Background Lung cancer is the leading cause of cancer related mortality. The most frequent histological subtypes are squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The differentiation of both has major implications for further molecular testing, treatment and thus patient outcome. Today, morphology is the gold standard to discern both entities but the differentiation is achievable in less than 80% of cases on biopsy material. Thus additional immunohistochemical stains with the need of multiple tissue sections may be required. Since usually only sparse biopsy material is available, diagnostic approaches should be tissuesparing in order to allow for predictive molecular analysis using the remaining tumor material. MALDI imaging is a new method to analyze hundreds of proteins or peptides on only one tissue section. Therefore this technology seems to be the ideal candidate to overcome limitations of current methods. The aim of this study is to identify a proteomic signature able to discriminate between SCC and ADC of the lung by MALDI imaging on formalin-fixed paraffin-embedded (FFPE) tissue material. Methods We investigated eight tissue microarrays (TMAs) consisting of needle core punches from 139 SCC and 168 ADC lung biopsies. Each TMA was examined by a pathologist that digitally marked cancer regions. Sections (5 μm thick) were on-tissue trypsin digested and sprayed with matrix solution using a robotic sprayer. Data were acquired using a MALDI-TOF/TOF at a spatial resolution of 100 μm. Each dataset was analyzed flexImaging (Bruker Daltonik) and SCiLS Lab (SCiLS GmbH) software. 83 SCC and 110 ADC cores were used to train the Linear Discriminant Analysis (LDA) model. An independent set consisting of 56 SCC and 58 ADC cores was used to validate the algorithm. Results The LDA classification model produced a set of m/z values capable of discriminating SCC and ADC of the lung. Results were in accordance with the diagnosis made on the resection specimens in 92.9% of the cases. Discriminant peptide species were subsequently identified. Whereas peptides of CK7 were found predominantly in ADC, peptides of CK15, CK5 and heat-shock protein beta-1 were identified almost exclusively in SCC. Conclusions While routine IHC has been reported to differentiate SCC and ADC of the lung in < 80% of cases in small biopsy specimens, our data demonstrate that MALDI imaging represents a valuable method to discriminate both subtypes in > 90% of cases and additionally saves tissue for subsequent molecular testing.
Differentiation of Squamous Cell Carcinoma and Adenocarcinoma of the Lung by MALDI Imaging Mass Spectrometry on FFPE Tissue Sections
Casadonte R;
2015-01-01
Abstract
Background Lung cancer is the leading cause of cancer related mortality. The most frequent histological subtypes are squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The differentiation of both has major implications for further molecular testing, treatment and thus patient outcome. Today, morphology is the gold standard to discern both entities but the differentiation is achievable in less than 80% of cases on biopsy material. Thus additional immunohistochemical stains with the need of multiple tissue sections may be required. Since usually only sparse biopsy material is available, diagnostic approaches should be tissuesparing in order to allow for predictive molecular analysis using the remaining tumor material. MALDI imaging is a new method to analyze hundreds of proteins or peptides on only one tissue section. Therefore this technology seems to be the ideal candidate to overcome limitations of current methods. The aim of this study is to identify a proteomic signature able to discriminate between SCC and ADC of the lung by MALDI imaging on formalin-fixed paraffin-embedded (FFPE) tissue material. Methods We investigated eight tissue microarrays (TMAs) consisting of needle core punches from 139 SCC and 168 ADC lung biopsies. Each TMA was examined by a pathologist that digitally marked cancer regions. Sections (5 μm thick) were on-tissue trypsin digested and sprayed with matrix solution using a robotic sprayer. Data were acquired using a MALDI-TOF/TOF at a spatial resolution of 100 μm. Each dataset was analyzed flexImaging (Bruker Daltonik) and SCiLS Lab (SCiLS GmbH) software. 83 SCC and 110 ADC cores were used to train the Linear Discriminant Analysis (LDA) model. An independent set consisting of 56 SCC and 58 ADC cores was used to validate the algorithm. Results The LDA classification model produced a set of m/z values capable of discriminating SCC and ADC of the lung. Results were in accordance with the diagnosis made on the resection specimens in 92.9% of the cases. Discriminant peptide species were subsequently identified. Whereas peptides of CK7 were found predominantly in ADC, peptides of CK15, CK5 and heat-shock protein beta-1 were identified almost exclusively in SCC. Conclusions While routine IHC has been reported to differentiate SCC and ADC of the lung in < 80% of cases in small biopsy specimens, our data demonstrate that MALDI imaging represents a valuable method to discriminate both subtypes in > 90% of cases and additionally saves tissue for subsequent molecular testing.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
