Prostate cancer is the most commonly diagnosed cancer in men, and the second most lethal. All stages of prostate cancer depend upon male sex hormones, also known as androgens, to grow because these hormones bind and activate androgen receptors. A class of drugs termed ‘antiandrogens’ can effectively treat prostate cancer because they bind to androgen receptors without activating them, thereby preventing androgens from binding. However, the efficacy of even highly potent antiandrogen drugs, such as enzalutamide is short-lived in many patients, and understanding the biological mechanisms that cause drug resistance is one of the major objectives in translational prostate cancer research.

Resistance can arise through mutations of the androgen receptor that result in the receptor being activated, rather than inhibited, by antiandrogen drugs. However, no such mutations are known yet for enzalutamide, and researchers are keen to understand whether they exist and, if so, to generate new drugs for prostate cancer that overcome them. To identify mutations that may lead to resistance, Balbas et al. designed a new screening method in human prostate cancer cells and showed that androgen receptors with a specific mutation (called F876L) can be activated by enzalutamide. More comprehensive biological studies showed that prostate cancer cells harboring the mutation continued to grow when treated with the drug. Balbas et al. also showed that this mutation can arise spontaneously in human prostate cancer cells treated long term with enzalutamide.

Balbas et al. reasoned that the mutation likely altered the way enzalutamide binds to the androgen receptor, and used computer-guided structural modeling of the complex formed by the receptor and the drug to investigate how this might occur. These studies indicated that the region of the androgen receptor containing the F876L mutation comes into direct contact with the drug, and provided a structural explanation for the loss of inhibition. Because these studies showed how enzalutamide might bind to the androgen receptor, they also suggested ways in which enzalutamide could be chemically modified to restore its inhibitory activity against the mutant receptor.

Balbas et al. then designed and synthesized a set of novel compounds, which the modeling data suggested could act as inhibitors of the mutant receptor. Several of these compounds inhibited the activity of both mutant and wild-type forms of the androgen receptor, and suppressed the growth of both enzalutamide-resistant and nonresistant prostate cancer cells.

The work of Balbas et al. outlines a general screening strategy for the discovery of clinically relevant mutations in cancer genes, and shows how in silico technologies can accelerate drug discovery in the absence of a crystal structure of a protein–drug complex. It also emphasizes how understanding the manner in which a drug binds its target can stimulate rational design of improved drug candidates.

The recent FDA approval of enzalutamide (formerly MDV3100) confirms the continued critical role AR signaling plays in castration-resistant prostate cancer (Tran et al., 2009; Scher et al., 2012). In spite of these promising results, patient responses to enzalutamide are varied and often short lived. Reactivation of AR signaling has been implicated in resistance to previous antiandrogen therapy (Linja et al., 2001; Chen et al., 2004), and one well-documented mechanism of reactivation is point mutation in the ligand-binding domain (LBD) of AR (Bergerat and Ceraline, 2009). Many of these mutations broaden ligand specificity, and some confer resistance by converting the AR antagonist into an agonist of the mutant receptor (Veldscholte et al., 1990; Haapala et al., 2001; Hara et al., 2003). Because previous work with targeted therapies that inhibit oncogenic kinases has shown that unbiased mutagenesis screens in preclinical models can identify a priori clinically relevant mutations that alter drug activity (Azam et al., 2003; Burgess et al., 2005), we designed a novel screening method to prospectively identify AR mutations that confer resistance to enzalutamide.

Mutagenesis screens to identify kinase inhibitor-resistant alleles of kinase targets such as BCR-ABL have relied upon cytokine-dependent test cells that become cytokine independent after introduction of the target kinase. Cells expressing drug-resistant kinase alleles selectively expand in the presence of drug, allowing rapid identification of mutations that confer drug resistance. There is no comparable strategy available for antiandrogens because introduction of AR does not confer a comparable growth advantage in AR-negative cells. We instead chose to identify and select cell populations with persistent AR transcriptional activity in the presence of enzalutamide. We reasoned that targeting the biological process of interest (transcriptional activation of a target gene), rather than a distal symptom of resistance to drug (i.e., persistent viability, elevated proliferation) might identify resistant clones more quickly. To this end, an AR-regulated EGFP reporter, with a probasin promoter and PSA enhancer elements driving EGFP expression (Pb.PSE.EGFP) (Chapel-Fernandes et al., 2006), was used to screen for and enrich cell populations bearing biologically active mutations (Figure 1A).

Figure 1 with 4 supplements see all

Download asset Open asset

Proof-of-concept studies were conducted with LNCaP cells cotransduced with Pb.PSE.EGFP and AR W741C, a well-characterized mutation that converts the AR antagonist bicalutamide into an agonist (Haapala et al., 2001; Hara et al., 2003). With starting ratios of 1:100 and 1:1000 cells overexpressing AR W741C to wild-type AR, respectively, we sorted cells with FACS that maintained EGFP expression after acute bicalutamide exposure. After four rounds of bicalutamide ‘selection’ and sorting, LNCaP cells expressing AR W741C dominated the final populations (Figure 1—figure supplement 1).

