PKM2 inhibition may reverse therapeutic resistance to transarterial chemoembolization in hepatocellular carcinoma

The Liver Cancer Institute (LCI) cohort is a publicly available HCC dataset available at the Gene Expression Omnibus (GEO, GSE14520; http://​www.​ncbi.​nlm.​nih.​gov/​geo). Data from this cohort has been previously described [10]. For this study, only the 105 patients who underwent TACE were selected from the LCI cohort. The Hong Kong cohort consists of 47 patients who underwent TACE, and has previously been described in detail [8]. Patients from these cohorts were assigned as TACE responders based on the previously described TACE Navigator gene signature utilizing NanoString technology (Seattle, Washington) [8]. NanoString was also utilized to evaluate for PKM2 expression in the Hong Kong cohort and PDCs. Gene expression as measured by NanoString counts, were log₂ transformed and converted to z-score within each cohort.

HCC cell line transcriptomic data was downloaded from the Cancer Cell Line Encyclopedia (CCLE) and log₂ transformed [11]. Expression data was then z-score transformed and the TACE Navigator prognostic signature was then applied. Based on this data, cell lines were classified as responder and non-responder like and used for downstream analysis.

HUH7 were cultured in Dulbecco’s modified Eagles Medium (Life Technologies) supplemented with 10% fetal bovine serum (FBS), penicillin, streptomycin and L-glutamine. Hep3B were cultured in Minimal Essential Medium (Life Technologies) supplemented with 10% FBS, penicillin, streptomycin, L-glutamine, non-essential amino acids and sodium pyruvate. SNU-387 were cultured in RPMI-1640 (Life Technologies) supplemented with 10% FBS, penicillin and streptomycin. SNU-475 were cultured in RPMI-1640 supplemented with 10% heat inactivated FBS, penicillin and streptomycin. PDCs (HCC3501, HCC 3796, HCC 4006, and HCC3258) were cultured in Dulbecco’s modified Eagles Medium: Nutrient Mixture F-12 (Life Technologies) supplemented with 10% FBS, non-essential amino acids, penicillin and streptomycin. pGFP-C-shLenti-PKM2 shRNA was purchased from Origene and packaged using FuGENE 6 transfection reagent (Promega). Cells lines were passaged less than 15X after thaw.

Protein lysates were separated on Bis-tris 4–12% gels (Life Technologies) and transferred to a nitrocellulose membrane (Life Technologies). Protein detection was performed using anti-PKM2 (Cell Signaling Technology, cat# 4053S) and anti-β-actin (Sigma-Aldrich, cat# A5316). All immunoblots are cropped for viewing. Total RNA was extracted using Trizol (Life Technologies) according to the manufacturer’s protocol. cDNA was synthesized using High Capacity cDNA Reverse Transcription Kit (Life Technologies). Gene expression was quantified by quantitative real-time transcription-polymerase chain reaction (qRT-PCR) using CFX384 Real Time System (BioRad) and SYBR Green PCR Master Mix (GenePharma). Gene expression levels were normalized to β-actin. The relative gene expression levels were detected and calculated using the ΔΔCt comparative method. The RT-PCR primer sequences are as follows: PKM2, 5′- ACTGTCCTCACCAAGTCTGG-3′ (forward) 5′-GAAGATGCCACGGTACAGGT-3′ (reverse); β-Actin, 5′-TTGTTACAGGAAGTCCCTTGCC-3′ (forward) 5′-ATGCTATCACCTCCCCTGTGT-3′ (reverse).

Glycolysis was measured using a commercially available kit (Cayman Chemical, cat#600450). Briefly, cells were seeded at 20,000 cells/well in triplicate in a 96-well plate in FBS free media appropriate for each cell line. Cells were placed in 37 °C incubator for 48 h. For hypoxia induced measurements cells were placed in a 37 °C incubator for 48 h in a closed 1% oxygen environment. After 48 h, plates were centrifuged at 1000 rpm for 5 min and 10 μl of supernatant was removed from each well and added to the reaction solution. The plate was incubated with shaking for 30 min and the absorbance at 490 nm was measured.

