Mechanism for controlled assembly of transcriptional condensates by Aire.

Transcriptional condensates play a crucial role in gene expression and regulation, yet their assembly mechanisms remain poorly understood. Here, we report a multi-layered mechanism for condensate assembly by autoimmune regulator (Aire), an essential transcriptional regulator that orchestrates gene expression reprogramming for central T cell tolerance. Aire condensates assemble on enhancers, stimulating local transcriptional activities and connecting disparate inter-chromosomal loci. This functional condensate formation hinges upon the coordination between three Aire domains: polymerization domain caspase activation recruitment domain (CARD), histone-binding domain (first plant homeodomain (PHD1)), and C-terminal tail (CTT). Specifically, CTT binds coactivators CBP/p300, recruiting Aire to CBP/p300-rich enhancers and promoting CARD-mediated condensate assembly. Conversely, PHD1 binds to the ubiquitous histone mark H3K4me0, keeping Aire dispersed throughout the genome until Aire nucleates on enhancers. Our findings showed that the balance between PHD1-mediated suppression and CTT-mediated stimulation of Aire polymerization is crucial to form transcriptionally active condensates at target sites, providing new insights into controlled polymerization of transcriptional regulators.

Punctuated evolution of prostate cancer genomes.

The analysis of exonic DNA from prostate cancers has identified recurrently mutated genes, but the spectrum of genome-wide alterations has not been profiled extensively in this disease. We sequenced the genomes of 57 prostate tumors and matched normal tissues to characterize somatic alterations and to study how they accumulate during oncogenesis and progression. By modeling the genesis of genomic rearrangements, we identified abundant DNA translocations and deletions that arise in a highly interdependent manner. This phenomenon, which we term "chromoplexy," frequently accounts for the dysregulation of prostate cancer genes and appears to disrupt multiple cancer genes coordinately. Our modeling suggests that chromoplexy may induce considerable genomic derangement over relatively few events in prostate cancer and other neoplasms, supporting a model of punctuated cancer evolution. By characterizing the clonal hierarchy of genomic lesions in prostate tumors, we charted a path of oncogenic events along which chromoplexy may drive prostate carcinogenesis.

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.

The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and the number of variants tested per loci. Using this strategy, we interrogated known prostate cancer (PCa) risk-associated loci and demonstrated that 35% of them harbor SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.

H3K27ac HiChIP in prostate cell lines identifies risk genes for prostate cancer susceptibility.

Genome-wide association studies (GWASs) have identified more than 200 prostate cancer (PrCa) risk regions, which provide potential insights into causal mechanisms. Multiple lines of evidence show that a significant proportion of PrCa risk can be explained by germline causal variants that dysregulate nearby target genes in prostate-relevant tissues, thus altering disease risk. The traditional approach to explore this hypothesis has been correlating GWAS variants with steady-state transcript levels, referred to as expression quantitative trait loci (eQTLs). In this work, we assess the utility of chromosome conformation capture (3C) coupled with immunoprecipitation (HiChIP) to identify target genes for PrCa GWAS risk loci. We find that interactome data confirm previously reported PrCa target genes identified through GWAS/eQTL overlap (e.g., MLPH). Interestingly, HiChIP identifies links between PrCa GWAS variants and genes well-known to play a role in prostate cancer biology (e.g., AR) that are not detected by eQTL-based methods. HiChIP predicted enhancer elements at the AR and NKX3-1 prostate cancer risk loci, and both were experimentally confirmed to regulate expression of the corresponding genes through CRISPR interference (CRISPRi) perturbation in LNCaP cells. Our results demonstrate that looping data harbor additional information beyond eQTLs and expand the number of PrCa GWAS loci that can be linked to candidate susceptibility genes.

Mutational heterogeneity in cancer and the search for new cancer-associated genes.

