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Small Molecule Therapeutics

A Genome-scale CRISPR Screen Identifies the ERBB and mTOR Signaling Networks as Key Determinants of Response to PI3K Inhibition in Pancreatic Cancer

Charlotte K. Milton, Annette J. Self, Paul A. Clarke, Udai Banerji, Federica Piccioni, David E. Root and Steven R. Whittaker
DOI: 10.1158/1535-7163.MCT-19-1131 Published July 2020

Abstract

KRAS mutation is a key driver of pancreatic cancer and PI3K pathway activity is an additional requirement for Kras-induced tumorigenesis. Clinical trials of PI3K pathway inhibitors in pancreatic cancer have shown limited responses. Understanding the molecular basis for this lack of efficacy may direct future treatment strategies with emerging PI3K inhibitors. We sought new therapeutic approaches that synergize with PI3K inhibitors through pooled CRISPR modifier genetic screening and a drug combination screen. ERBB family receptor tyrosine kinase signaling and mTOR signaling were key modifiers of sensitivity to alpelisib and pictilisib. Inhibition of the ERBB family or mTOR was synergistic with PI3K inhibition in spheroid, stromal cocultures. Near-complete loss of ribosomal S6 phosphorylation was associated with synergy. Genetic alterations in the ERBB–PI3K signaling axis were associated with decreased survival of patients with pancreatic cancer. Suppression of the PI3K/mTOR axis is potentiated by dual PI3K and ERBB family or mTOR inhibition. Surprisingly, despite the presence of oncogenic KRAS, thought to bestow independence from receptor tyrosine kinase signaling, inhibition of the ERBB family blocks downstream pathway activation and synergizes with PI3K inhibitors. Further exploration of these therapeutic combinations is warranted for the treatment of pancreatic cancer.

Introduction

The 10-year survival rate for pancreatic ductal adenocarcinoma (PDAC) has remained at just 3% for the past 40 years (1). Activation of the oncogene, KRAS, is one of the earliest genetic alterations detected in the development of PDAC and KRAS mutations are found in over 90% of cases (2, 3). Transgenic mouse models expressing oncogenic KrasG12D have demonstrated that mutant KRAS is an important driver in pancreatic cancer, as switching off Kras signaling results in tumor regression (4). Recently discovered inhibitors of KRAS12C have further validated the dependency of pancreatic, colon, and lung tumor models on oncogenic KRAS and demonstrated promising early clinical activity (5, 6).

There is a strong rationale for targeting PI3K in PDAC. Activation of the PI3K pathway is commonly observed in PDAC patient samples (7–9), regulates cell metabolism, growth, and survival, and is commonly implicated as a driver of human cancer (10). Importantly, phosphorylation of PI3K signaling markers, including AKT (Ser473), mTOR (Ser2448), and GSK3β (Ser9; ref. 11) or low expression of PTEN, a suppressor of PI3K signaling (12), is predictive of poor survival in pancreatic cancer. Moreover, the interaction between Ras and PI3Kα is essential for KrasG12D-induced tumorigenesis in mice (13). Notably, KrasG12D-driven murine PDAC tumors are dependent on PI3Kα (14, 15), but not PI3Kβ (15), or Craf (14) for tumorigenesis. Consequently, PI3K signaling is an attractive therapeutic target for PDAC. However, clinical trials of allosteric mTOR inhibitors, including temsirolimus (7), or everolimus (16), have shown limited activity in patients with gemcitabine-refractory, metastatic pancreatic cancer, likely due to loss of negative feedback on IRS1 and reactivation of PI3K (16). Multiple oncogenic pathways are engaged downstream of KRAS (17, 18), so it is perhaps unsurprising that targeting a single downstream effector may not be enough to affect cell viability. We hypothesize that PI3K inhibition selects for compensatory mechanisms sufficient to maintain tumor cell survival.

This study aimed to elucidate the mechanisms of intrinsic resistance to PI3K inhibition in pancreatic cancer and identify rational drug combinations to overcome them. Functional genomic screens have successfully identified loss-of-function events that drive drug resistance, finding NF1 loss to be a key driver of resistance to RAF inhibition in melanoma (19). We therefore employed a genome-scale synthetic lethal CRISPR screen to find loss-of-function events that could modulate sensitivity to PI3K inhibition. We discovered that the ERBB and mTOR signaling networks regulate response to PI3K inhibition in PDAC. Furthermore, we used a combination drug screen to prioritize clinically relevant targeted agents that synergize with PI3K inhibition to improve therapeutic response.

Materials and Methods

Cell lines and cell culture

Pancreatic cancer cell lines were a kind gift from Dr. Anguraj Sadanandam (The Institute of Cancer Research, London, United Kingdom), with the exception of PANC1, PATU8902, MIAPACA2, YAPC, and HEK293T cells, which were obtained from the ATCC. T47D cells were from the Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ). All cells were cultured in DMEM (Sigma) supplemented with 10 % FBS (FBS Good, Pan Biotech), with the exception of MIAPACA2, which was supplemented with 20% FBS. Human pancreatic stellate cells (PSC) were obtained from ScienCell laboratories. Recombinant growth factors were obtained from Bio-Techne. Cell lines were tested for Mycoplasma using the MycoAlert Mycoplasma Detection Kit (Lonza). Cell line authentication was not performed.

Small-molecule inhibitors

All small-molecule inhibitors were purchased from Selleck Chemicals: BYL719 (S2814), GDC0941 (S1065), pelitinib (S1392), everolimus (S1120), AZD8055 (S1555), AZD2014 (S2783), and BEZ235 (S1009). Stock solutions were prepared in dimethyl sulfoxide (DMSO) and stored at −20°C.

Cell proliferation assays

For GI50 determination, cells were seeded in 96-well plates. The next day, cells were treated with increasing concentrations of inhibitor or with DMSO alone. After a 72-hour incubation period, cell proliferation was quantified using CellTiter-Blue reagent (Promega) and normalized to DMSO-treated wells. GI50 values were calculated using nonlinear regression analysis in GraphPad Prism software. For population doubling experiments, cells were seeded at an initial density of 1 × 107 cells/flask in 225 cm2 flasks. Cells were allowed to proliferate to 80%–90% confluence before they were counted and then reseeded at the same initial density. Population doublings (PD) were calculated according to the equation below.

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For determination of maximum excess above bliss, cells were treated with a matrix of increasing concentrations of two inhibitors or DMSO. After a 72-hour incubation period, cell proliferation was quantified using CellTiter-Blue reagent and normalized to the DMSO-treated well. The Bliss independence model (20) was used to calculate synergy.

For colony assays, cells were seeded in 12-well plates. The next day, triplicate wells were treated with DMSO, the inhibitors alone, or the combinations. After 14 days, cells were washed with PBS and fixed in 4 % formaldehyde/PBS for 30 minutes. Cells were stained with 0.5% crystal violet in 70% ethanol and imaged using a FluoroChem E imaging system (Protein Simple). Colonies were quantified by solubilizing the crystal violet solution in 10% acetic acid and reading the absorbance at 595 nm using an EMax Plus Microplate Reader (Molecular Devices).

Spheroid growth assays

Human PSCs were cultured in DMEM/Nutrient Mixture F-12 Ham (Sigma Aldrich), supplemented with 1% GlutaMAX (Thermo Fisher Scientific), 1% Amphotericin B (Thermo Fisher Scientific), 1% Penicillin-Streptomycin (Sigma Aldrich), and 10% FBS (FBS Good, Pan Biotech). Cells were seeded in coculture with established PDAC cell lines at a starting density of 1 × 103 cells/well in 96-well Ultra-low Attachment Round Bottom Multi-well Plates (Nexcelom). Cells were seeded to form spheroids at a ratio of 1:1 PSCs to PDAC cell lines. The next day, cells were treated with DMSO, fixed concentrations of drugs or the desired combinations. Spheroid diameter was measured over a time period of 10 days, with measurements taken every 3–4 days. The first measurement was taken the day after cells were plated, before the addition of DMSO and drug treatments. Spheroid diameter was imaged and quantified using the Celigo Imaging Cytometer (Nexcelom) and is the average of at least three replicate spheroids. For viability staining, spheroids were incubated with 1 μmol/L calcein AM and 40 μg/mL propidium iodide for 30 minutes prior to imaging.

Cell lysis and Western blotting

After the desired treatment, cells were washed with cold PBS and lysed in NP40 buffer [0.5% NP40, 150 mmol/L NaCl, 50 mmol/L Tris pH 7.5, Pierce Protease and Phosphatase Inhibitor Mini Tablets (Life Technologies)]. Where detection of KRAS was necessary, cells were lysed in SDS buffer (1 % SDS, 10 mmol/L EDTA, 50 mmol/L Tris, pH 8). Bicinchoninic acid (Sigma) was used to determine protein concentration. Equal amounts of protein were separated by gel electrophoresis, using NuPAGE polyacrylamide gels (Life Technologies). Proteins were transferred to a nitrocellulose membrane using the iBlot 2 system (Life Technologies) and then blocked with LI-COR blocking buffer (LI-COR Biosciences). Membranes were incubated with the primary antibodies overnight at 4°C, followed by IRdye-conjugated secondary antibodies (LI-COR Biosciences) and detected using an Odyssey Fc imaging system (LI-COR Biosciences). Quantification of Western blots was performed using Image Studio Lite (LI-COR Biosciences). Details of the antibodies used can be found in Supplementary Table S1.

