TGFβ signaling limits lineage plasticity in prostate cancer

A forward genetic screen identifies Sirtuin1 as a driver of neuroendocrine prostate cancer

Although localized prostate cancer is relatively indolent, advanced prostate cancer manifests with aggressive and often lethal variants, including neuroendocrine prostate cancer (NEPC). To identify drivers of aggressive prostate cancer, we leveraged Sleeping Beauty (SB) transposon mutagenesis in a mouse model based on prostate-specific loss-of-function of Pten and Tp53 . Compared with control mice, SB mice developed more aggressive prostate tumors, with increased incidence of metastasis. Notably, a significant percentage of the SB prostate tumors display NEPC phenotypes, and the transcriptomic features of these SB mouse tumors recapitulated those of human NEPC. We identified common SB transposon insertion sites (CIS) and prioritized associated CIS-genes differentially expressed in NEPC versus non-NEPC SB tumors. Integrated analysis of CIS-genes encoding for proteins representing upstream, post-translational modulators of master regulators controlling the transcriptional state of SB -mouse and human NEPC tumors identified sirtuin 1 ( Sirt1 ) as a candidate mechanistic determinant of NEPC. Gain-of-function studies in human prostate cancer cell lines confirmed that SIRT1 promotes NEPC, while its loss-of-function or pharmacological inhibition abrogates NEPC. This integrative analysis is generalizable and can be used to identify novel cancer drivers for other malignancies.

Summary

Using an unbiased forward mutagenesis screen in an autochthonous mouse model, we have investigated mechanistic determinants of aggressive prostate cancer. SIRT1 emerged as a key regulator of neuroendocrine prostate cancer differentiation and a potential target for therapeutic intervention.

Treatment-induced stemness and lineage plasticity in driving prostate cancer therapy resistance

Most human cancers are heterogeneous consisting of cancer cells at different epigenetic and transcriptional states and with distinct phenotypes, functions, and drug sensitivities. This inherent cancer cell heterogeneity contributes to tumor resistance to clinical treatment, especially the molecularly targeted therapies such as tyrosine kinase inhibitors (TKIs) and androgen receptor signaling inhibitors (ARSIs). Therapeutic interventions, in turn, induce lineage plasticity (also called lineage infidelity) in cancer cells that also drives therapy resistance. In this Perspective, we focus our discussions on cancer cell lineage plasticity manifested as treatment-induced switching of epithelial cancer cells to basal/stem-like, mesenchymal, and neural lineages. We employ prostate cancer (PCa) as the prime example to highlight ARSI-induced lineage plasticity during and towards development of castration-resistant PCa (CRPC). We further discuss how the tumor microenvironment (TME) influences therapy-induced lineage plasticity. Finally, we offer an updated summary on the regulators and mechanisms driving cancer cell lineage infidelity, which should be therapeutically targeted to extend the therapeutic window and improve patients' survival.

Prognostic and immunological implications of heterogeneous cell death patterns in prostate cancer

BACKGROUND

Prostate cancer is one of the most common cancers in men with a significant proportion of patients developing biochemical recurrence (BCR) after treatment. Programmed cell death (PCD) mechanisms are known to play critical roles in tumor progression and can potentially serve as prognostic and therapeutic biomarkers in PCa. This study aimed to develop a prognostic signature for BCR in PCa using PCD-related genes.

MATERIALS AND METHODS

We conducted an analysis of 19 different modes of PCD to develop a comprehensive model. Bulk transcriptomic, single-cell transcriptomic, genomic, and clinical data were collected from multiple cohorts, including TCGA-PRAD, GSE58812, METABRIC, GSE21653, and GSE193337. We analyzed the expression and mutations of the 19 PCD modes and constructed, evaluated, and validated the model.

RESULTS

Ten PCD modes were found to be associated with BCR in PCa, with specific PCD patterns exhibited by various cell components within the tumor microenvironment. Through Lasso Cox regression analysis, we established a Programmed Cell Death Index (PCDI) utilizing an 11-gene signature. High PCDI values were validated in five independent datasets and were found to be associated with an increased risk of BCR in PCa patients. Notably, older age and advanced T and N staging were associated with higher PCDI values. By combining PCDI with T staging, we constructed a nomogram with enhanced predictive performance. Additionally, high PCDI values were significantly correlated with decreased drug sensitivity, including drugs such as Docetaxel and Methotrexate. Patients with lower PCDI values demonstrated higher immunophenoscores (IPS), suggesting a potentially higher response rate to immune therapy. Furthermore, PCDI was associated with immune checkpoint genes and key components of the tumor microenvironment, including macrophages, T cells, and NK cells. Finally, clinical specimens validated the differential expression of PCDI-related PCDRGs at both the gene and protein levels.