We next conducted the enzalutamide resistance screen with the Pb.PSE.EGFP reporter and a randomly mutagenized AR library. After five iterations of enzalutamide exposure and FACS sorting, we identified a population of cells with durable EGFP expression (Figure 1B). Moreover, enzalutamide promoted AR transcriptional activity in these cells, reflected by induction of EGFP expression compared to vehicle control (Figure 1B). Analysis of endogenous AR target gene expression confirmed that enzalutamide behaved as an agonist in the enriched cell population (Figure 1—figure supplement 2), and siRNA knockdown of AR showed that these pharmacologically induced changes remained AR dependent (Figure 1—figure supplement 3A,B).

To identify AR mutations in these cells, we amplified the exogenously expressed AR cDNA, and Sanger sequenced the PCR product. In two of three replicates, a single dominant point mutation emerged, resulting in the amino acid substitution F876L (Figure 1C). Importantly, this mutation clearly enriched throughout the selection process (Figure 1—figure supplement 4).

To validate the results of the screen, an AR F876L vector was engineered and transduced into parental LNCaP cells expressing the Pb.PSE.EGFP reporter. Treatment of these cells with enzalutamide resulted in a dose-dependent induction of EGFP expression (Figure 2A, Figure 2—figure supplement 1). We also introduced AR F876L cDNA into AR-negative CV1 cells along with an AR-dependent luciferase construct, and upon enzalutamide treatment, luciferase activity was induced ∼50-fold (Figure 2B). These results were comparable to those seen with the previously reported AR mutations T877A and W741C, which confer agonism to hydroxyflutamide and bicalutamide, respectively. Moreover, enzalutamide treatment potently induced nuclear localization of AR F876L (Figure 2—figure supplement 2A), and chromatin immunoprecipitation studies showed that enzalutamide recruited AR F876L to the enhancers of AR target genes (Figure 2—figure supplement 2B). Consistent with these results, endogenous AR target gene expression was either no longer repressed by enzalutamide (Figure 2C, left) or strongly induced by enzalutamide in cells expressing AR F876L (Figure 2C, right). A competition assay with 16β[¹⁸F]fluoro-5α-DHT (18F-FDHT), to measure relative AR binding affinity (Tran et al., 2009), showed that enzalutamide binds with higher affinity to AR F876L than wild-type AR (Figure 2—figure supplement 3), similar to what has been shown for hydroxyflutamide and the AR T877A mutant (Ozers et al., 2007). Notably, F876L similarly impacted the pharmacology of ARN-509 (Clegg et al., 2012), a structurally discrete antiandrogen sharing the bisaryl-thiohydantoin core motif (Figure 2B, Figure 2—figure supplements 4, 5).

Figure 2 with 8 supplements see all

Download asset Open asset

In vitro growth assays were conducted to examine the consequences of AR F876L expression on enzalutamide sensitivity in prostate cancer cell lines. Although enzalutamide treatment potently inhibits the growth of parental VCaP cells (Tran et al., 2009), overexpression of AR F876L entirely reversed this phenotype (Figure 2D). Enzalutamide also rescued the growth of VCaP/AR F876L cells in androgen-depleted media, similar to that seen with the endogenous androgen DHT (Figure 2E). Finally, these results were recapitulated in CWR22Pc cells, another prostate cancer cell line that is sensitive to enzalutamide (Figure 2—figure supplement 6A,B).

In vivo, LNCaP/AR cells overexpressing either AR WT or F876L were grafted subcutaneously into castrate SCID mice and time to tumor emergence/progression was determined in the presence or absence of drug. While the growth of wild-type AR tumors was almost completely inhibited by enzalutamide treatment, tumors expressing AR F876L grew rapidly in the presence of enzalutamide, similar to vehicle-treated tumors of either genotype (Figure 2F).

We next asked whether the F876L mutation spontaneously arises in antiandrogen-sensitive human prostate cancer models after prolonged treatment with enzalutamide or ARN-509. After culturing CWR22Pc cells in vitro with enzalutamide for several months, more than 50% of the cells expressed the F876L mutation (Table 1). Prolonged culture of these cells with ARN-509 also selected for a small population (∼1.3%) expressing AR F876L. In vivo, long-term enzalutamide or ARN-509 therapy in mice bearing LNCaP/AR xenograft tumors also resulted in the outgrowth of tumor cell populations expressing AR F876L (Table 1). Sequencing revealed that AR F876L predominated in one tumor (∼71%), was present at low frequency in four other tumors (∼1 to 2%), and that a distinct amino acid substitution at this residue, F876I, was enriched in one enzalutamide-resistant tumor (Table 2).

