TRAIL is a potent death-inducing ligand that mediates apoptosis through the extrinsic pathway and serves as an important endogenous tumor suppressor mechanism. Because tumor cells are often killed by TRAIL and normal cells are not, drugs that activate the TRAIL pathway have been thought to have potential clinical value. However, to date, most TRAIL-related clinical trials have largely failed due to the tumor cells having intrinsic or acquired resistance to TRAIL-induced apoptosis. Previous studies to identify resistance mechanisms have focused on targeted analysis of the canonical apoptosis pathway and other known regulators of TRAIL receptor signaling. To identify novel mechanisms of TRAIL resistance in an unbiased way, we performed a genome-wide shRNA screen for genes that regulate TRAIL sensitivity in sublines that had been selected for acquired TRAIL resistance. This screen identified previously unknown mediators of TRAIL resistance including angiotensin II receptor 2, Crk-like protein, T-Box Transcription Factor 2, and solute carrier family 26 member 2 (SLC26A2). SLC26A2 downregulates the TRAIL receptors, DR4 and DR5, and this downregulation is associated with resistance to TRAIL. Its expression is high in numerous tumor types compared with normal cells, and in breast cancer, SLC26A2 is associated with a significant decrease in relapse-free survival.

Implication: Our results shed light on novel resistance mechanisms that could affect the efficacy of TRAIL agonist therapies and highlight the possibility of using these proteins as biomarkers to identify TRAIL-resistant tumors, or as potential therapeutic targets in combination with TRAIL. Mol Cancer Res; 15(4); 382–94. ©2017 AACR.

The TRAIL was discovered by virtue of its high sequence homology to Fas ligand (1, 2). In contrast to TNF and FasL, TRAIL has a remarkable specificity for killing tumor cells while inducing little toxicity in normal cells. TRAIL-induced apoptosis is one of the body's physiologic mechanisms of tumor suppression, making it an ideal pathway to reactivate with therapeutics (3, 4). However, resistance to TRAIL-induced apoptosis is common in cancer and the overall efficacies of both monotherapy and combined treatment have been disappointing (5). Nevertheless, in numerous clinical trials with TRAIL receptor agonists, positive responses have been achieved in small subsets of patients (6).

TRAIL binds to five receptors: the functional death receptors (DR) 4 and DR5, the nonfunctional decoy receptors (DcR) 1 and DcR2, and the soluble TNFR member osteoprotegerin (OPG). Upon ligand binding, the trimerized functional TRAIL receptors form larger aggregates enabling the recruitment of the Fas Associated Death Domain (FADD), and procaspase 8 to the receptor. Together, these proteins form the core of the death-inducing signaling complex (DISC). Formation of the DISC enables dimerization of procaspase 8 into its active form, which is then usually cleaved to produce a soluble active protease. The activated caspase initiates activation of mitochondrial membrane permeabilization and downstream effector caspases, which ultimately cleave downstream proteins leading to the hallmarks of apoptosis including cleavage of structural proteins, DNA fragmentation, and membrane blebbing.

Theoretically, modulation of any of the pro- or antiapoptotic players that are involved in TRAIL signaling could influence the response to TRAIL. Numerous studies have been performed to understand the physiological mechanisms that tumor cells use to evolve resistance to TRAIL-induced apoptosis (7). Many cellular pathways have been linked to TRAIL resistance including ER-stress, proteasome-mediated degradation, protein folding, metabolism, and autophagy (7, 8). They each converge on the death receptor-mediated apoptosis pathway via multiple mechanisms, including regulation of the death receptors via protein translation (9, 10) and degradation (7, 11), as well as membrane localization via glycosylation (12). Other mechanisms include upregulation of competing pro-survival BH3 proteins (5), and regulation of DISC and apoptosome inhibitory molecules such as cellular FLICE-like inhibitory protein (FLIP) and the inhibitor of apoptosis protein, XIAP, respectively (13–15). All of these well-established resistance mechanisms involve interference with the core components of the TRAIL signaling pathway and many of these mechanisms affect any apoptosis signal. Insight into TRAIL-mediated resistance may reveal which combination therapies will be effective alongside TRAIL activation. In some cases, such as with proteasome inhibitors, it may be possible to overcome a broad range of resistance mechanisms (16).

Because tumor cells can utilize numerous and often unanticipated means to evade cell death, unbiased approaches can be useful to identify novel pathways that, when inhibited, may synergize with TRAIL activation. In this study, we used a genome-wide loss-of-function approach to discover novel mediators of TRAIL resistance. Using a lentiviral shRNA library designed to target the entire human genome, we discovered novel genes that when lost in TRAIL-resistant cells, can resensitize these cells to TRAIL agonists. These genes include, among others, the sulfate transporter SLC26A2. Here, we demonstrate that this novel mediator of TRAIL resistance evades apoptosis through its ability to downregulate the TRAIL receptors DR4 and DR5. Furthermore, we show that the expression of SLC26A2 is correlated with worsened prognosis in breast cancer.

Genome-wide loss-of-function shRNA screen

For the genome wide shRNA screen (17), the HIV-based GeneNet lentiviral Human 50 K library [pSIH1-H1-Puro, System Biosciences (SBI)], containing 200,000 shRNAs (three to five shRNAs for each of about 47,000 genes) with sequencing tags, was used with minor modifications to the manufacturer's protocol. Briefly, 2.5 × 106 BJAB-LexR cells, a subline of BJABs that have been made resistant to TRAIL and lexatumumab (an agonistic TRAIL-DR5 antibody; ref. 18) through long-term culture in increasing doses of lexatumumab were transduced with the library virus particles together with polybrene (final concentration 8 μg/mL). Parental, TRAIL-sensitive BJAB cells were transduced or mock transduced in parallel and exposed to puromycin selection to confirm transduction efficiency. One-week posttransduction, 90 × 106 cells were set up at a concentration of 0.5 million cells/mL and 24 hours later exposed to selection with recombinant TRAIL (Genentech) at a concentration of 100 ng/mL. Untreated library-transduced cells and untreated nontransduced cells were cultured in parallel. After TRAIL selection for 24 hours, the cells were washed and cultured for an additional 3 days before the cells were harvested and total RNA was isolated from transduced TRAIL-treated and untreated cells as well as nontransduced cells (negative control for next step vector DNA preparation) using TRIZOL (Thermo Fisher Scientific). The RNA was reverse transcribed using the vector-specific cDNA synthesis GNF/GNH primer from SBI and M-MLV reverse transcriptase (Epicentre) and then amplified by nested PCR, using a first-step amplification with Fwd GNF/GNH primer (5′-TGC ATG TCG CTA TGT GTT CTG GGA-3′) and Rev GNF/GNH primer (5′-CTC CCA GGC TCA GAT CTG GTC TAA-3′; SBI) and then a second-step amplification step with NRev Illumina (5′-CAA GCA GAA GAC GGC ATA CGA AGA AGC AAA AAG CAG AAT CGA AGA A-3′)and NFw Illumina-specific primers (5′-AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT CTT TCC CTA CAC GAC GCT TCC TGT CAG A-3′) containing Illumina-specific adapter sequences. The cDNA was quantified using a Bioanalyzer (Agilent) and sequenced on an Illumina Genome Analyzer (Illumina) using a specific sequencing primer (5′-ACA CTC TTT CCC TAC ACG ACG CTTCCT GCT AGA-3′), and the number of clusters for each shRNA sequence was identified.

Genome-wide loss-of-function shRNA screening analysis

The genome-wide loss-of-function shRNA screening data were analyzed using BiNGS! (Bioinformatics for Next Generation Sequencing; ref. 19) as previously described and validated (17, 20–22). In brief, sequencing data were mapped against the shRNA reference library using Bowtie (23). We then used negative binomial to model the count distribution in the sequencing data using edgeR (24). We computed the q-value of FDR for multiple comparisons for these shRNAs, and performed meta-analysis by combining adjusted P-values for all shRNAs representing the same gene using weighted Z-transformation (19). We used the associated P-value [P(wZ)] to sort lists of genes with differentially represented shRNAs.

