Introduction

Lung cancer is the leading cause of cancer deaths worldwide, with non-small cell lung cancer (NSCLC) as the prevalent form with a poor 5‐year survival rate of less than 15%1, 2. NSCLC is subdivided into three major histological types: squamous cell carcinoma, large cell carcinoma, and adenocarcinoma. Lung adenocarcinoma (LUAD) is the predominant histological type of lung cancer and accounts for about 40% of all cases. It is the most common subtype diagnosed in never-smokers3.

Recent advancements in high‐throughput molecular biology technologies have deepened our understanding of the pathology underlying NSCLC and highlighted the significant heterogeneity of NSCLC. Especially in LUAD, sequencing of entire cancer genomes has resulted in the identification of recurrent driver alterations in several genes (e.g., EGFR, BRAF, ALK, RET, ROS1, TP53) and frequently transformed signaling pathways. Thereby, new molecular subtypes of LUAD were defined and novel targeted treatment options could be developed.

Despite advances in personalized therapies as well as surgery, radiation and chemotherapy, longevity has not increased significantly. Thus, lung cancer patients’ prognosis is still poor and particularly dependent on the stage of disease while first diagnosis. Patients with stage I tumors can expect a 5‐year survival rate of up to 85%, for locally advanced disease, the survival drops to less than 30% and patients diagnosed with distant metastases have a miserable 5‐year survival rate of less than 5%2. However, diagnosis at an early stage is only achieved in each third case of lung cancer4. Therefore, in our study we focus on distant metastatic stage LUAD.

Among the various potential biomarkers, especially proteins are significant, because they represent the functional gene products and are comparatively stable5. They carry out most biological processes and are therefore directly involved in disease progression. However, complex regulatory systems controlling protein expression levels lead to dynamics of the proteome. Proteomics-based analyses, particularly mass spectrometry (MS), include the examination and classification of overall protein signatures in a quantitative manner. Differentially scaled proteomic technologies are applicable in various research settings. They are used to understand mechanisms of pathogenicity, in the analysis of diagnostic biomarkers, in order to detect differential expression patterns reacting to varying signals as well as functional examination of signaling pathways in several diseases. The collection of high-quality fresh tissue for proteomics-based clinical studies is intricate. Therefore, preserved formalin‐fixed and paraffin‐embedded (FFPE) tissues represent a valuable resource for retrospective studies with subsequent proteomic analyses6, 7.

We performed proteomic analysis using high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI–MS/MS) on a total of 38 FFPE samples corresponding to 14 patients with advanced LUAD and available tissue of matched distant metastases. Changes at the protein level between primary and metastatic tissue were detected and differentially expressed proteins were identified and validated using immunoblot. So far, proteomic studies in lung cancer mostly focused on early tumor detection and often used blood samples as sample origin (reviewed in5 and8). Even in tissue-based studies, metastatic samples were usually not included. Currently, there is only one proteomic study focusing on brain metastatic LUAD9. To our knowledge, this is thus the first proteomic study on matched pairs of primary and differently localized metastatic LUAD tissues providing a deeper insight into the proteomic changes during metastatic spread of LUAD.

Results

Our cohort comprised a total of 38 FFPE samples corresponding to 14 patients diagnosed with LUAD and accessible tissue of primary tumors as well as distant metastases (detailed sample information is given in Supplemental Table S1). Due to the limited availability of resected tissue samples especially of metastases we included several samples gained through clinical autopsies. Patient specific characteristics are summarized in Table 1. For each patient comprehensive molecular profiling was performed using fluorescence in-situ hybridization (FISH) and massive parallel sequencing (NGS, Table 1). None of the cases showed targetable gene alterations in EGFR, BRAF, ALK, RET or ROS1. One case was identified to carry an ERBB2 amplification and three cases showed the common KRAS p.G12C mutation. The most frequently mutated genes were TP53 in 57% and KRAS in 43%.

