Integrated single-cell analysis defines the epigenetic basis of castration-resistant prostate luminal cells.

Understanding prostate response to castration and androgen receptor signaling inhibitors (ARSI) is critical to improving long-term prostate cancer (PCa) patient survival. Here, we use a multi-omics approach on 229,794 single cells to create a mouse single-cell reference atlas for interpreting mouse prostate biology and castration response. Our reference atlas refines single-cell annotations and provides a chromatin context, which, when coupled with mouse lineage tracing, demonstrates that castration-resistant luminal cells are distinct from the pre-existent urethra-proximal stem/progenitor cells. Molecular pathway analysis and therapeutic studies further implicate AP1 (JUN/FOS), WNT/ß-catenin, FOXQ1, NF-κB, and JAK/STAT pathways as major drivers of castration-resistant luminal populations with relevance to human PCa. Our datasets, which can be explored through an interactive portal (https://visportal.roswellpark.org/data/tang/), can aid in developing combination treatments with ARSI for advanced PCa patients.

Developing Folate-Conjugated miR-34a Therapeutic for Prostate Cancer: Challenges and Promises.

Prostate cancer (PCa) remains a common cancer with high mortality in men due to its heterogeneity and the emergence of drug resistance. A critical factor contributing to its lethality is the presence of prostate cancer stem cells (PCSCs), which can self-renew, long-term propagate tumors, and mediate treatment resistance. MicroRNA-34a (miR-34a) has shown promise as an anti-PCSC therapeutic by targeting critical molecules involved in cancer stem cell (CSC) survival and functions. Despite extensive efforts, the development of miR-34a therapeutics still faces challenges, including non-specific delivery and delivery-associated toxicity. One emerging delivery approach is ligand-mediated conjugation, aiming to achieve specific delivery of miR-34a to cancer cells, thereby enhancing efficacy while minimizing toxicity. Folate-conjugated miR-34a (folate-miR-34a) has demonstrated promising anti-tumor efficacy in breast and lung cancers by targeting folate receptor α (FOLR1). Here, we first show that miR-34a, a TP53 transcriptional target, is reduced in PCa that harbors TP53 loss or mutations and that miR-34a mimic, when transfected into PCa cells, downregulated multiple miR-34a targets and inhibited cell growth. When exploring the therapeutic potential of folate-miR-34a, we found that folate-miR-34a exhibited impressive inhibitory effects on breast, ovarian, and cervical cancer cells but showed minimal effects on and targeted delivery to PCa cells due to a lack of appreciable expression of FOLR1 in PCa cells. Folate-miR-34a also did not display any apparent effect on PCa cells expressing prostate-specific membrane antigen (PMSA) despite the reported folate's binding capability to PSMA. These results highlight challenges in the specific delivery of folate-miR-34a to PCa due to a lack of target (receptor) expression. Our study offers novel insights into the challenges and promises within the field and casts light on the development of ligand-conjugated miR-34a therapeutics for PCa.

Developing folate-conjugated miR-34a therapeutic for prostate cancer treatment: Challenges and promises.

