Categories
Prostate cancer

Serial SBRT Potentially Beneficial in Oligometastatic Prostate Cancer

Serial stereotactic body radiation therapy (SBRT) directed by positron emission tomography (PET) using novel radiotracers may offer clinical benefits in patients with oligometastatic prostate cancer, according to a recent retrospective study.

The study’s findings suggest that a second course of SBRT for men with recurrent oligometastatic prostate cancer following an initial course of SBRT is an important therapeutic strategy to consider, Daniel H. Kwon, MD, of the University of California, San Francisco, and colleagues reported in Urologic Oncology.

The study included 25 men with 1-5 prostate cancer metastases detected on PSMA (prostate-specific membrane antigen) or fluciclovine PET and who underwent 2 consecutive courses of SBRT. At the time of the second SBRT course, PSMA and fluciclovine PET detected oligorecurrent disease in 17 (68%) and 8 (32%) patients, respectively. Of the 25 patients, 15 (60%) had castration-sensitive disease and 10 (40%) had castration-resistant disease.


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Following the second SBRT course, 16 patients (64%) achieved a 50% or greater decline in PSA level (PSA50), Dr Kwon’s team reported. The median progression-free survival (PFS) time was 11 months. The median survival time without initiation or intensification of androgen deprivation therapy (ADT) was 23.2 months. From the first SBRT course to the last follow-up (a median of 48 months), 7 patients (28%) remained ADT-free, Dr Kwon’s team reported.

A PSA50 response after the first SBRT course was significantly associated with a 64% decreased risk for disease progression and a 93% decreased risk for ADT initiation or intensification after the second SBRT course.

“Degree of biochemical response to first SBRT warrants further study as a potential predictor of PSA response, PFS, and ADT initiation/intensification-free survival following a subsequent SBRT course,” they concluded.

After controlling for potential confounders, the degree of biochemical response after the first SBRT course and concurrent change in systemic therapy with the second SBRT course were significantly associated with a greater likelihood of a PSA50 response and longer PFS after the second SBRT course, according to the investigators.

“Our study adds to the limited but growing literature supporting the feasibility and potential clinical benefit of serial metastasis-directed SBRT guided by novel radiotracers and PET imaging,” the authors wrote.

The authors wrote that their preliminary findings provide a rationale for larger and prospective studies of serial SBRT.

Reference

Kwon DH, Shakhnazaryan N, Shui D, et al. Serial stereotactic body radiation therapy for oligometastatic prostate cancer detected by novel PET-based radiotracers. Urol Oncol. Published online November 24, 2022. doi:10.1016/j.urolonc.2022.10.025

Categories
Prostate cancer

TACTICAL Awards- Class of 2022

The PCF TACTICAL Award-Class of 2022 recipients are:

Project Title: An Accelerated Platform using Lead-212 Targeted α-particle Therapy to Radically Improve Cancer Lethality of Prostate Cancer Theranostics using Novel Targets and Better Understanding of Resistance: the Cancer Lethality Lead Collaboration

Principal Investigators: Michael Hofman, MBBS (Peter MacCallum Cancer Centre), Mohammad Haskali, PhD (Peter MacCallum Cancer Centre), Luc Furic, PhD (Peter MacCallum Cancer Centre), Johannes Czernin, MD (University of California, Los Angeles), Christine Mona, PhD (University of California, Los Angeles), Caius Radu, MD (University of California, Los Angeles), Tom Hope, MD (University of California, San Francisco), Felix Feng, MD (University of California, San Francisco), Ken Herrmann, MD (Uniklinik Essen), Katharina Lückerath, PhD (Uniklinik Essen), Heidi Fettke, PhD (Peter MacCallum Cancer Centre), Elie Besserer, PhD (University of California, Los Angeles), Martin Sjoestroem, MD, PhD (University of California, San Francisco), Valeska von Kiedrowski, PhD (Uniklinik Essen)

Co-Investigators: Shahneen Sandhu, MBBS (Peter MacCallum Cancer Centre), Anna Trigos, PhD (Peter MacCallum Cancer Centre), Arun Azad, MD, PhD (Peter MacCallum Cancer Centre), Declan Murphy, FRACS (Peter MacCallum Cancer Centre), Giuseppe Carlucci, PhD (University of California, Los Angeles), Thuc Le, PhD (University of California, Los Angeles), Jonathan Tranel, PhD (University of California, San Francisco), Wolfgang Fendler, MD (Uniklinik Essen), Anna Pacelli, PhD (Uniklinik Essen)

Young Investigators: Marwa Rahimi, PhD (Peter MacCallum Cancer Centre), Magdalena Staniszewska, PhD (Uniklinik Essen)

Description:

  • Radioligand therapy (RLT) using Lutetium-177 PSMA, a small molecule with radioactive payload, is the latest FDA-approved treatment for men with lethal prostate cancer and improves survival and quality-of-life. While striking responses can occur, one-third of patients exhibit primary resistance and the patients who do respond will eventually recur. Thus, while RLT is highly promising, further optimization is needed.
  • An alternative approach is the use of alpha-emitting radioactive payloads, which have better tumor cell killing potential than the beta emitter Lutetium-177.
  • Michael Hofman and team are developing novel RLTs for prostate cancer that combine novel chemistry approaches to target prostate cells with the alpha emitter Lead-212.  Lead-212 has advantages over other radioemitters including ease of production using a novel desktop generator rather than nuclear reactor or cyclotron.
  • A cutting-edge chemistry approach will be used to develop and optimize high affinity cyclic peptides that can target prostate cancer proteins ROR1, DLL3 and others. These will be labeled with Lead-212 and compared with existing ligands targeting PSMA and FAP for RLT in preclinical prostate cancer imaging and treatment models. In-human biodistribution will be evaluated in pilot clinical trials.
  • The team will also use samples from advanced and lethal prostate cancer cases to characterize novel targets and biomarkers, and identify resistance mechanisms to RLT treatment.
  • If successful, this team will develop next-generation RLTs to radically improve outcomes for men with metastatic prostate cancer. The team aims to translate these theranostic agents for commercialization, regulatory approval and global adoption within 7-10 years. Not only would this improve lives for patients with prostate cancer but it would also have direct applicability in a broader range of cancers.

What this means to patients  The team will develop a new treatment called targeted alpha therapy using the radioactive substance Lead-212. This emits highly energic particles that can eradicate cancer whilst travelling less than the width of a human hair limiting side effects. This will be combined with a mRNA-based technology to discover new small molecules that lock onto cancer cells enabling targeted delivery of the radioactive payload. Additionally, we will characterize mechanisms of treatment resistance and develop biomarkers for better personalizing these treatments. We hope these discoveries will transform treatment for men with highly aggressive and lethal prostate cancers currently lacking effective diagnostics/therapies. The targets are also expressed in other cancers and may have equal or greater impact beyond prostate cancer.


 
Project Title: Developing Engineered Cell Therapies for Metastatic Castrate-Resistant Prostate Cancer to Increase Efficacy and Decrease Toxicity

Principal Investigators: Carl June, MD (University of Pennsylvania), Saul Priceman, PhD (City of Hope), Joseph Fraietta, PhD (University of Pennsylvania), Naomi Haas, MD (Abramson Cancer Center at the University of Pennsylvania Perelman School of Medicine), Avery Posey, PhD (University of Pennsylvania), Tanya Dorff, MD (City of Hope), John Maris, MD (Children’s Hospital of Philadelphia), James Gulley, MD, PhD (National Cancer Institute)

Co-Investigators: Vivek Narayan, MD (Abramson Cancer Center at the University of Pennsylvania Perelman School of Medicine), Priti Lal, MD (University of Pennsylvania), Regina Young, PhD (University of Pennsylvania), Evan Weber, PhD (Children’s Hospital of Philadelphia), Yi Xing, PhD (Children’s Hospital of Philadelphia), Nikolaous Sgourakis, PhD (Children’s Hospital of Philadelphia), Peter Choyke, MD (National Cancer Institute), Scott Norberg, DO (National Cancer Institute), Wiem Lassoued, PhD (National Cancer Institute), Jennifer Marte, MD, MPH (National Cancer Institute), Nan-Ping Weng, MD, PhD (National Institute on Aging), Peter Kuhn, PhD (University of Southern California), Kara Maxwell, MD, PhD (The Corporal Michael J. Crescenz VA Medical Center), Daniel Lee, MD (The Corporal Michael J. Crescenz VA Medical Center), Darshana Jhala, MD (The Corporal Michael J. Crescenz VA Medical Center)

Young Investigators: Andrew Rech, MD, PhD (University of Pennsylvania), Mark Yarmarkovich, PhD (Children’s Hospital of Philadelphia)

Description:

  • Chimeric Antigen Receptor (CAR) T cells are a powerful type of immunotherapy in which a patients’ own T cells are engineered to target and kill their cancer. CAR T cells have demonstrated dramatic efficacy and even cures in patients with various blood cancers, including lymphomas, certain types of leukemia, and multiple myeloma. CAR T cells are now under development for solid tumors including prostate cancer.
  • In prostate cancer, the efficacy and longevity of CAR T cells is limited by an immuno-suppressive tumor microenvironment. The team has previously identified multiple genes in T cells that when deleted may improve tumor-killing potential and overcome the suppressive tumor microenvironment.  In this project, they will comprehensively test the effects of deleting these genes in CAR T cell preclinical models. The team will also apply advanced analytic technologies to define the tumor and immune factors that impact CAR T cell therapy in patients with mCRPC, to identify new therapeutic targets for improving CAR T efficacy.
  • The efficacy of CAR T cells in combination with various other immunotherapies will be tested.
  • CAR T cells are engineered to target tumor-associated antigens, such as PSMA and PSCA in prostate cancer. The team will identify new target antigens in African American and racially diverse prostate cancer populations as well as in neuroendocrine-variant tumors, and test the efficacy of CAR T cells targeting these new antigens in preclinical models.
  • The efficacy of CAR T cells in solid tumors is also limited by genomic heterogeneity and loss of the target antigen. To overcome this barrier, the team will develop and test a “polytherapy” that combines CARs against multiple targets.
  • Together these preclinical studies, which employ prostate cancer models and samples and data from patients with mCRPC enrolled in CAR T cell trials, will create a roadmap for the development of more efficacious and safer next-generation CAR T cell therapies for mCRPC.
  • If successful, the team will launch a clinical trial testing the most promising CAR T clinical candidate(s) and strategies by year 3 of the project.

What this means for patients: This team will develop new clinic-ready CAR T cell therapies for mCRPC that can overcome the limitations of prior CAR T cell products for prostate cancer. This may ultimately lead to a powerful new arsenal of immunotherapy treatments for advanced prostate cancer patients, that are effective in racially diverse populations and against heterogenous advanced disease types. This knowledge may also advance immunotherapy in patients with other types of solid tumors.


 
Project Title: Novel Theranostic Agents for Neuroendocrine Prostate Cancer

Principal Investigators: Jason Lewis, PhD (Memorial Sloan Kettering Cancer Center), Michael Morris, MD (Memorial Sloan Kettering Cancer Center), Lisa Bodei, MD, PhD (Memorial Sloan Kettering Cancer Center), Himisha Beltran, MD (Dana-Farber Cancer Institute), Felix Feng, MD (University of California San Francisco), Samir Zaidi, MD, PhD (Memorial Sloan Kettering Cancer Center), John Poirier, PhD (NYU Grossman School of Medicine)

Co-Investigators: Kishore Pillarsetty, PhD (Memorial Sloan Kettering Cancer Center), Yu Chen, MD, PhD (Memorial Sloan Kettering Cancer Center), Anuradha Gopalan, MD (Memorial Sloan Kettering Cancer Center)

Young Investigators: Ryan Reddy, MD (Memorial Sloan Kettering Cancer Center), Salomon Tendler, MD, PhD (Memorial Sloan Kettering Cancer Center), Audrey Mauguen, PhD (Memorial Sloan Kettering Cancer Center)

Description:

  • Approximately 17% of patients with advanced prostate cancer eventually develop neuroendocrine prostate cancer (NEPC), which is typified by aggressiveness and extreme lethality.
  • Multiple challenges must be overcome to address the needs of NEPC patients, the most critical of which include developing a non-invasive diagnostic method for NEPC and developing novel treatment strategies that provide durable responses.
  • Jason Lewis and team will create novel diagnostic and therapeutic agents using newly developed antibodies that target the NEPC-specific protein, DLL3.
  • The team is conducting an ongoing clinical trial evaluating a DLL3-targeted imaging agent to identify patients with NEPC and collect biopsy samples of NEPC tumors. The team will use data and samples from this trial to validate DLL3 as a promising therapeutic and diagnostic (“theranostic”) target for patients with NEPC.
  • Novel DLL3-targeted theranostics developed in this project will be tested in preclinical NEPC models, and if promising, translated into clinical trials in patients.
  • The team will also develop a preclinical platform to identify and optimize DLL3 treatment combination strategies for future clinical trials.
  • If successful, this project will validate DLL3 as a theranostic target for the identification and treatment of patients with NEPC, and develop a novel clinic-ready DLL3-targeted PET-based agent and DLL3-targeted radioligand therapy.

What this means to patients:   This team will validate DLL3 as a theranostic target for the identification and treatment of patients with NEPC, a highly aggressive and currently untreatable form of advanced prostate cancer.  A novel DLL3-targeted PET-based diagnostic and DLL3-targeted radioligand therapy will be developed and readied for testing in clinical trials.  These studies will also aid in the identification of rational combinatorial strategies that synergize with DLL3-targeted agents. These results will lay the groundwork for the clinical trials of not only the theranostic proposed in this grant, but also for immunotherapeutics, antibody–drug conjugates, and other DLL3-directed strategies that are currently under development for both NEPC and small cell lung cancer (SCLC).


