Quantitative proteomics and phosphoproteomics on serial tumor biopsies from a sorafenib-treated HCC patient

Eva Dazert, Marco Colombi, Tujana Boldanova, Suzette Moes, David Adametz, Luca Quagliata, Volker Roth, Luigi Terracciano, Markus H. Heim, Paul Jenoe, and Michael N. Hall
PNAS February 2, 2016 113 (5) 1381-1386; published ahead of print January 19, 2016 https://doi.org/10.1073/pnas.1523434113

  1. Contributed by Michael N. Hall, December 9, 2015 (sent for review September 18, 2015; reviewed by Bernd Bodenmiller and Pierre-Alain Clavien)

Significance

Elucidation of evasive resistance to targeted therapies is a major challenge in cancer research. As a proof-of-concept study, we describe quantitative proteomics and phosphoproteomics on serial tumor biopsies from a sorafenib-treated hepatocellular carcinoma (HCC) patient. This approach reveals signaling pathway activity in a tumor and how it evades therapy. The described method will allow precision medicine based on phenotypic data. In particular, application of the method to a patient cohort will potentially identify new biomarkers, drug targets, and signaling pathways that mediate evasive resistance.

Abstract

Compensatory signaling pathways in tumors confer resistance to targeted therapy, but the pathways and their mechanisms of activation remain largely unknown. We describe a procedure for quantitative proteomics and phosphoproteomics on snap-frozen biopsies of hepatocellular carcinoma (HCC) and matched nontumor liver tissue. We applied this procedure to monitor signaling pathways in serial biopsies taken from an HCC patient before and during treatment with the multikinase inhibitor sorafenib. At diagnosis, the patient had an advanced HCC. At the time of the second biopsy, abdominal imaging revealed progressive disease despite sorafenib treatment. Sorafenib was confirmed to inhibit MAPK signaling in the tumor, as measured by reduced ribosomal protein S6 kinase phosphorylation. Hierarchical clustering and enrichment analysis revealed pathways broadly implicated in tumor progression and resistance, such as epithelial-to-mesenchymal transition and cell adhesion pathways. Thus, we describe a protocol for quantitative analysis of oncogenic pathways in HCC biopsies and obtained first insights into the effect of sorafenib in vivo. This protocol will allow elucidation of mechanisms of resistance and enable precision medicine.

Hepatocellular carcinoma (HCC) is a global health concern with an estimated 750,000 new cases per year (1). In more than 80% of cases, HCC arises in a setting of liver cirrhosis mainly of alcoholic or viral origin (2). The prognosis for HCC patients is poor, with less than 30% qualifying for curative treatments such as tumor resection or liver transplantation (2). Median survival time of patients that cannot be treated surgically is less than 1 y. Sorafenib is the only approved targeted therapy for HCC, prolonging median patient survival by ∼3 mo (3). Sorafenib is a multikinase inhibitor of Raf (B and C), vascular endothelial growth factor receptor (VEGFR), and platelet-derived growth factor receptor (PDGFR) (4), which presumably inhibits not only tumor cells but also endothelial cells responsible for tumor vascularization.

Resistance to a targeted cancer drug can be intrinsic or adaptive (5). Sorafenib is largely cytostatic (6), suggesting that intrinsic resistance is more common in tumors, although some reports describe tumor shrinkage upon sorafenib treatment (7). Studies involving HCC cell lines or immunohistochemical staining of tumor sections revealed that sorafenib resistance correlates with the up-regulation of several signaling pathways, including the mammalian target of rapamycin (mTOR) pathway as assayed by S6 S235/236 (8) and Akt S473 phosphorylation (9). Other potential resistance mechanisms involve epithelial-to-mesenchymal transition (EMT) and autophagy (10, 11). However, the molecular mechanisms of sorafenib resistance in patients are largely unknown. Understanding the pathways that confer intrinsic or adaptive resistance would allow precision medicine and increase treatment efficacy.

Proteomic analysis allows the identification of drug targets for cancer treatment and biomarkers for cancer classification or recurrence. In particular, MS is a powerful tool for resolving the complexity of cancer signaling pathways. With regard to HCC, qualitative proteomics has been performed on resected tumor material (12), laser-capture microdissected material from tissue sections (13, 14), and primary hepatocytes or serum derived from patients (15, 16). These studies (17, 18) identified HCC biomarkers such as glutamine synthetase and heat shock protein 70 (Hsp70) that are currently in use for diagnosis (19, 20). Quantitative proteomics has been performed on HCC resected tissue and serum (21, 22). Recently, proteomics has been performed on tumor biopsies of renal cell carcinoma patients (23). Several studies also have described phosphoproteomic analyses of resected HCC or other cancer material (24⇓–26), in some cases quantifying up to 8,000 phosphorylated sites (hereafter referred to as “phosphosites”) starting with 2 mg of protein (18, 27⇓⇓–30). However, to our knowledge, quantitative proteomics and phosphoproteomics, hereafter collectively referred to as “(phospho)proteomics,” have yet to be performed on tumor biopsies, possibly because biopsy material is nonrenewable and typically provides only a very small amount of protein. Importantly, quantitative (phospho)proteomics on serial biopsies taken before and during treatment has not been described. We note that although a biopsy procedure generates less material than a resection, it has the important advantage of capturing normally dynamic properties of a tumor, such as the phosphorylation status of signaling pathways. Biopsies are immediately snap-frozen upon removal from the patient and, unlike resected tissue, are obtained without causing ischemia or hypoglycemia in the collected tissue. Needle biopsies are taken routinely to diagnose and stage the disease. Another important consideration is a method to perform quantitative (phospho)proteomics, such as super-SILAC (“SILAC” is an acronym for “stable isotope labeling of amino acids in cell culture”), that allows direct comparison of biopsies obtained at different times or from different patients (31).

