1

ERBB3 overexpression is enriched in diverse patient populations with castration-sensitive

prostate cancer and is associated with a unique AR activity signature

Jordan E. Vellky (1,2), Brenna J. Kirkpatrick (1), Lisa C. Gutgesell (1), Mathias Morales (1),

Ryan M. Brown (1), Yaqi Wu (1), Mark Maienschein-Cline (3), Lucia D. Notardonato (4), Michael

S. Weinfeld (4), Ryan H. Nguyen (4), Eileen Brister (5), Maria Sverdlov (6), Li Liu (2,7), Ziqiao

Xu (2,7), Steven Kregel (8), Larisa Nonn (1,2), Donald J. Vander Griend (1,2), Natalie M.

Reizine (2,4)*

Affiliations:

1. Department of Pathology; The University of Illinois at Chicago; Chicago, IL 60637; USA

2. The University of Illinois Cancer Center; Chicago, IL 60637; USA

3. Research Informatics Core, Research Resources Center, The University of Illinois at

Chicago; Chicago, IL 60637; USA

4. UI Health Division of Hematology/Oncology, Department of Medicine, University of Illinois at

Chicago, Chicago, IL 60637; USA

5. Research Tissue Imaging Core, Department of Pathology, The University of Illinois at

Chicago, Chicago, IL 60612; USA

6. Research Histology Core, Research Resource Center, The University of Illinois at Chicago,

Chicago, IL 60612; USA

7. Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at

Chicago, Chicago, IL 60612; USA

8. Department of Cancer Biology, Loyola University Chicago, Chicago, IL 60660; USA

*Corresponding Author: Natalie M. Reizine

Mailing Address: 840 South Wood St. Suite 1020N M/C 787, Chicago, IL 60612

Email: nreizi2@uic.edu

Running Title: ERBB3 in Diverse CSPC Patients

Conflicts of Interest:

RHN has received compensation from GenomeWeb and Merck outside of the submitted work.

NR has served on advisory boards for Sanofi, Exelexis, Janssen, and received compensation

from AstraZeneca, EMD Serono, Merck, and Tempus outside the submitted work. All other

authors have no disclosures.

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Translational Relevance

ERBB3 (HER3) in prostate cancer is associated with poor clinical outcomes, including shorter

time to castration resistance and reduced overall survival. Prior studies have proposed its role in

androgen receptor signaling and postulated it as an actionable target in castration-resistant

prostate cancer. In this study, ERBB3 overexpression was found to be prevalent in racially

diverse men with castration-sensitive disease. ERBB3 mRNA expression was associated with

increased androgen receptor signaling and high serum PSA, and at the protein level, HER3 was

associated with low intraprostatic androgen, specifically in Black/African American men. Further

mechanistic studies demonstrate a direct relationship between HER3 expression and

enzalutamide sensitivity. We posit that ERBB3 overexpression may have prognostic

significance in assisting clinicians with therapy selection, particularly in the castration-sensitive

setting. Including ERBB3 as a biomarker in prospective trials could help stratify aggressive

disease and guide intelligent intensification of therapy. Downloaded from http://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-23-2161/3413312/ccr-23-2161.pdf by guest on 10 February 2024

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Abstract

Purpose

Despite successful clinical management of castration-sensitive prostate cancer (CSPC), the 5-

year survival rate for men with castration-resistant prostate cancer (CRPC) is only 32%.

Combination treatment strategies to prevent disease recurrence are increasing, albeit in

biomarker-unselected patients. Identifying a biomarker in CSPC to stratify patients who will

progress on standard-of-care therapy could guide therapeutic strategies.

Methods

Targeted deep sequencing was performed for the UI cohort (n=30), and immunostaining was

performed on a patient tissue microarray (n=149). Bioinformatic analyses identified pathways

associated with biomarker overexpression in the UI cohort, consolidated RNA-seq samples

accessed from dbGaP (n=664), and GSE209954 (n=68). Neutralizing antibody Patritumab and

ectopic HER3 overexpression were utilized for functional mechanistic experiments.

Results

We identified ERBB3 overexpression in diverse CSPC patient populations, where it was

associated with advanced disease at diagnosis. Bioinformatic analyses showed a positive

correlation between ERBB3 expression and the androgen response pathway despite low DHT

and stable expression of AR transcript in Black/African American men. At the protein level,

HER3 expression was negatively correlated with intraprostatic androgen in Black/African

American men. Mechanistically, HER3 promoted enzalutamide resistance in prostate cancer

cell line models and HER3-targeted therapy re-sensitized therapy-resistant prostate cancer cell

lines to enzalutamide.

Conclusions

In diverse CSPC patient populations, ERBB3 OE was associated with high AR signaling despite

low intraprostatic androgen. Mechanistic studies demonstrated a direct link between HER3 and

enzalutamide resistance. ERBB3 OE as a biomarker could thus stratify patients for

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intensification of therapy in castration-sensitive disease, including targeting HER3 directly to

improve sensitivity to AR-targeted therapies.

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Introduction

Prostate cancer is commonly diagnosed among men in the United States, resulting in

>34,000 deaths per year (1). The 5-year survival rate for localized disease remains high;

however, mortality increases substantially in metastatic disease, with a 5-year survival of only

32% (1). As an androgen-driven disease, prostate cancer is clinically managed with therapies

targeting the androgen receptor (AR), including androgen deprivation therapy (ADT) and

androgen receptor signaling inhibitors (ARSI). While these therapies are initially successful,

many cases progress to castration-resistant prostate cancer (CRPC). There are several

established mechanisms of progression to CRPC, with most cases occurring when AR gains

functionality through mutation, increased copy number, ligand promiscuity, the emergence of

ligand-independent variants, or retention of AR signaling through alternative receptors/cofactors (2-4). Verified risk factors for advancement to CRPC include age, family history, and

race (5). This is clinically significant given the racial disparity in prostate cancer mortality

observed in Black/AA men, where mortality rates are two to four times higher than those in

every other racial and ethnic group in the US (1, 6). Unfortunately, despite our improved

understanding of molecular changes and risk factors associated with CRPC, survival rates

remain low (3). While combination treatment strategies to prevent disease recurrence are

moving into the frontline for the management of advanced disease, recent landmark prospective

studies have been performed in biomarker-unselected patients (7, 8). This unselected

intensification strategy may ultimately result in overtreatment in a subset of patients. Therefore,

identifying a predictive biomarker in CSPC for the subset of patients that will progress on antiandrogen therapy could guide intelligent intensification to optimize targeted therapeutic

strategies in those most vulnerable to developing aggressive, resistant disease.

HER3 (ERBB3) has recently emerged as a potential actionable target for advanced

prostate cancer, where it was correlated to shorter time to castration resistance and shorter

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overall survival (9). In pre-clinical studies, direct HER3 inhibition or indirect targeting through its

ligand NRG1 decreased proliferation and migration in vitro and decreased tumor volume in vivo

(9-11). Notably, the scope of these studies is limited to targeting HER3 in advanced CRPC.