To further explore the function of Phe876 as the ‘gateway’ residue governing enzalutamide and ARN-509 pharmacology, we used site-directed mutagenesis to make additional amino acid substitutions at residue 876. A conservative F876Y substitution did not alter the pharmacology of either drug, but aliphatic substitutions structurally similar to F876L, such as F876I (also found in one xenograft with acquired resistance), conferred agonism to both enzalutamide and ARN-509 (Figure 2—figure supplement 7).

Notably, we observed that bicalutamide did not induce AR F876L transcriptional activity in our luciferase reporter assay, either at low (Figure 2B) or at high (Figure 2—figure supplement 7) concentrations, suggesting that it retains weak antagonist activity against this mutant. We conducted EGFP reporter assays to determine if AR F876L transcriptional activity is inhibited by bicalutamide, and found that while at low doses, it is an effective inhibitor of AR F876L, it loses potency at higher concentrations (Figure 2—figure supplement 8). We also observed only minimal growth inhibition in CWR22Pc cells expressing AR F876L with bicalutamide treatment (Figure 2—figure supplement 6A). These data, along with the knowledge that AR overexpression is a common resistance mechanism in patients with CRPC (Linja et al., 2001) and confers partial agonism on bicalutamide (Chen et al., 2004), suggest that bicalutamide is not a viable treatment option for patients who fail on enzalutamide due to AR F876L mutation.

That F876L so dramatically impacted the pharmacology of enzalutamide and ARN-509 suggested that a clear structural change in the drug–receptor complex might be occurring. This consideration prompted us to investigate the structural basis of this antagonism-to-agonism conversion. Because a crystal structure depicting AR bound to an antagonist does not yet exist, we performed structural modeling using ligand docking and molecular dynamics (MD) simulations (Karplus and McCammon, 2002; Jorgensen, 2004). In designing the study, we noted that both enzalutamide and ARN-509 share identical A-rings with bicalutamide (Figure 3A) and its derivative S1 that were respectively cocrystallized with the LBD of AR W741L and AR WT in agonist conformations (PDB ID 1Z95 and 2AXA) (Bohl et al., 2005a, 2005b). 2AXA was chosen as a structural template as it bears fewer amino acid substitutions compared to 1Z95. After initial quantum-mechanical geometry optimization of the small molecules, each was independently docked into AR WT or AR F876L with the mutually shared A-ring overlaid with that of S1, whereupon 10-ns explicit-solvent MD simulations were performed.

Figure 3 with 2 supplements see all

Download asset Open asset

The docked enzalutamide and ARN-509 molecules demonstrated strikingly different interaction patterns with AR compared to bicalutamide (Figure 3B,C). Notably, the thiohydantoin B-ring prevents the compound from accessing the ‘H12 pocket’ occupied by bicalutamide. Instead, the conformationally restricted thiohydantoin forces the C-ring to bind a region near the C terminus of helix 11 and the loop connecting helices 11 and 12, that we termed the ‘H11 pocket’. As seen in the MD simulations using the WT receptor with enzalutamide and ARN-509 (Figure 3D,E [in red], Figure 3—figure supplement 1), accommodation of the C-ring in this region is coupled to significant conformational rearrangements of residues on H11 and the H11–H12 connecting loop that prevents H12 from adopting the agonist conformation required for efficient coactivator recruitment. To investigate how the F876L mutation might alleviate antagonism, we performed similar MD simulations using the F876L receptor (Figure 3D,E [in cyan], Figure 3—figure supplement 1). For WT receptors in complex with enzalutamide and ARN-509, the average RMSDs to the crystal agonist conformation for the helix 11 terminus (residues 875–882) were measured at 2.24 and 1.94 Å, respectively, and those for the helix 12 terminus (residues 893–900) were 1.81 and 2.08 Å, respectively. For the F876L mutants, in comparison, the average RMSDs for helix 11 were somewhat lower at 1.01 and 1.70 Å for enzalutamide and ARN-509, respectively, and those for helix 12 went down to 1.37 Å for both ligands. The results demonstrate that despite inducing similar dislocations in the H11 pocket, the mutation allows the receptor to reposition H12 in a more agonist-like conformation that is compatible with coactivator recruitment (Figure 3D,E, Figure 3—figure supplement 1). A close look in the H11 pocket (Figure 3—figure supplement 2A,B) indicated the following: (1) F876 in AR WT likely interacts with the C-ring end of enzalutamide or ARN-509 through favorable pi stacking or van der Waals contacts, and (2) the loss of such favorable contacts upon F876L mutation concurrently affects the conformational choices of helices 11 and 12, which interact through both bonded and nonbonded forces. Importantly, these structural modeling results are consistent with the differential resistance profiles for enzalutamide and bicalutamide involving residues 741 and 876, respectively (Figure 2B).

