Article pubs.acs.org/jpr
Imaging Mass Spectrometry-based Molecular Histology Differentiates Microscopically Identical and Heterogeneous Tumors Emrys A. Jones,† Nicole Schmitz,‡ Cathelijn J. F. Waaijer,‡ Christian K. Frese,§,∥ Alexandra van Remoortere,† René J. M. van Zeijl,† Albert J. R. Heck,§,∥ Pancras C. W. Hogendoorn,‡ André M. Deelder,† A. F. Maarten Altelaar,§,∥ Judith V. M. G. Bovée,‡ and Liam A. McDonnell*,† †
Biomolecular Mass Spectrometry Unit, Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands § Biomolecular Mass Spectrometry and Proteomics Group, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands ∥ Netherlands Proteomics Centre, Utrecht, The Netherlands ‡
S Supporting Information *
ABSTRACT: Many tumors display significant cellular heterogeneity as well as molecular heterogeneity. Sensitive biomarkers that differentiate between diagnostically challenging tumors must contend with this heterogeneity. Mass spectrometry-based molecular histology of a patient series of heterogeneous, microscopically identical bone tumors highlighted the tumor cell types that could be characterized by a single profile and led to the identification of specific peptides that differentiate between the tumors.
KEYWORDS: MALDI, imaging mass spectrometry, intratumor heterogeneity, chondrosarcoma, molecular histology
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series of glioblastoma patient tissues.4 When applied to single tissue sections, the glioblastoma classifier could correctly demarcate the histological heterogeneity within the tumor tissues. In a similar study concerning protein imaging MS of soft-tissue sarcomas, Willems et al. demonstrated that while classifiers could be generated that demarcated histologically different regions within a tumor tissue, such histology-defined analyses underestimated the heterogeneity.9 In general terms, a classification analysis can only be as good as the data sets used to train the algorithm. When trained using imaging MS data sets from specific tumor groups, that is, those with distinct tumor histologies, then the classifier may only be used for such a defined exercise, for example, to assign status to less histologically distinct tissue samples. The need to train classification algorithms with well-characterized patient tissue samples makes them unsuitable as a discovery tool.10 For example, Willems et al. used imaging MS data sets from multiple patient tissue samples of high-grade and low-grade myxofibrosarcoma to train a classifier. When applied to imaging MS data sets from intermediate grade myxofibrosarcoma tissues the classifier could correctly highlight the high-grade-like and low-grade-like regions. However, the classifier could not
INTRODUCTION Mass spectrometry can generate profiles that contain hundreds of biomolecules directly from tissue. Spatially correlated mass spectrometry, imaging MS, reveals how each biomolecular ion varies across tissue samples.1 A number of mass spectrometric methods have been developed for imaging MS, enabling high sensitivity analysis of a range of molecular classes.2 Matrix assisted laser desorption/ionization (MALDI) is that most commonly used, principally because of the widespread commercial availability of high-performance mass spectrometers capable of imaging MS, and because it can be applied to different molecular classes only by altering the tissue preparation strategy.3 Alternative imaging MS techniques such as desorption electrospray ionization (DESI) are also beginning to demonstrate their clinical potential.4 By combining imaging MS with histology, MS profiles of specific pathological entities have been used to identify biomarkers of disease,5 and when combined with clinical outcomes identify signatures associated with prognosis6 and response to therapy.7 Examples that demonstrate the robustness of the technique include the generation of highly sensitive and specific imaging MS based classifiers that could differentiate six different types of adenocarcinoma and which involved the analysis of 171 patient tissue samples8 or the identification of tumor type and tumor grade specific lipid profiles in a large © XXXX American Chemical Society
Received: December 18, 2012
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dx.doi.org/10.1021/pr301190g | J. Proteome Res. XXXX, XXX, XXX−XXX
Journal of Proteome Research
Article
that is present in a specific region of tissue because only a fraction of pixels are analyzed. The high degree of cellular and morphological heterogeneity in chondrosarcoma, and the expected biomolecular heterogeneity, is ill suited to established biomarker discovery routines; it is essential to include these sources of heterogeneity to establish which cell types may be described by a single set of biomarkers.19 In this paper we demonstrate how multivariate methods may be simultaneously applied to all imaging MS data sets from a series of patient tissues. Consequently, the intratumor heterogeneity, interpatient heterogeneity and the differences between central and peripheral chondrosarcoma are explicitly included in the analysis. Using automated feature identification and extraction algorithms to focus the analysis solely on the well-defined peptide and protein signals present in the data sets, we have, for the first time, used MALDI imaging MS based-molecular histology to differentiate between microscopically identical and highly heterogeneous tumors. We assess how the intratumor heterogeneity highlighted by MALDI imaging MS correlates with the histological heterogeneity and determine which cell types may be characterized by single biomarkers.