Table 1 Patient cohort.
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To compare primary tumors and metastases on the proteome level, we performed HPLC and microflow ESI–MS/MS analysis using data-independent acquisition for exact quantification. Spectronaut analysis revealed 1405 distinct proteins identified across all samples (median 1003 per sample). 1055 were identified in ≥ 50% of the samples and were used for subsequent analyses. We first compared the pooled protein expression between primary tumors and metastases (Fig. 1 and Supplemental Table S2). 137 proteins (12.9%) were significantly (unadjusted p ≤ 0.05) differentially expressed between primaries or metastases. Of these 119 had a minimal fold change of 0.5 with overexpression in primaries (68) or metastases (51), respectively (Fig. 1A). The most frequent biological processes belonging to the proteins upregulated in metastases were the oxidation–reduction process, the mitochondrial electron transport, fatty acid beta-oxidation, and angiogenesis. For those upregulated in primaries these were complement activation, receptor-mediated endocytosis, the Fc-gamma receptor signaling pathway involved in phagocytosis, mRNA splicing, and the innate immune response. Of note, a number of metastasis-specific proteins were related to the extracellular matrix/stroma (e.g., the collagen subtypes COL4A2, COL18A1, and COL1A2). To evaluate the functional alterations more comprehensively, we used gene/protein set enrichment analyses (GSEA, Fig. 1B and Supplemental Table S3). Here, significantly enriched pathways were mostly found in the metastasis group (11 out of the top 15 pathways). In line with the biological processes, these were related to cellular energy metabolism, interestingly also mostly involving mitochondrial pathways. For all significantly regulated proteins (unadjusted p ≤ 0.05) a STRING protein–protein interaction network was created (Fig. 1C), which also showed a metastasis-linked cluster of metabolic proteins.

Figure 1
figure 1

Differential expression between primaries and their metastases. (A) Volcano plot with upregulated proteins in metastases on the left side (51, red) and in primaries on the right (68, blue), horizontal line is unadjusted p = 0.05, vertical lines are absolute log2 fold changes = 0.5; (B) Gene ontology (GO) pathways significantly enriched in primaries or metastases; GO terms ordered by false discovery rate (upward bars) with parallel display of the significance thresholds (0.05; dashed) and unadjusted p-values (downward bars); (C) STRING protein–protein interaction network of all significantly regulated proteins from (A); negative fold changes represent upregulation in metastases; only connections with more than 0.4 interaction score are shown; light grey visualizes the metastasis-linked cluster of proteins; circled candidate proteins underwent immunoblot validation.

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Due to the high variances in standard differential expression analysis, we used a second, orthogonal, and unsupervised evaluation approach to identify proteomic patterns across primary LUAD samples and metastases. This approach is generally applied to identify patterns across multiple types of quantitative data, including transcript and protein expression data (e.g.10). For unsupervised cluster analysis (Fig. 2 and Supplemental Table S4) rank determination by cophenetic correlation and dispersion revealed a distinct local maximum for k = 5 clusters with reasonable cluster separation and stability (Fig. 2A–C). Four of the five identified clusters were composed of a mixture of both primaries and metastatic samples, while one cluster included almost all metastases from one individual patient—highlighting the relevant interindividual heterogeneity. Similarly, a principal component analysis made some separation visible but explained only a minor variance (Fig. 2D). The similarity between matched pairs becomes evident, for example in patients 3, 10, 11, and 13. However, a clustering based on the metastatic locations is not visible. In order to show the effect of imputation, a principle component analysis (PCA) plot of the samples before (100% valid value filter, 334 values) in comparison to the one after imputation (50% valid value filter plus imputation) is given in Supplemental Fig. S1. Omission of imputation leads to less separation by the first two principal components, with a similar sample-wise pattern.

Figure 2
figure 2

Unsupervised cluster analysis (A) Consensus matrix for k = 5 clusters, color indicates stochastic reproducibility across independent runs; (B–C): Rank determination by cophenetic correlation (B) and dispersion (C); (D) Principal component analysis for the different samples; abbreviated localizations are given for each metastatic sample (ADR adrenal gland, HEP liver, KID kidney, OSS bone, OTH other).