Another notable insight from these simulations was that the substituent on position 4 of the B-ring (i.e., the geminal [gem]-dimethyl group on enzalutamide, the spirocyclobutyl ring on ARN-509; Figure 4A) was predicted to lie in close proximity to residues on H12 of the mutant receptor (Figure 3D,E). Moreover, the steric girth of the substituent appeared to impact the positioning of H12, as the bulkier spirocyclobutyl moiety on ARN-509 elicited greater H12 displacements in AR WT than did enzalutamide's gem-dimethyl group (Figure 3E).

Figure 4 with 8 supplements see all

Download asset Open asset

To restore the positioning of H12 into an antagonist conformation for AR F876L, we designed and synthesized a series of analogues bearing saturated hydrocarbon spirocycles of incrementally greater size and complexity on the B-ring of the enzalutamide scaffold (DR100-103, Figure 4A). We defined these ring extensions on the B-ring as the D-ring (Figure 3A). The merit of this approach was also supported by prior medicinal chemistry, which had shown that discrete bisaryl-thiohydantoin compounds bearing similar D-rings, were effective antagonists of AR WT (Jung et al., 2010). In this regard, we were optimistic that, minimally, the larger inhibitors we designed should be tolerated within the ligand-binding pocket.

Consistent with this precedent, the DR100-103 series inhibited the transcriptional activity of AR WT in the EGFP reporter assay (Figure 4A, Figure 4—figure supplement 1A,B). Whereas DR100-102 behaved as strong agonists for AR F876L (Figure 4—figure supplement 1A), (±)-DR103 potently inhibited the mutant receptor and antagonized DHT induction (Figure 4—figure supplements 1 and 2). We also found that (±)-DR103 was a more potent inhibitor of AR F876L than AR WT (Figure 4—figure supplement 3), a phenomenon we are currently working to understand.

This striking structure–activity relationship prompted us to empirically investigate the significance of the position of the gem-dimethyl group on the D-ring of (±)-DR103. Remarkably, a compound with the gem-dimethyl group on position 4 (rather than 3/5) of the D-ring (DR104) was a modest agonist of AR F876L (albeit an antagonist of AR WT). Moreover, a compound with gem-dimethyl groups at the 3 and 5 positions of the D-ring (DR105) inhibited AR F876L (and AR WT) (Figure 4—figure supplement 1C), further underscoring the biological importance of the steric interactions brought about by these moieties in the context of the mutant receptor. Encouragingly, transplanting the D-ring from (±)-DR103 onto the ARN-509 scaffold ([±]-DR106) also resulted in AR F876L inhibition (Figure 4—figure supplement 1B). We interpret this result to be supportive of the model advanced by the previous MD simulations, as the F876L substitution appeared to impact the ability of enzalutamide and ARN-509 to induce H12 conformational choices in a roughly equivalent manner. Finally, to underscore the importance of the steric interactions conferred by the D-ring, we synthesized DR107, a compound built on the enzalutamide scaffold bearing only hydrogen atoms at position 4 on the B-ring. This molecule was an agonist both for AR WT and AR F876L (Figure 4—figure supplement 1C), pointing directly to the pharmacological significance of interactions between H12 and the substituent at the position 4 of the B-ring.

In line with this pharmacology, (±)-DR103 inhibited the growth of prostate cancer cell lines expressing both the WT and mutant receptor (Figure 4B, Figure 4—figure supplement 4). (±)-DR103 also inhibited endogenous AR signaling and induced PARP cleavage (Figure 4—figure supplement 5). DR101, a close structural analogue that behaved as an agonist for AR F876L, did not inhibit cell growth at equivalent doses (Figure 4B). Finally, the dose of (±)-DR103 required to observe antiproliferative effects (10 μM) did not impact the growth of DU145 (an AR-null human prostate cancer cell line), supporting the specificity of the antiandrogen (Figure 4—figure supplement 6).

Structural modeling studies for (±)-DR103 reinforced our pharmacological model for AR antagonism by bisaryl-thiohydantoins. Unlike the results for enzalutamide and ARN-509, MD simulations using (S)-DR103 suggested that an agonist-like conformation of H12 cannot be achieved for either WT or mutant AR (Figure 4C, Figure 4—figure supplement 7). The modeling study instead showed that the D-ring on (S)-DR103 was capable of directly displacing the N-terminal residues of H12. A magnified view of the H12 pocket (Figure 4—figure supplement 8) shows that (S)-DR103 occupies a region in the H12 pocket that neither enzalutamide nor ARN-509 can access, thus imposing antagonist-like dislocation of helix 12. Whereas in the H11 pocket (Figure 4—figure supplement 8), (S)-DR103 did not show a significant difference in binding with residue L876 compared to either enzalutamide or ARN-509, which suggests that the restored antagonism was not achieved by simply regaining interactions at the mutation site in AR. Similar MD simulations for the complex of AR F876L and (R)-DR103 showed a slightly less pronounced H12 dislocation (Figure 4—figure supplement 7), suggesting that the two enantiomers might cause different levels of antagonism.