CONCLUSION

In conclusion, we developed a novel PCD-based prognostic feature that successfully predicted BCR in PCa patients and provided insights into drug sensitivity and potential response to immune therapy. These findings have significant clinical implications for the treatment of PCa.

Prostate cancer epidemiology and prognostic factors in the United States

Objective

Using the latest cohort study of prostate cancer patients, explore the epidemiological trend and prognostic factors, and develop a new nomogram to predict the specific survival rate of prostate cancer patients.

Methods

Patients with prostate cancer diagnosed from January 1, 1975 to December 31, 2019 in the Surveillance, Epidemiology, and End Results Program (SEER) database were extracted by SEER stat software for epidemiological trend analysis. General clinical information and follow-up data were also collected from 105 135 patients with pathologically diagnosed prostate cancer from January 1, 2010 to December 1, 2019. The factors affecting patient-specific survival were analyzed by Cox regression, and the factors with the greatest influence on specific survival were selected by stepwise regression method, and nomogram was constructed. The model was evaluated by calibration plots, ROC curves, Decision Curve Analysis and C-index.

Results

There was no significant change in the age-adjusted incidence of prostate cancer from 1975 to 2019, with an average annual percentage change (AAPC) of 0.45 (95% CI:-0.87~1.80). Among the tumor grade, the most significant increase in the incidence of G2 prostate cancer was observed, with an AAPC of 2.99 (95% CI:1.47~4.54); the most significant decrease in the incidence of G4 prostate cancer was observed, with an AAPC of -10.39 (95% CI:-13.86~-6.77). Among the different tumor stages, the most significant reduction in the incidence of localized prostate cancer was observed with an AAPC of -1.83 (95% CI:-2.76~-0.90). Among different races, the incidence of prostate cancer was significantly reduced in American Indian or Alaska Native and Asian or Pacific Islander, with an AAPC of -3.40 (95% CI:-3.97~-2.82) and -2.74 (95% CI:-4.14~-1.32), respectively. Among the different age groups, the incidence rate was significantly increased in 15-54 and 55-64 age groups with AAPC of 4.03 (95% CI:2.73~5.34) and 2.50 (95% CI:0.96~4.05), respectively, and significantly decreased in ≥85 age group with AAPC of -2.50 (95% CI:-3.43~-1.57). In addition, age, tumor stage, race, PSA and gleason score were found to be independent risk factors affecting prostate cancer patient-specific survival. Age, tumor stage, PSA and gleason score were most strongly associated with prostate cancer patient-specific survival by stepwise regression screening, and nomogram prediction model was constructed using these factors. The Concordance indexes are 0.845 (95% CI:0.818~0.872) and 0.835 (95% CI:0.798~0.872) for the training and validation sets, respectively, and the area under the ROC curves (AUC) at 3, 6, and 9 years was 0.7 or more for both the training and validation set samples. The calibration plots indicated a good agreement between the predicted and actual values of the model.

Conclusions

Although there was no significant change in the overall incidence of prostate cancer in this study, significant changes occurred in the incidence of prostate cancer with different characteristics. In addition, the nomogram prediction model of prostate cancer-specific survival rate constructed based on four factors has a high reference value, which helps physicians to correctly assess the patient-specific survival rate and provides a reference basis for patient diagnosis and prognosis evaluation.

Animal models of bone metastatic prostate cancer

Metastatic disease is a main cause of mortality in prostate cancer and remains to be incurable despite emerging new treatment agents. Development of novel treatment agents are confined within the boundaries of our knowledge of bone metastatic prostate cancer. Exploration into the underlying mechanism of metastatic tumorigenesis and treatment resistance will further expose novel targets for novel treatment agents. Up to date, many of these researches have been conducted with animal models which have served as classical tools that play a pivotal role in understanding the fundamental nature of cancer. The ability to reproduce the natural course of prostate cancer would be of profound value. However, currently available models do not reproduce the entire process of tumorigenesis to bone metastasis and are limited to reproducing small portions of the entire process. Therefore, knowledge of available models and understanding the strengths and weaknesses for each model is key to achieve research objectives. In this article, we take an overview of cell line injection animal models and patient derived xenograft models that have been applied to the research of human prostate cancer bone metastasis.