 
Project Title: Tactical Approaches to Repress oncogenic Gene Expression in prostatic Tumors (TARGET)

Principal Investigators: Arul Chinnaiyan, MD, PhD (University of Michigan), Sarki Abdulkadir, MD, PhD (Northwestern University)

Co-Investigators: Shaomeng Wang, PhD (University of Michigan), Ganesh Palapattu, MD (University of Michigan), Joshi Alumkal, MD (University of Michigan), Zachery Reichert, MD (University of Michigan), Ulka Vaishampayan, MD (University of Michigan), Maha Hussain, MD (Northwestern University), Gary Schiltz, PhD (Northwestern University), Yuzhuo Wang, PhD (University of British Columbia), Ke Ding, PhD (Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences), Xiaoju (George) Wang, PhD (University of Michigan), Jean Tien, PhD (University of Michigan), Yuanyuan Qiao, PhD (University of Michigan), Jeanne Stuckey, PhD (University of Michigan), Alex Tsodikov, PhD (University of Michigan), Jeremy Taylor, PhD (University of Michigan)

Young Investigators: Abhijit Parolia, PhD (University of Michigan), Lanbo Xiao, PhD (University of Michigan), Sarah Fenton, MD, PhD (Northwestern University)

Description:

  • The MYC protein is one of the most important oncogenes in human cancer, driving the development and progression of ~70% of all cancers, and is a critical driver of prostate cancer development and progression.
  • MYC would be an ideal target for cancer therapeutics; however, because of its unconventional and disorganized structure, it has proven difficult to target using standard drug development approaches. There are no approved direct MYC inhibitors.
  • Arul Chinnaiyan and team will use multiple novel drug development and chemistry approaches to develop cancer therapies that effectively target MYC.
  • Approaches that will be tested include small molecule inhibitors and PROTAC (proteolysis targeting chimera)-based degraders to target MYC.
  • The potential and efficacy of these therapies will be evaluated in preclinical prostate cancer models that span all clinical prostate cancer disease stages, and made ready for advancement into phase 1 clinical trials.
  • If successful, this team will develop a novel effective MYC-directed therapy for prostate cancer.  MYC is a major driver of many cancer types; thus, these studies have the potential to benefit huge numbers of patients with cancer.

What this means to patientsThis team will employ a suite of cutting-edge drug development techniques to develop an effective inhibitor of MYC, a major driver of ~70% of all cancers, including prostate cancer. These agents will be rapidly translated into clinical trial testing and have the potential to benefit a broad population of patients, including all clinical stages of prostate cancer and many types of cancer beyond prostate cancer.

Categories
Prostate cancer

Extensive androgen receptor enhancer heterogeneity in primary prostate cancers underlies transcriptional diversity and metastatic potential

AR enhancer usage is highly heterogeneous in primary PCa

Previously, we reported a total universe of 69,330 ARBS in a cohort of 88 primary prostate cancers with a mean tumor cell percentage >80%, averaging 7394 peaks per tumor with FRiP scores >1.5 (Supplementary Tables 24)3, which were processed in a standardized manner minimizing cell death and optimizing sample quality (see the “Methods” section). To annotate inter-tumor heterogeneity of enhancer usage, we ranked ARBS based on detected peaks in the fraction of tumors analyzed, revealing an unexpected high level of AR enhancer heterogeneity between tumors (Fig. 1a), with typical AR-inducible genes FKBP5 and KLK3 regulated by highly ranked ARBS enhancers 129 and 343 (enhID, Source Data), respectively. Based on ARBS prevalence in patients, we binned these ARBS in three categories: shared (SH; in AR sites identified in 68% or more of the patients), partially shared (PS; AR sites found in 2–67% or more of the patients) and unique peaks (UN; AR peaks observed in merely one patient).

Fig. 1: AR enhancer usage is heterogeneous in primary and normal PCa tissue.
figure 1

a Schematic overview: AR ChIP-seq identifies 69,330 AR Binding Sites (ARBS) in 88 patient tumor tissues ranked and binned on prevalence in patients, shared sites observed in 60–88 patients (SH-ARBS, blue), partially shared sites observed in 2–59 patients (PS-ARBS, yellow) and unique sites observed in 1 patient (UN-ARBS, purple). b ARBS ranking for 88 primary PCa tumors (blue, n = 69,330) with tumor ARBS presence in a panel of AR+ cell lines (red) and ARBS ranking for 15 normal prostate epithelium (orange, n = 27,500) with normal ARBS presence in a panel of AR+ cell lines (green). Sidebars indicate the percentage of cell line ARBS found in primary PCa tumors. c Boxplot quantification of primary tumor ranked ARBS (n = 69,330) presence in cell lines. Centerline, median; upper and lower quartiles; whiskers, 1.5 × interquartile range; points, outliers. Two-tailed Student’s t-test of means compared to LSHAR cells, ****p < 0.0001. Enrichment in SH-ARBS calculated by hypergeometric test, ****p < 0.0001, non-significant ns. d ARBS ranking normalized for a number of samples, comparing primary tumor (blue) and normal epithelium (orange). e ChIP-seq signal examples for peaks in ARBS categories, SH-ARBS enhID 1 which occurs in 88/88 patients, PS-ARBS enhID 2855 and observed in 45/88 patients and UN-ARBS enhID 47,348 in 1/88 patients. f Genome-wide AR ChIP-seq intensities for ARBS categories in individual tumors, line and secondary y-axis in blue on the right: SH-ARBS, line and secondary y-axis in yellow on the right: PS-ARBS, line in purple: UN-ARBS. g Genomic location distribution of ARBS for consensus of ARBS over all 88 tumors, gray: unstable consensus due to small amount of ARBS. h Transcription factor motif family enrichment at top 5% (SH), middle 5% (PS), and bottom 5% ARBS (UN) with z-score indicating prevalence. i Transcription factor motif presence for AR, FOXA1, and HOXB13 at ranked ARBS (top) and distribution across ranked ARBS (bottom). Centerline, median; upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers. Source data are provided in Source Data.

To which degree does AR binding in frequently-used PCa cell lines represent the enhancer heterogeneity found in tumors? We overlapped AR ChIP-seq data from treatment sensitive8,15 and resistant15,16,17 PCa cell lines with all ranked ARBS, displaying enrichment of commonly shared ARBS for all tested PCa cell lines and AR-transduced normal prostate LSHAR cells4. However, as a control for prostate selectivity, we observe that overlap of ARBS from PCa was largely absent in monocytic THP-1 cells18 and molecular apocrine ER/AR+ MDA-MDB453 breast cancer cells19(Fig. 1b, c, Supplementary Table 5). Of note, we observe a further shift towards SH-ARBS enrichment in the acquisition of therapy resistance for both bicalutamide-resistant LNCaPBR and enzalutamide-resistant LNCaP derivative 42DENZR. In normal epithelial prostate tissues (n = 15), we observed a highly similar heterogeneous ARBS ranking (n = 27,850, Fig. 1b, Supplementary Fig. 1A). Tumor and normal ranked ARBS rankings follow strikingly similar distributions considering the genetic heterogeneity of tumors10,11, suggesting that AR enhancer heterogeneity is not tumor-intrinsic, but instead patient-intrinsic (Fig. 1d, Supplementary Fig. 1G), corroborating our previous case-study identifying high inter-metastatic AR binding overlap from the same patient7.

As ChIP-seq data quality and peak calling can be impacted by technical limitations20, we performed extensive quality control analyses (NSC, RSC, FrIP, GC%, MSPC, and single mapped reads/AR ChIP-seq read correlation tests, Supplementary Figs. 1, 2) to test whether the observed inter-tumor AR heterogeneity results from technical artifacts. Samples contained comparable high fractions of tumor cells (Supplementary Table 2) and although total ARBS identified per sample fluctuated, AR expression levels for each sample did not correlate with the total number of AR ChIP-seq peaks, nor did read depth (RD) correlate with the total AR ChIP-seq peaks per sample, indicating biological variation between samples as opposed to technical variation (Supplementary Table 3, Supplementary Fig. 1E, F).

Strong ChIP-seq signals were found at SH-ARBS, while such signal on the individual patient level was weaker or absent at PS-ARBS. Interestingly, we identify UN-ARBS with clearly distinguishable signal from the background and of generally comparable intensity as PS-ARBS, showing that peak calling correctly identified UN-ARBS (exemplified in Fig. 1e, aggregate peaks quantified in Fig. 1f). Moreover, we detected occurrence of UN-ARBS in PCa cell lines (Fig. 1b) and alternative multiple sample peak calling (MSPC, see the “Methods” section) of ranked ARBS with multiple testing correction confirmed heterogeneous peaks as highly significant true-positives with comparable GC content, ARBS peak distribution and specific UN-ARBS signal over background (Supplementary Fig. 1B, C, E, H). Samples with low RD according to ENCODE4 Transcription Factor (TF) ChIP-seq standards had similar UN-ARBS and AR motif score distributions as those coming from samples with high RD (Supplementary Fig. 2). Moreover, no correlation was observed between samples with different RD quality categories and FRiP, NSC or RSC metrics (Supplementary Fig. 2A, C). Finally, UN-ARBS in quality outlier sample P349T did not contribute significantly to overall analyses (Supplementary Fig. 1E and Supplementary Fig. 2F, G).

As expected and previously reported3,4,21, ~80% of ARBS are present in introns or intergenic regions that are generally considered putative enhancers with cis-regulatory potential22 (Fig. 1g, Supplementary Fig. 1D). Interestingly, genomic distributions of ARBS were not equally distributed over consensus, with higher promoter enrichment in more heterogeneously occupied ARBS. Moreover, in all three ARBS categories, we found motifs for AR as well as canonical AR-interactors FOXA123 and HOXB134 (Fig. 1h, i, Supplementary Fig. 2H). Additionally, distributions of AR motif scores detected in UN-ARBS using MISP motif screen were equal for RD categories (Supplementary Fig. 2I). We tested our ARBS universe for significant overlaps in GIGGLE24 (see the “Methods” section), which analyses over 14,000 individual ChIP-seq databases for TF binding overlap, and confirmed binding of these classical prostate lineage TFs for SH- and PS-ARBS (Supplementary Fig. 1H). Cumulatively, these data support genuine enrichment of heterogeneous ARBS in sequencing analysis, with co-enrichment of TFs associated with canonical AR action. Interestingly, MED1 and RNA Polymerase II subunits, but not AR nor its classical interactors, were enriched in UN-ARBS in GIGGLE (Supplementary Fig. 1H). Like PS-ARBS, UN-ARBS are mostly associated with active TSS and enhancers (Supplementary Fig. 1J), whereas this analysis was uninformative for SH-ARBS due to the relatively small group size (n = 1201). Taken together, these GIGGLE analyses indicate occupancy by functional enhancer-binding proteins on patient-unique ARBS and stress their context-dependent nature.

Ranked ARBS have functional divergence on enhancer activity and mutation frequency

We tested ranked ARBS for bona fide enhancer activity and hormone dependency using available data from a massive parallel reporter assay testing enhancer potential for 7422 ARBS in LNCaP25 in vehicle versus DHT conditions (Fig. 2a), resulting in ARB’s enhancer potential that could be classified as inactive, inducible or constitutively active. Most-commonly shared ARBS were enriched for hormone-dependent enhancer activity (n = 286), relative to constitutively activate (n = 463) or inactive sites (n = 2467), suggesting hierarchical functional consequences of ARBS heterogeneity (Fig. 2b, c). The total set of 7422 ARBS analyzed in STARR-seq was expanded to 20,790 regions using a machine learning-based ARBS annotation, confirming our original conclusions with a more complete representation of total ARBS heterogeneity (Fig. 2b, c)25. To confirm these results, we performed additional STARR-seq targeted at 2495 randomly sampled heterogeneous ARBS to validate predictions of heterogeneous enhancer activity and confirmed the presence of active enhancers in heterogeneous ARBS, suggesting that a subset of these ARBS function as enhancers (Fig. 2b, c, Supplementary Fig. 3A, B). Finally, individual ARBS activity was validated for a subset of regions represented in the STARR-seq library for each element designation (inducible, constitutive and inactive) through luciferase assays, which confirmed their previously identified activity status and hormonal dependency (Supplementary Fig. 3C, D, Supplementary Table 6).

Fig. 2: Ranked ARBS have functional divergence on AR enhancer activity, mutational frequency, and presence of super enhancers in cell lines and tissue.
figure 2

a Enhancer activity for ranked ARBS found in EtOH over DHT LNCaP conditions in STARR-seq, machine learning predicted ARBS, and the second set of designed ARBS. Inset: zoomed-in STARR-seq regions for ARBS ranked 1–15,000. b Distribution of ARBS with enhancer activity identified in STARR-seq experiments. Inducible n = 286, constitutive n = 463, inactive n = 2467, predicted inducible n = 1237, predicted constitutive n = 1671, active n = 149 ARBS. Two-tailed Student’s t-test of means. Centerline, median; upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers. c ARBS ranking with presence of PCa risk single nucleotide polymorphisms (rSNP, and single nucleotide variations (SNV) identified in primary prostate cancer (cistrome Quantitative Trait Locus, cQTL; primary SNV, pSNV) and metastatic SNV, mSNV. Inset: distribution of rSNPs at ARBS among patients. Centerline, median; upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers. d Observed over expected background primary or metastatic mutation rate for ARBS rankings. Two-tailed Fisher’s exact test on untransformed values, *p < 0.05, **p < 0.01, ****p < 0.0001, ns = non-significant. e Ranked ARBS identified at super-enhancer genomic locations for PCa cell lines and tissue as reported by SEdb, SH-ARBS hypergeometric test of enrichment, ****p < 0.0001. f Genomic snapshot of PCAT1 and PCAT2 locus on Chr8 with ARBS prevalence in patients (blue numbers), rSNP rs710886 (black), primary SNVs (yellow), metastatic SNVs (purple), and VCaP SE (names in orange) presence. Source data are provided in Source Data.

Having established an association of AR enhancer heterogeneity in PCa with biological consequences on enhancer activity, we next investigated the impact of 764 risk single nucleotide polymorphisms (rSNPs)26,27,28 and single nucleotide variation (SNV) reported previously in primary PCa (n = 278,209)10 and in metastatic PCa (mPCa, n = 1,048,576)11 on primary ranked ARBS as SNVs accumulate during PCa progression. Moreover, we included germline allelic imbalance SNP data (cQTL, n = 4454; q < 0.05) which was called from our primary AR ChIP-seq data in a recent Cistrome-Wide Association Study (CWAS)29. Overlap of rSNPs with primary ranked ARBS was limited (52 out of 764 unique rSNPs) without any enrichment on particular ARBS, while germline cQTL SNPs and somatically acquired SNVs in primary and metastatic PCa are enriched in primary SH-ARBS (n = 1201, Fig. 2d, e), and these sites were previously associated with AR occupancy30.

Highly actively transcribed regions are characterized by high levels of clustered H3K27 acetylation regions, referred to as ‘super-enhancers’31,32, and describe tissue-specific-binding profiles that are typically AR-positive in PCa cell lines and tissue. These super-enhancers encompass ARBS found throughout ranked ARBS (Fig. 2f), as exemplified by recently reported VCaP SEs at PCAT1/2 regulating MYC expression during CRPC33, which is constituted by PS- and UN-ARBS that are affected by rSNPs and SNVs (Fig. 2g). Collectively these data show that ranked ARBS expose hierarchical enhancer activity, and display enrichment of primary and metastatic SNVs in SH-ARBS. Contrarily, super-enhancers are found scattered throughout the ARBS ranking, suggesting that PS- and UN-ARBS can functionally drive oncogenic processes.