However, HER3 has also been implicated in castration-sensitive prostate cancer (CSPC), where

several studies suggest crosstalk between HER3 and AR signaling (12-14). Because ARtargeted therapies are standard-of-care for clinical management of CSPC, signaling

mechanisms between HER3 and AR could be highly relevant to disease progression and the

development of therapeutic resistance; the clinical implications of this crosstalk, however, have

not been fully established. In this study, we identified racially diverse CSPC patient populations

with prevalent ERBB3 OE; in the UI cohort and others, ERBB3 RNA OE was enriched in

Black/AA men and was associated with aggressive disease at diagnosis. Considering the

enrichment of ERBB3 OE in Black/AA patients and the established crosstalk between ERBB3

and AR, we hypothesized that ERBB3 OE could provide prognostic value as a biomarker for

intensification of therapy in Black/AA men with CSPC and that this pathway can be targeted to

improve prostate cancer outcomes. Downloaded from http://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-23-2161/3413312/ccr-23-2161.pdf by guest on 10 February 2024

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Materials and Methods

Sample Collection and Sequencing

Treatment naïve tumor samples were collected from patients in the UI cohort through

biopsy or radical prostatectomy at UI Health (Table 1). Targeted deep sequencing was

performed on banked tissue samples as part of routine standard of care through Tempus

Laboratories (15, 16). The decision to send samples for sequencing was based on the following

criteria: 1) patients received care at UI Health for advanced prostate cancer between January

2022 and July 2022, 2) patients were eligible for sequencing per NCCN Guidelines on Prostate

Cancer V.1.2022, and 3) patients had tissue banked that was available to send. In total, 65

patient samples were sent for sequencing during this period; however, only 33 patients had

sufficient tissue quantity and quality for RNA sequencing. De-identified raw FASTQ sequencing

data was obtained from Tempus and stored in a secure server with password encryption.

Retrospective data analysis was performed on de-identified data after approval by an

institutional review board (IRB-STUDY2022-0907) and in accordance with the U.S. Department

of Health and Human Services and the Declaration of Helsinki. A waiver was granted for written

informed consent due to the retrospective nature of the study. De-identification of data was

maintained through all steps and generic identifiers were used to notate and analyze data.

Sequencing Analysis and Normalization

RNA FASTQ files were obtained from Tempus, and quality control of raw FASTQ data

was determined using FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/).

Sequences were trimmed using cutadapt to remove TruSeq adapters and to quality trim to a

minimum Phred score of 20 (17). Genome alignment was performed using STAR

(RRID:SCR_004463) with the hg38 human genome as reference (18). Genomic features were

quantified using FeatureCounts based on reverse-stranded libraries using default parameters

against Ensembl annotations for human genome hg38 (19, 20). Normalized gene expression

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levels were computed using edgeR in CPM units, including the TMM normalization factor (21).

Pairwise statistics between genes of interest using TPM (CPM-TMM) values was conducted

using a Mann-Whitney U test.

GSEA and AR Signature Score

TPM normalized RNA-seq data was sorted on ERBB3 levels using inclusive quartiles

where the ERBB3-high group are the patient IDs greater than or equal to the third quartile value

and the ERBB3-low group are the patient IDs lower than or equal to the first quartile value. All

genes associated with the high and low groups were extracted and Gene Set Enrichment

Analysis comparing ERBB3-high and ERBB3-low was done using GSEA (SeqGSEA,

RRID:SCR_005724) 4.3.2, as previously described (22). Settings altered from default in the

program include number of permutations set to 1000, permutation type set to phenotype,

expression data set to h.all.symbols.gmt[Hallmarks], Chip platform Human set to

ENSEMBL_Gene_ID_MSigD8v7.5.1.chip, enrichment statistic set to weighted, and metric for

ranking genes set to Signal2Noise (23). Using the normalized RNA-seq data, an AR signature

score was developed using a comprehensive list of AR target genes (24). The z-score [(x-µ)/σ]

was calculated for each gene independently and the z-score for each AR target gene was

summed to create a single AR signature score value per sample. This analysis was applied to

all patients in the UI cohort.

Acquisition and Analyses of Publicly-Available RNA-Seq Datasets

Access to publicly-available prostate cancer RNA-Seq datasets was requested and

approved via NIH dbGaP (25-30). IRB approval was obtained when necessary. FASTQ files

were trimmed and aligned to the genome using the same method as described above. A total of

664 RNA-seq datasets were further analyzed. Genomic features were quantified using

FeatureCounts as described above. Pairwise statistics between genes of interest using TPM

(CPM-TMM) values was conducted using a Mann-Whitney U test.

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The computational analysis for ancestry was performed as a de novo identification of

admixture groups within the experimental population, followed by a mapping of those groups to

pre-defined ancestries based on the GnoMAD database. All samples from publicly-available

RNA-seq samples and UI cohort were analyzed together. Variant calls for all RNA-seq samples

were called against reference hg38 using bcftools, mpileup, and bcftools call (multi-allelic caller,

flag -m), resulting in 15,7109,372 total variants (31). Variant calls with <8x coverage were

removed. Variants were filtered based on the following criteria: 1) minor allele frequency > 1%,

2) sample call rate >30%, 3) variant annotated in dbSNP, 4) GnoMAD allele frequency >1% in

at least one population, 5) not annotated in COSMIC or NCI60 (somatic mutations in cancer),

and 6) not annotated as an RNA editing site based on previously characterized RNA editing

sites from REDIportal (32). This resulted in 72,1010 final variants. De novo admixture groups

were obtained using Admixture, varying the number of admixture groups from k = 2 to 20, with

the optimal number (k=5) determined based on the minimum error (33). Allele frequencies were

compared to those from different ancestry groups in GnoMAD using Pearson correlations, and

the closest match was putatively assigned as the ancestry label for each admixture group.

Using the 5 module SNP construct for ancestry, we categorized each patient into White (>65%

NFE), Black (>65% AFR), or mixed (<65% of NFE and AFR). The mixed ancestry patients were

excluded from further analysis, leaving a total of 524 samples. This ancestry model was

validated in the UI Health Cohort, where patients that self-reported as Black/AA or

White/Caucasian matched 88% with ancestry determination at ≥70% threshold and 100% with

ancestry determination at >65% threshold (Supplemental Table 1). The number of AFR

patients identified from each dataset is shown in Supplemental Table 2.

Acquisition and Analysis of GSE209954

Data set GSE209954 was accessed using NCBI’s Gene Expression Omnibus (GEO,

RRID:SCR_005012) interface (6). Gene expression values for ERBB3 (identifier 3417249) were

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isolated from the series matrix file and stratified by race (African American – AAM, Non-African

American – NAAM). ERBB3 expression was normalized using SCAN.UPC package from

Bioconductor (RRID:SCR_006442), as previously described (6). Gleason score 4+3 samples

were included for analysis. A simple linear regression comparing pre-treatment PSA (ng/mL) to

normalized ERBB3 expression was calculated in GraphPad/Prism (v.9.4.1).

Tissue Microarray (TMA)

The TMA contains 1 mm cores from 149 UI Health radical prostatectomy patients (98

Black/African American, 51 White). A board-certified pathologist reviewed each core to confirm

pathology and Gleason grade. For each patient, an adjacent area of the prostate was snap

frozen and used to quantify intraprostatic testosterone (T) and dihydrotestosterone (DHT) levels

by LC-MS/MS analyses, as previously described (34). Clinical data includes tumor Gleason

score, body mass index (BMI), and smoking status for each patient represented on the TMA

(Table 2). This TMA has power >0.8 for an effect size of 0.13 by linear regression analyses.

