Sustained activation of EGFR-ERK1/2 signaling limits the therapeutic response to tigecycline-induced inhibition of mitochondrial translation in liver cancer

doi:10.21203/rs.3.rs-1452915/v1

Download PDF

Article

Sustained activation of EGFR-ERK1/2 signaling limits the therapeutic response to tigecycline-induced inhibition of mitochondrial translation in liver cancer

https://doi.org/10.21203/rs.3.rs-1452915/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Treatment of advanced liver cancer still faces great challenges. Identification of tumor dependencies is important for developing novel therapeutic strategies for liver cancer. Here, we identified mitochondrial translation as a major vulnerability of liver cancer by genome-wide CRISPR screen. Targeting mitochondrial translation by tigecycline showed therapeutic potential in liver cancer. Then, a compounds screen was applied to identify MEK inhibitors as synergistic drugs to tigecycline-insensitive liver cancer cells. Mechanistically, sustained activation of EGFR-ERK1/2-MYC cascade conferred the insensitivity to tigecycline, which was mediated by enhanced secretion of EREG and AREG. Moreover, glycolytic enzymes, such as HK2 and PKM2, and mitochondrial one-carbon enzyme SHMT2 were upregulated to stimulate glycolysis and maintain mitochondrial function, respectively, in a MYC-dependent manner. Tigecycline in combination with MEK inhibitor trametinib or EGFR inhibitor gefitinib showed synergistic antitumor effect in vitro and in vivo. Thus, our study provides a potential therapeutic strategy for liver cancer.

CRISPR screen

Mitochondrial translation

MEK

Glycolysis

OXPHOS

Conventional therapies for liver cancer, such as liver transplantation, tumour resection or ablation, are limited to early-stage tumours¹. For patients with advanced stages of liver cancer, first-line systemic therapies are sorafenib, lenvatinib, or atezolizumab plus bevacizumab. The combination of atezolizumab (anti-PD-L1 antibody) and bevacizumab (anti-VEGF antibody) had better overall and progression-free survival outcomes than single-drug treatment with sorafenib. However, the response rate of the combination therapy was only 27.3%². Hence, it is urgent to find further therapeutic approaches and strategies for liver cancer.

The complex regulatory networks that control tumor function make identifying therapeutic targets a major challenge. The use of CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated gene 9) screens has enabled the exploration of cancer driver genes and molecular mechanisms underlying drug resistance on a large scale³. For example, using a kinome-wide CRISPR-Cas9 genetic functional screen, we found that activation of epidermal growth factor receptor (EGFR) limits the response of liver cancer to lenvatinib. The combined regimen of the EGFR inhibitor gefitinib and lenvatinib provided a novel strategy for liver cancer patients with high expression of EGFR⁴. Recently, identifying cancer type-specific dependencies is becoming another critical strategy for cancer therapy. The CRISPR-Cas9 screens can provide new insights into therapeutic vulnerabilities of liver cancer. Using a kinome-focused genetic screen, we showed that pharmacological inhibition of the DNA-replication kinase CDC7 induces senescence selectively in liver cancer cells with mutations in TP53⁵. In addition, Liang and colleagues performed a genome-wide CRISPR-Cas9 screen and identified WEE1 kinase as a therapeutic vulnerability in liver cancer cells depleted of the ATRX chromatin remodeler gene⁶. However, potential therapeutic vulnerabilities of liver cancer remain to be investigated.

Here, we aim to further explore the dependencies of liver cancer through performing genome-wide CRISPR-Cas9 screen. Thus, we performed a genome-wide CRISPR screen in two liver cancer cell lines (SNU398 and HepG2) and identified mitochondrial translation as a major vulnerability of liver cancer cells. Emerging evidence suggests high level of mitochondrial translation may be required to support the bioenergetic requirements of cancer cells⁷. Besides, some studies showed that abnormal mitochondrial translation was involved in the process of epithelial-to-mesenchymal transition, cell cycle progression and apoptosis signaling^8,9. Therefore, therapeutic approaches that disrupt mitochondrial translation are showing great potentials in combating tumours.

Tigecycline, a FDA-approved broad spectrum antibiotic, can selectively inhibit mitochondrial translation^10,11. Previous studies showed that tigecycline could be used to treat hematological tumors by inhibition of mitochondrial translation^11,12. Limited studies only showed that tigecycline could enhance the activity of chemotherapy in liver cancer, and prevent the tumor relapse after resistance to sorafenib^13,14. However, the crucial role of mitochondrial translation and the rationale of tigecycline as a strategy for the treatment of liver cancer are still unclear.

In this study, we firstly report that the sustained activation of EGFR-ERK1/2 signaling pathway confers insensitivity to tigecycline therapy in liver cancer. Tigecycline in combination with MEK inhibitors or EGFR inhibitors achieves synergistic effects both in vitro and in vivo. Therefore, our findings suggest a therapeutic approach for liver cancer consisting of a combination of tigecycline and MEK inhibitors or EGFR inhibitors.

A genome-wide CRISPR-Cas9 screen identifies requirement of mitochondrial translation-related genes for liver cancer cells

To identify new vulnerabilities of liver cancer cells, we performed a genome-wide CRISPR-Cas9 genetic screen in SNU398 and HepG2 cells (Fig. 1a-c, Supplementary Fig. 1) and identified 455 genes commonly required in the two cells (Fig. 1d). According to the Gene Ontology (GO) gene set enrichment analyses of cellular component (CC) and biological process (BP), mitochondrial ribosome cellular component and mitochondrial translation biological process were highly enriched (Supplementary Fig. 2a, b). MitoCarta3.0 is an updated inventory of mammalian mitochondrial proteins using multiple experimental and computational approaches¹⁵. By comparing the common hits with the MitoCarta3.0 inventory, we found that 26 of these genes were implicated in mitochondrial translation (Fig. 1d, Supplementary Table 1). In view of this, we intend to study the role of mitochondrial translation in liver cancer cells. The differential expression of mitochondrial translation-related genes in The Cancer Genome Atlas (TCGA) liver hepatocellular carcinoma (LIHC) cohort was investigated. 24 mitochondrial translation-related genes were significantly upregulated in 50 tumour (T) tissues compared with corresponding non-tumour (N) tissues (Fig. 1e). Using GSVA to analyse the enrichment scores of the 26 mitochondrial translation-related genes between tumour and non-tumour tissues of HCC in TCGA, we found that mitochondrial translation-related genes were highly enriched in tumour tissues, indicating that mitochondrial translation is an important pathway for liver cancer cells (Supplementary Fig. 2c).

