas
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
13C Metabolic Flux Analysis ◽  
A Genome ◽  
Genome Scale

scholarly journals Prediction of Radiation Pneumonitis Using Genome-Scale Flux Analysis of RNA-Seq Derived From Peripheral Blood

2021 ◽  
Vol 8 ◽  
Author(s):  
Siqi Yang ◽  
Yi Yao ◽  
Yi Dong ◽  
Junqi Liu ◽  
Yingge Li ◽  
...  
Keyword(s):  
Peripheral Blood ◽  
Scoring System ◽  
Radiation Pneumonitis ◽  
Clinical Information ◽  
Tumor Stage ◽  
Flux Analysis ◽  
Rna Seq ◽  
A Genome ◽  
Significant Difference ◽  
Genome Scale

Purpose: Radiation pneumonitis (RP) frequently occurs during a treatment course of chest radiotherapy, which significantly reduces the clinical outcome and efficacy of radiotherapy. The ability to easily predict RP before radiotherapy would allow this disease to be avoided.Methods and Materials: This study recruited 48 lung cancer patients requiring chest radiotherapy. For each participant, RNA sequencing (RNA-Seq) was performed on a peripheral blood sample before radiotherapy. The RNA-Seq data was then integrated into a genome-scale flux analysis to develop an RP scoring system for predicting the probability of occurrence of RP. Meanwhile, the clinical information and radiation dosimetric parameters of this cohort were collected for analysis of any statistical associations between these parameters and RP. A non-parametric rank sum test showed no significant difference between the predicted results from the RP score system and the clinically observed occurrence of RP in this cohort.Results: The results of the univariant analysis suggested that the tumor stage, exposure dose, and bilateral lung dose of V5 and V20 were significantly associated with the occurrence of RP. The results of the multivariant analysis suggested that the exposure doses of V5 and V20 were independent risk factors associated with RP and a level of RP ≥ 2, respectively. Thus, our results indicate that our RP scoring system could be applied to accurately predict the risk of RP before radiotherapy because the scores were highly consistent with the clinically observed occurrence of RP.Conclusion: Compared with the standard statistical methods, this genome-scale flux-based scoring system is more accurate, straightforward, and economical, and could therefore be of great significance when making clinical decisions for chest radiotherapy.

scholarly journals Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

2020 ◽  
Author(s):  
Piyush Nanda ◽  
Pradipta Patra ◽  
Manali Das ◽  
Amit Ghosh
Keyword(s):  
Ethyl Acetate ◽  
Metabolic Flux ◽  
Carbon Sources ◽  
Growth Dynamics ◽  
Dry Weight ◽  
Metabolic Model ◽  
Flux Analysis ◽  
Specific Rate ◽  
A Genome ◽  
Genome Scale

Abstract Background Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to optimal fermentation only in oxygen limiting conditions that render it a suitable host for industrial-scale protein production. Ethyl acetate, an important industrial chemical, has been demonstrated to be a major overflow metabolite during aerobic batch cultivation with a specific rate of 0.12 g per g dry weight per hour. Here, we reconstruct a genome-scale metabolic model of the yeast to better explain the observed phenotypes and aid further hypothesis generation. Results We report the first genome-scale metabolic model, iPN730, using Build Fungal Model in KBase workspace. The inconsistencies in the draft model were semi-automatically corrected using literature and published datasets. The curated model comprises of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments (organelles). The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. The URC pyrimidine degradation pathway incorporated into the model enables it to grow on uracil or urea as the sole nitrogen source. Conclusion The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism, particularly that of pyrimidine metabolism and ethyl acetate production. Metabolic flux analysis using the model will enable hypotheses generation to gain a deeper understanding of metabolism in weakly Crabtree positive yeast and in fungal biodiversity in general.

scholarly journals Arabidopsis Genes Essential for Seedling Viability: Isolation of Insertional Mutants and Molecular Cloning