Secondary screen

For the secondary screen, a custom-made pooled “mini-library” of lentiviral HIV-based shRNA plasmids (pLKO.1) were ordered from the Functional Genomics Facilities, University of Colorado at Boulder. For each of 183 genes, 2 to 5 shRNAs were ordered. On Day 1, HEK293FT cells were plated in antibiotic-free DMEM media on 15 cm plates to achieve 60–80% confluency on the day of transfection, Day 2. The mini shRNA library was transfected into the cells along with packaging vectors pVSV-G, pPACKH1-GAG and pPACKH1-REV in the presence of Turbofect (Fermenta). In parallel, transfections were performed with a negative control vector and with a GFP-vector for confirming transduction efficiency. At Day 3, the media was replaced and at Day 4 and 5, virus particles were harvested. BJAB-LexR cells and BJAB cells were infected with the virus particles and cells were left untreated or were treated with recombinant TRAIL as described above. Genomic DNA was isolated using Qiagen DNeasy blood and tissue (Qiagen, cat. no. 69504). Amplification was performed by PCR with specific pLKO primers (Fwd: 5′-CTT GTG GAA AGG ACG AAA CAC CG-3′, Rev: 5′-CCA AAG ATC TCT GCT GCT C-S3′_) which were then digested with XhoI and ligated to previously annealed barcode linkers. A second round PCR was performed with Illumina adapter primers left (5′-CAA GCA GAA GAC GGC ATA CGA TGG AAA GGA CGA AAC ACC GG-3′) and right (5′-AAT GAT ACG GCG ACC ACC GAG ATC TAC ACT CTT TCC CTA CAC GAC GCT CTT CCG ATC T-3′). The DNA was quantified on a Bioanalyzer (Agilent) and sequenced on the Illumina Sequencer using custom sequencing primer FGS pLKO primer (5′-ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT-3′)

Gene-by-gene validations

For gene-by-gene validations, two to three shRNAs per gene in lentiviral HIV-based TRC shRNA plasmids (pLKO.1) were ordered from the Functional Genomics Facilities, University of Colorado at Boulder. The shRNAs are ordered by TCR number and the sequences are identical to those of Sigma-Aldrich Mission pLKO.1-puro vectors. Lentiviral particles were generated as described above and transduced into BJAB-LexR cells or MDA-MB231-TRAILR cells as described. As negative controls, nontargeting Mission pLKO.1-puro nontarget viral particles were generated and transduced to cells in parallel.

Cell culture

All cells were cultured at 37°C in humidified air supplemented with 5% CO2. 293FT cells were cultured in DMEM and supplemented with 10% FBS and penicillin/streptomycin. MDA-MB-231 cells were cultured in DMEM supplemented with 5% FBS, insulin, Hepes, and nonessential amino acids (Sigma). BJAB lymphoma cells were cultured in RPMI1640 with 10% FBS. The SLC26A2 expression plasmid purchased from Addgene was a kind gift from Dr. Antonio Rossi (Department of Molecular Medicine, University of Pavia, Section of Biochemistry, Pavia, Italy; ref. 25).

Cell line authentication

The BJAB-wt and LexR cell lines and the MDA-MB-231 SEN and TRAIL R cells were recently profiled (07/2016) to confirm their identity.

qRT-PCR

cDNA synthesis was performed using the iScript Kit (Bio-Rad) from total RNA that was isolated using the RNAeasy Extraction Kit (Qiagen). qRT-PCR reactions were run using ssoFast Evagreen supermix (Bio-Rad) and primers for either SLC26A2, (AGCTCCAAGGGATCATGGGAAAGTTGC, CATACTCAGCTTTCTGGTGTGGTAACAGC), DR4 (TGTACAATCACCGACCTTGACCA, AGCTAAGTCCCTGCACCACGA), DR5 (TCCTGGACTTCCATTTCCTG, TGCAGCCGTAGTCTTGATTG), CRKL, AGTR2, TBX2 (TTCCACAAACTGAAGCTGAC, GCTGTGTAATCTTGTCATTCTG), GAPDH (ACCCAGAAGACTGTGGATGG, TCTAGACGGCAGGTCAGGTC), or 18s (ACCCGTTGAACCCCATTCGTGA, GCCTCACTAAACCATCCAATCGG) with the Bio-Rad CFX96.

Western blot analysis

Whole cell extracts were harvested from equal numbers of cells for all conditions tested. After lysates were sonicated, they were loaded onto acrylamide gels and electophoresed, after which they were transferred to PVDF membranes. After blocking in 5% milk in Tris-buffered saline with 0.1% tween (TBST) for 1 hour, membranes were incubated in primary antibodies overnight with constant rocking at 4°C. After washing in TBST, membranes were incubated in the appropriate HRP-conjugated secondary antibodies for 1 hour at room temperature with constant rocking. The primary antibodies used were SLC26A2 [(1:500, Novus Biologicals 3F6), DR4 (1:500, AbCam #ab8414)], DR5 (1:500, AbCam #ab8416), and β-actin (1:10,000, Sigma-Aldrich).

Cell viability assays

The number of viable cells in transduced and treated cells was assessed using the CellTiter-Glo Luminescent Cell Viability Assay (Promega), a luminescent assay that quantifies ATP which is directly proportional to the number of viable cells, per manufacturer's instructions. Cells were set up at 0.25 × 106 cells/mL in white 96-well plates and 24 hours later serial dilutions of reagents [recombinant TRAIL (Genentech), doxorubicin (Sigma), or etoposide(Sigma)] were added in triplicate. Cells were incubated for 24 hours, luminescence was assayed within 5 hours of adding the CellTiter-Glo using a using a Turner Biosystems Modulus Microplate reader.

Propidium iodine staining

To assess propidium iodine (PI) positive BJAB cells via flow cytometry, we utilized the FITC Annexin-V Apoptosis Detection Kit I (BD Biosciences #556547) according to manufacturer's instructions. Specifically, 0.25 cells/mL were washed with PBS solution and then resuspended in 1× binding buffer to 1 × 106 cells/mL. One hundred microliters of the cell solution was transferred to a culture tube and incubated with 5 μL of FITC-Annexin V and 5 μL of PI (although FITC-Annxin V was not used for the current analysis) for 15 minutes at room temperature in the dark. A total of 400 μL of 1× binding buffer was added to stained cells prior to analysis with a Beckman Coulter FC500. Cells that underwent heat shock at 42° for 1 hour were used as a positive control for dead cells for gating purposes.

Caspase-3/7 apoptosis assay

A total of 1,000 MDA-MB-231 cells were plated in replicates of six in 96-well plates. Twenty-four hours later, cells were treated with serial dilutions of TRAIL and 5 μmol/L CellEvent caspase 3/7 (Invitrogen C10423). Cells were monitored every 2 hours with live cell, in vitro microscopy imaging via Incucyte Zoom (Essen BioScience).

Flow cytometry

Equal numbers of cells for all conditions tested were harvested and spun down via centrifugation and then washed three times in PBS supplemented with 0.5% BSA. Cells were resuspended in 100 μL of Flow Cytometry Staining Buffer (PBS + 1 mmol/L EDTA + 25 mmol/L HEPES pH 7.0 + 1.0% FBS) per 1 × 106 cells and were blocked with 1 μg/1 × 106 cells of anti-mouse IgG (Fc specific) antibody (M4280, Sigma Aldrich) for 15 minutes at room temperature. 10 μL/1 × 106 of PE-conjugated antibodies against DR4 (FAB347P, R&D Systems) and DR5 (FAB6311P, R&D Systems) were then added and allowed to incubate for 30 minutes at room temperature in the dark followed by three washes with flow cytometry staining buffer. The X-median or the geometric mean for percent positive cells was then analyzed with a Beckman Coulter FC500 where populations were gated based on unstained samples for each condition.

Data mining

Publicly available data sets were examined using either Oncomine or KMplot.com. For data analyzed though oncomine, studies with a P-value of 0.05 or greater and a fold change of two or greater were reported. For data analyzed through KMplot (2014 version), probe set 205097_at was used to analyze SLC26A2 expression in systemically untreated breast cancer patients, redundant samples were removed, unbiased data were excluded, and proportional hazards assumptions were checked. Results were split by the “auto select best cut off” and graphed such that the approximate bottom quartile are considered “low” expression and the remaining top three quartiles are considered “high” expression. Results are censored at threshold (26).

Micorarray gene expression data analysis

Parental and TRAIL-resistant MDA-MB-231 cells and WT BJAB and LEXR cells were treated with recombinant TRAIL at 100 ng/mL for 2 hours. RNA was isolated using TRIzol (Thermo Fisher Scientific) and was further purified (including a gDNA removal step) using the RNeasy PLUS Micro Kit (Qiagen). Isolated RNA from these samples were hybridized on Affymetrix HuGene 1.0 ST microarrays according to manufacturer's manual (Affymetrix). Gene expressions were normalized by using the Robust Multiarray Average (RMA; ref. 27) method as previously described using Affymetrix Power Tool. Gene Set Enrichment Analysis (GSEA; ref. 28) was performed on the normalized data using KEGG gene sets. Raw microarray data have been deposited into NCBI Gene Expression Omnibus with GSE82047.

