Biomedical subjects

Minh N Nguyen

Publications and source records attributed to Minh N Nguyen.

7 recordsLinked to original sources

PubMed2025

Tumor-immune partitioning and clustering algorithm for identifying tumor-immune cell spatial interaction signatures within the tumor microenvironment.

BACKGROUND: Growing evidence supports the importance of characterizing the organizational patterns of various cellular constituents in the tumor microenvironment in precision oncology. Most existing data on immune cell infiltrates in tumors, which are based on immune cell counts or nearest neighbor-type analyses, have failed to fully capture the cellular organization and heterogeneity. METHODS: We introduce a computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), that jointly measures immune cell partitioning between tumor epithelial and stromal areas and immune cell clustering versus dispersion. As proof-of-principle, we applied TIPC to a prospective cohort incident tumor biobank containing 931 colorectal carcinoma cases. TIPC identified tumor subtypes with unique spatial patterns between tumor cells and T lymphocytes linked to certain molecular pathologic and prognostic features. T lymphocyte identification and phenotyping were achieved using multiplexed (multispectral) immunofluorescence. In a separate hepatocellular carcinoma cohort, we replaced the stromal component with specific immune cell types-CXCR3+CD68+ or CD8+-to profile their spatial relationships with CXCL9+CD68+ cells. RESULTS: Six unsupervised TIPC subtypes based on T lymphocyte distribution patterns were identified, comprising two cold and four hot subtypes. Three of the four hot subtypes were associated with significantly longer colorectal cancer (CRC)-specific survival compared to a reference cold subtype. Our analysis showed that variations in T-cell densities among the TIPC subtypes did not strictly correlate with prognostic benefits, underscoring the prognostic significance of immune cell spatial patterns. Additionally, TIPC revealed two spatially distinct and cell density-specific subtypes among microsatellite instability-high colorectal cancers, indicating its potential to upgrade tumor subtyping. TIPC was also applied to additional immune cell types, eosinophils and neutrophils, identified using morphology and supervised machine learning; here two tumor subtypes with similarly low densities, namely 'cold, tumor-rich' and 'cold, stroma-rich', exhibited differential prognostic associations. Lastly, we validated our methods and results using The Cancer Genome Atlas colon and rectal adenocarcinoma data (n = 570). Moreover, applying TIPC to hepatocellular carcinoma cases (n = 27) highlighted critical cell interactions like CXCL9-CXCR3 and CXCL9-CD8. CONCLUSIONS: Unsupervised discoveries of microgeometric tissue organizational patterns and novel tumor subtypes using the TIPC algorithm can deepen our understanding of the tumor immune microenvironment and likely inform precision cancer immunotherapy.

Humans↗

PubMed2006

Two-stage support vector regression approach for predicting accessible surface areas of amino acids.

We address the problem of predicting solvent accessible surface area (ASA) of amino acid residues in protein sequences, without classifying them into buried and exposed types. A two-stage support vector regression (SVR) approach is proposed to predict real values of ASA from the position-specific scoring matrices generated from PSI-BLAST profiles. By adding SVR as the second stage to capture the influences on the ASA value of a residue by those of its neighbors, the two-stage SVR approach achieves improvements of mean absolute errors up to 3.3%, and correlation coefficients of 0.66, 0.68, and 0.67 on the Manesh dataset of 215 proteins, the Barton dataset of 502 nonhomologous proteins, and the Carugo dataset of 338 proteins, respectively, which are better than the scores published earlier on these datasets. A Web server for protein ASA prediction by using a two-stage SVR method has been developed and is available (http://birc.ntu.edu.sg/~ pas0186457/asa.html).

Amino Acids↗

PubMed2006

Use of Intralipid for kinetic analysis of HDL apoC-III: evidence for a homogeneous kinetic pool of apoC-III in plasma.

Apolipoprotein C-III (apoC-III) is an important regulator of lipoprotein metabolism. Radioisotope and stable isotope kinetic studies show differing results in relation to the kinetics of apoC-III in HDL. Kinetic analysis of HDL apoC-III may be difficult because of its low concentration, as well as the presence of other apoproteins at higher concentration, in the HDL fraction. We used Intralipid(R) (IL), known to preferentially extract apoC proteins from plasma, as a means of extracting apoC-III from HDL before apoprotein separation by isoelectric focusing gel electrophoresis for the measurement of tracer enrichment. Protein purity was assessed by an isoleucine-to-leucine (Ile/Leu) ratio, as apoC-III contains no isoleucine. We compared apoC-III kinetics in 14 men using a bolus infusion of deuterated leucine. The Ile/Leu ratio for IL-extracted HDL (IL-HDL) apoC-III (3.0 +/- 0.7%) was not different from that of VLDL apoC-III (2.6 +/- 0.6%) but was significantly lower than that of untreated HDL apoC-III (9.0 +/- 2.9%) (P < 0.001). The isotopic enrichment curves and fractional catabolic rates (FCRs) for IL-HDL apoC-III were not different from those of VLDL apoC-III. In contrast, HDL apoC-III had significantly lower isotopic enrichments and FCRs than IL-HDL apoC-III (P < 0.001). In conclusion, this simple IL method can be used to isolate apoC-III from HDL with minimal interference from other HDL apoproteins, and it demonstrates that the kinetics of apoC-III in VLDL and HDL are similar, supporting the concept of a single kinetically homogeneous pool of apoC-III in plasma.

Apolipoprotein C-III↗

PubMed2006

Apolipoproteins C-III and A-V as predictors of very-low-density lipoprotein triglyceride and apolipoprotein B-100 kinetics.

