Hanna C. Diehl

Identification and validation of potential new biomarkers for prostate cancer diagnosis and prognosis using 2D-DIGE and MS.

This study was designed to identify and validate potential new biomarkers for prostate cancer and to distinguish patients with and without biochemical relapse. Prostate tissue samples analyzed by 2D-DIGE (two-dimensional difference in gel electrophoresis) and mass spectrometry (MS) revealed downregulation of secernin-1 (P < 0.044) in prostate cancer, while vinculin showed significant upregulation (P < 0.001). Secernin-1 overexpression in prostate tissue was validated using Western blot and immunohistochemistry while vinculin expression was validated using immunohistochemistry. These findings indicate that secernin-1 and vinculin are potential new tissue biomarkers for prostate cancer diagnosis and prognosis, respectively. For validation, protein levels in urine were also examined by Western blot analysis. Urinary vinculin levels in prostate cancer patients were significantly higher than in urine from nontumor patients (P = 0.006). Using multiple reaction monitoring-MS (MRM-MS) analysis, prostatic acid phosphatase (PAP) showed significant higher levels in the urine of prostate cancer patients compared to controls (P = 0.012), while galectin-3 showed significant lower levels in the urine of prostate cancer patients with biochemical relapse, compared to those without relapse (P = 0.017). Three proteins were successfully differentiated between patients with and without prostate cancer and patients with and without relapse by using MRM. Thus, this technique shows promise for implementation as a noninvasive clinical diagnostic technique.

A catalogue of proteins released by colorectal cancer cells in vitro as an alternative source for biomarker discovery.

Improved methods for the early diagnosis of colorectal cancer by way of sensitive and specific tumour markers are highly desirable. Therefore, efficient strategies for biomarker discovery are urgently needed. Here we present an approach that is based on the direct experimental access to proteins released by SW620 human colorectal cancer cells in vitro. A 2‐D map and a catalogue of this subproteome – here termed the secretome – were established comprising more than 320 identified proteins which translate into approximately 220 distinct genes. As the majority of the secretome constituents were nominally cellular proteins, we directly compared the secretome and the total proteome by 2‐D‐DIGE analysis. We provide evidence that unspecific release through cell death, classical secretion, ectodomain shedding, and exosomal release contribute to the secretome in vitro, presumably reflecting the mechanisms in vivo which lead to the occurrence of tumour‐specific proteins in the circulation. These data together with the fact that the SW620 secretome catalogue, as presented here, does comprise a large number of known and novel biomarker candidates, validates our approach to isolate and characterize the tumour cell secretome in vitro as a rich source for tumour biomarkers.

Spatial and molecular resolution of diffuse malignant mesothelioma heterogeneity by integrating label-free FTIR imaging, laser capture microdissection and proteomics

Diffuse malignant mesothelioma (DMM) is a heterogeneous malignant neoplasia manifesting with three subtypes: epithelioid, sarcomatoid and biphasic. DMM exhibit a high degree of spatial heterogeneity that complicates a thorough understanding of the underlying different molecular processes in each subtype. We present a novel approach to spatially resolve the heterogeneity of a tumour in a label-free manner by integrating FTIR imaging and laser capture microdissection (LCM). Subsequent proteome analysis of the dissected homogenous samples provides in addition molecular resolution. FTIR imaging resolves tumour subtypes within tissue thin-sections in an automated and label-free manner with accuracy of about 85% for DMM subtypes. Even in highly heterogeneous tissue structures, our label-free approach can identify small regions of interest, which can be dissected as homogeneous samples using LCM. Subsequent proteome analysis provides a location specific molecular characterization. Applied to DMM subtypes, we identify 142 differentially expressed proteins, including five protein biomarkers commonly used in DMM immunohistochemistry panels. Thus, FTIR imaging resolves not only morphological alteration within tissue but it resolves even alterations at the level of single proteins in tumour subtypes. Our fully automated workflow FTIR-guided LCM opens new avenues collecting homogeneous samples for precise and predictive biomarkers from omics studies.

Tissue MALDI Mass Spectrometry Imaging (MALDI MSI) of Peptides.

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a technique to visualize molecular features of tissues based on mass detection. This chapter focuses on MALDI MSI of peptides and provides detailed operational instructions for sample preparation of cryoconserved and formalin-fixed paraffin-embedded (FFPE) tissue. Besides sample preparation we provide protocols for the MALDI measurement, tissue staining, and data analysis. On-tissue digestion and matrix application are described for two different commercially available and commonly used spraying devices: the SunCollect (SunChrom) and the ImagePrep (Bruker Daltonik GmbH).

Molecular profiles of thyroid cancer subtypes: Classification based on features of tissue revealed by mass spectrometry imaging☆

Determination of the specific type of thyroid cancer is crucial for the prognosis and selection of treatment of this malignancy. However, in some cases appropriate classification is not possible based on histopathological features only, and it might be supported by molecular biomarkers. Here we aimed to characterize molecular profiles of different thyroid malignancies using mass spectrometry imaging (MSI) which enables the direct annotation of molecular features with morphological pictures of an analyzed tissue. Fifteen formalin-fixed paraffin-embedded tissue specimens corresponding to five major types of thyroid cancer were analyzed by MALDI-MSI after in-situ trypsin digestion, and the possibility of classification based on the results of unsupervised segmentation of MALDI images was tested. Novel method of semi-supervised detection of the cancer region of interest (ROI) was implemented. We found strong separation of medullary cancer from malignancies derived from thyroid epithelium, and separation of anaplastic cancer from differentiated cancers. Reliable classification of medullary and anaplastic cancers using an approach based on automated detection of cancer ROI was validated with independent samples. Moreover, extraction of spectra from tumor areas allowed the detection of molecular components that differentiated follicular cancer and two variants of papillary cancer (classical and follicular). We concluded that MALDI-MSI approach is a promising strategy in the search for biomarkers supporting classification of thyroid malignant tumors. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.