To validate the results of the CRISPR screen, we selected three representative mitochondrial translation-related genes, GFM1 (a mitochondrial translation factor), MRPL4 (a protein of large mitochondrial ribosomal subunit), and MRPS23 (a protein of small mitochondrial ribosomal subunit), by interfering with their expression through shRNA-mediated knockdown. Knockdown of GFM1, MRPL4, or MRPS23 in SNU398 and HepG2 cells resulted in not only short-term and long-term cell proliferation inhibition (Fig. 1f-h, Supplementary Fig. 3a, b), but also the downregulation of translation products, such as CYTB (complex III), MTCO2 (complex IV), and ATP6 (complex V), which are components of the respiratory chain complex (Supplementary Fig. 3c, d), indicating that knockdown of mitochondrial translation-related genes leads to impaired mitochondrial translation.

Prognostic significance of mitochondrial translation-related genes

To investigate the prognostic value of mitochondrial translation-related genes, we collected tumour material and clinical data from 243 HCC patients from the Naval Military Medical University Affiliated Eastern Hepatobiliary Hospital. Univariate and multivariate analyses for protein levels (as determined by immunohistochemistry on tumour material) and clinicopathological features showed that GFM1, MRPL4, and MRPS23 were independent prognostic markers of overall survival (OS) and time to relapse (TTR) (Supplementary Table 2–4). Kaplan-Meier survival analyses showed that HCC patients with high expression of GFM1, MRPL4, or MRPS23, had worse OS and shorter TTR (Fig. 2a-f). Using consensus clustering analysis for the 26 genes, the HCC cohort from TCGA could be divided into three subclusters. Patients in the cluster 2 represented generally low levels of 26 mitochondrial translation-related genes and low GSVA score of mitochondrial translation, but high survival rate (P = 0.0060) (Fig. 2g-i). These data support the notion that high expression of mitochondrial translation-related genes is associated with poor outcome in HCC.

Tigecycline inhibits mitochondrial translation in liver cancer cells

Tigecycline, the only FDA-approved antibiotic of the glycylcyclines, has been shown to specifically inhibit mitochondrial translation^11,16. According to cell viability assays, we found tigecycline had significant activity against the majority of liver cancer cells tested (Fig. 3a). Most liver cancer cells were sensitive to tigecycline, especially SNU398, Huh6, and HepG2 cells. In contrast, PLC/PRF/5, Li7, and MHCC97H cells were considered to be relatively insensitive because their half maximal inhibitory concentration (IC₅₀) exceeded 30 µM (Fig. 3a). Moreover, long-term cell proliferation assays using tigecycline also validated that SNU398, Huh6, and HepG2 cells were sensitive to tigecycline, while PLC/PRF/5, Li7, and MHCC97H cells were not (Supplementary Fig. 4).

Tigecycline could inhibit the process of mitochondrial translation of both sensitive and insensitive cells, as evidenced by the notion that this drug can downregulate protein levels of CYTB, MTCO2, and ATP6 in both sensitive and insensitive cells (Fig. 3b). In vivo experiments showed that tigecycline could inhibit the growth of liver cancer cells without affecting the body weight of mice (Fig. 3c-e). IHC staining analyses confirmed that protein levels of mitochondrially-translated MTCO2, CYTB, and ATP6 were decreased in tumour tissues removed from mice treated with tigecycline (Fig. 3f). Therefore, we conclude that tigecycline inhibits the mitochondrial translation of all liver cancer cells but has major anti-proliferative effects in a subset of liver cancer cells.

Compound screen identifies MEK inhibitors as synergistic with tigecycline

To find drugs that have synergistic effects of tigecycline in liver cancer cells, we performed a compound screen in tigecycline-insensitive cells (Fig. 4a). The top 20 compounds with synergistic effects in MHCC97H cells were then selected for the second-round screen in three insensitive cell lines: MHCC97H, PLC/PRF/5, and Li7 (Fig. 4b). The second-round screen showed mitogen-activated protein kinase kinase (MEK) inhibitors and receptor tyrosine kinase (RTK) inhibitors such as trametinib, cobimetinib, selumetinib, ceritinib, crizotinib, and cabozantinib had favorable synergies of tigecycline (Fig. 4c). Incucyte cell proliferation, short-term and long-term cell proliferation assays showed that the MEK inhibitors trametinib and cobimetinib increased the sensitivity of MHCC97H, PLC/PRF/5, and Li7 cells to tigecycline (Fig. 4d, e, Supplementary Fig. 5a-d). However, ceritinib, crizotinib, and cabozantinib had no synergistic effects in long-term cell growth assays (data not shown). Thus, we identify MEK inhibitors as drugs that display synergy with tigecycline.

Combined regimen of tigecycline and trametinib inhibits both oxidative phosphorylation and glycolysis

Given that tigecycline inhibited mitochondrial translation, thereby potentially affecting oxidative phosphorylation (OXPHOS), we measured the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of sensitive and insensitive cells treated with tigecycline. These results showed that tigecycline could reduce the basal OCR, maximal respiration and ATP production of OXPHOS of both tigecycline-sensitive and -insensitive cells (Supplementary Fig. 6a, b). However, the effects on glycolysis were quite different. Compensatory enhancement of glycolysis and glycolytic capacity was only observed in tigecycline-insensitive cells, which might explain the differences in sensitivity of liver cancer cells to tigecycline (Supplementary Fig. 6c, d). Compared to tigecycline treatment alone, the combination of trametinib and tigecycline could further reduce the basal OCR, maximal respiration, and ATP production of OXPHOS in tigecycline-insensitive cells. Additionally, compensatory enhancement of glycolysis and glycolytic capacity was inhibited (Fig. 5a, b, Supplementary Fig. 7a-e). Besides, tigecycline caused changes in the activities of key enzymes in glycolysis pathway. After treatment with tigecycline, the activities of hexokinase, pyruvate kinase, and the content of extracellular lactate increased in MHCC97H and PLC/PRF/5 cells, but decreased after addition of trametinib (Fig. 5c-e).