Prostate cancer (PCa) remains a common cancer with high mortality in men due to its heterogeneity and the emergence of drug resistance. A critical factor contributing to its lethality is the presence of prostate cancer stem cells (PCSCs), which can self-renew, long-term propagate tumors and mediate treatment resistance. MicroRNA-34a (miR-34a) has shown promise as an anti-PCSC therapeutic by targeting critical molecules involved in cancer stem cell (CSC) survival and functions. Despite extensive efforts, the development of miR-34a therapeutics still faces challenges, including non-specific delivery and delivery-associated toxicity. One emerging delivery approach is ligand-mediated conjugation, aiming to achieve specific delivery of miR-34a to cancer cells, thereby enhancing efficacy while minimizing toxicity. Folate-conjugated miR-34a (folate-miR-34a) has demonstrated promising anti-tumor efficacy in breast and lung cancers by targeting folate receptor α (FOLR1). Here, we first show that miR-34a, a TP53 transcriptional target, is reduced in PCa that harbors TP53 loss or mutations and that miR-34a mimic, when transfected into PCa cells, downregulated multiple miR-34a targets and inhibited cell growth. When exploring the therapeutic potential of folate-miR-34a, we found that folate-miR-34a exhibited impressive inhibitory effects on breast, ovarian and cervical cancer cells but showed minimal effects on and targeted delivery to PCa cells due to a lack of appreciable expression of FOLR1 in PCa cells. Folate-miR-34a also did not display any apparent effect on PCa cells expressing prostate-specific membrane antigen (PMSA) despite the reported folate's binding capability to PSMA. These results highlight challenges in specific delivery of folate-miR-34a to PCa due to lack of target (receptor) expression. Our study offers novel insights on the challenges and promises within the field and cast light on the development of ligand-conjugated miR-34a therapeutics for PCa.

STAT3/LKB1 controls metastatic prostate cancer by regulating mTORC1/CREB pathway.

Prostate cancer (PCa) is a common and fatal type of cancer in men. Metastatic PCa (mPCa) is a major factor contributing to its lethality, although the mechanisms remain poorly understood. PTEN is one of the most frequently deleted genes in mPCa. Here we show a frequent genomic co-deletion of PTEN and STAT3 in liquid biopsies of patients with mPCa. Loss of Stat3 in a Pten-null mouse prostate model leads to a reduction of LKB1/pAMPK with simultaneous activation of mTOR/CREB, resulting in metastatic disease. However, constitutive activation of Stat3 led to high LKB1/pAMPK levels and suppressed mTORC1/CREB pathway, preventing mPCa development. Metformin, one of the most widely prescribed therapeutics against type 2 diabetes, inhibits mTORC1 in liver and requires LKB1 to mediate glucose homeostasis. We find that metformin treatment of STAT3/AR-expressing PCa xenografts resulted in significantly reduced tumor growth accompanied by diminished mTORC1/CREB, AR and PSA levels. PCa xenografts with deletion of STAT3/AR nearly completely abrogated mTORC1/CREB inhibition mediated by metformin. Moreover, metformin treatment of PCa patients with high Gleason grade and type 2 diabetes resulted in undetectable mTORC1 levels and upregulated STAT3 expression. Furthermore, PCa patients with high CREB expression have worse clinical outcomes and a significantly increased risk of PCa relapse and metastatic recurrence. In summary, we have shown that STAT3 controls mPCa via LKB1/pAMPK/mTORC1/CREB signaling, which we have identified as a promising novel downstream target for the treatment of lethal mPCa.

Integrated single-cell analysis defines the epigenetic basis of castration-resistant prostate luminal cells.

Understanding prostate response to castration and androgen receptor signaling inhibitors (ARSI) is critical to improving long-term prostate cancer (PCa) patient survival. Here we use a multi-omics approach on 229,794 single cells to create a mouse single-cell reference atlas better suited to interpreting mouse prostate biology and castration response. Our reference atlas refines single-cell annotations and provides chromatin context, which, when coupled with mouse lineage tracing demonstrates that the castration-resistant luminal cells are distinct from the pre-existent urethra-proximal stem/progenitor cells. Molecular pathway analysis and therapeutic studies further implicate JUN/FOS, WNT/B-Catenin, FOXQ1, NFkB, and JAK/STAT pathways as the major drivers of castration-resistant luminal populations with high relevance to human PCa. Importantly, we demonstrate the utility of our datasets, which can be explored through an interactive portal (https://visportal.roswellpark.org/data/tang/), to aid in developing novel combination treatments with ARSI for advanced PCa patients.

Evidence for context-dependent functions of KDM5B in prostate development and prostate cancer.