Major international projects are underway that are aimed at creating a comprehensive catalogue of all the genes responsible for the initiation and progression of cancer. These studies involve the sequencing of matched tumour-normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false-positive findings that overshadow true driver events. We show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumour-normal pairs and discover extraordinary variation in mutation frequency and spectrum within cancer types, which sheds light on mutational processes and disease aetiology, and in mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and enable the identification of genes truly associated with cancer.

Complementary genomic approaches highlight the PI3K/mTOR pathway as a common vulnerability in osteosarcoma.

Osteosarcoma is the most common primary bone tumor, yet there have been no substantial advances in treatment or survival in three decades. We examined 59 tumor/normal pairs by whole-exome, whole-genome, and RNA-sequencing. Only the TP53 gene was mutated at significant frequency across all samples. The mean nonsilent somatic mutation rate was 1.2 mutations per megabase, and there was a median of 230 somatic rearrangements per tumor. Complex chains of rearrangements and localized hypermutation were detected in almost all cases. Given the intertumor heterogeneity, the extent of genomic instability, and the difficulty in acquiring a large sample size in a rare tumor, we used several methods to identify genomic events contributing to osteosarcoma survival. Pathway analysis, a heuristic analytic algorithm, a comparative oncology approach, and an shRNA screen converged on the phosphatidylinositol 3-kinase/mammalian target of rapamycin (PI3K/mTOR) pathway as a central vulnerability for therapeutic exploitation in osteosarcoma. Osteosarcoma cell lines are responsive to pharmacologic and genetic inhibition of the PI3K/mTOR pathway both in vitro and in vivo.

Detecting Small Cell Transformation in Patients with Advanced EGFR Mutant Lung Adenocarcinoma through Epigenomic cfDNA Profiling.

PURPOSE: Histologic transformation to small cell lung cancer (SCLC) is a mechanism of treatment resistance in patients with advanced oncogene-driven lung adenocarcinoma (LUAD) that currently requires histologic review for diagnosis. Herein, we sought to develop an epigenomic cell-free DNA (cfDNA)-based approach to noninvasively detect small cell transformation in patients with EGFR mutant (EGFRm) LUAD. EXPERIMENTAL DESIGN: To characterize the epigenomic landscape of transformed (t)SCLC relative to LUAD and de novo SCLC, we performed chromatin immunoprecipitation sequencing (ChIP-seq) to profile the histone modifications H3K27ac, H3K4me3, and H3K27me3; methylated DNA immunoprecipitation sequencing (MeDIP-seq); assay for transposase-accessible chromatin sequencing; and RNA sequencing on 26 lung cancer patient-derived xenograft (PDX) tumors. We then generated and analyzed H3K27ac ChIP-seq, MeDIP-seq, and whole genome sequencing cfDNA data from 1 mL aliquots of plasma from patients with EGFRm LUAD with or without tSCLC. RESULTS: Analysis of 126 epigenomic libraries from the lung cancer PDXs revealed widespread epigenomic reprogramming between LUAD and tSCLC, with a large number of differential H3K27ac (n = 24,424), DNA methylation (n = 3,298), and chromatin accessibility (n = 16,352) sites between the two histologies. Tumor-informed analysis of each of these three epigenomic features in cfDNA resulted in accurate noninvasive discrimination between patients with EGFRm LUAD versus tSCLC [area under the receiver operating characteristic curve (AUROC) = 0.82-0.87]. A multianalyte cfDNA-based classifier integrating these three epigenomic features discriminated between EGFRm LUAD versus tSCLC with an AUROC of 0.94. CONCLUSIONS: These data demonstrate the feasibility of detecting small cell transformation in patients with EGFRm LUAD through epigenomic cfDNA profiling of 1 mL of patient plasma.

Epigenomic signatures of sarcomatoid differentiation to guide the treatment of renal cell carcinoma.