Computer classification and construction of a novel prognostic signature based on moonlighting genes in prostate cancer

Advanced prostate cancer (PRAD) patients have poor prognosis and rising morbidity despite the ongoing iteration of molecular therapeutic agents. As newly discovered proteins with several functions, Moonlighting proteins have showed an important role in tumor progression but has not been extensively investigated in PRAD. Our study aimed to identify moonlighting-related prognostic biomarkers and prospective PRAD therapy targets. 103 moonlighting genes were gathered from previous literatures. A PRAD classification and multivariate Cox prognostic signature were constructed using dataset from The Cancer Genome Atlas (TCGA). Subsequently, we tested our signature’s potential to predict biochemical failure-free survival (BFFS) using GSE21032, a prostate cancer dataset from Gene Expression Omnibus (GEO). The performance of this signature was demonstrated by Kaplan-Meier (KM), receiver operator characteristic (ROC), areas under ROC curve (AUC), and calibration curves. Additionally, immune infiltration investigation was conducted to determine the impact of these genes on immune system. This signature’s influence on drug susceptibility was examined using CellMiner’s drug database. Both training and validation cohorts demonstrated well predictive capacity of this 9-gene signature for PRAD. The 3-year AUCs for TCGA-PRAD and GSE21032 were 0.802 and 0.60 respectively. It can effectively classify patients into various biochemical recurrence risk groups. These genes were also assessed to be connected with tumor mutation burden (TMB), immune infiltration and therapy. This work created and validated a moonlighting gene signature, revealing fresh perspectives on moonlighting proteins in predicting prognosis and improving treatment of PRAD.

A N7-Methylguanine-Related Gene Signature Applicable for the Prognosis and Microenvironment of Prostate Cancer

Background

Despite the constant iteration of small-molecule inhibitors and immune checkpoint inhibitors, PRAD (prostate adenocarcinoma) patients with distant metastases and biochemical recurrence maintain a poor survival outcome along with an increasing morbidity in recent years. N⁷-Methylguanine, a new-found type of RNA modification, has demonstrated an essential role in tumor progression but has hardly been studied for its effect on prostate carcinoma. The current study aimed to seek m⁷G (N7-methylguanosine) related prognostic biomarkers and potential targets for PRAD treatment.

Methods

42 genes related to m⁷G were collected from former literatures and GSEA (Gene Set Enrichment Analysis) website. Then, RNA-seq (RNA sequencing) and clinical data from TCGA-PRAD (The Cancer Genome Atlas-Prostate) cohort were retrieved to screen the differentially expressed m⁷G genes to further construct a multivariate Cox prognostic model for PRAD. Next, GSE116918, a prostate cancer cohort acquired from GEO (Gene Expression Omnibus) database, was analyzed for the external validation group to assess the ability to predict BFFS (biochemical failure-free survival) of our m⁷G prognostic signature. Kaplan-Meier, ROC (receiver operator characteristic), AUC (areas under ROC curve), and calibration curves were adopted to display the performance of this prognostic signature. In addition, immune infiltration analysis was implemented to evaluate the effect of these m⁷G genes on immunoinfiltrating cells. Correlation with drug susceptibility of the m⁷G signature was also analyzed by matching drug information in CellMiner database.

Results

The m⁷G-related prognostic signature, including three genes (EIF3D, EIF4A1, LARP1) illustrated superior prognostic ability for PRAD in both training and validation cohorts. The 5-year AUC were 0.768 for TCGA-PRAD and 0.608 for GSE116918. It can well distinguish patients into different risk groups of biochemical recurrence (p =1e-04 for TCGA-PRAD and p =0.0186 for GSE116918). Immune infiltration analysis suggested potential regulation of m⁷G genes on neutrophils and dendritic cells in PRAD.

Conclusions

A m⁷G-related prognostic signature was constructed and validated in the current study, giving new sights of m⁷G methylation in predicting the prognostic and improving the treatment of PRAD.

Immunohistochemical and genetic characteristics of HPV-associated endocervical carcinoma with an invasive stratified mucin-producing carcinoma (ISMC) component

Invasive stratified mucin-producing carcinoma (ISMC) is a recently described entity of human papillomavirus (HPV)-associated endocervical adenocarcinoma with phenotypic plasticity and aggressive clinical behavior. To identify the cell of origin of ISMC, we investigated the immunohistochemical expression of cervical epithelial cell markers (CK7, PAX8, CK5/6, p63, and CK17), stemness markers (ALDH1 and Nanog), and epithelial-mesenchymal transition (EMT) markers (Snail, Twist, and E-cadherin) in 10 pure and mixed type ISMCs with at least 10% of ISMC component in the entire tumor, seven usual type endocervical adenocarcinomas (UEAs), and seven squamous cell carcinomas (SCCs). In addition, targeted sequencing was performed in 10 ISMCs. ISMC was significantly associated with larger tumor size (p = 0.011), more frequent lymphovascular invasion and lymph node metastasis (p < 0.001), higher FIGO stage (p = 0.022), and a tendency for worse clinical outcomes (p = 0.056) compared to other HPV-associated subtypes. ISMC showed negative or borderline positivity for PAX8, CK5/6, and p63, which were distinct from UEA and SCC (p < 0.01). Compared to UEA and SCC, ISMC showed higher expression for ALDH1 (p = 0.119 for UEA and p = 0.009 for SCC), Snail (p = 0.036), and Twist (p = 0.119), and tended to show decreased E-cadherin expression (p = 0.083). In next-generation sequencing analysis, ISMC exhibited frequent STK11, MET, FANCA, and PALB2 mutations compared to conventional cervical carcinomas, and genes related to EMT and stemness were frequently altered. EMT-prone and stemness characteristics and peripheral expression of reserve cell and EMT markers of ISMC suggest its cervical reserve cell origin. We recommend PAX8, CK5/6, and p63 as diagnostic triple biomarkers for ISMC. These findings highlight the distinct biological basis of ISMC.