As is evident from previous work with ABL kinase inhibitors for chronic myeloid leukemia and antivirals for HIV and hepatitis (Shah et al., 2004; Glickman and Sawyers, 2012), understanding mechanisms of drug resistance is a crucial first step in developing strategies to prevent or overcome it. With its recent approval, the case for defining mechanisms that overcome enzalutamide therapy is timely and compelling. Because AR mutations are a cause of clinical resistance to antiandrogens (flutamide and bicalutamide) (Veldscholte et al., 1990; Haapala et al., 2001), and previous work has shown that clinically relevant mutations can be discovered from screening platforms in preclinical models, we prospectively searched for such mutations in the context of enzalutamide using a novel saturation mutagenesis approach. This screen revealed that mutation of Phe 876 to Leu converts enzalutamide and ARN-509 into AR agonists and confers resistance to drug-induced growth inhibition in vitro and in vivo. Importantly, this mutation was also recovered ‘spontaneously’ from enzalutamide-sensitive cell line and xenograft models treated with prolonged enzalutamide therapy.

That prostate cancer can spontaneously acquire gain-of-function mutations in AR (rather than acquiring mutations that simply preclude inhibitor binding) underscores the special challenge in pharmacologically overcoming this mechanism of resistance. By borrowing insight from studies of the progesterone receptor, showing that a single amino acid can determine sensitivity to RU486, and structural analyses of the estrogen receptor (Benhamou et al., 1992; Shiau et al., 1998), our attention was immediately directed to establishing and testing a structural model of the AR/enzalutamide complex to explain enzalutamide's curious pharmacology in the context of AR F876L. Using MD simulations, a novel binding mode for the drug was identified, which provided a compelling explanation for how antagonism is retained against the bicalutamide-resistant Trp 741 mutation. More importantly, the MD simulations argued that an altered spatial orientation of enzalutamide within the AR LBD might explain the onset of agonism, as the F876L mutation appeared to reposition the drug to eliminate steric clashes that promoted H12 dislocation in AR WT. Reassuringly, several larger compounds that the MD simulations predicted could restore H12 dislocation (the ‘D-ring’ series) effectively antagonized AR F876L.

Because the discovery of this mutation and its companion pharmacology provided the basis for our structural model, it is difficult to envision how the importance of the D-ring might have otherwise emerged from previous (and ongoing) chemical screening efforts. Among the ∼100 bisaryl-thiohydantoins published to date, several compounds bearing structurally similar moieties to our bioactive series were essentially indistinguishable from enzalutamide, ARN-509, or other leading agents in conventional cell-based assays. Our own focused chemical screen further speaks to the unusually complicated pharmacobiology of AR F876L, as subtle changes in the position of geminal dimethyl moieties on DR103-5 radically impacted the respective bioactivity of the drugs.

Our success predicting the pharmacology of candidate inhibitors with MD simulations argues for a novel workflow by which in silico screening could guide future antiandrogen drug discovery (pending a cocrystal structure of an AR/antagonist complex). The data indicating that ∼50% of patients fail to respond to enzalutamide has somewhat overshadowed the importance of the discovery of the bisaryl-thiohydantoin chemotype for AR, and the ongoing enthusiasm for developing better drugs based on this motif is most visibly reflected by the clinical trial with ARN-509. In this regard, our structural model provides a powerful tool to further refine the chemotype into drug candidates with improved properties.

Collectively, these findings demonstrate the importance of coordinated mutagenesis, structural modeling, and medicinal chemistry studies in designing drugs against an important cancer target for which appropriate drug affinity and binding conformation are mutually indispensible for competent inhibition. We are optimistic that discovery of the AR F876L mutation will facilitate solution of the enzalutamide/AR complex by X-ray crystallography. As for the potential clinical impact of a priori discovery of drug–resistance mutations to novel cancer drugs, our previous experience with the ABL kinase inhibitor dasatinib in chronic myeloid leukemia serves as an example. Within 2 years of reporting dasatinib-resistant mutations in BCR-ABL in a preclinical model, analogous mutations were recovered from dasatinib-resistant chronic myeloid leukemia patients (Burgess et al., 2005; Shah et al., 2007). We hope that this report will guide a similar search for AR mutations in prostate cancer patients who develop clinical resistance to enzalutamide. Routine rebiopsy of tumor tissue in men with castration-resistant prostate cancer is challenging due to the high frequency of osteoblastic bone lesions consisting primarily of stromal tissue. Blood-based assays for AR mutation detection may be a compelling alternative, based on recent success in detection of tumor-specific mutations in circulating plasma DNA from patients with other cancers (Diehl et al., 2008; Leary et al., 2012).