Renal cell carcinoma with sarcomatoid differentiation (sRCC) is associated with poor survival and a heightened response to immune checkpoint inhibitors (ICIs). Two major barriers to improving outcomes for sRCC are the limited understanding of its gene regulatory programs and the low diagnostic yield of tumor biopsies due to spatial heterogeneity. Herein, we characterized the epigenomic landscape of sRCC by profiling 107 epigenomic libraries from tissue and plasma samples from 50 patients with RCC and healthy volunteers. By profiling histone modifications and DNA methylation, we identified highly recurrent epigenomic reprogramming enriched in sRCC. Furthermore, CRISPRa experiments implicated the transcription factor FOSL1 in activating sRCC-associated gene regulatory programs, and FOSL1 expression was associated with the response to ICIs in RCC in two randomized clinical trials. Finally, we established a blood-based diagnostic approach using detectable sRCC epigenomic signatures in patient plasma, providing a framework for discovering epigenomic correlates of tumor histology via liquid biopsy.

First-line Systemic Therapy Following Adjuvant Immunotherapy in Renal Cell Carcinoma: An International Multicenter Study.

BACKGROUND AND OBJECTIVE: Adjuvant pembrolizumab significantly improved overall survival (OS) in renal cell carcinoma (RCC), but real-world data on sequential treatment are scarce. We sought to evaluate the clinical outcomes of first-line (1L) systemic therapy following adjuvant immune oncology (IO)-based regimens. METHODS: A retrospective study including patients with recurrent RCC following adjuvant IO across 29 international institutions was conducted. The primary endpoint was progression-free survival (PFS) on 1L systemic therapy estimated using the Kaplan-Meier method. Preplanned subanalyses of clinical outcomes by type of 1L systemic therapy, recurrence timing, and International Metastatic RCC Database Consortium (IMDC) risk groups were performed. Treatment-related adverse events leading to treatment discontinuation, dose reduction, or corticosteroid use were assessed. KEY FINDINGS AND LIMITATIONS: A total of 94 patients were included. Most received adjuvant pembrolizumab (n = 37, 39%), atezolizumab (n = 28, 30%), or nivolumab + ipilimumab (n = 15, 16%). The cohort included 49 (52%) patients who had recurrence within 3 mo of the last adjuvant IO dose, whereas 45 (48%) recurred beyond 3 mo. Bone metastases were significantly higher in tumors recurring at <3 mo (10/49, 20%) than those recurring at >3 mo (1/45, 2.2%; p = 0.008). Most patients received 1L vascular endothelial growth factor-targeted therapy (VEGF-TT; n = 37, 39%), IO + VEGF-TT (n = 26, 28%), or IO + IO (n = 12, 13%). The remaining underwent local therapy. The median follow-up for the 1L systemic therapy cohort was 15 mo. The 18-mo PFS and OS rates were 45% (95% confidence interval [CI]: 34-60) and 85% (95% CI: 75-95), respectively. Treatment-related adverse events occurred in 32 (42%) patients and included skin toxicity (n = 7, 9.2%), fatigue (n = 6, 7.9%), and diarrhea/colitis (n = 4, 5.3%). Limitations included selecting patients from large academic centers and the short follow-up period. CONCLUSIONS AND CLINICAL IMPLICATIONS: A subset of patients with recurrent RCC following adjuvant IO respond to systemic therapies, including VEGF-TT and IO-based regimens. Notably, patients with favorable-risk disease may derive more benefit from VEGF-TT than from IO therapies in this setting. Future approaches utilizing radiographic tools and biomarker-based liquid biopsies are warranted to detect occult metastatic disease and identify candidate patients for adjuvant IO therapy. PATIENT SUMMARY: Adjuvant pembrolizumab significantly improved overall survival in renal cell carcinoma (RCC). There are limited data on clinical outcomes after the recurrence of RCC tumors following adjuvant immunotherapy. In this study, we find that patients respond to subsequent systemic therapies across different treatment options.

Utilizing Electronic Health Records (EHR) and Tumor Panel Sequencing to Demystify Prognosis of Cancer of Unknown Primary (CUP) patients.

Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its original site and accounts for 3-5% of all cancers. It does not have established targeted therapies, leading to poor outcomes. We developed OncoNPC, a machine learning classifier trained on targeted next-generation sequencing data from 34,567 tumors from three institutions. OncoNPC achieved a weighted F1 score of 0.94 for high confidence predictions on known cancer types (65% of held-out samples). When applied to 971 CUP tumors from patients treated at the Dana-Farber Cancer Institute, OncoNPC identified actionable molecular alterations in 23% of the tumors. Furthermore, OncoNPC identified CUP subtypes with significantly higher polygenic germline risk for the predicted cancer type and significantly different survival outcomes, supporting its validity. Importantly, CUP patients who received first palliative intent treatments concordant with their OncoNPC-predicted cancer sites had significantly better outcomes (H.R. 0.348, 95% C.I. 0.210 - 0.570, p-value 2.32 × 10-5). OncoNPC thus provides evidence of distinct CUP subtypes and offers the potential for clinical decision support for managing patients with CUP.

Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary.

Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its primary site and accounts for 3-5% of all cancers. Established targeted therapies are lacking for CUP, leading to generally poor outcomes. We developed OncoNPC, a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors across 22 cancer types from three institutions. Oncology NGS-based primary cancer-type classifier (OncoNPC) achieved a weighted F1 score of 0.942 for high confidence predictions ([Formula: see text]) on held-out tumor samples, which made up 65.2% of all the held-out samples. When applied to 971 CUP tumors collected at the Dana-Farber Cancer Institute, OncoNPC predicted primary cancer types with high confidence in 41.2% of the tumors. OncoNPC also identified CUP subgroups with significantly higher polygenic germline risk for the predicted cancer types and with significantly different survival outcomes. Notably, patients with CUP who received first palliative intent treatments concordant with their OncoNPC-predicted cancers had significantly better outcomes (hazard ratio (HR) = 0.348; 95% confidence interval (CI) = 0.210-0.570; P = [Formula: see text]). Furthermore, OncoNPC enabled a 2.2-fold increase in patients with CUP who could have received genomically guided therapies. OncoNPC thus provides evidence of distinct CUP subgroups and offers the potential for clinical decision support for managing patients with CUP.

The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer.

We introduce Promoter-Enhancer-Guided Interaction Networks (PENGUIN), a method for studying protein-protein interaction (PPI) networks within enhancer-promoter interactions. PENGUIN integrates H3K27ac-HiChIP data with tissue-specific PPIs to define enhancer-promoter PPI networks (EPINs). We validated PENGUIN using cancer (LNCaP) and benign (LHSAR) prostate cell lines. Our analysis detected EPIN clusters enriched with the architectural protein CTCF, a regulator of enhancer-promoter interactions. CTCF presence was coupled with the prevalence of prostate cancer (PrCa) single nucleotide polymorphisms (SNPs) within the same EPIN clusters, suggesting functional implications in PrCa. Within the EPINs displaying enrichments in both CTCF and PrCa SNPs, we also show enrichment in oncogenes. We substantiated our identified SNPs through CRISPR/Cas9 knockout and RNAi screens experiments. Here we show that PENGUIN provides insights into the intricate interplay between enhancer-promoter interactions and PPI networks, which are crucial for identifying key genes and potential intervention targets. A dedicated server is available at https://penguin.life.bsc.es/ .

A biallelic multiple nucleotide length polymorphism explains functional causality at 5p15.33 prostate cancer risk locus.

To date, single-nucleotide polymorphisms (SNPs) have been the most intensively investigated class of polymorphisms in genome wide associations studies (GWAS), however, other classes such as insertion-deletion or multiple nucleotide length polymorphism (MNLPs) may also confer disease risk. Multiple reports have shown that the 5p15.33 prostate cancer risk region is a particularly strong expression quantitative trait locus (eQTL) for Iroquois Homeobox 4 (IRX4) transcripts. Here, we demonstrate using epigenome and genome editing that a biallelic (21 and 47 base pairs (bp)) MNLP is the causal variant regulating IRX4 transcript levels. In LNCaP prostate cancer cells (homozygous for the 21 bp short allele), a single copy knock-in of the 47 bp long allele potently alters the chromatin state, enabling de novo functional binding of the androgen receptor (AR) associated with increased chromatin accessibility, Histone 3 lysine 27 acetylation (H3K27ac), and ~3-fold upregulation of IRX4 expression. We further show that an MNLP is amongst the strongest candidate susceptibility variants at two additional prostate cancer risk loci. We estimated that at least 5% of prostate cancer risk loci could be explained by functional non-SNP causal variants, which may have broader implications for other cancers GWAS. More generally, our results underscore the importance of investigating other classes of inherited variation as causal mediators of human traits.