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Figure 3 visualizes the 10% most cluster-relevant proteins (protein score > 90th percentile)11. The overlap between these 106 cluster-relevant proteins and those differentially expressed in pooled comparison comprised nine proteins (ARHGDIB, HNRNPA1, SRI, CYRIB/FAM49B, HNRNPL, HK1, IGKC, PAFAH1B2 and ATP5A1) and was used to choose proteins likely involved in metastasis for further validation. Due to their potential role in tumorigenesis HK1 and ATP5A (upregulated in metastases) as well as SRI and ARHGDIB (upregulated in primaries) were selected. Quantitative expression was measured in n = 6 primaries, n = 8 matched metastases and n = 2 additional metastatic samples using immunoblotting (Fig. 4). Significant differential expression (p < 0.05) could be confirmed for HK1 and ATP5A, both upregulated in metastases compared to matched primary tumors in immunoblot as well as LC–MS/MS analyses (Fig. 4A, B). An exemplary immunoblot reflecting differential expression is shown in Fig. 4C. All immunoblots are provided as original TIFF files in Supplemental Figs. S2–S7 with a corresponding sample matrix given as Supplemental Table S5. SRI and ARHGDIB did not show significant differences in the immunoblot analysis.

Figure 3
figure 3

Expression heatmap of cluster-relevant proteins. Log2-normalized and zscore-transformed expression data for the 10% most relevant proteins for the clusters from Fig. 2; Missing values in grey.

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Figure 4
figure 4

Immunoblot validation. (A) Immunoblot results; Normalized densitometric intensities of n = 5 primaries and n = 8 metastases; Whiskers represent interquartile range; p values are from Mann–Whitney-U; light lines link sample pairs; (B) Mass spectrometric normalized intensities of the samples from (A); (C) Exemplary immunoblot; STD = Standards for cross-blot normalization; Sample type blue = Primary; Sample type red = Metastasis; Full-width blots cropped for the specific protein bands; Blot #2 for the quantification of Sorcin with a separate loading control, which matches its molecular weight; complete original blots are presented as Supplemental Figs. S2–S7 with a corresponding sample matrix as Supplemental Table S5; quantitatively compared blots were generated during the same experiment and processed in parallel.

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In total, our analyses identified several metabolic proteins with differential expression between primary LUAD and matched distant metastases. HK1 and ATP5A could be validated. However, we also observed considerable inter-individual heterogeneity.

Discussion

Lung cancer is the leading cause of cancer-related mortality worldwide and lung adenocarcinoma (LUAD) is the most common form of lung cancer with a poor 5-year survival rate of less than 15%1. Prognosis for lung cancer patients strongly depends on the stage of disease at time of diagnosis and the presence of metastasis is the major factor for low survival rates2. Therefore, there is an urgent need to discover processes and signaling pathways involved in metastasis formation in LUAD. In our study we compared the proteomic profiles measured by high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI–MS/MS) of primary LUAD samples to those of matched distant metastases.

Our cohort comprised a total of 38 FFPE samples corresponding to 14 patients diagnosed with LUAD and accessible tissue of primary tumors as well as distant metastases. The most frequently mutated genes in our cohort were TP53 in 50% and KRAS in 29%, reflecting a typical distribution in a LUAD cohort. KRAS mutation is known to be the most common gain-of-function alteration, accounting for around 30% of LUADS in western countries12.

In recent years, proteomic studies have become a widely used research tool in analyzing cancer biology, complementing the results of genetic profiling. As most biological functions are carried out by proteins, protein profiles can often represent even more accurately a disease state and thus be a more reliable and quantitative tool to discover new cancer biomarkers. Mass spectrometry (MS) techniques allow the identification of differentially expressed proteins in small quantities of tumor samples13, 14. As fresh frozen tissue with corresponding clinical data is often not available for retrospective analyses, several studies showed the feasibility of using stored FFPE tissues for MS-based comprehensive proteomic profiling6, 7. So far, most proteomic studies on lung cancer focused on the differentiation of histological subtypes or early diagnosis of malignant disease15,16,17,18,19,20,21. A very recent study analyzed also distant metastatic tissue, but included only brain metastases9. To our knowledge, our study is now the first proteomic study on matched pairs of primary and differently located metastatic LUAD tissues providing a deeper insight into the proteomic changes during the metastatic spread of LUAD.

