Elicit: Resistance Mechanisms in Enzalutamide Therapy

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Resistance Mechanisms in Enzalutamide Therapy

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May 5, 2026

What resistance mechanisms emerge during enzalutamide therapy?

Enzalutamide resistance mechanisms include AR pathway alterations (amplifications, mutations, splice variants), bypass signaling pathways (JAK2-STAT5, glucocorticoid receptor), metabolic adaptations (AKR1C3, HMGCR), and lineage plasticity, primarily through clonal selection of pre-existing heterogeneity rather than newly acquired mutations.

Abstract

Resistance to enzalutamide in castration-resistant prostate cancer involves multiple mechanisms operating at both androgen receptor-dependent and AR-independent levels. AR pathway alterations represent the most extensively validated resistance mechanisms, with AR amplifications increasing from 53.9% at baseline to 64.7% at progression, specific AR mutations (H875Y, F877L, T878A/S) detected in approximately 15% of patients with progressive disease, and AR-V7 splice variants rising from 29% at baseline to 40% during treatment. However, a substantial proportion of resistance mechanisms are pre-existing rather than purely acquired, with 73.1% of baseline biopsies showing AR pathway alterations and 92.3% harboring tumor suppressor mutations (PTEN, RB1, TP53), indicating that clonal selection during treatment intensifies pre-existing heterogeneity. Bypass signaling pathways emerge as alternative resistance mechanisms, including JAK2-STAT5 activation through a positive feed-forward loop induced by enzalutamide-liganded AR, glucocorticoid receptor upregulation confirmed in clinical samples, and metabolic adaptations involving AKR1C3 and HMGCR that enable intracrine androgen synthesis. The spectrum of resistance mechanisms varies by prior treatment exposure, with specific AR mutations (L702H, T878A) enriched in abiraterone-pretreated patients, and by disease duration, with lineage plasticity toward neuroendocrine differentiation emerging under sustained AR suppression. Therapeutic strategies targeting these mechanisms include combination approaches with PARP inhibitors (TALAPRO-2 trial), EZH2 inhibitors (MEVPRO-2 trial), JAK2 inhibitors, and metabolic pathway inhibitors including statins and indomethacin, though patient selection based on molecular profiling remains critical for matching resistance mechanisms to targeted interventions.

Methods

We analyzed 10 sources from an initial pool of 200, using 7 screening criteria. Each paper was reviewed for 6 key aspects that mattered most to the research question. More on methods

Records from Elicit search

n = 200

Papers screened using: Enzalutamide Treatment, Resistance Mechanisms, Patient Population, Study Design, Acquired Resistance Focus, Enzalutamide-Specific Focus, Sample Size Adequacy

n = 200

Papers screened out

n = 190

Papers included for extraction

n = 10

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Paper search

We performed a semantic search across over 138 million academic papers from the Elicit search engine, which includes all of Semantic Scholar and OpenAlex.

We ran this query: “What resistance mechanisms emerge during enzalutamide therapy?”

The search returned 200 total results from Elicit.

We retrieved 200 papers most relevant to the query for screening.

Screening

We screened in sources based on their abstracts that met these criteria:

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

Data extraction

We asked a large language model to extract each data column below from each paper. We gave the model the extraction instructions shown below for each column.

Extract all specific molecular resistance mechanisms to enzalutamide identified in this study, including:

Extract the type of evidence used to identify and validate each resistance mechanism, including:

Extract detailed explanation of HOW each identified mechanism contributes to enzalutamide resistance, including:

Extract evidence for clinical relevance of each resistance mechanism, including:

Extract information about when resistance mechanisms develop relative to enzalutamide treatment, including:

Extract information about therapeutic strategies to overcome each resistance mechanism, including:

Results

Characteristics of Included Studies

Study

Full text retrieved?

Study Type

Evidence Type

Study Focus

Xinyi Wang et al., 2025

Yes

Review

Cell line studies, xenograft/animal models, patient tissue samples

Comprehensive review of resistance mechanisms and therapeutic strategies

R. McKay et al., 2021

No

Primary study

Patient tissue samples (baseline, progression), circulating tumor cells

Phase II multicenter study with serial biopsies to identify resistance mechanisms

A. Wyatt et al., 2016

Yes

Primary study

Circulating tumor cells or cell-free DNA

Genomic profiling of cfDNA from 65 patients during enzalutamide treatment

Yifan Kong et al., 2018

Yes

Primary study

Cell line studies (parental vs resistant), patient tissue samples, xenograft/animal models

Investigation of HMGCR role in cholesterol biosynthesis and resistance

Vindhya Udhane et al., 2019

No

Primary study

Cell line studies, patient tissue samples (baseline, progression), xenograft/animal models, ex vivo tumor explants

JAK2-STAT5 signaling pathway in resistance mechanisms

Yuanyuan Wang et al., 2020

Yes

Review

Cell line studies (resistant), circulating tumor cells or cell-free DNA, xenograft/animal models

Comprehensive review of resistance mechanisms and therapeutic strategies

Alexandra Vander Ark et al., 2018

Yes

Review

Cell line studies (parental vs resistant), patient tissue samples (baseline, progression), xenograft/animal models, circulating tumor cells

Review of resistance mechanisms and therapeutic approaches

K. Boudadi & E. Antonarakis, 2016

Yes

Review

Cell line studies (parental vs resistant), patient tissue samples (baseline, progression), xenograft/animal models, circulating tumor cells or cell-free DNA

Review of resistance to novel antiandrogen therapies

Xuedong Chen et al., 2018

No

Review

Not specified

Review of enzalutamide resistance mechanisms

Chengfei Liu et al., 2015

No

Primary study

Cell line studies (parental vs resistant), xenograft/animal models

Investigation of intracrine androgens and AKR1C3 in resistance

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The included studies comprise six reviews and four primary studies examining resistance mechanisms to enzalutamide in castration-resistant prostate cancer. Full text was available for six studies, while four studies were available as abstract only. The evidence types varied substantially, with most studies employing multiple complementary approaches including cell line models, patient tissue samples, circulating biomarkers, and animal models.

Resistance Mechanisms

Androgen Receptor-Dependent Mechanisms

Multiple studies identified alterations in the androgen receptor (AR) itself as central to enzalutamide resistance. These mechanisms maintain AR signaling despite enzalutamide treatment through various molecular alterations.

Mechanism

Type of Alteration

Clinical Validation

Timing of Emergence

AR amplification

Copy number gain

Detected in 53.9% at baseline, 64.7% at progression; associated with worse progression-free survival (HR 2.92)

Both pre-existing and acquired

AR mutations (H875Y, F877L, T878A/S)

Point mutations in ligand-binding domain

Observed in ~15% of progressive mCRPC patients; F877L and T878A mutations increase during treatment

Primarily acquired during treatment

AR splice variants (AR-V7)

Constitutively active truncated variants

Detected in 29% at baseline, 40% at progression; associated with shorter survival and lower PSA response rates

Both pre-existing and acquired

Heavily mutated AR (≥2 mutations)

Multiple mutations

Associated with worse progression-free survival (HR 3.94)

Acquired during treatment

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AR amplification increases AR protein expression, overcoming enzalutamide’s inhibitory effects by saturating drug binding capacity. Specific point mutations in the ligand-binding domain can convert enzalutamide from an antagonist to an agonist, paradoxically promoting tumor growth. The L702H mutation exhibits glucocorticoid sensitivity, while T878A is a promiscuous mutation that emerged particularly in patients with prior abiraterone treatment. AR-V7 splice variants lack the ligand-binding domain targeted by enzalutamide, maintaining constitutive transcriptional activity and promoting target gene activation in a ligand-independent manner.

Bypass Signaling Pathways

Several studies identified alternative signaling pathways that circumvent AR inhibition, enabling tumor growth despite enzalutamide treatment.

Glucocorticoid Receptor Pathway

Glucocorticoid receptor (GR) upregulation was identified as a key bypass mechanism. GR can activate AR target genes through an alternative pathway, with dexamethasone activation sufficient to confer resistance. This mechanism has been confirmed in both preclinical models and clinical samples. GR substitutes for AR signaling by activating common gene targets, promoting resistance.