Enhancer-specific copy number alterations at heterogeneous ARBS drive transcriptional output in metastatic disease

Next to SNVs, large structural events like copy number alterations (CNAs) are frequently observed in mPCa as drivers of progression11,34. In metastatic disease, CNAs of enhancers with tumor-driving potential for AR11,13,35, FOXA136, and HOXB135 have been reported. A previously reported large sequencing database of 101 mPCa tissues11 reported that 23 mPCa patients had known CNA gains at exclusively the AR enhancer locus, of which PS- and UN-ARBS were previously identified as tumor-associated4 and metastatic-associated5 ARBS (Fig. 3a, Supplementary Table 7, Source Data). We reconfirmed previously reported promoter–enhancer interactions by integrating ranked ARBS using H3K27ac Hi-ChIP data5,37. Numerous ARBS are in close proximity to a single promoter in 3D genome space, confirming previously reported VCaP AR ChIA-PET data38. For example, we identify chromatin loops between PS- and UN-ARBS and the AR promoter (Fig. 3a). As expected, large CNA gains and losses in mPCa patients11 affect ARBS with enhancer–promoter interaction irrespective of ranking, including PS- and UN-ARBS, that potentially affect the expression of tumor-driving genes (Fig. 3b, Supplementary Fig. 4). Moreover, we observe a multitude of structural variations (SVs) in these sites, most notably deletions, inversions or tandem duplications at well-described PCa SV loci like tumor suppressors SPOPL at chr2q and DCC/BCL2 at chr18q (Fig. 3b).

Fig. 3: Enhancer-specific copy number alterations at ranked ARBS affect transcription at interacting PCa-dependent genes in metastatic disease.
figure 3

a Genomic snapshot of enhancer region upstream of AR locus with H3K27ac Hi-ChIP interaction data, number of primary patients with ARBS, primary tumor ARBS (TARBS), metastasis-associated ARBS and metastatic patient enhancer-specific copy number gains (23/101 patients). b ARBS ranking showing H3K27ac Hi-ChIP interactions with gene promoters and ARBS affected by copy number gains and losses and SVs such as deletions, inversions, and tandem duplications. False color scale for CNAs and SVs indicate occurrence. c PCa cell line VCaP, 22Rv1, and LNCaP gene dependencies (CERES effectivity score) for interacting ARBS affected by copy number gains (red) and loss (blue). The predominant CNA is shown, i.e. copy number gains for oncogenes occur at much higher frequencies than losses. d ARBS interacting with promoter of essential PCa genes, plotted for prevalence in patients with color denoting predominant copy number gains (red) and losses (blue) at these loci. e Metastatic PCa patient RNA-seq log2fold expression changes over copy number neutral samples (ref call) for patients with CNAs exclusively at ARBS or exclusively at gene coding sequences. ^ denotes only single patient expression value in mPCa cohort, dark gray not present in mPCa cohort. Source data are provided in Source Data.

To globally assess tumor-driving enhancer amplifications and losses, we used the cancer gene dependency repository (DepMap)39 and investigated whether CNA-affected ARBS interact with gene loci regulating essential genes in PCa cell lines (VCaP, 22Rv1, and LNCaP, Supplementary Table 8). Based on these analyses, we found 25 essential genes interacting with CNA-affected ARBS, including previously described PCa drivers AR11,13, GRHL240, and HOXB135 (Fig. 3c). These genes interact with PS- and UN-ARBS with a majority of ARBS found in <20 patients (Fig. 3d). Importantly, CNAs at exclusively these ARBS frequently altered corresponding mPCa gene expression11 to a comparable extent as CNAs at exclusively gene coding sequences (Fig. 3e), as was evident for AR-upregulated (AR and ZBTB10) and AR-downregulated (IDI1, CITED2, and BCCIP) genes. These findings underline how mPCa CNA-affected ARBS are only found in a minority of primary PCa tissues, which later during mPCa have transcriptional consequences for critical PCa tumor-driving genes.

Transcriptional variation is associated with less-commonly shared ARBS

We predicted which ARBS influence gene expression most in our cohort by modeling H3K27ac-based HiChIP interacting ARBS–promoter pairs and matched gene expression from 88 primary patients in a generalized linear model (GLM), which generalizes linear regression of ARBS occupancy in patients to their transcriptional response. For example, the complete ARBS landscape of CITED2 (PCa tumor-driving gene, Fig. 3e) has a high degree of ARBS heterogeneity including many PS- and UN-ARBS scattered between individual tumors on Chr6 (Fig. 4a).

Fig. 4: Transcriptional variability is associated with less-commonly shared ARBS.
figure 4

a Primary PCa tumor AR ChIP-seq log-transformed MACS peak scores for ARBS with H3K27ac HiChIP interaction with CITED2 gene promoter. Gray: no ARBS detected in AR ChIP-seq, Right: Matched log-transformed TMM-normalized RNA-seq expression levels for patients. b Generalized linear model (GLM) -log(p-values) from fitting log-transformed MACS peak scores (predictor) with CITED2 gene expression (response), ARBS were filtered for overlapping H3K27ac presence in LNCaP. Top: Observed model p-value and simulated p-value obtained from permutation tests (n = 1000) based on likelihood ratio test. c LNCaP single-cell chromatin accessibility for three cell clusters at CITED2 locus with filtered CICERO co-accessibility scores of links for CITED2 enhancers in 80, 54, 10, 8, and 1 patient(s). d Genomic snapshot of CITED2 locus with LNCaP AR ChIP-seq, ranked ARBS from tissue ChIP-seq with CITED2 H3K27ac Hi-ChIP promoter–enhancer pairs found in 80, 54, 10, 8, and 1 patient(s) and design of Cas9 sgRNA pairs (orange, 2 sgRNAs per arrow). e Normalized expression levels of CITED2 over β-actin as measured by RT-qPCR 40 days after infection with non-targeting control (N), sgRNA pair 12 (12), the pool of all sgRNAs guides (p) with gDNA PCR verification of cas9 cut from the same isolate for CITED2 interacting ARBS found in 80, 54, 10, 8, and 1 patient(s). Orange arrows denote cut DNA fragments, nt = nucleotide weight. Representative experiment, center line, mean; error bars, SD; two-tailed Student’s t-test of means on technical replicates, *p < 0.05, **p < 0.01, ***p < 0.001. f LNCaP proliferation z-score for 878 sgRNAs targeted at AR enhancer locus in Cas9 perturbation or dCas9-KRAB inhibition tiling assay with dotted lines denoting ranked ARBS in 2, 13, 1, and 23 primary tumors. Shaded areas denote 95% confidence interval. g LNCaP proliferation z-score for sgRNAs in ARBS found in 2, 13, 1, and 23 primary tumors, with control comprising z-scores of all other sgRNAs in this region. sgRNAs per ARBS, AR2: 29, AR13: 49, AR23: 49, AR1: 4, ctrl: 1573. Centerline, median; upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers. Two-tailed Student’s t-test of means with AR23 as reference group, *p < 0.05, **p < 0.01,***p < 0.001 ****p < 0.0001, ns non-significant. Source data are provided in Source Data.

The CITED2 GLM shows that an ARBS found in 54 primary tumors, and not the most-commonly shared site found in 80 tumors, was most significantly associated with gene expression differences (Fig. 4b), with model assumptions such as linearity of points and limited individual point influence holding (Supplementary Fig. 5A). Additionally, as expected from trends observed in genome-wide AR ChIP-seq peak strengths in the three ARBS categories (Supplementary Fig. 1E, F), we find a clear negative correlation between ChIP-seq peak strength and ARBS category across all ARBS and patients (Supplementary Fig. 5B, C). In total, 2026 ARBS regulating 1901 unique genes were identified by GLMs to significantly associate genome-wide AR ChIP-seq with expression differences (p < 0.001).

As these observations are from bulk measurements, single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) from LNCaP14 was used to infer promoter–enhancer interactions and ARBS accessibility throughout cell cycle phases. Although few ARBS were cell-cycle phase-specific in arrested bulk LNCaP41 and AR activity fluctuates among scATAC-seq clusters, there is an enrichment of ranked ARBS and de novo AR and FOXA1 motifs in differentially accessible chromatin (Supplementary Fig. 6). Links are observed between most heterogeneously bound ARBS and the CITED2 promoter in LNCaP (Fig. 4c), in agreement with LNCaP H3K27ac Hi-ChIP data5.

Subsequently, for CITED2 we assessed which proximal ARBS have a functional impact on transcriptional output. For this, we genetically deleted the entire ARBS through CRISPR-Cas9-mediated genome editing by transducing LNCaP with Cas9 and confirming its activity in a polyclonal population (Supplementary Fig. 7A, B), designing guide pairs and combining these in pools to maximize the chance of a KO in case of a non-effective single guide. Guide pairs and pools were targeted to ARBS edges identified with scATAC-seq, which had AR binding in LNCaP (Fig. 4d) and we confirmed successful Cas9 deletion through genomic DNA PCR. We observed a concomitant drop in CITED2 expression for the CITED2 enhancer found in 54 primary tumors (CI54), which was predicted to most significantly affect transcription, although effect sizes varied between guide pair and guide pools (Fig. 4e, Supplementary Fig. 7D). Additionally, as an orthogonal method for Cas9 deletion, we employed CRISPR interference through a modified Suntag-system42 which enables recruitment of 10 repressive KRAB effectors at a locus through sgRNAs (Suntag-KRAB, validated in Supplementary Fig. 7A, C). Suntag-KRAB repression at the CITED2 enhancers confirms that CI54 most significantly affects transcription (Supplementary Fig. 7F), while Cas9-mediated CI54 deletion in the CITED2 expressing AR+ breast cancer cell line MDA-MB453 did not result in a transcriptional decrease, as this cell line does not use enhancer CI54 (Supplementary Fig. 7A, G, H). These data suggest that less-frequently shared ARBS can have the largest impact on transcriptional output, which could be attributed to varying degrees of functional redundancy among ARBS.

To further confirm these findings, we focused on the AR locus, for which numerous inter-tumor heterogeneous ARBS loop to the TSS (Fig. 3a) and with confirmed AR binding and transcriptional activity in our primary patient cohort (Supplementary Fig. 8A). Using two orthogonal CRISPR drop-out screens tiling 878 sgRNAs across the entire AR enhancer region on ChrX13, AR13 (found in 13 primary tumors) proved most-critical for tumor cell proliferation, in contrast to the more-commonly shared AR23 or less-common AR2 (Fig. 4f, g). Jointly, these data further confirm that heterogeneous ARBS can impact cellular fitness to a larger degree than more commonly shared ARBS. Interestingly, exclusively AR13 shows strong HOXB13 motif enrichment (Supplementary Fig. 8B), and HOXB13 was detected at AR13 for LNCaP and 22Rv1 cells using ChIP-seq15 (Supplementary Fig. 8C), providing a possible explanation for these observations given HOXB13’s critical nature in regulating AR-transcriptional function5.

To further investigate this, we confirmed the differential proliferation effects of AR23 and AR13 enhancer perturbation using LNCaP:Suntag-KRAB and observed similar trends in proliferation defects (Supplementary Fig. 8D, Fig. 4f, g), with AR13 perturbation having the biggest impact on cell proliferation as opposed to AR23, underlining the impact of heterogeneous ARBS on cellular fitness. Finally, we performed HOXB13 ChIP-qPCR on LNCaP:Suntag-KRAB with either NT or AR13 sgRNAs and observed that perturbation of AR13 through KRAB-mediated heterochromatinization leads to a sharp decrease of HOXB13 binding at this locus compared to NT (Supplementary Fig. 8E). These results confirm that HOXB13 binding at AR13 influences PCa cellular fitness through enhancing AR transcription.

Metastasis-associated heterogeneous ARBS in poor-outcome primary tumors drive tumor-promoting gene expression pathways

Prior studies have identified ARBS found selectively enriched in normal tissue (NARBS) over primary tumors (TARBS)4, metastasis-associated sites (met-ARBS), or those found in primary PCa5 and ARBS linked to good and poor outcomes8. As such, ARBS alterations specific to different states of PCa progression and disease outcomes have been established. With TARBS representing a general feature of primary PCa, we observe an expected TARBS enrichment for SH-ARBS, with NARBS poorly represented in our tumor samples. In contrast, met-ARBS are found mostly in heterogeneous ARBS, suggesting that heterogeneous ARBS contribute to disease progression (Fig. 5a, Supplementary Fig. 9A). In agreement with this observation, good outcome ARBS are more prevalent at SH-ARBS as compared to poor outcome ARBS (Supplementary Table 9). Our 88-patient cohort was designed as a case-control study based on biochemical recurrence (BCR)3, enabling us to independently confirm the clinical implications of AR enhancer heterogeneity. We observed a significant difference between cases and controls in good/poor outcome ARBS ratios (Fig. 5b, Supplementary Fig. 9B ratios good:poor: (1) >1.2 good, (2) 1.2 > mixed > 0.8, (3) poor < 0.8), independently confirming poor outcome ARBS being more-heterogeneously distributed among tumors and highlighting the predictive power of these previously reported sites.

Fig. 5: Metastasis-associated heterogeneous ARBS in poor-outcome primary tumors drive different gene expression pathways.
figure 5

a Normal tissue and tumor enriched ARBS (NARBS and TARBS, respectively), primary sites, metastatic associated ARBS (met-ARBS), good and poor outcome sites presence in ranked ARBS. SH-ARBS (blue) or PS + UN-ARBS (yellow) enrichment through a two-sided hypergeometric test of enrichment, ****p < 0.0001, non-significant ns. Inset: zoom-in on outcome sites for ranked ARBS 1 through 15,000. b Distribution of ratio of outcome sites per primary PCa patient split for BCR development (case) or not (control). Ratios good:poor; (1) >1.2 good (blue), (2) 1.2 > mixed > 0.8 (purple), (3) poor < 0.8 (red). Two-sided Fisher’s exact test, *p < 0.05. c Distribution of TARBS (T, red dotted line) and Met-ARBS (M, purple line) in ranked ARBS. Centerline, median; upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers. Two-tailed Student’s t-test of means, ****p < 0.0001 d Histograms of TARBS (red) or Met-ARBS (purple) counted in patients, split for BCR development (case) or not (control). Two-sided Kolmogorov–Smirnov test of distribution, ****p < 0.0001. e Euler diagram of a number of genes with GO regulatory potential score >0.5 for met-ARBS which are specific for primary patients with biochemical recurrence (BCR) development (case) or without (control). f Gene set enrichment analysis (GSEA) for hallmarks of cancer collection using GO RP score >0.05, for genes linked to case- (left) and control-specific (right) met-ARBS in less than 10% of patients. g Transcription factor motif analysis of case/control specific met-ARBS with motif z-scores. h Heatmap clustering (k = 2) for Taylor (FDR < 0.01) and Grasso cohort z-score expression levels from patient tissues filtered for case-specific met-ARBS genes <10% of patients. Source data are provided in Source Data.