We identified 1405 proteins across all samples with 1055 shared by at least 50% of the samples. Our differential expression analysis between primary tumors and their corresponding metastases revealed 137 proteins significantly upregulated in primaries or metastases respectively. Another recent LC–MS-based proteomic study on 22 LUAD patients using fresh frozen tissue samples revealed 365 and 366 proteins differentially expressed in early-stage (I-II) or advanced-stage (III-IV) LUAD compared to normal tissue, respectively22. Comparable to our study, the authors identified 155 proteins dysregulated between early- and advanced-stage tumors. Their PCA showed a clear separation between four clusters corresponding to different stages and normal vs. tumor tissue. As in our cluster analysis as well as PCA the similarity between matched pairs of the same patient becomes evident and emphasizes the importance of using matched tissue samples for comparative analysis, as we did in our study.

Recently, Gillette et al. published a comprehensive proteogenomic characterization of 110 LUAD and 101 matched normal adjacent tissues (cryopulverized tissue). They revealed four subgroups defined by key driver mutations, country, and gender and identified new therapeutic targets. The study, however, did not include stage IV cancers with distant metastases. It is thus not surprising, that there is no overlap with the herein identified candidate proteins23. Another large deep-scale proteogenomics study of LUAD in Taiwanese population24 and a comprehensive proteogenomics analysis of 103 LUAD in chinese patients25 were published in recent years.

There are several proteomic studies on LUAD tumor progression that compare different stages of the disease. Kawamura et al. identified 81 proteins significantly differentially expressed in stage IA compared to IIIA LUAD26. Further analysis revealed NAPSA to be significantly reduced expressed in advanced stage tumors as well as hAG-2 highly expressed in stage IIIA vs. IA LUAD. Additionally, differential expression of hAG-2 was related to regional lymph node metastasis27. Also, the study of Hsu et al. focused on lymph node metastasis in LUAD28. They identified 133 differentially expressed proteins and selected six of them for further validation (ERO1L, PABPC4, RCC1, RPS25, NARS, and TARS). All of these studies were based on non-metastatic cases and further work identifying biomarkers for distant metastasis formation in LUAD is still lacking. Therefore, our study included only cases with distant metastasis and no early-stage tumors.

A recent study by Woldmar et al.9 conducted proteomic profiling on 20 surgically resected primary and brain metastatic LUAD samples. They identified 1496 proteins differentially expressed between primary tumors and corresponding metastases. Pathways activated in primary tumors were associated with the immune system, cell–cell/matrix interactions and migration, whereas metastatic tumor samples displayed overrepresentation of pathways related to metabolism, translation or vesicle formation. In part, these results correspond to the pathways connected with differentially expressed proteins we detected in our study. Similar to Woldmar et al. we found distant metastases to be for example associated with metabolic processes, whereas primary tumors showed amongst others overrepresentation of pathways related to the immune system. However, several particular pathways as well as individual biomarker candidates identified in the different studies do not correspond. This might be due to the fact that instead of analyzing only brain metastases we included also distant metastases of other locations.

Using gene/protein set enrichment analyses we mostly detected significantly enriched pathways in the metastasis group (11 out of the top 15 pathways). In line with the biological processes associated with differentially expressed proteins, these were related to cellular energy metabolism, especially involving mitochondrial pathways. The importance of mitochondrial processes for lung cancer initiation and progression is also described in other studies (e.g.29 or reviewed in30). Of note, Chuang et al. discovered a specifically altered mitochondrial functionality related to the metastatic cell state of LUAD and that this association could also be used therapeutically31.