We describe quantitative (phospho)proteomic analyses of needle biopsies of HCC and matched nontumor tissue from a human patient. These analyses provide a global snapshot of signaling pathways in the biopsy material. Analyzing serial biopsies taken from a patient before and during therapy, we measured differences in signaling pathways between tumor and matched nontumor control tissue and the changes in these signaling pathways upon sorafenib treatment. Our findings provide insight into mechanisms of tumor progression and resistance to cancer therapy.

Results

Experimental Setup for Quantitative (Phospho)Proteomics on Biopsies.

To monitor signaling pathways in HCC, we established an experimental protocol to perform (phospho)proteomics on tumor biopsies (Fig. 1A). The mean wet weight of eight examined biopsies, including biopsies used in pilot experiments, was 21 mg, from which, on average, ∼0.9 mg of total protein was extracted for proteome and phosphoproteome analyses (Fig. 1B). After testing many permutations (Table S1), urea-based protein extraction combined with strong cation exchange chromatography (SCX) and titanium dioxide (TiO2) phosphopeptide enrichment (32) was implemented as the most effective protocol (Fig. 1C).

Fig. 1.
Fig. 1.

Experimental setup for quantitative (phospho)proteomics on biopsies. (A) Schematic overview of proposed study. (B) Mean wet weight and recovered protein of eight liver biopsies. (C) Workflow for quantitative (phospho)proteomics. Five different human HCC cell lines (numbered 1–5) were used to prepare the super-SILAC spike-in standard. (D and E) Proteins (D) and phosphosites and phosphoproteins (E) identified and quantified in biopsy of hemochromatosis patient. CTRL, control; TU, tumor.

View this table:
Table S1.

Additional experimental procedures that were tested at each step of the sample preparation and their outcome

To perform quantitative (phospho)proteomics, we generated a spike-in standard consisting of a mixture of cell extracts of five HCC cell lines metabolically labeled with heavy isotopes (13C- and 15N-arginine and lysine), a method commonly known as super-SILAC (31). The spike-in standard was validated by measuring a 1:1 mix of the labeled standard with corresponding unlabeled extracts (Fig. S1 A and B). Over 90% of all heavy-to-light (H/L) protein ratios were <1.5 (Fig. S1C), confirming the high labeling efficiency and thereby the validity of the spike-in standard. From 1 mg of spike-in standard, we could identify ∼4,500 proteins, and nearly all (80%) proteins could be quantified in two of three MS measurements (Fig. S1D).

Fig. S1.
Fig. S1.

Characterization of liver-specific super-SILAC spike-in standard. (A and B) Log2-transformed H/L ratios of proteins (A) and phosphosites (B) measured in heavy super-SILAC spike-in standard mixed 1:1 with unlabeled standard. (C) Percent of ratios of proteins determined in A. (D) Identified and quantified proteins measured with the super-SILAC spike-in standard from A. (E) Integration of numbers from Fig. 1 D and E. (F) Venn diagrams displaying the overlap between light biopsy-specific (Left) and heavy cell-line–specific (Right) proteins/phosphosites.

To determine the sensitivity of our experimental protocol (Fig. 1C) on biopsy material, we measured the proteome and phosphoproteome (Datasets S1 and S2) of a liver biopsy from a patient with hereditary hemochromatosis. Approximately 1.4 mg of total protein was extracted from the biopsy, from which ∼5,200 proteins were identified (Fig. 1D); 98% of those proteins could be quantified. Approximately 8,500 phosphosites, corresponding to about 3,000 phosphoproteins, were identified (Fig. 1E); 92% of those phosphoproteins could be quantified. In total, combining the proteome and phosphoproteome, we were able to quantify ∼6,000 proteins, of which ∼1,800 were found in both datasets (Fig. S1E). The high percentage of quantified proteins reflects good coverage of the liver biopsy by the spike-in standard (Fig. S1F). Thus, the spike-in standard was suitable for quantitative (phospho)proteomics on liver biopsies.

Next, we sought to determine the coverage of signaling pathways in our quantified (phospho)proteome. We quantified 234 kinases and several components of prominent signaling pathways involved in HCC, as well as important HCC biomarkers, in the (phospho)proteome (Table S2). Thus, our overall experimental protocol allows monitoring of cancer-related signaling pathways in liver biopsies.

View this table:
Table S2.

Cancer-related signaling pathways quantified in proteome of hemochromatosis patient biopsy

Quantitative Pre- and On-Therapy (Phospho)Proteomics on Biopsies from a Sorafenib-Treated HCC Patient.

Next, we examined if our experimental protocol could quantify signaling changes in an HCC patient upon sorafenib treatment, in particular in ontreatment biopsies compared with pretreatment biopsies. For this proof-of-principle study, a 62-y-old male patient with alcoholic liver cirrhosis and an advanced HCC was asked to donate additional biopsies for research purposes (Fig. 2A). Biopsies of the primary tumor and control nontumor tissue were taken from the patient 7 wk before sorafenib treatment and then again 14.5 wk later, after 7.5 wk of sorafenib treatment. Shortly thereafter, treatment was stopped, and the patient died 2 mo later. HCC was confirmed by histopathological examination. The pre- and ontreatment tumor biopsies were 50% and 90% HCC, respectively (Fig. 2B). Blood levels of the HCC biomarker α-fetoprotein (AFP) (Fig. 2A) and tumor size increased throughout the observation period. A follow-up imaging study after 8 wk of sorafenib treatment documented a portal vein occlusion (probable tumor invasion of the portal vein) and ascites, suggesting progressive disease. As a consequence, sorafenib treatment was stopped.