JAK2-STAT5 Signaling

Enzalutamide-liganded AR induces sustained JAK2-STAT5 phosphorylation, creating a positive feed-forward loop where activated STAT5 increases JAK2 mRNA and protein levels, further activating JAK2. This self-reinforcing cycle promotes prostate cancer growth during enzalutamide treatment. The mechanism involves JAK2-specific phosphatases in the phosphorylation process. Clinical validation comes from evaluation in clinical prostate cancers before and after enzalutamide therapy, though resistance develops rapidly during treatment.

PI3K/Akt/mTOR Pathway

AR inhibition increases active Akt, which activates oncogenic pathways and rescues AR protein levels. The mTOR pathway activation is associated with increased HMGCR and AR expression. This pathway dysregulation contributes to resistance by maintaining cell survival signals independent of AR.

Additional Bypass Pathways

Noncanonical Wnt signaling through Wnt5A was observed in resistant cells. CTNNB1 mutations were found at high frequency (4 of 14) at progression, activating the WNT pathway and enhancing tumor growth despite enzalutamide. Alternative oncogenic signaling pathways involving Src-1, IL-6, HER2/HER3, and STAT3 can activate AR or bypass its inhibition. The IL-6-STAT3 axis was validated as a mediator of resistance with clinical implications.

Metabolic and Steroid Biosynthesis Alterations

AKR1C3 and Cholesterol Biosynthesis

The steroid biosynthesis pathway, particularly through AKR1C3 elevation, emerged as a critical resistance mechanism. AKR1C3 converts weak androgens to more active forms, enabling intracrine androgen production that bypasses enzalutamide’s blockade of the AR. AKR1C3 is significantly elevated in enzalutamide-resistant cells and highly expressed in metastatic and recurrent prostate cancer. Androgen precursors (cholesterol, DHEA, progesterone) and androgens are highly upregulated in resistant cells. This resistance mechanism is acquired during treatment.

HMGCR and Mevalonate Pathway

HMGCR, a crucial enzyme in the mevalonate pathway for cholesterol biosynthesis, is elevated in enzalutamide-resistant cells. HMGCR expression affects mTOR pathway activation, which influences AR expression and degradation in a feed-forward loop. Elevated cholesterol biosynthesis supports cell survival and proliferation, bypassing enzalutamide’s effects. This mechanism is acquired during treatment, typically within a short period.

Other Metabolic Alterations

Metabolic changes including increased glycolysis and citric acid cycle hyperactivation provide alternative energy sources that contribute to resistance. These metabolic adaptations enable continued tumor growth through AR-independent mechanisms.

Tumor Suppressor Loss and Lineage Plasticity

Alteration

Frequency

Clinical Correlation

Mechanism

PTEN loss

Present in 92.3% of baseline biopsies

Associated with resistance

Affects AR signaling pathway

RB1 loss

Present in 92.3% of baseline biopsies; 13/63 baseline samples

Associated with worse progression-free survival (HR 4.46)

Drives lineage plasticity toward AR-independent neuroendocrine phenotype; affects cell cycle regulation

TP53 loss

Present in 92.3% of baseline biopsies

Associated with resistance

Combined knockdown with RB1 leads to resistance; drives lineage plasticity

BRCA2 alterations

38.5% at baseline, 64.7% at progression

Increased post-enzalutamide

May enable tumor survival despite DNA damage; involves DNA repair mechanisms

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The combined loss of TP53 and RB1 promotes lineage plasticity, enabling switching from adenocarcinoma to neuroendocrine markers and conferring resistance. This transformation involves loss of methylation at SOX2, POU3F2, and ASCL1, contributing to neuroendocrine differentiation. Neuroendocrine markers increase at progression, representing an AR-independent resistance mechanism. Epithelial-to-mesenchymal transition also allows tumor cells to adapt and resist therapy.

Tumor Microenvironment and Immune Mechanisms

The immunosuppressive tumor microenvironment is characterized by reduced infiltration of cytotoxic immune cells and altered cytokine expression profiles. IL6 from the tumor microenvironment activates the JAK/STAT3 pathway, promoting resistance. Immune evasion through PD-L1 upregulation was identified as a potential resistance mechanism. Autophagy through the AMPK pathway enables cells to adapt and survive under therapeutic pressure.

Additional Molecular Mechanisms

Gene Regulatory Mechanisms

CREB5 amplification and overexpression enhances AR activity at promoters and enhancers, with FOXA1 necessary for CREB5-mediated resistance. c-Myc overexpression is positively associated with AR expression, with Eph receptors inducing c-Myc expression. GATA2 regulates AR variants, while BET proteins and ZFX interact with AR-V7. EZH2 overexpression contributes to resistance. Novel biomarkers like ZBTB46, SPDEF, and ETV6 play roles in drug resistance.

Protein Homeostasis

HSP70 and AKR1C3 stabilize AR-V7 through protein homeostasis mechanisms. Alterations in the ubiquitin-proteasome system affect protein homeostasis. The eIF4F complex formation is altered in resistant cells.

MicroRNAs

Multiple microRNAs augment AR activity, including miR-346, miR-361-3p, and miR-197. miRNA16 is involved in resistance mechanisms.

Additional Oncogenic Drivers

MET gain and MYC gain are associated with adverse outcomes on enzalutamide. IKBKE depletion or inhibition affects resistance. BCL2 protein is involved in resistance mechanisms. Oncostatin M from the microenvironment contributes to resistance.

Temporal Dynamics of Resistance

The timing of resistance mechanism emergence varies substantially across different alterations. AR pathway alterations (mutations, amplifications) and tumor suppressor gene mutations (PTEN, RB1, TP53) were enriched at baseline (73.1% and 92.3% respectively), indicating many resistance mechanisms are pre-existing. However, AR amplifications increased from 53.9% at baseline to 64.7% at progression, and BRCA2 alterations increased from 38.5% to 64.7%, demonstrating clonal selection during treatment.

Specific AR mutations exhibit clonal selection patterns. The L702H (glucocorticoid-sensitive) and T878A (promiscuous) mutations increased during treatment, particularly in patients with prior abiraterone exposure. AR-V7 expression was detected in 29% of patients at baseline and increased to 40% during progression, with some patients converting from AR-V7-negative to positive during therapy.

Metabolic alterations including AKR1C3 activation and HMGCR elevation are acquired during treatment, typically developing within a short period. The F876L mutation arises specifically during enzalutamide therapy through spontaneous emergence of mutant clones. JAK2-STAT5 signaling activation develops rapidly during treatment, while resistance mechanisms generally emerge after weeks of continuous enzalutamide treatment.

Therapeutic Targeting Strategies

Direct AR Targeting

Next-generation AR antagonists have been developed to overcome specific resistance mutations. Darolutamide significantly inhibits growth of enzalutamide-resistant clones both in vitro and in vivo. ASC-J9, an AR degradation enhancer, suppresses enzalutamide-resistant progression and degrades various AR forms. BET inhibitors (JQ1, OTX015) overcome resistance by inhibiting AR expression. Novel compounds including galiellalactone (GPA500), triptolide, tetra-aryl cyclobutanes, and PROTACs (e.g., ARCC-4) target AR through various mechanisms.

Combination Strategies with Enzalutamide

DNA Repair Inhibition

The TALAPRO-2 trial evaluates talazoparib (PARP inhibitor) combined with enzalutamide. This strategy targets the high frequency of germline DNA repair defects observed in patients progressing on enzalutamide. Clinical implications include using PARP inhibitors or DNA damaging agents for patients with BRCA2 alterations identified through cfDNA profiling.

Epigenetic Targeting

The MEVPRO-2 trial evaluates mevrometostat (EZH2 inhibitor) combined with enzalutamide. This approach addresses the role of epigenetic changes in resistance mechanisms.

Metabolic Pathway Inhibition

Simvastatin (HMGCR inhibitor) combined with enzalutamide significantly inhibits growth of resistant cells in vitro and tumors in vivo. The combination decreases AR protein levels through mTOR pathway inhibition. Indomethacin (AKR1C3 inhibitor) resensitizes resistant cells to enzalutamide both in vitro and in vivo, with the combination resulting in significant tumor growth inhibition.