Lentiviral production

HEK293T cells were seeded at a density of 2.4 × 106 cells/plate in 10-cm plates. The next day cells were transfected with shRNA/sgRNA plasmid (3 μg) and the packaging plasmids psPAX2 (2.1 μg) and pmD2.G (0.9 μg) using 30 μL lipofectamine per transfection. Cells were incubated for 72 hours at 37°C, after which the supernatant was collected and stored in 0.5 mL aliquots at −80°C for future experiments. Each batch of lentivirus was titrated on cells to determine concentration needed for 100% infection efficiency.

shRNA

MISSION shRNA plasmids (pLKO.1) were obtained from Sigma-Aldrich. The pLKO.1-LacZ and -Luciferase targeting shRNA plasmids were from the Genetic Perturbation Platform (The Broad Institute). TRC numbers and target sequences for shRNA plasmids are shown in Supplementary Table S2. Cells were transduced with lentivirus as described previously (19). Cell proliferation was quantified using CellTiter-Blue reagent (Promega) and normalized to cells transduced with control lentivirus. Gene dependency scores were calculated on the basis of the dependency index described by Singh and colleagues (21).

Drug combination screen

Cells were plated in 384-well plates and the Echo 550 Liquid Handler (Labcyte) was used to dispense 20 nL of each compound from a library of 485 FDA-approved drugs and tool compounds (selected by the Cancer Research UK Cancer Therapeutics Unit and purchased from Selleckchem) onto the plates to give the final concentration of 800 nmol/L on the cells. Plates were then treated with either 100 nL of DMSO, BYL719, or GDC0941, to give a final concentration of 10 μmol/L BYL719 or 1 μmol/L GDC0941. After a 96-hour incubation period, cell proliferation was quantified using CellTiter-Blue reagent (Promega). Synergy was calculated using the Bliss independence model, as described previously. The Bliss score for each combination is the mean of three replicates.

CRISPR

LentiCRISPRv2 (was a gift from Feng Zhang, Addgene plasmid #52961; ref. 22) were digested with Esp3I at 37°C overnight (New England Biolabs, NEB). Oligos were designed to include each sgRNA target sequence (Supplementary Table S3) according to the “Zhang Lab General Cloning Protocol,” available at https://www.addgene.org/crispr/zhang/. Each pair of oligos was phosphorylated and annealed with T4 PNK enzyme (NEB). Each oligo duplex was then ligated into the appropriate vector using the quick ligase enzyme (NEB) at 16°C overnight. Lentiviral plasmids were transformed into Stbl3 bacteria (Invitrogen), according to the manufacturer's instructions, and then plated on ampicillin (50 mg/mL)-selective agar plates. Single colonies were then amplified, extracted, and used to produce lentivirus as described above. Before generation of lentivirus, each amplified plasmid was sequenced to ensure successful sgRNA sequence integration. To generate clonal cell populations expressing each plasmid, cells were first transduced with the virus. Cells were transduced with the lentiCRISPRv2 lentivirus, as this also contains the vector for Cas9 expression. Cells successfully transduced with lentiCRISPRv2 were selected for using 10 μg/mL blasticidin, respectively. After 7 days of selection, cells were seeded in 96-well plates at a density of 0.5 cells/well to select for clonal populations. These were expanded under continued antibiotic selection until sufficient cell numbers were generated. Stocks were frozen down in FBS with 10% DMSO and stored in liquid nitrogen.

Generation of Cas9 cell lines

Cell lines were engineered to express Cas9 by centrifugation of 4 × 106 cells with (pXPR101) Cas9 lentivirus (1:1), in the presence of 8 μg/mL polybrene for 1 hour at 37°C. Cells were incubated with fresh media overnight, before cells were trypsinized and pooled for selection. Cells were incubated with 10 μg/mL blasticidin for 7 days to select for successfully infected cells. In parallel, cells were plated in 6-well plates for determination of infection efficiency. To assess Cas9 activity, parental and Cas9-expressing cells were infected with a lentivirus encoding both EGFP and a sgRNA targeting EGFP (pXPR_011-sgEGFP). Successfully transduced cells were selected for using 2 μg/mL puromycin, until all cells of a “no infection control” were dead. Cells were assayed by flow cytometry to assess EGFP expression. The activity of Cas9 was taken as the proportion of EGFP-negative cells in the Cas9-transduced population.

Genome-wide synthetic lethal screen protocol

Cells were seeded in 12-well plates at a density of 3 × 106 cells/well in 2 mL media. Cells were infected with the Avana4 lentiviral library (Broad Institute, 74,687 sgRNAs targeting 18,407 genes; refs. 23, 24) in four infection replicates. Cells were infected with a predicted representation of 500 cells/sgRNA after selection and with the volume of virus/well that gave approximately 40% infection efficiency. Cells were centrifuged at 2,000 rpm for 2 hours at 30°C in the presence of lentivirus and 8 μg/mL polybrene, followed by incubation in fresh media overnight. Cells were pooled and seeded into T225 flasks at a density of 1 × 107 cells/flask for selection with 2 μg/mL puromycin for 7 days and passaged as necessary. In parallel, cells were seeded in 6-well plates to determine infection efficiency. After 7 days of selection, MIAPACA2 cells were split into three arms and treated with either 0.02% DMSO, 10 μmol/L BYL719, or 1 μmol/L GDC0941. Throughout the screen, cells were passaged as necessary, maintaining a total representation of 500 cells/sgRNA in each replicate. After eight population doublings, cells from each arm were collected and cell pellets stored at −80°C. Genomic DNA was extracted using the QIAamp DNA Blood Maxi Kit (Qiagen). PCR amplification and next-generation sequencing (NGS) was conducted as described previously (23). Briefly, sequencing adaptors and sample barcodes were added to sgRNA sequences from gDNA and pDNA samples by PCR. Samples were purified with Agencourt AMPure XP SPRI beads (Beckman Coulter A63880) and then sequenced on a HiSeq2000 (Illumina). Reads were counted by searching for the CACCG sequence of each sgRNA insert and then mapping the 20 nucleotide sgRNA sequence to a reference file of all sgRNAs in the library and assigned to the treatment condition based on the appended barcode.

Focused minipool screen protocol

The custom minipool lentiviral library [3,067 sgRNAs targeting 296 top hit genes (one gene was accidentally omitted), 496 nontargeting control sgRNAs, and 201 sgRNAs targeting essential genes] was prepared as described previously (23, 24). Plasmid DNA (pDNA) was sequenced by NGS to quantify the abundance of each sgRNA in the pool. The pDNA pool was then transfected into HEK 293T cells to produce lentivirus according to the “Large Scale Lentiviral Production” protocol available at https://portals.broadinstitute.org/gpp/public/resources/protocols. Each cell line was infected with the custom minipool lentiviral library in four infection replicates. Cells were infected with a predicted representation of 2,000 cells/sgRNA, after selection, and with the volume of virus/well that gave approximately 40% infection efficiency, as determined previously. The transduction and selection protocol used was the same as in the genome-wide screen and is described above. After 7 days of selection, cells were split into three arms and treated with either 0.02% DMSO, BYL719, or GDC0941. Throughout the screen, cells were passaged to maintain a representation of 2,000 cells/sgRNA. After eight population doublings, cells from each arm were collected and genomic DNA was extracted and sequenced, as in the whole-genome screen.

CRISPR screen analysis

The abundance of each sgRNA in each replicate was quantified by calculating the Log2(sequencing reads/million) (RPM), according to the formula below.

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The log2 fold change (LFC) from the early time point was calculated by normalizing RPM for each sgRNA in each replicate to that in an early time point control taken 3 days after selection with puromycin. The LFC between the DMSO and drug-treated arms was calculated as the difference in average LFC across three replicates. This was used to rank individual sgRNAs according to their selective depletion or enrichment in the drug-treated arms. Top scoring genes were ranked according to the number of independent high scoring sgRNAs targeting the same gene, according to the STARS gene-ranking algorithm (23). To assess depletion of essential genes from the population, as a positive control for successful gene editing, the RPM for each sgRNA was normalized to the plasmid DNA to calculate the LFC from baseline. The list of 885 core essential genes was kindly provided by Dr. Marco Licciardello (The Institute of Cancer Research, London, United Kingdom) and is compiled from the genes that were consistently and significantly depleted in all cell lines tested from three previous publications (25–27). To assess the statistical significance of the overlap between genes that modulated the response to BYL719 or GDC0941, the representation factor was calculated as below.

The associated probability was calculated by exact hypergeometric probability as detailed

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(http://nemates.org/MA/progs/representation.stats.html).

Gene set enrichment analysis of genome-wide screen

A list of all hit genes was compiled from those that were that were enriched or depleted from the BYL719- or GDC0941-treated arms of the genome-wide CRISPR screen (FDR < 0.3). This list was used to interrogate the Reactome (28) and KEGG (29, 30) gene sets within the Molecular signatures database (31, 32) available at http://software.broadinstitute.org/gsea/msigdb.

Results

Loss-of-function CRISPR screen in PDAC cells identifies RTK and mTOR signaling networks as key determinants of response to PI3K inhibition

To discover loss-of-function events that modulate sensitivity to PI3K inhibition in PDAC, we conducted a genome-wide CRISPR screen anchored to the PI3Kα-selective inhibitor BYL719 (alpelisib; ref. 33) or the pan-class I PI3K inhibitor GDC0941 (pictilisib; ref. 34). In vitro, pancreatic cell lines were resistant to BYL719 and GDC0941, compared with the PI3Kα-dependent breast cancer cell line, T47D (Fig. 1A; Supplementary Fig. S1A; ref. 35). Resistance was observed despite inhibition of PI3K–AKT signaling, highlighting a disconnect between pathway inhibition and inhibition of cell proliferation in the pancreatic cells (Fig. 1B; Supplementary Fig. S1E). We selected MIAPACA2 cells for the genome-wide screen, as they were resistant to both BYL719 and GDC0941 and dependent on KRAS for proliferation (Supplementary Fig. S1A–S1C). We engineered this cell line to stably express Cas9 (MIAPACA2_Cas9) and confirmed that Cas9 expression did not alter the response to PI3K inhibition by BYL719 or the pan-PI3K inhibitor GDC0941 (Supplementary Fig. S1D and S1E).