indicate whether individual nodules within the high-grade-like and low-grade-like regions of tissue could be further subdivided.9 A subsequent multivariate investigation of the tissues revealed intratumor heterogeneity within the low-gradelike regions.11 Biomarker discovery experiments are typically defined by the tissue’s histology. Such an approach functions well for tumors with relatively well-differentiated histologies but are less practical for histologically heterogeneous tumors and does not take into account the biomolecular intratumor heterogeneity present in many tumors. Recent multiregion sequencing data has demonstrated that the branched evolution of tumors is reflected in the tumor’s heterogeneity12 and specific clones within the primary tumor play a critical role in the development of drug resistance and metastasis.13 Only imaging MS has the necessary data acquisition throughput to be able to examine biomolecular intratumor heterogeneity. While a number of studies have indicated how imaging MS may reveal intratumor heterogeneity,4,9,11,14 the role of the heterogeneity with regard to patient diagnosis or prognosis has not been ascertained. The two main variants of chondrosarcoma, a malignant cartilage-forming tumor of bone, present an example of tumors that are not suited to established histology-defined analyses. Chondrosarcoma can occur in the medulla (central chondrosarcoma, ∼75%) or at the bone surface (peripheral chondrosarcoma, ∼10%).15 They are morphologically identical and highly heterogeneous, composed of irregularly shaped lobules of cartilage separated by vascularized fibrous tissue.15,16 Chondrosarcoma are notoriously resistant to conventional chemo- and radiotherapy, leaving surgery as the mainstay of treatment. Central and peripheral chondrosarcoma arise from benign tumors with different genetic backgrounds, which had led to the hypothesis that the tumors were genetically distinct with different treatment targets. However, recent results have demonstrated that peripheral chondrosarcomas rarely contain the mutations in the EXT (hereditary multiple exostoses) genes of their benign precursor tumor (osteochondromas),17 and ∼50% central chondrosarcomas contain the mutations of the IDH (isocitrate dehydrogenase) genes of their benign precursor tumor (enchondromas).18 We have used MALDI imaging MS to investigate the molecular differences of central and peripheral chondrosarcoma. This has necessitated the application of molecular-histology capabilities for the analysis of these histologically identical, heterogeneous tumors. Established methods for biomarker discovery using MALDI imaging MS seek proteins differentially detected throughout each histologically selected region of tissue, that is, uniformly detected at higher/lower levels. The uniformity requirement has arisen because entire MALDI imaging MS data sets, containing thousands of pixel-associated mass spectra per tissue, are not compatible with current biomarker discovery data analysis pipelines. Instead, representative mass spectra are randomly selected from the histologically defined regions, averaged and analyzed as independent measurements using algorithms originally developed for protein profiling (i.e., no spatial component). Biomarker discovery of protein extracts seek proteins consistently and specifically associated with the pathology. When applied to MALDI imaging MS data, these routines seek biomarkers that are detected in every representative mass spectrum; the data analysis strategy does not differentiate between an inconsistent biomarker and one
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MATERIALS AND METHODS
Tissue Samples
All tissue samples were handled in a coded fashion, according to Dutch national ethical guidelines (Code for Proper Secondary Use of Human Tissue, Dutch Federation of Medical Scientific Societies). The selection of central and peripheral chondrosarcomas was restricted to grade II chondrosarcomas to remove the variability in protein expression between histological grades. Five micrometer thick tissue sections were cut at −20 °C using a cryomicrotome and stained with hematoxylin and eosin (H&E) to check the diagnosis and viability of the tissue. Slides were re-evaluated histologically and classified according to the 2002 World Health Organization criteria.20 Ten central and ten peripheral tumors snap-frozen in liquid nitrogen with a tumor content >80% and with a necrotic content