Epigenomic charting and functional annotation of risk loci in renal cell carcinoma.

While the mutational and transcriptional landscapes of renal cell carcinoma (RCC) are well-known, the epigenome is poorly understood. We characterize the epigenome of clear cell (ccRCC), papillary (pRCC), and chromophobe RCC (chRCC) by using ChIP-seq, ATAC-Seq, RNA-seq, and SNP arrays. We integrate 153 individual data sets from 42 patients and nominate 50 histology-specific master transcription factors (MTF) to define RCC histologic subtypes, including EPAS1 and ETS-1 in ccRCC, HNF1B in pRCC, and FOXI1 in chRCC. We confirm histology-specific MTFs via immunohistochemistry including a ccRCC-specific TF, BHLHE41. FOXI1 overexpression with knock-down of EPAS1 in the 786-O ccRCC cell line induces transcriptional upregulation of chRCC-specific genes, TFCP2L1, ATP6V0D2, KIT, and INSRR, implicating FOXI1 as a MTF for chRCC. Integrating RCC GWAS risk SNPs with H3K27ac ChIP-seq and ATAC-seq data reveals that risk-variants are significantly enriched in allelically-imbalanced peaks. This epigenomic atlas in primary human samples provides a resource for future investigation.

Nucleosome Patterns in Circulating Tumor DNA Reveal Transcriptional Regulation of Advanced Prostate Cancer Phenotypes.

Advanced prostate cancers comprise distinct phenotypes, but tumor classification remains clinically challenging. Here, we harnessed circulating tumor DNA (ctDNA) to study tumor phenotypes by ascertaining nucleosome positioning patterns associated with transcription regulation. We sequenced plasma ctDNA whole genomes from patient-derived xenografts representing a spectrum of androgen receptor active (ARPC) and neuroendocrine (NEPC) prostate cancers. Nucleosome patterns associated with transcriptional activity were reflected in ctDNA at regions of genes, promoters, histone modifications, transcription factor binding, and accessible chromatin. We identified the activity of key phenotype-defining transcriptional regulators from ctDNA, including AR, ASCL1, HOXB13, HNF4G, and GATA2. To distinguish NEPC and ARPC in patient plasma samples, we developed prediction models that achieved accuracies of 97% for dominant phenotypes and 87% for mixed clinical phenotypes. Although phenotype classification is typically assessed by IHC or transcriptome profiling from tumor biopsies, we demonstrate that ctDNA provides comparable results with diagnostic advantages for precision oncology. SIGNIFICANCE: This study provides insights into the dynamics of nucleosome positioning and gene regulation associated with cancer phenotypes that can be ascertained from ctDNA. New methods for classification in phenotype mixtures extend the utility of ctDNA beyond assessments of somatic DNA alterations with important implications for molecular classification and precision oncology. This article is highlighted in the In This Issue feature, p. 517.

Liquid biopsy epigenomic profiling for cancer subtyping.

Although circulating tumor DNA (ctDNA) assays are increasingly used to inform clinical decisions in cancer care, they have limited ability to identify the transcriptional programs that govern cancer phenotypes and their dynamic changes during the course of disease. To address these limitations, we developed a method for comprehensive epigenomic profiling of cancer from 1 ml of patient plasma. Using an immunoprecipitation-based approach targeting histone modifications and DNA methylation, we measured 1,268 epigenomic profiles in plasma from 433 individuals with one of 15 cancers. Our assay provided a robust proxy for transcriptional activity, allowing us to infer the expression levels of diagnostic markers and drug targets, measure the activity of therapeutically targetable transcription factors and detect epigenetic mechanisms of resistance. This proof-of-concept study in advanced cancers shows how plasma epigenomic profiling has the potential to unlock clinically actionable information that is currently accessible only via direct tissue sampling.