Immunohistochemistry Staining and Analysis

Five-micron sections of the prostate cancer TMA were deparaffinized and stained on

BOND RX automated stainer according to the BOND Polymer Refine Detection Kit (Leica

Biosystems, #DS9800) protocol. Briefly, after deparaffinization, sections were subjected to heatbased antigen retrieval with BOND Epitope Retrieval Solution 2 (pH 9.0, Leica Biosystems,

#AR9640) for 20 min at 100ºC. Endogenous peroxidase activity and non-specific binding was

blocked by sequentially treating samples with peroxidase block (BOND Polymer Refine

Detection Kit) and Background sniper protein block (Biocare Medical, #BS966) for 15 min at

room temperature. Sections were then incubated with anti-HER3/ERBB3 antibody (1:100, Cell

Signaling Technology #12708, RRID:AB_2721919) for 60 min, and signal was detected using

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DAB. All sections were counterstained with hematoxylin and mounted with Micromount media

(Leica Microsystems, #3801730).

Slides were scanned at 20x resolution on an AT2 scanner and image analysis was

performed using HALO version 3.5 and HALO AI (Indica Labs, Albuquerque, NM, USA). Wholeslide images were segmented into individual TMA cores using the HALO TMA module. A

pathologist reviewed all cores and identified regions of each core that were not consistent with

the tissue description in the TMA key. Those regions were excluded from analysis. Within each

core, tissue areas were identified, and artifacts were excluded using a modified version of the

HALO AI QC Slide V2 classifier. Epithelial regions were identified via a custom-trained HALO AI

MiniNet classifier. The HALO Area Quantification Module was used to separate DAB stain for

ERBB3 using color deconvolution and set a threshold for positive staining. In epithelial-classified

areas, each pixel was compared to the ERBB3 staining threshold and percentage of the area

positive for the stain was reported.

Statistical Analysis of TMA

Statistical analysis of the TMA was done in collaboration with the UI Cancer Center

Biostatistics Shared Resource Core. Due to skewness of the HER3 and hormone distributions,

log-transformation was applied when necessary. T-tests or analysis of variance (ANOVA)

models were used to compare HER3 expressions between demographic categories. Spearman

or Pearson correlations between log-transformed HER3 and ordinal (BMI, Gleason Sum) or

continuous variables (age, PSA, androgen levels T and DHT). Multivariate regression models

with backward model selection method were employed to identify factors associated with (log)

HER3. Interactions between androgens and race were tested in the identifications of factors that

modified the correlations between androgens and HER3. All statistical tests were 2-sided,

controlling for Type I error probability of 0.05. Statistical analyses were performed in SAS 9.4.

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Cell Lines and Materials

Human prostate cancer cell lines were cultured as previously described (35). Briefly,

LNCaP (CVCL_0395) and CWR-R1 (CVCL_4833) cells were maintained in RPMI-1640 ATCC

Modification (Gibco, Waltham, MA) supplemented with 10% fetal bovine serum (FBS) and 1%

penicillin/streptomycin (Corning, Corning, NY). LAPC4 (CVCL_4744) cells were maintained in

IMDM (Hyclone, Logan, UT) supplemented with 10% FBS, 1% P/S, and 1nM R1881 (SigmaAldrich, St. Louis, MO). VCaP (CVCL_2235) cells were maintained in DMEM (Gibco, Waltham,

MA), supplemented with 10% FBS and 1% P/S. BT474 (CVCL_0179) cells were maintained in

McCoy’s 5a Modified medium (Gibco, Waltham, MA) supplemented with 20% FBS and 1% P/S.

MDA PCa 2b (CVCL_4748) cells were maintained in BRFF-HPC1 media (Athena ES, Baltimore,

MD) supplemented with 20% FBS and 1% P/S. ENZR

cells were maintained growth medium

supplemented with 10μM enzalutamide as previously described (35). VCaP (CRL-2876), MDA

PCa 2b (CRL-2422), and BT474 (HTB-20) cells were obtained from ATCC. LNCaP, LAPC4, and

CWR-R1 cells were generously provided by Dr. John Isaacs at Johns Hopkins University and

validated as previously described (35). Cell authentications were performed at The University of

Arizona Genetics lab, and all cell lines were confirmed as negative for mycoplasma using the

Universal Mycoplasma Detection Kit (ATCC, Manassas, VA). For phospho-HER3 detection,

cells were treated with 100ng/mL NRG1 (Peprotech, Waltham, MA) for 15 minutes. Patritumab

was obtained from MedChemExpress (Monmouth Junction, NJ). For proliferation assays, cells

were treated with 10μM Patritumab for 5 days. For Western blot validation, cells were treated

with 10μM Patritumab for 2 hours with or without 100ng/mL NRG1 for 15 minutes. Lentiviral

vectors pLV[Exp]-Puro-EF1A>hERBB3 (HER3-OE, VB900139-0636jnd) and pLV[Exp]-PuroEF1A>ORF_Non-targeting stuffer (NT, VB010000-9298rtf) were obtained from Vector Builder

(Vector Builder, Chicago, IL). Each vector was independently incubated at a multiplicity of 8 with

5mg/mL Polybrene for 20 minutes in OPTI-MEM (Thermo Fisher Scientific, Waltham, MA). Virus

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was added to cells for 24 hours, followed by 24 hours recovery in full media. Infected cells were

selected in 1ug/mL of Puromycin (Thermo Fisher Scientific, Waltham, MA).

Western Blot

Western blotting was performed as previously described (35). Briefly, cells were

collected in RIPA buffer, sonicated, and protein was quantified using the Pierce BCA kit

(Thermo Fisher Scientific, Waltham, MA). 40μg of lysate was run on 4-15% Mini-PROTEAN

precast polyacrylamide gels (BioRad, Hercules, CA). Protein was transferred to nitrocellulose

membranes and blocked according to the antibody specifications (5% bovine serum albumin

(BSA) or non-fat milk). Primary antibodies used are detailed in Supplemental Table 3.

Membranes were incubated with secondary antibodies – goat anti-rabbit IRDye 800 CW (LICOR Biosciences Cat# 926-32223, RRID:AB_621845) or goat anti-mouse IRDye 680 (LI-COR

Biosciences Cat# 926-32222, RRID:AB_621844) – and images were captured using an infrared

Odyssey scanner (LI-COR Biosciences, Lincoln, NE).

Cell Proliferation Assay

CWR-R1 and LNCaP (NT, HER3-OE) cells were plated at a density of 10,000 cells per

well and MDA PCa 2b cells at 15,000 cells per well in 96-well plates. Cells were treated 24

hours later, and proliferation analysis was performed using IncuCyte S3 Live-Cell Analysis

System imaging every 4 hours for 68-72 hours (Sartorius, Gottingen, Germany). Analysis was

completed in the Incucyte software with cell counts normalized to initial number of cells (Hour

0). A 2-way ANOVA with Tukey’s multiple comparison test was used for statistical analysis.