Fig. 2.
Fig. 2.

Clinical data of the sorafenib-treated HCC patient. (A) Clinical history of the sorafenib-treated HCC patient. The green bar indicates sorafenib treatment. PVO, portal vein occlusion. (B) H&E staining of HCC patient biopsies. (Scale bars: 100× magnification, 200 μm; 200× magnification, 100 μm.)

The wet weights of the pre-sorafenib control, pre-sorafenib tumor, on-sorafenib control and on-sorafenib tumor biopsies were 40, 11.2, 30.4, and 31.9 mg, which yielded 1.4, 0.17, 1.12, and 0.52 mg of protein, respectively. Thus, the tumor biopsies were smaller and yielded less protein than the control nontumor biopsies (Fig. 3A), probably because of tumor fibrosis that hampered manipulation.

Fig. 3.
Fig. 3.

Statistics of quantitative (phospho)proteomics on biopsies from a sorafenib-treated HCC patient before and during therapy. Graphs show the number of proteins (A) and phosphosites and phosphoproteins (B) identified and quantified in two of three measurements per biopsy.

We analyzed the (phospho)proteome of the tumor and nontumor control biopsies obtained before and during sorafenib treatment (Datasets S3 and S4). In each control biopsy, about 5,000 proteins were identified, of which about 4,000 (∼80%) could be quantified. In agreement with the lower protein yield, the number of proteins identified in the tumor biopsies ranged from 3,000–4,000, of which about 80% could be quantified (Fig. 3A). Approximately 3,800 phosphorylated sites in about 1,500 phosphoproteins were detected in each control biopsy, and 1,000–2,000 phosphosites in ∼500–1,000 phosphoproteins were detected in the tumor biopsies (Fig. 3B). The lower number of detected phosphoproteins in the tumor biopsies was again the result of lower protein yields. As expected, the (phospho)proteomes of the tumor and control biopsies largely overlapped (Fig. S2 A and B). Finally, combining data from the proteome and phosphoproteome, we quantified 4,379 proteins in, for example, the pre-sorafenib control biopsy in two of three measurements (Fig. S2C). Of those, 3,365 proteins were measured only in the proteome, 332 proteins were found exclusively in the phosphoproteome, and 682 were in both datasets. These results are comparable to the results obtained with the control biopsy from the hemochromatosis patient analyzed previously (Fig. S1E). The slightly lower number of phosphorylated proteins detected in the biopsies from the HCC patient could be explained by reduced tissue quality.

Fig. S2.
Fig. S2.

Statistics of quantitative (phospho)proteomics on biopsies from a sorafenib-treated HCC patient before and during therapy. Venn diagrams showing overlap of quantified proteins (A) and phosphosites (B) in the HCC patient biopsies from Fig. 3. (C) Integration of numbers from Fig. 3. (D) Formula used to calculate normalized change upon treatment used in E and Fig. 5C. (E) Extracted quantitative results for HCC biomarkers.

As a proof of principle, we analyzed the expression of the three most common HCC biomarkers, glutamine synthetase, glypican3, and Hsp70 (33). As shown in Fig. S2 D and E, glutamine synthetase expression was higher in the tumor than in nontumor tissue and increased further upon sorafenib treatment. Glypican3 and Hsp70 were higher in the tumor but only upon sorafenib treatment. This finding suggests that the tumor progressed during sorafenib treatment, in agreement with the observed increase in serum AFP levels and tumor size.

How did quantified protein and phosphorylation levels change significantly between biopsies? As shown in the volcano plots of Fig. S3A, 140 proteins were significantly up-regulated and 142 were down-regulated in the pretreatment tumor compared with the matched nontumor tissue, whereas 290 proteins were up-regulated and 177 were down-regulated in the treated tumor compared with its matched control. With regard to phosphorylation levels (Fig. S3B), 82 phosphosites were up-regulated and 8 were down-regulated in the pretreatment tumor compared with matched nontumor tissue, and 70 phosphosites were up-regulated and 19 were down-regulated in the treated tumor compared with matched nontumor tissue. Thus, our quantitative analysis of only ∼1 mg of protein recovered from biopsies provided good coverage of the (phospho)proteome and detected significant changes in protein expression and phosphorylation. Importantly, even from a small biopsy containing much less protein (0.17 mg), about 1,010 phosphosites could be detected, of which 424 could be reliably quantified.

Fig. S3.
Fig. S3.

Significant changes in quantitative pre- and on-therapy (phospho)proteomes. (A and B) Volcano plots displaying the comparison of control and tumor biopsy proteins (A) and phosphosites (B) before and during sorafenib treatment. (C) Pathway enrichment analysis (software indicated) on proteins that are potential adaptive resistance factors (group 4) from Fig. 4A.

Expression and Phosphorylation Clusters in Response to Sorafenib.

