Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.
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ABSTRACT:
SUBMITTER: Fernandez EA
PROVIDER: S-EPMC8294562 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:DNA sequencing is likely to become a standard typing method in forensics in the near future. We define a microhaplotype to be a locus with two or more single nucleotide polymorphisms (SNPs) that occur within a short segment of DNA (e.g., 200 bp) that can be covered by a single sequence run and collectively define a multiallelic locus. Microhaplotypes can be highly informative for many forensic questions, including detection of mixtures of two or more sources in a DNA sample, a common problem in forensic practice. RESULTS:When all alleles are equally frequent, the probability of detecting three or more alleles in a mixture is at maximum. The classical population genetics concept of effective number of alleles at a locus, termed Ae, converts the unequal allele frequencies at a locus into a value that is equivalent to some number of equally frequent alleles, allowing microhaplotype loci to be ranked. The expectations for the ability to qualitatively detect mixtures are given for different integer values of Ae, and the cumulative probabilities of detecting mixtures based on testing multiple microhaps are shown to exceed 95% with as few as five loci with average Ae values of even slightly greater than 3.0. CONCLUSIONS:Microhaplotypes with Ae values of >3 will be exceedingly useful in ordinary forensic practice. Based on our studies, 3-SNP microhaplotypes will sometimes meet this criterion, but 4-SNP microhaplotypes can even exceed this criterion and have values >4.
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Project description:BackgroundConfounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogenous biospecimens offer a promising solution, however the performance of such methods depends entirely on the library of methylation markers being used for deconvolution. Here, we introduce a novel algorithm for Identifying Optimal Libraries (IDOL) that dynamically scans a candidate set of cell-specific methylation markers to find libraries that optimize the accuracy of cell fraction estimates obtained from cell mixture deconvolution.ResultsApplication of IDOL to training set consisting of samples with both whole-blood DNA methylation data (Illumina HumanMethylation450 BeadArray (HM450)) and flow cytometry measurements of cell composition revealed an optimized library comprised of 300 CpG sites. When compared existing libraries, the library identified by IDOL demonstrated significantly better overall discrimination of the entire immune cell landscape (p = 0.038), and resulted in improved discrimination of 14 out of the 15 pairs of leukocyte subtypes. Estimates of cell composition across the samples in the training set using the IDOL library were highly correlated with their respective flow cytometry measurements, with all cell-specific R (2)>0.99 and root mean square errors (RMSEs) ranging from [0.97 % to 1.33 %] across leukocyte subtypes. Independent validation of the optimized IDOL library using two additional HM450 data sets showed similarly strong prediction performance, with all cell-specific R (2)>0.90 and R M S E<4.00 %. In simulation studies, adjustments for cell composition using the IDOL library resulted in uniformly lower false positive rates compared to competing libraries, while also demonstrating an improved capacity to explain epigenome-wide variation in DNA methylation within two large publicly available HM450 data sets.ConclusionsDespite consisting of half as many CpGs compared to existing libraries for whole blood mixture deconvolution, the optimized IDOL library identified herein resulted in outstanding prediction performance across all considered data sets and demonstrated potential to improve the operating characteristics of EWAS involving adjustments for cell distribution. In addition to providing the EWAS community with an optimized library for whole blood mixture deconvolution, our work establishes a systematic and generalizable framework for the assembly of libraries that improve the accuracy of cell mixture deconvolution.