Data Availability

Publicly-available data was accessed through dbGaP with the following accession

numbers: phs001141.v1.p1, phs000909.v1.p1, phs000915.v2.p2, phs000310.v1.p1,

phs000985.v1.p1, phs000443.v1.p1, and phs001698.v1.p1 (25-30). For the UI Health cohort,

the data that support the findings of this study are available on request from the corresponding

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author, N.R. The data are not publicly available because the subjects were not consented for

the publication or distribution of their raw sequencing data.

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Results

ERBB3 Overexpression is Enriched in a Diverse Population of CSPC Patients

The UI patient cohort was procured using an IRB-approved retrospective analysis of

next-generation sequencing results for patients with prostate cancer. Sequencing data for RNA

expression was available in 33 patients. The median age of patients in the UI cohort was 63.4

(range 51.9-77.7 years) (Table 1). 23/33 (69.7%) of UI patients self-reported as African

American/Black and 7/33 (21.2%) self-reported as Non-African American/White, with 4/7

(57.1%) reporting Hispanic ethnicity (Table 1). Within this population, 69.7% of patients

presented with de novo metastases, indicating advanced disease at diagnosis (Table 1).

Moreover, 72.3% of patients had a Gleason Score sum greater than or equal to 8, while 15.2%

had Gleason Score sum less than or equal to 7 (12.1% unknown Gleason Score) (Table 1). Of

the 33 patients, three were excluded from further analysis (one due to insufficient RNA yield

during tissue processing, two due to progression to CRPC prior to sequencing). Ultimately, all

analyzed samples (n=30) were sequenced from treatment naïve tumors.

Using a positivity threshold identified during genomic analysis, ERBB3 overexpression

(OE) was detected in 40% of patients (Figure 1A). High ERBB3 expression was associated with

more advanced disease; ERBB3 expression was significantly higher in patients with de novo

metastases (M1) at diagnosis compared to patients without metastases (M0) (Figure 1B).

Applying the same threshold for ERBB3 OE determined above, 48% of de novo metastatic

samples had ERBB3 OE compared to 14% OE in localized disease (Figure 1B). Interestingly,

there was no association between ERBB3 expression and Gleason score, suggesting ERBB3

as a prognostic marker, independent of Gleason score (Figure 1C). Canonical alterations

associated with prostate cancer (TMPRSS2-ERG, PTEN, SPOP, TP53, etc.) were assessed in

the UI patient cohort, where there were no consistent alterations concurrent with ERBB3 OE

(Supplemental Table 4). One of the unique demographic characteristics of this patient

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population is self-reported racial and ethnic diversity. Because race is a risk factor for

aggressive disease, we stratified ERBB3 OE by race and ethnicity, resulting in 1/3 (33%) of selfidentified White patients with ERBB3 OE, and 11/27 (41%) of self-identified Black and Hispanic

patients with ERBB3 OE. This suggests a potential enrichment of ERBB3 expression in

underrepresented minority populations. Though the overall sample size is relatively small

(n=30), the UI cohort provides an opportunity to assess the predictive and prognostic

implications of ERBB3 OE, as well as its association with advanced prostate cancer in a racially

diverse population.

To determine if the relationship between ERBB3 OE and race was consistent in larger

data sets, we compiled prostate cancer RNA sequencing data from publicly available dbGaP

samples. Self-reported race was not available in these datasets, so we applied a SNP

admixture analysis to determine ancestry in 664 patient samples. ERBB3 expression was

assessed in prostate cancer progression (benign n=61, localized n=125, metastases n=310,

CRPC n=130) and Benign Prostatic Hyperplasia (BPH, n=38), where ERBB3 was significantly

higher in primary localized prostate cancer and metastases vs. benign (Figure 1D). Consistent

with our finding in the UI cohort, ERBB3 normalized expression (TPM) was significantly higher

in AFR samples vs. NFE in both localized and metastatic disease, suggesting this relationship is

not stage specific (Figures 1E-F). Pie charts depicting the percent of ERBB3 OE in AFR vs.

NFE showed a higher proportion of ERBB3 OE in AFR vs. NFE in both localized prostate

cancer and metastases (Figures 1E-F). There was not a statistically significant difference

between ERBB3 TPM in AA samples from UI cohort vs. dbGaP samples, suggesting a direct

comparison is possible (Supplemental Figure 1A). Taken together, these data suggest that

ERBB3 RNA OE occurs in a subset of castration-sensitive prostate samples, where it was

associated with more aggressive disease stage at diagnosis and was enriched in Black/AFR

patients versus White/NFE. Determining the significance of ERBB3 OE in CSPC could thus

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provide insight into better treatment strategies for this subset of patients with aggressive

disease.

HER3 Expression Inversely Correlates with Intraprostatic DHT in Black/AA Men

To address ERBB3 expression at the protein level, we quantified ERBB3 protein (HER3)

in treatment-naïve primary prostate samples from a diverse patient cohort for which we also

have quantification of intraprostatic androgen concentrations (34) (Table 2, Figure 2). HER3

positivity was quantified in luminal epithelial cells of prostate cancer cores, with normal tissue

exclusion (Figure 2A). HER3 percent positivity was calculated for each core, where there was

no significant difference in expression between Black/AA and White patients (Figure 2B).

Because of the established crosstalk between HER3 and AR signaling, we assessed the

relationship between intraprostatic androgen (dihydrotestosterone, DHT) and HER3 in these

patients. Interestingly, there was a significant inverse relationship between HER3 expression

and intraprostatic DHT in Black/AA patients that did not exist in White patients (Figure 2C-D).

This observation could be clinically significant in the context of racial disparity in prostate cancer

mortality for Black men in the US (1, 6). Because AR signaling inhibitors competitively bind AR

to block DHT, these therapies may not be as effective in low DHT conditions, potentially

warranting alternative therapeutic strategies in these patients. A better understanding of the

signaling pathways active downstream of ERBB3/HER3 in Black/AA compared to White men

could provide insight into its role in prostate cancer etiology and disparities.

Androgen Signaling is Prevalent when ERBB3 is Overexpressed

To determine the signaling pathways that are associated with ERBB3 OE, we performed

gene set enrichment analysis (GSEA) on ERBB3 high (top quartile) versus ERBB3 low (bottom

quartile) samples from the UI and consolidated publicly available RNA-seq samples (dbGaP)

(Figure 3). Demographics for ERBB3 high and low quartiles from the UI cohort and dbGaP

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samples used for gene set enrichment analysis show most of the UI cohort patients selfidentified as Black/AA, and most of the patients accessed from dbGaP were White/NFE

according to the admixture analysis (Figure 3A). For the UI cohort, KEGG pathway enrichment

plots identified Androgen Response (normalized enrichment score, NES = 1.77; false discovery

rate, FDR = 0.09) to be enriched in ERBB3 high samples, and Inflammatory Response (NES = -

1.69; FDR = 0.16) to be enriched in ERBB3 low samples (Figure 3B). Similarly, in the publiclyavailable RNA-seq samples (dbGaP), KEGG pathway analysis showed enrichment of Androgen

Response (Hallmark) (NES = 1.79, FDR =0.09) in ERBB3 high samples and enrichment of

Epithelial Mesenchymal Transition (EMT) (Hallmark) (NES = -1.85, FDR =0.06) in ERBB3 low

samples. To further investigate the association between the androgen response pathway and

