| Added by | pmartino |
|---|---|
| Group name | EquipePM |
| Item Type | Journal Article |
| Title | MALDI imaging mass spectrometry and chemometric tools to discriminate highly similar colorectal cancer tissues |
| Creator | Mas et al. |
| Author | S. Mas |
| Author | A. Torro |
| Author | N. Bec |
| Author | C. Gongora |
| Author | C. Larroque |
| Author | P. Martineau |
| Author | A. de Juan |
| Author | S. Marco |
| Abstract | 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. |
| Publication | Talanta |
| Pages | 120455 |
| Date | October 10, 2019 |
| Journal Abbr | Talanta |
| Language | en |
| DOI | 10.1016/j.talanta.2019.120455 |
| ISSN | 0039-9140 |
| URL | http://www.sciencedirect.com/science/article/pii/S0039914019310884 |
| Accessed | 2019/12/04 - 20:05:08 |
| Library Catalog | ScienceDirect |
| Extra | 00000 |
| Tags | Chemometrics, last, mabimprove, MALDI imaging, original, top, Tumor heterogeneity |
| Date Added | 2019/12/10 - 17:00:22 |
| Date Modified | 2024/03/01 - 09:41:42 |
| Notes and Attachments | hal-03003933.docx (Attachment) Mas et al. - 2019 - MALDI imaging mass spectrometry and chemometric to.pdf (Attachment) Mas et al. - 2020 - MALDI imaging mass spectrometry and chemometric to.pdf (Attachment) PubMed entry (Attachment) ScienceDirect Snapshot (Attachment) |
Institut de Recherche en Cancérologie de