We performed unsupervised hierarchical clustering to identify groups of proteins and phosphosites that display similar expression patterns across all four biopsies, i.e., the two nontumor control biopsies and the two tumor biopsies, each minus and plus sorafenib. The heat maps in Fig. 4 display proteins and phosphosites that were quantified in at least two of three measurements per biopsy and that passed an ANOVA-based multisample test for statistically significant up- or down-regulation, with a false discovery rate (FDR) of 2% or 5%. Column-wise clustering of triplicate measurements of a given biopsy demonstrated that interbiopsy biological variation was greater than intrabiopsy experimental variation. Row-wise clustering of proteins and phosphosites (hereafter collectively referred to as “factors”) revealed five prominent groups based on tissue distribution and response to sorafenib (Fig. 4 and Datasets S5 and S6). Group 1 contains factors that are decreased in the tumor biopsies relative to nontumor biopsies. These factors could be tumor suppressors whose absence in the tumor conferred intrinsic resistance. Group 2 includes factors that are increased only in the tumor and only before sorafenib treatment. These factors could be oncogenic sorafenib targets. Importantly, phosphorylation of ribosomal protein S6 kinase (Rsk-2)-S369, a site phosphorylated by the MAPK Erk, is in this group (see below). Group 3 consists of factors that are down-regulated specifically in the tumor before treatment. These factors behave as expected for a tumor suppressor whose presence is restored upon sorafenib treatment. Group 4 contains factors that are up-regulated specifically in the tumor upon sorafenib treatment. These are factors possibly involved in tumor progression and adaptive resistance. Members of group 4 could be targeted by a second drug in combination with sorafenib to achieve more effective treatment. Group 5 contains factors that are up-regulated specifically in the tumor biopsies, with and without sorafenib. These could be oncogenic, intrinsic resistance factors. The clinical significance of the factors in the above groups requires further characterization and confirmation.

Fig. 4.
Fig. 4.

Expression and phosphorylation clusters in response to sorafenib. Unsupervised hierarchical clustering using Euclidian distance of the proteome (A) and the phosphoproteome (B) data from at least two of three measurements per biopsy. Numbers correspond to groups in A.

We performed pathway enrichment analysis on the proteins that could be adaptive resistance factors, i.e., the proteins of group 4 (Fig. S3C). Using four different over-representation analysis methods, we found several annotation terms that were identified more than once, e.g., lysosome, glycolysis and pyruvate metabolism, apoptosis, and the c-myc pathway. Interestingly, these terms are consistent with the notion that sorafenib inhibits vascularization, thus limiting oxygen and nutrient availability in the tumor.

Changes in MAPK Pathway Activity upon Sorafenib Treatment.

We next investigated the effect of sorafenib on its known target in vivo. To this end, we examined phosphorylation changes in the Raf–MAPK/ERK kinase–ERK (Raf–Mek–Erk) MAPK pathway (34⇓⇓–37) and downstream substrates in the tumor in response to sorafenib (Fig. 5). In particular, we detected and quantified phosphorylation of C-Raf-S621, B-Raf-S729, Rsk-2-S369, eIF4B (eukaryotic translation initiation factor 4B)-S422, Filamin-A-S2152, raptor-S863 (mTORC1 component), S6-S236, and -S240, and CAD (carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase)-S1859. Phosphorylation of the sites in C-Raf and Rsk-2 decreased in the tumor upon sorafenib treatment, albeit significantly only for Rsk-2. This effect, which was even more pronounced when normalized to matched nontumor tissue, indicates that sorafenib treatment inhibited Raf–Erk signaling. Phosphorylation of the pyrimidine biosynthesis protein CAD also decreased in the tumor upon treatment but still remained at a high level. Conversely, phosphorylation of Filamin-A-S2152 and S6-S240 increased in the tumor upon sorafenib treatment. The Filamin-A site appeared as a potential adaptive resistance factor, as revealed by hierarchical clustering of the phosphoproteome (Fig. 4B). The high level of eIF4B phosphorylation in the tumor both before and during sorafenib treatment suggests that eIF4B may be an intrinsic resistance factor (see above). Finally, B-Raf-S729, raptor-S863, and S6-S236 phosphorylation did not change significantly upon sorafenib treatment. The quantitative decrease in C-Raf and Rsk-2 phosphorylation upon sorafenib treatment suggests that sorafenib indeed inhibited Raf–Erk signaling in the tumor. Importantly, to our knowledge, this is the first demonstration that sorafenib inhibits MAPK signaling in a cancer patient. However, the elevated levels of eIF4B, Filamin-A, S6-S240, and CAD phosphorylation suggest that sorafenib treatment failed to inhibit signaling distal to Rsk-2. This lack of sorafenib responsiveness may suggest that an alternative pathway compensates for the block in Raf–Erk signaling. Further analysis, including a cohort of patients, may reveal the identity of the compensatory pathway and allow better-informed treatment.

Fig. 5.
Fig. 5.

Changes in MAPK pathway (34⇓⇓–37) activity upon sorafenib treatment. (A) Quantitative data for proteins and phosphosites are color coded: gray, protein quantified in proteome; white, protein not quantified in proteome; blue, phosphosite down-regulated in tumor upon treatment; black, phosphosite unchanged; red, phosphosite up-regulated in tumor upon treatment; red cross, inhibitory phosphosite; black star, phosphosite constitutively high in tumor. (B) Quantitative results (−log2-transformed mean ± SD of H/L ratios of each biopsy normalized to control pre-sorafenib biopsy) of prominent signaling nodes from A. S, single ratio measurement only; *, significant in ANOVA-based two-sample test comparing the biopsies, FDR 5% (quantified in at least two of three measurements per biopsy). (C) Normalized change upon treatment (see Fig. S2D): −log2 [(TU/CTRL on-sorafenib)/(TU/CTRL pre-sorafenib)] of prominent signaling nodes from A. *, significant in ANOVA-based multisample test over all four biopsies, FDR 5% (quantified in at least two of three measurements per biopsy).