Finally, we investigated whether heterogeneous ARBS plays a role in PCa progression to metastatic disease. We observed a striking enrichment of met-ARBS at PS- and UN-ARBS over primary disease TARBS (Fig. 5c). Moreover, we separated TARBS and met-ARBS based on their presence in patients with a high chance of BCR (case) or with a low chance of BCR (control), and observed a significant enrichment of met-ARBS in cases over control patients in PS- and UN-ARBS, whereas no difference in TARBS enrichment is found in both patient populations (Fig. 5d).

We observed that met-ARBS were selectively enriched in PS- and UN-ARBS in patients whose tumors ultimately progressed. To confirm these observations on the transcriptional level, we calculated Gene Ontology Regulatory Potential (GO-RP) scores of the bottom 10% of case-specific and control-specific met-ARBS (RPscore > 0.05, Supplementary Fig. 9C, D), identifying distinct sets of genes (Fig. 5e) representing different kinds of pathways (Fig. 5f). GO-RP uses not the only distance between enhancers and promoters, but also adjusts these scores and ranks elements based on the integration of ChIP-seq and expression data to accurately identify target genes. Notably, heterogeneous case met-ARBS regulate hallmarks of cancer pathways involved in cholesterol synthesis43, mTORC1 signaling44, androgen response, and WNT beta-catenin signaling44, whereas the P53 pathway, which is often inactivated in mPCa10,45, was activated by heterogeneous control met-ARBS. Moreover, individual metastasis-promoting genes such as proto-oncogene RET46 or migration and invasiveness-related genes like CDH17, CDH18, ITGB5, and ITGB7, or osteoclast-promoting TF FOS2L47 involved in the formation of bone metastases are found in this set and are regulated by heterogeneous met-ARBS detected in cases. With comparable enrichment for TF motifs for both groups (Fig. 5g), transcriptomics data from Taylor48 and Grasso49 cohorts show that the most-heterogeneous ARBS found in primary tumors that ultimately relapse regulate genetic programs selectively altered in mPCa (Fig. 5e, h, Supplementary Fig. 9E).

Categories
Prostate cancer

Acute effect of high-intensity interval aerobic exercise on serum myokine levels and resulting tumour-suppressive effect in trained patients with advanced prostate cancer

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  • Categories
    Prostate cancer

    Active Surveillance, Exercise, Vegan Diets and Healthy Fat: Your Questions Answered (Part 2)

    Dr. Stacey Kenfield, Associate Professor in the Departments of Urology and Epidemiology & Biostatistics at UCSF, was a recent guest on PCF’s monthly webinar series hosted by CEO Dr. Charles Ryan. She discussed her research on Prostate 8, a collection of lifestyle changes that have been shown to reduce a patient’s risk of prostate cancer recurrence or death from prostate cancer.

    Dr. Kenfield has followed up with her answers to common questions posed before and during the webinar, covering recommendations for active surveillance, the role of alcohol drinking, resistance training, and healthy fats. (Read more in Part 1 here on eggs, dairy, and supplements.)

    Are there differences in the exercise and diet recommendations for men with early prostate cancer diagnosis on active surveillance than for those with more advanced disease?

    Most of the studies to date examining post-diagnostic diet and clinical outcomes have focused on men diagnosed with localized prostate cancer, including those who receive primary treatment (e.g., radical prostatectomy, radiation, androgen deprivation therapy) and those on active surveillance. The studies we mentioned during the webinar included both of these groups and did not separate out men on active surveillance from the group receiving primary treatment. Since men on active surveillance were included in these studies, we would recommend the same guidance. There are limited studies to date in men with more advanced disease examining the diet and clinical outcomes.

    What is the relationship between alcohol drinking and prostate cancer?

    Specifically for prostate cancer, some research shows that modest alcohol intake after a diagnosis of prostate cancer (1 drink on 3-5 days per week) – may be beneficial. A U-shaped relationship was observed in that study, with most benefit at modest level (vs. no alcohol intake or heavier intake). Note that this is actually less than the general recommendation in the Dietary Guidelines for Americans for men limiting intake to 2 or fewer drinks per day.

    Of note, if you are a non-drinker, it is not recommended to start drinking alcohol to prevent prostate cancer. Please discuss with your doctor based on your personal health situation.

    How is a plant-based diet beneficial?

    A plant-based diet is associated with better heart health (lower risk of type 2 diabetes and cardiovascular disease), healthier body weight, and lower risk of death (overall death and death from cardiovascular disease). This means that the bulk of what you eat comes from plant foods, but that meat, dairy, eggs, fish, and other seafood makes up a smaller proportion of total food. Research also shows benefits of certain plant-based diets specifically on prostate cancer outcomes, such as a 19% lower risk of fatal prostate cancer. Read more here.

    If a person is on a vegan diet (no fish), how can they get the recommended amounts of omega-3 fatty acids?

    The recommended adequate intake (AI) for alpha-linolenic acid (ALA), a type of omega-3 essential fatty acid from plant-based sources, is 1,600 mg/day for men. Plant-based sources include: vegetable oils (especially high in flaxseed oil, walnut oil, canola oil, and soybean oil), nuts (especially walnuts), flax seeds, chia seeds, hemp seeds, and pumpkin seeds. For example, one serving of walnuts can fulfill an entire day’s requirement with 1 ounce (14 walnut halves) containing 2,570 mg of ALA. One ounce of chia seeds contains 5,060 mg of ALA. One tablespoon of flaxseed oil contains 7,260 mg of ALA. Aim for at least one rich source of omega-3 fatty acids in your diet every day. Add a tablespoon of canola or soybean oil in salad dressing or in cooking, eat a handful of walnuts, or add ground flaxseed mixed into oatmeal.

    Keep in mind that long-chain omega-3 fatty acids called eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) consumed directly from eating fish are thought to have more significant health benefits than ALA from plant-based foods. It’s estimated that up to 10% of ALAs are converted to EPA/DHA, so a tablespoon of flaxseed oil is worth about 700 mg of EPA and DHA. For reference, the 2020-2025 Dietary Guidelines for Americans recommend 8-10 ounces of seafood per week; 8 ounces of fatty fish is equivalent to 250-500 mg combined of EPA and DHA per day. If you are getting this equivalency level from plant-based ALA sources, we wouldn’t recommend a supplement. If you have trouble incorporating a rich omega-3 plant-based food source daily, adding a high-quality algae (plant-based) or fish oil supplement may be recommended. Consult with your doctor first before adding an omega-3 supplement. Read more here and here.

    What is the guidance on amount and intensity of resistance training?

    Perform muscle strengthening exercises at least 2 days per week. Choose 8-10 different exercises that work major muscle groups (legs, hips/glutes, chest, arms, core). Aim for 2-3 sets of 8-12 repetitions for each exercise. Choose a weight or resistance that allows you to perform the 8-12 repetitions, but not more, with proper form. When you first increase the weight, you may only be able to do 6-10 repetitions with proper form. Work up to 8-12 repetitions. When you start to exceed that range, increase the weight again, so that you can continue to progress. Separate sessions by at least 24 hours to allow your muscles time to recover.

    Additional resources:

    UCSF’s Department of Urology Resources for Healthy Living.

    Global Action Plan (GAP-4) trial of exercise in patients with metastatic prostate cancer

    PCF’s wellness guide, The Science of Living Well, Beyond Cancer

    Categories
    Prostate cancer

    Active Surveillance, Exercise, Vegan Diets and Healthy Fat: Your Questions Answered (Part 2)

    Dr. Stacey Kenfield, Associate Professor in the Departments of Urology and Epidemiology & Biostatistics at UCSF, was a recent guest on PCF’s monthly webinar series hosted by CEO Dr. Charles Ryan. She discussed her research on Prostate 8, a collection of lifestyle changes that have been shown to reduce a patient’s risk of prostate cancer recurrence or death from prostate cancer.

    Dr. Kenfield has followed up with her answers to common questions posed before and during the webinar, covering recommendations for active surveillance, the role of alcohol drinking, resistance training, and healthy fats. (Read more in Part 1 here on eggs, dairy, and supplements.)

    Are there differences in the exercise and diet recommendations for men with early prostate cancer diagnosis on active surveillance than for those with more advanced disease?

    Most of the studies to date examining post-diagnostic diet and clinical outcomes have focused on men diagnosed with localized prostate cancer, including those who receive primary treatment (e.g., radical prostatectomy, radiation, androgen deprivation therapy) and those on active surveillance. The studies we mentioned during the webinar included both of these groups and did not separate out men on active surveillance from the group receiving primary treatment. Since men on active surveillance were included in these studies, we would recommend the same guidance. There are limited studies to date in men with more advanced disease examining the diet and clinical outcomes.

    What is the relationship between alcohol drinking and prostate cancer?

    Specifically for prostate cancer, some research shows that modest alcohol intake after a diagnosis of prostate cancer (1 drink on 3-5 days per week) – may be beneficial. A U-shaped relationship was observed in that study, with most benefit at modest level (vs. no alcohol intake or heavier intake). Note that this is actually less than the general recommendation in the Dietary Guidelines for Americans for men limiting intake to 2 or fewer drinks per day.

    Of note, if you are a non-drinker, it is not recommended to start drinking alcohol to prevent prostate cancer. Please discuss with your doctor based on your personal health situation.

    How is a plant-based diet beneficial?

    A plant-based diet is associated with better heart health (lower risk of type 2 diabetes and cardiovascular disease), healthier body weight, and lower risk of death (overall death and death from cardiovascular disease). This means that the bulk of what you eat comes from plant foods, but that meat, dairy, eggs, fish, and other seafood makes up a smaller proportion of total food. Research also shows benefits of certain plant-based diets specifically on prostate cancer outcomes, such as a 19% lower risk of fatal prostate cancer. Read more here.

    If a person is on a vegan diet (no fish), how can they get the recommended amounts of omega-3 fatty acids?

    The recommended adequate intake (AI) for alpha-linolenic acid (ALA), a type of omega-3 essential fatty acid from plant-based sources, is 1,600 mg/day for men. Plant-based sources include: vegetable oils (especially high in flaxseed oil, walnut oil, canola oil, and soybean oil), nuts (especially walnuts), flax seeds, chia seeds, hemp seeds, and pumpkin seeds. For example, one serving of walnuts can fulfill an entire day’s requirement with 1 ounce (14 walnut halves) containing 2,570 mg of ALA. One ounce of chia seeds contains 5,060 mg of ALA. One tablespoon of flaxseed oil contains 7,260 mg of ALA. Aim for at least one rich source of omega-3 fatty acids in your diet every day. Add a tablespoon of canola or soybean oil in salad dressing or in cooking, eat a handful of walnuts, or add ground flaxseed mixed into oatmeal.

    Keep in mind that long-chain omega-3 fatty acids called eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) consumed directly from eating fish are thought to have more significant health benefits than ALA from plant-based foods. It’s estimated that up to 10% of ALAs are converted to EPA/DHA, so a tablespoon of flaxseed oil is worth about 700 mg of EPA and DHA. For reference, the 2020-2025 Dietary Guidelines for Americans recommend 8-10 ounces of seafood per week; 8 ounces of fatty fish is equivalent to 250-500 mg combined of EPA and DHA per day. If you are getting this equivalency level from plant-based ALA sources, we wouldn’t recommend a supplement. If you have trouble incorporating a rich omega-3 plant-based food source daily, adding a high-quality algae (plant-based) or fish oil supplement may be recommended. Consult with your doctor first before adding an omega-3 supplement. Read more here and here.

    What is the guidance on amount and intensity of resistance training?

    Perform muscle strengthening exercises at least 2 days per week. Choose 8-10 different exercises that work major muscle groups (legs, hips/glutes, chest, arms, core). Aim for 2-3 sets of 8-12 repetitions for each exercise. Choose a weight or resistance that allows you to perform the 8-12 repetitions, but not more, with proper form. When you first increase the weight, you may only be able to do 6-10 repetitions with proper form. Work up to 8-12 repetitions. When you start to exceed that range, increase the weight again, so that you can continue to progress. Separate sessions by at least 24 hours to allow your muscles time to recover.

    Additional resources:

    UCSF’s Department of Urology Resources for Healthy Living.

    Global Action Plan (GAP-4) trial of exercise in patients with metastatic prostate cancer

    PCF’s wellness guide, The Science of Living Well, Beyond Cancer

    Categories
    Prostate cancer

    Extensive germline-somatic interplay contributes to prostate cancer progression through HNF1B co-option of TMPRSS2-ERG

    The presented study complies with all relevant ethical regulations and was approved by the University of Oulu and the Shanghai Jiao Tong University School of Medicine Affiliated Ruijin Hospital. All participating patients provided written informed consent. Patients were not monetarily compensated.

    Cell culture

    The cell lines used in the work (Supplementary Table 4) are 22Rv1 (CRL-2505, ATCC), LNCaP (CRL-1740, ATCC), VCaP (CRL-2876, ATCC), DU145 (HTB-81, ATCC), PC3 (CRL-1435, ATCC), V16A78, A549 (CCL-185, ATCC), RWPE1 (CRL-11609, ATCC), and 293 T (CRL-11268, ATCC). All cell lines were confirmed to be mycoplasma free during our study. As described above, most of cells lines were originally purchased from ATCC (American Type Culture Collection). Cell morphology and growth rate of the cell lines used in this study were similar to previous reports. These cell lines have been authenticated by STR fingerprinting. The cells were cultured under the conditions of 37 °C and 5% CO2. VCaP and 293 T were grown in Dulbecco’s Modified Eagle’s Medium (DMEM) (11965092, Thermo FisherInvitrogen), for culturing DU145 we used Eagle’s Minimum Essential Medium (EMEM) (30-2003, ATCC). Also, LNCaP, 22Rv1 and V16A were grown in Roswell Park Memorial Institute Medium (RPMI 1640) (R8758, Sigma) and finally A549 and PC3 was grown in F12-K (30-2004, Invitrogen). The cell culture media were supplied with a final concentration of 10% fetal bovine serum (16000044, Thermo Fisher) and 1% of penicillin and streptomycin (15140122, Thermo Fisher)). RWPE1 cells were grown in Keratinocyte-Serum Free Medium. Keratinocyte-SFM Kit including epidermal growth factor (EGF) and bovine pituitary extract (BPE) supplements were purchased from Invitrogen (17005-042, Invitrogen). The VCaP cells were cultured in the charcoal-stripped media wherein activating the androgen receptor signaling through a dihydrotestosterone (DHT) (Olli A. Jänne lab, University of Helsinki) treatment with final concentration of 100 nM for 24 h.

