APPLICATION NOTE. Highly reproducible and Comprehensive Proteome Profiling of Formalin-Fixed Paraffin-Embedded (FFPE) Tissues Slices

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Octavia Goodman
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APPLICATION NOTE Highly reproducible and Comprehensive Proteome Profiling of Formalin-Fixed Paraffin-Embedded (FFPE) Tissues Slices INTRODUCTION Preservation of tissue biopsies is a critical step to

1 APPLICATION NOTE Highly reproducible and Comprehensive Proteome Profiling of Formalin-Fixed Paraffin-Embedded (FFPE) Tissues Slices INTRODUCTION Preservation of tissue biopsies is a critical step to This has enabled the build up of large biobanks of enable a variety of biochemical analyses that lead formalin-fixed paraffin-embedded (FFPE) tissues for to a better understanding of the molecular features more than a century. responsible for a given disease state. The FFPE samples from these biobanks offer great At present, two major protocols are commonly used for potential for a better understanding of health and long term storage and preservation of clinical samples: disease and for the discovery of new diagnostic/ direct freezing of the tissue or fixation using formalin stratification markers and therapeutic targets. and subsequent embedding into paraffin. Where direct freezing of the sample requires an elaborate infrastructure and has only been available for a few decades, fixation of the tissue in formalin can be easily executed and long term storage at room temperature can be achieved.

2 INTRODUCTION & METHODS At the present, analysis of the accessible proteome of fresh frozen biopsies is routinely carried out achieving high analytical depth and high reproducibility. However, proteomics studies of comparable quality in FFPE tissue samples are still very limited. One particularly challenging aspect to efficient protein extraction remains the chemical reversal of crosslinks generated during formalin fixation, which is reflected in the low number of proteins quantified and poor reproducibility in such samples. Here, we present a new quantitative proteomic workflow for FFPE tissue samples. The sample preparation relies on heatassisted protein extraction and high-energy sonication for tissue disruption. In summary, the results presented in this application note demonstrate that our optimized protocol achieves reproducible protein extraction from FFPE tissue samples and combined with HRM TM mass spectrometry enables consistent quantification of more than 5,000 proteins, thus achieving equal performance to that of other proteomics studies carried out on fresh frozen tissue samples. Collectively, our workflow permits access to the valuable protein information stored in FFPE tissue slices, hence potentially revealing the molecular features characteristic of a specific disease state. The protocol has been optimized to achieve efficient protein extraction, enabling the reproducible quantification of proteomes from FFPE tissue slices. In this application note, we demonstrate the superior performance of Biognosys proteomic workflow for the comprehensive analysis of six FFPE tissue samples from three patients diagnosed with lung cancer. Three samples from lung cancer tissues and three of their adjacent healthy tissues were used to assess the reproducibility of the developed protocol. Each sample was prepared in triplicate and the resulting peptides were analyzed by LC-MS/MS using Biognosys Hyper Reaction Monitoring (HRM TM ) acquisition mode.

3 METHODS The FFPE tissues were cut into 5µm slices (3 healthy tissues and 3 cancer tissues: 2 squamous cell carcinoma, 1 adenocarcinoma). All biological samples were prepared in triplicates. The method for sample preparation will be published soon. Prior to data acquisition, all samples were spiked with irt peptides (Biognosys). Peptide samples (2 µg) were analyzed on a Q Exactive HF mass spectrometer (Thermo Scientific) by a 2h gradient in a dataindependent acquisition fashion (DIA, SWATH-like, HRM TM ). The reported mass differences to the unmodified peptide sequences were matched to the unimod database ( to identify dynamic modifications introduced by the FFPE procedure. Spectronaut was used for the analysis of the DIA data. All DIA data were filtered by a 1% FDR on precursor and protein level. Statistical analysis of the data was also performed in Spectronaut (t-test, q-value<0.01, foldchanges > 2 fold). Biological interpretation of the data was done by Ingenuity Pathway Analysis (IPA, QIAGEN Bioinformatics). For data analysis of the DIA data, a spectral library was generated based on 10 high ph reverse phase fractions of pooled healthy and cancer samples. These fractionated samples were analyzed by the same gradient and LC-MS setup using a data-dependent acquisition (DDA) method. DDA data were searched against the human UniProt database with MaxQuant 1 (PSM and protein FDR=1%) and a spectral library was built in Spectronaut 2 (Biognosys). To identify FFPE specific modifications, the MaxQuant search was repeated using the dependent search option.

