The Use of Mass Spectrometry
Imaging to Predict Treatment
Response of Patient-Derived Xenograft Models of Triple-Negative Breast
Cancer
- Publication date
- 2015
- Publisher
Abstract
In recent years,
mass spectrometry imaging (MSI) has been shown
to be a promising technique in oncology. The effective application
of MSI, however, is hampered by the complexity of the generated data.
Bioinformatic approaches that reduce the complexity of these data
are needed for the effective use in a (bio)medical setting. This holds
especially for the analysis of tissue microarrays (TMA), which consist
of hundreds of small tissue cores. Here we present an approach that
combines MSI on tissue microarrays with principal component linear
discriminant analysis (PCA-LDA) to predict treatment response. The
feasibility of such an approach was evaluated on a set of patient-derived
xenograft models of triple-negative breast cancer (TNBC). PCA-LDA
was used to classify TNBC tumor tissues based on the proteomic information
obtained with matrix-assisted laser desorption ionization (MALDI)
MSI from the TMA surface. Classifiers based on two different tissue
microarrays from the same tumor models showed overall classification
accuracies between 59 and 77%, as determined by cross-validation.
Reproducibility tests revealed that the two models were similar. A
clear effect of intratumor heterogeneity of the classification scores
was observed. These results demonstrate that the analysis of MALDI-MSI
data by PCA-LDA is a valuable approach for the classification of treatment
response and tumor heterogeneity in breast cancer