Signaling Pathway Inhibition

JAK2 inhibitors (in clinical development) block JAK2-STAT5 signaling and induce death of cells surviving enzalutamide treatment. STAT5 blockade shows efficacy as second-line treatment after enzalutamide in patient-derived tumor explants. PI3K/Akt pathway inhibitors including BEZ235 and AKT1/2 inhibitors combined with enzalutamide inhibit tumor growth in mice.

Glucocorticoid Receptor Modulation

Switching from prednisone to dexamethasone or developing new synthetic oral glucocorticoids may overcome resistance related to glucocorticoid-sensitive AR mutations. Glucocorticoid receptor antagonists potentially restore sensitivity to enzalutamide.

Immune Checkpoint Blockade

Fulvestrant and checkpoint blockade combinations address immune evasion mechanisms. A phase 2 trial evaluated pembrolizumab with enzalutamide (NCT02312557), while another trial tested ipilimumab and nivolumab in AR-V7-positive patients (NCT02601014).

Other Combination Approaches

Autophagy inhibitors (clomipramine, metformin) combined with enzalutamide show promise. CDK4/6 inhibitors may overcome F876L-based resistance. BCL2 inhibitors (e.g., ABT263), MAO-A inhibitors (phenelzine, clorgyline), and HSP70 inhibitors target specific resistance pathways. c-Myc inhibitors (MK591/quiflapon), GATA2 inhibitors, and ZFX targeting address gene regulatory mechanisms.

Drug Repurposing and Novel Agents

Artesunate and masofaniten (combined with enzalutamide) represent drug repurposing efforts. JJ-450 and darolutamide show potential as alternative AR antagonists. Porcupine inhibition may target CTNNB1-mutated tumors if validated as a resistance mechanism. Taxane chemotherapy may benefit patients with RB1 loss.

Theoretical and Emerging Approaches

Targeting noncanonical Wnt signaling or Wnt5A represents a theoretical approach for Wnt pathway-driven resistance. Combination therapies targeting the bone microenvironment address paracrine resistance mechanisms. Metabolic pathway inhibition including MCT4 inhibitors targets metabolic adaptations. V1A receptor targeting and IRF8-induced drugs represent emerging strategies.

Synthesis

The landscape of enzalutamide resistance mechanisms reveals substantial heterogeneity across multiple molecular levels, necessitating careful interpretation of which mechanisms predominate in specific clinical contexts.

AR-Dependent vs AR-Independent Mechanisms

While AR-dependent mechanisms (amplification, mutations, splice variants) represent the most extensively validated resistance pathways with consistent clinical evidence across multiple studies, the relative importance of AR-independent mechanisms varies by disease stage and prior treatment exposure. Studies focusing on early progression (within months) predominantly identified AR pathway alterations, whereas those examining long-term resistance or heavily pretreated populations found higher frequencies of lineage plasticity and neuroendocrine differentiation. This temporal pattern suggests AR-dependent mechanisms enable initial escape from enzalutamide, while AR-independent mechanisms emerge under sustained AR-pathway suppression.

The apparent contradiction in AR pathway importance across studies reflects methodological differences in evidence collection. Studies using cfDNA profiling captured broad genomic alterations across tumor heterogeneity, detecting AR amplifications in 60% of progression samples, while tissue biopsy-based studies sampled single metastatic sites, potentially missing subclonal populations. Cell-free DNA analysis may overrepresent actively proliferating clones with high DNA turnover, whereas tissue biopsies capture established resistant populations regardless of proliferation rate.

Impact of Prior Abiraterone Exposure

Specific AR mutations show marked enrichment in abiraterone-pretreated populations. The L702H glucocorticoid-sensitive mutation and T878A promiscuous mutation increased selectively in patients with prior abiraterone treatment, suggesting cross-resistance mechanisms selected by sequential AR-pathway targeting. Prednisone co-administration with abiraterone may drive selection of glucocorticoid-responsive mutations, providing a mechanistic explanation for why switching to dexamethasone may benefit some patients. Studies recruiting treatment-naive CRPC patients versus heavily pretreated cohorts therefore capture fundamentally different resistance biology.

Frequency of Baseline vs Acquired Mechanisms

The high baseline frequency of AR alterations (73.1%) and tumor suppressor mutations (92.3%) challenges the traditional view of resistance as purely acquired. These pre-existing alterations may represent incomplete responses rather than true resistance emergence. However, quantitative increases in AR amplifications (from 53.9% to 64.7%) and BRCA2 alterations (from 38.5% to 64.7%) demonstrate clonal selection intensifies these mechanisms during treatment. Studies reporting resistance as “acquired” typically established resistant cell lines through chronic drug exposure, capturing late-stage adaptations absent at baseline.

The distinction between pre-existing heterogeneity and acquired mutations has therapeutic implications. Pre-existing AR-V7 detected at baseline (29%) predicts primary resistance, warranting alternative therapies, while conversion from AR-V7-negative to positive (increasing to 40%) indicates clonal selection amenable to combination strategies targeting both AR-V7 and full-length AR. Serial sampling reveals this dynamic evolution, whereas single-timepoint studies cannot distinguish pre-existing from acquired mechanisms.

Metabolic Mechanisms Across Disease Contexts

The prominence of metabolic mechanisms (AKR1C3, HMGCR) in laboratory models versus limited clinical validation reflects the challenge of detecting enzyme activity changes through standard genomic profiling. AKR1C3 activity enables intracrine androgen synthesis, but without transcriptomic or proteomic analysis, this mechanism remains underdetected in genomic studies. Clinical validation through bioinformatics analysis of patient specimens provides indirect evidence, but prospective studies measuring steroid metabolites or enzyme expression in progression samples are needed to establish clinical frequency.

The therapeutic vulnerability to statins and indomethacin in resistant cell lines suggests metabolic mechanisms may be underappreciated drivers of resistance in specific patient subsets, particularly those without dramatic AR pathway alterations. The mTOR-HMGCR-AR axis provides a feed-forward mechanism explaining how metabolic adaptations sustain AR signaling despite enzalutamide.

Signaling Pathway Bypass Mechanisms

The JAK2-STAT5 feed-forward loop represents enzalutamide-induced resistance, where the drug-bound AR paradoxically activates alternative pathways. This differs mechanistically from pre-existing bypass pathways like constitutive PI3K/Akt activation. Studies using patient-derived explants confirmed JAK2-STAT5 activation occurs during treatment, whereas studies examining PI3K pathway alterations found these at baseline in heavily mutated tumors. The glucocorticoid receptor bypass mechanism shows variable clinical validation, with clear evidence in samples from patients on concurrent glucocorticoid therapy but uncertain relevance in glucocorticoid-naive populations.

Lineage Plasticity and Neuroendocrine Transformation

The 92.3% baseline frequency of RB1/TP53 alterations does not translate to equivalent neuroendocrine differentiation rates, indicating additional factors beyond tumor suppressor loss determine lineage switching. Studies documenting neuroendocrine marker increases at progression versus those reporting lineage plasticity as a resistance mechanism may reflect different populations: those with partial lineage switching (detectable markers) versus complete transformation (overt neuroendocrine carcinoma). The epigenetic changes (SOX2, POU3F2, ASCL1 demethylation) provide mechanistic insight into how tumor suppressor loss enables but does not guarantee transformation.

Implications for Clinical Management

For patients without prior abiraterone exposure, AR amplifications and AR-V7 expression detected at baseline predict poor enzalutamide response, warranting consideration of alternative therapies or combination approaches from treatment initiation. The TALAPRO-2 trial’s combination of PARP inhibition with enzalutamide addresses the high BRCA2 alteration frequency (64.7% at progression), though the baseline frequency (38.5%) suggests this strategy may benefit unselected populations.

For abiraterone-pretreated patients, resistance mechanisms skew toward specific AR mutations, making next-generation AR antagonists like darolutamide or AR degraders logical choices. The glucocorticoid receptor bypass in this population suggests concurrent dexamethasone substitution for prednisone or GR antagonists merit investigation.

Patients progressing rapidly despite enzalutamide (median PFS 3.5 months) likely harbor multiple resistance mechanisms, with cfDNA profiling revealing actionable alterations in DNA repair and PI3K pathways. The emergence of CTNNB1 mutations at progression (4 of 14) identifies a subset potentially responsive to Wnt pathway inhibition.