ERBB3 OE, we selected several canonical androgen response pathway genes to compare

expression in ERBB3 high vs. low samples (Figure 3C). As expected, ERBB3 high samples had

a significantly higher expression of ERBB3, KLK3, NKX3-1, TMPRSS2, and ZBTB10 in both the

UI and dbGaP samples (Figure 3C). There was not a statistically significant difference in other

receptor tyrosine kinase family members (ERBB2, EGFR) or ligand NRG1 in ERBB3 high vs.

low (Supplemental Figure 1B). Interestingly, AR transcript was significantly higher in the

ERBB3 high samples from dbGaP, but not in the UI cohort (Figure 3C). Finally, leading edge

analysis of the genes driving the enrichment of Androgen Response (Hallmark) were compared

between the UI cohort and samples from dbGaP using a heatmap (Figure 3D). 31 leading edge

genes were similar between the two datasets, 7 genes were specific to the UI cohort, and 30

genes were specific to the dbGaP samples (Figure 3E). While AR signaling was prevalent in

both patient datasets, the genes driving the Androgen Response hallmark are different,

potentially suggesting distinct pathways mediating activation of AR signaling. Given the

prevalence of ERBB3 OE, the inverse relationship between ERBB3 and intraprostatic DHT, and

the differential leading edge analysis driving Androgen Response hallmark in Black/AA men, it is

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likely that the signaling mechanisms mediating the ERBB3/AR axis may have differential clinical

implications.

ERBB3 Expression Correlates with an AR Signature Score and High Serum PSA in

Black/AA Men

To assess the clinical significance of ERBB3/AR signaling in Black/AA men with CSPC,

we evaluated the relationship between ERBB3, a clinically adaptable AR signature score, and

pre-treatment serum PSA in the UI cohort and in publicly available dataset GSE209954. The AR

signature score was based on a summation of z-scores for 34 AR-target genes conserved

across reported AR chromatin immunoprecipitation experiments (24). A heatmap of the UI

cohort patients showed most ERBB3 high patients had a correspondingly high AR signature

score (Figure 4A). To determine if this phenomenon was consistent in other racially diverse

datasets, we compared ERBB3 expression and AR signature score in GSE209954, which

contains 33 primary prostate cancer samples (Gleason 7, 4+3) from African American (AAM)

men (6). Like the UI cohort, ERBB3 expression appeared to correlate with a positive AR

signature score (Figure 4B). To quantify this apparent relationship between ERBB3 and AR

signaling, we performed a linear regression analysis, where there was a statistically significant

positive correlation between ERBB3 expression and the summed AR signature score for the UI

cohort (R2

= 0.3033, p=0.009) and GSE209954 (R2

= 0.5962, p<0.0001) (Figure 4C). We next

assessed the relationship between ERBB3 expression and pre-treatment serum PSA in both the

UI cohort and GSE209954. As expected, serum PSA was significantly increased in the ERBB3

high patients compared to ERBB3 low (p=0.0224) in the UI cohort (Figure 4D). Using both the

African American (AAM) and Non-African American (NAAM) samples from the GSE209954

dataset, we directly evaluated the interface between ERBB3, pre-treatment serum PSA, and

race. With all samples combined, there was no correlation between pre-treatment serum PSA

and ERBB3 expression (p=0.3528) (Figure 4E). However, when stratified by race, there was a

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statistically significant correlation between ERBB3 expression and serum PSA in Black/AAM

men (p=0.0244) that did not exist in the NAAM samples (p=0.5156) (Figure 4E). Since all

patients analyzed from this dataset have the same Gleason score (4+3) and disease stage

(<T2c, (6)) this relationship between PSA and ERBB3 appears to be independent of disease

burden. Additionally, the UI cohort and external dataset GSE209954 both suggest that pretreatment serum PSA is significantly higher in Black/AA patients with high ERBB3, but not White

patients. These data support our earlier finding that AR signaling is increased in Black/AA

patients with high ERBB3 expression, and further support the idea that the crosstalk between

ERBB3 and AR is clinically important as a prognostic biomarker in diverse patients with prostate

cancer.

HER3 Promotes Enzalutamide Resistance in Prostate Cancer Models

Our data demonstrates that ERBB3/HER3 is associated with androgen signaling in a

subset of CSPC patients, suggesting it may play a role in sensitivity to AR-targeted therapeutics

such as enzalutamide (ENZ). To directly assess the functional relationship between HER3 and

ENZ sensitivity, we performed mechanistic gain- and loss-of-function experiments in prostate

cancer cell line models. In a panel of prostate cancer cell lines, HER3 was highly expressed in

CWR-R1 and MDA PCa 2b cells and lowly expressed in LNCaP and VCaP cells (Figure 5A). In

previously published enzalutamide-adapted (enzalutamide-resistant, ENZR

) CWR-R1, LNCaP,

and VCaP cells treated with HER3 ligand NRG1, pHER3 was increased in ENZR

lines vs.

parental, suggesting increased HER3 responsiveness in models of enzalutamide resistance

(Figure 5B). pHER2/HER2 were also increased in CWR-R1 ENZR

vs. parental, but not LNCaP

or VCaP ENZR

(Figure 5B). Conversely, in androgen-adapted LNCaPs grown in R1881 for 6

months, pHER3/HER3, but not pHER2/HER2, were decreased in the high androgen condition

(10nM R1881) compared to control (0nM) (Figure 5C). To determine if HER3 is necessary for

enzalutamide resistance, we assessed the growth of CWR-R1 and MDA PCa 2b cells after

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treatment with HER3 inhibitor Patritumab in combination with ENZ (Figure 5D, Supplemental

Figure 2A). ENZ alone did not decrease proliferation, but when co-treated with Patritumab, cell

proliferation decreased significantly compared to either treatment alone (p<0.0001) (Figure 5D).

Western blots confirmed reduction of pHER3 with Patritumab (Figure 5E, Supplemental

Figure 2B). Interestingly, proliferation of CWR-R1 and MDA PCa 2b cells was decreased after

co-treatment with enzalutamide and Patritumab-Deruxtecan, an antibody drug conjugate (ADC)

that is under clinical development in other disease states (Supplemental Figure 2C-D). To

determine if HER3 is sufficient for enzalutamide resistance, we ectopically over-expressed

HER3 in LNCaP cells and assessed proliferation after treatment with ENZ (Figure 5F,

Supplemental Figure 2). In control cells (non-targeted vector, NT), cell proliferation decreased

after treatment with 10µM ENZ; however, proliferation in the HER3-OE cells did not decrease

after treatment with ENZ, suggesting HER3-OE is sufficient for ENZ-resistance (Figure 5F).