Tigecycline activates the EGFR-ERK1/2-MYC cascade

To explore the molecular basis of liver cancer cells insensitivity to tigecycline and how trametinib might enhance this sensitivity, we conducted RNA sequencing of MHCC97H and PLC/PRF/5 cells following treatment with tigecycline. Compared tigecycline to control groups, we found 357 upregulated and 261 downregulated genes in MHCC97H cells, and 603 upregulated and 574 downregulated genes in PLC/PRF/5 cells (Supplementary Fig. 8a, Supplementary Table 5–6). KEGG pathways analyses showed shared upregulated pathways and no shared downregulated pathway of MHCC97H and PLC/PRF/5 cells (Fig. 6a, Supplementary Fig. 8b). Sankey diagrams of upregulated genes showed that tigecycline treatment significantly enriched the MAPK signaling pathway and one carbon pool by folate pathway compared to control treated cells (Fig. 6a). qRT-PCR was then performed to investigate the expression of genes involved in one carbon pool by folate pathway, including SHMT2, ALDH1L2, MTHFD1L and MTHFD2. Results showed that mRNA level of SHMT2 was markedly elevated with treatment of tigecycline in MHCC97H and PLC/PRF/5 cells (Fig. 6b, Supplementary Fig. 8c). These results suggest that the MAPK cascade is enhanced in tigecycline-insensitive cells with treatment of tigecycline and SHMT2 may be involved in the low response of liver cancer cells to tigecycline.

Next, we performed an assay to measure activation (tyrosine phosphorylation) of human receptor tyrosine kinases. Results showed that phosphorylation of epidermal growth factor receptor (EGFR) and hepatocyte growth factor receptor (HGFR) are further activated following treatment with tigecycline (Fig. 6c). Combined data analysis for RNA sequencing, mRNA levels of amphiregulin (AREG) and epiregulin (EREG), ligands of EGFR in ERBB signaling pathway, were upregulated in tigecycline-treated cells. Therefore, we focused on EGFR signaling in subsequent studies (Fig. 6a). qRT-PCR experiments confirmed that mRNA levels of AREG and EREG were increased following treatment with tigecycline (Supplementary Fig. 9a). Since AREG and EREG are growth factors that can interact with EGFR in an autocrine fashion, ELISA was then performed to measure the levels of AREG and EREG in the supernatants. Results showed that AREG and EREG were significantly increased in the cell culture supernatants with treatment of tigecyline (Fig. 6d, e, Supplementary Fig. 9b, c). Moreover, Western blot experiments indicated that EGFR pathway was activated, which subsequently the levels of p-MEK1/2 and p-ERK1/2 were increased in tigecycline-treated cells. Inhibition of EGFR by gefitinib could also reduce the levels of p-MEK1/2 and p-ERK1/2 and had a synergistic effect of inhibition on liver cancer cells with tigecycline (Fig. 6f, g, Supplementary Fig. 9d, e). It has been described that MAPK cascade can transcriptionally activate MYC expression, thereby increasing the transcription of MYC downstream genes^17,18. Considering the increase of glycolysis after treatment with tigecycline, protein levels of hexokinase 2 (HK2), pyruvate kinase M1/2 (PKM2), and lactate dehydrogenase A (LDHA) were detected by Western blot. Results showed the increase in their protein levels after treatment with tigecycline and a decrease after addition of trametinib (Fig. 6h, Supplementary Fig. 9f). Our data suggest that a feedback activation loop involving in the EGFR-ERK1/2-MYC cascade may mediate insensitivity of liver cancer cells to tigecycline.

To explore whether responses of liver cancer cells to tigecycline could be identified by the level of activation of the MAPK cascade, we performed Western blot analyses and found sustained activation of ERK1/2 signaling by tigecycline in tigecycline-insensitive cells, while this signaling was only transiently activated in tigecycline-sensitive cells, indicating that sustained feedback activation of ERK1/2 is crucial for liver cancer cells insensitivity to tigecycline (Supplementary Fig. 10a, b).

To understand how MYC, an important downstream target of the MAPK cascade, influenced OXPHOS and glycolysis after treatment with tigecycline, we conducted shRNA-mediated knockdown of MYC in MHCC97H and PLC/PRF/5 cells. After addition of tigecycline, knockdown of MYC not only reduced the expression of SHMT2 and glycolytic enzymes-encoding genes such as HK2, PKM2, and LDHA (Fig. 6i, Supplementary Fig. 11a), but also decreased the levels of OCR and ECAR in MHCC97H and PLC/PRF/5 cells (Fig. 6j, k, Supplementary Fig. 11b-d).

To confirm feedback activation of glycolysis when treated with tigecycline, we knocked down HK2 in MHCC97H and PLC/PRF/5 cells with shRNA (Supplementary Fig. 12a). Results exhibited that the levels of OCR were enhanced and ECAR were declined after knockdown of HK2, while the levels of OCR and ECAR were decreased in the presence of tigecycline (Supplementary Fig. 12b, c). Our findings indicate that inhibition of feedback activation of glycolysis might result in energy depletion when treated with tigecycline, which was further supported by the synergistic effect of glycolysis inhibitor 2-Deoxy-D-glucose (2-DG) and tigecycline in liver cancer cells (Supplementary Fig. 12d).

To explore the role of SHMT2 in the treatment of tigecycline, we stably knocked down SHMT2 in liver cancer cells. In the presence of tigecycline, the expression of SHMT2 increased to partially maintain the suppressed mitochondrial function, while knockdown of SHMT2 in MHCC97H and PLC/PRF/5 cells led to downregulation of mitochondrial translation products, including CYTB, MTCO2, and ATP6 (Supplementary Fig. 13a). The usage of tigecycline in SHMT2-knockdown cells further reduced the OCR levels compared with only treated with tigecycline (Supplementary Fig. 13b). Thus, knockdown of mitochondrial folate enzyme SHMT2 in treatment of tigecycline resulted in further impaired mitochondrial translation and defective OXPHOS.