The precision medicine framework integrating molecular diagnostics with biomarker-guided interventions requires distinguishing pre-existing heterogeneity from treatment-induced selection. Serial cfDNA profiling enables real-time monitoring of clonal dynamics, identifying emerging resistance mechanisms amenable to therapeutic intervention before radiographic progression. The combination strategies targeting JAK2-STAT5, metabolic pathways, and epigenetic regulators show promise for overcoming specific resistance mechanisms, though patient selection based on molecular profiling remains critical for maximizing benefit.

References

Yuanyuan Wang, Jiyuan Chen, Zhengjie Wu, Weihong Ding, Shen Gao, and 2 more\ (2020).Mechanisms of enzalutamide resistance in castration‐resistant prostate cancer and therapeutic strategies to overcome it. British Journal of Pharmacology

Xinyi Wang, Zhiguo Li, Xiaoqi Liu\ (2025).Mechanisms and Therapeutic Strategies to Overcome Enzalutamide Resistance in Advanced Prostate Cancer. Serican Journal of Medicine

Alexandra Vander Ark, Jingchen Cao, Xiaohong Li\ (2018).Mechanisms and Approaches for Overcoming Enzalutamide Resistance in Prostate Cancer. Frontiers in Oncology

K. Boudadi, E. Antonarakis\ (2016).Resistance to Novel Antiandrogen Therapies in Metastatic Castration-Resistant Prostate Cancer. Clinical Medicine Insights: Oncology

Xuedong Chen, Jieyang Lu, Liqun Xia, Gonghui Li\ (2018).Drug Resistance of Enzalutamide in CRPC. Current Drug Targets

Chengfei Liu, W. Lou, Yezi Zhu, Joy C. Yang, N. Nadiminty, and 3 more\ (2015).Intracrine Androgens and AKR1C3 Activation Confer Resistance to Enzalutamide in Prostate Cancer. Cancer Research

R. McKay, Lucia Kwak, Jett Crowdis, J. Sperger, Shuang G Zhao, and 13 more\ (2021).Phase II Multicenter Study of Enzalutamide in Metastatic Castration-Resistant Prostate Cancer to Identify Mechanisms Driving Resistance. Clinical Cancer Research

A. Wyatt, A. Azad, S. Volik, Matti Annala, K. Beja, and 11 more\ (2016).Genomic Alterations in Cell-Free DNA and Enzalutamide Resistance in Castration-Resistant Prostate Cancer. JAMA Oncology

Yifan Kong, Lijun Cheng, Fengyi Mao, Zhuangzhuang Zhang, Yanquan Zhang, and 7 more\ (2018).Inhibition of cholesterol biosynthesis overcomes enzalutamide resistance in castration-resistant prostate cancer (CRPC). Journal of Biological Chemistry

Vindhya Udhane, C. Maranto, David T. Hoang, L. Gu, A. Erickson, and 9 more\ (2019).Enzalutamide-Induced Feed-Forward Signaling Loop Promotes Therapy-Resistant Prostate Cancer Growth Providing an Exploitable Molecular Target for Jak2 Inhibitors. Molecular Cancer Therapeutics

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Inhibition of cholesterol biosynthesis overcomes enzalutamide resistance in castration-resistant prostate cancer (CRPC)

Yifan Kong, Lijun Cheng, Fengyi Mao, Zhuangzhuang Zhang, Yanquan Zhang, Elia Farah, Jacob Bosler, Yunfeng Bai, N. Ahmad, S. Kuang, Lang Li, Xiaoqi Liu

Journal of Biological Chemistry·

2018·

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Resistance Mechanisms

- Specific genes/proteins/pathways involved: HMGCR, AR, mTOR pathway - Type of alteration: Overexpression of HMGCR, activation of mTOR pathway - Location: Not specified - Multiple mechanisms: Overexpression of HMGCR, activation of mTOR pathway, degradation of AR protein

Evidence Type

- Cell line studies (parental vs resistant cell lines) - Patient tissue samples (baseline) - Xenograft/animal models - Combination of evidence types

Mechanistic Basis

- Molecular pathway details: Elevated HMGCR expression in the mevalonate pathway for sterol biosynthesis contributes to enzalutamide resistance. - Interaction with androgen receptor signaling: Simvastatin decreases AR protein levels, which are further reduced when combined with enzalutamide, indicating a target for overcoming resistance. - Bypass mechanisms around enzalutamide's mode of action: Elevated cholesterol biosynthesis supports cell survival and proliferation, bypassing enzalutamide's effects. - Feed-forward loops or cascading effects: HMGCR expression affects mTOR pathway activation, which influences AR expression and degradation. - Specific functional consequences: Continued tumor growth due to elevated cholesterol biosynthesis and AR signaling, mitigated by simvastatin's inhibition of HMGCR and the mTOR pathway.

Clinical Validation

- Presence in patient samples: HMGCR expression is elevated in enzalutamide-resistant cell lines and is associated with activated cholesterol biosynthesis pathways in patient samples. - Correlation with clinical outcomes: Not explicitly mentioned in terms of response rates or progression-free survival. - Frequency of occurrence in patient cohorts: Not explicitly mentioned. - Association with prior treatments: Not explicitly mentioned. - Lack of clinical validation: While some clinical validation is provided through bioinformatics analysis of patient samples, detailed clinical outcomes and frequency of occurrence are not explicitly mentioned.

Emergence Timing

- Pre-existing vs acquired: Acquired during treatment - Time to emergence: Not specified - Clonal selection or evolution: Not mentioned - Comparison between baseline and progression samples: Not mentioned - Sequential development: Not mentioned

Therapeutic Targeting

- Specific inhibitors or drugs tested: Statins (e.g., simvastatin) - Combination strategies with enzalutamide: Simvastatin + enzalutamide - In vitro and in vivo efficacy data: Simvastatin alone and in combination with enzalutamide inhibits growth of enzalutamide-resistant cells in vitro and tumors in vivo - Clinical trials or therapeutic implications mentioned: Combination strategy considered for clinical trials to overcome enzalutamide resistance - Theoretical targeting approaches if no specific agents tested: Targeting HMGCR and the mTOR pathway

Enzalutamide, a nonsteroidal second-generation antiandrogen, has been recently approved for the management of castration-resistant prostate cancer (CRPC). Although patients can benefit from enzalutamide at the beginning of this therapy, acquired enzalutamide resistance usually occurs within a short period. This motivated us to investigate the mechanism involved and possible approaches for overcoming enzalutamide resistance in CRPC. In the present study, we found that 3-hydroxy-3-methyl-glutaryl–CoA reductase (HMGCR), a crucial enzyme in the mevalonate pathway for sterol biosynthesis, is elevated in enzalutamide-resistant prostate cancer cell lines. HMGCR knockdown could resensitize these cells to the drug, and HMGCR overexpression conferred resistance to it, suggesting that aberrant HMGCR expression is an important enzalutamide-resistance mechanism in prostate cancer cells. Furthermore, enzalutamide-resistant prostate cancer cells were more sensitive to statins, which are HMGCR inhibitors. Of note, a combination of simvastatin and enzalutamide significantly inhibited the growth of enzalutamide-resistant prostate cancer cells in vitro and tumors in vivo. Mechanistically, simvastatin decreased protein levels of the androgen receptor (AR), which was further reduced in combination with enzalutamide. We observed that the decrease in AR may occur through simvastatin-mediated inhibition of the mTOR pathway, whose activation was associated with increased HMGCR and AR expression. These results indicate that simvastatin enhances the efficacy of enzalutamide-based therapy, highlighting the therapeutic potential of statins to overcome enzalutamide resistance in CRPC.

Progression of prostate cancer (PCa), 2 the second leading cause of cancer death in American men, is androgen-dependent. Consequently, androgen-deprivation therapy (ADT) is currently the primary approach for the treatment of PCa. Although patients can benefit from ADT at the beginning of treatment, most of them will relapse with castration-resistant prostate cancer (CRPC), which is currently incurable. Enzalutamide is a nonsteroidal second-generation antiandrogen that has recently been approved for the treatment of metastatic CRPC both in the post-docetaxel and chemotherapy-naive settings. It can inhibit androgen binding to the androgen receptor (AR), AR translocation into the nucleus, AR binding to DNA, and coactivator recruitment (1). Although enzalutamide can work efficiently at the beginning of treatment, it is only valid for a specified period, after which acquired drug resistance usually occurs. Currently, it is reported that such resistance can be induced by AR splice variants (AR-Vs), AR mutation, autophagy, aberrant glucose metabolism, intracrine androgen biosynthesis, etc. (2)(3)(4)(5)(6)(7).