RESULTS & DISCUSSIONS To assess the reproducibility of the FFPE sample preparation workflow, three 5µm slices were cut from three healthy and three cancer FFPE tissue samples (Figure 1a); resulting

4 RESULTS & DISCUSSIONS To assess the reproducibility of the FFPE sample preparation workflow, three 5µm slices were cut from three healthy and three cancer FFPE tissue samples (Figure 1a); resulting in 18 samples. Each tissue slice was processed separately. The sample preparation workflow resulted in 40 to 200µg of peptides per slice (Figure 1b), sufficient for state of the art LC-MS analysis. The spectral library that was created for the analysis of the HRM data comprised of 125,081 peptide precursors and 8,003 proteins. The size of this library was comparable to a lung cancer library we generated from fresh frozen tissue in another study, with an overlap of more than 6,500 proteins (77%) 3. This finding demonstrates that our sample preparation protocol enables a similar depth of analysis using FFPE tissue when compared to that of fresh frozen tissue. Next, we analyzed whether modifications were introduced by the FFPE procedure. Additional data analysis with MaxQuant revealed that lysine methylation is a unique modification in FFPE tissue samples and it has previously been reported as an FFPE specific modification 4. Protein quantification was only influenced to a minor extent by the methylated peptides (Pearson correlation = 0.93). Application of the spectral library enabled the quantification of a total of 105,121 peptide precursors and 6,894 proteins. On average 5,298 proteins per sample were quantified (Figure 1c), which is comparable to other lung cancer studies using fresh frozen tissue 5. Another critical parameter of the sample preparation is its reproducibility. The %CVs based on the sample preparation replicates, were between 5 and 10% on the protein level (Figure 1d), demonstrating an excellent reproducibility of our sample preparation workflow. Figure 1: Sample Preparation Overview B Peptide Yield per Slice A FFPE Lung Sample Specimen Peptide Yield [ug] H1 H2 H3 C1 C2 C3 Fig. 1a: Specimen of a lung FFPE sample prior to sample preparation. Fig 1b: Total amount of extracted peptides per 5µm FFPE tissue slice. Amounts per biological sample were averaged and standard deviations are shown per sample preparation triplicates. Peptides were quantified by NanoDrop.

1d: Sample preparation %CV per sample preparation triplicate.

5 Figure 1: Sample Preparation Overview C Precursor Level Peptide Level Protein Level D Sample Preparation %CV Distribution Fig. 1c: Overview of quantified proteins, peptides and peptide precursors per 2µg injection. Numbers were averaged per sample preparation triplicate and standard deviations are shown. Fig. 1d: Sample preparation %CV per sample preparation triplicate. For further validation, two different FFPE tissue types from other sources were processed in the same manner and comparable results were achieved in terms of sample preparation reproducibility and number of quantified proteins. We also observed increased complexity of the proteome in the lung cancer samples (average 5,423 proteins per sample) compared to the healthy cohort (average 5,172; Figure 1c), which has been reported before in lung cancer 5. Global analysis of the data showed that the sample preparation replicates clustered closely together. The healthy and the cancer cohorts were well separated and also the squamous cell carcinoma from the adenocarcinoma samples (visualized as heatmap in Figure 2a). This analysis clearly indicates that the observed differences are driven by the biological differences of the samples and not by variances introduced by the sample preparation. As we were also interested in the differently abundant proteins, we performed a statistical comparison. In total 2,047 proteins were found to be differentially abundant between the healthy and cancer samples (q value < 0.01, min 2-fold change).

Figure 2: Global Data Analysis A B Fig. 2a: Heatmap as generated by Spectronaut. H1-H3 are the healthy tissue samples (green). SSC equals squamous cell carcinoma (red), AC adenocarcinoma (orange).

6 Figure 2: Global Data Analysis A B Fig. 2a: Heatmap as generated by Spectronaut. H1-H3 are the healthy tissue samples (green). SSC equals squamous cell carcinoma (red), AC adenocarcinoma (orange). The numbers represent the matched healthy tissue. Proteins are sorted by decreasing intensity. Intensity is color-coded from dark blue (10.6, low) to yellow (23.1, high). Fig. 2b: Volcano plot as generated by Spectronaut. Candidate proteins are marked red. Proteins without a significantly differential abundance are marked grey. The majority of the proteins (1,227) were more abundant in the cancer cohort (visualized as volcano plot in Figure 2b). Our study is in line with a recent lung cancer study of fresh frozen tissue 5, 18 out of the 20 most differentially abundant proteins were also significantly altered in their abundance in our dataset (median q-value = 3e-13). The biological interpretation of the data in IPA revealed that the majority of the differentially abundant proteins are related to cancer, identified as the top disease in the analysis (1,779 proteins, p = 9e-5 to 9e-18). Additionally, three upstream regulators linked to cancer were identified in the analysis. The most significantly enriched regulator was CST5 (p = 3.9e-24), which is described as a mediator of tumor suppression by p53 6. Further, the well-studied oncogene MYC 7 (p = 1.4e-10) was found to be activated in our study and the IPA analysis also revealed an inhibition of SMARCA4 (p = 1.8e-7), a gene whose inactivation was reported to promote lung cancer aggressiveness 8. The most significantly enriched canonical pathway was EIF2 signaling (Figure 3a, p = 5.7e-20), which is involved in translational initiation and is therefore a key step in protein synthesis. This pathway is of particular interest in cancer research as potential therapeutic target 9,10. The expression level of most proteins in this pathway were very consistently elevated by 2-fold in the cancer cohort (depicted for translation initiation factors and ribosomal proteins in Figure 3b), exemplifying the high quality of the quantitative data.