Blocking EGFR-ERK1/2-MYC cascade sensitizes to tigecycline in vivo

To validate the tigecycline drug combination in vivo, we used the tigecycline-insensitive MHCC97H subcutaneous tumour model. Compared to the control group, treatment of tigecycline or trametinib alone represented no obvious tumour suppressive effect, while the combination of tigecycline and trametinib showed a more effective inhibition of tumour growth without affecting the weight of mice (Fig. 7a, b, Supplementary Fig. 14a, b). Protein levels of MYC, glycolytic enzymes such as HK2 and LDHA, SHMT2, and p-ERK1/2 in tumour tissues from tigecycline group were increased compared to control. The combination of tigecycline and trametinib reduced the protein levels of these genes induced by feedback activation (Fig. 7c, Supplementary Fig. 14c, d). On the other hand, the combination treatment further inhibited the expression of mitochondrial translation products such as MTCO2, ATP6, and CYTB compared to tigecycline monotherapy, indicating the involvement of impaired mitochondrial translation and defective OXPHOS in the xenografts treated with the drug combination (Supplementary Fig. 14c, d). Our data suggest that combined regimen of tigecycline and trametinib can significantly suppress tumour growth in vivo by reducing glycolysis and OXPHOS.

It has been described that tigecycline had an immunomodulatory activity under the stimulus of staphylococcal superantigen¹⁹. We investigated the combination therapies in immune-competent mice with mouse liver cancer cells (Hepa1-6) transplanted subcutaneously. Trametinib or gefitinib had markedly synergistic effects with tigecycline in immune-competent mice, without affecting the weight of mice (Fig. 7d, e, Supplementary Fig. 15a). IHC analyses of markers of mouse immune cells showed increased CD4⁺ T cells, CD8⁺ T cells, and NKG2D⁺ cells (a marker of NK cells) in the combination groups (tigecycline combined with trametinib or tigecycline combined with gefitinib) compared to control or tigecycline group. Whereas, the number of F4/80⁺ cells (a marker of macrophages) was decreased in the combination group compared to the tigecycline group (Fig. 7f, Supplementary Fig. 15b). Therefore, this remodeling of immune pattern may further improve the efficacy of the combination therapies.

Tumours exhibit reprogrammed metabolic activities that promote cancer progression²⁰. Research on cancer metabolism has mainly focused on glycolysis in the past. The rapid rise in studies on the role of mitochondria in tumour reprograming has uncovered new insights and therapeutic opportunities²¹. Recent studies have highlighted the heterogeneity and flexibility of tumour metabolism between tumours and even within different regions of solid tumours. A rigid or static focus on cellular autonomic pathways, such as glycolysis, would not be conducive to the precise treatment of tumours²². However, identifying metabolic vulnerabilities susceptible to therapeutic targeting remains a challenge³. The emergence of functional genetic screens provides a possible solution to this conundrum²³. Through a genome-wide CRISPR screen, we found that mitochondrial translation may be a main vulnerability for liver cancer cells. Our data indicate that liver cancer cells can have different dependencies on mitochondrial translation, indicating the heterogeneity in tumour cell metabolism and the need for different strategies targeting tumour vulnerabilities.

Based on a high-resolution CRISPR screen, it was found that colorectal carcinoma cell HCT116 was specifically dependent on mitochondrial activity. However, the specific mechanism and its clinical significance were not described²⁴. In this study, we found through a genome-wide CRISPR screen that inhibition of mitochondrial translation has anti-tumour activity and may become a novel therapeutic strategy for liver cancer. This finding has clinical significance because FDA-approved tigecycline has inhibitory properties on mitochondrial translation. For tigecycline-sensitive liver cancer cells, tigecycline serves as a potential therapeutic agent to inhibit mitochondrial translation and OXPHOS. As for tigecycline-insensitive cells, tigecycline inhibited mitochondrial translation as well. However, due to the increase of compensatory glycolysis and the decrease in dependence on OXPHOS, cells still survived under tigecycline treatment. Through a compound library screen, we identified MEK inhibitors as a class of drugs that can sensitize tigecycline-insensitive cells to this drug. From a perspective of energy metabolism, the combination regimen of tigecycline and MEK inhibitors reduced the increase of compensatory glycolysis and avoided the energy shift between glycolysis and OXPHOS. For such metabolic plasticity or hybrid metabolism of cancer cells, our strategy is to inhibit the main energy metabolism of the tumour to achieve tumour starvation.

In this study, we observed that secretion of EREG and AREG increased following treatment with tigecycline, resulting in activation of EGFR and downstream signaling. It has been reported that mitochondrial dysfunction and mitochondrial stress adaptation induce expression and secretion of AREG²⁵. We found that EREG and AREG induced glycolysis by activating the EGFR-ERK1/2 cascade and the subsequent transcription of its downstream gene MYC, which represents the novelty ofmechanism of our results. MYC is involved in the development and maintenance of most human tumors, including HCC²⁶. MYC regulates the expression of glycolytic enzymes, such as HK2 and PKM2, to stimulate glycolysis. Besides, we found that the expression of SHMT2 was elevated by MYC after treatment with tigecycline. Ye et al. reported that the mitochondrial enzyme SHMT2 was induced in MYC-transformed cells under hypoxia²⁷. However, their studies did not report that SHMT2 can be regulated by MYC through mitochondrial translation inhibitors or inhibition of OXPHOS. Loss of the mitochondrial folate enzyme SHMT2 (rather than other folate enzymes) led to inhibited mitochondrial translation and defective OXPHOS, mainly because SHMT2 is involved in providing methyl donors and producing the taurine methyluridine base at the wobble position of select mitochondrial tRNAs²⁸. We propose that under the stress of inhibiting mitochondrial translation, the high expression of SHMT2 is to maintain partial mitochondrial translation function. This effect disappeared when the expression of SHMT2 was suppressed after inhibition of EGFR-ERK1/2-MYC cascade, resulting in further reduction of OXPHOS levels.