Pathway analysis

Genome-scale integrated analysis of gene networks (GIANT; ref. 29) was used with a confidence cut off of 0.5 and a maximum gene cut off of 20 across all tissue types.

A genome-wide loss-of-function screen in a cell model of acquired TRAIL resistance

To perform a screen to identify novel mechanisms of TRAIL resistance, we selected the Burkitt lymphoma cell line, BJAB, which is inherently sensitive to TRAIL, and its TRAIL-resistant subline, LexR. The BJAB LexR subline was previously made resistant to TRAIL-induced apoptosis via increasing exposure to Lexatumumab, an agonistic antibody to the functional TRAIL receptor, TNF, and has been previously characterized to show a marked increase in cell growth in the presence of Lexatumumab as compared to parental counterparts, BJAB-wild-type (wt) cells (16). A cell viability assay confirmed that BJAB-LexR cells are significantly more resistant to TRAIL than are BJAB-wt cells (Fig. 1A). Importantly, this induced resistance is specific to the TRAIL pathway, as the BJAB-LexR cells do not show increased resistance to other apoptosis inducing chemotherapeutics including doxorubicin or etoposide (Supplementary Fig. S1A and S1B).

Figure 1.

Genome-wide loss-of-function shRNA screening of a TRAIL-resistant lymphoma cell line. Lymphoma cell lines BJAB-LexR (TRAIL resistant) were treated with indicated concentrations of recombinant TRAIL for 24 hours (A). Relative viability compared with untreated cells was determined by CellTiter 96 AQueous Non-Radioactive Cell Proliferation Assay. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA with a Bonferroni posttest. B, Schematic of shRNA screen: BJAB-LexR cells were transduced with a genome-wide lentiviral shRNA library and treated with recombinant TRAIL (100 ng/mL) for 24 hours. C, RNA was extracted after transduction with the shRNA library and pre- and post-treatment with TRAIL and analyzed by deep sequencing. Results show clear differences in the shRNA representation between untreated and TRAIL-treated BJAB-LexR cells. D, Schematic of secondary shRNA screen: BJAB-LexR cells were treated with a lentiviral shRNA sublibrary and treated with recombinant TRAIL (100 ng/mL) for 24 hours. Hits confirmed from the primary and secondary screens were then individually tested with individual shRNAs.

Figure 1.

Genome-wide loss-of-function shRNA screening of a TRAIL-resistant lymphoma cell line. Lymphoma cell lines BJAB-LexR (TRAIL resistant) were treated with indicated concentrations of recombinant TRAIL for 24 hours (A). Relative viability compared with untreated cells was determined by CellTiter 96 AQueous Non-Radioactive Cell Proliferation Assay. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA with a Bonferroni posttest. B, Schematic of shRNA screen: BJAB-LexR cells were transduced with a genome-wide lentiviral shRNA library and treated with recombinant TRAIL (100 ng/mL) for 24 hours. C, RNA was extracted after transduction with the shRNA library and pre- and post-treatment with TRAIL and analyzed by deep sequencing. Results show clear differences in the shRNA representation between untreated and TRAIL-treated BJAB-LexR cells. D, Schematic of secondary shRNA screen: BJAB-LexR cells were treated with a lentiviral shRNA sublibrary and treated with recombinant TRAIL (100 ng/mL) for 24 hours. Hits confirmed from the primary and secondary screens were then individually tested with individual shRNAs.

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Using the BJAB system, we performed a genome-wide loss-of-function screen. To perform the screen, we introduced a library of lentiviral particles expressing shRNAs targeting the entire human genome to BJAB cells with forced resistance to LexR (BJAB-LexR) and then subjected the cells to either no treatment or TRAIL treatment (Fig. 1B). In this screen, over-representation of a particular shRNA after TRAIL treatment implies that the target gene promotes TRAIL-induced apoptosis and, conversely, an under-representation of a certain shRNA after TRAIL treatment implies that the target promotes resistance against TRAIL-induced apoptosis. As shown in Fig. 1C, there were clear differences in the representation of different shRNAs before and after TRAIL treatment, indicating that selection of certain shRNAs has, in fact, taken place. After analysis of the altered shRNAs in the BJAB-LexR TRAIL–treated and untreated cells, we identified 580 candidate resistance genes that were within the statistical cut-off, E < 2. We selected 182 candidates from this gene list, and constructed a secondary library consisting of pooled shRNAs (2 to 5 shRNAs per gene) specifically targeting these genes. The top candidates confirmed in this secondary screen were then validated using specific shRNAs. We confirmed that knockdown (KD) of Angiotensin II Receptor 2 (AGTR2), Crk-like protein (CRKL), T-Box Transcription Factor 2 (TBX2), and solute carrier family 26 (anion exchanger), member 2 (SLC26A2) in LexR cells (Fig. 2A–D) significantly reduced TRAIL resistance (Fig. 2E–H) with the reduction of resistance correlating to the level of KD seen with the two shRNAs.

Figure 2.

AGTR2, CRKL, TBX2, and SLC26A2 mediate resistance to TRAIL in BJAB cells. TRAIL-resistant BJAB-LexR cells were lentivirally transduced with either a control, nontargeting shRNA vector (nontargeting) or with either of two different individual shRNAs (sh1, sh2) targeting AGTR2 (A), CRKL (B), TBX2 (C), and SLC26A2 (D). KD was determined by qRT-PCR. The expression is relative to the expression of GAPDH and was normalized to nontargeting shRNA vector-expressing cells. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA with a Bonferroni posttest. (E-H) BJAB-LexR cells transduced with either nontargeting shRNA vector or with either of two different individual shRNAs targeting AGTR2 (E), CRKL (F), TBX2 (G), and SLC26A2 (H) were treated with varying concentrations of recombinant TRAIL for 24 hours. Survival as a percentage of untreated cells was determined by CellTiter-Glo Luminescent Cell Viability Assay. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA.

Figure 2.

AGTR2, CRKL, TBX2, and SLC26A2 mediate resistance to TRAIL in BJAB cells. TRAIL-resistant BJAB-LexR cells were lentivirally transduced with either a control, nontargeting shRNA vector (nontargeting) or with either of two different individual shRNAs (sh1, sh2) targeting AGTR2 (A), CRKL (B), TBX2 (C), and SLC26A2 (D). KD was determined by qRT-PCR. The expression is relative to the expression of GAPDH and was normalized to nontargeting shRNA vector-expressing cells. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA with a Bonferroni posttest. (E-H) BJAB-LexR cells transduced with either nontargeting shRNA vector or with either of two different individual shRNAs targeting AGTR2 (E), CRKL (F), TBX2 (G), and SLC26A2 (H) were treated with varying concentrations of recombinant TRAIL for 24 hours. Survival as a percentage of untreated cells was determined by CellTiter-Glo Luminescent Cell Viability Assay. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA.

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Novel TRAIL-resistant genes identified in a lymphoma model also mediate resistance in breast cancer

Next, we asked whether the effects of AGTR2, CRKL, TBX2, and SLC26A2 on TRAIL resistance were unique to BJAB cells or if similar results could be observed in other tumor types. To this end, we developed a similar model of TRAIL resistance in MDA-MB-231 breast cancer cells through long-term exposure to increasing concentrations of a recombinant TRAIL ligand, generating a TRAIL-resistant subline, 231-TRAILR. 231-TRAILR cells are resistant to TRAIL specifically, but are not generally resistant to apoptosis because they retain sensitivity to doxorubicin and etoposide, similar to that of the parental cells (Fig. 3A; Supplementary Fig. S2A and S2B). In concordance with what we found in the BJAB-LexR system, knocking down AGTR2, CRKL, TBX2, and SLC26A2 (Fig. 3B–E), all rendered the MDA-MB-231-TRAILR breast cancer cells significantly more sensitive to TRAIL-induced apoptosis (Fig. 3F–I).

Figure 3.

AGTR2, CRKL, TBX2, and SLC26A2 mediate resistance to TRAIL in MDA-MB-231 breast cancer cells. A, Parental, TRAIL-sensitive MDA-MB-231 cells (231 SEN) and MDA-MB-231 cells made TRAIL resistant through long-term culture in increasing concentrations of recombinant TRAIL (231 TRAILR) were treated at indicated concentrations of recombinant TRAIL for 24 hours. Relative viability compared with untreated cells was determined by CellTiter-Glo Luminescent Cell Viability Assay. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA with a Bonferroni posttest. B–E, 231 TRAILR cells were lentivirally transduced with either a control, nontargeting shRNA vector (nontargeting), or with either of two different individual shRNAs (sh1, sh2) targeting AGTR2 (B), CRKL (C), TBX2 (D), and SLC26A2 (E). KD was determined by qRT-PCR. The expression is relative to the expression of GAPDH and was normalized to nontargeting shRNA vector-expressing cells. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA with a Bonferroni posttest. F–I, 231 TRAILR cells transduced with either a control, nontargeting shRNA vector (nontargeting) or with either of two different individual shRNAs (sh1, sh2) targeting AGTR2 (F), CRKL (G), TBX2 (H), and SLC26A2 (I) were treated with varying concentrations of recombinant TRAIL for 24 hours. Survival as a percentage of untreated cells was determined by CellTiter-Glo Luminescent Cell Viability Assay. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA.