In our study, the overlap between the 137 differentially expressed proteins and 106 most relevant proteins identified by cluster analysis revealed 9 candidate proteins involved in metastasis formation of LUAD. Of these, four were chosen for validation by immunoblotting: Hexokinase 1 and ATP Synthase F1 Subunit Alpha (HK1, ATP5A, upregulated in metastases) as well as Sorcin and RhoGDP Dissociation Inhibitor Beta (SRI, ARHGDIB, upregulated in primaries). All four candidates have previously been reported to be likely involved in tumorigenesis and partially even in lung cancer. For example, overexpression and amplification of the calcium-binding protein Sorcin has been described for different cancer entities, including lung cancer32. Additionally, the association between SRI overexpression and resistance to gemcitabine could repeatedly be shown. Qu et al. identified 14 proteins related to gemcitabine resistance in NSCLC cell lines, among them SRI33, which has previously been found to be overexpressed in several multidrug-resistant cell lines34. Also, ARHGDIB is reported to be involved in lung cancer tumorigenesis35. It was initially shown to be a metastasis suppressor in bladder cancer and later found to be lost in many metastatic tumors36.

In our validation, significant differential expression could be confirmed for HK1 and ATP5A, both upregulated in metastases compared to matched primary tumors in immunoblot and LC–MS/MS analyses. ATP5A itself has not yet been described to be associated with lung cancer, but another ATP synthase subunit could already be identified as biomarker for LUAD by Chen and colleagues37. They identified nine enzymatic proteins significantly overexpressed in LUAD compared to adjacent normal lung tissue using 2DGE and MALDI-MS or peptide sequencing, including the ATP synthase subunit D (ATP5D). Additionally, it has been reported that inhibiting the ATP synthase suppresses proliferation and growth of lung cancer cells38. ATP5A is furthermore described as shared drug target for aging and dementia39. The hexokinase HK1 is involved in glycolysis (and in part bound to the mitochondrial outer membrane). Its herein observed differential expression thus corresponds to the detected metastasis-linked cluster of metabolic proteins, mostly involving mitochondrial pathways. We found HK1 to be overexpressed in metastases compared to primary tumors. So far, HK1 was rather described to be expressed in normal tissues, whereas cancer cells often show additional or alternative expression of the HK2 isoform40, 41. HK2 was detected to be required for tumor initiation and maintenance in mouse models of KRAS-driven lung cancer40 and HK1 knock-out lung cancer cells expressing only HK2 were shown to be sensitive to HK2 silencing-induced cytostasis41. In hepatocellular cells HK1 expression correlates with resistance to tyrosine kinase inhibition and its function could be impaired by Lonidamine, a glycolysis inhibitor that inhibits the activity of mitochondrially bound hexokinases42, 43. In order to exclude that differential expression of HK1 and ATP5A is caused by an underlying tissue-specific expression we checked protein expression using the human protein atlas44. Both proteins are described to be expressed ubiquitously in a non-tissue-specific manner, especially without enhanced expression in any of the herein analyzed localizations. Our validation cohort comprised samples from the discovery cohort. Therefore, an additional validation on a larger and independent cohort would be desirable in the future.

We observed heterogeneous protein expression profiles of matched primary tumors and their distant metastases across patients. Nonetheless, several mostly metabolic proteins were associated with the metastatic state. HK1 and ATP5A could be identified and validated as candidate proteins. These findings give a better understanding of tumor progression and metastasis formation and might help to improve biomarker-based diagnosis and prognosis prediction.

Methods

Study design and sample selection

This study has been granted approval by the ethics committee of the University Luebeck (project code AZ 16-277, AZ 16-278). The ethics committee assesses the appropriateness of the design of the retrospective study, in which the samples were included completely anonymized. The requirement for obtaining informed consent has been waived. All investigations were carried out in adherence to the principles in the Declaration of Helsinki.

In total, 38 samples corresponding to 14 patients with advanced lung adenocarcinoma and available tissue of matched distant metastases were identified. Of these, primary tumor tissue from 9 patients and metastases tissue from 12 patients were harvested in clinical autopsies. Patients were annotated by sex, age at diagnosis and smoking status. Detailed information on pretreatment with chemotherapy, localizations, and number of analyzed metastases for each patient is shown in Table 1.

