Journal Highlight: Denaturing high‐performance liquid chromatography and principal component analysis for identification of DNA point mutations in breast cancer and lymphoma samples

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  • Published: Nov 5, 2018
  • Author: separationsNOW
  • Channels: Laboratory Informatics
thumbnail image: Journal Highlight: Denaturing high‐performance liquid chromatography and principal component analysis for identification of DNA point mutations in breast cancer and lymphoma samples

Principal component analysis has been used to analyze the HPLC chromatograms from 3 different genes acquired under denaturing conditions to identify point mutations.

Denaturing high‐performance liquid chromatography and principal component analysis for identification of DNA point mutations in breast cancer and lymphoma samples

Journal of Chemometrics, 32, 2018, e3053 online
Yocanxóchitl Perfecto‐Avalos, Raquel Cuevas‐Díaz Durán, Luis Villela, Alejandro Garcia‐Gonzalez, Ricardo Javier Díaz‐Domínguez, Tania Loyo, Miguel Ángel Gutiérrez‐Monreal, Juan Manuel Esparza‐Treviño, Carlos Rocha‐Inclán, Rocío Rojo, Eduardo Cárdenas‐Cantú, Jezreel Pantaléon‐García, Sean‐Patrick Scott

Abstract: DNA mutations are identified by techniques that use the knowledge of the wild‐type DNA sequence and its mutated variant. The involved analytic methods must be accurate, rapid, and sustainable, if a clinical application is pursued. High‐performance liquid chromatography under denaturing conditions is a useful technique to screen mutations. Denaturing high‐performance liquid chromatography resultant chromatograms are suitable for feature extraction analysis with multivariate methods such as principal component analysis. In this work, principal component analysis was applied to analyze the chromatograms from 3 different genes. Fragments with verified wild‐type sequence were used as reference and samples with sequence unknown were tested. A statistical characterization based on Tukey's boxplot equation of principal component scores allowed us to analyze the distance distribution between reference and sample clusters to establish a classification criterion: an outlier could represent a mutated sample, and a typical value could be a wild‐type sample. Identified outliers were further analyzed by sequencing and proved to carry a mutation. From 72 datasets with a total of 4258 injections, we successfully assessed the classification criterion, identifying mutated samples in lymphoma and breast cancer patients with ratio of prediction Gmean = [0.89, 1.00]. Compared with sequencing analysis, this procedure reduced time and costs.

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