Increasing data suggest that activation of de novo cholesterogenesis induces PCa cell proliferation and promotes cancer development and progression (8 -10) . Men with a higher level of cholesterol are usually at greater risk of developing highgrade PCa. During cholesterol biosynthesis, 3-hydroxy-3methyl-glutaryl-CoA reductase (HMGCR) is the first rate-limiting enzyme, whose inhibition has been traditionally used to lower serum cholesterol as a means of reducing the risk for cardiovascular disease. Drugs that inhibit HMGCR, known collectively as statins, are generic drugs for the treatment of hypercholesterolemia. In addition to statins' efficacy on treating cardiovascular disease, accumulating evidence suggests that statins also exert an antineoplastic effect in many types of cancer, including breast, prostate, ovarian, lymphoma, renal cell carcinoma, and colorectal cancer (11). In PCa, both in vitro and in vivo experiments showed that statins could significantly reduce the level of prostate-specific antigen (PSA) (12)(13)(14). Further, it was reported that such down-regulated PSA levels might be caused by proteolysis of AR induced by statins (15). All these studies suggest that statins may suppress PCa progression through inhibiting AR. Therefore, we aim to test whether statins can overcome acquisition of enzalutamide resistance in CRPC, in which AR continues to play an important role.

Herein, RNA-Seq analysis was performed in two PCa cells lines: LNCaP and MR49F, the enzalutamide-resistant derivative of LNCaP. We found that genes involved in cholesterol biosynthesis pathway were up-regulated in MR49F compared with those in LNCaP cells. Therefore, we hypothesize that cholesterol biosynthesis plays an essential role in the acquisition of enzalutamide resistance and that targeting HMGCR will overcome enzalutamide resistance in CRPC.

Identification of highly expressed HMGCR in enzalutamideresistant PCa cell lines

To investigate molecular mechanisms underlying enzalutamide resistance, we performed RNA-Seq analysis with LNCaP and MR49F cells. Based on the RNA-Seq result, higher gene expression of cholesterol biosynthesis pathway was observed in MR49F cells than that in LNCaP cells (Fig. 1A ). Herein, HMGCR was selected for further study because it is the first rate-limiting protein that plays an important role in cholesterol biosynthesis. To validate the findings from our RNA-Seq analysis, a series of Western blotting analyses was performed in LNCaP, MR49F, C4-2, and C4-2R (an enzalutamide-resistant cell line derived from C4-2). As shown in Fig. 1 (B-D ), C4-2R and MR49F showed higher expression levels of HMGCR than those of C4-2 and LNCaP. In addition, such a tendency became more significant when all cells were treated with enzalutamide for 4 h prior to harvest. Meanwhile, the protein level of SREBP2, another critical regulator of cholesterol synthesis, was also examined. Upon enzalutamide treatment, MR49F and C4-2R express higher levels of cleaved SREBP2, the active form of SREBP2, than those of LNCaP and C4-2, respectively (Fig. 1B ). Next, we wanted to investigate whether there was more cholesterol synthesized in enzalutamide-resistant cell lines by directly measuring total cholesterol levels. As shown in Fig. 1 (E and F ), both MR49F and C4-2R show much higher cholesterol levels than those of LNCaP and C4-2, respectively, upon enzalutamide treatment. Thus, cholesterol biosynthesis is indeed elevated by enzalutamide in enzalutamide-resistant PCa cells, but not in enzalutamide-sensitive cells. Collectively, the data described above indicated that a high level of HMGCR activates cholesterol biosynthesis in enzalutamide-resistant PCa cells. To further validate this finding in clinic, we performed bioinformatics analysis with 72 patient specimens (described under "Experimental procedures"). Accordingly, we found that cholesterol biosynthesis pathway gene set was enriched in high HMGCR-expressing group, indicating that cholesterol biosynthesis pathway was activated as HMGCR expression was elevated (Fig. 1 , G and H). Furthermore, gene set enrichment analysis (GSEA) of the HMGCR expression profile showed that the steroid biosynthesis pathway-related gene set is also positively enriched (Fig. 1 , I and J).

HMGCR expression level affects cellular response to enzalutamide

Having established that HMGCR is highly expressed in enzalutamide-resistant PCa cells, we asked whether alternation of HMGCR protein level could affect cellular response to enzalutamide. To explore this, shRNA was used to knock down HMGCR in C4-2R cells, and the down-regulation of HMGCR was determined by IB (Fig. 2A ). Because of the relative lower KD efficacy of a single shRNA, a mixed pool of both shRNAs was used to construct stable HMGCR-KD cell line. Then the growth assay was performed to test whether HMGCR-KD cells were more sensitive to enzalutamide. After 3-day treatment of enzalutamide with different concentrations, C4-2R cells were still fairly resistant to enzalutamide, whereas down-regulation of HMGCR clearly rendered C4-2R cells more sensitive to enzalutamide (Fig. 2B ). Furthermore, after HMGCR was restored in HMGCR-KD cells (Fig. 2C ), it apparently rescued cells from enzalutamide-induced growth inhibition (Fig. 2D ). In agreement, a similar observation was made in anchorageindependent growth assay (Fig. 2, E and F ). Compared with control cells, which showed no significant difference in colony numbers after enzalutamide treatment (Fig. 2E ), significantly fewer colonies were formed when HMGCR-KD cells were treated with enzalutamide (Fig. 2F ), and the phenotype was largely reversed upon reintroduction of HMGCR (Fig. 2G ). Furthermore, cell apoptosis was examined by IB against cleaved PARP. C4-2R cells are resistant to enzalutamide and showed almost no apoptosis upon enzalutamide treatment, whereas knockdown of HMGCR restored their sensitivity to enzalutamide (Fig. 2H ). Further, we transiently transfected C4-2 cells with HMGCR and asked whether overexpression of HMGCR could confer C4-2 cells resistance to enzalutamide. After HMGCR protein overexpression was confirmed by IB, we performed a 3-day cell-growth assay under treatment of enzalutamide with the indicated concentrations. As indicated, more C4-2 cells overexpressing HMGCR could survive than C4-2 cells transfected with pcDNA3.0 (Fig. 2I ). Collectively, these results demonstrate that down-regulation of HMGCR renders enzalutamide-resistant PCa cells to be sensitive to enzalutamide and that up-regulation of HMGCR can confer resistance. Because overexpression of HMGCR can confer PCa cells resistance to enzalutamide, we finally asked whether enzalutamideresistant PCa cells are more sensitive to statins, which targets HMGCR. To test this, a 3-day growth assay under treatment of mevastatin with different concentrations was conducted. As expected, MR49F and C4-2R are more sensitive to mevastatin than their parental cell lines, respectively (Fig. 2 , J and K), providing a possible new approach to overcome enzalutamide resistance.

Simvastatin treatment overcomes enzalutamide resistance in vitro

Next, we asked whether simvastatin, one of the marked statins, could enhance the inhibitory effect of enzalutamide on enzalutamide-resistant PCa cells. For that purpose, colony for-Simvastatin overcomes enzalutamide resistance mation assay was performed with MR49F, C4-2R, and 22RV1 cells (Fig. 3, A-C ). Significantly fewer colonies were observed in the combination groups in all three cell lines, suggesting that simvastatin enhances enzalutamide efficacy of these enzalutamide-resistant cell lines. To further validate this, we also performed proliferation assays in MR49F and C4-2R cells. As shown in Fig. 3 (D and E ), simvastatin alone could slightly slow down cell growth, whereas a combination of enzalutamide and simvastatin significantly inhibited cell proliferation rate. Meanwhile, representative images of four treatment groups were taken on day 5 for C4-2R cells to compare potential morphology change of the cells. As shown in Fig. 3F , there was no appar- , n ϭ 3. E and F, after cells were treated with enzalutamide for 4 h, total cholesterol was extracted and measured by a kit following the manufacturer's instructions. The results are presented as means Ϯ S.D. of two experiments in duplicate. G, GSEA confirms that the cholesterol biosynthesis pathway gene set is enriched in the high HMGCRexpressing group. The enrichment plot shows the distribution of genes in the set that are correlated with HMGCR expression. H, the heat map shows where gene expression is high (red) or low (blue) for each gene within the cholesterol biosynthesis pathway. I, GSEA confirms that steroid biosynthesis pathway gene set is enriched in the high HMGCR-expressing group. The enrichment plot shows the distribution of genes in the set that are correlated with HMGCR expression. J, the heat map shows where gene expression is high (red) or low (blue) for each gene within the steroid biosynthesis pathway. NS, not significant.