Figure 3.

AGTR2, CRKL, TBX2, and SLC26A2 mediate resistance to TRAIL in MDA-MB-231 breast cancer cells. A, Parental, TRAIL-sensitive MDA-MB-231 cells (231 SEN) and MDA-MB-231 cells made TRAIL resistant through long-term culture in increasing concentrations of recombinant TRAIL (231 TRAILR) were treated at indicated concentrations of recombinant TRAIL for 24 hours. Relative viability compared with untreated cells was determined by CellTiter-Glo Luminescent Cell Viability Assay. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA with a Bonferroni posttest. B–E, 231 TRAILR cells were lentivirally transduced with either a control, nontargeting shRNA vector (nontargeting), or with either of two different individual shRNAs (sh1, sh2) targeting AGTR2 (B), CRKL (C), TBX2 (D), and SLC26A2 (E). KD was determined by qRT-PCR. The expression is relative to the expression of GAPDH and was normalized to nontargeting shRNA vector-expressing cells. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA with a Bonferroni posttest. F–I, 231 TRAILR cells transduced with either a control, nontargeting shRNA vector (nontargeting) or with either of two different individual shRNAs (sh1, sh2) targeting AGTR2 (F), CRKL (G), TBX2 (H), and SLC26A2 (I) were treated with varying concentrations of recombinant TRAIL for 24 hours. Survival as a percentage of untreated cells was determined by CellTiter-Glo Luminescent Cell Viability Assay. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA.

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Of the genes identified, the sulfate transporter, SLC26A2, had the most robust effect when assessed across both systems. This gene is understudied in the context of cancer or drug resistance, and thus we decided to pursue it further. To better explore the mechanism by which SLC26A2 mediates resistance in breast cancer cells, we established clonal isolate sublines of the 231-TRAILR line with stable KD of SLC26A2 expression using two different shRNA constructs. shRNA1 (sh1) targets SLC26A2 in the 3′UTR of the gene, whereas shRNA2 (sh2) targets the coding region. KD of SLC26A2 with either of these constructs selectively enhanced TRAIL sensitivity in 231-TRAILR cells, but did not affect sensitivity to other general chemotherapeutics including doxorubicin and etoposide (Fig. 4A and B; Supplementary Fig. S3A and S3B). As a test of the specificity of the KD, we rescued SLC26A2 expression with a construct containing only the coding sequence. This construct restored TRAIL resistance in the cells expressing the shRNA1 KD because it lacks the 3′UTR and thus is resistant to shRNA1 KD, but did not restore resistance in cells expressing the shRNA2 construct that targets the coding region (Fig. 4A and B), thus demonstrating that the KD of SLC26A2 is sequence-specific. Taken together, these data demonstrate that SLC26A2 is a novel mediator of TRAIL resistance.

Figure 4.

TRAIL-resistant MDA-MB-231 cells are resensitized to TRAIL by KD of SLC26A2. 231 SEN and 231 TRAILR cells transduced with a nontargeting control shRNA or with shRNA 1 that targets SLC26A2 in the 3′UTR of the gene (RES + sh1; A), such that the shRNA will not KD exogenous SLC26A2 or with shRNA 2 that targets SLC26A2 in the coding region and can target both exogenous and endogenous proteins were treated with indicated concentrations of recombinant TRAIL for 24 hours (B). Survival as a percentage of untreated cells was determined by CellTiter-Glo Luminescent Cell Viability Assay. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA with a Bonferroni posttest.

Figure 4.

TRAIL-resistant MDA-MB-231 cells are resensitized to TRAIL by KD of SLC26A2. 231 SEN and 231 TRAILR cells transduced with a nontargeting control shRNA or with shRNA 1 that targets SLC26A2 in the 3′UTR of the gene (RES + sh1; A), such that the shRNA will not KD exogenous SLC26A2 or with shRNA 2 that targets SLC26A2 in the coding region and can target both exogenous and endogenous proteins were treated with indicated concentrations of recombinant TRAIL for 24 hours (B). Survival as a percentage of untreated cells was determined by CellTiter-Glo Luminescent Cell Viability Assay. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by two-way ANOVA with a Bonferroni posttest.

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KD of SLC26A2 leads to enhanced expression of DR4 and DR5

To determine if the increase in viability observed with SLC26A2 KD in LEXR cells was due to cell death, we stained BJAB-LEXR cells with PI, a nuclear stain that is excluded by live cells and therefore indicative of late-apoptotic or necrotic cells. Uptake of PI, corresponding to cell death was quantified by flow cytometry. Cell death was dramatically increased in both BJAB-SLC26A2 KD lines as compared to the nontargeting control line (Fig. 5A). In the adherent MDA-MB-231 cell lines, we performed an activated-caspase 3/7 assay by measuring green fluorescence with Incucyte live cell imaging after the addition of CellEvent caspase-3/7 and TRAIL. Indeed, we observed a significant increase in activated caspase 3/7 in the MDA-MB-231 TRAILR SLC26A2 KD lines as compared to the nontargeting control (Fig. 5B). Taken together, these results indicate that the decrease of viable cells observed with loss of SLC26A2 is due to increased apoptosis in TRAIL-resistant cell lines.

Figure 5.

SLC26A2 regulates the TRAIL receptors, DR4 and DR5, in MDA-MB-231 cells. A, Flow cytometry analysis in BJAB-LEXR cells with stable, lentiviral, shRNAs against SLC26A2 or a nontargeting shRNA after 24 hours of treatment with TRAIL (20 ng/mL) to assess PI-positive cells as an indication of dead or late apoptotic cells. Representative experiment is shown (of two experiments). B, MDA-231 SENS and TRAILR cells plated in replicates of six were treated with TRAIL (100 ng/mL) and a green fluorogenic substrate for activated-caspase-3/7. Green fluorescence was measured every 2 hours with 100 ng/mL TRAIL by light microscopy using real-time in vitro imaging. Statistical significance assessed by two-way ANOVA with a Tukey posttest. A representative figure is shown (of two experiments). C, Triplicate samples of 231 SEN and 231 TRAILR were analyzed for RNA expression of SLC26A2, DR4, and DR5, using microarray analysis. The expression is presented as a heatmap relative to an average expression of a combination of constitutive housekeeping genes. RNA and protein were isolated from 231 SEN cells and 231 TRAILR cells as well as 231 TRAILR cells transduced with a nontargeting control shRNA (noncoding) or with either of two shRNA targeting SLC26A2 (SLC26A2 sh1 and SLC26A2 sh2, respectively). mRNA expression of SLC26A2 (D), DR4 (E), and DR5 (F) was determined by qRT-PCR. The expression is relative to the expression of GAPDH and was normalized to the nontargeting shRNA vector-expressing cells. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by one-way ANOVA. Protein expression of these genes as well as β-actin was analyzed by Western blot analysis as shown in G. Surface expression of DR4 and DR5 was analyzed by flow cytometry as depicted in H and I, respectively. Representative experiments are shown (of ≥3 experiments). Statistical significance assessed by one-way ANOVA.

Figure 5.

SLC26A2 regulates the TRAIL receptors, DR4 and DR5, in MDA-MB-231 cells. A, Flow cytometry analysis in BJAB-LEXR cells with stable, lentiviral, shRNAs against SLC26A2 or a nontargeting shRNA after 24 hours of treatment with TRAIL (20 ng/mL) to assess PI-positive cells as an indication of dead or late apoptotic cells. Representative experiment is shown (of two experiments). B, MDA-231 SENS and TRAILR cells plated in replicates of six were treated with TRAIL (100 ng/mL) and a green fluorogenic substrate for activated-caspase-3/7. Green fluorescence was measured every 2 hours with 100 ng/mL TRAIL by light microscopy using real-time in vitro imaging. Statistical significance assessed by two-way ANOVA with a Tukey posttest. A representative figure is shown (of two experiments). C, Triplicate samples of 231 SEN and 231 TRAILR were analyzed for RNA expression of SLC26A2, DR4, and DR5, using microarray analysis. The expression is presented as a heatmap relative to an average expression of a combination of constitutive housekeeping genes. RNA and protein were isolated from 231 SEN cells and 231 TRAILR cells as well as 231 TRAILR cells transduced with a nontargeting control shRNA (noncoding) or with either of two shRNA targeting SLC26A2 (SLC26A2 sh1 and SLC26A2 sh2, respectively). mRNA expression of SLC26A2 (D), DR4 (E), and DR5 (F) was determined by qRT-PCR. The expression is relative to the expression of GAPDH and was normalized to the nontargeting shRNA vector-expressing cells. Representative experiments are shown (of ≥3 experiments) with each experiment performed in triplicate. Statistical significance assessed by one-way ANOVA. Protein expression of these genes as well as β-actin was analyzed by Western blot analysis as shown in G. Surface expression of DR4 and DR5 was analyzed by flow cytometry as depicted in H and I, respectively. Representative experiments are shown (of ≥3 experiments). Statistical significance assessed by one-way ANOVA.