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Project description:Complete separation of chemicals in a complex mixture is far from being achieved even with the current high-performance separation technology, such as gas chromatography-mass spectrometry (GC-MS). Several deconvolution techniques based on multivariate curve resolution (MCR), or model peak methods, which are represented by AMDIS, have been developed to address the above-mentioned issue. The model peak methods have been developed to provide easy-to-use tools, including AMDIS, but are limited for MCR with approximation methods. The objective of this study was to provide an easy-to-use deconvolution tool based on the MCR approach for GC-MS data. The spectral deconvolution tool based on non-negative matrix factorization (NMF), which calculates outputs using an approximation method, was implemented as a free web platform, namely, GC Mixture Touch, clarifying the effects of the parameters required for the deconvolution. The GC Mixture Touch was applied to the actual mixture sample of road dust spiked with chemical standards. The recommended parameter settings for smoothing of the chromatogram, the number of ranks, and the NMF algorithm for the deconvolution were clarified through the study. The performance with the suggested parameters was evaluated with respect to compound identification for the actual sample. All of the test compounds in the sample were correctly identified with the GC Mixture Touch, outperforming AMDIS with respect to the identification. The GC Mixture Touch is easy to use on the web even for users without programming skills. This is expected to enhance the application of the NMF-based deconvolution, and it should prove helpful in finding the compounds hidden in complex mixtures that are difficult to find using conventional approaches.
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Project description:The principle cause of cardiovascular disease (CVD) is atherosclerosis, a chronic inflammatory condition characterized by immunologically complex fatty lesions within the intima of arterial vessel walls. Dendritic cells (DCs) are key regulators of atherosclerotic inflammation, with mature DCs generating pro-inflammatory signals within vascular lesions and tolerogenic DCs eliciting atheroprotective cytokine profiles and regulatory T cell (Treg) activation. Here, we engineered the surface chemistry and morphology of synthetic nanocarriers composed of poly(ethylene glycol)-b-poly(propylene sulfide) copolymers to selectively target and modulate DCs by transporting the anti-inflammatory agent 1, 25-Dihydroxyvitamin D3 (aVD) and ApoB-100 derived antigenic peptide P210. Polymersomes decorated with an optimized surface display and density for a lipid construct of the P-D2 peptide, which binds CD11c on the DC surface, significantly enhanced the cytosolic delivery and resulting immunomodulatory capacity of aVD in vitro. Intravenous administration of the optimized polymersomes achieved selective targeting of DCs in atheroma and spleen compared to all other cell populations, including both immune and CD45- cells, and locally increased the presence of tolerogenic DCs and cytokines. aVD-loaded polymersomes significantly inhibited atherosclerotic lesion development in high fat diet-fed ApoE-/- mice following 8 weeks of administration. Incorporation of the P210 peptide generated the largest reductions in vascular lesion area (~33%, p<0.001), macrophage content (~55%, p<0.001), and vascular stiffness (4.8-fold). These results correlated with an ~6.5-fold increase in levels of Foxp3+ regulatory T cells within atherosclerotic lesions. Our results validate the key role of DC immunomodulation during aVD-dependent inhibition of atherosclerosis and demonstrate the therapeutic enhancement and dosage lowering capability of cell-targeted nanotherapy in the treatment of CVD.
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Project description:BACKGROUND:To facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells. METHODS:Our deconvolution method was developed by choosing ?-support vector regression (?-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT. RESULTS:In silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT. CONCLUSIONS:We developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.
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Project description:Remarkable developments in immuno-oncology have changed the landscape of gastric cancer (GC) treatment. Because immunotherapy intervenes with tumor immune response rather than directly targeting tumor cells, it is important to develop a greater understanding of tumor immunity. This review paper summarizes the tumor immune reaction and immune escape mechanisms while focusing on the role of T cells and their co-inhibitory signals, such as the immune checkpoint molecules programmed death-1 and programmed deathligand 1 (PD-L1). This paper also describes past clinical trials of immunotherapy for patients with GC and details their clinical implications. Strong predictive markers are essential to improve response to immunotherapy. Microsatellite instability, Epstein-Barr virus, PD-L1 expression, and tumor mutational burden are now regarded as potent predictive markers for immunotherapy in patients with GC. Novel immunotherapy and combination therapy targeting new immune checkpoint molecules such as lymphocyte-activation gene 3, T cell immunoglobulin, and mucin domain containing-3, and indoleamine 2,3-dioxygenase have been suggested, and trials are ongoing to evaluate their safety and efficacy. Immunotherapy is an important treatment option for patients with GC and has great potential for improving patient outcome, and further research in immuno-oncology should be carried out.
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