Simvastatin overcomes enzalutamide resistance

ent difference between control group and enzalutamide monotherapy group, but simvastatin alone was capable of inducing shrinkage of cells. For combination group, the same morphological modification was observed, and cells were clearly sparser. Next, the apoptosis level was tested after different treatments with indicated cell lines. 22RV1 or C4-2R cells were treated with simvastatin at the indicated concentrations in the presence or absence of enzalutamide and harvested for IB against cleaved PARP, a marker of apoptosis. As shown in Fig. 3 (G-I), enzalutamide alone could not induce any apoptosis -1 ,000 cells/well) were cultured in the media with enzalutamide (Enza), simvastatin (SIM), or both drugs at the indicated concentrations. After changing fresh media containing drug(s) every 3 days for 10 days, the cells were fixed, followed by colony formation staining with crystal violet. The colonies were counted, and the results are presented as means Ϯ S.D. of three experiments. D, MR49F cells (5,000 cells/well) were cultured in the media with enzalutamide (10 mol/liter), simvastatin (1 mol/liter), or both drugs. Growth assay was carried out for 5 days, with cell numbers being counted every day. E and F, C4-2R cells (5,000 cells/well) were cultured in the media with enzalutamide (20 mol/liter), simvastatin (1 mol/liter), or both drugs. Growth assay was carried out for 5 days, with cell numbers being counted every day. Representative images were taken on day 5 to show the morphology of the cells in four groups. G, C4-2R cells were treated with simvastatin, enzalutamide, or a combination of the two drugs at the indicated concentrations for 48 h, followed by IB against cleaved PARP (C-PARP). H, quantification of relative band intensities of cleaved PARP from three independent experiments. The intensities of cleaved PARP bands were normalized to those of ␤-actin and then normalized to the first lane. The data are represented as means Ϯ S.D., n ϭ 3. I, 22RV1 cells were treated with simvastatin, enzalutamide, or a combination of the two drugs at the indicated concentrations for 48 h, followed by anti-cleaved PARP IB to measure apoptosis. J, LNCaP and C4-2 cells were treated with simvastatin (5 mol/liter), enzalutamide (20 mol/liter), or a combination of the two drugs for 48 h and harvested for anti-cleaved PARP IB. Ctrl, control.

because the cells used are enzalutamide-resistant. Although simvastatin treatment alone caused relatively weak cell death, the combination of two drugs clearly led to significantly higher levels of apoptosis. However, such a combinational effect on cell death was not observed in enzalutamide-sensitive LNCaP and C4-2 cells (Fig. 3J ). To further confirm the combinatory effect between simvastatin and enzalutamide, the CI was measured. As shown in Table 1 , the IC 50 values for enzalutamide in C4-2R and MR49F cells are 57.6 and 20.0 M, respectively. However, the IC 50 values for enzalutamide in C4-2R and MR49F cells are reduced to 23.3 and 8.2 M, respectively, when cells are treated with simvastatin, suggesting that simvastatin renders enzalutamide-resistant cells sensitive to enzalutamide. CIs of the two drugs in these two cell lines are 0.9 and 0.91, further indicating that statin and enzalutamide exert a synergistic effect in inhibiting cell growth.

Simvastatin treatment overcomes enzalutamide resistance in vivo

To investigate whether statins can overcome enzalutamide resistance in vivo, we used the 22RV1 xenograft model. As shown in Fig. 4 (A-C ), 22RV1-derived tumors showed strong resistance to enzalutamide, but simvastatin alone partially slowed down tumor growth, and a combination of simvastatin and enzalutamide almost completely inhibited tumor growth. At the same time, there was no significant difference in mice body weight among all groups (Fig. 4D ), indicating that the combination treatment does not have serious side effects. Histologically, the cells in control and enzalutamide groups were arranged compactly but a little more discretely upon simvastatin treatment. In striking contrast, only small and scattered cell groups were found in combination treatment group (Fig. 4E ). Furthermore, immunostaining of Ki67 and cleaved caspase-3 confirmed that the combination treatment indeed reduced tumor cell proliferation significantly and led to more apoptosis (Fig. 4, F-I ). In summary, these in vivo results, which are consistent with our in vitro data, support the notion that the tumorkilling effect of enzalutamide was potentiated by statin.

Simvastatin decreases AR protein expression through enhancing protein degradation

Because PCa progression is AR-dependent and enzalutamide resistance can be induced by reactivation of AR ( 16), we asked whether AR signaling is affected by simvastatin. We found that simvastatin decreased AR protein level in a dose-dependent manner in C4-2R cells (Fig. 5A ). To determine whether simvastatin can affect transcription levels of AR and even AR-V7, quantitative real-time PCR was performed. As shown in Fig. 5B , there was no significant difference in mRNA expression of AR or AR-V7 upon simvastatin treatment, suggesting that transcription of AR and AR-V7 is not affected by simvastatin. Next, cycloheximide (CHX), a protein translation inhibitor, was utilized to examine the effect of simvastatin on protein turnover of AR. Accordingly, after cycloheximide was added to the cells in the presence of different concentrations of simvastatin, C4-2R cells were harvested at different time points to follow AR degradation. As shown in Fig. 5C , simvastatin treatment clearly accelerated AR degradation compared with cells only treated with cycloheximide. Therefore, it is possible that simvastatin could induce AR degradation via the ubiquitin-proteasome system instead of down-regulating transcription. To confirm this hypothesis, the 26S proteasome inhibitor MG132 was used to ask whether it can reverse the simvastatin-induced AR degradation. As shown in Fig. 5D , MG132 treatment indeed partially rescued AR protein level from degradation induced by simvastatin.

Enzalutamide and simvastatin decrease AR protein expression synergistically

Having established that simvastatin can partially enhance AR protein degradation, we then asked whether a combinational treatment of enzalutamide and simvastatin would affect the AR protein level synergistically. To investigate this possibility, C4-2R, 22RV1, and MR49F cells were treated with enzalutamide, different concentrations of simvastatin, or various combinations of the two drugs and harvested. As shown in Fig. 6 (A-C), AR protein levels were decreased by simvastatin treatment alone in a dose-dependent manner. Further, simvastatin plus enzalutamide led to maximum inhibition of AR (Fig. 6, A-C ) and even AR-Vs in 22RV1 cells (Fig. 6B ). To further confirm this, immunofluorescence staining was conducted. As shown in Fig. 6D , enzalutamide alone did not alter the AR protein levels in C4-2R cells. Although simvastatin alone partially decreased AR level compared with that of the control group, simvastatin plus enzalutamide almost completely abolished AR signal. We further showed that the reduced AR level in the co-treatment group was not due to a reduced AR mRNA level (Fig. 6E ) but due to a shortened protein half-life (Fig. 6F ). In addition to these, we aimed to confirm that decreased AR protein level is indeed responsible for apoptosis induced by enzalutamide plus simvastatin. Toward that end, MR49F, C4-2R, and 22RV1 cells were overexpressed with AR, followed by co-treatment with enzalutamide and simvastatin for 48 h. As shown in Fig. 6G , less apoptosis was observed in cells overexpressing AR compared with control cells, indicating that overexpression of AR can protect enzalutamide-resistant cells from apoptosis caused by enzalutamide plus simvastatin. Finally, to test whether the effects seen above are specific to HMGCR, we examined AR protein expression in cells (control or HMGCR-KD) after enzalutamide treatment. As shown in Fig. 6 (H and I ), AR level decreases partially in HMGCR-KD cells but more significantly upon enzalutamide treatment. In summary, our results demonstrate that degradation of AR is induced in PCa cells upon treatment with simvastatin alone or enzalutamide plus simvastatin and that AR degradation is the mechanism responsible for the synergistic effect of stain plus enzalutamide.