Figure 1.
Figure 1.

A whole-genome CRISPR screen implicates receptor tyrosine kinase and mTOR signaling networks as modulators of response to PI3K inhibition. A, Proliferation of 14 human pancreatic cell lines (all KRAS-mutant except BxPC3) and one breast cancer cell line (T47D KRAS wild-type) was measured after incubation with increasing concentrations of BYL719 for 72 hours. Proliferation was quantified using CellTiter-Blue and compound GI50 values were calculated using GraphPad Prism. Mean cell proliferation, relative to DMSO control, is plotted ± SE (n = 3). B, Phosphorylation of AKT and PRAS40 was assessed by Western blotting after incubation with increasing concentrations of BYL719 for 2 hours, in the cell lines MIAPACA2, PANC1, and T47D (n = 2). C, Overview of genome-wide CRISPR screening method. Cas9-expressing MIAPACA2 cells were infected with the Avana4 pooled CRISPR library and split into three arms. Cells were passaged for eight population doublings in the presence of DMSO, BYL719, or GDC0941. The abundance of each sgRNA was assessed by NGS, under each condition. D, Following successful transduction of MIAPACA2_Cas9 cells with the Avana4 sgRNA library, cells were cultured in the presence of DMSO, 10 μmol/L BYL719, or 1 μmol/L GDC0941 for at least eight population doublings. Cells were counted every 3–5 days and population doublings are the mean of four replicates ± SE. E, The average log-fold change (LFC) in sgRNA abundance was determined for BYL719-treated samples versus the DMSO controls. STARS analysis was used to rank genes that when targeted by CRISPR, enhanced the antiproliferative activity of BYL719. F, STARS analysis was used as in E to rank genes that when targeted by CRISPR, suppressed the antiproliferative activity of BYL719, and conferred resistance to the drug. G, Venn diagram analysis of hit genes to identify shared enhancers/suppressors of BYL719 and GDC0941 activity. H, GSEA highlights key pathways that modulate sensitivity to PI3K inhibition. Significantly enriched pathways from the Reactome and KEGG databases were nominated from a list of the hit genes (FDR < 0.3) that were enriched or depleted in the genome-wide CRISPR screen with either BYL719 or GDC0941.

MIAPACA2_Cas9 cells were transduced with Avana4 lentiviral library (23, 24). Cells were split into DMSO-, BYL719-, or GDC0941-treated arms and passaged for eight population doublings (Fig. 1C). Cell proliferation rate was slowed by incubation with 10 μmol/L BYL719 or 1 μmol/L GDC0941 (Fig. 1D), concentrations at which PI3K signaling was near-completely inhibited. Our choice of inhibitor concentration was driven by a balance of attaining near-complete pathway inhibition at a concentration that permits sufficient cell proliferation for the screen to be performed within approximately 3–4 weeks. Although the concentration of BYL719 used was quite high, we hypothesized that by using two chemically distinct inhibitors, off-target effects could be accounted for by focusing on hits observed with both inhibitors. Cells were harvested, genomic DNA (gDNA) extracted, and sgRNAs amplified and barcoded by PCR. Next-generation sequencing (NGS) was employed to quantify the abundance of each sgRNA in each experimental arm. This analysis demonstrated that sgRNAs targeting essential genes or KRAS, a known dependency of this cell line, were depleted from the population, whereas nontargeting sgRNAs were not (Supplementary Fig. S2A). The STARS algorithm (23, 24) was used to rank genes with multiple scoring sgRNAs that were selectively depleted (enhancers of the antiproliferative effect) or enriched (suppressors of the antiproliferative effect) in each arm of the screen compared with DMSO (Fig. 1E and F; Supplementary Fig. S2B and S2C; Supplementary Tables S4–S7).

Overall, there was a large degree of overlap among the hit genes [false discovery rate (FDR) of <0.3] for which sgRNAs were depleted or enriched with BYL719 or GDC0941 treatment (Fig. 1G; Supplementary Table S8). Out of 82 and 34 genes for which sgRNAs enhanced the antiproliferative effect in the BYL719- and GDC0941-treated arms, respectively, 11 of the hits were common to both treatment arms (representation factor 75, P < 1.192 × 10−18). There was a greater degree of overlap between the genes for which sgRNAs were enriched with drug treatment, with 63 genes common to both drugs, out of a total of 135 and 120 genes that were enriched with BYL719 and GDC0941, respectively (representation factor of 74, P < 6.147 × 10−109). Strikingly, sgRNAs targeting multiple negative regulators of PI3K/mTOR signaling were enriched in the drug-treated populations, implying that loss of these genes promotes resistance to PI3K inhibition. Indeed, the top-ranking sgRNAs that were enriched in both drug-treated arms targeted TSC1 and TSC2, which inhibit the activity of RHEB and downstream mTORC1 signaling (36). Other sgRNAs enriched under drug treatment targeted PTEN, DDIT4 (36), AKT1S1 (37), and RALGAPB (38). Guide RNA–targeting genes encoding proteins of the mTORC1 network, mTOR kinase, RAPTOR, and RRAGC were significantly depleted from the BYL719-treated arm, suggesting that loss of mTORC1 sensitizes cells to BYL719 treatment (39, 40). This implicates the mTORC1 complex as a key mediator of resistance to PI3K inhibition. Given that loss of PTEN, TSC1, or TSC2 confers resistance to PI3K inhibition and loss of mTOR sensitizes to PI3K inhibition, this provides important validation of the screening conditions, as these events are known to modulate sensitivity to PI3K inhibitors (41, 42).

We used the Molecular Signatures Database (MSigDB; refs. 31, 32) to investigate the 297 genes that modulated response to either BYL719 or GDC0941 and found enrichment for multiple pathways involved in PI3K signaling, including mTOR, AKT (PKB), and insulin signaling (Fig. 1H). Multiple RTK signaling pathways, including the ERBB family (in particular EGFR and ERBB2) as well as FGFR and PDGF, were enriched among the hit genes that were significantly enriched or depleted from the genome-wide CRISPR screen (FDR < 0.3).

“Signaling by EGFR in cancer” was the most highly enriched pathway and is of particular interest in pancreatic cancer as the EGFR inhibitor erlotinib has shown some modest activity in patients with pancreatic cancer (43). Moreover, guides targeting genes associated with the internalization and degradation of activated ERBB family receptors were enriched in the drug-treated populations. Suppressor hit genes included AP2S1, AP2B1, and AP2M1, which encode subunits of the adaptor protein 2 (AP-2) complex and are involved in clathrin-dependent endocytosis of activated EGFR (44). We hypothesize that loss of the AP-2 complex would result in sustained EGFR signaling. PRKACA encodes a catalytic subunit of protein kinase A (PKA), which phosphorylates and inhibits EGFR (45), and facilitates its internalization and ubiquitination (46). Overall, loss of these genes may result in activation of EGFR, thereby promoting resistance to PI3K inhibition.

Minipool validation screen further implicates RTK signaling as a modulator of sensitivity to PI3K inhibition in multiple cell lines

Penetrant synthetic lethal interactions, which demonstrate similar effects across diverse cellular models, may have greater therapeutic benefit as they could overcome the molecular heterogeneity that exists within tumors (47). Therefore, to prioritize penetrant synthetic lethal effects, we generated a minipool targeting 296 hit genes from the genome-wide screen, including those hits identified with either BYL719 or GDC0941, for a secondary validation screen. We tested this library in MIAPACA2 cells and in three additional KRAS-mutant pancreatic cancer cell lines. All cell lines chosen for the validation screen were of the QM subtype of PDAC as this represents the subtype with the poorest prognosis and therefore the most urgent clinical unmet need (48). We confirmed that Cas9 expression did not alter response to PI3K inhibition (Supplementary Fig. S3A). We selected concentrations of BYL719 and GDC0941 that inhibited PI3K signaling, but still permitted cell proliferation (Supplementary Fig. S3B). Cells were then transduced with the minipool library. After puromycin selection, cells were treated with DMSO, BYL719, or GDC0941 and passaged for approximately eight population doublings (Supplementary Fig. S4A). sgRNA abundance was determined as in the whole-genome screen. For each cell line, the abundance of nontargeting sgRNAs was not changed compared with the plasmid DNA, but sgRNAs targeting essential genes were depleted, indicating that transduction led to successful gene editing (Supplementary Fig. S4B).

STARS analysis was used to prioritize genes with multiple top-scoring sgRNAs that were either enriched or depleted from the drug-treated arms (Supplementary Tables S9–S24). To discover penetrant hits, genes were ranked according to their average STARS score across all four cell lines (Fig. 2A). Reassuringly, there was considerable overlap between the hit genes that could modulate sensitivity to BYL719 and GDC0941. MEMO1, UBE2H, MIOS, and YPEL5 were the top four hit genes that, when lost, sensitized to both BYL719 and GDC0941 across all four cell lines. Targeting of PTEN, TSC1, TSC2, FRYL, PDCD10, and NF2 were the top six hit genes that drove resistance to both PI3K inhibitors. Notably, sgRNAs targeting KEAP1 were enriched in the presence of PI3K inhibition suggesting resistance could also be driven by NFE2L2/NRF2-mediated activation of an antioxidant stress response pathway (49). As STARS only uses the top 10% of sgRNAs to rank genes, we also analyzed the minipool screen based on the average LFC of all sgRNAs for each cell line and then ranked each gene in the minipool based on the average LFC across all cell lines. Reassuringly, both analysis approaches showed agreement (Supplementary Fig. S4C). We also confirmed that sgRNAs targeting the top-ranking genes identified in the primary screen also showed significant enrichment or depletion in the MIAPACA2 cell line in the secondary screen (Supplementary Fig. S4D). This suggested good concordance between the primary and secondary screens in MIAPACA2 cells.