Western blots confirmed overexpression of HER3 compared to the non-targeted control vector

(Figure 5G, Supplemental Figure 2). Taken together, these results indicate that HER3 is

necessary and sufficient for enzalutamide resistance and support a clinical approach targeting

HER3 to prevent or reverse enzalutamide resistance. Downloaded from http://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-23-2161/3413312/ccr-23-2161.pdf by guest on 10 February 2024

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Discussion

In this study, we identified ERBB3 OE in a subset of castration-sensitive prostate cancer

diagnoses. This mRNA OE was enriched in Black/AA men and was associated with more

advanced disease at clinical presentation. Bioinformatic analysis of this ERBB3 high population

revealed a relationship between ERBB3 and increased AR signaling. This association between

ERBB3 and the androgen signaling axis was confirmed in external data sets and in patient

tissue samples where HER3 expression was negatively correlated with intraprostatic

dihydrotestosterone (DHT) levels in AA patients. Taken together, these data suggest that

Black/AA patients with ERBB3 overexpression have significantly higher pre-treatment PSA and

AR signaling, but low intraprostatic DHT. Mechanistically, this inverse relationship could suggest

a pre-therapeutic ligand-independent activation of AR signaling. In this context, AR signaling

inhibitors, which directly compete with DHT for binding to AR, may not be as effective or

durable, implicating high ERBB3/HER3 expression as a biomarker for patients who will benefit

from intensification of therapy in the castration-sensitive setting. Here, we demonstrated that

HER3 was necessary and sufficient to confer ENZ resistance in prostate cancer cell line models

and that these models accurately represent the inverse relationship we observed between

androgens and HER3 from patient tissue analyses. A better understanding of ERBB3 OE and

its role as a potential mechanism of AR-targeted therapeutic resistance could improve

therapeutic outcomes in this subset of patients.

Receptor tyrosine kinases have been implicated in AR signaling as a mechanism for

hormone-independent prostate cancer for decades (36). However, Phase II clinical trials

targeting EGFR (ERBB1) and HER2 (ERBB2) in CRPC have not yet proven to be clinically

significant (37-41). Recently, HER3 (ERBB3) has been implicated in CRPC, where it was

associated with disease recurrence and shorter overall survival (9). Following promising preclinical data, several Phase I clinical trials targeting HER3 using monoclonal antibodies or

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antibody-drug conjugates are currently recruiting CRPC patients (NCT05057013,

NCT05588609, NCT05785741). Our study suggests an additional setting for targeting HER3:

CSPC. However, ERBB3/HER3 data in primary prostate cancer has historically been

inconsistent. In some studies, ERBB3/HER3 increases throughout prostate cancer progression

where it is associated with poor prognosis, while others show ERBB3-low primary PC radical

prostatectomy samples were associated with worse prognosis (9, 42, 43). Additionally, several

studies implicate nuclear HER3 as either an indicator of progression to hormone-refractory

prostate cancer, or the lack of nuclear HER3 as predictor of biochemical recurrence (44, 45).

Potential explanations for the controversy surrounding ERBB3/HER3 in prostate cancer is that it

may serve as a biomarker in a specific cellular localization (nuclear vs. cytosolic vs. membrane)

or may only be relevant in a particular subset of patients. Further investigation into stratification

for ERBB3/HER3 biomarker and/or drug targeting studies should be considered moving

forward.

In the UI patient cohort, we see ERBB3 overexpression as a subtype of CSPC that has

not been described previously. Because our patient population is more racially diverse than

many previous prostate cancer sequencing cohorts, it is possible that ERBB3 overexpression

has been overlooked in previous CSPC studies due to lack of diversity in samples analyzed.

Recently, an increasing number of studies are investigating racial disparities in prostate cancer

incidence and mortality (1, 6, 46). The cause of the racial disparity in prostate cancer is

multifactorial and not yet fully elucidated, stemming from a combination of inequitable access to

health resources, lack of representation in research, and epigenetic sequelae of environmental

factors (e.g. stress, metabolism, diet). Although race is an inherently social category,

biogeographical ancestry analyses have shown that there is a strong concordance between

molecular-based ancestry groups and racial groups (47, 48). Identification of molecular

expression and activity patterns that vary by racial group may provide a key to addressing

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outcomes related to racial disparities, as currently existing treatments have not been developed

with attention to the inclusion of diverse patient populations. In one recent study, racial

disparities were assessed in veterans who have relatively equal access to healthcare through

Veterans Affairs; despite this equal access setting with standardized treatment regimens,

Black/AA men were younger at diagnosis and had higher AR signaling, more advanced disease,

and increased residual metastatic burden compared to White men (46, 49). However, a general

lack of inclusion of diverse patients in biomarker identification studies has resulted in information

disparity for prognosis and targeted therapeutic options. Many of the parameters assessed in

the current study support previous findings for Black/AA men with prostate cancer including

more advanced disease at diagnosis and increased AR signaling. In this context, ERBB3

overexpression may serve as a biomarker for aggressive disease, stratifying patients that will

most likely benefit from intensification of therapy. Validating ERBB3 OE as a biomarker and

identifying additional genomic alterations within this vulnerable population has high potential to

impact patient care and improve treatment outcomes.

While our data generally supports other recent findings, there are some limitations. First,

clinical outcomes for the UI cohort are not yet available. While initial analyses of this cohort and

others suggest a relationship between ERBB3 OE and overall survival (Supplemental Figure

3A) and time to progression (Supplemental Figure 3B), investigation in datasets with more

detailed outcomes is warranted to develop ERBB3 OE as a biomarker and/or target in CSPC.

Second, retrospective disparities research is impeded by a lack of patient demographic

information in comprehensive data sets like dbGaP. To address this, we utilized an admixture

analysis using SNP data to determine ancestry. While this is a useful bioinformatics tool, it is

limited by the lack of information regarding other factors contributing to disparities (e.g. access

to healthcare, environmental factors). Therefore, it should only be used as a tool to assess

genetic drivers of disparity, and other sociological studies should address additional contributing

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factors. Third, based on our data, ERBB3 mRNA expression could be a biomarker for poor

prognosis; however, this differential expression was not captured at the protein level by IHC

analysis. This inconsistency could be due to technical limitations of IHC, where the total protein

expression may not be the biomarker, but rather functionally active membrane-bound HER3

would correlate better with the mRNA data for Black/AA men. Additionally, the TPM values for

ERBB3 showed expression in all samples, with higher expression in the ERBB3 OE subset. It is

possible that IHC is not reflecting these quantitative changes due to lack of assay sensitivity.

Finally, we demonstrated that ERBB3 overexpression was associated with increased AR

signaling despite lower intraprostatic DHT in Black/AA men. This is somewhat anti-intuitive in

terms of mechanisms for CRPC progression; AR signaling is frequently increased in CRPC but

has been shown to coincide with increased intraprostatic DHT (50, 51). In our ERBB3 OE

samples, the increased AR signaling does not appear to be mediated by the ligand, DHT, which

may indicate an alternative activation of AR signaling in Black/AA men. This ligand-independent

activation of AR by receptor tyrosine kinases has been proposed previously in prostate cancer

cell line models, where HER2/HER3 dimerization induced AR activation in an androgendepleted environment (12); however, the clinical implications of this aberrant AR signaling in the

context of racial disparities has not been explored. This prospective model, and the functionality

and therapeutic implications of ERBB3/HER3 in racially diverse CSPC patients, should be

further investigated.