Immunomodulatory role of tigecycline in tumour has rarely been reported. Provinciali et al. found that vitamin E improved the efficacy of tigecycline by increased levels of NK cells cytotoxicity in an animal model of wounds infected with meticillin-resistant Staphylococcus aureus²⁹. An in vitro study showed murine lymphocytes treated with only tigecycline neither influenced percentage and absolute counts of CD4⁺ T and CD8⁺ T cells nor affected the regulatory/suppressive T cells³⁰. In this study, we compared the effects of tigecycline treatment to control group on tumour growth in vivo using the immune-competent mice with mouse liver cancer cells (Hepa1-6) transplanted subcutaneously. We found that the number of CD8⁺ T cells dropped slightly, while the number of CD4⁺ T cells, NK cells and macrophages did not change significantly. In combination with tigecycline and trametinib or gefitinib, the number of CD4⁺ T, CD8⁺ T, and NK cells increased and macrophages decreased compared to tigecycline treatment. In nude mice with MHCC97H cells transplanted subcutaneously, tumors in the combination group showed very slow growth, while tumors in the combination groups shrank rapidly in immunocompetent mouse model, which suggests infiltrating immune cells enhance the efficacy of the combined regimens.

This study has potential clinical and translational significance in liver cancer. Tigecycline is a broad-spectrum antibiotic used against many Gram-positive and Gram-negative pathogens. Its safety should be similar to standard antimicrobial therapy for complicated skin and soft tissue infections and complicated intra-abdominal infections in adults³¹. A multicenter, controlled, randomized clinical trial uncovered that the combination of piperacillin/tazobactam and tigecycline (50 mg/12 hours intravenously) was safe, effective and well tolerated in high-risk hematologic patients with cancer with febrile neutropenia³². Though tumour suppression was not disclosed in the trial, it appears that tigecycline (50 mg/12 hours intravenously) is tolerated in cancer patients. Recent studies have revealed the anti-tumour activity of tigecycline and an in vitro study of tigecycline in the treatment of chronic myeloid leukemia is under way (NCT02883036). More efforts are still needed to bring tigecycline to the clinical treatment of cancer. In this study, we identified the feedback activation of EGFR-ERK1/2-MYC cascade as the primary mechanism of the low response of some liver cancer cells to mitoribosome-targeting antibiotic tigecycline. MEK inhibitors or EGFR inhibitors conferred tigecycline sensitivity by blocking this feedback activation. Clinical trials of dabrafenib and MEK inhibitor trametinib in patients with advanced or metastatic BRAF V600-mutant anaplastic thyroid cancer, melanoma, and non-small-cell lung cancer (NSCLC) revealed that this therapeutic regimen had strong clinical activity and good tolerability^33–35. EGFR inhibitor gefitinib has been approved by FDA for the treatment of NSCLC³⁶. Our previous study showed that the combined treatment of lenvatinib plus gefitinib had favorable clinical responses in 12 patients with advanced HCC (NCT04642547)⁴. Since these drugs have been approved for clinical use or are part of ongoing clinical trials, clinical testing of the combination of tigecycline and MEK inhibitors or EGFR inhibitors for the treatment of liver cancer should be feasible in the near future.

In this study, we characterized the crucial role of mitochondrial translation in liver cancer cells. Tigecycline is a potential therapeutic agent for liver cancer through inhibiting mitochondrial translation. Sustained activation of EGFR-ERK1/2-MYC cascade limits the response of liver cancer cells to tigecycline. Blocking the EGFR-ERK1/2-MYC cascade by EGFR inhibitor gefitinib or MEK inhibitor trametinib sensitizes the effects of liver cancer cells to tigecycline both in vitro and in vivo, which inhibits the energy metabolism of glycolysis and OXPHOS (Fig. 7g).

Cells and cell culture conditions

SNU398, Huh6, HepG2, SNU449, SNU423, SNU387, Huh7, Hep3B, PLC/PRF/5, Li7 and MHCC97H cells were cultured in DMEM with 10% FBS, glutamine and penicillin-streptomycin (Gibco) at 37℃ and 5% CO2.Mycoplasma contamination was excluded via a PCR-based method

Compounds and antibodies

Compounds

Tigecycline (S1403), Cobimetinib (S8041), Gefitinib (S1025) and 2-Deoxy-D-glucose (S4701) were purchased from Selleck Chemicals. Trametinib (HY-10999) was purchased from MedChemExpress.

Antibodies

GFM1 (ab171945), Ki67 (ab15580), CD4 (ab183685), and NKG2D (ab203353) were purchased from Abcam. p-ERK1/2 (4370), ERK1/2 (4695), p-MEK1/2 (9154), p-EGFR (3777), EGFR (4267), MYC (5065) and F4/80 (70076) were purchased from Cell signaling technology. MRPL4 (27484-1-AP), MRPS23 (18345-1-AP), CYTB (55090-1-AP), MTCO2 (55070-1-AP), ATP6 (55313-1-AP), SHMT2 (11099-1-AP), MEK1/2 (11049-1-AP), PKM2 (15822-1-AP), HK2 (22029-1-AP), MYC (67447-1-lg, for IHC) and β-actin (HRP-60008) were purchased from Proteintech. LDHA (A1146) was purchased from ABclone. CD8a (14-0808-82) was purchased from ThermoFisher.

Genome-wide CRISPR screen

On the design of the genome-wide CRISPR library, 75748 sgRNAs targeting 19114 human genes, 50 negative genes and 50 positive genes were applied. By lentiviral transduction, the genome-wide CRISPR library was ferried into SNU398 and HepG2 cells. A differential test between T0 and Tu for each sgRNA was conducted by DESeq2. The MAGeCK Robust Rank Algorithm was used to calculate to represent sgRNA prioritization. The detailed method was described in our previous study⁴.

Short hairpin RNA (shRNA)-mediated knockdown

Lentiviral vectors containing shRNA against GFM1, MRPL4, MRPS23, SHMT2, MYC, and HK2 came from the Human TRC shRNA Library. PLKO is TRC cloning vector. Lentivirus transduction was conducted using polyplus regents according to the instructions and followed by drug selection (2 µg/ml puromycin for 3–5 days).

shGFM1: #1 GCTCACATTGATTCTGGGAAA, #2 GCTGAAGACTTGAAAGTGAAT;

shMRPL4: #1 CTGCACATCATGGACTCCCTA, #2 CTCTAGGCTTAAGACCTTCAA ;

shMRPS23: #1 CAAAGCTCAGTATCATGGTTT, #2 CTGGCTGAATGTTGAGCTATT ;

shSHMT2: #1 CCGGAGAGTTGTGGACTTTAT, #2 CCGAATCAACTTTGCCGTGTT;

shMYC: #1 CCCAAGGTAGTTATCCTTAAA, #2 CCTGAGACAGATCAGCAACAA;

shHK2: #1 GCCTGGCTAACTTCATGGATA, #2 ACTGAGTTTGACCAGGAGATT.