Figure 2.
Figure 2.

The ERBB family can sustain pancreatic cancer cell proliferation and mediate resistance to PI3K inhibition associated with S6 phosphorylation. A, A secondary screen of top-ranking hits from the primary screen was conducted in MIAPACA2, PANC1, PATU8902, and PANC03.27 cells treated with BYL719 or GDC0941. For each cell line, STARS analysis was used to rank genes that enhanced or suppressed the antiproliferative activity of BYL719 or GDC0941. Genes were then ranked according to the mean STARS score across all four cell lines. Genes with a STARS score of 4 or more in three or more cell lines are highlighted in red. Gray boxes indicate the gene was not ranked by STARS. B, PANC03.27 and MIAPACA2 cells expressing Cas9 were transduced with lentiviral vectors encoding sgRNAs targeting GFP, UBE2H, or MEMO1 to generate cell populations with reduced expression of either UBE2H or MEMO1. Cells were lysed 7 days after transfection and cell lysates were analyzed by Western blotting (n = 3). C, Cells as in B were treated with increasing concentrations of BYL719 or GDC0941. After 4 days of drug treatment, cell proliferation was quantified using CellTiter-Blue. Mean cell proliferation, relative to the DMSO-treated control, is plotted ± SE (n = 3). D, PATU8988S cells were cultured in the presence of BSA, EGF, HRG, or IGF1 (100 ng/mL) with 10% FBS and treated with a range of concentrations of BYL719. Cell proliferation was quantified after 72 hours using CellTiter-Blue. The mean GI50 is plotted ± SE (n = 6) and statistical significance compared with the BSA control was determined by one-way ANOVA. E, PATU8988S cells were cultured in the presence of BSA, EGF, HRG, or IGF1 (100 ng/mL) with 10% FBS and treated with DMSO (0.1%) or BYL719 (2 μmol/L) for 72 hours. Cell lysates were analyzed by Western blotting (n = 3).

We focussed on two hits – MEMO1 and UBE2H, as they were both related to ERBB family signaling. Knockout of MEMO1 or UBE2H by CRISPR/Cas9 was confirmed (Fig. 2B) and enhanced the antiproliferative effect of BYL719 in PANC03.27 and MIAPACA2 (Fig. 2C). Both MEMO1 and UBE2H regulate signal transduction by the ERBB family and IGF1R (50, 51); therefore, we hypothesized that stimulation of RTKs with specific ligands could promote resistance to PI3K inhibition. First, by culturing cells in low serum (0.1% FBS) AKT, PRAS40 and S6 phosphorylation were all decreased (Supplementary Fig. S5A), suggesting removal of growth factors could dampen signaling, even in the setting of oncogenic KRAS. Furthermore, cotreatment with BYL719 caused a near-complete suppression of AKT, PRAS40, and S6 phosphorylation. Low serum reduced cell proliferation by approximately 50% relative to 10% serum as did treatment with BYL719 (Supplementary Fig. S5B). Low serum and BYL719 treatment modestly suppressed cell proliferation further but to a lesser degree compared with drug treatment in 10% serum. This may reflect either the reduced proliferation rate of the cells in low serum and/or a decrease in PI3K signaling under low serum conditions. Interestingly, the addition of EGF, heregulin HRG and insulin-like growth factor 1 (IGF1) significantly increased the GI50 concentration for BYL719 (Fig. 2D). HRG conferred the greatest degree of resistance to BYL719, associated with sustained AKT and S6 phosphorylation in the presence of BYL719 (Fig. 2E). Notably, despite EGF strongly activating the EGFR receptor and causing the expected downregulation of EGFR expression (52), it was not as effective as HRG in driving resistance to BYL719. Taken together, these data suggest that the ERBB family can drive resistance to PI3K inhibition in PDAC cells.

Combination drug screen nominates clinically relevant inhibitors of ERBB and mTOR signaling as sensitizers to PI3K inhibition

To identify clinically relevant inhibitors of RTK signaling that synergized with PI3K inhibition, we used an established library of 485 FDA-approved drugs and tool compounds, alone and in combination with 10 μmol/L BYL719 or 1 μmol/L GDC0941. The library was screened in MIAPACA2 cells at a concentration of 800 nmol/L, a concentration empirically chosen for a balance between being sufficient to modulate the target in cells, but not so high as to induce off target effects. Nevertheless, some synergistic interactions may not be detected for those compounds that were used at a too high or too low concentration. The Bliss independence model (20) was used to calculate synergy for each drug combination (Supplementary Tables S25 and S26). To identify hits common to both PI3K inhibitors, the Bliss score for BYL719 was plotted against that for GDC0941 (Fig. 3A). The compounds were also ranked on the basis of their average Bliss score for both PI3K inhibitors (Fig. 3B). Notably, the ERBB family inhibitor pelitinib demonstrated greatest synergy with both PI3K inhibitors. Another ERBB family inhibitor, dacomitinib, also demonstrated synergy with both compounds. Multiple inhibitors of mTOR also ranked highly, including KU-0063794, rapamycin, ridaforolimus, everolimus, and WYE-354. KU-0063794 inhibits mTORC1 and mTORC2 kinase activity and, given that it drove greater synergy than mTORC1 allosteric inhibitors such as rapamycin, suggests that dual mTORC1/2 inhibitors may elicit greater synergy with PI3K inhibitors. The ERBB family inhibitor, pelitinib (53), and the mTORC1/2 kinase inhibitor, AZD2014 (54), were selected to validate the synergistic interaction between inhibition of PI3K and ERBB or mTOR signaling. The combination of BYL719 and pelitinib or AZD2014 synergistically inhibited proliferation of pancreatic cells in both short- (Fig. 3C) and long-term (Fig. 3D) assays. This highlighted the capacity of the ERRB family and the mTOR pathway to drive resistance to PI3K inhibition.

Figure 3.
Figure 3.

A combinatorial drug screen identifies mTOR and ERBB family inhibitors as potent enhancers of PI3K inhibition. A, MIAPACA2 cells were treated with a library of 487 FDA-approved drugs and tool compounds at a concentration of 800 nmol/L in the presence or absence of 10 μmol/L BYL719 or 1 μmol/L GDC0941. After 96 hours, cell proliferation was quantified by CellTiter-Blue assay. Proliferation was normalized to a DMSO-treated control on each plate and the Bliss independence model was used to calculate synergy with BYL719 or GDC0941 for each of the compounds in triplicate. The mean Bliss score for each compound in combination with BYL719 was plotted against the mean Bliss score for each compound in combination with GDC0941. B, The mean Bliss score for each compound in combination with BYL719 and GDC0941 was calculated and used to rank compounds. The top 20 compounds are shown. C, Cells were incubated with a matrix of increasing concentrations of BYL719 and either pelitinib or AZD2014 for 144 hours. Cell viability was measured using CellTiter-Blue and normalized to DMSO-treated wells (a shift from blue to red indicates loss of proliferation). The Bliss independence model was used to calculate synergy where a shift from green to red indicates increasing synergy (n = 3). D, Cells were incubated with the indicated compounds or the combinations in triplicate for 14 days. Cells were fixed and stained with 0.5% crystal violet. E, Spheroids consisting of a coculture of MIAPACA2 cells and PSCs (1:1) were allowed to establish for 24 hours and were then treated with the indicated compounds or the combination for 9 days. Dual calcein AM (viable cells) and propidium iodide (nonviable cells) staining was performed. Images were obtained using the Celigo imaging cytometer. F, The diameter of the spheroid was measured every 3–4 days by quantification of images recorded on the Celigo. Colony diameter is plotted as the mean of at least three independent spheroids ± SE (n = 2). Statistical significance was determined by one-way ANOVA with Tukey multiple comparisons test. G, Propidium iodide staining of spheroids was performed after 4 days and fluorescence intensity of the spheroid was quantified on the Celigo (n = 6). Mean fluorescence intensity is represented by the horizontal bar. Statistical significance was determined by one-way ANOVA with Tukey multiple comparisons test.

Using a spheroid coculture of MIAPACA2 cells with activated PSCs—thought to better-model tumor–stromal interactions and the 3D tumor environment in vivo compared with 2D culture on plastic (55, 56), the combination of BYL719 and pelitinib or AZD2014 robustly inhibited spheroid growth (Fig. 3E and F). Propidium iodide staining of spheroids after 4 days of treatment demonstrated a significant increase in dead or dying cells with the combination of BYL719 with either pelitinib or AZD2014 versus single agents (Fig. 4G). Overall, we have clearly demonstrated that combined inhibition of PI3Kα and either ERBB or mTOR is synergistic in multiple models of PDAC.

Figure 4.
Figure 4.

Inhibition of S6 phosphorylation is associated with synergistic, antiproliferative effects of combined PI3K and mTOR or pan-ERBB family inhibition. A, MIAPACA2 and T47D cells were exposed to the indicated concentrations of BYL719 for 2 hours or MIAPACA2_Cas9 cell clones lacking expression of p110α (knocked out via CRISPR-Cas9) were analyzed by Western blotting for PI3K pathway activation using the indicated antibodies. NIC, no infection control. B, Percentage cell proliferation after incubation with 10 μmol/L BYL719 for 72 hours relative to DMSO (n = 3) is plotted against percentage S6 phosphorylation, relative to DMSO, quantified from Western blots after incubation with 10 μmol/L BYL719 for 2 hours (n = 2). Each point represents a different cell line from a panel of 12 PDAC cells. Linear regression analysis in GraphPad Prism was used to determine the correlation between the two variables. C, MIAPACA2 cells were incubated with DMSO (0.1%) BYL719 (10 μmol/L), AZD2014 (250 nmol/L), pelitinib (1 μmol/L), or the indicated combinations for 72 hours and cell lysates were analyzed by Western blotting (n = 2).