Prostate cancer (PCa) is one of the leading causes of cancer-related deaths worldwide. Prostate tumorigenesis and PCa progression involve numerous genetic as well as epigenetic perturbations. Histone modification represents a fundamental epigenetic mechanism that regulates diverse cellular processes, and H3K4 methylation, one such histone modification associated with active transcription, can be reversed by dedicated histone demethylase KDM5B (JARID1B). Abnormal expression and functions of KDM5B have been implicated in several cancer types including PCa. Consistently, our bioinformatics analysis reveals that the KDM5B mRNA levels are upregulated in PCa compared to benign prostate tissues, and correlate with increased tumor grade and poor patient survival, supporting an oncogenic function of KDM5B in PCa. Surprisingly, however, when we generated prostate-specific conditional Kdm5b knockout mice using probasin (Pb) promoter-driven Cre: loxP system, we observed that Kdm5b deletion did not affect normal prostate development but instead induced mild hyperplasia. These results suggest that KDM5B may possess context-dependent roles in normal prostate development vs. PCa development and progression.

Author Correction: LRIG1 is a pleiotropic androgen receptor-regulated feedback tumor suppressor in prostate cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Intron retention is a hallmark and spliceosome represents a therapeutic vulnerability in aggressive prostate cancer.

The role of dysregulation of mRNA alternative splicing (AS) in the development and progression of solid tumors remains to be defined. Here we describe the first comprehensive AS landscape in the spectrum of human prostate cancer (PCa) evolution. We find that the severity of splicing dysregulation correlates with disease progression and establish intron retention as a hallmark of PCa stemness and aggressiveness. Systematic interrogation of 274 splicing-regulatory genes (SRGs) uncovers prevalent genomic copy number variations (CNVs), leading to mis-expression of ~68% of SRGs during PCa development and progression. Consequently, many SRGs are prognostic. Surprisingly, androgen receptor controls a splicing program distinct from its transcriptional regulation. The spliceosome modulator, E7107, reverses cancer aggressiveness and inhibits castration-resistant PCa (CRPC) in xenograft and autochthonous PCa models. Altogether, our studies establish aberrant AS landscape caused by dysregulated SRGs as a hallmark of PCa aggressiveness and the spliceosome as a therapeutic vulnerability for CRPC.

LRIG1 is a pleiotropic androgen receptor-regulated feedback tumor suppressor in prostate cancer.

LRIG1 has been reported to be a tumor suppressor in gastrointestinal tract and epidermis. However, little is known about the expression, regulation and biological functions of LRIG1 in prostate cancer (PCa). We find that LRIG1 is overexpressed in PCa, but its expression correlates with better patient survival. Functional studies reveal strong tumor-suppressive functions of LRIG1 in both AR+ and AR- xenograft models, and transgenic expression of LRIG1 inhibits tumor development in Hi-Myc and TRAMP models. LRIG1 also inhibits castration-resistant PCa and exhibits therapeutic efficacy in pre-established tumors. We further show that 1) AR directly transactivates LRIG1 through binding to several AR-binding sites in LRIG1 locus, and 2) LRIG1 dampens ERBB expression in a cell type-dependent manner and inhibits ERBB2-driven tumor growth. Collectively, our study indicates that LRIG1 represents a pleiotropic AR-regulated feedback tumor suppressor that functions to restrict oncogenic signaling from AR, Myc, ERBBs, and, likely, other oncogenic drivers.

Enhanced efficacy of sitravatinib in metastatic models of antiangiogenic therapy resistance.