Quantitative reverse-transcription PCR

EZ-press RNA Purification Kit (EZBioscience) was used to extract total RNA from cells. cDNA was acquired using Color Reverse Transcription Kit with gDNA remover (EZBioscience). Quantitative PCR reactions were conducted using 2×Color SYBR Green qPCR Master Mix (EZBioscience). GFM1 forward, 5'-GAGGAAGCAGGTTAATTGGAAGG-3'; GFM1 reverse, CCCAGAATCAATGTGAGCTGAGA; MRPL4 forward, CTGGGTCTCAAAGTGGCACTG; MRPL4 reverse, AACAGCCGGGATCAAGTTGAA; MRPS23 forward, GAAACCGTAGGGAGCATCTTC; MRPS23 reverse, GCGTCATATACGTCAAACCACA; SHMT2 forward, CCCTTCTGCAACCTCACGAC; SHMT2 reverse, TGAGCTTATAGGGCATAGACTCG; ALDH1L2 forward, GCTGAAGTTGGCACTAATTGGC; ALDH1L2 reverse, TGAACACCCCTACTACTCGGT; MTHFD1L forward, CTGCCTTCAAGCCGGTTCTT;MTHFD1L reverse, TTTCCTGCATCAAGTTGTCGT; MTHFD2 forward, CTGCGACTTCTCTAATGTCTGC; MTHFD2 reverse,CTCGCCAACCAGGATCACA; β-actin forward, CATGTACGTTGCTATCCAGGC;β-actin reverse, CTCCTTAATGTCACGCACGAT.

Immunohistochemistry (IHC)

Tissue microarray contained 243 HCC samples from the Naval Military Medical University Affiliated Eastern Hepatobiliary Hospital in Shanghai, China. Patients did not receive any preoperative anticancer therapy. Ethical approval was gained from the Eastern Hepatobiliary Hospital Research Ethics Committee and written informed consent was obtained from each enrolled patient. The method of IHC staining referred to our previous study⁴. As for H-score assessment, intensity score of staining was categorized into four levels: 0 for no staining; 1⁺ for light staining visible only at high magnification; 2⁺ for intermediate staining and 3⁺ for dark staining visible even at low magnification. The percentage of cells at different intensities was determined by ImageJ. H score = 1×(% of 1⁺ cells) + 2×(% of 2⁺ cells) + 3×(% of 3⁺ cells). Due to the differences in gene expression, we divided the patients into high and low expression groups according to the median value of H score³⁷. As for estimating infiltrated immune cells, we counted the number of positive cells at high magnification.

Gene set variation analysis (GSVA)

GSVA is a method to evaluate variation of pathway activity in an unsupervised manner³⁸. The list of 135 mitochondrial translation-related genes was downloaded from http://www.gsea-msigdb.org/gsea/downloads.jsp. For 50 pairs of tumour and corresponding non-tumour tissues in HCC, GSVA scores of mitochondrial translation of all mitochondrial translation-related genes were estimated by “GSVA” R package. Besides, GSVA score of mitochondrial translation was also calculated in subclusters stratified by 26 mitochondrial translation-related genes.

Long-term cell growth assays and short-term cell growth assays

Long-term cell growth assays

Cells were seeded into six-well plates at a density of 20,000–50,000 cells per well. After static adherence, drugs were added into wells at the given concentrations for 10–14 days. Culture medium was changed twice a week and drugs were supplemented accordingly. Cells were fixed with 4% paraformaldehyde for 10 min and stained with 0.1% crystal violet.

short-term cell growth assays

Cells were seeded into 96-well plates at a density of 2,000–5,000 cells per well. After 24 hours, drugs were added at the indicated concentrations for three days. Cell Counting Kit-8 (CCK8) (Dojindo) was used to measure the absorbance at 450 nm according to the instructions.

Western blots

Cells were lysed in RIPA buffer contained with phosphatase and protease inhibitor cocktails (Sigma). Lysates were incubated on ice for 10 min, then centrifuged at 12,000g, 4°C, for 10 min. Protein quantification was conducted using BCA protein assay Kit. Quantified proteins lysates were separated with 10%-15% SDS-PAGE and transferred to PVDF membrane. After blocking with 5% skim milk, the PVDF membranes were incubated with primary antibody (1:1000 dilution) overnight at 2–8°C on a rocking platform shaker. Secondary antibodies were used at a 1:5,000–5,0000 dilution of anti-rabbit or anti-mouse HRP-conjugated secondary antibodies (Bioworld).

Compounds screen

A FDA-approved drug library containing 1957 compounds (MedChemExpress) was used to perform compounds screen in MHCC97H cells in the initial screen process. MHCC97H cells were seeded into 96-well plates at a density of 3,500 cells per well and divided into two groups: control and tigecycline treatment (25 µM). 2 µM compounds were added into each well of these groups for three days respectively. Compared with control group, top 20 compounds that inhibited cell proliferation in tigecycline group were chosen for the second-round screen in MHCC97H (tigecycline, 25 µM), PLC/PRF/5 (tigecycline, 10 µM), and Li7 (tigecycline, 25 µM) cells. The screen was conducted with two replicates of each cell line. Under treatment of compounds, the cell viability of control group was normalized against control conditions (untreated cells) after subtraction of background signal, and tigecycline group was nomalized against single tigecycline-treated cells. The proliferation inhibition was calculated between normalized control cells and tigecycline-treated cells under treatment of compounds.

Incucyte cell proliferation assays

MHCC97H, PLC/PRF/5 and Li7 cells were counted and seeded into 96-well plates at a density of 2,000–4,000 cells per well. After static adherence, drugs were added into wells at the given concentrations. The confluence of cells was monitored automatically using IncuCyte ZOOM every 4 hours (Essen Bioscience).