Pathway Enrichment Analysis Reveals Potential Mechanisms of Sorafenib Resistance.

To obtain insight into global changes occurring in the biopsied tissues, two different pathway enrichment analyses were performed using MetaCore (Fig. S4). In the first analysis (Fig. S4A and Dataset S7), matched pretreatment tumor and nontumor biopsies were compared directly. The proteome showed a strong enrichment of metabolic pathways, in particular proteins involved in androgen metabolism. Enzymes with a role in the “backdoor” pathway of androgen activation [AKR1C1–3 (Aldo-keto reductase C1–C3)] (38) were up-regulated in the tumor before treatment and increased even further in the tumor following treatment (Fig. S5A and Dataset S3). The enzyme AKR1C3 has been described to increase aggressiveness in prostate cancer (39). Increased expression of AKR1C3 together with the observed expression of 17β-HSD6, which catalyzes the final step of dihydrotestosterone synthesis (40), and decreased expression of 17β-HSD2, which inactivates dihydrotestosterone (41), suggests enhanced local production of active androgens and possibly androgen-dependent cancer cell proliferation. We also observed decreased expression of enzymes involved in degradation of such hormones (UGT2B15, UGT2B17, UGT1A4) in the tumor compared with control tissue, likely indicating a loss of liver-specific characteristics (42). Interestingly, elevated levels of active androgens represent a well-described risk factor for HCC development, accounting for the higher incidence of HCC among males (43). Finally, the proteome also was enriched in immune response pathways, possibly indicative of inflammation in the tumor. The phosphoproteome exhibited enrichment in pathways involved in cell adhesion, translation, and insulin signaling, all pathways commonly implicated in cancer.

Fig. S4.
Fig. S4.

Pathway enrichment analysis reveals potential mechanisms of sorafenib resistance. (A) Pathway enrichment of tumor vs. control pre-sorafenib performed by MetaCore. (B) Pathway enrichment of normalized changes upon treatment performed by MetaCore. Only proteins/phosphosites significantly changed by more than 0.75-fold up or down (log2) were taken for enrichment analysis. Color-coding indicates affiliation to overall pathway groups.

Fig. S5.
Fig. S5.

Significant changes in androgen metabolism and cell adhesion pathways. (A) Extracted quantitative results for enzymes involved in androgen metabolism from MetaCore pathway enrichment in Fig. S4A (−log2-transformed mean ± SD of H/L ratios of each biopsy normalized to the control pre-sorafenib biopsy). All displayed enzymes were changed significantly in ANOVA-based multisample tests over all four biopsies, FDR 2% (quantified in at least two of three measurements per biopsy). (B) Pathway map of nonjunctional mechanism of cell adhesion generated using MetaCore (Fig. S4). The left half of the protein icon and the left phosphate group represent tumor vs. control pre-sorafenib; the right half of the protein icon and the right phosphate group represent the normalized change upon treatment (see Fig. S2D); white, not detected; gray, not significantly changed; blue, significantly decreased more than 0.75-fold (log2); red, significantly increased more than 0.75-fold (log2).

In the second analysis (Fig. S4B and Dataset S8), we compared tumor tissue obtained before and during sorafenib treatment. In this case, changes were normalized to the matched nontumor control tissues (Fig. S2D). This analysis identifies changes that are sorafenib-induced or changes caused by tumor progression; it is not possible to distinguish these two scenarios, because untreated biopsies from the second time point are not available. The proteome displayed a strong enrichment of pathways involved in central carbon metabolism, suggesting an increase in glycolysis and a decrease in Krebs cycle components in the sorafenib-treated tumor. The increased glycolytic enzymes were aldolase, enolase, and pyruvate kinase; the decreased Krebs cycle enzymes were isocitrate dehydrogenase, α-ketoglutarate dehydrogenase, and succinate dehydrogenase (Dataset S3). This finding is in agreement with the recent observation that increased glycolysis correlates with sorafenib resistance (44) and that sorafenib inhibits vascularization. Furthermore, both the proteome and phosphoproteome revealed enrichment of pathways involved in cell proliferation, including the MAPK pathway, as described above (Fig. 5).

Both MetaCore analyses revealed significant enrichment of cell adhesion pathways (Fig. 6 and Fig. S5B). Interestingly, vimentin and the extracellular matrix (ECM) proteins fibronectin and vitronectin were up-regulated, and desmoplakin, plakoglobin, the junctional adhesion molecules JAM-1/2 and zona occludens-1 (ZO-1), the putative tumor suppressor p120 catenin, and the cytoskeleton regulator α-catenin were down-regulated in the tumor upon treatment. Collectively, these changes suggest that EMT occurred in the tumor, during tumor progression and/or in response to sorafenib (45, 46). Importantly, EMT correlates with sorafenib resistance and tumor invasiveness (10, 47), whereas the ECM plays a role in drug resistance in a process called “cellular adhesion molecule-dependent drug resistance” (CAM-DR) (48, 49). Both processes may have contributed to increased tumor invasiveness and the failure of sorafenib treatment in the patient. In agreement with this notion, a portal vein occlusion (probable tumor invasion of the portal vein), a finding interpreted as progressive disease, was observed in the patient after 8 wk of sorafenib treatment.