Cells were seeded at 500 cells/well in a 6 well plate and cultured for 10 days in both normoxia and 1% oxygen in parallel. Cells were then washed with ice cold 1x PBS (Life Technologies) followed by ice cold fixation with methanol for 30 min. After fixation, cells were washed with dH₂O followed by 2 h of 0.05% crystal violet staining and subsequent washings with dH₂O. Colonies were quantified via manual counting in triplicates.

Xcelligence (ACEA Bioscience) assays were performed for cell proliferation, cell invasion and cell migration and measured by cell index. Cell index is a measure of cell proliferation within each well of the plate as defined by (impedance at time point – impedance without cells) / nominal impedance value. Assays were performed in closed 1% hypoxic environment. Cells were seeded at 1000 cells/well in E-plates (ACEA Bioscience, cat # 5232368001), with appropriate medium in quadruplets. For TACE simulation assay, appropriate wells were treated with doxorubicin (Sigma, cat# D1515) and/or shikonin, (Sigma, cat#S7576) at the IC₅₀ appropriate for each cell line (Supplemental Table 1). Cell specific IC₅₀ was determined based on data from the Genomics of Drug Sensitivity in Cancer database [12]. Proliferation was measured over 72 h. For migration and invasion assay, 30,000 cells/well were plated on a CIM plate (ACEA Bioscience, cat #5665817001) with Matrigel (for invasion only) in a 1:200 dilution with appropriate media. Migration and invasion were measured over 24 h. For cell proliferation, migration and invasion experiments were performed in triplicate.

Relative apoptosis activity was measured via Caspase-3/7 activity by using the Apo-ONE homogeneous Caspase 3/7 assay (Promega, cat#G7792). Briefly cells were seeded in cell specific media at 7500 cells/well in a 96 well plate and placed in a 37 °C incubator overnight. Cells were then placed in a hypoxic incubator for 48 h. The assay was then performed per the manufacturer’s protocol and fluorescence was measured (excitation: 485 nm, emission: 535 nm).

Cells were seeded in cell specific media at 7500 cells/well in a white 96 well plate and placed in a 37 °C incubator for 24 h. For doxorubicin-only experiments, two-fold drug dilutions were performed. For doxorubicin and shikonin experiments, a similar two-fold doxorubicin dilution was performed. Following dilutions, shikonin was added at the cell specific IC₅₀. One plate was placed in a normal 37 °C incubator and the other plate was placed in hypoxia. Normoxia/hypoxia experiments were performed concurrently. After 48-h incubation, plates were removed from the incubator and allowed to reach room temperature (RT). At this point Cell-Titer Glo (Promega) was added to each well. Plates were placed on an orbital shaker for 2 min and then incubated for 10 min at RT. Luminescence was then measured. Viability assays are relative to the normoxia untreated control. For combination index experiments, a shikonin two-fold dilution was performed in addition to the previously described. To calculate the combination index, a constant ratio of 1:1 doxorubicin and shikonin was used. Viability and drug sensitivity assays were also performed with cisplatin (Milipore Sigma, cat# 232120) in the same manner. Combination index constant ratio was performed at a ratio of 4:1 cisplatin to shikonin given the relatively high IC₅₀ of cisplatin in HCC cell lines.

PDCs were cultured in AlgiMatrix 3D Culture System (Life Technologies) in the method described by Takai et al. [13]. Briefly, one million cells were seeded and cultured in cell specific media for 2 weeks at which point they were treated with doxorubicin or shikonin alone as well as in combination and placed in hypoxia for 48 h. Haematoxylin and eosin staining was performed before and after treatment. For the collection of spheroids, the matrix was dissolved using Algimatrix Dissolving Buffer (Life Technologies) per the manufacturer’s instructions. Organoids were then pelleted by centrifugation at 300 g for 5 min. After collection, organoids were seeded in a white 96 cell plate with the addition of Cell Titer Glo 3D Cell Viability Assay (Promega, cat# G9683). Plates were placed on an orbital shaker and allowed to incubate per the manufacturer’s instructions. Luminescence was then measured, and viability was calculated relative to the untreated control.