Resistance to PI3K inhibition is associated with sustained mTORC1 activity and can be overcome with mTOR and ERBB family inhibitors

The CRISPR and drug combination screens suggested that under PI3Kα inhibition, pancreatic cancer cells depend on mTOR signaling for proliferation. Therefore, we hypothesized that inadequate suppression of mTORC1 signaling underlies the resistance of pancreatic cells to BYL719. Indeed, phosphorylation of S6 (Ser240/244, catalyzed by p70S6K) was not suppressed by BYL719 treatment in resistant PDAC cells, but was seen in the sensitive breast cancer cell line, T47D, whereas phospho-AKT was suppressed in both the sensitive and insensitive lines (Fig. 4A). Moreover, while CRISPR knockout of p110α decreased phosphorylation of AKT (Ser473) and PRAS40 (Thr246), phosphorylation of S6 (Ser240/244) was maintained (Fig. 4A). Across a panel of 12 pancreatic cell lines, the inhibition of phospho-S6 (Ser240/244) achieved with 10 μmol/L BYL719 closely correlated with the effect on cell proliferation (Fig. 4B). This suggests that mTOR signaling is uncoupled from PI3K in pancreatic cell lines and that this limits response to PI3Kα inhibition. Hence, we suggest that inhibition of phospho-S6 (Ser240/244) is an important and independent predictor of response to BYL719 versus other more proximal markers of PI3K signaling.

Our results suggest that inhibition of PI3K alone does not inhibit proliferation and that combination with mTORC1 inhibition is required. However, clinical trials of allosteric mTOR inhibitors in pancreatic cancer have been unsuccessful likely due to loss of negative feedback on IRS1 (7, 16) and, as shown herein, pancreatic cancer cells are resistant to single-agent inhibition of mTORC1 in vitro (Supplementary Fig. S6A), despite suppression of S6 phosphorylation (Supplementary Fig. S6B). We propose that inhibition of both PI3K and mTORC1 signaling is essential to inhibit cell proliferation. In line with this, the mTORC1/2 kinase inhibitors dactolisib (BEZ235) and AZD8055 (a closely related analogue of AZD2014) displayed potent antiproliferative activity in pancreatic cancer cell lines (Supplementary Fig. S6C and S6D) and this was associated with inhibition of phospho-S6 (Ser240/244) and at approximately 10-fold higher concentrations, inhibition of phospho-AKT (Ser473), as expected by dual inhibition of mTORC1 and mTORC2 (Supplementary Fig. S6E and S6F).

We studied the effect of the combinations of BYL719 with pelitinib or AZD2014 on PI3K–mTOR signaling. BYL719 alone resulted in near-complete suppression of phospho-AKT (Ser473), but decreases in phospho-S6 (Ser240/244) were not sustained. (Fig. 4C; Supplementary Fig. S7A and S7B). Similarly, AZD2014 or pelitinib could not sustain inhibition of both PI3K-AKT and mTORC1 signaling for 72 hours. Only the combination of these agents with BYL719 was sufficient to durably inhibit signaling at both nodes (Fig. 4C; Supplementary Fig. S7A and S7B). Given that inhibition of ERBB signaling could also decrease MAPK pathway activity, we also assessed the effect of pelitinib alone and in combination with BYL719 on ERK1/2 phosphorylation. However, no robust inhibition was observed, suggesting that decreased MAPK pathway activity was not contributing to the antiproliferative activity of this combination (Supplementary Fig. S7B).

Genetic alteration of the ERBB–PI3K signaling axis correlates with poor survival of patients with PDAC

To seek clinical relevance for our findings, we investigated how expression of selected genes, implicated by both our CRISPR and drug screens, related to clinical outcomes in PDAC by interrogating publicly available TCGA “provisional data” in cBioPortal (57, 58). Ninety-one percent of patients in this dataset have KRAS-mutant tumors. Genetic alterations of the ERBB family and PI3K signaling axis were present in 40% of 149 cases (Fig. 5A) and associated with poor survival among patients with PDAC, with a significant decrease in median survival from 23 months to 16 months (Fig. 5B). Therefore, genetic alterations in the ERBB family and PI3K signaling pathways are common in patients with PDAC and may contribute to a poor clinical outcome.

Figure 5.
Figure 5.

Alterations in genes encoding the ERBB family and selected downstream signaling mediators is associated with reduced pancreatic cancer patient survival. A, Genetic alteration of the ERBB–PI3K signaling axis was assessed using cBioPortal and the TCGA “provisional dataset” for 149 pancreatic cancer patients. Sixty of 149 patients (40%) displayed alterations in one or more of the indicated genes. B, Overall survival of patients with pancreatic cancer with or without genetic alterations of the genes shown in A. Significance was assessed by Log-rank Mantel–Cox test.

Discussion

Overcoming acquired resistance to targeted therapies is arguably the major challenge facing drug discovery for the treatment of cancer. As exemplified by our CRISPR and drug combination screens, mechanisms of resistance to PI3K inhibition in PDAC converge on signaling through mTORC1. Incomplete suppression of mTORC1 underlies intrinsic resistance to PI3K inhibition and correlates with drug response. This was also predictive of response to PI3Kα inhibition in cell lines and patient tumors in PI3K-dependent breast cancer (41). Taken together, this suggests that mTORC1 has utility as a biomarker of PI3K inhibition and loss of mTOR signaling, combined with PI3K inhibition, is necessary to inhibit tumor growth. Interestingly, a drug-modifier CRISPR screen with the KRAS inhibitor MRTX849 also identified mTOR depletion as an enhancer of drug activity and validated the combination of MRTX849 with everolimus and AZD2014. Consistent with our data, near-complete suppression of phospho-S6 was associated with an antiproliferative effect (6).

Our data suggest that alternative pathways may compensate for PI3K inhibition to reactivate mTORC1. PI3K signaling is regulated by growth factors, as removal of FBS is sufficient to inhibit signaling through AKT and PRAS40 in PDAC cells. EGF confers resistance to BYL719 in head and neck cancer (59) and IGF1 and neuregulin 1 (also known as heregulin, HRG) drive resistance to PI3K inhibition in PIK3CA-mutant breast cancer (41). IGF1 is of interest in pancreatic cancer as it is found at high levels in tumor stroma (60). The greatest protective effect was associated with reactivation of PI3K signaling by HRG and suggests that ligand-mediated ERBB family activation participates in driving resistance to PI3K inhibition in PDAC, even in the context of oncogenic KRAS.

Numerous regulators of RTK signaling were implicated in resistance to PI3K inhibition in both CRISPR screens. Of these, MEMO1 interacts with IGF1R and all four ERBB family members (50, 51) and mediates activation of MAPK and PI3K signaling (51). MEMO1 also interacts with IRS1 and prevents dephosphorylation and deactivation of IRS1 signaling (50). UBE2H is involved in insulin and PI3K signaling in skeletal muscle and cooperates with the E3-ubiquitin ligase, Mitsugumin 53 (MG53 or TRIM72), to ubiquitinate and downregulate IRS1, which is important for negative feedback regulation of IGF1 and insulin signaling and inhibition of skeletal myogenesis (61). Loss of these genes sensitized cells to PI3K inhibition, demonstrating that RTK signaling is a clear determinant of response to PI3K inhibition.

The combination drug screen suggested that targeting of mTOR or the ERBB family could circumvent resistance to PI3K inhibition. The ERBB family consists of four receptor tyrosine kinases, which are activated by ligand binding and regulate the RAS, MAPK, and PI3K pathways (62). However, KRAS is activated downstream of EGFR signaling, implying that EGFR may have little relevance in tumors driven by constitutively activated RAS signaling. In support of this, activating mutations in KRAS drive resistance to EGFR inhibitors in colorectal cancer (63, 64). Conversely, in PDAC clinical trials, addition of the EGFR inhibitor, erlotinib, to gemcitabine resulted in a modest survival benefit, suggesting that tumors may still partially rely on EGFR signaling (43). In line with this, we show that genetic alterations of ERBB and PI3K pathway members in PDAC patient tumors associates with poor survival and may provide potential patient selection criteria for this drug combination, which warrants further investigation as a novel therapeutic strategy in PDAC. The use of gene expression signatures, to classify pancreatic cancers into distinct subtypes that exhibit vulnerabilities to specific drugs, also has the potential to inform treatment decisions (48). For example, we focused on the QM subtype of pancreatic cancer, so this patient population would be a rational choice for preliminary investigations. Furthermore, around 50% of clinical pancreatic samples are EGFR positive and overexpression correlates with poor survival (43). Upregulation of EGFR occurs selectively in PanINs and early stages of PDAC in mice (65, 66), which implicates this receptor in tumor development. Moreover, mouse models show that Kras-driven tumorigenesis is dependent on Egfr, as genetic inactivation of Egfr blocks induction of PanINs and PDAC (65, 66). Similarly, studies of KRAS-mutant NSCLC show that ERBB family signaling amplifies the activity of mutant KRAS in in vivo models and loss of ERBB family signaling impairs tumor development (67, 68). Furthermore, studies conducted with the KRAS inhibitors AMG510 or MRTX849 have shown synergistic antiproliferative activity with ERBB family inhibitors (5, 6). Studies in pancreatic organoid models have shown synergy between either MEK or AKT inhibitors when combined with ERBB family inhibitors (but not with EGFR inhibition), underscoring the need to completely suppress ERBB signaling for activity (69). Pelitinib is a potent and irreversible EGFR inhibitor that also has activity against other ERBB family members, most notably HER2 (70, 71). Therefore, the synergistic activity is likely not due to inhibition of EGFR alone, but through more durable inhibition of all ERBB family receptors. These data clearly support a role for the ERBB family in mutant KRAS signaling and KRAS-driven tumorigenesis and cell proliferation across tumor types.