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To understand mechanistically how SLC26A2 could affect cell death, we performed gene expression analysis in the MDA-MB-231 SENS and TRAILR cell lines and cross-referenced this data with that of our shRNA screen. This analysis showed increased SLC26A2 expression in the MDA-MB-231-TRAILR cell line as compared with its sensitive counterpart (Fig. 5C). Interestingly, we also noted that expression of the TRAIL receptors, DR4 and DR5, were decreased in the array analyses (Fig. 5C). The increase of SLC26A2 and concomitant decrease of DR4 and DR5 expression in MDA-MB-231-TRAILR cells when compared to their sensitive counterparts was confirmed using qRT-PCR (Fig. 5D–F) and Western blot analysis (Fig. 5G). Importantly, there was also reduced surface protein expression of DR4 and DR5 as assessed by flow cytometry in the MDA-MB-231-TRAILR cells as compared to the SEN cells (Fig. 5H and I). These results were confirmed in the BJAB cell system, where we again observed an increase in SLC26A2 mRNA expression and a corresponding decrease in surface protein expression of DR4 and DR5 in the BJAB-LexR cells as compared to the BJAB-wt cells (Supplementary Fig. S4A–S4C).

Because KD of SLC26A2 reversed resistance to TRAIL and because downregulation of DR4 and DR5, crucial mediators of TRAIL apoptosis, is associated with acquired TRAIL resistance, we next assessed whether TRAIL sensitivity, SLC26A2 expression, and expression of DR4 and DR5 were linked. To this end, we analyzed the expression of these receptors in MDA-MB-231-TRAILR sublines with stable SLC26A2 KD. Notably, TRAIL-resistant MDA-MB-231 cells transduced with nontargeting shRNA had approximately the same DR4 and DR5 expression as nontransduced TRAIL-resistant cells. However, in MDA-MB-231-TRAILR cells transduced with shRNAs targeting SLC26A2, the mRNA, protein in whole cell lysates, and surface protein levels of DR4 and DR5 were restored (Fig. 5H and I). Together, these data demonstrate that SLC26A2 is regulating the overall levels of DR4 and DR5, and more importantly, surface levels of DR4 and DR5.

SLC26A2 expression correlates with worsened disease in human tumors

Because many tumors are known to be resistant to TRAIL, we asked whether the levels of SLC26A2 may be elevated in human tumors, as a means to induce TRAIL resistance. Indeed, numerous public gene expression datasets, spanning multiple tumor types, show a significant increase in SLC26A2 expression in tumor as compared to normal tissue (Table 1; ref. 30–40). In addition, elevated SLC26A2 expression correlates with metastasis or worsened prognosis in numerous tumor types (41–47). Importantly, in a cohort of 1,000 untreated breast cancer patients (taken from multiple studies using KMplot; ref. 26), high SLC26A2 expression correlated with a significant decrease in relapse free survival and almost significant decrease in distant metastasis free survival (Fig. 6 and Supplementary Fig. S5). Together, these data demonstrate that SLC26A2 is a marker for cancer versus normal as well as an indicator for poor prognosis in breast cancer and other cancers, and suggest that it may regulate tumor progression at least in part via its ability to mediate TRAIL resistance.

Table 1.

SLC26A2 overexpression is correlated with worsened disease

Tumor typeDifferential expressionFold changePDataset
Breast Metastatic event at 1 year vs. no event at 1 year 2.282 9.88E−10 Minn Breast 2 
Breast Metastatic site vs. primary site 2.485 0.005 Weigelt Breast 
Brain Glioblastoma dead at 5 years vs. alive 2.531 6.02E−4 Nutt Brain 
Lung Lung adenocarcioma - advanced N stage 2.125 0.001 Beer Lung 
Breast Ductal breast carcinoma- ERBB2/ER/PR negative vs. positive 5.313 5.75E−6 Richardson Breast 2 
Breast Invasive ductal breast carcinoma stroma vs. normal 2.214 8.23E−5 Karnoub Breast 
Colon Colon adenocarcinoma dead at 1 year vs. alive 2.695 0.003 TCGA Colorectal 
Brain Glioblastoma vs. normal 2.002 6.38E−18 Sun Brain 
Lymphoma Anaplastic large cell lymphoma vs. normal 5.641 2.79E−8 Piccaluga Lymphoma 
Lymphoma Angioimmunoblastiv T-cell lymphoma vs. normal 3.689 5.32E−8 Piccaluga Lymphoma 
Lymphoma Unspecified peripheral T-cell lymphoma vs. normal 3.489 6.22E−13 Piccaluga Lymphoma 
Ovarian Ovarian clear cell adenocarcinoma vs. normal 2.353 8.41E−4 Lu Ovarain 
Prostate Prostatic intraepithelial neoplasia epithelia vs. normal 3.646 1.53E−4 Tomlins Prostate 
Liver Hepatocellular carcinoma vs. normal 3.239 5.64E−5 Wurmbach Liver 
Renal Nonhereditary clear cell renal carcinoma vs. normal 2.371 6.45E−7 Beroukhim Renal 
Melanoma Skin basal cell carcinoma vs. normal 2.337 2.15E−4 Riker Melanoma 
Melanoma Metastaic site vs. primary site 2.079 5.01E−5 Xu Melanoma 
Renal Renal Wilms tumor vs. normal 2.210 0.004 Yusenko Renal 
Brain Glioblastoma vs. normal 2.328 5.46E−5 Murat Brain 
Brain Anaplastic oligodendroglioma vs. normal 2.384 1.86E−5 French Brain 
Tumor typeDifferential expressionFold changePDataset
Breast Metastatic event at 1 year vs. no event at 1 year 2.282 9.88E−10 Minn Breast 2 
Breast Metastatic site vs. primary site 2.485 0.005 Weigelt Breast 
Brain Glioblastoma dead at 5 years vs. alive 2.531 6.02E−4 Nutt Brain 
Lung Lung adenocarcioma - advanced N stage 2.125 0.001 Beer Lung 
Breast Ductal breast carcinoma- ERBB2/ER/PR negative vs. positive 5.313 5.75E−6 Richardson Breast 2 
Breast Invasive ductal breast carcinoma stroma vs. normal 2.214 8.23E−5 Karnoub Breast 
Colon Colon adenocarcinoma dead at 1 year vs. alive 2.695 0.003 TCGA Colorectal 
Brain Glioblastoma vs. normal 2.002 6.38E−18 Sun Brain 
Lymphoma Anaplastic large cell lymphoma vs. normal 5.641 2.79E−8 Piccaluga Lymphoma 
Lymphoma Angioimmunoblastiv T-cell lymphoma vs. normal 3.689 5.32E−8 Piccaluga Lymphoma 
Lymphoma Unspecified peripheral T-cell lymphoma vs. normal 3.489 6.22E−13 Piccaluga Lymphoma 
Ovarian Ovarian clear cell adenocarcinoma vs. normal 2.353 8.41E−4 Lu Ovarain 
Prostate Prostatic intraepithelial neoplasia epithelia vs. normal 3.646 1.53E−4 Tomlins Prostate 
Liver Hepatocellular carcinoma vs. normal 3.239 5.64E−5 Wurmbach Liver 
Renal Nonhereditary clear cell renal carcinoma vs. normal 2.371 6.45E−7 Beroukhim Renal 
Melanoma Skin basal cell carcinoma vs. normal 2.337 2.15E−4 Riker Melanoma 
Melanoma Metastaic site vs. primary site 2.079 5.01E−5 Xu Melanoma 
Renal Renal Wilms tumor vs. normal 2.210 0.004 Yusenko Renal 
Brain Glioblastoma vs. normal 2.328 5.46E−5 Murat Brain 
Brain Anaplastic oligodendroglioma vs. normal 2.384 1.86E−5 French Brain 
Figure 6.