For all statistical analyses, a p < 0.05 was considered statistically significant. Statistical tests included 2-tailed Student’s t-test for two groups or one-way ANOVA for multiple groups where appropriate. For clinical data, descriptive statistics were calculated for all variables of interest. Continuous variables were summarized using means, whereas categorical variables were summarized using frequency and percentages. Comparisons of categorical variables were performed using the chi-square or Fisher’s exact test whereas continuous variables were compared with the two-sided Student’s t-test. Overall survival (OS) was calculated based on the provided survival times utilizing the Kaplain-Meier method and log-rank test. Following a univariable analysis, a Cox proportional hazards model was constructed using all variables with p < 0.05. Genomic analyses were performed using BRB-ArrayTools version 4.6.0 (Bethesda, MD). Experimental statistics were performed using GraphPad Prism 7.01 (San Diego, CA). Clinical statistics were performed using STATA 14.0 (College Station, TX). Combination index was calculated using the Chou-Talalay method and Compusyn software [14, 15].

We first set out to identify signaling pathways associated with TACE resistance by utilizing the TACE Navigator gene signature, which includes 14 genes (ASNS, CDK1, DNASE1L3, FBXL5, GOT2, GRHPR, IARS, LGALS3, LHFPL2, MFGE8, MKI67, PEBP1, TNFSF10 and UBB). The TACE navigator was used to identify two distinct subpopulations, TACE responders and non-responders. Gene expression analyses between the responders and non-responders resulted in 1726 differentially expressed genes, as described previously [8]. To identify genes that are important drivers of resistance and functionally linked to TACE, we correlated the gene expression of the 15 genes from the TACE Navigator to the 1726 differentially expressed genes using Spearman Rank. We reasoned that only genes that are functionally important for TACE resistance will be correlated with all 15 genes. We filtered out genes that were correlated with survival and correlated with all 14 genes from the TACE navigator, resulting in 105 genes (FDR < 0.05) (Fig. 1a). We then performed hierarchical clustering of TACE patients based on our 105 correlated genes and observed two distinct populations (Fig. 1b). We next performed KEGG pathway gene set enrichment analyses to identify unique signaling pathways associated with TACE response. We found correlation with 11 unique KEGG pathways, including glycolysis and gluconeogenesis (Fig. 1c). Given our previous findings that TACE resistance is associated with hypoxic reprograming [8], we investigated the glycolysis and gluconeogenesis pathway, which includes PKM2, Glucose-6-Phosphatase Catalytic Subunit (G6PC) and Phosphoenolpyruvate Carboxykinase 1 (PCK1) (Fig. 1d). These genes were among the most differentially expressed between responders and non-responders (Fig. 1b) and we chose to further investigate PKM2 due to its known role in hypoxia and its interaction with the hypoxia regulatory gene HIF-1α [16].

We next set out to examine the functional significance of each of these genes by examining their relative gene expression in responders and non-responders and whether their expression correlated with overall patient survival. We found that in a cohort of patients that received TACE, PKM2 was elevated in non-responders compared to responders (Fig. 1e; log fold change 1.14, p < 0.01). PKM2 expression using a median cutoff was also associated with significantly attenuated overall survival in this patient cohort (Fig. 1f; median survival High: 27.5 months vs Undefined, p < 0.0001). Conversely, we found that gluconeogenesis related genes, G6PC and PCK1, had significantly higher expression amongst responders, which was associated with better overall survival (PCK1: median survival High: Undefined, Low: 47.1 months, p = 0.05; G6PC: High: Undefined, Low: 47.1 months, p = 0.04) (Supplementary Figure 1). These findings indicate that PKM2 is more likely associated with non-responders and the glycolysis pathway. To ensure that these findings were not specific to a single subset of HCC patients, we set out to validate our findings in the Hong Kong cohort (n = 47). From this analysis, we found PKM2 was significantly elevated in non-responders and associated with a worse overall survival (High: 27.8 months, Low: Undefined, p = 0.05) (Supplementary Figure 2), consistent with the LCI cohort.