We have shown that pancreatic cancer cell lines are predominantly resistant to inhibition of PI3K via sustained mTOR signaling, despite effective inhibition of upstream PI3K signaling, indicating that alternative pathways can maintain mTORC1 activation and promote proliferation. Our genetic and pharmacologic data show that dual inhibition of PI3K and mTORC1 signaling achieves greater antiproliferative activity than targeting a single node. Notably, dual mTORC1/2 kinase inhibitors, such as AZD2014, achieve this at low nanomolar concentrations that are pharmacologically relevant, whereas higher concentrations of mTORC1 allosteric inhibitors are required to achieve similar effects. This suggests a potential benefit of inhibiting both mTORC1 and mTORC2. Furthermore, this can also be achieved by combined inhibition of PI3Kα and ERBB family signaling, indicating that the ERBB family is important for sustaining mTORC1 activity in the presence of PI3K inhibition, even in the context of mutant KRAS (Supplementary Fig. S8). Given the role of ERBB family activity in enhancing signaling through KRAS in NSCLC (67, 68), one may speculate that inhibition of mTOR, downstream of ERBB family inhibition, may result from attenuation of RTK-driven wild-type KRAS signaling (72). However, we did not detect robust inhibition of ERK phosphorylation in response to pelitinib (alone or with BYL719), suggesting this was not MAPK dependent. This study provides the basis for future translational work in xenograft and genetically engineered mouse models of pancreatic cancer, to determine the tolerability and efficacy of combined PI3K and mTOR kinase inhibitors or irreversible ERBB family inhibitors. Reassuringly, the combination of PI3K and pan-ERBB inhibitors has been tested in KRAS or PIK3CA-driven xenografts and genetically engineered mouse models confirming this therapeutic strategy is tolerated and efficacious in vivo (73–75). Our data suggest potential pharmacodynamic biomarkers to monitor drug response and guide dosing strategies. Positive results may renew interest in these classes of therapeutic agents for this challenging cancer type.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Authors' Contributions

Conception and design: C.K. Milton, S.R. Whittaker

Development of methodology: C.K. Milton, S.R. Whittaker

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): C.K. Milton, A.J. Self, D.E. Root, S.R. Whittaker

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): C.K. Milton, F. Piccioni, D.E. Root, S.R. Whittaker

Writing, review, and/or revision of the manuscript: C.K. Milton, P.A. Clarke, U. Banerji, S.R. Whittaker

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A.J. Self, F. Piccioni, S.R. Whittaker

Study supervision: P.A. Clarke, U. Banerji, S.R. Whittaker

Acknowledgments

This work has been funded by The Institute of Cancer Research, Pancreatic Cancer UK and the Louis Nicholas Residuary Charitable Trust (to S.R. Whittaker). We thank Drs. Muge Sarper and Amine Sadok at The Institute of Cancer Research for advice on the spheroid, coculture assays and Drs Mark Stubbs and Rosemary Burke, also at The Institute of Cancer Research, for access to the compound library and assistance with ECHO dispensing.

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.

Footnotes

  • Note: Supplementary data for this article are available at Molecular Cancer Therapeutics Online (http://mct.aacrjournals.org/).

  • Mol Cancer Ther 2020;19:1423–35

  • Received December 13, 2019.
  • Revision received February 17, 2020.
  • Accepted April 6, 2020.
  • Published first May 5, 2020.