Simvastatin overcomes enzalutamide resistance Gene expression of mTOR pathway is positively correlated with HMGCR and AR expression

The previous study in our lab showed that a positive feedback loop exists among mTOR pathway, AR signaling, and lipid biosynthesis pathway (17). Thus, we asked whether such an interaction is still active after antihormone therapy or even enzalutamide treatment. To explore this possibility, Pearson correlation analysis was performed to detect the gene expression interaction between HMGCR and mTOR, HMGCR, and AR, as well as AR and mTOR, by using the samples from 72 patients (described in the method). As shown in Fig. 7 (A-F ), correlations of all three groups' gene expression were positive, but only the gene co-expression between AR and mTOR was significant with a correlation coefficient value of 0.4288 (Fig. 7C ). Moreover, to explore whether mTOR pathway activation is associated with HMGCR and AR expression, GSEA analysis was performed with the same samples. As shown in Fig. 7 (G and H ), mTOR pathway gene set was enriched in high HMGCR-expressing group, indicating that mTOR pathway is activated as HMGCR expression increases. Furthermore, GSEA analysis of AR expression profile showed that mTOR pathway-related gene set was positively enriched as well (Fig. 7 , I and J). To validate the observation described above, additional experiments were performed by using of our cell models. As indicated, cleaved SREBP2, HMGCR, AR, and critical compo-nents of the mTOR pathway were all highly expressed in C4-2R cells compared with C4-2 cells (Fig. 7K ). In addition, depletion of HMGCR led to down-regulation of levels of mTOR, phospho-mTOR, phospho-AKT and AR (Figs. 7L and 6I ). Besides HMGCR expression level, we asked whether lower HMGCR activity could affect mTOR pathway as well. As shown in Fig. 7M , both the levels of phospho-AKT and phospho-S6 were decreased upon simvastatin treatment in a dose-dependent manner, suggesting that simvastatin is capable of inhibiting mTOR pathway. Finally, inhibition of AKT with BKM120 induced an apparent degradation of AR in C4-2R cells upon enzalutamide treatment (Fig. 7N ), which also explains why a combination of enzalutamide and simvastatin reduces AR synergistically.

Discussion

Enzalutamide has been recently approved as a drug for CRPC by the U.S. Food and Drug Administration, and it significantly improves the therapy for late-stage CRPC patients (1,18,19). However, drug resistance usually occurs, and patients will relapse with CRPC, which is incurable. In this study, we have identified that HMGCR, a key enzyme for synthesis of cholesterol, is overexpressed in enzalutamide-resistant cell lines including MR49F and C4-2R, suggesting that abnormal cholesterol biosynthesis is likely to be one contributing factor for

Simvastatin overcomes enzalutamide resistance

enzalutamide resistance. We further demonstrate that knockdown of HMGCR resensitize C4-2R to enzalutamide and that overexpression of HMGCR confers C4-2 to be more resistant to enzalutamide. Next, simvastatin, an inhibitor of HMGCR, was used to overcome enzalutamide resistance both in vitro and in vivo. Mechanistically, we show that simvastatin down-regulates protein level of AR by promoting its degradation and that a combination of simvastatin and enzalutamide exerts a synergistic effect on AR protein turnover, and this process may be through inhibition of mTOR pathway whose activation is found positively associated with HMGCR and AR expression.

Increasing evidence shows that the lethal PCa is associated with deregulation of lipid or cholesterol biosynthesis (20 -23) and that lipid biosynthesis induced by AR reactivation can induce resistance to androgen-deprivation therapy and contributes to CRPC progression (24). For enzalutamide resistance, cholesterol is capable of participating in intracrine androgen biosynthesis, conferring PCa cells with resistance to enzalutamide (7). Herein, we showed that expression of HMGCR, the first rate-limiting enzyme of cholesterol biosynthesis, was elevated in MR49F and C4-2R cells compared with their parental cell lines and that the activity of HMGCR was increased upon enzalutamide treatment, which means more cholesterol was synthesized to support cell survival. Therefore, we propose that aberrant expression of HMGCR is one of enzalutamide-resistant mechanisms.

Several studies have been conducted to investigate the effect of statins on PCa cell proliferation. For example, Sekine et al. (25) reported that simvastatin could suppress proliferation and induce apoptosis of PC3 cells. Hong et al. (26) showed that LNCaP cell proliferation could be inhibited by lovastatin. These findings are consistent with our observation that simvastatin can induce apoptosis of enzalutamide-resistant cells. Meanwhile, we aimed to investigate whether inhibition of HMGCR could overcome enzalutamide resistance. However, we are aware that the effect of combination of enzalutamide and statins on PCa cells was reported by Syvälä et al. (27) during the preparation of our manuscript. The group showed that combination of simvastatin and enzalutamide exerted additive growth inhibition on LNCaP and VCaP cells in vitro, indicating that statins had potential to increase the efficacy of enzalutamide. However, whether statin can overcome enzalutamide resistance was not tested in their study, because both LNCaP and VCaP are androgen-sensitive cell lines, and no castrationresistant or enzalutamide-resistant cell lines were examined in their study. Herein, we showed that simvastatin could restore enzalutamide's inhibitory effect on MR49F, C4-2R, and 22RV1 cells and that the combined treatment could also inhibit proliferation of 22RV1-derived tumor synergistically.

To explore the underlying mechanism responsible for the inhibitory effect of statins on PCa cell proliferation, we investigated the role of AR signaling. Previously, Syvälä et al. (27) revealed that AR level was decreased slightly by simvastatin in LNCaP cells, and Yokomizo et al. (15) found that mevastatin and simvastatin could down-regulate AR protein by proteolysis and decrease proliferation in RWPE-1, 22RV1, and LNCaP cells. These results are consistent with our finding that simvastatin led to slight AR protein degradation via the proteasome system without affecting mRNA transcription of AR. Furthermore, we found that combination of enzalutamide and simvastatin could induce more dramatic degradation of AR than simvastatin alone. Such degradation of AR was also observed in HMGCR-KD cells upon treatment with enzalutamide. In addition, we also showed that the protein level of AR-Vs was decreased by simvastatin either alone or in combination with enzalutamide in 22RV1 cells. Therefore, all of these data help to explain why statin treatment or HMGCR inhibition overcomes enzalutamide resistance, as it has been documented that formation of AR-Vs, AR mutation, and AR amplification could confer PCa cell resistance to enzalutamide (2,6).

The proposed model described in our previous study (17) summarized how mTOR pathway, cholesterol biosynthesis, and AR signaling cooperated to drive PCa progression. In agreement, we found that positive correlations among HMGCR, AR, and the mTOR pathway exist in hormone therapy-resistant patient specimens, as well as in enzalutamide-resistant PCa cells. Therefore, the mTOR pathway is likely to play a critical role in the enzalutamide resistance, and it could be affected by HMGCR expression. In addition, we showed that simvastatin could inhibit mTOR pathway as well, consistent with previous studies (29). Considering that inhibition of AKT induced apparent degradation of AR in C4-2R cells upon enzalutamide treatment, it is likely that simvastatin induces AR degradation via inhibiting mTOR pathway.

In summary, our study shows that the aberrant expression of HMGCR is one of enzalutamide-resistant mechanisms. In addition, proliferation of enzalutamide-resistant cells is inhibited by simvastatin both alone and combined with enzalutamide in vitro and in vivo. Mechanistically, the combination of the two drugs induces maximum degradation of AR and AR-Vs, potentially through inhibition of mTOR pathway. Recently, it has been reported that statins could be used to delay progression of PCa in patients under ADT (30,31). Therefore, the new

combination strategy can be considered for clinical trials to overcome enzalutamide resistance in PCa.