Finally, to establish the influence of PKM2 on survival amongst other known prognostic clinical variables, we constructed a Cox Proportional-Hazards Model (Supplemental Table 2). In the univariable analysis, elevated PKM2, BCLC stage B and C, local invasion and cirrhosis were all found to impact survival, which were included in the multivariable model. Multivariable analysis revealed high PKM2 expression was associated with a three-fold increased risk of death (HR 3.02, 95% CI: 1.50–6.04, p = 0.002). In addition, BCLC stage C was also found to be significantly associated with death but to a lesser extent (HR 2.62, 95% CI 1.30–5.27, p = 0.007). Given these findings, we hypothesized that PKM2 was associated with TACE resistance and warranted further investigation.

To investigate TACE resistance, we first determined HCC cell lines with the similar transcriptome profiles that we observed within our patient cohorts using the 27 HCC cell lines. Using the 14 TACE Navigator genes, we performed hierarchical clustering analyses, which yielded two groups defined as responder and non-responder based on the expression patterns resembling of HCC patients (Fig. 2a). We then selected two responder-like cell lines, Hep3B and HUH7, as well as two non-responder-like cell lines, SNU-387 and SNU-475 for downstream functional studies as these lines were available in our cell repository. To confirm the responder-like and non-responder-like phenotype, we tested the IC₅₀ of the most commonly used TACE chemotherapeutics, doxorubicin and cisplatin, in both normoxia and hypoxia, a condition mimicking the TACE procedure. We observed that in the responder-like cell lines, there was a decrease in the IC₅₀ of doxorubicin (Fold change: Hep3B: -1.7, HUH7: − 3.45) and cisplatin (Fold change: Hep3B -1.14, HUH7–4.89) whereas in the non-responder-like cell lines, the IC₅₀ increased for both doxorubicin (Fold change: SNU-387: 1.58, SNU-475: 1.74) and cisplatin (Fold change: SNU-387: 1.45, SNU-475: 3.27) when subjected to hypoxia (Fig. 2b). We next characterized the cell lines by determining relative PKM2 mRNA and protein expression under both normoxic and hypoxic conditions. Accordingly, the non-responder-like cell lines had higher PKM2 mRNA and protein expression in normoxia compared to responders, which was further enhanced under hypoxic conditions (Fig. 2c). To assess the functional role of PKM2 expression in glucose metabolism, glycolysis activity was assessed. The non-responder like cells have a higher baseline glycolysis activity as well as enhanced activity in hypoxia (Fig. 2d). Together, these findings are indicative of a difference in glycolysis utilization between responder and non-responder like cell lines.

We next investigated the functional role of PKM2 in both responder- and non-responder-like HCC cell lines. We measured lactate production in HCC cells treated with shPKM2 lentivirus and scramble control. PKM2 knockdown resulted in a significant decrease in lactate production across all cell lines (Fig. 3a). Among responder-like cells, Hep3B demonstrated a 24.2% reduction (p < 0.01) while HUH7 demonstrated a similar 20.3% reduction (p < 0.01). Conversely, non-responder-like cells experienced a greater lactate reduction (42.0% in SNU-387, 40.4% in SNU-475, p < 0.01). Apoptosis activity was also increased in all cell lines with a greater effect in the non-responder-like cells (% increase: SNU-387: 55.6%, p < 0.01; SNU-475: 53.5%, p < 0.01) than in responder-like cells (% increase: Hep3B: 11.7%, p = 0.02; HUH7: 14.0%, p = 0.02) (Fig. 3b). To test the effect of decreased glycolysis activity on cellular viability and metastatic potential, we performed colony formation, migration and invasion assays. Cell viability, as measured by colony formation, demonstrated that all cell lines were affected, with non-responder-like cells illustrating a greater effect (% decrease: SNU-387: 59.1% p = 0.05; SNU-475: 43.9%, p = 0.02) than what was observed in responder-like cells (% decrease: Hep3B: 20.8%, p < 0.01; HUH7: 20.8%, p < 0.01) (Fig. 3c). Cell migration was significantly decreased amongst all cell lines with PKM2 knockdown (Fold change: Hep3B 0.61, p < 0.01; HUH7: 0.66, p = 0.02; SNU-387: 0.73, p = 0.03; SNU-475: 0.45, p < 0.01) (Fig. 3d), whereas a reduction in PKM2 had no significant effect on invasion (Fold change: Hep3B: 0.76, p = 0.20; HUH7: 0.77, p = 0.06; SNU-387: 0.78, p = 0.07; SNU-475: 0.74, p = 0.10) (Fig. 3e). These data indicate that PKM2 knockdown disrupts glycolytic activity, thus affecting cancer-associated phenotypes. In addition, these data suggest that inhibition of PKM2 may be a useful step to overcome TACE resistance.