References

  1. 1.↵
    Cancer Research UK Cancer Statistics for the UK. Available from: http://www.cancerresearchuk.org/health-professional/cancer-statistics/.
  2. 2.↵
    1. Kamisawa T,
    2. Wood LD,
    3. Itoi T,
    4. Takaori K
    . Pancreatic cancer. The Lancet 2016;388(10039):73–85.
    OpenUrl
  3. 3.↵
    1. Witkiewicz AK,
    2. McMillan EA,
    3. Balaji U,
    4. Baek G,
    5. Lin WC,
    6. Mansour J,
    7. et al.
    Whole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets. Nat Commun 2015;6:6744.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Collins MA,
    2. Bednar F,
    3. Zhang YQ,
    4. Brisset JC,
    5. Galban S,
    6. Galban CJ,
    7. et al.
    Oncogenic Kras is required for both the initiation and maintenance of pancreatic cancer in mice. J Clin Invest 2012;122:639–53.
    OpenUrlCrossRefPubMed
  5. 5.↵
    1. Canon J,
    2. Rex K,
    3. Saiki AY,
    4. Mohr C,
    5. Cooke K,
    6. Bagal D,
    7. et al.
    The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature 2019;575:217–23.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Hallin J,
    2. Engstrom LD,
    3. Hargis L,
    4. Calinisan A,
    5. Aranda R,
    6. Briere DM,
    7. et al.
    The KRAS(G12C) inhibitor MRTX849 provides insight toward therapeutic susceptibility of KRAS-mutant cancers in mouse models and patients. Cancer Discov 2020;10:54–71.
    OpenUrlAbstract/FREE Full Text
  7. 7.↵
    1. Javle MM,
    2. Shroff RT,
    3. Xiong H,
    4. Varadhachary GA,
    5. Fogelman D,
    6. Reddy SA,
    7. et al.
    Inhibition of the mammalian target of rapamycin (mTOR) in advanced pancreatic cancer: results of two phase II studies. BMC Cancer 2010;10:368.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Edling CE,
    2. Selvaggi F,
    3. Buus R,
    4. Maffucci T,
    5. Di Sebastiano P,
    6. Friess H,
    7. et al.
    Key role of phosphoinositide 3-kinase class IB in pancreatic cancer. Clin Cancer Res 2010;16:4928–37.
    OpenUrlAbstract/FREE Full Text
  9. 9.↵
    1. Asano T,
    2. Yao Y,
    3. Zhu J,
    4. Li D,
    5. Abbruzzese JL,
    6. Reddy SA
    . The PI 3-kinase/Akt signaling pathway is activated due to aberrant Pten expression and targets transcription factors NF-kappaB and c-Myc in pancreatic cancer cells. Oncogene 2004;23:8571–80.
    OpenUrlCrossRefPubMed
  10. 10.↵
    1. Yuan TL,
    2. Cantley LC
    . PI3K pathway alterations in cancer: variations on a theme. Oncogene 2008;27:5497–510.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Kennedy AL,
    2. Morton JP,
    3. Manoharan I,
    4. Nelson DM,
    5. Jamieson NB,
    6. Pawlikowski JS,
    7. et al.
    Activation of the PIK3CA/AKT pathway suppresses senescence induced by an activated RAS oncogene to promote tumorigenesis. Mol Cell 2011;42:36–49.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Morran DC,
    2. Wu J,
    3. Jamieson NB,
    4. Mrowinska A,
    5. Kalna G,
    6. Karim SA,
    7. et al.
    Targeting mTOR dependency in pancreatic cancer. Gut 2014;63:1481–9.
    OpenUrlAbstract/FREE Full Text
  13. 13.↵
    1. Gupta S,
    2. Ramjaun AR,
    3. Haiko P,
    4. Wang Y,
    5. Warne PH,
    6. Nicke B,
    7. et al.
    Binding of ras to phosphoinositide 3-kinase p110alpha is required for ras-driven tumorigenesis in mice. Cell 2007;129:957–68.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Eser S,
    2. Reiff N,
    3. Messer M,
    4. Seidler B,
    5. Gottschalk K,
    6. Dobler M,
    7. et al.
    Selective requirement of PI3K/PDK1 signaling for Kras oncogene-driven pancreatic cell plasticity and cancer. Cancer Cell 2013;23:406–20.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Baer R,
    2. Cintas C,
    3. Dufresne M,
    4. Cassant-Sourdy S,
    5. Schonhuber N,
    6. Planque L,
    7. et al.
    Pancreatic cell plasticity and cancer initiation induced by oncogenic Kras is completely dependent on wild-type PI 3-kinase p110alpha. Genes Dev 2014;28:2621–35.
    OpenUrlAbstract/FREE Full Text
  16. 16.↵
    1. Wolpin BM,
    2. Hezel AF,
    3. Abrams T,
    4. Blaszkowsky LS,
    5. Meyerhardt JA,
    6. Chan JA,
    7. et al.
    Oral mTOR inhibitor everolimus in patients with gemcitabine-refractory metastatic pancreatic cancer. J Clin Oncol 2009;27:193–8.
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    1. She QB,
    2. Halilovic E,
    3. Ye Q,
    4. Zhen W,
    5. Shirasawa S,
    6. Sasazuki T,
    7. et al.
    4E-BP1 is a key effector of the oncogenic activation of the AKT and ERK signaling pathways that integrates their function in tumors. Cancer Cell 2010;18:39–51.
    OpenUrlCrossRefPubMed
  18. 18.↵
    1. She QB,
    2. Solit DB,
    3. Ye Q,
    4. O'Reilly KE,
    5. Lobo J,
    6. Rosen N
    . The BAD protein integrates survival signaling by EGFR/MAPK and PI3K/Akt kinase pathways in PTEN-deficient tumor cells. Cancer Cell 2005;8:287–97.
    OpenUrlCrossRefPubMed
  19. 19.↵
    1. Whittaker SR,
    2. Theurillat JP,
    3. Van Allen E,
    4. Wagle N,
    5. Hsiao J,
    6. Cowley GS,
    7. et al.
    A genome-scale RNA interference screen implicates NF1 loss in resistance to RAF inhibition. Cancer Discov 2013;3:350–62.
    OpenUrlAbstract/FREE Full Text
  20. 20.↵
    1. Bliss CI
    . The toxicity of poisons applied jointly. Ann Appl Biol 1939;26:585–615.
    OpenUrlCrossRef
  21. 21.↵
    1. Singh A,
    2. Greninger P,
    3. Rhodes D,
    4. Koopman L,
    5. Violette S,
    6. Bardeesy N,
    7. et al.
    A gene expression signature associated with "K-Ras addiction" reveals regulators of EMT and tumor cell survival. Cancer Cell 2009;15:489–500.
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. Sanjana NE,
    2. Shalem O,
    3. Zhang F
    . Improved vectors and genome-wide libraries for CRISPR screening. Nat Methods 2014;11:783–4.
    OpenUrlCrossRefPubMed
  23. 23.↵
    1. Doench JG,
    2. Fusi N,
    3. Sullender M,
    4. Hegde M,
    5. Vaimberg EW,
    6. Donovan KF,
    7. et al.
    Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol 2016;34:184–91.
    OpenUrlCrossRefPubMed
  24. 24.↵
    1. Piccioni F,
    2. Younger ST,
    3. Root DE
    . Pooled lentiviral-delivery genetic screens. Curr Protoc Mol Biol 2018;121:3211–121.
    OpenUrl
  25. 25.↵
    1. Blomen VA,
    2. Majek P,
    3. Jae LT,
    4. Bigenzahn JW,
    5. Nieuwenhuis J,
    6. Staring J,
    7. et al.
    Gene essentiality and synthetic lethality in haploid human cells. Science 2015;350:1092–6.
    OpenUrlAbstract/FREE Full Text
  26. 26.↵
    1. Hart T,
    2. Chandrashekhar M,
    3. Aregger M,
    4. Steinhart Z,
    5. Brown KR,
    6. MacLeod G,
    7. et al.
    High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 2015;163:1515–26.
    OpenUrlCrossRefPubMed
  27. 27.↵
    1. Wang T,
    2. Birsoy K,
    3. Hughes NW,
    4. Krupczak KM,
    5. Post Y,
    6. Wei JJ,
    7. et al.
    Identification and characterization of essential genes in the human genome. Science 2015;350:1096–101.
    OpenUrlAbstract/FREE Full Text
  28. 28.↵
    1. Fabregat A,
    2. Jupe S,
    3. Matthews L,
    4. Sidiropoulos K,
    5. Gillespie M,
    6. Garapati P,
    7. et al.
    The reactome pathway knowledgebase. Nucleic Acids Res 2018;46:D649–D55.
    OpenUrlCrossRefPubMed
  29. 29.↵
    1. Kanehisa M,
    2. Furumichi M,
    3. Tanabe M,
    4. Sato Y,
    5. Morishima K
    . KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 2017;45:D353–61.
    OpenUrlCrossRefPubMed
  30. 30.↵
    1. Kanehisa M,
    2. Goto S. KEGG
    : kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27–30.
    OpenUrlCrossRefPubMed
  31. 31.↵
    1. Liberzon A,
    2. Subramanian A,
    3. Pinchback R,
    4. Thorvaldsdottir H,
    5. Tamayo P,
    6. Mesirov JP
    . Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011;27:1739–40.
    OpenUrlCrossRefPubMed
  32. 32.↵
    1. Subramanian A,
    2. Tamayo P,
    3. Mootha VK,
    4. Mukherjee S,
    5. Ebert BL,
    6. Gillette MA,
    7. 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.
    OpenUrlAbstract/FREE Full Text
  33. 33.↵
    1. Furet P,
    2. Guagnano V,
    3. Fairhurst RA,
    4. Imbach-Weese P,
    5. Bruce I,
    6. Knapp M,
    7. et al.
    Discovery of NVP-BYL719 a potent and selective phosphatidylinositol-3 kinase alpha inhibitor selected for clinical evaluation. Bioorg Med Chem Lett 2013;23:3741–8.
    OpenUrlCrossRefPubMed
  34. 34.↵
    1. Folkes AJ,
    2. Ahmadi K,
    3. Alderton WK,
    4. Alix S,
    5. Baker SJ,
    6. Box G,
    7. et al.
    The identification of 2-(1H-indazol-4-yl)-6-(4-methanesulfonyl-piperazin-1-ylmethyl)-4-morpholin-4-yl-t hieno[3,2-d]pyrimidine (GDC-0941) as a potent, selective, orally bioavailable inhibitor of class I PI3 kinase for the treatment of cancer. J Med Chem 2008;51:5522–32.
    OpenUrlCrossRefPubMed
  35. 35.↵
    1. Fritsch C,
    2. Huang A,
    3. Chatenay-Rivauday C,
    4. Schnell C,
    5. Reddy A,
    6. Liu M,
    7. et al.
    Characterization of the novel and specific PI3Kα inhibitor NVP-BYL719 and development of the patient stratification strategy for clinical trials. Mol Cancer Ther 2014;13:1117–29.
    OpenUrlAbstract/FREE Full Text
  36. 36.↵
    1. Huang J,
    2. Manning BD
    . The TSC1-TSC2 complex: a molecular switchboard controlling cell growth. Biochem J 2008;412:179–90.
    OpenUrlAbstract/FREE Full Text
  37. 37.↵
    1. Sancak Y,
    2. Thoreen CC,
    3. Peterson TR,
    4. Lindquist RA,
    5. Kang SA,
    6. Spooner E,
    7. et al.
    PRAS40 is an insulin-regulated inhibitor of the mTORC1 protein kinase. Mol Cell 2007;25:903–15.
    OpenUrlCrossRefPubMed
  38. 38.↵
    1. Martin TD,
    2. Chen XW,
    3. Kaplan RE,
    4. Saltiel AR,
    5. Walker CL,
    6. Reiner DJ,
    7. et al.
    Ral and Rheb GTPase activating proteins integrate mTOR and GTPase signaling in aging, autophagy, and tumor cell invasion. Mol Cell 2014;53:209–20.
    OpenUrlCrossRefPubMed
  39. 39.↵
    1. Sancak Y,
    2. Bar-Peled L,
    3. Zoncu R,
    4. Markhard AL,
    5. Nada S,
    6. Sabatini DM
    . Ragulator-Rag complex targets mTORC1 to the lysosomal surface and is necessary for its activation by amino acids. Cell 2010;141:290–303.
    OpenUrlCrossRefPubMed
  40. 40.↵
    1. Sancak Y,
    2. Peterson TR,
    3. Shaul YD,
    4. Lindquist RA,
    5. Thoreen CC,
    6. Bar-Peled L,
    7. et al.