Fig. 6.
Fig. 6.

Pathway enrichment analysis indicates EMT upon sorafenib treatment. Pathway map of junctional mechanism of cell adhesion generated using MetaCore (Fig. S4). The left half of the protein icon and the left phosphate group represent tumor vs. control pre-sorafenib; the right half of the protein icon and the right phosphate group indicate normalized change upon treatment (see Fig. S2D); white, not detected; gray, not significantly changed; blue, significantly reduced more than 0.75-fold (log2); red, significantly increased more than 0.75-fold (log2).

Discussion

We describe a protocol for quantitative (phospho)proteomic analysis of needle biopsies. We used this protocol to examine control and tumor biopsies obtained from an HCC patient before and during sorafenib treatment. The small, needle biopsies yielded 0.17–1.4 mg of protein from which we were able to quantify 2,000–4,000 proteins and 500–2,100 phosphosites. To our knowledge, this is the first quantitative phosphoproteomic analysis of serial biopsies from a cancer patient. Phosphorylation in the Raf–Erk–Rsk pathway was reduced in the sorafenib-treated tumor, indicating that the drug was indeed effective in inhibiting its target kinase, Raf. Interestingly, phosphorylation of downstream targets was not similarly inhibited, suggesting that a compensatory pathway(s) may have been active in the tumor, possibly accounting for the treatment failure observed in the patient. Pathway enrichment analysis suggests EMT and CAM-DR as processes potentially involved in tumor progression and treatment failure. The protocol described here could eventually be translated to the clinic to provide precision therapy and thus more effective treatment of patients.

Studies analyzing phosphorylation in biopsies thus far have used antibody-based immunohistochemistry, immunoblotting, or reverse-phase protein arrays (8). In contrast, our protocol allows quantitative analysis of multiple pathways in parallel and even the detection of new phosphorylation sites. A critical factor is the quality of the biopsy material. The coaxial needle biopsy technique used allows sampling multiple times at the same site on the tumor. It is essential that one of the biopsy samples is evaluated by a pathologist for diagnosis and grading of the HCC. Importantly, the pathologist also provides information on the relative amounts of vital tumor cells, stromal cells, fibrosis, and sclerosis in the biopsy, and the amount of nonmalignant liver tissue. An accurate pathological evaluation is even more important for biopsies of infiltrative type HCC that often do not have discrete boundaries in imaging studies. Based on the results of the pathological assessment, one can determine whether HCC biopsies obtained at different times during disease progression can be compared. For serial biopsies before and during treatment, we sampled the same tumor nodule. However, tumor shape can change over time. Given this limitation in the serial biopsy procedure, it is imperative to use robust statistics for the analysis of (phospho)proteomic data. Only changes of more than twofold or changes rated as significant by ANOVA-based tests are taken into consideration. Importantly, we did not observe a bias toward up- or down-regulated proteins that could be correlated with the percentage of tumor tissue present in the biopsy.

We obtained evidence for EMT and up-regulation of ECM proteins in the tumor upon treatment. This finding suggests that the tumor progressed toward a more aggressive and invasive phenotype. Indeed, the patient displayed portal vein occlusion (probably caused by tumor invasion of the portal vein), suggesting progressive disease. Furthermore, EMT and the up-regulation of ECM proteins could be one of the potential compensatory pathways driving progression of HCC upon sorafenib treatment, as observed in the patient. Data from a cohort of patients will provide a more complete picture of how HCC progresses and the mechanisms of sorafenib resistance. The described protocol could facilitate the search for new therapeutic options for HCC and also could be applied to other cancers treated with other drugs to further the understanding of carcinogenesis and cancer drug resistance in general. Another future perspective is to identify predictive molecular patterns that would aid in patient stratification.

Materials and Methods

Study Design and Patient Biopsy Selection.

Patients were recruited in the Clinic for Gastroenterology and Hepatology of the University Hospital Basel, Switzerland. They gave written informed consent to donate liver biopsy specimens for research. The pilot experiment biopsies and the hemochromatosis patient biopsy were collected to build up a biobank; the HCC patient biopsies were collected as part of a study protocol, number EK 152/10. Both protocols were approved by the Ethics Committee of the Canton Basel, Switzerland. The biopsies were performed under ultrasound guidance using a coaxial needle technique allowing repetitive sampling from the same part of a focal lesion with a full-core biopsy instrument (BioPince; Angitech). Diagnosis of HCC was made by histopathological analysis.

Other Methods.

For further information on materials and methods, see SI Materials and Methods.

SI Material and Methods

Preparation of Super-SILAC Spike-In Standard.

A super-SILAC (31) spike-in standard was prepared from the following HCC cell lines: Huh7 of high and low passage, HepG2, Huh6, and Hep3B, kindly provided by Ralf Bartenschlager (University of Heidelberg, Heidelberg). For labeling, cells were grown in DMEM with 10% dialyzed FCS (molecular weight cutoff, 10 kDa), 100 IU/mL penicillin, 100 μg/mL streptomycin, 2 mM l-glutamine, 1× nonessential amino acids without proline, containing heavy (R10K8, 13C- and 15N-labeled) or light (R0K0) amino acids (Dundee Cell Products). After 8–10 passages, incorporation of the labeled amino acids was tested by LC/MS/MS. Once labeling reached 90–100%, the heavy labeled cells were expanded to prepare large amounts of the heavy super-SILAC standard. Additionally, cell lines grown in light amino acids were expanded to prepare a corresponding unlabeled standard. At ∼80–90% confluence, each cell line was harvested and lysed (see below). Protein concentration was determined by Bradford assay, and cell extracts from each cell line were mixed at a 1:1:1:1:1 ratio. Aliquots were snap-frozen in liquid nitrogen and stored at −80 °C.