Tyrosine kinase inhibitors (TKIs) that primarily target angiogenesis are approved to treat several cancers in the metastatic setting; however, resistance is common. Sequential treatment or 'switching' from one TKI to another following failure can be effective, but predicting which drugs will have cross-over sensitivity remains a challenge. Here we examined sitravatinib (MGCD516), a spectrum-selective TKI able to block MET, TAM (TYRO3, AXL, MerTK) and multiple receptor families (including PDGFRs, VEGFRs, and Ephs). Transcriptomic analysis of several mouse and human cell lines revealed diverse molecular changes after resistance to two TKIs (sunitinib and axitinib) with multiple sitravatinib targets found to be upregulated. Sitravatinib treatment in vitro resulted in enhanced anti-proliferative effects in resistant cells and was improved compared to TKIs with similar target profiles. In vivo, primary tumor growth inhibition after sitravatinib treatment in mice was enhanced in resistant tumors and metastasis suppression improved when tumors were surgically removed. Together, these results suggest that the diverse and often inconsistent compensatory signaling mechanisms found to contribute to TKI resistance may paradoxically improve the tumor-inhibiting effects of broad-spectrum TKIs such as sitravatinib that are able to block multiple signaling pathways. Sitravatinib in the second-line setting following antiangiogenic TKI treatment may have enhanced inhibitory effects in local and disseminated disease, and improve outcomes in patients with refractory disease.

A Transient Pseudosenescent Secretome Promotes Tumor Growth after Antiangiogenic Therapy Withdrawal.

VEGF receptor tyrosine kinase inhibitors (VEGFR TKIs) approved to treat multiple cancer types can promote metastatic disease in certain limited preclinical settings. Here, we show that stopping VEGFR TKI treatment after resistance can lead to rebound tumor growth that is driven by cellular changes resembling senescence-associated secretory phenotypes (SASPs) known to promote cancer progression. A SASP-mimicking antiangiogenic therapy-induced secretome (ATIS) was found to persist during short withdrawal periods, and blockade of known SASP regulators, including mTOR and IL-6, could blunt rebound effects. Critically, senescence hallmarks ultimately reversed after long drug withdrawal periods, suggesting that the transition to a permanent growth-arrested senescent state was incomplete and the hijacking of SASP machinery ultimately transient. These findings may account for the highly diverse and reversible cytokine changes observed in VEGF inhibitor-treated patients, and suggest senescence-targeted therapies ("senotherapeutics")-particularly those that block SASP regulation-may improve outcomes in patients after VEGFR TKI failure.

Linking prostate cancer cell AR heterogeneity to distinct castration and enzalutamide responses.

Expression of androgen receptor (AR) in prostate cancer (PCa) is heterogeneous but the functional significance of AR heterogeneity remains unclear. Screening ~200 castration-resistant PCa (CRPC) cores and whole-mount sections (from 89 patients) reveals 3 AR expression patterns: nuclear (nuc-AR), mixed nuclear/cytoplasmic (nuc/cyto-AR), and low/no expression (AR-/lo). Xenograft modeling demonstrates that AR+ CRPC is enzalutamide-sensitive but AR-/lo CRPC is resistant. Genome editing-derived AR+ and AR-knockout LNCaP cell clones exhibit distinct biological and tumorigenic properties and contrasting responses to enzalutamide. RNA-Seq and biochemical analyses, coupled with experimental combinatorial therapy, identify BCL-2 as a critical therapeutic target and provide proof-of-concept therapeutic regimens for both AR+/hi and AR-/lo CRPC. Our study links AR expression heterogeneity to distinct castration/enzalutamide responses and has important implications in understanding the cellular basis of prostate tumor responses to AR-targeting therapies and in facilitating development of novel therapeutics to target AR-/lo PCa cells/clones.

Tumor-Independent Host Secretomes Induced By Angiogenesis and Immune-Checkpoint Inhibitors.