    The Rag GTPases bind raptor and mediate amino acid signaling to mTORC1. Science 2008;320:1496–501.
    OpenUrlAbstract/FREE Full Text
  41. 41.↵
    1. Elkabets M,
    2. Vora S,
    3. Juric D,
    4. Morse N,
    5. Mino-Kenudson M,
    6. Muranen T,
    7. et al.
    mTORC1 inhibition is required for sensitivity to PI3K p110α inhibitors in PIK3CA-mutant breast cancer. Sci Transl Med 2013;5:196ra99.
    OpenUrlAbstract/FREE Full Text
  42. 42.↵
    1. Juric D,
    2. Castel P,
    3. Griffith M,
    4. Griffith OL,
    5. Won HH,
    6. Ellis H,
    7. et al.
    Convergent loss of PTEN leads to clinical resistance to a PI(3)Kα inhibitor. Nature 2015;518:240–4.
    OpenUrlCrossRefPubMed
  43. 43.↵
    1. Moore MJ,
    2. Goldstein D,
    3. Hamm J,
    4. Figer A,
    5. Hecht JR,
    6. Gallinger S,
    7. et al.
    Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: a phase III trial of the National Cancer Institute of Canada Clinical Trials Group. J Clin Oncol 2007;25:1960–6.
    OpenUrlAbstract/FREE Full Text
  44. 44.↵
    1. Rappoport JZ,
    2. Simon SM
    . Endocytic trafficking of activated EGFR is AP-2 dependent and occurs through preformed clathrin spots. J Cell Sci 2009;122(Pt 9):1301–5.
    OpenUrlAbstract/FREE Full Text
  45. 45.↵
    1. Barbier AJ,
    2. Poppleton HM,
    3. Yigzaw Y,
    4. Mullenix JB,
    5. Wiepz GJ,
    6. Bertics PJ,
    7. et al.
    Transmodulation of epidermal growth factor receptor function by cyclic AMP-dependent protein kinase. J Biol Chem 1999;274:14067–73.
    OpenUrlAbstract/FREE Full Text
  46. 46.↵
    1. Salazar G,
    2. Gonzalez A
    . Novel mechanism for regulation of epidermal growth factor receptor endocytosis revealed by protein kinase A inhibition. Mol Biol Cell 2002;13:1677–93.
    OpenUrlAbstract/FREE Full Text
  47. 47.↵
    1. Brough R,
    2. Gulati A,
    3. Haider S,
    4. Kumar R,
    5. Campbell J,
    6. Knudsen E,
    7. et al.
    Identification of highly penetrant Rb-related synthetic lethal interactions in triple negative breast cancer. Oncogene 2018;37:5701–18.
    OpenUrl
  48. 48.↵
    1. Collisson EA,
    2. Sadanandam A,
    3. Olson P,
    4. Gibb WJ,
    5. Truitt M,
    6. Gu S,
    7. et al.
    Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med 2011;17:500–3.
    OpenUrlCrossRefPubMed
  49. 49.↵
    1. Port J,
    2. Muthalagu N,
    3. Raja M,
    4. Ceteci F,
    5. Monteverde T,
    6. Kruspig B,
    7. et al.
    Colorectal tumors require NUAK1 for protection from oxidative stress. Cancer Discov 2018;8:632–47.
    OpenUrlAbstract/FREE Full Text
  50. 50.↵
    1. Sorokin AV,
    2. Chen J
    . MEMO1 a new IRS1-interacting protein, induces epithelial-mesenchymal transition in mammary epithelial cells. Oncogene 2013;32:3130–8.
    OpenUrlCrossRefPubMed
  51. 51.↵
    1. Jiang K,
    2. Yang Z,
    3. Cheng L,
    4. Wang S,
    5. Ning K,
    6. Zhou L,
    7. et al.
    Mediator of ERBB2-driven cell motility (MEMO) promotes extranuclear estrogen receptor signaling involving the growth factor receptors IGF1R and ERBB2. J Biol Chem 2013;288:24590–9.
    OpenUrlAbstract/FREE Full Text
  52. 52.↵
    1. Heldin CH,
    2. Westermark B,
    3. Wasteson A
    . Desensitisation of cultured glial cells to epidermal growth factor by receptor down-regulation. Nature 1979;282:419–20.
    OpenUrlCrossRefPubMed
  53. 53.↵
    1. Erlichman C,
    2. Hidalgo M,
    3. Boni JP,
    4. Martins P,
    5. Quinn SE,
    6. Zacharchuk C,
    7. et al.
    Phase I study of EKB-569, an irreversible inhibitor of the epidermal growth factor receptor, in patients with advanced solid tumors. J Clin Oncol 2006;24:2252–60.
    OpenUrlAbstract/FREE Full Text
  54. 54.↵
    1. Pike KG,
    2. Malagu K,
    3. Hummersone MG,
    4. Menear KA,
    5. Duggan HM,
    6. Gomez S,
    7. et al.
    Optimization of potent and selective dual mTORC1 and mTORC2 inhibitors: the discovery of AZD8055 and AZD2014. Bioorg Med Chem Lett 2013;23:1212–6.
    OpenUrlCrossRefPubMed
  55. 55.↵
    1. Gaviraghi M,
    2. Tunici P,
    3. Valensin S,
    4. Rossi M,
    5. Giordano C,
    6. Magnoni L,
    7. et al.
    Pancreatic cancer spheres are more than just aggregates of stem marker-positive cells. Biosci Rep 2011;31:45–55.
    OpenUrlAbstract/FREE Full Text
  56. 56.↵
    1. Tape CJ,
    2. Ling S,
    3. Dimitriadi M,
    4. McMahon KM,
    5. Worboys JD,
    6. Leong HS,
    7. et al.
    Oncogenic KRAS regulates tumor cell signaling via stromal reciprocation. Cell 2016;165:910–20.
    OpenUrlCrossRefPubMed
  57. 57.↵
    1. Cerami E,
    2. Gao J,
    3. Dogrusoz U,
    4. Gross BE,
    5. Sumer SO,
    6. Aksoy BA,
    7. et al.
    The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012;2:401–4.
    OpenUrlAbstract/FREE Full Text
  58. 58.↵
    1. Gao J,
    2. Aksoy BA,
    3. Dogrusoz U,
    4. Dresdner G,
    5. Gross B,
    6. Sumer SO,
    7. et al.
    Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013;6:pl1.
    OpenUrlAbstract/FREE Full Text
  59. 59.↵
    1. Elkabets M,
    2. Pazarentzos E,
    3. Juric D,
    4. Sheng Q,
    5. Pelossof RA,
    6. Brook S,
    7. et al.
    AXL mediates resistance to PI3Kalpha inhibition by activating the EGFR/PKC/mTOR axis in head and neck and esophageal squamous cell carcinomas. Cancer Cell 2015;27:533–46.
    OpenUrlCrossRefPubMed
  60. 60.↵
    1. Bergmann U,
    2. Funatomi H,
    3. Yokoyama M,
    4. Beger HG,
    5. Korc M
    . Insulin-like growth factor I overexpression in human pancreatic cancer: evidence for autocrine and paracrine roles. Cancer Res 1995;55:2007–11.
    OpenUrlAbstract/FREE Full Text
  61. 61.↵
    1. Yi JS,
    2. Park JS,
    3. Ham YM,
    4. Nguyen N,
    5. Lee NR,
    6. Hong J,
    7. et al.
    MG53-induced IRS-1 ubiquitination negatively regulates skeletal myogenesis and insulin signalling. Nat Commun 2013;4:2354.
    OpenUrlCrossRefPubMed
  62. 62.↵
    1. Yarden Y,
    2. Sliwkowski MX
    . Untangling the ErbB signalling network. Nat Rev Mol Cell Biol 2001;2:127–37.
    OpenUrlCrossRefPubMed
  63. 63.↵
    1. Karapetis CS,
    2. Khambata-Ford S,
    3. Jonker DJ,
    4. O'Callaghan CJ,
    5. Tu D,
    6. Tebbutt NC,
    7. et al.
    K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 2008;359:1757–65.
    OpenUrlCrossRefPubMed
  64. 64.↵
    1. Van Emburgh BO,
    2. Arena S,
    3. Siravegna G,
    4. Lazzari L,
    5. Crisafulli G,
    6. Corti G,
    7. et al.
    Acquired RAS or EGFR mutations and duration of response to EGFR blockade in colorectal cancer. Nat Commun 2016;7:13665.
    OpenUrl
  65. 65.↵
    1. Navas C,
    2. Hernandez-Porras I,
    3. Schuhmacher AJ,
    4. Sibilia M,
    5. Guerra C,
    6. Barbacid M
    . EGF receptor signaling is essential for k-ras oncogene-driven pancreatic ductal adenocarcinoma. Cancer Cell 2012;22:318–30.
    OpenUrlCrossRefPubMed
  66. 66.↵
    1. Ardito CM,
    2. Gruner BM,
    3. Takeuchi KK,
    4. Lubeseder-Martellato C,
    5. Teichmann N,
    6. Mazur PK,
    7. et al.
    EGF receptor is required for KRAS-induced pancreatic tumorigenesis. Cancer Cell 2012;22:304–17.
    OpenUrlCrossRefPubMed
  67. 67.↵
    1. Kruspig B,
    2. Monteverde T,
    3. Neidler S,
    4. Hock A,
    5. Kerr E,
    6. Nixon C,
    7. et al.
    The ERBB network facilitates KRAS-driven lung tumorigenesis. Sci Transl Med 2018;10. pii: eaao2565.
    OpenUrl
  68. 68.↵
    1. Moll HP,
    2. Pranz K,
    3. Musteanu M,
    4. Grabner B,
    5. Hruschka N,
    6. Mohrherr J,
    7. et al.
    Afatinib restrains K-RAS-driven lung tumorigenesis. Sci Transl Med 2018;10. pii: eaao2301.
    OpenUrlCrossRefPubMed
  69. 69.↵
    1. Ponz-Sarvise M,
    2. Corbo V,
    3. Tiriac H,
    4. Engle DD,
    5. Frese KK,
    6. Oni TE,
    7. et al.
    Identification of resistance pathways specific to malignancy using organoid models of pancreatic cancer. Clin Cancer Res 2019;25:6742–55.
    OpenUrlAbstract/FREE Full Text
  70. 70.↵
    1. Schaefer G,
    2. Shao L,
    3. Totpal K,
    4. Akita RW
    . Erlotinib directly inhibits HER2 kinase activation and downstream signaling events in intact cells lacking epidermal growth factor receptor expression. Cancer Res 2007;67:1228–38.
    OpenUrlAbstract/FREE Full Text
  71. 71.↵
    1. Wissner A,
    2. Overbeek E,
    3. Reich MF,
    4. Floyd MB,
    5. Johnson BD,
    6. Mamuya N,
    7. et al.
    Synthesis and structure-activity relationships of 6,7-disubstituted 4-anilinoquinoline-3-carbonitriles. The design of an orally active, irreversible inhibitor of the tyrosine kinase activity of the epidermal growth factor receptor (EGFR) and the human epidermal growth factor receptor-2 (HER-2). J Med Chem 2003;46:49–63.
    OpenUrlCrossRefPubMed
  72. 72.↵
    1. Young A,
    2. Lou D,
    3. McCormick F
    . Oncogenic and wild-type Ras play divergent roles in the regulation of mitogen-activated protein kinase signaling. Cancer Discov 2013;3:112–23.
    OpenUrlAbstract/FREE Full Text
  73. 73.↵
    1. Belmont PJ,
    2. Jiang P,
    3. McKee TD,
    4. Xie T,
    5. Isaacson J,
    6. Baryla NE,
    7. et al.
    Resistance to dual blockade of the kinases PI3K and mTOR in KRAS-mutant colorectal cancer models results in combined sensitivity to inhibition of the receptor tyrosine kinase EGFR. Sci Signal 2014;7:ra107.
    OpenUrlAbstract/FREE Full Text
  74. 74.↵
    1. Brady SW,
    2. Zhang J,
    3. Seok D,
    4. Wang H,
    5. Yu D
    . Enhanced PI3K p110alpha signaling confers acquired lapatinib resistance that can be effectively reversed by a p110alpha-selective PI3K inhibitor. Mol Cancer Ther 2014;13:60–70.
    OpenUrlAbstract/FREE Full Text
  75. 75.↵
    1. Young CD,
    2. Pfefferle AD,
    3. Owens P,
    4. Kuba MG,
    5. Rexer BN,
    6. Balko JM,
    7. et al.
    Conditional loss of ErbB3 delays mammary gland hyperplasia induced by mutant PIK3CA without affecting mammary tumor latency, gene expression, or signaling. Cancer Res 2013;73:4075–85.
    OpenUrlAbstract/FREE Full Text
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A Genome-scale CRISPR Screen Identifies the ERBB and mTOR Signaling Networks as Key Determinants of Response to PI3K Inhibition in Pancreatic Cancer
Charlotte K. Milton, Annette J. Self, Paul A. Clarke, Udai Banerji, Federica Piccioni, David E. Root and Steven R. Whittaker
Mol Cancer Ther July 1 2020 (19) (7) 1423-1435; DOI: 10.1158/1535-7163.MCT-19-1131
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