Seahorse analysis

Cells were seeded into XFe96 microplate at the density of 4,000–10,000 cells per well. Tigecyline or/and MEK inhibitors were added to wells for three days before Seahorse glycolysis or mitochondrial stress analyses. The Seahorse glycolysis and mitochondrial stress test kits were used in combination with the Seahorse XFe96 Analyzer (Agilent Technologies, Santa Clara, CA) as described by the manufacturer. Glucose (10 mM), oligomycin (1 µM), and 2-DG (50 mM) were used in the glycolysis stress test. Oligomycin (1.5 µM), Carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP, 1 µM), rotenone and antimycin A (0.5 µM) were applied in mitochondrial stress test. Results were analyzed by the Seahorse Wave 2.6.1 software.

RNA sequencing and data analysis

MHCC97H and PLC/PRF/5 cells were treated with DMSO or 10 µM tigecycline for three days followed by RNA-sequencing. Compared tigecycline to control groups, differentially expressed genes (DEGs) were identified and KEGG analysis of these genes was performed. Top enriched pathways and hub genes were visualized using Sankey diagram.

Human phospho-receptor tyrosine kinase arrays

Human phospho-receptor tyrosine kinase (Phospho-RTK) arrays were applied in the detection of kinase signaling after treatment of tigecycline in MHCC97H cells, according to the manufacturer’s instructions (R&D).

Enzyme-linked immunosorbent assay (ELISA)

Cell culture supernatants were collected and analyzed for relative autocrine EREG and AREG by ELISA analyses according to the manufacturer’s instructions. Human EREG and AREG ELISA kits were purchased from Mlbio.

In vivo experiments

All mice were operated according to protocols approved by the Shanghai Medical Experimental Animal Care Commission and Shanghai Cancer Institute. SNU398 and MHCC97H cells (5×10⁶ per mouse) were injected into right shoulder of 6-week-old BALB/c nude mice (male, 6 mice per group). Hepa1-6 cells (1×10⁷) were injected into right shoulder of 4-6-week-old C57BL/6 mice (male, 6 mice per group). Based on the measurement of vernier caliper, tumour volume was assessed by the modified ellipsoidal formula: tumour volume = ½ length×width². When tumours grew to about 100 mm³, nude mice were randomly assigned for 5 days/week to receive either solvent, tigecycline (50 mg/kg, intraperitoneal injection), trametinib (0.25 mg/kg, oral gavage), and combination of tigecycline plus trametinib once daily. Immune-competent mice were assigned for 5 days/week to receive either solvent, tigecycline (50 mg/kg, intraperitoneal injection), trametinib (0.25 mg/kg, oral gavage), gefitinib (80 mg/kg, oral gavage), tigecycline plus trametinib, and tigecycline plus gefitinib once daily. In the combination group, drugs with each compound administered at the same dose and schedule as a single drug.

Galle, P. R. et al. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. Journal of Hepatology 69, 182–236 (2018).
Finn, R. S. et al. Atezolizumab plus Bevacizumab in Unresectable Hepatocellular Carcinoma. N Engl J Med 382, 1894–1905 (2020).
Wang, C., Cao, Y., Yang, C., Bernards, R. & Qin, W. Exploring liver cancer biology through functional genetic screens. Nat Rev Gastroenterol Hepatol 18, 690–704 (2021).
Jin, H. et al. EGFR activation limits the response of liver cancer to lenvatinib. Nature 595, 730–734 (2021).
Wang, C. et al. Inducing and exploiting vulnerabilities for the treatment of liver cancer. Nature 574, 268–272 (2019).
Liang, J. et al. Genome-Wide CRISPR-Cas9 Screen Reveals Selective Vulnerability of ATRX -Mutant Cancers to WEE1 Inhibition. Cancer Res 80, 510–523 (2020).
Criscuolo, D., Avolio, R., Matassa, D. S. & Esposito, F. Targeting Mitochondrial Protein Expression as a Future Approach for Cancer Therapy. Front. Oncol. 11, 797265 (2021).
Wang, F., Zhang, D., Zhang, D., Li, P. & Gao, Y. Mitochondrial Protein Translation: Emerging Roles and Clinical Significance in Disease. Front. Cell Dev. Biol. 9, 675465 (2021).
Pecoraro, A., Pagano, M., Russo, G. & Russo, A. Ribosome Biogenesis and Cancer: Overview on Ribosomal Proteins. IJMS 22, 5496 (2021).
Dong, Z. et al. Biological Functions and Molecular Mechanisms of Antibiotic Tigecycline in the Treatment of Cancers. IJMS 20, 3577 (2019).
Škrtić, M. et al. Inhibition of Mitochondrial Translation as a Therapeutic Strategy for Human Acute Myeloid Leukemia. Cancer Cell 20, 674–688 (2011).
Kuntz, E. M. et al. Targeting mitochondrial oxidative phosphorylation eradicates therapy-resistant chronic myeloid leukemia stem cells. Nat Med 23, 1234–1240 (2017).
Tan, J., Song, M., Zhou, M. & Hu, Y. Antibiotic tigecycline enhances cisplatin activity against human hepatocellular carcinoma through inducing mitochondrial dysfunction and oxidative damage. Biochemical and Biophysical Research Communications 483, 17–23 (2017).
Meßner, M. et al. Metabolic implication of tigecycline as an efficacious second-line treatment for sorafenib-resistant hepatocellular carcinoma. FASEB j. 34, 11860–11882 (2020).
Rath, S. et al. MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Research 49, D1541-D1547 (2021).
Kim, H.-J., Maiti, P. & Barrientos, A. Mitochondrial ribosomes in cancer. Seminars in Cancer Biology 47, 67–81 (2017).
Yang, W. et al. ERK1/2-dependent phosphorylation and nuclear translocation of PKM2 promotes the Warburg effect. Nat Cell Biol 14, 1295–1304 (2012).
Pan, Z. et al. LIMK1 nuclear translocation promotes hepatocellular carcinoma progression by increasing p-ERK nuclear shuttling and by activating c-Myc signalling upon EGF stimulation. Oncogene 40, 2581–2595 (2021).
Saliba, R., Paasch, L. & El Solh, A. Tigecycline attenuates staphylococcal superantigen-induced T-cell proliferation and production of cytokines and chemokines. Immunopharmacology and Immunotoxicology 31, 583–588 (2009).
Vander Heiden, M. G. & DeBerardinis, R. J. Understanding the Intersections between Metabolism and Cancer Biology. Cell 168, 657–669 (2017).
Oliveira, G. L., Coelho, A. R., Marques, R. & Oliveira, P. J. Cancer cell metabolism: Rewiring the mitochondrial hub. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 1867, 166016 (2021).
Kim, J. & DeBerardinis, R. J. Mechanisms and Implications of Metabolic Heterogeneity in Cancer. Cell Metabolism 30, 434–446 (2019).
Wei, L. et al. Genome-wide CRISPR/Cas9 library screening identified PHGDH as a critical driver for Sorafenib resistance in HCC. Nat Commun10, 4681 (2019).
Hart, T. et al. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell163, 1515–1526 (2015).
Wang, S.-F. et al. Mitochondrial stress adaptation promotes resistance to aromatase inhibitor in human breast cancer cells via ROS/calcium up-regulated amphiregulin-estrogen receptor loop signaling. Cancer Letters 523, 82–99 (2021).
Dauch, D. et al. A MYC-aurora kinase A protein complex represents an actionable drug target in p53-altered liver cancer. Nat Med 22, 744–753 (2016).
Ye, J. et al. Serine Catabolism Regulates Mitochondrial Redox Control during Hypoxia. Cancer Discovery 4, 1406–1417 (2014).
Morscher, R. J. et al. Mitochondrial translation requires folate-dependent tRNA methylation. Nature 554, 128–132 (2018).
Provinciali, M. et al. Vitamin E improves the in vivo efficacy of tigecycline and daptomycin in an animal model of wounds infected with meticillin-resistant Staphylococcus aureus. Journal of Medical Microbiology 60, 1806–1812 (2011).
Jasiecka-Mikołajczyk, A., Jaroszewski, J. J. & Maślanka, T. Effect of tigecycline on the production of selected cytokines and counts of murine CD4 + and CD8 + T cells-an in vitro study. Pol J Vet Sci 21, 819–822 (2018).
Stein, G. E. & Babinchak, T. Tigecycline: an update. Diagnostic Microbiology and Infectious Disease 75, 331–336 (2013).
Bucaneve, G. et al. Results of a Multicenter, Controlled, Randomized Clinical Trial Evaluating the Combination of Piperacillin/Tazobactam and Tigecycline in High-Risk Hematologic Patients With Cancer With Febrile Neutropenia. JCO 32, 1463–1471 (2014).
Subbiah, V. et al. Dabrafenib and Trametinib Treatment in Patients With Locally Advanced or Metastatic BRAF V600-Mutant Anaplastic Thyroid Cancer. JCO 36, 7–13 (2018).
Robert, C. et al. Improved Overall Survival in Melanoma with Combined Dabrafenib and Trametinib. N Engl J Med 372, 30–39 (2015).
Planchard, D. et al. Dabrafenib plus trametinib in patients with previously untreated BRAFV600E-mutant metastatic non-small-cell lung cancer: an open-label, phase 2 trial. The Lancet Oncology 18, 1307–1316 (2017).
Du, X. et al. Acquired resistance to third-generation EGFR-TKIs and emerging next-generation EGFR inhibitors. The Innovation 2, 100103 (2021).
Mazières, J. et al. Evaluation of EGFR protein expression by immunohistochemistry using H-score and the magnification rule: Re-analysis of the SATURN study. Lung Cancer 82, 231–237 (2013).
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7 (2013).