    Plasmids and gene cloning

    Human cDNA library was used to amplify HNF1B open reading frame (ORF) that was cloned into pLVET-IRES-GFP and pcDNA3.1 vectors (Supplementary Table 9). Wild type ERG was also amplified from the same library and cloned into pcDNA3.1. The cDNA of TMPRSS2-ERG fusion was cloned from VCaP into pcDNA3.1. HNF1B-D (domains of full-length HNF1B NM_000458), HNF1B-POU and HNF1B-T of HNF1B sub-domains were cloned into pcDNA3.1, respectively, to express three recombinant proteins for testing protein interaction between HNF1B and TMPRSS2-ERG at domain levels. Primer sequences, cloning methods and enzymes are shown in Supplementary Tables 5 and 15.

    Construction of reporter plasmids

    Each enhancer or promoter region was amplified from human genomic DNA and cloned into pGL4.10 [luc2] (E6651, Promega) (Supplementary Table 9) containing a SNP (rs718960, rs7405696, rs11651052, rs9901746, rs11263763 or rs12453443) region. Each of these six SNPs was cloned with two different alleles obtained by site-directed mutagenesis. In addition, three SNP (rs2955626 or rs461251 and rs684232) regions were cloned. The enhancers were cloned into the BamHI site (in both orientations), and the promoters of HNF1B, VPS53, FAM57A or GEMIN4 into the EcoRV/HindIII sites of pGL4.10 [luc2] vector, respectively. Both orientations can facilitate testing enhancer activity of SNP-containing regions regardless of the promoter location. The constructs were transient, reversely transfected into LNCaP or VCaP (treated with DHT or ETH) cells with a Renilla Luciferase control plasmid pGL4.75 [hRluc/CMV] (E6931, Promega) by using X-treme GENE HP DNA Transfection Reagent (06366236001, Roche). The experiments were performed on the 96-well white plates with each well containing 100 μl medium of 3 × 105 22Rv1 and LNCaP cells/ml or 9 × 105 VCaP cells/ml. After incubation at 5% CO2 and 37 °C for 48 h, the luciferase activity was measured with Dual-Glo Luciferase Assay System (E2940, Promega). At least three replicate wells were used per construct and the data were statistically analyzed with a two-tailed Student’s t test. Primer sequences, cloning methods and enzymes are shown in Supplementary Tables 5 and 15.

    Protein blot analysis

    Cell pellet was resuspended in lysis buffer (600 mM Nacl, 1% Triton X-100 in PBS, freshly added 1 x protease inhibitor) and sonicated (Q800R sonicator, Q Sonica). The sample was centrifuged, and the supernatant was collected. The amount of protein was measured with Pierce BCA Protein Assay Kit (23225, Thermo Fisher Scientific) based on the manufacturer’s protocol and 30 μg of protein lysate of each sample was separated by electrophoresis in 7.5% or 12% SDS-PAGE gel and transferred into 0.45 μm Immobilon-P PVDF Membrane (IPVH00010, Millipore) using a Semi-Dry transfer cell (Trans-Blot SD, Bio-Rad). After transfer, the membrane was blocked for minimum 30 min at room temperature using blocking buffer (5% nonfat milk in TBST) while gently shaking. The blocked membrane then was incubated with antibody diluted in blocking buffer (1:1000 (Ab μl: blocking buffer μl): rabbit polyclonal anti-HNF1B, mouse monoclonal anti-HNF-1B and mouse monoclonal anti-FLAG. 1:5000: mouse monoclonal anti-V5, mouse monoclonal anti-V5-HRP, mouse monoclonal anti-ERG, rabbit monoclonal anti-ERG) at 4 °C for 16 h with gentle rotation. After incubation, the membrane was washed three times each 10 min using TBST. Anti-rabbit IgG or anti-mouse IgG was used as secondary antibody (Thermo Fisher) with 1:5000 dilution into blocking buffer and the incubation took place on a rotor at room temperature for 1 h. Afterwards, the membrane was washed three times each with 15 min using TBST. Finally, the membrane was developed with Lumi-Light Western Blotting Substrate (12015200001, Roche) or SuperSignal West Femto Maximum Sensitivity Substrate (34095, Thermo Fisher Scientific) according to the protocol and exposed with Fujifilm LAS-3000 Imager. Original blots are provided in the Source data file. For more information about the antibodies see Supplementary Table 6.

    Ectopic expression via transient transfection

    293 T cells were used for transient transfection with pcDNA3.1 constructs (Supplementary Tables 9, 14). Mixer A of pcDNA3.1 construct and P3000 reagent (L3000015, Thermo Fisher Scientific) was diluted with Opti-MEM. Mixer B of lipofectamine 3000 reagent (L3000015, Thermo Fisher Scientific) was diluted with Opti-MEM (11058021, Thermo Fisher). We mix A and B which were incubated at room temperature for 15 min and added into 70-80% confluent seeded cells. The cells were incubated 24-48 h before harvesting.

    Co-immunoprecipitation

    Co-Immunoprecipitation was performed for examining the endogenous interaction of HNF1B with TMPRSS2-ERG in VCaP with DHT treatment. The ectopic interaction of HNF1B or HNF1B domains with ERG cloned into pcDNA3.1 and transfected in 293 T (cloning primers listed in Supplementary Table 5). Cells were harvested and lysed with 0.5 ml cold immunoprecipitation buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1% Triton X-100, 10% Glycerol, 1 mM EDTA and 1% protease inhibitor cocktail). Keep Cell lysates on ice for 30 min with few vortex periods in between. Sonicate in 4 °C water bath for 20 s. Centrifuge for 30 min at 4 °C and supernatant was incubated with 30 μl protein-G Magnetic Beads to pre-clear crude cell extract of proteins which can bind non-specifically to the beads at 4 °C for 1 h. Keep the supernatant and add 5 μg of antibody (5 μg: mouse monoclonal anti-V5, rabbit monoclonal IgG and rabbit monoclonal anti-ERG, mouse monoclonal anti-FLAG) (Supplementary Table 6) with incubation at 4 °C overnight. Add 30 μl of fresh protein-G Magnetic Beads and incubate at 4 °C for 6 h followed by washing five times with Immunoprecipitation buffer. Furthermore, resuspend beads in 30 μl of 2 x SDS sample loading buffer and incubate at 95 °C for 5 min and finally use the supernatant on SDS-PAGE gel for electrophoresis separation.

    siRNA transfections

    Individual set of two siRNAs (Qiagen) against HNF1B or ERG were tested in knockdown efficiency and compared with the siRNA negative-control (Qiagen) by RT-qPCR. For cell proliferation assays, we used the same set of two siRNAs (Qiagen) against HNF1B compared with negative and positive control siRNA (Qiagen). 8 × 105 of VCaP and LNCaP cells were used in reverse transfection for 6-well plate, respectively. siRNA transfection was performed with HiPerFect Transfection Reagent (301705, Qiagen) with a final concentration of 50 nM siRNA (see Supplementary Table 7 for the siRNAs used).

    RNA isolation and real time quantitative PCR

    RNeasy Mini Kit (74106, QIAGEN) was applied for RNA isolation and RNase-Free DNase (79254, QIAGEN) was used during the isolation to remove DNA. cDNA was synthesized from 2 μg RNA by either the High-Capacity cDNA Reverse Transcription Kit (4368814, Applied Biosystems) or the iScript Reverse Transcription Supermix (1708840, Bio-Rad). After cDNA synthesis we used SYBR Select Master Mix (4472920, Applied Biosystems) and high specificity primers for the quantitative RT-PCR reactions. The results were normalized with beta-actin control and for each gene’s analysis made triplicates. Primers used for RT-qPCR in Supplementary Table 8.

    CRISPR/Cas9-mediated genome editing analysis

    CRISPR design tool (http://crispr.mit.edu/or crispor.tefor.net) was used to prepare the pair of oligos (sgRNA-top and sgRNA-bottom) attached in Supplementary Table 8. Most of the experiment was performed according to the previous protocol89. For annealing process 1 μl sgRNA-top (100 μM) and 1 μl sgRNA-bottom (100 μM) were mixed with 1 x T4 ligation buffer, 1 μl T4 PNK and 6 μl ddH2O. The oligos were phosphorylated and annealed in a thermocycler at 37 °C for 30 min followed with 95 °C for 5 min; ramp down to 25 °C with 5 °C/min. Then, the annealed oligos were inserted into pSpCas9 (BB)−2A-Puro and the plasmids were transfected in 22Rv1 and V16A cells with 70-80% confluency. 0.6 μg of total amount of Cas9 plasmids designed for the same SNP region, with 1:1 ratio or 1:1:1:1 ratio, which added into cells by using Lipofectamine 3000 in 24-well plate according to the protocol. Medium was changed after 24 h and replaced with medium containing 1 μg/ml puromycin (P9620, Merck). Afterwards, the successfully transfected cells were isolated to single cells by dilution or FACS. The single cells were seeded into 96-well plates and after 2-3 weeks the positive clones were examined further by genotyping and RT-qPCR determination of gene expression.

    Chromatin immunoprecipitation (ChIP)

    ChIP assay was carried out based mainly on previous study62. The cells were cross-linked in a final concentration of 1% formaldehyde in medium for 10 min at room temperature with gently shaking. The final concentration of 125 mM glycine was added to stop the reaction and incubated for minimum 5 min with slight shake. Cells were harvested and the pellet was resuspended in hypotonic lysis buffer (20 mM Tris-Cl, pH 8.0, with 10 mM KCl, 10% glycerol, 2 mM DTT, and freshly added cOmplete protease inhibitor cocktail (04693159001, Roche) and incubated up to 1 h on a rotor at 4 °C. Afterwards, the pellet was washed twice with cold PBS and resuspended in SDS lysis buffer (50 mM Tris-HCl, pH 8.1, with 0.5% SDS, 10 mM EDTA, and freshly added cOmplete Protease Inhibitor). Sonication (Q800R sonicator, Q Sonica) was performed as far as the chromatin had size 250-500 bp. Later, 70 μl of Dynabead protein G (10004D, Invitrogen) were washed twice with blocking buffer (0.5% BSA in IP buffer) and incubated with 8 μg of antibody (Rabbit polyclonal anti- HNF1B, Rabbit polyclonal IgG, Rabbit monoclonal IgG, Mouse polyclonal IgG, Rabbit monoclonal anti-ERG, Anti-rabbit Androgen Receptor, H3K4me1, H3K4me2, H3K4me3 and H3K27ac) (examined antibodies in Supplementary Table 6) in 1 ml of 0.5% BSA in IP buffer (20 mM Tris-HCl, pH8.0, with 2 mM EDTA, 150 mM NaCl, 1% Triton X-100, and freshly added Protease inhibitor cocktail) for 10 h at 4 °C on rotor. After incubation the supernatant was removed, and the sonicated chromatin lysate (200-250 μg) was diluted in 1.3 ml of IP buffer and was added into the beads-antibody complex with incubation at 4 °C for at least 12 hrs on rotor. Afterwards, the beads-antibody complex was washed one time with wash buffer I (20 mM Tris-HCl, pH 8.0, with 2 mM EDTA, 0.1%SDS, 1% Triton X-100, and 150 mM NaCl) and once with buffer II (20 mM Tris-HCl pH, 8.0, with 2 mM EDTA, 0.1% SDS, 1% Triton X-100, and 500 mM NaCl), followed by two times of washing with buffer III (10 mM Tris-HCl, pH 8.0, with 1 mM EDTA, 250 mM LiCl, 1% deoxycholate, and 1% NP-40) and two times with buffer IV (10 mM Tris-HCl, pH 8.0, and 1 mM EDTA). 50 μl of extraction buffer (10 mM Tris-HCl, pH 8.0, 1 mM EDTA, and 1% SDS) were added to extract from the beads the DNA-protein complex by incubating and shaking at 65 °C for 20 min (repeat same step with another 50 μl of extraction buffer). Proteinase K (AM2548, Thermo Fisher Scientific) with final concentration 1 mg/ml and NaCl with final concentration 0.3 M were added into the extracted DNA-protein complex and incubated at shaking heat block for 16 h at 65 °C in 1000 rpm to reverse-crosslink of the protein-DNA interactions. DNA was purified with MinElute PCR Purification Kit (28006, QIAGEN) followed by ChIP-qPCR with primers that targeted DNA binding genome sequences (see Supplementary Table 8). ChIP library was prepared according to manufacturer’s protocol TruSeq Sample Preparation Best Practices and Troubleshooting Guide (Illumina). Finally, the sample were sequenced and analyzed.

    Quantitative analysis of chromosome conformation capture assay

    Quantitative analysis of chromosome conformation capture assay (3C-qPCR) was performed as described in the Hagege et al. protocol82. The primers used for these assays are listed in Supplementary Table 8. The cells were trypsinized and resuspended in PBS with 10% FBS. 1 × 107 cells were cross-linked in PBS with 10% FBS and 1% formaldehyde for 10 min at room temperature. To stop the crosslinking reaction, we added 0.57 ml of 2.5 M glycine (ice cold). The pellets of VCaP with treatment and A549 were resuspended in 5 ml cold lysis buffer and incubate for 13 min on ice. Then we centrifuge at 400 g at 4 °C and remove the supernatant and keep the pelleted nuclei, which were collected by centrifugation and used for digestion. We continue on digestion step of sample with HindIII restriction enzyme to digest chromatin DNA and the digestion efficiency was verified. The digested nuclear lysate was used in the ligation step. After ligation, for purification of DNA we increased the volume of sample to dilute DTT presented in the sample with 7 ml distilled water, 1.5 ml of 2 M sodium acetate pH 5.6 and 35 ml ethanol. After washing pellet with 70% ethanol and dry the pellet, and resuspended it in 150 μl of 10 mM Tris pH 7.5. DNA was desalted with centrifuge filters (Microcon DNA Fast Flow) (MRCF0R100, Millipore). For TaqMan qPCR, we used 1 μl of the 3 C sample (100 ng/μl), 5 μl of Quanti tech probe PCR mix (QIAGEN), 1 μl of Taqman probe (1.5 μM), 1 μl of Test + Constant primer (5 μM) and 2 μl distilled H2O. We performed standard curve of each primer using serial dilution of control template, containing amplified fragments across each of 7 HindIII cut sites and mix them together. Values of intercept and slope from the standard curve were used to evaluate the ligation product using the following equation: Value = 10 (Ct-intercept)/slope. These values were finally normalized to ERCC3 (loading control).