Protein Extraction and Digestion.

Fresh biopsies were immediately snap-frozen in liquid nitrogen. For protein extraction, each frozen biopsy was crushed into a fine powder in an in-house–constructed metal mortar cooled on dry ice (cryogenic grinding) and was transferred into a cooled 1.5-mL tube containing 150–400 μL lysis buffer [50 mM Tris⋅HCl (pH 8.0), 8 M urea, 150 mM NaCl, 1 mM PMSF, Complete Mini Protease Inhibitors (Roche), 100 mM sodium pyrophosphate/β-glycerophosphate/NaF/NaN3/para-Nitrophenylphosphate]. The biopsy lysate was vortexed vigorously for 5 min, sonicated twice in a VWR Ultrasonic cleaner bath (USC300T) for 1 min, and homogenized with a Teflon pestle in a Heidolph homogenizer (RZR 2052 Control) at 300 rpm for 2 min. Samples were rotated for 30 min at 4 °C and centrifuged at 10,600 × g in a table top centrifuge for 10 min at 15 °C. Protein concentration was measured with a Bradford assay. Heavy super-SILAC spike-in standard was added to the light biopsy protein lysate in a 1:1 ratio. Next, proteins were reduced with 10 mM DTT for 1 h at 37 °C and were alkylated with 50 mM iodoacetamide for 30 min at room temperature in the dark, both with gentle shaking. Urea concentration was lowered to 4 M with 50 mM Tris⋅HCl, pH 8.0. Lysates were digested with two rounds of endoproteinase LysC (Wako) at an enzyme-to-protein ratio of 1:100 at 37 °C for 2 h. Next, the urea concentration was lowered to 1 M. Lysates were digested with two rounds of trypsin (Worthington) at a 1:50 ratio overnight and at a 1:100 ratio for 2.5 h at 37 °C. Digestion was stopped with TFA to a final concentration of 0.5%. Digests were centrifuged for 2 min at 1,500 × g and were desalted on a C18 SepPak cartridge (50-mg column for peptide load capacity up to 2.5-mg) (Waters) (50) with 0.1% TFA for loading and washing and 0.5% AcOH/80% AcCN for elution. Peptide concentration was estimated at 280 nm, and peptides were dried in the SpeedVac.

SCX.

SCX fractionation was done according to ref. 50 with modifications. The dried peptides were resuspended in 1.5 mL of SCX buffer A [5 mM KH2PO4 (pH 2.65), 30% AcCN], sonicated briefly, and centrifuged at 10,600 × g in a table top centrifuge. The HiTrap SP cartridge (GE Healthcare) was equilibrated three times with 1 mL of SCX buffer A, then was washed three times with 1 mL of SCX buffer B [5 mM KH2PO4 (pH 2.65), 30% AcCN containing 500 mM KCl] and was re-equilibrated three times with 1 mL of SCX buffer A. Peptides were applied onto the column and washed three times with 1 mL SCX buffer A. Flowthrough and washes were collected separately as fractions. The bound peptides were stepwise desorbed with 1 mL each of SCX buffer A containing 50 mM, 100 mM, 150 mM, 250 mM, 350 mM, and 500 mM KCl (optionally 10 mM and 25 mM), and each fraction was collected individually. Peptide concentration was estimated at 280 nm. Fractions were dried in the SpeedVac and desalted on C18 columns (The Nest Group) of varying size adjusted to the peptide content with 0.1% TFA for loading and washing and 0.5% AcOH/80% AcCN for elution. Twenty percent of each fraction was separated for LC/MS/MS analysis as the proteome.

Phosphopeptide Enrichment.

Phosphopeptide enrichment was performed with TiO2-coupled beads (GL Sciences Inc.) as described previously (32). The phosphopeptide pools were desalted on MicroSpin columns or Stage Tips and dried in the SpeedVac.

LC/MS/MS Analysis.

The dried phospho-/peptides were dissolved in 20 μL of 0.1% AcOH, 0.005% TFA and injected into the LC/MS. Proteomes and phosphoproteomes were analyzed by capillary LC/MS/MS using a homemade separating column (0.075 mm × 18 cm) packed with Reprosil C18 reverse-phase material (2.4-μm particle size; Dr. Maisch). The column was connected on-line to an Orbitrap Elite FT hybrid instrument (Thermo Scientific). The solvents used for peptide separation were 0.1% acetic acid in water/0.005% TFA (solvent A) and 0.1% acetic acid/0.005% TFA and 80% AcCN in water (solvent B). Two microliters of sample were injected with a Proxeon nLC capillary pump (Thermo Scientific) set to 250 nL/min. For proteome measurements, a linear gradient from 0–40% solvent B in solvent A in 190 min was delivered with the nano pump at a flow rate of 250 nL/min. After 190 min solvent B was increased to 75% in 5 min. For phosphoproteome measurements, a linear gradient from 0–40% solvent B in solvent A in 95 min was delivered with the nano pump at a flow rate of 250 nL/min. After 95 min solvent B was increased to 75% in 25 min. The eluting peptides were ionized at 2.5 kV. The mass spectrometer was operated in data-dependent mode, precursor scan was done in the Orbitrap set to 120,000 resolution, and fragment ions were mass analyzed in the LTQ instrument. A top-20 method was run. For phosphoproteomes, the LTQ instrument was set to multistage activation.