Acknowledgements

This work was funded by grants from the National Natural Science Foundation ofChina (82073039, 82011530441, 81920108025), Shanghai Rising-StarProgram(19QA1408200), Shanghai Municipal Science and Technology Project(20JC1411100), 111 project (B21024) and Shanghai Jiao Tong University School ofMedicine (YG2019GD01).

Author contributions

W. Q., H. J.,and R. B. conceptualized and supervised the study. Y. Z.,and S. W. performed all the experimental assays and wrote the original draft. W. W., J. L., H. L., Q. J., J. Z., X. Z., R. Y., Q. W., and Y. S.,provided constructive comments and discussion. C. L., and R. L. B. performed bioinformatics analyses. S. Y. collected the HCC tissues and supervised the study.

Competing interests:

The authors declare that they have no competing interests.

Data and materials availability：

Alldata associated with this study are present in the paper or the SupplementaryMaterials. The RNA-seq data will be deposited in the Gene ExpressionOmnibus (GEO) repository before publication. Reasonable request for materials may be made bycontacting W. Q ([email protected]).

There is NO Competing Interest.

SupplementaryTable14.pdf
Supplementary Table 1-4
SupplementaryTable5.xlsx
Supplementary Table 5
SupplementaryTable6.xlsx
Supplementary Table 6
SupplementaryFigure.pdf
Supplementary Figure
SupplementaryInformation.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Sustained activation of EGFR-ERK1/2 signaling limits the therapeutic response to tigecycline-induced inhibition of mitochondrial translation in liver cancer

Status:

Version 1

Abstract

Figures

Introduction

Results

A genome-wide CRISPR-Cas9 screen identifies requirement of mitochondrial translation-related genes for liver cancer cells

Prognostic significance of mitochondrial translation-related genes

Tigecycline inhibits mitochondrial translation in liver cancer cells

Compound screen identifies MEK inhibitors as synergistic with tigecycline

Combined regimen of tigecycline and trametinib inhibits both oxidative phosphorylation and glycolysis

Tigecycline activates the EGFR-ERK1/2-MYC cascade

Blocking EGFR-ERK1/2-MYC cascade sensitizes to tigecycline in vivo

Discussion

Conclusion

Methods

Cells and cell culture conditions

Compounds and antibodies

Compounds

Antibodies

Genome-wide CRISPR screen

Short hairpin RNA (shRNA)-mediated knockdown

Quantitative reverse-transcription PCR

Immunohistochemistry (IHC)

Gene set variation analysis (GSVA)

Long-term cell growth assays and short-term cell growth assays

Long-term cell growth assays

short-term cell growth assays

Western blots

Compounds screen

Incucyte cell proliferation assays

Seahorse analysis

RNA sequencing and data analysis

Human phospho-receptor tyrosine kinase arrays

Enzyme-linked immunosorbent assay (ELISA)

In vivo experiments

References

Declarations

Additional Declarations

Supplementary Files

Status:

Version 1