    Lentiviral constructs, lentivirus production and infection

    HNF1B was cloned into the lentivirus plasmid pLVET-IRES-GFP for ectopic expression (see Supplementary Table 5). Two set of shRNA constructs in the pLKO.1-puro vector targeting HNF1B, VPS53, FAM57A or GEMIN4 (Merck) were applied for knockdown assays. More information on the shRNA constructs can be found in Supplementary Table 7. Lentiviral constructs were produced with the third-generation packaging system in human embryonic kidney (HEK) 293 T cells (ATCC, CRL-11268) which were seeded the previous day into 3.5-cm plate in a 70%–80% confluency. At the day of transfection, the medium was replaced with 1 ml low glucose DMEM (Invitrogen) containing 10% FBS, 0.1% penicillin-streptomycin. A mix of four plasmids was made in a ratio 1:1:1:3 in a total amount of 10 μg (pVSVG-envelope plasmid, pMDLg/pRRE-packaging plasmid, pRSV-Rev-packaging plasmid and lentiviral transfer vector) (Supplementary Table 9) and diluted in Opti-MEM with Lipofectamine 2000. 24 h later the medium was replaced with 2 ml fresh medium. After that time, the virus-containing medium was collected every 24 h for 3 d and then was centrifuged at 95 g for 5 min and the supernatant was filtered with 0.45 μm filter unit place on syringe. Then the sample was collected and frozen with liquid nitrogen before stored at −80 °C. For virus transduction into the desired cells seeded 24 h before transduction in 3.5 cm plate, the final concentration of 8 μg/ml polybrene (Sigma) was added in 1.4 ml medium and 0.6 ml lentivirus-containing medium. Then the culture medium of target cells was replaced with the above prepared mix and incubate for 24 h at 37 °C and 5% CO2. In case of puromycin (Sigma) selection construct, after 24 h the medium was replaced with pre-warmed medium, and 48 h after transduction the medium was changed with fresh medium containing puromycin in a final concentration of 2 μg/ml. Cells without virus transduction were used as control to determine cell survival status upon puromycin selection. For the GFP expression constructs, 48 h after transduction the cells were sorted positively by fluorescence activated cell sorting (FACS) using BD FACS Aria flow cytometer (BD Biosciences).

    Cell proliferation assays

    The experiments were performed on the 96-well plates with each well containing 100 μl medium of 2 × 103 V16A, PC3, DU145, RWPE1 or 22Rv1 cells and 8 × 103 VCaP cells per well, respectively, in an incubation period of 4-6 d with 5% CO2 and 37 °C. The Cell Proliferation Kit II XTT (11465015001, Roche) was used according to the manufacturer. Cell proliferation was examined at indicated time points by XTT colorimetric assay (absorbance at 450 nm). At least three replicate wells were prepared per condition and the data were statistically analyzed with a two-tailed Student’s t test. The information on critical commercial assays can be available in Supplementary Table 12.

    Wound healing assays

    Cells were seeded into 96-well imageLock plates with the appropriate culture medium that can allow to grow near 100% confluence. Then we used WoundMaker tool to create homogenous scratch wounds and cells were washed twice with PBS. Culture medium was added into each well. The wound areas of each well were imaged every 2 h for max 180 h using Essen BioScience IncuCyte Live-Cell Imaging System.

    Gene expression correlation analysis

    We performed the co-expression analysis to evaluate the expression correlation between HNF1B, ERG and FAM57A from multiple independent cohorts with benign and cancerous prostate tissues. The co-expression tests were also applied in scenarios considering TMPRSS2-ERG status. Both Pearson’s product-moment correlation and Spearman’s rank correlation rho methods were applied in all linear expression correlation tests. Genes were ranked according to Pearson coefficient value in a descending order to identify the gene that is most co-expressed with HNF1B in a genome-wise scale.

    Survival analysis

    Survival analysis was applied to assess the impact of HNF1B cell cycle signature, ERG & HNF1B target gene signature and ERG & HNF1B eGene signature on PCa prognosis and survival in multiple independent cohorts. The survival analyses were performed and visualized as Kaplan-Meier plots using R package “Survival” (v.3.2.3)90,91. Patients were stratified into two groups based on the median value of the z-score summed signature scores. Function “Surv” was first employed to create the survival models with “time-to-event” and “event status” as input from clinical cohorts. Then signature scores was further followed to fit to the models by function “survfit”. The Cox proportional-hazards model92 was applied to investigate the hazard ratio for assessing the association between patients’ survival time and gene expression or signature scores.

    Expression quantitative trait loci (eQTL) analysis

    To evaluate the associations between genotypes of SNPs and HNF1B expression level, we performed the expression quantitative trait loci (eQTL) analysis by R package “MatrixEQTL” v.2.293 in Wisconsin and TCGA cohorts94,95, which comprised of 466 normal and 389 prostate tumor samples, respectively. To examine whether T2E fusion affects the eQTL signal, we matched available T2E information to the existing TCGA cohort and further stratified patients into fusion-positive and -negative PCa tumors consisting of 160 and 228 samples, respectively. The eQTL analysis was applied by fitting a linear regression model between the expression and the genotype data, other parameters were left as default (pvOutputThreshold = 0.05, errorCovariance = numeric ()”). The transcriptional profiling in TCGA cohort was assessed by RNA-Seq. The TCGA cohort was genotyped on Affymetrix SNP array 6. The relevant SNP genomic locations are listed in Supplementary Table 13.

    Enrichment analysis of HNF1Β SNPs in ERG-positive PCa tumors

    To investigate whether the SNPs were associated with TMPRSS2-ERG fusion-positive tumors in PCa, we performed an independent association study in a Chinese prostate biopsy cohort96 and radical prostatectomy cohort. Briefly, a consecutive prostate biopsy cohort and prostatectomy cohort with biospecimen started from October 2017 to December 2021 at a tertiary hospital in Shanghai, China. ERG was regularly stained in biopsy tissue samples via IHC. IHC was performed on the 4-μm-thick FFPE tissue sections using commercially available antibodies against ERG (Agilent Technologies Singapore). Antibody staining was detected using a universal immunoperoxidase polymer method (Envision-kit; Dako, Carpinteria, CA, US). A Dako automated immunohistochemistry system (Dako, Carpinteria, CA, US) was used according to the manufacturer’s protocol. Likewise, the IHC results were independently interpreted by two experienced pathologists: Xiaoqun Yang and Chaofu Wang. Genotyping was performed in most of the samples using Illumina Asian Screening Array. Imputation was performed thereafter97. A posterior probability of >0.90 was applied to call genotypes during imputation and the same quality control procedure for excluding genotyped SNPs was applied to imputed SNPs. Genotyping data of HNF1B with a ± 100 kb window was extracted (n = 1,662). SNPs were excluded if they had: (1) genotype call rate < 90% (n = 1,335); (2) minor allele frequency (MAF) <0.01 (n = 53); or (3) p < .05 for the Hardy–Weinberg Equilibrium (HWE) test. The study was approved by the Institutional Review Board (IRB) of Ruijin Hospital, Shanghai, China. A total of 1,543 samples with ERG expression information were found to have genotyping data (October 2017-December 2021). The demographic characteristics of these patients are shown in Supplementary Table 1. Assuming that positive ERG expression based on IHC is due to ERG fusion, 136 ERG-positive PCa cases out of 791 (17.2%) cases were observed in this cohort (30 ERG-positive biopsies negative to PCa to be excluded). This frequency was similar to the reported ERG fusion frequencies in Asian population33,34,35,36.

    RNA-sequencing (RNA-seq) and differential expression analysis

    Preparation of RNA samples was made with VCaP cells, which were treated with 100 nM DHT, and reversely transfected with two different siRNAs targeting HNF1B and negative siRNA control and incubated for 72 h at 37 °C each with two biological replicates. For the RNA sequencing in VCaP cells treated with either negative control siRNAs or siRNAs against HNF1B, raw sequence data were first pre-processed with FastQC (v.0.11.4) to assess read quality. SortMeRna was applied to identify and filter rRNA98 to limit the rRNA quantity in FastQ files. The filtered data was resubmitted for a QC assessment by FastQC (v.0.11.4) to ensure the validity of the filtering steps. Trimmomatic v.0.3999 was employed to process reads for quality trimming and adapter removal with default parameters: TruSeq3-SE.fa:2:30:10 SLIDINGWINDOW: 5:20. A final FastQC (v.0.11.4) run was performed to ensure the success of previous quality control steps. The processed reads were aligned against the human genome assembly hg19 using TopHat2 v.2.1.1100 with default settings; parameter for library type was set as “fr-firststrand”. HTSeq v.0.11.0 (htseq-count) was employed to quantitate aligned sequencing reads against gene annotation from UCSC and with parameters “-s reverse, –i gene_id”. Differential expression analysis was performed from read count matrix using Bioconductor package DESeq2 v.1.16.1101. Genes with low expressions (<5 cumulative read count across samples) were filtered out before analysis. A threshold of P < 0.05 was applied to generate the differentially expressed gene list. Statistical test was applied to control or treatment to ensure high correlations between biological replicates. Data was normalized using method variance Stabilizing Transformation (VST) and the heatmap presenting differentially expressed genes between siRNA Control and siRNAs HNF1B samples was generated using R package “pheatmap” v.1.0.12. All the relevant software and algorithms are listed in Supplementary Table 10.

    Gene set enrichment analysis

    We applied Gene Set Enrichment Analysis (GSEA) v.4.0.3 to interpret the RNA-Seq results upon knockdown of HNF1B. The pre-ranked gene list was obtained by calculation of data following formula sign (logFC)*-log(p value), and data were sorted in a descending order. GSEAPreranked test102 was used to test the enrichment of genes with phenotype in Hallmark gene sets. Parameters were set as follows: Enrichment statistic = “weighted”, Max size (exclude larger sets) = 5000, number of permutations =1000. All other parameters were remained as default. The GSEA enrichment plots were generated using R packages “clusterProfiler” v.3.14.3103 and “enrichplot” v.1.12.0104.

    Chromatin immunoprecipitation sequencing (ChIP-seq)

    The HNF1B ChIP-seq library was sequenced to generate 35-76 bp single-end reads. The HNF1B and ERG replicates were sequenced and produced 150 bp single-end reads. FastQC (v.0.11.4) was applied to assess the quality of raw data and followed by Trimmomatic v.0.3999 for quality control. The trimmed reads were mapped into the human genome assembly hg19 using Bowtie2 v.2.4.4105. MACS2 v. 2.2.7.1106 was employed for peak calling using default parameters. HOMER v.4.11107, UCSC, samtools v.1.9108, bedtools v.2.27.1109, deepTools v.3.3.2110 and IGV v.2.4.10 tools were used for peak annotation and generating big wiggle and TDF formats. Bioconductor package ChIPseeker v.1.18.0111 was applied to perform downstream peak annotation analysis.

    Development of the HNF1B/ERG derived signatures

    The HNF1B cell cycle signature, composed of 33 genes, was derived from the five top enriched cell cycle related pathways via GSEA, then further being intersected with the 207-upregulated genes from the RNA-Seq upon HNF1B knockdown. We defined the differentially expressed genes from the RNA-Seq as HNF1B knockdown signature. The HNF1B knock-down upregulated signature score was defined as a z-score sum of the 207 HNF1B upregulated genes by RNA-seq measurement. For the HNF1B and ERG direct target gene signature, we first converted the 207 upregulated gene symbols to Entrez IDs. Bedtools v.2.27.1109 was used to identify common peaks from HNF1B and ERG ChIP-Seq binding signals. Function “annotatePeaks.pl” from HOMER v.4.11 was applied for annotating HNF1B and ERG common peaks. The 207-upregulated gene list and the gene list from HNF1B and ERG common binding peaks were intersected, and thus resulted in a 51-gene list, defined as HNF1B and ERG direct target gene signature. For the eQTL gene (eGene) signature, we screened 13 proxy SNPs enriched in HNF1B and ERG common binding sites from Haploreg v.4.1112. We then set R2 ≥ 0.8 as a threshold, which resulted in a total eight proxy SNPs with 17 corresponding eQTL genes. We defined these 17 genes as HNF1B and ERG eGene signature. Signature scores were calculated as weighted sums of normalized expression of the genes from each signature.

    Meta-analysis

    The pooled HR was calculated by a fixed effect model113, as the I2 statistic was less than 30% or the fixed effects P value for the I2 statistic was greater than 0.10, indicating insignificant heterogeneity across studies114. The meta-analysis for investigation of the association between the HNF1B cell cycle signature and patient prognosis across studies was performed using the “metafor” package v.3.4.0115 in R environment v.4.2.0.

    Multivariate analysis

    We investigated the association of the PCa patient overall survival and biochemical recurrence with the HNF1B cell cycle signature and clinical variables including age, Gleason score, PSA, tumor stage, ERG-fusion status, seminal vesical status and extraprostatic extension status. The Cox proportional hazards model was applied for to investigate the relation between patient prognosis and the HNF1B cell cycle signature together with a set of covariates described above. Samples were stratified into two groups with higher and lower expression by comparing to the median value of the HNF1B cell cycle signature or by the continuous value of the HNF1B cell cycle signature score.

    Statistical analysis and data visualization

    All statistical analyses were performed using RStudio116,117 v.1.2.5033 with R environment v.3.6.3 or unless specified. Statistical analyses were applied across normal prostate, tumor and metastatic tissues from multiple cohorts. Mann–Whitney U test was used for gene expression in clinical cohorts with two groups, while Kruskal-Wallis H test was applied for cohorts having three groups or more. R package “Survival” was applied in all Survival analysis. Statistical analyses for all Kaplan-Meier curves were calculated using log-rank test. HNF1B signature scores were calculated from the z-score sum of panels of gene expression levels. For microarray-based expression profiling, we selected gene probes with lowest p values. Circos maps were generated using Circos (v.0.67)118. Asterisks indicate the significance level (*p < 0.05; **p < 0.01; ***p < 0.005). P value <0.05 was considered to be statistically significant.

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    Categories
    Prostate cancer

    ARID1A loss induces polymorphonuclear myeloid-derived suppressor cell chemotaxis and promotes prostate cancer progression

    The study is performed with all relevant ethical regulations of the Institutional biomedical research ethics committee of Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences.

    Cell culture

    Cell lines were purchased from American Type Culture Collection (293 T, CRL-11268; C4-2, CRL-3314; PC-3, CRL-1435; 22RV-1, CRL-2505; DU145, HTB-81; HCT116, CCL-247; RAW 264.7, TIB-71; Myc-CaP, CRL-3255), Cell Bank, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences (A549, SCSP-503; Jurkat, SCSP-513) or Nanjing Cobioer Biosciences Co., LTD, P. R. China (OCI-LY10, CBP60558). C4-2, PC-3, 22RV-1 and Jurkat cells were cultured in RPMI 1640 media supplemented with 10% FBS at 37 °C under 5% CO2. 293 T, Myc-CaP, DU145, A549, HCT116, Raw264.7, and OCI-Ly10 cells were cultured in DMEM media at the same growth conditions. Cell lines were tested to confirm lack of mycoplasma contamination. The culture and experiments of organoid cells generated in this study were performed according to previously reported63,64.

