by Keyword: Tumor heterogeneity

By year:[ 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 ]

Mas, S., Torro, A., Fernández, L., Bec, N., Gongora, C., Larroque, C., Martineau, P., de Juan, A., Marco, S., (2020). MALDI imaging mass spectrometry and chemometric tools to discriminate highly similar colorectal cancer tissues Talanta 208, 120455

Intratumour heterogeneity due to cancer cell clonal evolution and microenvironment composition and tumor differences due to genetic variations between patients suffering of the same cancer pathology play a crucial role in patient response to therapies. This study is oriented to show that matrix-assisted laser-desorption ionization-Mass spectrometry imaging (MALDI-MSI), combined with an advanced multivariate data processing pipeline can be used to discriminate subtle variations between highly similar colorectal tumors. To this aim, experimental tumors reproducing the emergence of drug-resistant clones were generated in athymic mice using subcutaneous injection of different mixes of two isogenic cell lines, the irinotecan-resistant HCT116-SN50 (R) and its sibling human colon adenocarcinoma sensitive cell line HCT116 (S). Because irinotecan-resistant and irinotecan-sensitive are derived from the same original parental HCT116 cell line, their genetic characteristics and molecular compositions are closely related. The multivariate data processing pipeline proposed relies on three steps: (a) multiset multivariate curve resolution (MCR) to separate biological contributions from background; (b) multiset K-means segmentation using MCR scores of the biological contributions to separate between tumor and necrotic parts of the tissues; and (c) partial-least squares discriminant analysis (PLS-DA) applied to tumor pixel spectra to discriminate between R and S tumor populations. High levels of correct classification rates (0.85), sensitivity (0.92) and specificity (0.77) for the PLS-DA classification model were obtained. If previously labelled tissue is available, the multistep modeling strategy proposed constitutes a good approach for the identification and characterization of highly similar phenotypic tumor subpopulations that could be potentially applicable to any kind of cancer tissue that exhibits substantial heterogeneity. © 2019 Elsevier B.V.

Keywords: Chemometrics, Colorectal cancer, MALDI imaging, Multivariate analysis, Tumor heterogeneity