10 August 2016

SCiLS contributed to the following publications:

Abstract: Histopathological subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (ADC) and squamous cell carcinoma (SqCC) is of utmost relevance for treatment stratification. However, current immunohistochemistry (IHC) based typing approaches on biopsies are imperfect, therefore novel analytical methods for reliable subtyping are needed. We analyzed formalin-fixed paraffin-embedded tissue cores of NSCLC by Matrix-assisted laser desorption/ionization (MALDI) imaging on tissue microarrays to identify and validate discriminating MALDI imaging profiles for NSCLC subtyping. 110 ADC and 98 SqCC were used to train a Linear Discriminant Analysis (LDA) model. Results were validated on a separate set of 58 ADC and 60 SqCC. Selected differentially expressed proteins were identified by tandem mass spectrometry and validated by IHC. The LDA classification model incorporated 339 m/z values. In the validation cohort, in 117 cases (99.1%) MALDI classification on tissue cores was in accordance with the pathological diagnosis made on resection specimen. Overall, three cases in the combined cohorts were discordant, after reevaluation two were initially misclassified by pathology while one was classified incorrectly by MALDI. Identification of differentially expressed peptides detected well-known IHC discriminators (CK5, CK7), but also less well known differentially expressed proteins (CK15, HSP27). In conclusion, MALDI imaging on NSCLC tissue cores as small biopsy equivalents is capable to discriminate lung ADC and SqCC with a very high accuracy. In addition, replacing multislide IHC by an one-slide MALDI approach may also save tissue for subsequent predictive molecular testing. We therefore advocate to pursue routine diagnostic implementation strategies for MALDI imaging in solid tumor typing.

Abstract: A standardized workflow for matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI imaging MS) is a prerequisite for the routine use of this promising technology in clinical applications. We present an approach to develop standard operating procedures for MALDI imaging MS sample preparation of formalin-fixed and paraffin-embedded (FFPE) tissue sections based on a novel quantitative measure of dataset quality. To cover many parts of the complex workflow and simultaneously test several parameters, experiments were planned according to a fractional factorial design of experiments (DoE). The effect of ten different experiment parameters was investigated in two distinct DoE sets, each consisting of eight experiments. FFPE rat brain sections were used as standard material because of low biological variance. The mean peak intensity and a recently proposed spatial complexity measure were calculated for a list of 26 predefined peptides obtained by in silico digestion of five different proteins and served as quality criteria. A five-way analysis of variance (ANOVA) was applied on the final scores to retrieve a ranking of experiment parameters with increasing impact on data variance.

 

Two new papers are available online to which SCiLS contributed