7 Figure 3: IPA Analysis A B Another interesting finding was the enrichment of the complement system. Nearly every protein of the pathway was found to be decreased in the cancer cohort; only C1QBP, the inhibitor of the system, showed an elevated protein level (pathway overview depicted in Figure 3b). Previous studies have shown that cancer cells inhibit the complement system to escape the immune response 11,12 and also that elevated levels of C1QBP were already reported in cancer and discussed as potential target for therapeutics 13. C In summary, our sample preparation workflow combined with HRM TM mass spectrometry enables the proteomics analysis of FFPE tissue samples with comparable depth and reproducibility to fresh frozen tissue samples. Fig. 3a. TOP10 canonical pathways as reported by IPA analysis. The numbers on top of the bars indicate the number of proteins in the pathway. Fig. 3b. Boxplots of the log2 fold chances of the three major protein classes of EIF2 signaling. Fig. 3c. Complement system pathway analysis. Quantified proteins are marked by pink circles and changes in the protein abundance are color-coded. Blue indicates a decreased level of the protein in the cancer cohort and red an elevated level in the cancer cohort.

8 CONCLUSIONS The initial biological interpretation of the data in IPA exemplified how the analysis of FFPE tissues can be leveraged to reveal deep insights into a disease, here demonstrated for lung cancer. Biognosys high content protein profiling is now available for FFPE tissue- unlocking a wealth of biological information in a sample type that was previously considered nearly inaccessible for proteomics studies. The high availability of FFPE tissue samples holds great potential for a deeper understanding of various diseases including the discovery of new potential therapeutic targets and biomarkers. REFERENCES 1. Tyanova, et al. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, (2016). 2. Bruderer, R. et al. Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues. Mol. Cell. Proteomics 14, (2015). 3. Bruderer, R. et al. DDA-free DIA algorithm applied to data generated on a novel fast scanning Orbitrap instrument. Proceedings of ASMS Conference in Indianapolis (2017). 4. Bauden, M., et al. Characterization of histone-related chemical modifications in formalin-fixed paraffin-embedded and fresh-frozen human pancreatic cancer xenografts using LC-MS/MS. Lab. Investig. 97, (2017). 5. Tenzer, S. et al. Integrated quantitative proteomic and transcriptomic analysis of lung tumor and control tissue: a lung cancer showcase. Oncotarget 7, (2016). 6. Huenten, S. & Hermeking, H. p53 directly activates cystatin D/CST5 to mediate mesenchymal-epithelial transition: a possible link to tumor suppression by vitamin D3. Oncotarget 6, (2015). 7. Harris, C. C. et al. Role of oncogenes and tumour suppressor genes in human lung carcinogenesis. IARC Sci. Publ (1991). 8. Orvis, T. et al. BRG1/SMARCA4 Inactivation Promotes Non-Small Cell Lung Cancer Aggressiveness by Altering Chromatin Organization. Cancer Res. 74, (2014). 9. Bhat, M. et al. Targeting the translation machinery in cancer. Nat. Rev. Drug Discov. 14, (2015). 10. Koromilas, A. E. Roles of the translation initiation factor eif2 serine 51 phosphorylation in cancer formation and treatment. Biochim. Biophys. Acta - Gene Regul. Mech. 1849, (2015). 11. Afshar-Kharghan, V. The role of the complement system in cancer. J. Clin. Invest. 127, (2017). 12. Pio, R., Corrales, L. & Lambris, J. D. The role of complement in tumor growth. Adv. Exp. Med. Biol. 772, (2014). 13. McGee, A. M., et al. The mitochondrial protein C1QBP promotes cell proliferation, migration and resistance to cell death. Cell Cycle 10, (2011). INTERESTED IN HEARING MORE ABOUT BIOGNOSYS SOLUTIONS? Contact us at info@biognosys.com to schedule a meeting with our experts or visit our Science Hub for additional resources we have available (papers, tutorials, webinars etc.).

Digitizing the Proteomes From Big Tissue Biobanks

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