We hypothesized that PKM2 knockdown could improve TACE efficacy. We assessed the effect of PKM2 knockdown on the cell’s response to doxorubicin and cisplatin, two commonly used chemotherapeutic drugs in TACE. IC₅₀ values were calculated in hypoxia for each cell line treated with or without shPKM2 knockdown and scramble control. In all cell lines, we noted a reduction in the hypoxia IC₅₀ of these two chemotherapeutics (Fig. 4a-b). Among responder-like cells treated with shPKM2, Hep3B demonstrated a 0.56 (p < 0.01) and 0.41 (p < 0.01) fold change in the IC₅₀ of doxorubicin and cisplatin compared to scramble control, respectively, whereas HUH7 demonstrated a 0.32 (p < 0.01) and 0.31 (p < 0.01) fold change in the IC₅₀ of doxorubicin and cisplatin. In addition, non-responder like cells treated with shPKM2, SNU-387 demonstrated a 0.13 (p < 0.01) and 0.18 (p < 0.01) fold change in the IC₅₀ of doxorubicin and cisplatin while SNU-475 demonstrated a 0.07 (p < 0.01) and 0.08 (p < 0.01) fold change in the IC₅₀ of doxorubicin and cisplatin respectively in hypoxia.

Next, we designed an in vitro TACE simulation assay to capture the effect of both chemotherapy and acute hypoxia in which cells were treated at their IC₅₀ of doxorubicin and placed into a closed 1% oxygen system to induce acute hypoxia. Given that doxorubicin is the most commonly used TACE agent worldwide, and its slightly larger reduction in IC₅₀ in PKM2 knockdown compared to cisplatin, we elected to perform further characterization of in vitro TACE model using doxorubicin. Cellular proliferation was then measured over 72 h. As expected, the responder cell lines had a significant response to doxorubicin alone (Fold change: Hep3B: 0.22, p < 0.01; HUH7: 0.01, p < 0.01), which was slightly improved when cells were treated with shPKM2 (Fold change: Hep3B: 0.09, p < 0.01; HUH7: 0.05, p < 0.01) (Fig. 4c Left panels). Conversely, amongst the non-responder cell lines, SNU387 had no response to doxorubicin (Fold Change:1.24 p = 0.33) whereas SNU475 had a minimal response (Fold Change: 0.87, p < 0.01) (Fig. 4c, right panels). When non-responder cells were treated with shPKM2 and doxorubicin, both non-responder cell lines had a drastic reduction of cell growth (Fold Change: SNU-387: 0.03, p < 0.01); SNU-475: 0.12, p < 0.01) (Fig. 4c, Right panels). These data suggest that PKM2 inhibition was able to inhibit cell growth in all cell lines, resulting in improved TACE-like efficacy in vitro (Fig. 4d).

Shikonin, a naphthoquinone, has been shown to inhibit glycolysis through PKM2 specific inhibition [17]. We next sought to evaluate whether shikonin had similar inhibitory effects in our TACE response cell models. We first tested the effect of shikonin on the sensitization of cancer cells to either doxorubicin (Fig. 5a) or cisplatin (Fig. 5c). In the responder-like cell lines, Hep3B demonstrated a reduction in the area under the curve (AUC) in hypoxia of 31% (p < 0.01) with doxorubicin and 31% (p < 0.01) with cisplatin whereas HUH7 demonstrated a 53% reduction (p < 0.01) and a 5% reduction (p = 0.02) with doxorubicin and cisplatin, respectively. The non-responder-like cell lines also experienced a decrease in the AUC with combination chemotherapy and shikonin. SNU-387 demonstrated an 82% (p < 0.01) and 85% (p < 0.01) reduction in AUC with doxorubicin and cisplatin, respectively, whereas SNU-475 demonstrated a 94% (p < 0.01) reduction in AUC with doxorubicin and a 95% (p < 0.01) AUC reduction with cisplatin. To determine the interaction between both doxorubicin and shikonin and cisplatin and shikonin in the non-responder like cell lines, we calculated the combination index via the Chou-Talalay method (Fig. 5b, d). We observed that doxorubicin and shikonin are synergistic at higher level of cytotoxicity (Fig. 5b), whereas the combination of shikonin and cisplatin interact in an additive manner (Fig. 5d). These data indicate that the combination therapy may be a viable option to improve TACE.