Histological and molecular pathological characterization

Histological analyses on formalin-fixed/paraffin-embedded (FFPE) tumor blocks were performed in the Institute of Pathology of the University Hospital Schleswig–Holstein, Campus Luebeck. Histology of each case including growth pattern was assessed by senior pathologists experienced in lung pathology. Using H&E-stained slides, tumor areas were marked and tumor cell content was estimated.

For each case, tissue areas with preferably high tumor cell content (mean: 69%, standard deviation: 19%, CV: 0.27, Supplemental Table S1) were selected for nucleic acid extractions. Isolation of genomic DNA was performed using the Maxwell RSC DNA FFPE Kit and the Maxwell RSC instrument (Promega, Fitchburg WI, U.S.A.). DNA samples were quantified using the Qubit fluorimeter (TermoFisher, Waltham MA, U.S.A.). To identify genetic alterations in AKT1, ALKMUT, BRAF, CTNNB1, DDR2, EGFR, ERBB2Mut, ERBB4, FBXW7, FGFR1, FGFR2, FGFR3, KRAS, MAP2K1, METMut, NRAS, NOTCH1, PIK3CA, PTEN, STK11, SMAD4 and TP53 massive parallel sequencing using the Ion AmpliSeq Colon and Lung Cancer Research Panel v2 and Ion PGM sequencing platform (ThermoFisher Scientific) were used. Additionally, the possible presence of ALK, RET or ROS1 translocations as well as amplifications of MET and ERBB2 in the primary tumors was investigated by fluorescence in-situ hybridization (FISH) using the corresponding ZytoLight probes (ALK Z-2124, RET Z-2148, ROS1 Z-2144, MET Z-2087, ERBB2 Z-2017, ZytoVision, Bremerhaven, Germany).

Protein extraction

For each primary tumor or metastasis tissue areas with preferably high tumor cell content were selected for proteomic analysis and 45 µm sections were cut off and stored at room temperature. To solubilize the proteins 1 ml Heptane was added to each sample, vortexed for 10 s. After 1.5 h at room temperature, 50 µl Methanol were added and the samples were vortexed again. The samples were centrifuged for 2 min at 9000×g at room temperature, the supernatant was removed, and the samples dried out for 5 min at room temperature. The QProteome® FFPE Tissue Kit (Qiagen, USA) was used for protein extraction. Subsequently, total protein concentration was determined in triplets using the fluorescence-based EZQ™ Protein Quantification Kit (Life Technologies, USA). Fluorescence visualization was carried out with the Typhoon™ FLA 9000 laser scanner (GE Healthcare). Densitometric analysis was performed using the ImageQuant™ TL software (GE Healthcare).

For each sample 100 µl lysate containing 25 µg protein were purified using methanol and chloroform. The protein pellet was washed with ethanol and dissolved in 1% RapiGest (Waters, USA) in 25 mM Ammonium bicarbonate (ABC) buffer. Proteins were reduced with 50 mM Dithiothreitol (DTT) and incubated at 37 °C at 950 rpm for 1 h. Afterwards, 100 mM iodoacetamide (in ABC buffer) was used to alkylate the proteins by shaking the samples at 37 °C with 950 rpm for 1 h. Proteins were digested using 25 ng/µl Trypsin (Sigma-Aldrich, USA) in ABC buffer over night at 37 °C. Trifluoroacetic acid (5%) was added and the samples were incubated at 950 rpm at 37 °C. The samples were centrifuged and the supernatant was transferred into a new tube, dried out by vacuum centrifugation for 3 days and stored at − 80 °C until further analysis.

Proteomic analysis by high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI–MS/MS)

With minor adjustments, proteomic analysis was performed as described previously45. The samples were solubilized in 2% acetonitrile/0.5% formic acid. Luna C18 (2) (5 μm, 20 × 0.3 cm; Phenomenex, USA) was used as trap column and the samples were desalted for 5 min. An analytical column (LC Column, 3 μm C18 (2), 150 mm × 0.3 mm, Phenomenex, USA) was used to separate the peptides. Analyzation with mass spectrometer and following SWATH (sequential window acquisition of all theoretical mass spectra) were performed according to Sauer et. al.45. Thereby, the collision energy (CE) was set to 10 and the updated SWATH Variable Window Calculator V2.0 was used to define the precursor isolation windows.