SLC26A2 expression correlates with worsened prognosis in breast cancer. With the use of KMplot (53), 1,000 untreated breast cancer patients were divided into two groups with either high SLC26A2 expression (approximately top 3 quartiles) or low SLC26A2 expression (approximately bottom quartile). Patients with high SLC26A2 expression have a decreased probability of relapse-free survival.

Figure 6.

SLC26A2 expression correlates with worsened prognosis in breast cancer. With the use of KMplot (53), 1,000 untreated breast cancer patients were divided into two groups with either high SLC26A2 expression (approximately top 3 quartiles) or low SLC26A2 expression (approximately bottom quartile). Patients with high SLC26A2 expression have a decreased probability of relapse-free survival.

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Much enthusiasm initially surrounded the promise of TRAIL agonist therapy as a specific means to target cancers. However, the lack of anticancer activity in a significant number of patients displayed during phase II clinical trials has been disappointing (5). The last decade has revealed insight into multiple missteps that occurred with the initial TRAIL agonist therapies, most specifically because there are many routes of resistance that tumors can acquire to escape death induced by TRAIL, and it is critical to both develop strategies to circumvent resistance and identify upfront the most likely responders for successful use of TRAIL receptor–targeted therapies. Despite this new understanding, neither a successful combination therapy nor the use of biomarkers for TRAIL sensitivity, to identify the patients who are likely to benefit, have moved into clinical trials.

To find novel resistance markers and obtain a more general view of TRAIL resistance mechanisms beyond the known mechanisms that generally involve direct participants in the core TRAIL pathway, we performed a genome wide shRNA loss of function screen in TRAIL-resistant cell lines (Fig. 1). This unbiased approach allowed for the discovery of unique genes that were not previously linked to TRAIL signaling or drug resistance in general. Using gene-specific shRNAs in two different cell line systems (encompassing different tumor types), we confirmed that downregulation of four specific genes resensitize resistant cell lines to TRAIL agonist therapy (AGTR2, CRKL, TBX2, and SLC26A2; Figs. 2 and 3). Importantly, none of these genes are involved in the core TRAIL signaling pathway, nor are they general regulators of apoptosis, emphasizing the ability of the genome wide unbiased screen to find new unanticipated regulators of TRAIL resistance.

Interestingly, we discovered a connection between these seemingly unrelated TRAIL resistance genes discovered in our screen. This connection was discovered by performing genome-scale integrated-analysis of gene networks (GIANT) across all tissues (29). Restricting the analysis to highly significant relationships (relationship confidence >0.5) with a maximum number of 20 genes to form the network, we found that all four genes are connected (Fig. 7A) through a functional network that is significantly associated with a number of pathways, processes, and diseases, the majority of which are relevant to cancer (Supplementary Table S1). Interestingly, microarray analysis of BJAB WT and LEXR TRAIL-treated samples as well as MDA-231 SENS and TRAILR TRAIL-treated samples, followed by GSEA of significantly changed genes reveals that a number of similar pathways are enriched (Supplementary Table S2 and S3). In particular, the ErbB/EGFR signaling pathway (or KEGG pathways that contain large portions of this pathway) is significantly associated with the functional network created between the four confirmed genes, as well as in the microarray analyses from both the BJAB and MDA-MB-231 cell lines (Fig. 7B and C). These results strongly suggest that ErbB signaling may be relevant for TRAIL resistance. Importantly, downregulation of ErbB/EGFR has been previously associated with increasing TRAIL sensitivity. Specifically, decreased expression of ErbB with either trastuzumab or antisense oligodeoxynucleotides sensitizes ErbB overexpressing breast and ovarian cells to TRAIL-mediated apoptosis (48). Additional studies have mechanistically linked EGFR to TRAIL resistance via the Bcl-2 family member myeloid cell leukemia 1 (Mcl-1; ref. 49) as well as cytochrome C release and caspase-3–like activation (50).

Figure 7.

Pathway analysis identifies a functional network between AGTR2, CRKL, TBX2, and SLC26A2. A, Use of GIANT (29) with AGTR2, CRKL, TBX2, and SLC26A2 as inputs analyzed across all tissue types (relationship confidence > 0.5 and max number of genes set to 20). B and C, Microarray analyses were performed on mRNA from BJAB WT and LEXR samples (B) and MDA-231 SENS and TRAILR samples after treatment with TRAIL (100 ng/mL), followed by GSEA analysis (C). B, The KEGG pancreatic signaling pathway (consisting mostly of ErbB/EGFR signaling pathways) is displayed with core genes upregulated (enriched) in the trail-treated BJAB-LEXR samples as compared with TRAIL-treated BJAB-WT samples in red. C, The KEGG ErbB signaling pathway is displayed with core genes upregulated (enriched) in the TRAIL-treated MDA-231 TRAILR samples as compared with the TRAIL-treated MDA-231 SENS samples.

Figure 7.

Pathway analysis identifies a functional network between AGTR2, CRKL, TBX2, and SLC26A2. A, Use of GIANT (29) with AGTR2, CRKL, TBX2, and SLC26A2 as inputs analyzed across all tissue types (relationship confidence > 0.5 and max number of genes set to 20). B and C, Microarray analyses were performed on mRNA from BJAB WT and LEXR samples (B) and MDA-231 SENS and TRAILR samples after treatment with TRAIL (100 ng/mL), followed by GSEA analysis (C). B, The KEGG pancreatic signaling pathway (consisting mostly of ErbB/EGFR signaling pathways) is displayed with core genes upregulated (enriched) in the trail-treated BJAB-LEXR samples as compared with TRAIL-treated BJAB-WT samples in red. C, The KEGG ErbB signaling pathway is displayed with core genes upregulated (enriched) in the TRAIL-treated MDA-231 TRAILR samples as compared with the TRAIL-treated MDA-231 SENS samples.

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Although numerous potential TRAIL-resistant genes were identified in our screen, here we focused on the anion exchange channel SLC26A2. We demonstrate that knocking down this gene, which has never been previously been implicated in cancer drug sensitivity, significantly resensitizes resistant cells to TRAIL agonist therapy in both lymphoma cells and breast cancer cells. The effect was not a general proapoptotic effect as apoptosis induced by other apoptosis-inducing agents were not affected (Fig. S3). Moreover, when SLC26A2 was reintroduced into cells with knocked down SLC26A2, the cells became resensitized to TRAIL-induced apoptosis indicating that expression of this gene is both necessary and sufficient to confer selective TRAIL resistance (Fig. 4). Expression of SLC26A2 led to downregulation of the death receptors DR4 and DR5, suggesting a plausible mechanism by which SLC26A2 counteracts TRAIL-induced apoptosis (Fig. 5C-I).

Although SLC26A2 has not previously been associated with pro-tumorigenic phenotypes, examination of tumor microarray datasets reveals increased expression of SLC26A2 in tumor tissue as compared with normal tissue across multiple tumor types including Wilms tumor, glioblastoma, lymphoma, ovarian, breast, hepatocellular, skin, and renal carcinoma (Table 1; refs. 30–47). Furthermore, other datasets show a correlation between elevated SLC26A2 expression and worsened prognosis or metastasis, particularly in glioblastoma, melanoma, breast, and lung cancer (Table 1 and Fig. 6). As SLC26A2 is understudied, there are no known specific inhibitors to block this channel, although other anion channel inhibitors have already moved into clinical trials for the treatment of cancer, suggesting that targeting SLC26A2 may be feasible (51).

TRAIL receptor-targeted therapy has so far been disappointing in the clinic, underscoring how inadequate understanding of potential resistance mechanisms prior to starting clinical studies can contribute to the failure of targeted therapies (52). Innovative, genome-wide studies, such as that described here, provide a way to gain a comprehensive understanding of potential resistance mechanisms that cancer cells can employ to evade TRAIL-induced apoptosis, or to evade apoptosis induced by other targeted agents. In addition, these approaches can also uncover strategic combinational therapies that could synergize with TRAIL agonist therapies in clinic. Although the majority of patients seem unresponsive to TRAIL monotherapies, survival curves indicate that a small percentage of patients do benefit. Development of a panel of strategic molecular biomarkers using the approaches described here may allow for a way to better identify those patients who may benefit from TRAIL receptor-targeted therapies.

No potential conflicts of interest were disclosed.

