MALDI imaging-based identification of prognostically relevant signals in bladder cancer using large-scale tissue microarrays.

OBJECTIVE: Although most patients with urinary bladder cancer present with noninvasive and low-malignant stages of the disease, about 20% eventually develop life-threatening metastatic tumors. This study was designed to evaluate the potential of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to identify molecular markers predicting the clinical course of bladder cancer. MATERIALS AND METHODS: We employed MALDI-MSI to a bladder cancer tissue microarray including paraffin-embedded tissue samples from 697 patients with clinical follow-up data to search for prognostically relevant associations. RESULTS: Analysis of our MALDI imaging data revealed 40 signals in the mass spectra (m/z signals) associated with epithelial structures. The presence of numerous m/z signals was statistically related to one or several phenotypical findings including tumor aggressiveness (stage, grade, or nodal status; 30 signals), solid (5 signals) or papillary (3 signals) growth patterns, and increased (6 signals) or decreased (12 signals) cell proliferation, as determined by Ki-67 immunohistochemistry. Two signals were linked with tumor recurrence in noninvasive (pTa category) tumors, of which one was also related to progression from pTa-category to pT1-category disease. The absence of one m/z signal was linked with decreased survival in the subset of 102 muscle-invasive cancers. CONCLUSION: Our data demonstrate the suitability of combining MSI and large-scale tissue microarrays to simultaneously identify and validate clinically useful molecular markers in urinary bladder cancer.

MALDI imaging on tissue microarrays identifies molecular features associated with renal cell cancer phenotype.

AIM: To identify molecular features associated with clinico-pathological parameters in renal cell cancer. MATERIALS AND METHODS: Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging was employed for a kidney cancer tissue microarray containing tissue samples from 789 patients for which clinical follow-up data were available. RESULTS: A comparison of mass spectrometric signals with clinico-pathological features revealed significant differences between papillary and clear cell renal cell cancer. Within the subgroup of clear cell RCC, statistical associations with tumor stage (seven signals, p<0.01 each), Fuhrman grade (seven signals, p<0.0001 each), and presence of lymph node metastases (10 signals, p<0.01 each) were found. In addition, the presence of one signal was significantly linked to shortened patient survival (p=0.0198). CONCLUSION: Our data pinpoint towards various molecules with potential relevance in renal cell cancer. They also demonstrate that the combination of the MALDI mass spectrometry imaging and large-scale tissue microarray platforms represents a powerful approach to identify clinically-relevant molecular cancer features.

MALDI imaging on large-scale tissue microarrays identifies molecular features associated with tumour phenotype in oesophageal cancer.

AIMS: Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) and tissue microarray (TMA) technologies were jointly utilized to search for molecular features associated with clinicopathological parameters in oesophageal cancer. METHODS AND RESULTS: Two TMAs from formalin-fixed tissue samples, including 300 adenocarcinomas and 177 squamous cell carcinomas with clinical follow-up data, were analysed. MALDI-MSI analysis revealed 72 distinct mass per charge (m/z) signals associated with tumour cells, 48 of which were found in squamous cell carcinomas only, and 12 of which were specific for adenocarcinomas. In adenocarcinomas, six signals were linked to early-stage (pT1-T2) tumours (two signals) and the presence (one signal) or absence (three signals) of lymph node metastasis. In squamous cell carcinomas, 24 signals were strongly linked to different phenotypic features, including tumour stage (four signals), histological grade (four signals), and lymph node metastasis (three signals). CONCLUSIONS: The high number of m/z signals that were found to be significantly linked to one or more phenotypic features of oesophageal cancer highlights the power of MALDI-MSI in the analysis of high-density TMAs. The data also emphasise substantial biological differences between adenocarcinomas and squamous cell carcinomas.