Here, we have provided evidence of ERBB3 OE in a subset of diverse CSPC patients

where it correlated with the AR signaling pathway activity. Additional bioinformatic and

immunohistologic analyses showed that this AR signaling activity occurs despite stable

expression of the androgen receptor, and at the protein level, in low intraprostatic androgen

conditions. Taken together, these data suggest this AR signaling is being maintained despite

low availability of androgen. In terms of clinical management, this unique induction of AR

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signaling may indicate that AR-targeted therapies alone are not sufficient for this subtype of

CSPC and that ERBB3 OE may be a biomarker for patients that will benefit from intensification

of therapy in the castration-sensitive setting. Further, our pre-clinical data suggests that directly

targeting HER3 in prostate cancer with ERBB3/HER3-OE could increase sensitivity to

enzalutamide. Based on these findings, prospective validation studies for ERBB3 OE in CSPC

clinical trials and/or clinical trials targeting HER3 in conjunction with AR-targeted therapies have

strong potential to better clinical management of prostate cancer. Importantly, ERBB3 OE is

enriched in a diverse patient cohort, potentially suggesting a contribution to racial disparities, yet

also informing potential targeted therapeutic strategies in diverse individuals. With better

inclusion of more diverse patient cohorts in future analyses, we can close this knowledge gap,

improving stratification and selection of patients with prostate cancer who may benefit from

intensification and personalization of therapy. Downloaded from http://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-23-2161/3413312/ccr-23-2161.pdf by guest on 10 February 2024

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Acknowledgements

We acknowledge the support of the University of Illinois at Chicago Department of Pathology

and the Vander Griend and Nonn lab members for their helpful input. We acknowledge Tempus

Laboratories for support with RNA-sequencing and the Center for Research Informatics for

assistance with data analysis. We would like to thank the UIC Pathology Core and the UI

Cancer Center for assistance with staining and analyzing tissue microarray data. Additionally,

we acknowledge Dr. Zhengjia (Nelson) Chen for biostatistical assistance in the development of

the AR signature score. We would like to thank our funding sources: JEV is supported by the

Chicago KUH FORWARD U2CDK129917 and TL1DK132769, MM-C is supported in part by

NCATS UL1TR002003, RHN is a recipient of the Robert A. Winn Diversity in Clinical Trials

Career Development Award, funded by Bristol Myers Squibb Foundation, LN is supported by

DOD-CDMRP PCRP Health Disparity Research Awards PC170484, PC190699 and

R21CA231610, and NR is supported by the University of Illinois Cancer Center’s American

Cancer Society Institutional Research Grant IRG-22-149-01-IRG. Finally, we would like to

express our gratitude to the courageous patients who inspired this work. Downloaded from http://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-23-2161/3413312/ccr-23-2161.pdf by guest on 10 February 2024

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Table 1. Patient Characteristics in the UI Health Cohort and dbGaP Samples

UI Cohort n (%)

dbGaP Samples n (% of reported)

Sample Size 33 541

Age at baseline (years)

Percent reported 100% 15.0%

Median 63.4 72

Range 51.9 – 77.7 39-92

Self-Reported Race

Percent reported 100% 14.8%

African American/Black 23 (69.7) 1 (1.25)

White 7 (21.2) 77 (96.3)

Unknown 2 (6.0) 0 (0)

American Indian 1 (3.0) 1 (1.25)

Asian 0 (0) 1 (1.25)

Self-Reported Ethnicity

Percent reported 100% 0%

Non-Hispanic 27 (81.8)

Hispanic 6 (18.2)

Baseline PSA

Percent reported 100% 4.62%

Median 226 5.3

Range 7.97 – 5800 2.8-13.7

Stage at Diagnosis

Percent reported 100% 0%

Localized (N0) 4 (12.1)

Regional (N1) 6 (18.2)

Metastatic (de novo) 23 (69.7)

Tissue Sequenced

Percent reported 100% 100%

Localized tumor 33 (100) 101 (19.3)

Metastases 310 (59.2)

CRPC 115 (21.9)

NEPC 15 (2.86)

Gleason Sum

Percent reported 100% 4.62%

≤ 7 5 (15.2) 19 (76)

≥ 8 24 (72.3) 6 (24)

Unknown 4 (12.1) 0 (0)

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Table 2: Patient Characteristics in the Tissue Microarray

Categorical

Variables

n (%) HER3 (log)

Mean (SD)

p-value

Race

Black

White

98 (65.8)

51 (34.2)

2.74 (0.86)

2.81 (0.81)

0.55†

BMI

Underweight

Normal

Overweight

Obesity

2 (1.3)

35 (24.2)

53 (35.6)

58 (38.9)

3.07 (0.21)

2.90 (0.78)

2.85 (0.90)

2.60 (0.82)

0.0050!!

Gleason Sum

56789

1 (0.7)

12 (8.1)

116 (77.9)

7 (4.7)

13 (8.7)

2.27 (0.78)

2.80 (0.83)

2.73 (0.86)

2.77 (0.84)

3.03 (0.70)

0.49!!

Smoking Status

Former

Current

Non-Smoker

50 (33.6)

51 (34.2)

48 (32.2)

2.69 (0.80)

3.00 (0.79)

2.61 (0.88)

0.0048!

Continuous

Variables

Mean (SD) Correlation

w/ HER3 (log)

*

p-value

Log HER3 2.77 (0.84)

Age 61 (6.5) 0.039 0.53

PSA 15.0 (44.0) -0.066 0.29

Log T (pg/mg) 1.93 (6.69) 0.163 0.0118

Log DHT (pg/mg) 1.62 (1.31) -0.066 0.31 ‡ Paired t-test † Two-sample t-tests

! ANOVA F-tests

!! Spearman Correlation * Pearson Correlation Coefficient Downloaded from http://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-23-2161/3413312/ccr-23-2161.pdf by guest on 10 February 2024

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Figure Legends

Figure 1: ERBB3 overexpression is enriched in a diverse CSPC patient population and is

associated with advanced disease at diagnosis. A) ERBB3 was overexpressed in 40% of

patients in the UI Cohort (n=30). B) When stratified for M stage of disease at diagnosis, ERBB3

expression was significantly higher in patients with de novo metastatic (M1, n=23) vs. M0 (n=7).

Using a positivity threshold of 350 TPM, 14% of M0 patients had ERBB3 OE, while 48% of

patients with de novo metastatic disease at diagnosis had ERBB3 OE. Race/ethnicity for each

patient is shown with colored dots (Black/Hispanic = purple, White = blue). C) When stratified for

Gleason score at diagnosis, ERBB3 expression was not significantly different between Gleason

score ≤7, 8, or 9+ in the UI Cohort. D) With additional samples from consolidated publicly

available RNA-sequencing datasets, ERBB3 expression was significantly higher in localized

prostate cancer (n=125) and metastases (n=310) vs. benign (n=61). E) In the localized prostate

cancer samples from consolidated RNA-sequencing data, ERBB3 expression was significantly

higher in samples with SNP profiles indicative of African ancestry (AFR, n=22) vs. Non-Finnish

European ancestry (NFE, n=102). Applying the positivity threshold for ERBB3 OE to localized

sample set showed 17% ERBB3 OE in NFE ancestry and 30% ERBB3 OE in AFR ancestry. F)

In the metastatic samples from consolidated RNA-sequencing data, ERBB3 expression was

significantly higher in samples with SNP profiles indicative of African ancestry (AFR, n=16) vs.