The levels of various circulating blood proteins can change in response to cancer therapy. Monitoring therapy-induced secretomes (TIS) may have use as biomarkers for establishing optimal biological effect (such as dosing) or identifying sources of toxicity and drug resistance. Although TIS can derive from tumor cells directly, nontumor "host" treatment responses can also impact systemic secretory programs. For targeted inhibitors of the tumor microenvironment, including antiangiogenic and immune-checkpoint therapies, host TIS could explain unexpected collateral "side effects" of treatment. Here, we describe a comparative transcriptomic and proteomic analysis of host TIS in tissues and plasma from cancer-free mice treated with antibody and receptor tyrosine kinase inhibitors (RTKI) of the VEGF, cMet/ALK, and PD-1 pathways. We found that all cancer therapies elicit TIS independent of tumor growth, with systemic secretory gene change intensity higher in RTKIs compared with antibodies. Our results show that host TIS signatures differ between drug target, drug class, and dose. Notably, protein and gene host TIS signatures were not always predictive for each other, suggesting limitations to transcriptomic-only approaches to clinical biomarker development for circulating proteins. Together, these are the first studies to assess and compare "off-target" host secretory effects of VEGF and PD-1 pathway inhibition that occur independent of tumor stage or tumor response to therapy. Testing treatment impact on normal tissues to establish host-mediated TIS signatures (or "therasomes") may be important for identifying disease agnostic biomarkers to predict benefits (or limitations) of drug combinatory approaches. Mol Cancer Ther; 17(7); 1602-12. ©2018 AACR.

Histone 2B-GFP Label-Retaining Prostate Luminal Cells Possess Progenitor Cell Properties and Are Intrinsically Resistant to Castration.

The existence of slow-cycling luminal cells in the prostate has been suggested, but their identity and functional properties remain unknown. Using a bigenic mouse model to earmark, isolate, and characterize the quiescent stem-like cells, we identify a label-retaining cell (LRC) population in the luminal cell layer as luminal progenitors. Molecular and biological characterizations show that these luminal LRCs are significantly enriched in the mouse proximal prostate, exhibit relative dormancy, display bipotency in both in vitro and in vivo assays, and express a stem/progenitor gene signature with resemblance to aggressive prostate cancer. Importantly, these LRCs, compared with bulk luminal cells, maintain a lower level of androgen receptor (AR) expression and are less androgen dependent and also castration resistant in vivo. Finally, analysis of phenotypic markers reveals heterogeneity within the luminal progenitor cell pool. Our study establishes luminal LRCs as progenitors that may serve as a cellular origin for castration-resistant prostate cancer.

Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach.

Rapid improvements in the detection and tracking of early-stage tumor progression aim to guide decisions regarding cancer treatments as well as predict metastatic recurrence in patients following surgery. Mathematical models may have the potential to further assist in estimating metastatic risk, particularly when paired with in vivo tumor data that faithfully represent all stages of disease progression. Herein, we describe mathematical analysis that uses data from mouse models of spontaneous metastasis developing after surgical removal of orthotopically implanted primary tumors. Both presurgical (primary tumor) growth and postsurgical (metastatic) growth were quantified using bioluminescence and were then used to generate a mathematical formalism based on general laws of the disease (i.e., dissemination and growth). The model was able to fit and predict pre/postsurgical data at the level of the individual as well as the population. Our approach also enabled retrospective analysis of clinical data describing the probability of metastatic relapse as a function of primary tumor size. In these data-based models, interindividual variability was quantified by a key parameter of intrinsic metastatic potential. Critically, our analysis identified a highly nonlinear relationship between primary tumor size and postsurgical survival, suggesting possible threshold limits for the utility of tumor size as a predictor of metastatic recurrence. These findings represent a novel use of clinically relevant models to assess the impact of surgery on metastatic potential and may guide optimal timing of treatments in neoadjuvant (presurgical) and adjuvant (postsurgical) settings to maximize patient benefit.

The Challenges of Modeling Drug Resistance to Antiangiogenic Therapy.