Protein Identification and Data Processing.

The LC/MS/MS data were analyzed with MaxQuant, version 1.4.0.3 (51) and searched against the human Swiss-Prot database (October 29, 2013) from the UniProtKB (UniProt Knowledgebase). A mass tolerance of 20 ppm was allowed for the first search, and a mass tolerance of 10 ppm was allowed for the main search. Oxidation (Met), acetylation (protein N terminus), and phosphorylation on serine, threonine, and tyrosine were set as variables, and carbamidomethylation (C) was set as a fixed modification. Two missed cleavages were allowed. Arg10 and Lys8 were set as heavy labels. Peptide FDR, protein FDR, and site decoy fraction were set to 2%. For protein quantification, a ratio count of 1 was applied. The protein and pSTY datasets were exported into a FileMaker Pro-12 databank. Class I (≥75% localization probability) and class II phosphosites (≥50% localization probability) were kept for further analysis. The phosphosites found in the TiO2-enriched dataset (phosphoproteome) and in the proteome were combined into one phosphoproteome file. If a phosphosite was found in both datasets, only the one from the TiO2-enriched phosphoproteome was selected. For further analysis, the normalized ratio H/L was chosen. For easier visualization of the direction of regulation, all normalized ratios H/L were −log2 transformed prior to any further calculation (e.g., before forming the ratio TU vs CTRL). To avoid confusion, y axes of the corresponding graphs and of the columns in Datasets S3 and S4 were only labeled with log2. All data were subsequently analyzed with Perseus (see below). Normalized change upon treatment was calculated as follows: −log2 [(tumor/control) on sorafenib/(tumor/control) pre-sorafenib] (Fig. S2D).

Statistical Analysis.

For statistical data analysis, volcano plots, and heat maps, the R-based Perseus program, version 1.4.0.2 (52), was used. For the volcano plots, an ANOVA two-sample t test was performed, adjusting S0 to 1, number of randomizations to 250, and FDR to 2% for the proteome and to 5% for the phosphoproteome. The ANOVA-based two-sample tests compared control pre-sorafenib to tumor pre-sorafenib, control on sorafenib to tumor on sorafenib, or tumor pre-sorafenib to tumor on sorafenib biopsies. For the heat maps, an ANOVA-based multisample t test was performed, adjusting S0 to 1, number of randomizations to 250, and FDR to 2% for the proteome and to 5% for the phosphoproteome. Z-scoring was performed without grouping and only with entries that were tagged as positive following the multisample analysis. Finally, for unsupervised hierarchical clustering, the distance was set to Euclidian, the linkage to average, and the maximal numbers of clusters to 300.

Pathway Enrichment Analysis.

Pathway enrichment for the adaptive resistance factor group was performed with (i) WebGestalt (bioinfo.vanderbilt.edu/webgestalt/) (53) using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Pathway Commons database, with (ii) ConsensusPathDB (consensuspathdb.org) using KEGG, and with (iii) Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/) (54) using the GOTERM database, all at highest stringency. Settings for the WebGestalt: protein list was run against the list of Homo sapiens EntrezGene protein coding, database KEGG or Pathway Commons, statistical method hyper-geometrical, multiple test adjustment BH. Settings for the ConsensusPathDB: overrepresentation analysis was run, and the protein list was run against the background list of all our measured proteins, database KEGG. Settings for DAVID: functional annotation clustering was run, and the protein list was run against the background list of all our measured proteins, databases GOTERM_CC/BP_FAT and INTERPRO.

Pathway enrichment for the complete proteome and phosphoproteome was performed using MetaCore (https://portal.genego.com). Two groups were analyzed: first, control vs. tumor before treatment, and second normalized changes upon treatment. Log2 fold changes were extracted, and only factors that changed significantly (passed the ANOVA t test) and by more than 0.75-fold (log2) were included. P values of enrichment were translated into a heat map by color coding. Selected hit pathway maps from this MetaCore analysis were chosen to visualize pathway changes and involved proteins/phosphosites.

Acknowledgments

We acknowledge support from the Goldschmidt and Jacobson Foundation (E.D.), the European Research Council (Mechanisms of Evasive Resistance in Cancer), and the Swiss National Science Foundation (M.N.H.).

Footnotes

  • ↵1To whom correspondence should be addressed. Email: m.hall{at}unibas.ch.

  • Author contributions: E.D., M.H.H., P.J., and M.N.H. designed research; E.D., T.B., S.M., L.Q., and P.J. performed research; T.B., L.T., M.H.H., and P.J. contributed new reagents/analytic tools; E.D., M.C., D.A., V.R., L.T., M.H.H., P.J., and M.N.H. analyzed data; and E.D., M.H.H., P.J., and M.N.H. wrote the paper.

  • Reviewers: B.B., University of Zurich; and P.-A.C., University Hospital of Zurich.

  • The authors declare no conflict of interest.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1523434113/-/DCSupplemental.

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Quantitative (phospho)proteomics of tumor biopsies
Eva Dazert, Marco Colombi, Tujana Boldanova, Suzette Moes, David Adametz, Luca Quagliata, Volker Roth, Luigi Terracciano, Markus H. Heim, Paul Jenoe, Michael N. Hall
Proceedings of the National Academy of Sciences Feb 2016, 113 (5) 1381-1386; DOI: 10.1073/pnas.1523434113
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