OBJECTIVE: We investigated the associations between plasma very-low-density lipoprotein (VLDL)-apolipoprotein (apo)C-III and apoA-V concentrations and the kinetics of VLDL-apoB-100 and VLDL triglycerides in 15 men. We also explored the relationship between these parameters of VLDL metabolism and VLDL-apoC-III kinetics. METHODS AND RESULTS: ApoC-III, apoB, and triglyceride kinetics in VLDL were determined using stable isotopes and multicompartmental modeling to estimate production rate (PR) and fractional catabolic rate (FCR). Plasma VLDL-apoC-III concentration was significantly and inversely associated with the FCRs of VLDL triglycerides (r=-0.610) and VLDL-apoB (r=-0.791), and positively correlated with the PR of VLDL-apoC-III (r=0.842). However, apoA-V concentration was not significantly associated with any of the kinetic variables. There was a significant association (P<0.01) between the PRs of VLDL triglycerides and VLDL-apoB (r=0.641), and between the FCRs of VLDL triglycerides and VLDL-apoB (r=0.737). In multiple regression analysis, plasma VLDL-apoC-III concentration was a significant predictor of VLDL triglyceride FCR (beta-coefficient=-0.575) and VLDL-apoB FCR (beta-coefficient=-0.839). CONCLUSIONS: Our findings suggest that increased VLDL-apoC-III concentrations resulting from an overproduction of VLDL-apoC-III are strongly associated with the delayed catabolism of triglycerides and apoB in VLDL. We also demonstrated that the kinetics of VLDL triglycerides and apoB are closely coupled. Our data do not support a role for plasma apoA-V in regulating VLDL kinetics.

Aged↗

PubMed2006

Factorial study of the effect of n-3 fatty acid supplementation and atorvastatin on the kinetics of HDL apolipoproteins A-I and A-II in men with abdominal obesity.

BACKGROUND: Disturbed HDL metabolism in insulin-resistant, obese subjects may account for an increased risk of cardiovascular disease. Fish oils and atorvastatin increase plasma HDL cholesterol, but the underlying mechanisms responsible for this change are not fully understood. OBJECTIVE: We studied the independent and combined effects of fish oils and atorvastatin on the metabolism of HDL apolipoprotein A-I (apo A-I) and HDL apo A-II in obese men. DESIGN: We conducted a 6-wk randomized, placebo-controlled, 2 x 2 factorial intervention study of the effects of fish oils (4 g/d) and atorvastatin (40 mg/d) on the kinetics of HDL apo A-I and HDL apo A-II in 48 obese men with dyslipidemia with intravenous administration of [d3]-leucine. Isotopic enrichments of apo A-I and apo A-II were measured with gas chromatography-mass spectrometry with kinetic parameters derived from a multicompartmental model (SAAM II). RESULTS: Fish oils and atorvastatin significantly decreased plasma triacylglycerols and increased HDL cholesterol and HDL2 cholesterol (P < 0.05 for main effects). A significant (P < 0.02) main effect of fish oils was observed in decreasing the fractional catabolic rate of HDL apo A-I and HDL apo A-II. This was coupled with a significant decrease in the corresponding production rates, accounting for a lack of treatment effect on plasma concentrations of apo A-I and apo A-II. Atorvastatin did not significantly alter the concentrations or kinetic parameters of HDL apo A-I and HDL apo A-II. None of the treatments altered insulin resistance. CONCLUSIONS: Fish oils, but not atorvastatin, influence HDL metabolism chiefly by decreasing both the catabolism and production of HDL apo A-I and HDL apo A-II in insulin-resistant obese men. Addition of atorvastatin to treatment with fish oils had no additional effect on HDL kinetics compared with fish oils alone.

Abdominal Fat↗

PubMed2005

Prediction of protein relative solvent accessibility with a two-stage SVM approach.

Information on relative solvent accessibility (RSA) of amino acid residues in proteins provides valuable clues to the prediction of protein structure and function. A two-stage approach with support vector machines (SVMs) is proposed, where an SVM predictor is introduced to the output of the single-stage SVM approach to take into account the contextual relationships among solvent accessibilities for the prediction. By using the position-specific scoring matrices (PSSMs) generated by PSI-BLAST, the two-stage SVM approach achieves accuracies up to 90.4% and 90.2% on the Manesh data set of 215 protein structures and the RS126 data set of 126 nonhomologous globular proteins, respectively, which are better than the highest published scores on both data sets to date. A Web server for protein RSA prediction using a two-stage SVM method has been developed and is available (http://birc.ntu.edu.sg/~pas0186457/rsa.html).

Amino Acids↗

PubMed2003

Multi-class support vector machines for protein secondary structure prediction.

The solution of binary classification problems using the Support Vector Machine (SVM) method has been well developed. Though multi-class classification is typically solved by combining several binary classifiers, recently, several multi-class methods that consider all classes at once have been proposed. However, these methods require resolving a much larger optimization problem and are applicable to small datasets. Three methods based on binary classifications: one-against-all (OAA), one-against-one (OAO), and directed acyclic graph (DAG), and two approaches for multi-class problem by solving one single optimization problem, are implemented to predict protein secondary structure. Our experiments indicate that multi-class SVM methods are more suitable for protein secondary structure (PSS) prediction than the other methods, including binary SVMs, because their capacity to solve an optimization problem in one step. Furthermore, in this paper, we argue that it is feasible to extend the prediction accuracy by adding a second-stage multi-class SVM to capture the contextual information among secondary structural elements and thereby further improving the accuracies. We demonstrate that two-stage SVMs perform better than single-stage SVM techniques for PSS prediction using two datasets and report a maximum accuracy of 79.5%.

Computational Biology↗