Conception and design: L.Y. Dimberg, K. Behbakht, A. Thorburn, H.L. Ford

Development of methodology: L.Y. Dimberg, A.-C. Tan, H.L. Ford

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): L.Y. Dimberg, C.G. Towers, K. Behbakht, T.J. Hotz, C.C. Porter, A.-C. Tan

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): L.Y. Dimberg, C.G. Towers, J. Kim, A.-C. Tan, H.L. Ford

Writing, review, and/or revision of the manuscript: L.Y. Dimberg, C.G. Towers, K. Behbakht, J. Kim, C.C. Porter, A.-C. Tan, A. Thorburn, H.L. Ford

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S. Fosmire

Study supervision: L.Y. Dimberg, H.L. Ford

We gratefully acknowledge the substantial contribution of Joshua Cabrera in the acquisition, analysis, and interpretation of the data presented in this publication. Because Mr. Cabrera passed away before this article was written, listing him as a co-author is not consistent with the policies of the journal. We would also like to thank Jackie Thorburn and Lubna Qamar for technical assistance with experiments performed in this manuscript. Finally, we would like to acknowledge Rani Powers for her insight into GIANT pathway analysis.

This work was supported by NIH GrantCA124545 (to A. Thorburn, K. Behbakht, and H. Ford), Department of Defense (DOD) postdoctoral fellowship BC093627 and Swedish Research Council postdoctoral fellowship 2009-618 (to L. Dimberg), DOD Ovarian Cancer Idea AwardOC06143 (to K. Behbakht), and the Bioscience Discovery and Evaluation Grant (to A. Thorburn, K. Behbakht, and H. Ford). C. Towers was funded by the UC Denver AMC Molecular Biology Program T32 training grant, NIH-RO1 Diversity Supplement to R01-CA157790, and the UNCF/MERCK Graduate Fellowship. The flow cytometry, functional genomics and genomics shared resource were funded by the Cancer Center support grant (CA046934).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Pitti
RM
,
Marsters
SA
,
Ruppert
S
,
Donahue
CJ
,
Moore
A
,
Ashkenazi
A
. 
Induction of apoptosis by Apo-2 ligand, a new member of the tumor necrosis factor cytokine family
.
J Biol Chem
1996
;
271
:
12687
90
.
2.
Wiley
SR
,
Schooley
K
,
Smolak
PJ
,
Din
WS
,
Huang
CP
,
Nicholl
JK
, et al
Identification and characterization of a new member of the TNF family that induces apoptosis
.
Immunity
1995
;
3
:
673
82
.
3.
Ashkenazi
A
,
Pai
RC
,
Fong
S
,
Leung
S
,
Lawrence
DA
,
Marsters
SA
, et al
Safety and antitumor activity of recombinant soluble Apo2 ligand
.
J Clin Invest
1999
;
104
:
155
62
.
4.
Walczak
H
,
Miller
RE
,
Ariail
K
,
Gliniak
B
,
Griffith
TS
,
Kubin
M
, et al
Tumoricidal activity of tumor necrosis factor-related apoptosis-inducing ligand invivo
.
Nat Med
1999
;
5
:
157
63
.
5.
Dimberg
LY
,
Anderson
CK
,
Camidge
R
,
Behbakht
K
,
Thorburn
A
,
Ford
HL
. 
On the TRAIL to successful cancer therapy? Predicting and counteracting resistance against TRAIL-based therapeutics
.
Oncogene
2013
;
32
:
1341
50
.
6.
von Pawel
J
,
Harvey
JH
,
Spigel
DR
,
Dediu
M
,
Reck
M
,
Cebotaru
CL
, et al
Phase II trial of mapatumumab, a fully human agonist monoclonal antibody to tumor necrosis factor-related apoptosis-inducing ligand receptor 1 (TRAIL-R1), in combination with paclitaxel and carboplatin in patients with advanced non-small-cell lung cancer
.
Clin Lung Cancer
2014
;
15
:
188
96
.
7.
Trivedi
R
,
Mishra
DP
. 
Trailing TRAIL resistance: novel targets for TRAIL sensitization in cancer cells
.
Front Oncol
2015
;
5
:
69
.
8.
Gump
JM
,
Staskiewicz
L
,
Morgan
MJ
,
Bamberg
A
,
Riches
DW
,
Thorburn
A
. 
Autophagy variation within a cell population determines cell fate through selective degradation of Fap-1
.
Nat Cell Biol
2014
;
16
:
47
54
.
9.
Graff
JR
,
Konicek
BW
,
Carter
JH
,
Marcusson
EG
. 
Targeting the eukaryotic translation initiation factor 4E for cancer therapy
.
Cancer Res
2008
;
68
:
631
4
.
10.
Fan
S
,
Li
Y
,
Yue
P
,
Khuri
FR
,
Sun
SY
. 
The eIF4E/eIF4G interaction inhibitor 4EGI-1 augments TRAIL-mediated apoptosis through c-FLIP Down-regulation and DR5 induction independent of inhibition of cap-dependent protein translation
.
Neoplasia
2010
;
12
:
346
56
.
11.
Johnson
TR
,
Stone
K
,
Nikrad
M
,
Yeh
T
,
Zong
WX
,
Thompson
CB
, et al
The proteasome inhibitor PS-341 overcomes TRAIL resistance in Bax and caspase 9-negative or Bcl-xL overexpressing cells
.
Oncogene
2003
;
22
:
4953
63
.
12.
Wagner
KW
,
Punnoose
EA
,
Januario
T
,
Lawrence
DA
,
Pitti
RM
,
Lancaster
K
, et al
Death-receptor O-glycosylation controls tumor-cell sensitivity to the proapoptotic ligand Apo2L/TRAIL
.
Nat Med
2007
;
13
:
1070
7
.
13.
Yang
JK
. 
FLIP as an anti-cancer therapeutic target
.
Yonsei Med J
2008
;
49
:
19
27
.
14.
Cummins
JM
,
Kohli
M
,
Rago
C
,
Kinzler
KW
,
Vogelstein
B
,
Bunz
F
. 
X-linked inhibitor of apoptosis protein (XIAP) is a nonredundant modulator of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-mediated apoptosis in human cancer cells
.
Cancer Res
2004
;
64
:
3006
8
.
15.
Ng
CP
,
Bonavida
B
. 
X-linked inhibitor of apoptosis (XIAP) blocks Apo2 ligand/tumor necrosis factor-related apoptosis-inducing ligand-mediated apoptosis of prostate cancer cells in the presence of mitochondrial activation: sensitization by overexpression of second mitochondria-derived activator of caspase/direct IAP-binding protein with low pl (Smac/DIABLO)
.
Mol Cancer Ther
2002
;
1
:
1051
8
.
16.
Menke
C
,
Bin
L
,
Thorburn
J
,
Behbakht
K
,
Ford
HL
,
Thorburn
A
. 
Distinct TRAIL resistance mechanisms can be overcome by proteasome inhibition but not generally by synergizing agents
.
Cancer Res
2011
;
71
:
1883
92
.
17.
Porter
CC
,
Kim
J
,
Fosmire
S
,
Gearheart
CM
,
van Linden
A
,
Baturin
D
, et al
Integrated genomic analyses identify WEE1 as a critical mediator of cell fate and a novel therapeutic target in acute myeloid leukemia
.
Leukemia
2012
;
26
:
1266
76
.
18.
Menke
C
,
Goncharov
T
,
Qamar
L
,
Korch
C
,
Ford
HL
,
Behbakht
K
, et al
TRAIL receptor signaling regulation of chemosensitivity invivo but not invitro
.
PLoS One
2011
;
6
:
e14527
.
19.
Kim
J
,
Tan
AC
. 
BiNGS!SL-seq: a bioinformatics pipeline for the analysis and interpretation of deep sequencing genome-wide synthetic lethal screen
.
Methods Mol Biol
2012
;
802
:
389
98
.
20.
Spreafico
A
,
Tentler
JJ
,
Pitts
TM
,
Tan
AC
,
Gregory
MA
,
Arcaroli
JJ
, et al
Rational combination of a MEK inhibitor, selumetinib, and the Wnt/calcium pathway modulator, cyclosporin A, in preclinical models of colorectal cancer
.
Clin Cancer Res
2013
;
19
:
4149
62
.
21.
Sullivan
KD
,
Padilla-Just
N
,
Henry
RE
,
Porter
CC
,
Kim
J
,
Tentler
JJ
, et al
ATM and MET kinases are synthetic lethal with nongenotoxic activation of p53
.