Next, we performed in vitro TACE simulation with the addition of shikonin and doxorubicin. Consistent with the above observations, responder-like cells had a significant decrease in cellular proliferation with doxorubicin alone (Fold change: Hep3B: 0.27, p < 0.01; HUH7: 0.31, p < 0.01), which was further enhanced with the combination therapy of shikonin and doxorubicin (Fold change: Hep3B: 0.15, p < 0.01; HUH7:0.10, p < 0.01) (Fig. 5e, Left panels). Conversely, doxorubicin alone had a small reduction in cellular proliferation in both non-responder like cell lines (Fold change: SNU-387: 0.64, p < 0.01; SNU-475: 0.81, p = 0.04) but with the addition of shikonin therapeutic efficacy was significantly increased which resulted in a 0.11 (p < 0.01) fold change in the SNU-387 cells and a 0.13 (p < 0.01) fold change in the SNU-475 cells (Fig. 5e, Right panels). These data suggest that resistance can be overcome with the addition of a PKM2 inhibitor during the TACE procedure, which has the potential to greatly improve treatment response. Additionally, we observed that combination therapy with doxorubicin and shikonin significantly improved in vitro TACE response in all cell lines (Fig. 5f). This data may suggest that combination shikonin and doxorubicin has the potential to improve TACE efficacy even in those patients with expected favorable outcomes.

To test the hypothesis that combination shikonin and doxorubicin improves TACE response in pre-clinical models, we used a 3D model utilizing PDCs to better approximate a tumor-like environment. Given our findings that PKM2 enrichment both confers a negative prognostic outcome as well as is associated with TACE resistance, we elected to screen 33 HCC PDCs for PKM2 expression. Utilizing the NanoString platform, median relative PKM2 mRNA expression was 13.33 (Fig. 6a). We then selected two representative lines from both the PKM2-high and PKM2-low groups and generated 3D organoids. In a similar manner to our 2D TACE in vitro assay, organoids were treated at cell line specific IC₅₀s of both shikonin and doxorubicin and subjected to hypoxia. Organoid viability was then assessed. PKM2 low cells behaved in a similar manner to the responder-like cells in 2D culture with both cell lines demonstrating a statistically significant decrease in viability upon doxorubicin treatment alone (Viability fold change: HCC 3796: 0.64, p < 0.01; HCC 4006: 0.67, p = 0.03) (Fig. 6b, Left panels). Conversely, PKM2 high cells behaved in a similar manner when compared to the TACE non-responder cells. Both HCC 3258 and HCC 3501 demonstrated resistance to doxorubicin treatment alone (Viability fold change: HCC 3258: 0.74, p = 0.37; HCC 3501: 0.91, p = 0.34) whereas when treated with the combination of doxorubicin and shikonin, resistance is overcome resulting in a 0.27 (p = 0.01) and 0.49 (p < 0.01) fold change in HCC 3258 and HCC 3501 respectively (Fig. 6b, Right panels). In addition to the improved response in non-responders, we also noted a significant difference in the response to doxorubicin and combination treatment regardless of PKM2 expression (Fig. 6c). Finally, we examined the histologic characteristics of the PKM2 High organoids both before and after combination therapy. In a pretreatment condition, HCC spheres displayed a well circumscribed structure whereas following combination treatment they appeared more disorganized and irregular. Similarly, the results seen in our 2D model, we observed increased efficacy of our in vitro TACE assay when non-responder like organoids were treated with combination PKM2 inhibitor and standard chemotherapy.