Drug resistance remains an ongoing challenge for the majority of patients treated with inhibitors of the vascular endothelial growth factor (VEGF) pathway, a key regulator of tumor angiogenesis. Preclinical models have played a significant role in identifying multiple complex mechanisms of antiangiogenic treatment failure. Yet questions remain about the optimal methodology to study resistance that may assist in making clinically relevant choices about alternative or combination treatment strategies. The origins of antiangiogenic treatment failure may stem from the tumor vasculature, the tumor itself, or both together, and preclinical methods that define resistance are diverse and rarely compared. We performed a literature search of the preclinical methodologies used to examine resistance to VEGF pathway inhibitors and identified 109 papers from more than 400 that use treatment failure as the starting point for mechanistic study. We found that definitions of resistance are broad and inconsistent, involve only a small number of reagents, and derive mostly from in vitro and in vivo methodologies that often do not represent clinically relevant disease stages or progression. Together, this literature analysis highlights the challenges of studying inhibitors of the tumor microenvironment in the preclinical setting and the need for improved methodology to assist in qualifying (and quantifying) treatment failure to identify mechanisms that will help predict alternative strategies in patients.

Neoadjuvant antiangiogenic therapy reveals contrasts in primary and metastatic tumor efficacy.

Thousands of cancer patients are currently in clinical trials evaluating antiangiogenic therapy in the neoadjuvant setting, which is the treatment of localized primary tumors prior to surgical intervention. The rationale is that shrinking a tumor will improve surgical outcomes and minimize growth of occult micrometastatic disease-thus delaying post-surgical recurrence and improving survival. But approved VEGF pathway inhibitors have not been tested in clinically relevant neoadjuvant models that compare pre- and post-surgical treatment effects. Using mouse models of breast, kidney, and melanoma metastasis, we demonstrate that primary tumor responses to neoadjuvant VEGFR TKI treatment do not consistently correlate with improved post-surgical survival, with survival worsened in certain settings. Similar negative effects did not extend to protein-based VEGF pathway inhibitors and could be reversed with altered dose, surgical timing, and treatment duration, or when VEGFR TKIs are combined with metronomic 'anti-metastatic' chemotherapy regimens. These studies represent the first attempt to recapitulate the complex clinical parameters of neoadjuvant therapy in mice and identify a novel tool to compare systemic antiangiogenic treatment effects on localized and disseminated disease.

Classical mathematical models for description and prediction of experimental tumor growth.

Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: 1) to determine a statistical model for description of the measurement error, 2) to establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) to assess the models' ability to forecast future tumor growth. The models included in the study comprised the exponential, exponential-linear, power law, Gompertz, logistic, generalized logistic, von Bertalanffy and a model with dynamic carrying capacity. For the breast data, the dynamics were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power, with excellent prediction scores (≥80%) extending out as far as 12 days in the future. For the lung data, the Gompertz and power law models provided the most parsimonious and parametrically identifiable description. However, not one of the models was able to achieve a substantial prediction rate (≥70%) beyond the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement. For instance, forecast success rates went from 14.9% to 62.7% when using the power law model to predict the full future tumor growth curves, using just three data points. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic.

Modeling spontaneous metastatic renal cell carcinoma (mRCC) in mice following nephrectomy.

One of the key challenges to improved testing of new experimental therapeutics in renal cell carcinoma (RCC) is the development of models that faithfully recapitulate early- and late-stage metastatic disease progression. Typical tumor implantation models utilize ectopic or orthotopic primary tumor implantation, but few include systemic spontaneous metastatic disease that mimics the clinical setting. This protocol describes the key steps to develop RCC disease progression stages similar to patients. First, it uses a highly metastatic mouse tumor cell line in a syngeneic model to show orthotopic tumor cell implantation. Methods include superficial and internal implantation into the sub-capsular space with cells combined with matrigel to prevent leakage and early spread. Next it describes the procedures for excision of tumor-bearing kidney (nephrectomy), with critical pre- and post- surgical mouse care. Finally, it outlines the steps necessary to monitor and assess micro-and macro-metastatic disease progression, including bioluminescent imaging as well provides a detailed visual necropsy guide to score systemic disease distribution. The goal of this protocol description is to facilitate the widespread use of clinically relevant metastatic RCC models to improve the predictive value of future therapeutic testing.