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE46 partner repository with the dataset identifier PXD042604. Corresponding raw file names can be obtained from Supplemental Table S1.

SWATH data processing

The software tool Spectronaut v13.2 (Biognosys, Switzerland) was used for the SWATH data processing. First a hybrid spectral library was established from all 38 SWATH runs and five pooled DDA runs using Spectronaut with default settings. The hybrid spectral library was subsequently searched using the default settings with Spectronauts pulsar search engine. The false discovery rate (FDR) was set to 1% at the peptide precursor level and protein level, respectively. Additionally, all proteins considered in this study were identified by at least two peptides. The human UniProtKB/Swiss-Prot database47 was used for protein inference from identified peptides.

Immunoblot

Immunoblotting was performed as described previously6. The primary antibodies were anti-HK1 (1:500; monoclonal mouse IgG; antibodies- online ABIN933202, Aachen, Germany), anti-ATP5A (1:1000; monoclonal rabbit IgG; abcam ab176569, Cambridge, UK), anti-Sorcin A (1:1000; polyclonal rabbit IgG, antibodies-online ABIN5014335, Aachen, Germany), anti-ARHGDIB (1:500;polyclonal rabbit IgG, antibodies-online ABIN2855594, Aachen, Germany) as well as loading controls anti-Cofilin (1:1000; Cell Signaling Technology 5175S, Danvers, USA) and anti-HPRT (1:100; Santa-Cruz sc-376938, Dallas, USA). Secondary antibodies were 1:2500 goat anti-rabbit IgG (ThermoFisher 31460, Schwerte, Germany) and 1:2500 goat anti-mouse IgG (ThermoFisher 31430, Schwerte, Germany).

Conditions for relative protein quantitation were ensured48 and the linear ranges determined beforehand. Sample-specific protein abundances were normalized to the mean of the same-gel standards prior to normalization to loading controls.

Bioinformatics and statistical analyses

Data processing and statistical analyses were performed in Python (2.7.17 and 3.9.9) using the modules nimfa 1.4.0, gseapy 0.10.8 (permutation_type = 'phenotype', permutation_num = 100, method = 't_test', processes = 4, seed = 7), matplotlib 2.2.5, numpy 1.16.1, sklearn 0.20.4 (including decomposition.PCA with default settings), pandas 0.24.2, scipy 1.2.2, and seaborn 0.9.1. The raw data was filtered for proteins quantified in at least 50% of all samples. Data was normalized using Normicsmedian49 based on the top 100 invariant proteins. Significance for differential expression was calculated with Mann–Whitney-U tests (unadjusted due to comparison to orthogonal unsupervised evaluation). Due to the unequal number of metastases per primary, a more conservative unpaired statistical approach was chosen over paired statistical tests to avoid biased weights across samples. Additionally, Benjamini–Hochberg adjusted p-values are included as an additional worksheet (“adjusted”) in Supplemental Table S2. Unsupervised non-negative matrix factorization was performed on all proteins for k = 2 up till k = 10, with missing values replaced by the mean of all valid values. The mean was chosen over minimum/low values or other more sophisticated methods as a conservative approach (to reduce power rather than introducing biases) in this setting of relatively high missingness (at random) and known performance heterogeneity in FFPE samples in line with suggestions from the literature50. Overall, missing values were not imputed for any test, except for PCA and unsupervised cluster analysis. The local maximum at k = 5 was chosen as it demonstrated a distinctive peak for both cophenetic correlation and dispersion. Relevance scores were computed as implemented in the nimfa package51 defined by Kim and Park11. For gene set enrichment analyses (GSEA) the 2018 gene ontology terms for biological processes were used. The STRING network was created on string-db.org52.