1. 1. Azam M
   2. Latek RR
   3. Daley GQ

   (2003) Mechanisms of autoinhibition and STI-571/imatinib resistance revealed by mutagenesis of BCR-ABL

   Cell 112:831.

   https://doi.org/10.1016/S0092-8674(03)00190-9

   • Google Scholar
2. 1. Bayly CI
   2. Cieplak P
   3. Cornell WD
   4. Kollman PA

   (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the Resp model

   J Phys Chem 97:10269.

   https://doi.org/10.1021/j100142a004

   • Google Scholar
3. 1. Benhamou B
   2. Garcia T
   3. Lerouge T
   4. Vergezac A
   5. Gofflo D
   6. Bigogne C

   et al. (1992) A single amino acid that determines the sensitivity of progesterone receptors to RU486

   Science 255:206.

   https://doi.org/10.1126/science.1372753

   • Google Scholar
4. 1. Bergerat JP
   2. Ceraline J

   (2009) Pleiotropic functional properties of androgen receptor mutants in prostate cancer

   Hum Mutat 30:145.

   https://doi.org/10.1002/humu.20848

   • Google Scholar
5. 1. Bohl CE
   2. Gao W
   3. Miller DD
   4. Bell CE
   5. Dalton JT

   (2005a) Structural basis for antagonism and resistance of bicalutamide in prostate cancer

   Proc Natl Acad Sci USA 102:6201.

   https://doi.org/10.1073/pnas.0500381102

   • Google Scholar
6. 1. Bohl CE
   2. Miller DD
   3. Chen J
   4. Bell CE
   5. Dalton JT

   (2005b) Structural basis for accommodation of nonsteroidal ligands in the androgen receptor

   J Biol Chem 280:37747.

   https://doi.org/10.1074/jbc.M507464200

   • Google Scholar
7. 1. Brooks BR
   2. Bruccoleri RE
   3. Olafson BD
   4. States DJ
   5. Swaminathan S
   6. Karplus M

   (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations

   J Comput Chem 4:187.

   https://doi.org/10.1002/jcc.540040211

   • Google Scholar
8. 1. Brooks BR
   2. Brooks CL
   3. Mackerell AD
   4. Nilsson L
   5. Petrella RJ
   6. Roux B

   et al. (2009) CHARMM: the biomolecular simulation program

   J Comput Chem 30:1545.

   https://doi.org/10.1002/jcc.21287

   • Google Scholar
9. 1. Brunger AT
   2. Karplus M

   (1988) Polar hydrogen positions in proteins: empirical energy placement and neutron-diffraction comparison

   Proteins 4:148.

   https://doi.org/10.1002/prot.340040208

   • Google Scholar
10. 1. Burgess MR
    2. Skaggs BJ
    3. Shah NP
    4. Lee FY
    5. Sawyers CL

    (2005) Comparative analysis of two clinically active BCR-ABL kinase inhibitors reveals the role of conformation-specific binding in resistance

    Proc Natl Acad Sci USA 102:3395.

    https://doi.org/10.1073/pnas.0409770102

    • Google Scholar
11. 1. Chapel-Fernandes S
    2. Jordier F
    3. Lauro F
    4. Maitland N
    5. Chiaroni J
    6. de Micco P

    et al. (2006) Use of the PSA enhancer core element to modulate the expression of prostate- and non-prostate-specific basal promoters in a lentiviral vector context

    Cancer Gene Ther 13:919.

    https://doi.org/10.1038/sj.cgt.7700966

    • Google Scholar
12. 1. Chen CD
    2. Welsbie DS
    3. Tran C
    4. Baek SH
    5. Chen R
    6. Vessella R

    et al. (2004) Molecular determinants of resistance to antiandrogen therapy

    Nat Med 10:33.

    https://doi.org/10.1038/nm972

    • Google Scholar
13. 1. Clegg NJ
    2. Wongvipat J
    3. Joseph JD
    4. Tran C
    5. Ouk S
    6. Dilhas A

    et al. (2012) ARN-509: a novel antiandrogen for prostate cancer treatment

    Cancer Res 72:1494.

    https://doi.org/10.1158/0008-5472.CAN-11-3948

    • Google Scholar
14. 1. Cornell WD
    2. Cieplak P
    3. Bayly CI
    4. Kollman PA

    (1993) Application of Resp charges to calculate conformational energies, hydrogen-bond energies, and free-energies of solvation

    J Am Chem Soc 115:9620.

    https://doi.org/10.1021/ja00074a030

    • Google Scholar
15. 1. Dagvadorj A
    2. Tan SH
    3. Liao Z
    4. Cavalli LR
    5. Haddad BR
    6. Nevalainen MT

    (2008) Androgen-regulated and highly tumorigenic human prostate cancer cell line established from a transplantable primary CWR22 tumor

    Clin Cancer Res 14:6062.

    https://doi.org/10.1158/1078-0432.CCR-08-0979

    • Google Scholar
16. 1. Diehl F
    2. Schmidt K
    3. Durkee KH
    4. Moore KJ
    5. Goodman SN
    6. Shuber AP

    et al. (2008) Analysis of mutations in DNA isolated from plasma and stool of colorectal cancer patients

    Gastroenterology 135:489.

    https://doi.org/10.1053/j.gastro.2008.05.039

    • Google Scholar
17. Book

    1. Frisch MJ
    2. Trucks GW
    3. Schlegel HB
    4. Scuseria GE
    5. Robb MA
    6. Cheeseman JR

    et al. (2004)

    Gaussian 03, Revision C.02

    Wallingford, CT: Gaussian, Inc..