Non-Finnish European ancestry (NFE, n=291). Applying the positivity threshold for ERBB3 OE

to localized sample set showed 14% ERBB3 OE in NFE ancestry and 50% ERBB3 OE in AFR

ancestry. TPM = transcripts per million, *p<0.05, ***p<0.01,****p<0.0001. Downloaded from http://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-23-2161/3413312/ccr-23-2161.pdf by guest on 10 February 2024

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Figure 2: HER3 Expression Inversely Correlates with Intraprostatic DHT in AA Men. A) A

tissue microarray containing prostate tumor specimens from African American and White men

was stained for HER3 and analyzed for HER3 positivity. Representative images of the TMA

quantification pipeline are shown; normal areas of the cores were excluded from analysis (norm.

exclusion) and HER3 was specifically quantified in epithelial cells (epithelial mask). B) HER3

percent positivity was not significantly different between AA and White prostate tumor cores (ns

= not significant). C) HER3 expression (log) was negatively correlated with intraprostatic DHT

(log) in AA men (p=0.0255), but not in White men (p=0.4485). D) Representative images of

HER3 staining (brown) in AA and White cores with High vs. Low intraprostatic DHT.

Hematoxylin was used as a nuclear counterstain (blue). Downloaded from http://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-23-2161/3413312/ccr-23-2161.pdf by guest on 10 February 2024

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Figure 3: ERBB3 Expression is Associated with Androgen Signaling. A) Demographics for

ERBB3 high and low quartiles from the UI cohort and consolidated publicly-available RNA-seq

samples (dbGaP) used for gene set enrichment analysis. Race for each patient analyzed is

represented in a heatmap where purple is AA/AFR and blue is White/NFE. B) Gene set

enrichment analysis between ERBB3 high and low samples in the UI cohort showed enrichment

of Androgen Response (Hallmark) (NES = 1.77, FDR =0.09) in ERBB3 high vs. low patients and

enrichment of Inflammatory Response (Hallmark) (NES = -1.69, FDR =0.16) in ERBB3 low vs.

high patients. C) Gene set enrichment analysis between ERBB3 high and low samples in the

samples from the dbGaP cohort showed enrichment of Androgen Response (Hallmark) (NES =

1.79, FDR =0.09) in ERBB3 high vs. low patients and enrichment of Epithelial Mesenchymal

Transition (EMT) (Hallmark) (NES = -1.85, FDR =0.06) in ERBB3 low vs. high patients. D) To

confirm the androgen response signature is prevalent in ERBB3 high patients, expression of

select AR target genes was assessed in ERBB3 high vs. low patients in the UI cohort and in

dbGaP samples. ERBB3 expression was significantly higher in ERBB3 high patients vs. ERBB3

low, as expected. AR transcript was not statistically different in ERBB3 high vs. low in the UI

cohort, but significantly higher in ERBB3 high vs. low patients in dbGaP samples. KLK3, NKX3-

1, TMPRSS2, and ZBTB10 were all significantly increased in ERBB3 high vs. low patients. E.

Leading edge analysis of the genes driving the enrichment of Androgen Response (Hallmark)

were compared between the UI cohort and samples from dbGaP using a heatmap where grey

indicated leading edge genes and white indicated non-leading edge genes. 31 leading edge

genes were similar between the two datasets. 7 genes were specific to the UI cohort, and 30

genes were specific to the samples mined from dbGaP. NES = normalized enrichment score,

FDR = false discovery rate, FC = fold change, ns = not significant, *p<0.05, **p<0.01,

***p<0.001, ****p<0.0001.

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Figure 4: ERBB3 Expression Correlates with an AR Signature Score and High Serum PSA

in AA Men. A) ERBB3 (log) TPM values for each patient from the UI Cohort are shown in a

heatmap alongside the log transformed TPM values of the AR target genes that make up the

AR signature score.. B) ERBB3 (log) TPM values for each patient from GSE209954 are shown

in a heatmap alongside the log transformed TPM values of the AR target genes that make up

the AR signature score. C) A simple linear regression of ERBB3 expression (TPM) and AR

signature Score Sum showed a statistically significant positive correlation between ERBB3

expression and AR signaling in the UI Cohort (R2

= 0.3033, p = 0.0009) and GSE209954 (R2

=

0.5962, p < 0.0001). Each patient is represented by a dot with the best-fit line (solid line)

bracketed by the 95% confidence intervals (dashed lines). D) Pre-treatment serum PSA (ng/mL)

was significantly higher in the ERBB3 high patients from the UI cohort vs. ERBB3 low (*p<0.05).

E) In external dataset GSE209954 containing African American (AAM) and Non-African

American (Non-AAM) patients, ERBB3 expression was not significantly correlated with pretreatment PSA (R2

= 0.01309, p = 0.3528) in all samples. However, when stratified by race,

there was a significant positive correlation between ERBB3 expression and pre-treatment PSA

in AAM patients (R2

= 0.1530, p = 0.0244) that did not exist in Non-AAM patients (R2

= 0.0129, p

= 0.5156). Each patient is represented by a dot with the best-fit line (solid line) bracketed by the

95% confidence intervals (dashed lines). Downloaded from http://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-23-2161/3413312/ccr-23-2161.pdf by guest on 10 February 2024

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Figure 5: HER3 Promotes Enzalutamide Resistance in Prostate Cancer Models. A) Protein

expression of phospho-HER3 (pHER3), HER3, phospho-HER2 (pHER2), HER2, AR, and Betaactin was assessed in an array of prostate cancer cell lines (LNCaP, VCaP, CWR-R1, MDA

PCa 2b, LAPC4). BT474 (breast cancer) was used as a positive control for HER2/3 expression

and Beta-actin was used as a loading control. B) Protein expression of pHER3, HER3, pHER2,

HER2, AR) was assessed in enzalutamide-resistant (ENZR

) CWR-R1, LNCaP, and VCaP cells.

ENZR

cells were maintained in 10 µM enzalutamide and all cells were treated with 100ng/mL

NRG1 for 15 minutes prior to protein collection. Beta-actin was used as a loading control. C)

LNCaP were maintained in 0, 1, or 10nM R1881 for 6 months to create an androgen-adapted

cell line. Cells were treated with 100ng/mL NRG1 for 15 minutes prior to protein collection.

pHER3 and HER3 were decreased in the high androgen condition (10nM R1881) compared to

control (0nM R1881). pHER2 and HER2 were increased in low androgen (1nM R1881), but not

different in the 10nM condition vs. control. Beta-actin was used as a loading control. D) Cell

proliferation was assessed in CWR-R1 cells after treatment with HER3 inhibitor Patritumab in

combination with ENZ. ENZ alone did not decrease proliferation, but when co-treated with

Patritumab, cell proliferation decreased significantly compared to either treatment alone (n=5).

E) Western blots confirmed reduction of pHER3 with Patritumab (10µM for 2 hours) after NRG1

stimulation (100ng/mL for 15 minutes). pHER2 was also decreased after Patritumab treatment,

but to a lesser degree. Beta-actin was used as a loading control. F) A representative cell

proliferation curve in LNCaP HER3 overexpression (HER3-OE) vs. non-targeted control (NT)

vectors after treatment with 10µM ENZ. Proliferation decreased in the NT control cells after

treatment with 10µM ENZ; however, the HER3-OE cells did not decrease after treatment with

ENZ, suggesting HER3-OE is sufficient for ENZ-resistance (n=3). G) Western blots confirmed

over-expression of HER3 compared to NT control. A 2-way ANOVA with Tukey’s multiple

comparison test was used for statistics; ****p<0.0001, ns = not significant.

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