Genetics ◽  
2001 ◽  
Vol 159 (4) ◽  
pp. 1765-1778
Author(s):  
Gregory J Budziszewski ◽  
Sharon Potter Lewis ◽  
Lyn Wegrich Glover ◽  
Jennifer Reineke ◽  
Gary Jones ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Translocation ◽  
Gene Families ◽  
Mutant Phenotype ◽  
Lethal Mutant ◽  
A Genome ◽  
Genes Encoding ◽  
High Level ◽  
Mutant Lines ◽  
Genome Scale

Abstract We have undertaken a large-scale genetic screen to identify genes with a seedling-lethal mutant phenotype. From screening ~38,000 insertional mutant lines, we identified >500 seedling-lethal mutants, completed cosegregation analysis of the insertion and the lethal phenotype for >200 mutants, molecularly characterized 54 mutants, and provided a detailed description for 22 of them. Most of the seedling-lethal mutants seem to affect chloroplast function because they display altered pigmentation and affect genes encoding proteins predicted to have chloroplast localization. Although a high level of functional redundancy in Arabidopsis might be expected because 65% of genes are members of gene families, we found that 41% of the essential genes found in this study are members of Arabidopsis gene families. In addition, we isolated several interesting classes of mutants and genes. We found three mutants in the recently discovered nonmevalonate isoprenoid biosynthetic pathway and mutants disrupting genes similar to Tic40 and tatC, which are likely to be involved in chloroplast protein translocation. Finally, we directly compared T-DNA and Ac/Ds transposon mutagenesis methods in Arabidopsis on a genome scale. In each population, we found only about one-third of the insertion mutations cosegregated with a mutant phenotype.

scholarly journals CTIM-03. PHASE II STUDY OF REGORAFENIB IN BEVACIZUMAB REFRACTORY RECURRENT GLIOBLASTOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii33-ii33
Author(s):  
Yasmeen Rauf ◽  
Cathy Schilero ◽  
David Peereboom ◽  
Manmeet Ahluwalia
Keyword(s):  
Dose Escalation ◽  
Therapeutic Option ◽  
Objective Response ◽  
Progression Free Survival ◽  
Recurrent Glioblastoma ◽  
Weekly Dose ◽  
Cellular Functions ◽  
Molecule Inhibitor ◽  
Secondary Endpoints ◽  
Magnetic Resonance Imaging Mri

Abstract BACKGROUND Most patients with glioblastoma (GBM) receive bevacizumab as part of their treatment. There is no good therapeutic option after bevacizumab failure. Regorafenib has potent preclinical antitumor activity and long-lasting anti-angiogenic activity as measured by dynamic contrast enhanced (DCE) – magnetic resonance imaging (MRI). Regorafenib is a small molecule inhibitor of multiple membrane-bound and intracellular kinases involved in normal cellular functions and in pathologic processes such as oncogenesis, tumor angiogenesis, and maintenance of the tumor microenvironment. METHODS Patients with progression of GBM after treatment with Bevacizumab will be eligible for the study. Oral administration of Regorafenib at 160 mg once daily will be administered for 3 weeks on /1 week off. Weekly dose escalation of regorafenib from 80 mg to 160 mg/day will be employed as per the Redos strategy. Patients start the treatment 80 mg/day in week 1, with weekly dose escalation to 120 mg in week 2, then 160 mg week in 3 if no significant drug-related toxicities are observed. They will be continued on treatment with Regorafenib 160 md /day till tumor progression or toxicity. They will get MRI brain every 4 weeks during the study. RESULTS Primary endpoint is median Overall survival. Secondary endpoints include progression free survival at 6 months and the median time to progression and objective response rate using the modified RANO criteria. The overall safety and tolerability of regorafenib by CTCAE version 5.0. will also be reported. CONCLUSION This is an ongoing clinical trial.

Sign in / Sign up

Export Citation Format

Share Document