    Human tumor samples and TMA analysis

    The use of pathological specimens, as well as the review of all pertinent patient records, was approved by the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. All patient samples were collected by the Department of Pathology with approval from the Research Ethics Committee of Daping Hospital, Army Medical University, and informed consent was obtained from the patients. To determine the correlation of ARID1A expression, NF-κB signaling activity and protein levels of CXCL2, CXCL3 in PCa tissues, a total of 42 prostate samples (GS > 7) were collected during surgical radical prostatectomy (Supplementary Table 2). Immunostaining of TMA (adjacent normal tissues: 40; tumor sample: 100; Supplementary Table 1) were performed by Department of Pathology at the Daping Hospital, Army Medical University by using anti-ARID1A (Cell Signaling Technology; 12354 S, clone: D2A8U, 1:500), CD8 (Abcam; ab189926, clone: EPR10640(2), 1:200), CD15 (Abcam; ab220182, clone: FUT4/815, 1:500), P65 (Cell Signaling Technology; 8242 S, clone: D14E12, 1:500). The tissue samples and clinical parameters of PCa patients who underwent radical prostatectomy were collected, including age at diagnosis, baseline serum PSA level, GS, adverse pathological features (extraprostatic extension, seminal vesicle invasion, lymph node invasion, and positive surgical margins), and follow-up PSA levels. No patients received adjuvant therapy until biochemical recurrence. For ARID1A and P65, staining intensity was scored following a three-tiered system, yielding a staining index ranging from 1 to 9 (extensive, strong staining). CD8+ and CD15+ cells were measured as the number of CD8 and CD15 positive cells in each TMA core compared to the total cells number. Low expression of ARID1A and P65 were defined by a staining index below 6, whereas staining scores if 6–9 were considered high expression. CD8+ and CD15+ cells were measured based on the number of CD8 and CD15 positive cells versus the total cells number. The median positive numbers were set as the cut offs to divide the patients. IHC results were quantified by pathologists blinded to the outcome. All the analyses were conducted or confirmed by two certified clinical pathologists independently (QL and JJ).

    Animal models and experiment assays

    All mice were maintained in a specific-pathogen-free (SPF) facility, and all related protocols were performed in compliance with the Guide for the Care and Use of Laboratory Animals and were approved by the Institutional biomedical research ethics committee of Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences. The maximal tumor size/burden permitted (2 cm3) by ethics committee was not exceeded in the study. Mice were housed under specific-pathogen-free conditions with standard food and water ad libitum in a 12 h light and 12 h dark cycle. Humidity and ambient temperature were maintained between 45–65% and 20–24 °C, respectively. Arid1a-floxed mice were provided by Dr. Hongbin Ji (Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences). Pten-floxed mice were generated by Hong Wu4. The PBCre/+ transgenic mice were obtained from Fen Wang65. For GEMM studies, all mice are maintained in C57BL/6 background, and the histology analysis and molecular characterizations are described as below. Male mice were sacrificed for analysis at 3- and 4-month-old. For subcutaneous injection, 4–6-week-old male mice (C57BL/6, FVB or Rag1−/− mice; Shanghai SLAC Laboratory Animal Co., Ltd) were injected with 1 × 106 cells. The tumor size was measured every 4 days using calipers in two dimensions to generate a tumor volume using the following formula: 0.5 × (length × width2). 10-week-old PtenPC−/− and PtenPC−/−; Arid1aPC−/− mice received castration. Two weeks later, Enzalutamide (MedKoo Biosciences, 201821) was dissolved in DMSO as 90 mg/ml stock solution, which was diluted in corn oil (Sigma) to make 2.5 mg/ml working solution. Mice were given 100 μl I.P. intraperitoneal injections three times a week for 2 months66. More details about the number and age of animals used in each experiment, see the corresponding figure legends.

    Histology quantifications

    Histological features were graded using previously described nomenclature and criteria67. In brief, HGPIN was characterized by the intraglandular proliferation of crowding cells with atypia and cribriform formation or the development of multilayered, solid glandular structures. Adenocarcinoma displays an abnormal architectural glandular pattern with disturbance of benign epithelial-stromal relationships, indicated by the presence of atypical cells breaking the basal membrane. Quantitative histological results are derived from five random slides of each mouse in the entire animal cohort, as previously described68,69. In brief, for each animal, five random fields were captured, each of which was further divided into four quadrants. In each quadrant, the most advanced histological feature was recorded for quantification. Thus, the number of each lesion subtype in each experimental group was established, and the percentage of different lesion subtypes was compared.

    Expression plasmids, shRNA, sgRNA, CRISPRa, and CRISPRi

    pLenti-puro-ARID1A was purchased from Addgene for overexpression. The full-length human IKKα, IKKβ, and NEMO cDNA were cloned into pcDNA5-Flag (Invitrogen), pcDNA3.1 (Myc or HA tag) (Invitrogen) or pLVX-IRES-puro (Clontech) to generate expression plasmids. ARID1A mutants were constructed as described previously. The Arid1a sense and antisense KD1 and KD2 oligonucleotides were annealed and cloned into pLKO.1-Puro (Addgene). pLentiCRISPRv2-sgRNAs targeting Arid1a gene locus were used to knock out the gene in cells. sgRNAs for CRISPRa or CRISPRi were designed by CRISPick (https://portals.broadinstitute.org/gppx/crispick/public). Briefly, dSV40-dCas9-10×GCN4-mCherry construct was generated from dSV40-dCas9-10×GCN4 (Addgene, #107310) by inserting a P2A-mCherry cassette for sorting purpose. Control and sgRNA targeting the enhancer of A20 were cloned into sgRNA-SPH vector. The dCas9-GCN4 expressed Myc-CaP cells were infected with control and Arid1a-sgRNA lentivirus. CRISPR-mediated endogenous A20 activation is achieved by SPH activator system as previously reported70. For CRISPRi, sgRNAs were cloned into lenti-dCas9-KRAB-puro vector (Addgene) and stably transfected into Myc-CaP cells. The siRNA, sgRNA, and shRNA sequences are listed in Supplementary Table 3.

    Infections and transfections

    Lentivirus was used to establish individual stable cells, and empty vector was used as the controls for overexpression, shRNA-based knockdown and sgRNA-based knockout. 1 × 105 cells were plated into 6 well plates the day before infection. When the cells reached 50% confluence, 2 mL of lentiviral supernatant with 10 μg/mL polybrene was added and removed 24 hr later. Infected cells were selected in 2 μg/mL puromycin or 5 μg/mL basticidin. For transfections, cells were transfected with siRNA duplexes or plasmid by using Lipofectamine (Invitrogen) according to manufacturer’s instructions. One day before transfection, cells were plated into 6 well plates. We diluted 100 µM oligonucleotide or 2 ug plasmid and 2 µl Lipofectamine respectively in 50 µl of Opti-MEM. While complexes were formed after 15–20 min, we added and incubated them with the cells at 37 °C for 4 h. Remove the medium from the cells and add 2 mL of fresh medium to each well until harvest.

    Immunostaining and IHC

    ARID1A (Sigma-Aldrich; HPA005456, 1:500), CD4 (Abcam; ab183685, clone: EPR19514, 1:500), CD8 (Abcam; ab209775, clone: EPR20305, 1:200), Ly6G (BioLegend; 127601, clone: 1A8, 1:500), SMAα (Sigma-Aldrich; A2547, clone: 1A4, 1:4000), AR (Santa Cruz Biotechnology; SC816S, clone: N-20, 1:500), CK8 (Abcam; ab154301, 1:4000) and Ki67 (Cell Signaling Technology; 12202, clone: D3B5, 1:500) antibody were used for immunohistochemistry. Formalin-fixed paraffin-embedded (FFPE) tissue sections were de-paraffinized. The tissue sections were blocked first for 1 h in MOM Blocking reagent (Vector Labs; MKB-2213). Sections were then incubated sequentially with the primary antibody overnight and followed by 1 h incubation with biotinylated anti-mouse (Vector Labs; BA-9200-1.5) or rabbit (Vector Labs; BA-1000-1.5) secondary antibodies in a 1:500 dilution. The Streptavidin-HRP and DAB detection kit (Vector Labs) were used according to the manufacturer’s instructions. For quantification, positive cells were counted from at least three random slides of each mouse for a total of 4–8 pairs.

    Reagent and immunotherapy

    The chemicals used in vitro and in vivo assays are as follows: SB225002 (0.1 μM for the in vitro assays and 2 mg/kg for the mouse treatment, MedChemExpress; HY-16711), JSH-23 (5 μM for the in vitro assays and 2 mg/kg for the mouse treatment, MedChemExpress; HY-13982); RS504393 (0.2 μM for the in vitro assays, MedChemExpress; HY-15418); MG132 (Selleck Chemical; S2619s); cycloheximide (BioVision; 1041-1 G). SB225002 and JSH-23 in DMSO were diluted in corn oil for in vivo administration through intraperitoneal injection every other day or gavage daily, respectively. For immunotherapy, antibody intraperitoneal injection was started when subcutaneous tumor volume reached ~100 mm3 or PtenPC−/−; Arid1aPC−/− mice were 2.5-month-old. The following antibodies were injected alone or in combination: anti-mouse PD1 (BioXCell; Clone: RMP1-14, BE0146); anti-mouse CTLA4 (BioXCell; Clone: 9H10, BE0131); anti-mouse Ly6G (BioXCell; clone: 1A8, BE0075-1); and their respective isotype IgG controls. PD1/CTLA4 antibody were simultaneously administrated to the mice. Treatment was administered twice a week through intraperitoneal injections at a dosage of 200 μg/injection/antibody, and subcutaneous tumor volume was monitored every 4 days. CXCL2 was depleted from conditioned media by incubation with mouse antibody against Mip2/Cxcl2 (R&D Systems; MAB452).

    Cytokine measurement

    Protein lysis of PCa specimen was used to determine CXCL2 and CXCL3 levels by single-plex sandwich ELISA kits (Elabscience; E-EL-H1904c and E-EL-H1905c). The amount of CXCL2 and CXCL3 protein in the serum and protein lysis of prostate tissues from PtenPC−/− and PtenPC−/−; Arid1aPC−/− mice was determined using mouse CXCL2 and CXCL3 specific ELISA kits (Elabscience; E-EL-M0019c and Beijing BioRab Technology Co. Ltd.; ZN2584). IFN-γ levels were determined using a single-plex sandwich ELISA (Senxiong Biotech). The assay was performed according to the manufacturer’s instructions.

    PMN-MDSC isolation and transwell assay

    PMN-MDSCs were isolated from prostate tumors of PtenPC−/−; Arid1aPC−/− mice sorted by FACS (CD45+ CD11b+ Ly6G+ Ly6Clow) and plated in RPMI1640 medium. PMN-MDSCs (1 × 105 cells/well) were seeded in the top chamber of the transwell (Corning). Conditioned media from cultured Myc-CaP cell lines (with or without Arid1a knockout; with or without SB225002, JSH-23, RS504393 or CXCL2 antibody pre-treatment) were collected and added to the bottom layer of the transwell. After 4 h incubation, cells that migrated to the bottom chamber were counted. These experiments were performed in triplicate, and statistical significance was assessed using two-tailed unpaired t-test.

    CyTOF

    Tumor cells were isolated using Mouse Tumor Dissociation Kit (Miltenyi Biotec; 130-096-730) and were depleted of red blood cells using RBC Lysis Buffer (BioLegend; 420301). Cells were stimulated for 4 h at 37 °C with 10% FBS RPMI1640 medium supplemented with cell activation cocktail (BioLegend; 423303; 1:500 dilution). Cells were Fc-blocked by CD16/CD32 antibody (Biolegend; 156604) and incubated with CyTOF surface antibody cocktails for 30 min at 4 °C. For intracellular staining, cells were permeabilized using fixation/permeabilization buffer solution (BD Biosciences). Cells were washed twice and incubated with CyTOF intracellular antibody mix for 1 h at room temperature. For singlet discrimination, cells were washed and incubated with Cell-ID Intercalator-Ir (Fluidigm 201192 A) overnight at 4 °C. The samples were submitted to the Flow Cytometry and run using CyTOF Instrumentation (DVS Science), and were analyzed by FlowJo and Cytobank. Cell populations were identified as T cells (CD45+ CD3e+), CD4+ T cells (CD45+ CD3e+ CD8a CD4+), CD8+ T cells (CD45+ CD3e+ CD8a+ CD4), PMN-MDSC (CD45+ CD11b+ F4/80low Ly6G+ Ly6Clow), M-MDSC (CD45+ CD11b+ F4/80low Ly6G Ly6Chigh) and macrophage (CD45+ CD11b+ F4/80high). CyTOF staining panels are detailed as follows: Ly6G (conjugated to 141Pr, DVS-Fluidigm; clone: 1A8, 3141008B), CD4 (conjugated to 145Nd, DVS-Fluidigm; clone: RM4-5, 3145002B), CD8a (conjugated to 146Nd, DVS-Fluidigm; clone: 53-6.7, 3146003B), CD45 (conjugated to 147Sm, DVS-Fluidigm; clone: 30-F11, 3147003B), CD11b (conjugated to 148Nd, DVS-Fluidigm; clone: M1/70, 3148003B), CD3e (conjugated to 152Sm, DVS-Fluidigm; clone: 145-2C11, 3152004B), F4/80 (conjugated to 159Tb, DVS-Fluidigm; clone: BM8, 3159009B), Ly6C (conjugated to 162Dy, DVS-Fluidigm; clone: HK1.4, 3162014B) IFNγ (conjugated to 165Ho, DVS-Fluidigm; clone: XMG1.2, 3165003B).