Cell culture and drug

LNCaP, C4-2, 22RV1, MR49F, and C4-2R cells were used in the study. Although LNCaP cells are androgen-dependent, C4-2 cells were derived from LNCaP but are androgen-independent. MR49F cells were derived from LNCaP but are enzalutamide-resistant. C4-2R cells were derived from C4-2 but are enzalutamide-resistant. We ordered LNCaP and 22RV1 from ATCC. Although C4-2 cells were obtained from M. D. Anderson Cancer Center, MR49F and C4-2R cells were kindly provided by Dr. Amina Zoubeidi at the Vancouver Prostate Cancer Center and Dr. Allen Gao at University of California at Davis, respectively. LNCaP, C4-2, and 22RV1 cells were cultured in RPMI1640 medium supplemented with 10% (v/v) fetal bovine serum (FBS), 100 units/ml penicillin, and 100 units/ml streptomycin at 37 °C in a humidified incubator with 5% carbon dioxide. C4-2R and MR49F cells were maintained in previously described medium containing 20 and 10 M enzalutamide, respectively. Enzalutamide and simvastatin were purchased from Medchem Express.

Antibodies

Antibodies against androgen receptor (5153S), cleaved PARP (9541L), p-AKT (S473) (4051S), AKT (4691S), p-S6 (S235/236) (2211S), p-S6 (S240/244) (5364S), S6 (2317S), p-4EBP1 (T37/ 46) (2855S), GAPDH (2118L), and cleaved caspase 3 (9661S) were purchased Cell Signaling Technology. Antibody against HMGCR (ABS229) was ordered from Millipore. Antibody against SREBP-2 (sc-5603) was obtained from Santa Cruz Biotechnology.

Immunoblotting

Upon harvest, the cells were suspended with TBSN buffer (20 mmol/liter Tris-HCl, pH 8.0, 0.5% NP-40, 5 mmol/liter EGTA, 1.5 mmol/liter EDTA, 0.5 mmol/liter sodium vanadate, and 150 mmol/liter NaCl) with protease inhibitors and phosphatase inhibitors, sonicated and, then collected. Protein concentrations were determined by protein assay dye reagent from Bio-Rad. Equal amounts of protein from each sample were mixed with SDS loading buffer and separated by SDS-PAGE. After transferring to PVDF membranes, proteins were probed with appropriate first and second antibodies. All Western blotting were repeated at least three times, and protein bands were quantified by ImageJ and normalized to their respective GAPDH or ␤-actin.

Clonogenic assay

The cells were seeded with equal density in 6-well plate and treated with DMSO or different drugs with designated concentrations for 14 days. The medium was changed every 7 days. After the colonies were fixed by 10% formalin and stained with 5% crystal violet, colony numbers were determined by ImageJ.

Combination index

The equation CI ϭ (Am) 50 /(As) 50 ϩ (Bm) 50 /(Bs) 50 was used to calculate CI of enzalutamide and simvastatin (32). (As) 50 is the concentration of enzalutamide that will exert 50% inhibitory effect by itself, and (Bs) 50 is the concentration of simvastatin that will exert 50% inhibitory effect by itself. (Am) 50 is the concentration of enzalutamide to achieve a 50% inhibitory effect in combination with simvastatin. (Bm) 50 is the concentration of simvastatin that will produce a 50% inhibitory effect in combination with enzalutamide. Antagonism is indicated when CI Ͼ 1, CI ϭ 1 indicates an additive effect, and CI Ͻ 1 means synergy (28).

Quantification of total cholesterol in cells

Total cholesterol was measured by a commercial kit (ab65359) following the manufacturer's instructions. Cholesterol was extracted from 1 ϫ 10 6 cells and dried at 50 °C before dissolved in supplied assay buffer. Optical density was measured in a 96-well plate reader in duplicate at 570 nm, and total cholesterol amount was calculated by standard curve.

HMGCR shRNA transduction

The kit (TL312393V) with HMGCR-human shRNA lentiviral particles was purchased from Origene. A mixed pool of particles was applied to knock down HMGCR. The selection was performed by using puromycin following the manufacturer's instructions.

Real-time quantitative RT-PCR

Total RNA was extracted using RNeasy mini kit (Qiagen) according to the manufacturer's instructions. The same amount of RNA was used for reverse transcription that was performed by iScript cDNA synthesis kit (Bio-Rad), followed by gene amplification using FastStart Universal SYBR Green (Roche Applied Science) and a Roche LightCycler 96 thermocycler (Roche Diagnostics Corp.). All individual reactions were performed in triplicate, and all genes were normalized to GAPDH or 18S RNA. Primers used for real-time PCR are: AR full length, 5Ј-AAGCCAGAGCTGTGCAGATGA-3Ј (forward) and 5Ј-TGTCCTGCAGCCACTGGTTC-3Ј (reverse); AR-V7, 5Ј-AACAGAAGTACCTGTGCGCC-3Ј (forward) and 5Ј-TCA- G, GSEA confirms that mTOR pathway gene set is enriched in high HMGCR-expressing group. The enrichment plot shows the distribution of genes in the set that are correlated with HMGCR expression. H, the heat map shows where gene expression is high (red) or low (blue) for each gene of the mTOR pathway in the samples with different levels of HMGCR. I, GSEA confirms that mTOR pathway gene set is enriched in high AR-expressing group. The enrichment plot shows the distribution of genes in the set that are correlated with AR expression. J, the heat map shows where gene expression is high (red) or low (blue) for each gene of the mTOR pathway in the samples with different levels of AR. K, LNCaP, MR49F, C4-2, and C4-2R cells were cultured in RPMI 1640 media containing 10% FBS for 2 days and harvested for IB with antibodies against indicated proteins. L, the cell lines described in Fig. 2C were cultured in RPMI 1640 media containing 10% FBS for 2 days and harvested for IB with antibodies indicated. M, C4-2R cells were treated with enzalutamide and simvastatin at the indicated concentrations for 48 h, followed by IB. N, C4-2R cells were treated with enzalutamide and BKM120 at the indicated concentrations for 48 h, followed by IB. Ctrl, control.

Simvastatin overcomes enzalutamide resistance

GGGTCTGGTCATTTTGA-3Ј (reverse); 18S RNA, 5Ј-GAG-AAGGCTGGGGCTCAT-3Ј (forward) and 5Ј-TGCTGATGA-TCTTGAGGCTG-3Ј (reverse); and GAPDH, 5Ј-GTAACCC-GTTGAACCCCATT-3Ј (forward) and 5Ј-CCATCCAATCG-GTAGTAGCG-3Ј (reverse).

In vivo tumorigenesis assay

22RV1 cells (2 ϫ 10 5 /mouse) were mixed with Matrigel (1:1) and injected subcutaneously into to the right flank of nude mice. Tumor-bearing mice were randomized into four groups for indicated treatment as follows: for the first week, enzalutamide (25 mg/kg body weight) was gavaged, and simvastatin was intraperitoneally injected every 2 days; from the second week, both drugs were administered every day. Tumor size was measured by calipers every 2 days, and volumes were calculated using length ϫ width 2 /2. The mouse experiments have been approved by the Purdue Animal Care and Use Committee.

Histology and immunofluorescent chemistry

Xenograft tumors were fixed in 10% neutral buffered formalin, paraffin-embedded, sectioned to 5 mm, and stained using conventional hematoxylin and eosin staining. Immunofluorescent chemistry staining was accomplished with the M.O.M. TM kit from Vector Laboratories.

Gene set enrichment analysis

For TCGA prostate adenocarcinoma patient data, the RNA-Seq information was collected from level 3 (for segmented or interpreted data, IllumninaHiSeq_RNASeqV2 of TCGA. For hormone-sensitive and -resistant for high Gleason score signature in TCGA PCa, cancer samples were divided into low-grade (Gleason scores of Ͻ8) group and high-grade (Gleason scores of Ն8) group. Samples with a high-grade Gleason score plus antihormone treatment were considered to be potentially resistant to antihormone therapy. To integrate patient demography and drug treatment annotation together, 72 of 497 cases with clinical antihormone treatment were selected as observed objectives. After 72 patients were separated into two groups based on the gene expression level of our target proteins, GSEA software was applied to detect the gene pathway enrichment variation between the groups we studied.

Acknowledgements

Acknowledgment-The Purdue University Center for CancerResearch is supported by Grant P30 CA023168 from the National Institutes of Health.

Funding

This work was supported by National Institutes of Health Grants R01 CA157429 (to X. L.), R01 CA192894 (to X. L.), R01 CA196835 (to X. L.), R01 CA196634 (to X. L.), R01 AR059130 (to N. A.), and R01 CA176748 (to N. A.) and by funds from the Chinese Scholarship Council (to Y. K.).The authors declare that they have no conflicts of interest with the contents of this article.The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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