    • Google Scholar
18. 1. Glickman MS
    2. Sawyers CL

    (2012) Converting cancer therapies into cures: lessons from infectious diseases

    Cell 148:1089.

    https://doi.org/10.1016/j.cell.2012.02.015

    • Google Scholar
19. 1. Haapala K
    2. Hyytinen ER
    3. Roiha M
    4. Laurila M
    5. Rantala I
    6. Helin HJ

    et al. (2001) Androgen receptor alterations in prostate cancer relapsed during a combined androgen blockade by orchiectomy and bicalutamide

    Lab Invest 81:1647.

    https://doi.org/10.1038/labinvest.3780378

    • Google Scholar
20. 1. Hara T
    2. Miyazaki J
    3. Araki H
    4. Yamaoka M
    5. Kanzaki N
    6. Kusaka M

    et al. (2003)

    Novel mutations of androgen receptor: a possible mechanism of bicalutamide withdrawal syndrome

    Cancer Res 63:149.

    • Google Scholar
21. 1. Jorgensen WL

    (2004) The many roles of computation in drug discovery

    Science 303:1813.

    https://doi.org/10.1126/science.1096361

    • Google Scholar
22. 1. Jung ME
    2. Ouk S
    3. Yoo D
    4. Sawyers CL
    5. Chen C
    6. Tran C

    et al. (2010) Structure-activity relationship for thiohydantoin androgen receptor antagonists for castration-resistant prostate cancer (CRPC)

    J Med Chem 53:2779.

    https://doi.org/10.1021/jm901488g

    • Google Scholar
23. 1. Karplus M
    2. McCammon JA

    (2002) Molecular dynamics simulations of biomolecules

    Nat Struct Biol 9:646.

    https://doi.org/10.1038/nsb0902-646

    • Google Scholar
24. 1. Koboldt DC
    2. Zhang Q
    3. Larson DE
    4. Shen D
    5. McLellan MD
    6. Lin L

    et al. (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing

    Genome Res 22:568.

    https://doi.org/10.1101/gr.129684.111

    • Google Scholar
25. 1. Leary RJ
    2. Sausen M
    3. Kinde I
    4. Papadopoulos N
    5. Carpten JD
    6. Craig D

    et al. (2012) Detection of chromosomal alterations in the circulation of cancer patients with whole-genome sequencing

    Sci Transl Med 4:162ra154.

    https://doi.org/10.1126/scitranslmed.3004742

    • Google Scholar
26. 1. Li H
    2. Durbin R

    (2009) Fast and accurate short read alignment with Burrows-Wheeler transform

    Bioinformatics 25:1754.

    https://doi.org/10.1093/bioinformatics/btp352

    • Google Scholar
27. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N

    et al. (2009) The sequence alignment/map format and SAMtools

    Bioinformatics 25:2078.

    https://doi.org/10.1093/bioinformatics/btp352

    • Google Scholar
28. 1. Linja MJ
    2. Savinainen KJ
    3. Saramäki OR
    4. Tammela TL
    5. Vessella RL
    6. Visakorpi T

    (2001) Amplification and overexpression of androgen receptor gene in hormone-refractory prostate cancer

    Cancer Res 61:3550.

    https://doi.org/10.1038/sj.bjc.6601127

    • Google Scholar
29. 1. Momany FA
    2. Rone R

    (1992) Validation of the general purpose Quanta®3.2/CHARMm® force-field

    J Comput Chem 13:888.

    https://doi.org/10.1002/jcc.540130714

    • Google Scholar
30. 1. Ozers MS
    2. Marks BD
    3. Gowda K
    4. Kupcho KR
    5. Ervin KM
    6. De Rosier T

    et al. (2007) The androgen receptor T877A mutant recruits LXXLL and FXXLF peptides differently than wild-type androgen receptor in a time-resolved fluorescence resonance energy transfer assay

    Biochemistry 46:683.

    https://doi.org/10.1021/bi061321b

    • Google Scholar
31. 1. Pettersen EF
    2. Goddard TD
    3. Huang CC
    4. Couch GS
    5. Greenblatt DM
    6. Meng EC

    et al. (2004) UCSF Chimera—a visualization system for exploratory research and analysis

    J Comput Chem 25:1605.

    https://doi.org/10.1002/jcc.20084

    • Google Scholar
32. 1. Scher HI
    2. Fizazi K
    3. Saad F
    4. Taplin ME
    5. Sternberg CN
    6. Miller K

    et al. (2012)

    Increased survival with enzalutamide in prostate cancer after chemotherapy

    N Engl J Med.