    Lymphocyte staining and flow cytometry

    Tissues were dissected, minced into small pieces and further digested by 1 mg/ml Collagenase Type II (Thermo Fisher Scientific; 17101015), 1 mg/ml Collagenase Type IV (Thermo Fisher Scientific; 17104019) and 0.1 mg/ml DNase I recombinant (Sigma-Aldrich; 4536282001) at 37 °C for 30–60 min. One million cells were incubated with 1 μl CD16/CD32 antibody (Biolegend; 156604) to block the Fc receptor at 4 °C for 10 min. Cell suspension were incubated with cell surface antibodies at 4 °C for 30 min. For intracellular cytokine staining, cells were stimulated for 4 h at 37 °C with cell activation cocktail (BioLegend; 423303). After permeabilized with FOXP3 Fixation/Permeabilization Buffer (eBioscience; eBio 00-5523) according to the manufacturer’s protocol, cell suspension was incubated with IFNγ, FOXP3, and Ki67 antibody at 4 °C for 30 min. Then, cells were analyzed on a Gallios analyzer (Beckman Coulter Life Sciences). Data were analyzed with FlowJo v.10 (FlowJo LLC). All FACS antibodies were used in a dilution of 1:100. F4/80 (BV510, BioLegend; clone: BM8, 123135), CD11b (BV605, BioLegend; clone: M1/70, 101237), Ly6G (PE-Cy7, Bio-Legend; clone: 1A8, 127617), Ly6C (APC, BioLegend; clone: HK1.4, 128016), CD45 (APC-Cy7, BioLegend; clone: 30-F11, 103116), CD4 (PE-Cy7, BioLegend; clone: GK1.5, 100422), CD8 (AF700, BioLegend; clone: 53-6.7, 100730), FOXP3(APC, eBioscience; clone: 236 A/E7, 17-4777), Ki67 (BV421, BioLegend; clone: 16A8, 652411) and IFNγ (FITC, BioLegend; clone: XMG1.2, 505806).

    T cell suppression assay

    PMN-MDSCs were isolated from 3-month-old PtenPC−/−; Arid1aPC−/− mice prostate tumors. CD8+ T cells were isolated from spleen of wild-type C57BL/6 mice. A T cell suppression assay was performed using PMN-MDSCs sorted by FACS (CD45+ CD11b+ Ly6G+ Ly6Clow) and CFSE (Invitrogen)-labeled MACS-sorted (Miltenyi Biotec; 130-104-075) CD8+ T cells in anti-CD3- and anti-CD28-coated 96-well plates at an MDSC/ T cell ratio of 0:4, 1:4, with 1 × 105 PMN-MDSCs. CFSE intensity was quantified 96 h later with peaks identified by FACS. CFSE peaks indicated the division times. Division times 0–2 and 3–4 were defined as low proliferation and high proliferation, respectively.

    RNA isolation and real-time PCR

    Total RNA was extracted using TRIzol (Invitrogen) according to the manufacturer’s instructions. First-strand cDNA was synthesized by HiScript II Q RT SuperMix (Vazyme) for qPCR. Real-time PCR was performed with FastStart Universal SYBR Green Master (Roche) on QuantStudio 7 Flex Real Real-Time PCR System (Applied Biosystems). The primers used for real-time PCR are shown Supplementary Table 3. The 2–ΔΔCt method was used to calculate relative expression changes.

    IP and IB analysis

    For IP assays, cells were lysed and washed in HEPES lysis buffer (20 mM HEPES, pH 7.4, 200 mM NaCl, 1.5 mM MgCl2, 2 mM EGTA, 0.5% NP-40, 1 mM NaF, 1 mM Na3VO4 and 1 mM PMSF) supplemented with protease-inhibitor cocktail (Roche). Cell lysates were incubated overnight at 4 °C with indicated primary antibody and protein A/G agarose beads (Roche) or anti-flag M2 agarose (Sigma). Beads were centrifuged at 1000 g for 5 min at 4 °C to remove the supernatant, washed four times with the IP buffer and boiled SDS-loading buffer for 10 min at 95 °C. Samples were run on SDS-PAGE gel analyzed by western blotting. Full scan blots, see the Source Data file.

    Chromatin-immunoprecipitation assays

    The ChIP assays were performed using ChIP kit (Catalog no. 17-371; Millipore). The procedure was according to the kit instruction manual provided by the manufacturer. Briefly, 1 × 107 Myc-CaP cells were fixed by 1% formaldehyde, fragmented by sonication to shear the chromatin to 400–1000 bp. The sheared crosslinked chromatin was incubated with IgG, anti-BRG1 (Abcam, clone: EPNCIR111A, ab110641), anti-BAF155 (Santa Cruz Biotechnology; clone: G-7, sc-365543X), anti-ARID1A (Cell Signaling Technology; clone: D2A8U, 12354 S) and anti-ARID1B (Cell Signaling Technology; clone: E9J4T, 92964 S) antibodies (10 μg antibody for each ChIP reaction) overnight followed by Protein G conjugated agarose beads incubation. The precipitated DNA was amplified by primers and quantified by QuantStudio 7 Flex Real Real-Time PCR System (Applied Biosystems). ChIP primer sequences can be found in the Supplementary Table 3.

    Enhancer RNA (eRNA) assay

    According to the manufacturer’s guidelines (Cell-Light TM EU Nascent RNA Capture Kit; RiboBio), nascent RNA was labeled in WT and Arid1a KO Myc-CaP cells by ethynyl-labeled uridine (EU). Subsequently, the resulting EU-labeled RNA was detected via Cu (I)-catalyzed click chemistry that introduced a Biotin tag for RNA purification. At last, streptavidin-purified RNA was applied to reverse transcriptase-mediated cDNA synthesis and further qPCR analysis.

    In vitro kinase assay

    Recombinant proteins of Flag-ARID1A (substrate) and HA-IKKβ (enzyme) were first prepared and purified from E. coli. In a typical phosphorylation reaction, 1 μg Flag-ARID1A protein was incubated with HA-IKKβ in a 50 μl kinase reaction buffer (50 mM Tris-HCl, 5 mM MgCl2, 30 μM ATP) at 37 °C for 1 h. Phosphorylation of ARID1A was analyzed by western blotting with a thiophosphate ester rabbit monoclonal antibody (Abcam; ab92570). Full scan blots, see the Source Data file.

    Gel filtration

    Gel-filtration chromatography analyses were performed with Superose 6 Increase 10/300 GL columns (GE Healthcare; 29-0915-96). Briefly, C4-2 cells were lysed in 1 mL Hypotonic Buffer containing protease inhibitors (Roche). Nuclear pellets were collected and washed twice with Hypotonic Buffer, and then lysed in HEPES lysis buffer. After lysing for 20 min on ice, cell debris were removed and the nuclear extraction was further filtered through a 0.45 mm syringe filter before loaded onto a Superose 6 Increase 10/300 GL columns. A total of 20 μl samples from each fraction were analyzed by IB using indicated antibodies. Full scan blots, see the Source Data file.

    GST pull-down assay

    Full length HA-IKKβ protein was obtained from in-vitro translation kit (catalog no. L1170; Promega). BL21 E. coli transformed with empty vector or pGEX-GST-ARID1A-F2 truncated plasmid was induced by Isopropyl-1-thio-β-D-galactopyranoside (IPTG) (0.5 mM) at 16 °C for 5 h. After sonication, incubate the cell lysate with glutathione-Sepharose beads for 1 h at 4 °C. Recombinant proteins binding to beads were incubated with HA-IKKβ protein for 3 h at 4 °C. Beads were subsequently harvested through centrifugation and washed three times by 0.2% NP-40 buffer before boiled and loaded to SDS-PAGE. Full scan blots, see the Source Data file.

    Organoid generation

    The mouse prostatic organoid formation assay was performed according to previously reported63,64. Mouse prostate tissue was minced and digested in 5 mg/ml Collagenase type II (Life Technologies;17101-015) with 10 μM Y-27632 dihydrochloride and incubated at 37 °C for 1 h. The dissociated tissue pellets were resuspended in TrypLE (Gibco; 12605-010) with 10 μM Y-27632 for further digestion for 15 min at 37 °C. The dissociated cell suspensions were stained for 30 min on ice with the following antibody: CD24-FITC (BioLegend; clone: M1/69, 101806), PE conjugated CD49f (eBioscience; clone: GoH3, 12-0495-82) and DAPI (1 ng/μl, Sigma; D8417). For organoids formation assay, cells were suspended using organoid medium, and mixed with Matrigel (1:1). The cell suspension was seeded into 24 well culture plate with the cell number of 2000 in a volume of 50 μl per well. The number and size of the organoids were determined on day 9.

    RNA-seq and analysis

    Cells were isolated from 3-month-old PtenPC−/− and PtenPC−/−; Arid1aPC−/− mice (n = 3) through isolating EpCAM+; CD45 cells. For subcutaneous tumors, Myc-CaP expressing sgARID1A and control vectors inoculated subcutaneously into FVB mice and treated with or without JSH-23 and epithelial cells were sorted for RNA-seq. Total RNA was extracted using TRIzol reagent (Invitrogen), and then subjected for sequencing by RiboBio (Guangzhou, China). Samples were demultiplexed into paired-end reads using Illumina’s bcl2fastq conversion software v2.20. The reference genome was indexed using bowtie2-build, and reads were aligned onto the GRCm38/mm10 mouse reference genome using TopHat2 with strand-specificity and allowing only for the best match for each read. The aligned file was used to calculate strand-specific read count for each gene using HTSeq-count (version 0.13.5). The significance was operated by setting fold changes threshold at level of 1.5 and p < 0.05. Heatmaps were generated using the pheatmap (1.0.12) package in R (4.2.0). For gene enrichment analysis (GSEA), The ZHANG_ RESPONSE_TO_IKK_INHIBITOR_AND_TNF_UP (223 genes) and HALLMARK_ TNFA_SIGNALING_VIA_NF-κB (200 genes) signature were derived from GSEA C2: curated gene sets and hallmark gene sets. Similar transcriptome sequencing and analysis were also performed by WT and Arid1a KO Myc-CaP cells. Weighted GSEA enrichment statistic and Signal2Noise or Diff_of_Classes metric for ranking genes were used.

    ChIP-seq and data analysis

    Chromatin-immunoprecipitation experiments were carried out by WT and Arid1a KO Myc-CaP cells using the ChIP kit (Catalog no. 17-371; Millipore). Chromatin from 3 × 106 cells was used for each ChIP reaction with 10 μg of the target protein antibodies: anti-BRG1 (Abcam, clone: EPNCIR111A, ab110641), anti-H3K27ac (Cell Signaling Technology; clone: D5E4, 8173 S), anti-H3K4me1 (Cell Signaling Technology; clone: D1A9, 5326) and anti-H3K4me3 (Cell Signaling Technology; clone: C42D8, 9751 S). Purified DNA was then prepared for sequencing (Illumina). Libraries were quantified and sequenced on the Illumina HiSeq 2500 Sequencer (125-nucleotide read length). Reads mapped to the same genomic positions were filtered by PICARD-MarkDuplicates (Galaxy Version 2.18.2.2), and the nonredundant reads were used for peak calling. MACS2-callpeak (Galaxy Version 2.1.1.20160309.6) was used for performing peak calling with the threshold, p-value ≤ 0.005. ChIP peak profile plots and read-density heat maps were generated using deepTools, and cistrome overlap analyses were carried out using the ChIPseeker (Galaxy Version 1.18.0). Galaxy available pipelines were utilized for analysis (https://usegalaxy.org/).

    ATAC-seq and data analysis

    50,000 Myc-CaP cells with or without Arid1a KO were suspended in cytoplasmic lysis buffer (CER-I from the NE-PER kit, Invitrogen; 78833). Nuclei were resuspended in 50 μl of 1× TD buffer, then incubated with 2–2.5 μl Tn5 enzyme for 30 min at 37 °C (Nextera DNA Library Preparation Kit; FC-121-1031). Samples were purified and PCR-amplified with the NEBNext High-Fidelity 2X PCR Master Mix (NEB; M0541L). ATAC-seq libraries were sequenced on the Illumina HiSeq 2500 (125-nucleotide read length, paired end). Paired-end fastq files were trimmed and uniquely aligned to the GRCm38/mm10 mouse genome assembly using BWA. Duplicated reads were removed by PICARD-MarkDuplicates (Galaxy Version 2.18.2.2). Filtered bam files were used for peak calling by MACS2 and an initial threshold q-value of 0.01 as cutoff. Bigwig files were then visualized using the UCSC genome browser, and the final figures were assembled using Adobe Illustrator.

    Analysis of ARID1A, NF-κB, and MDSC signature in human PCa patients

    Analysis in human tumor datasets was carried out as previously described7,68,69. Differentially expressed genes between epithelial cells of 3-month-old PtenPC−/−; Arid1aPC−/− versus PtenPC−/− mouse prostates were defined as the Arid1a signature. The NF-κB signature was derived from TIAN_TNF_SIGNALING_VIA_NFKB (26 genes), SCHOEN_SCHOENNFKB_SIGNALING (34 genes), ZHANG_RESPONSE_TO _IKK_INHIBITOR_AND_TNF_UP (223 genes) and HALLMARK_TNFA_SIGNALING_ VIA_NFKB (200 genes) from GSEA C2: curated gene sets (the former 3) and hallmark gene sets. MDSC signature (39 genes) genes were generated as previously described16. To define the degree of Arid1a signature manifestation within the profiles from an external human tumor dataset, we used the previously described t-score metric7,69. For example, the t-score was defined for each external profile as the two-sided t-statistic comparing the average of the Arid1a-induced genes with the average of the Arid1a-repressed genes (For computing gene signature scores based on expression profile data from human dataset, genes were first z-normalized to the SD from the median across the dataset). The t-score contrasted the patterns of the “Arid1a-induced” genes against those of the “Arid1a-repressed” genes which did not include Arid1a mRNA itself, to derive a single value denoting coordinate expression of the two gene sets. NF-κB and MDSC signature scores were calculated using the method as previously described16. Specifically, we used the ssGSEA algorithm (GSVA Ver_1.45.5) to assign an enrichment score of genes in each gene list above for each sample. Higher ssGSEA scores correspond to more joint upregulation of genes in each signature.

    Statistical analysis

    GraphPad Prism 8.0 was used for statistical calculations. For all comparisons between two groups of independent datasets, two-tailed unpaired t-test was performed, p-value and standard error of the mean (SEM) were reported. For comparisons among more than two groups (>2), one-way or two-way ANOVA followed by multiple comparison were performed, p-values and SEM were reported; and p-values were adjusted by multiple testing corrections (Bonferroni) when applicable. For quantification of tumor cell or immune cell density, images of tumor sections with IF or IHC staining were captured by using microscope. The positive cells were counted. Three fields in each were randomly selected for tumor cell or immune cell density analysis and statistical analysis was performed by using t-test. The two-tailed Pearson correlation between ARID1A expression/signature and NF-κB activity, MDSC signature were calculated using Graphpad Prism 8.0, and difference of proportion were determined by two-tailed Fisher’s exact test. Patient recurrence was determined by Kaplan–Meier analysis. Statistical testing was performed with the log-rank test. For genetically engineered mouse modes (GEMMs) analysis, the examinations were performed dependent on animal available. All the experiments were independently repeated at least three times (with at least 5 biological repeats in total) with the similar time course and treatment. The xenograft assays were results of one-time experiment with sufficient animal number indicated in figure legend. In all figures, not significant (ns), p < 0.05 () and p < 0.01 ().

    Reporting summary

    Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    Categories
    Prostate cancer

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