Nat Chem Biol
2012
;
8
:
646
54
.
22.
Casas-Selves
M
,
Kim
J
,
Zhang
Z
,
Helfrich
BA
,
Gao
D
,
Porter
CC
, et al
Tankyrase and the canonical Wnt pathway protect lung cancer cells from EGFR inhibition
.
Cancer Res
2012
;
72
:
4154
64
.
23.
Langmead
B
,
Trapnell
C
,
Pop
M
,
Salzberg
SL
. 
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome
.
Genome Biol
2009
;
10
:
R25
.
24.
Robinson
MD
,
McCarthy
DJ
,
Smyth
GK
. 
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
.
Bioinformatics
2010
;
26
:
139
40
.
25.
Bonafe
L
,
Hastbacka
J
,
de la Chapelle
A
,
Campos-Xavier
AB
,
Chiesa
C
,
Forlino
A
, et al
A novel mutation in the sulfate transporter gene SLC26A2 (DTDST) specific to the Finnish population causes de la Chapelle dysplasia
.
J Med Genet
2008
;
45
:
827
31
.
26.
Gyorffy
B
,
Lanczky
A
,
Eklund
AC
,
Denkert
C
,
Budczies
J
,
Li
Q
, et al
An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients
.
Breast Cancer Res Treat
2010
;
123
:
725
31
.
27.
Irizarry
RA
,
Hobbs
B
,
Collin
F
,
Beazer-Barclay
YD
,
Antonellis
KJ
,
Scherf
U
, et al
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
.
Biostatistics
2003
;
4
:
249
64
.
28.
Subramanian
A
,
Tamayo
P
,
Mootha
VK
,
Mukherjee
S
,
Ebert
BL
,
Gillette
MA
, et al
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles
.
Proc Natl Acad Sci U S A
2005
;
102
:
15545
50
.
29.
Greene
CS
,
Krishnan
A
,
Wong
AK
,
Ricciotti
E
,
Zelaya
RA
,
Himmelstein
DS
, et al
Understanding multicellular function and disease with human tissue-specific networks
.
Nat Genet
2015
;
47
:
569
76
.
30.
Karnoub
AE
,
Dash
AB
,
Vo
AP
,
Sullivan
A
,
Brooks
MW
,
Bell
GW
, et al
Mesenchymal stem cells within tumour stroma promote breast cancer metastasis
.
Nature
2007
;
449
:
557
63
.
31.
Sun
L
,
Hui
AM
,
Su
Q
,
Vortmeyer
A
,
Kotliarov
Y
,
Pastorino
S
, et al
Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain
.
Cancer Cell
2006
;
9
:
287
300
.
32.
Piccaluga
PP
,
Agostinelli
C
,
Califano
A
,
Rossi
M
,
Basso
K
,
Zupo
S
, et al
Gene expression analysis of peripheral T cell lymphoma, unspecified, reveals distinct profiles and new potential therapeutic targets
.
J Clin Invest
2007
;
117
:
823
34
.
33.
Lu
KH
,
Patterson
AP
,
Wang
L
,
Marquez
RT
,
Atkinson
EN
,
Baggerly
KA
, et al
Selection of potential markers for epithelial ovarian cancer with gene expression arrays and recursive descent partition analysis
.
Clin Cancer Res
2004
;
10
:
3291
300
.
34.
Tomlins
SA
,
Mehra
R
,
Rhodes
DR
,
Cao
X
,
Wang
L
,
Dhanasekaran
SM
, et al
Integrative molecular concept modeling of prostate cancer progression
.
Nat Genet
2007
;
39
:
41
51
.
35.
Wurmbach
E
,
Chen
YB
,
Khitrov
G
,
Zhang
W
,
Roayaie
S
,
Schwartz
M
, et al
Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma
.
Hepatology
2007
;
45
:
938
47
.
36.
Beroukhim
R
,
Brunet
JP
,
Di Napoli
A
,
Mertz
KD
,
Seeley
A
,
Pires
MM
, et al
Patterns of gene expression and copy-number alterations in von-Hippel Lindau disease-associated and sporadic clear cell carcinoma of the kidney
.
Cancer Res
2009
;
69
:
4674
81
.
37.
Riker
AI
,
Enkemann
SA
,
Fodstad
O
,
Liu
S
,
Ren
S
,
Morris
C
, et al
The gene expression profiles of primary and metastatic melanoma yields a transition point of tumor progression and metastasis
.
BMC Med Genomics
2008
;
1
:
13
.
38.
Yusenko
MV
,
Kuiper
RP
,
Boethe
T
,
Ljungberg
B
,
van Kessel
AG
,
Kovacs
G
. 
High-resolution DNA copy number and gene expression analyses distinguish chromophobe renal cell carcinomas and renal oncocytomas
.
BMC Cancer
2009
;
9
:
152
.
39.
Murat
A
,
Migliavacca
E
,
Gorlia
T
,
Lambiv
WL
,
Shay
T
,
Hamou
MF
, et al
Stem cell-related "self-renewal" signature and high epidermal growth factor receptor expression associated with resistance to concomitant chemoradiotherapy in glioblastoma
.
J Clin Oncol
2008
;
26
:
3015
24
.
40.
French
PJ
,
Swagemakers
SM
,
Nagel
JH
,
Kouwenhoven
MC
,
Brouwer
E
,
van der Spek
P
, et al
Gene expression profiles associated with treatment response in oligodendrogliomas
.
Cancer Res
2005
;
65
:
11335
44
.
41.
Minn
AJ
,
Gupta
GP
,
Siegel
PM
,
Bos
PD
,
Shu
W
,
Giri
DD
, et al
Genes that mediate breast cancer metastasis to lung
.
Nature
2005
;
436
:
518
24
.
42.
Weigelt
B
,
Glas
AM
,
Wessels
LF
,
Witteveen
AT
,
Peterse
JL
,
van't Veer
LJ
. 
Gene expression profiles of primary breast tumors maintained in distant metastases
.
Proc Natl Acad Sci U S A
2003
;
100
:
15901
5
.
43.
Nutt
CL
,
Mani
DR
,
Betensky
RA
,
Tamayo
P
,
Cairncross
JG
,
Ladd
C
, et al
Gene expression-based classification of malignant gliomas correlates better with survival than histological classification
.
Cancer Res
2003
;
63
:
1602
7
.
44.
Beer
DG
,
Kardia
SL
,
Huang
CC
,
Giordano
TJ
,
Levin
AM
,
Misek
DE
, et al
Gene-expression profiles predict survival of patients with lung adenocarcinoma
.
Nat Med
2002
;
8
:
816
24
.
45.
Richardson
AL
,
Wang
ZC
,
De Nicolo
A
,
Lu
X
,
Brown
M
,
Miron
A
, et al
X chromosomal abnormalities in basal-like human breast cancer
.
Cancer Cell
2006
;
9
:
121
32
.
46.
The Cancer Genome Atlas Network
. 
Comprehensie molecular characterization of human colon and rectal cancer
.
Nature
2012
;
487
:
330
7
.
47.
Xu
L
,
Shen
SS
,
Hoshida
Y
,
Subramanian
A
,
Ross
K
,
Brunet
JP
, et al
Gene expression changes in an animal melanoma model correlate with aggressiveness of human melanoma metastases
.
Mol Cancer Res
2008
;
6
:
760
9
.
48.
Cuello
M
,
Ettenberg
SA
,
Clark
AS
,
Keane
MM
,
Posner
RH
,
Nau
MM
, et al
Down-regulation of the erbB-2 receptor by trastuzumab (herceptin) enhances tumor necrosis factor-related apoptosis-inducing ligand-mediated apoptosis in breast and ovarian cancer cell lines that overexpress erbB-2
.
Cancer Res
2001
;
61
:
4892
900
.
49.
Henson
ES
,
Gibson
EM
,
Villanueva
J
,
Bristow
NA
,
Haney
N
,
Gibson
SB
. 
Increased expression of Mcl-1 is responsible for the blockage of TRAIL-induced apoptosis mediated by EGF/ErbB1 signaling pathway
.
J Cell Biochem
2003
;
89
:
1177
92
.
50.
Gibson
EM
,
Henson
ES
,
Haney
N
,
Villanueva
J
,
Gibson
SB
. 
Epidermal growth factor protects epithelial-derived cells from tumor necrosis factor-related apoptosis-inducing ligand-induced apoptosis by inhibiting cytochrome c release
.
Cancer Res
2002
;
62
:
488
96
.
51.
Lang
F
,
Stournaras
C
. 
Ion channels in cancer: future perspectives and clinical potential
.
Philos Trans R Soc Lond B Biol Sci
2014
;
369
:
20130108
.
52.
Lemke
J
,
von Karstedt
S
,
Zinngrebe
J
,
Walczak
H
. 
Getting TRAIL back on track for cancer therapy
.
Cell Death Differ
2014
;
21
:
1350
64
.
53.
Gyorffy
B
,
Surowiak
P
,
Budczies
J
,
Lanczky
A
. 
Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer
.
PLoS One
2013
;
8
:
e82241
.