Integrative molecular analyses define correlates of high B7-H3 expression in metastatic castrate-resistant prostate cancer.

B7-H3 (CD276) is an immune checkpoint overexpressed in prostate cancer with minimal expression in normal tissues and associated with poor prognosis, making it an excellent therapy target. We interrogated B7-H3 expression and its regulation in metastatic castration-resistant prostate cancer (mCRPC). We found greater expression of B7-H3 transcript relative to other immunotherapy targets (CTLA-4, PD-L1/2), including in tumors that lacked expression of prostate-specific membrane antigen (PSMA). Enzalutamide-resistant mCRPC cells demonstrated increased amounts of B7-H3, and this was associated with resistance signaling pathways. Using a machine-learning algorithm, the gene network of B7-H3 was strongly correlated with androgen receptor (AR) and AR co-factor (HOXB13, FOXA1) networks. In mCRPC samples, the B7-H3 promoter and distal enhancer regions exhibited enhanced transcriptional activity and were directly bound by AR and its co-factors. Altogether, our study characterizes molecular profiles and epigenetic regulation of B7-H3-expressing mCRPC tumors, which informs optimal precision-oncology approaches for mCRPC patients.

ASCL1 activates neuronal stem cell-like lineage programming through remodeling of the chromatin landscape in prostate cancer.

Treatment with androgen receptor pathway inhibitors (ARPIs) in prostate cancer leads to the emergence of resistant tumors characterized by lineage plasticity and differentiation toward neuroendocrine lineage. Here, we find that ARPIs induce a rapid epigenetic alteration mediated by large-scale chromatin remodeling to support activation of stem/neuronal transcriptional programs. We identify the proneuronal transcription factor ASCL1 motif to be enriched in hyper-accessible regions. ASCL1 acts as a driver of the lineage plastic, neuronal transcriptional program to support treatment resistance and neuroendocrine phenotype. Targeting ASCL1 switches the neuroendocrine lineage back to the luminal epithelial state. This effect is modulated by disruption of the polycomb repressive complex-2 through UHRF1/AMPK axis and change the chromatin architecture in favor of luminal phenotype. Our study provides insights into the epigenetic alterations induced by ARPIs, governed by ASCL1, provides a proof of principle of targeting ASCL1 to reverse neuroendocrine phenotype, support luminal conversion and re-addiction to ARPIs.

HOXB13 suppresses de novo lipogenesis through HDAC3-mediated epigenetic reprogramming in prostate cancer.

HOXB13, a homeodomain transcription factor, critically regulates androgen receptor (AR) activities and androgen-dependent prostate cancer (PCa) growth. However, its functions in AR-independent contexts remain elusive. Here we report HOXB13 interaction with histone deacetylase HDAC3, which is disrupted by the HOXB13 G84E mutation that has been associated with early-onset PCa. Independently of AR, HOXB13 recruits HDAC3 to lipogenic enhancers to catalyze histone deacetylation and suppress lipogenic regulators such as fatty acid synthase. Analysis of human tissues reveals that the HOXB13 gene is hypermethylated and downregulated in approximately 30% of metastatic castration-resistant PCa. HOXB13 loss or G84E mutation leads to lipid accumulation in PCa cells, thereby promoting cell motility and xenograft tumor metastasis, which is mitigated by pharmaceutical inhibition of fatty acid synthase. In summary, we present evidence that HOXB13 recruits HDAC3 to suppress de novo lipogenesis and inhibit tumor metastasis and that lipogenic pathway inhibitors may be useful to treat HOXB13-low PCa.