To identify molecular features associated with clinico-pathological parameters and TMPRSS2-ERG fusion status in prostate cancer, we employed MALDI mass spectrometric imaging (MSI) to a prostate cancer tissue microarray (TMA) containing formalin-fixed, paraffin-embedded tissues samples from 1,044 patients for which clinical follow-up data were available. MSI analysis revealed 15 distinct mass per charge (m/z)-signals associated to epithelial structures. A comparison of these signals with clinico-pathological features revealed statistical association with favorable tumor phenotype such as low Gleason grade, early pT stage or low Ki67 labeling Index (LI) for four signals (m/z 700, m/z 1,502, m/z 1,199 and m/z 3,577), a link between high Ki67LI for one signal (m/z 1,013) and a relationship with prolonged time to PSA recurrence for one signal (m/z 1,502; p 5 0.0145). Multiple signals were associated with the ERG-fusion status of our cancers. Two of 15 epithelium-associated signals including m/z 1,013 and m/z 1,502 were associated with detectable ERG expression and five signals (m/z 644, 678, 1,044, 3,086 and 3,577) were associated with ERG negativity. These observations are in line with substantial molecular differences between fusion-type and non-fusion type prostate cancer. The signals observed in this study may characterize molecules that play a role in the development of TMPRSS2-ERG fusions, or alternatively reflect pathways that are activated as a consequence of ERG-activation. The combination of MSI and large-scale TMAs reflects a powerful approach enabling immediate prioritization of MSI signals based on associations with clinico-pathological and molecular data.Prostate cancer is a leading cause of cancer-related mortality in males. Worldwide, more than 600,000 men are diagnosed with prostate cancer each year. 1 Although the majority of prostate cancers are detected at early stages as a result of prostate specific antigen (PSA) screening, many patients present with advanced and metastatic cancer at the time of diagnosis. 2 It is hoped that a better understanding of the molecular biology of prostate cancer will help to improve prostate cancer diagnosis and therapy.Screening methods such as DNA, RNA, and protein arrays have led to the identification of multiple candidate gene alterations that occur in usually small fractions of prostate cancers. [3][4][5] Most of these alterations have not been further analyzed for clinical significance, partly because the necessary tissue samples with clinical annotation were lacking in the laboratories where the screening methods were utilized. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometric imaging (MSI) on a tissue microarray (TMA) is an interesting concept for screening for clinically relevant biomarkers. [6][7][8] This approach combines a systematic search for novel biomarkers by mass spectrometry with a simultaneous analysis of the newly found parameters on hundreds of tumors with clinical follow-up data.In this study, we applied MSI to a TMA contai...

For identification of clinically relevant masses to predict status, grade, relapse and prognosis of colorectal cancer, we applied Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) to a tissue micro array containing formalin-fixed and paraffin-embedded tissue samples from 349 patients. Analysis of our MALDI-IMS data revealed 27 different m/z signals associated with epithelial structures. Comparison of these signals showed significant association with status, grade and Ki-67 labeling index. Fifteen out of 27 IMS signals revealed a significant association with survival. For seven signals (m/z 654, 776, 788, 904, 944, 975 and 1013) the absence and for eight signals (m/z 643, 678, 836, 886, 898, 1095, 1459 and 1477) the presence were associated with decreased life expectancy, including five masses (m/z 788, 836, 904, 944 and 1013) that provided prognostic information independently from the established prognosticators pT and pN. Combination of these five masses resulted in a three-step classifier that provided prognostic information superior to univariate analysis. In addition, a total of 19 masses were associated with tumor stage, grade, metastasis and cell proliferation. Our data demonstrate the suitability of combining IMS and large-scale tissue micro arrays to simultaneously identify and validate clinically useful molecular marker. Copyright © 2017 John Wiley & Sons, Ltd.