    • Google Scholar
33. 1. Shah NP
    2. Tran C
    3. Lee FY
    4. Chen P
    5. Norris D
    6. Sawyers CL

    (2004) Overriding imatinib resistance with a novel ABL kinase inhibitor

    Science 305:399.

    https://doi.org/10.1126/science.1099480

    • Google Scholar
34. 1. Shah NP
    2. Skaggs BJ
    3. Branford S
    4. Hughes TP
    5. Nicoll JM
    6. Paquette RL

    et al. (2007) Sequential ABL kinase inhibitor therapy selects for compound drug-resistant BCR-ABL mutations with altered oncogenic potency

    J Clin Invest 117:2562.

    https://doi.org/10.1172/JCI30890DS1

    • Google Scholar
35. 1. Shiau AK
    2. Barstad D
    3. Loria PM
    4. Cheng L
    5. Kushner PJ
    6. Agard DA

    et al. (1998) The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen

    Cell 95:927.

    https://doi.org/10.1016/S0092-8674(00)81717-1

    • Google Scholar
36. 1. Tran C
    2. Ouk S
    3. Clegg NJ
    4. Chen Y
    5. Watson PA
    6. Arora V

    et al. (2009) Development of a second-generation antiandrogen for treatment of advanced prostate cancer

    Science 324:787.

    https://doi.org/10.1126/science.1168175

    • Google Scholar
37. 1. Trapnell C
    2. Pachter L
    3. Salzberg SL

    (2009) TopHat: discovering splice junctions with RNA-Seq

    Bioinformatics 25:1105.

    https://doi.org/10.1093/bioinformatics/btp120

    • Google Scholar
38. 1. Veldscholte J
    2. Ris-Stalpers C
    3. Kuiper GG
    4. Jenster G
    5. Berrevoets C
    6. Claassen E

    et al. (1990) A mutation in the ligand binding domain of the androgen receptor of human LNCaP cells affects steroid binding characteristics and response to anti-androgens

    Biochem Biophys Res Commun 173:534.

    https://doi.org/10.1016/S0006-291X(05)80067-1

    • Google Scholar
39. 1. Watson PA
    2. Chen YF
    3. Balbas MD
    4. Wongvipat J
    5. Socci ND
    6. Viale A

    et al. (2010) Constitutively active androgen receptor splice variants expressed in castration-resistant prostate cancer require full-length androgen receptor

    Proc Natl Acad Sci USA 107:16759.

    https://doi.org/10.1073/pnas.1012443107

    • Google Scholar

Author details

1. Minna D Balbas

   1. Louis V. Gerstner, Jr. Graduate School of Biomedical Sciences, Memorial Sloan-Kettering Cancer Center, New York, United States
   2. Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, United States

   Contribution

   MDB, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   MDB, Co-inventor of patent applications covering the AR mutation and novel chemical matter described in this manuscript.

2. Michael J Evans

   Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, United States

   Contribution

   MJE, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   MJE, Co-inventor of a patent application covering the novel chemical matter described in this manuscript.

3. David J Hosfield

   Ben May Department for Cancer Research, University of Chicago, Chicago, United States

   Contribution

   DJH, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   DJH, Co-inventor of a patent application covering the novel chemical matter described in this manuscript.

4. John Wongvipat

   Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, United States

   Contribution

   JW, Conception and design, Acquisition of data, Analysis and interpretation of data

   Competing interests

   No competing interests declared.

5. Vivek K Arora

   Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, United States

   Contribution

   VKA, Conception and design, Acquisition of data, Analysis and interpretation of data

   Competing interests

   No competing interests declared.

6. Philip A Watson

   Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, United States

   Contribution

   PAW, Conception and design, Acquisition of data, Analysis and interpretation of data

   Competing interests

   PAW, Co-inventor of a patent application covering the AR mutation described in this manuscript.

7. Yu Chen

   Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, United States

   Contribution

   YC, Conception and design of the saturation mutagenesis screen

   Competing interests

   No competing interests declared.

8. Geoffrey L Greene

   Ben May Department for Cancer Research, University of Chicago, Chicago, United States

   Contribution

   GLG, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   GLG, Co-inventor of a patent application covering the novel chemical matter described in this manuscript.

9. Yang Shen

   Toyota Technological Institute at Chicago, Chicago, United States

   Contribution

   YS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   yangshen@ttic.edu

   Competing interests

   YS, Co-inventor of a patent application covering the novel chemical matter described in this manuscript.

10. Charles L Sawyers

    1. Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, United States
    2. Howard Hughes Medical Institute, Memorial Sloan-Kettering Cancer Center, New York, United States

    Contribution

    CLS, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    sawyersc@mskcc.org

    Competing interests

    CLS, Senior editor, eLife; co-inventor of enzalutamide and ARN-509; co-founder of Aragon Pharmaceuticals; co-inventor of patent applications covering the AR mutation and novel chemical matter described in this manuscript.

• 4,777

  views

• 831

  downloads

• 335

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.