Lineage plasticity in cancer: a shared pathway of therapeutic resistance

Lineage plasticity, the ability of cells to transition from one committed developmental pathway to another, has been proposed as a source of intratumoural heterogeneity and of tumour adaptation to an adverse tumour microenvironment including exposure to targeted anticancer treatments. Tumour cell conversion into a different histological subtype has been associated with a loss of dependency on the original oncogenic driver, leading to therapeutic resistance. A well-known pathway of lineage plasticity in cancer - the histological transformation of adenocarcinomas to aggressive neuroendocrine derivatives - was initially described in lung cancers harbouring an EGFR mutation, and was subsequently reported in multiple other adenocarcinomas, including prostate cancer in the presence of antiandrogens. Squamous transformation is a subsequently identified and less well-characterized pathway of adenocarcinoma escape from suppressive anticancer therapy. The increased practice of tumour re-biopsy upon disease progression has increased the recognition of these mechanisms of resistance and has improved our understanding of the underlying biology. In this Review, we provide an overview of the impact of lineage plasticity on cancer progression and therapy resistance, with a focus on neuroendocrine transformation in lung and prostate tumours. We discuss the current understanding of the molecular drivers of this phenomenon, emerging management strategies and open questions in the field.

From genomics to functions: preclinical mouse models for understanding oncogenic pathways in prostate cancer

Next-generation sequencing has revealed numerous genomic alterations that induce aberrant signaling activities in prostate cancer (PCa). Among them are pathways affecting multiple cancer types, including the PI3K/AKT/mTOR, p53, Rb, Ras/Raf/MAPK, Myc, FGF, and Wnt signaling pathways, as well as ones that are prominent in PCa, including alterations in genes of AR signaling, the ETS family, NKX3.1, and SPOP. Cross talk among the oncogenic pathways can confer PCa resistance to therapy, particularly in advanced tumors, which are castration-resistant or show neuroendocrine features. Various experimental models, such as cancer cell lines, animal models, and patient-derived xenografts and organoids have been utilized to dissect PCa progression mechanisms. Here, we review the current preclinical mouse models for studying the most commonly altered pathways in PCa, with an emphasis on their interplays. We highlight the power of genetically engineered mouse models (GEMMs) in translating genomic discoveries into understanding of the functions of these oncogenic events in vivo. Developing and analyzing PCa mouse models will undoubtedly continue to offer new insights into tumor biology and guide novel rationalized therapy.

TGIF transcription factors repress acetyl CoA metabolic gene expression and promote intestinal tumor growth

In this study, Shah et al. show that Tgifs, which repress gene expression by binding directly to DNA or interacting with transforming growth factor β (TGFβ)-responsive SMADs, promote adenoma growth in the context of mutant Apc (adenomatous polyposis coli). Their findings suggest that Tgifs play an important role in regulating basic energy metabolism in normal cells and that this function of Tgifs is amplified in some cancers.

Tgif1 (thymine–guanine-interacting factor 1) and Tgif2 repress gene expression by binding directly to DNA or interacting with transforming growth factor (TGF) β-responsive SMADs. Tgifs are essential for embryogenesis and may function in tumor progression. By analyzing both gain and loss of Tgif function in a well-established mouse model of intestinal cancer, we show that Tgifs promote adenoma growth in the context of mutant Apc (adenomatous polyposis coli). Despite the tumor-suppressive role of TGFβ signaling, transcriptome profiling of colon tumors suggests minimal effect of Tgifs on the TGFβ pathway. Instead, it appears that Tgifs, which are up-regulated in Apc mutant colon tumors, contribute to reprogramming metabolic gene expression. Integrating gene expression data from colon tumors with other gene expression and chromatin-binding data identifies a set of direct Tgif target genes encoding proteins involved in acetyl CoA and pyruvate metabolism. Analysis of both tumor and nontumor tissues indicates that these genes are targets of Tgif repression in multiple settings, suggesting that this is a core Tgif function. We propose that Tgifs play an important role in regulating basic energy metabolism in normal cells, and that this function of Tgifs is amplified in some cancers.