Co-clinical Analysis of a Genetically Engineered Mouse Model and Human Prostate Cancer Reveals Significance of NKX3.1 Expression for Response to 5α-reductase Inhibition

Metformin Overcomes the Consequences of NKX3.1 Loss to Suppress Prostate Cancer Progression

BACKGROUND

The antidiabetic drug metformin has known anticancer effects related to its antioxidant activity; however, its clinical benefit for prostate cancer (PCa) has thus far been inconclusive. Here, we investigate whether the efficacy of metformin in PCa is related to the expression status of NKX3.1, a prostate-specific homeobox gene that functions in mitochondria to protect the prostate from aberrant oxidative stress.

OBJECTIVE

To investigate the relationship of NKX3.1 expression and metformin efficacy in PCa.

DESIGN, SETTING, AND PARTICIPANTS

Functional studies were performed in vivo and in vitro in genetically engineered mouse models and human LNCaP cells, and organotypic cultures having normal or reduced/absent levels of NKX3.1. Correlative studies were performed using two independent retrospective tissue microarray cohorts of radical prostatectomies and a retrospective cohort of prostate biopsies from patients on active surveillance.

INTERVENTION

Metformin was administered before or after the induction of oxidative stress by treatment with paraquat.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Functional endpoints included analyses of histopathology, tumorigenicity, and mitochondrial function. Correlative endpoints include Kaplan-Meier curves and Cox proportional hazard regression models.

RESULTS AND LIMITATIONS

Metformin reversed the adverse consequences of NKX3.1 deficiency following oxidative stress in vivo and in vitro, as evident by reduced tumorigenicity and restored mitochondrial function. Patients with low NKX3.1 expression showed a significant clinical benefit from taking metformin.

CONCLUSIONS

Metformin can overcome the adverse consequences of NKX3.1 loss for PCa progression by protecting against oxidative stress and promoting normal mitochondrial function. These functional activities and clinical correlates were observed only with low NKX3.1 expression. Thus, the clinical benefit of metformin in PCa may depend on the status of NKX3.1 expression.

PATIENT SUMMARY

Prostate cancer patients with low NKX3.1 are likely to benefit most from metformin treatment to delay disease progression in a precision interception paradigm.

lncRNA STAT4-AS1 Inhibited TH17 Cell Differentiation by Targeting RORγt Protein

Objective

From our previous study, we obtained long noncoding RNA (lncRNA) STAT4-AS1, which is related to asthma through high-throughput screening. However, we could not determine the specific mechanism involved and in response to this. We further designed this study.

Results

First, we found that lncRNA STAT4-AS1 was downregulated in T cells from patients with asthma when compared to healthy controls. Next, we confirmed that lncRNA STAT4-AS1 was significantly negatively correlated with T helper 17 (TH17) differentiation in vitro experiments. The decreases of STAT4-AS1 promoted TH17 differentiation, while the increases of STAT4-AS1 inhibited TH17 differentiation. Subsequently, through RNA pull-down, RNA-binding protein immunoprecipitation (RIP), and dual luciferase reporter assay, we found that STAT4-AS1 could inhibit the binding of retinoid-related orphan receptor-γt (RORγt) protein with an IL-17A promoter after binding with RORγt protein. Fluorescence in situ hybridization (FISH) and nuclear-cytoplasmic separation assay showed that STAT4-AS1 is bonded to RORγt in the cytoplasm, preventing RORγt from entering the nucleus.

Conclusion

Overall, STAT4-AS1 directly targets RORγt protein, inhibits the mutual binding of RORγt and IL-17 gene promoter, and eventually inhibits TH17 differentiation. To this end, STAT4-AS1 as a potential target may confer applications in the clinical treatment and diagnosis of TH17-related diseases.

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer

Background

In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design.

Objective

In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches in light of their application to therapeutic response modeling in cancer.

Conclusion

We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

Exploring liver cancer biology through functional genetic screens

As the fourth leading cause of cancer-related death in the world, liver cancer poses a major threat to human health. Although a growing number of therapies have been approved for the treatment of hepatocellular carcinoma in the past few years, most of them only provide a limited survival benefit. Therefore, an urgent need exists to identify novel targetable vulnerabilities and powerful drug combinations for the treatment of liver cancer. The advent of functional genetic screening has contributed to the advancement of liver cancer biology, uncovering many novel genes involved in tumorigenesis and cancer progression in a high-throughput manner. In addition, this unbiased screening platform also provides an efficient tool for the exploration of the mechanisms involved in therapy resistance as well as identifying potential targets for therapy. In this Review, we describe how functional screens can help to deepen our understanding of liver cancer and guide the development of new therapeutic strategies.

NKX3.1 Localization to Mitochondria Suppresses Prostate Cancer Initiation

Mitochondria provide the first line of defense against the tumor-promoting effects of oxidative stress. Here we show that the prostate-specific homeoprotein NKX3.1 suppresses prostate cancer initiation by protecting mitochondria from oxidative stress. Integrating analyses of genetically engineered mouse models, human prostate cancer cells, and human prostate cancer organotypic cultures, we find that, in response to oxidative stress, NKX3.1 is imported to mitochondria via the chaperone protein HSPA9, where it regulates transcription of mitochondrial-encoded electron transport chain (ETC) genes, thereby restoring oxidative phosphorylation and preventing cancer initiation. Germline polymorphisms of NKX3.1 associated with increased cancer risk fail to protect from oxidative stress or suppress tumorigenicity. Low expression levels of NKX3.1 combined with low expression of mitochondrial ETC genes are associated with adverse clinical outcome, whereas high levels of mitochondrial NKX3.1 protein are associated with favorable outcome. This work reveals an extranuclear role for NKX3.1 in suppression of prostate cancer by protecting mitochondrial function. SIGNIFICANCE: Our findings uncover a nonnuclear function for NKX3.1 that is a key mechanism for suppression of prostate cancer. Analyses of the expression levels and subcellular localization of NKX3.1 in patients at risk of cancer progression may improve risk assessment in a precision prevention paradigm, particularly for men undergoing active surveillance.See related commentary by Finch and Baena, p. 2132.This article is highlighted in the In This Issue feature, p. 2113.

Co-Clinical Trials: An Innovative Drug Development Platform for Cholangiocarcinoma

Cholangiocarcinoma (CCA), a group of malignancies that originate from the biliary tract, is associated with a high mortality rate and a concerning increase in worldwide incidence. In Thailand, where the incidence of CCA is the highest, the socioeconomic burden is severe. Yet, treatment options are limited, with surgical resection being the only form of treatment with curative intent. The current standard-of-care remains adjuvant and palliative chemotherapy which is ineffective in most patients. The overall survival rate is dismal, even after surgical resection and the tumor heterogeneity further complicates treatment. Together, this makes CCA a significant burden in Southeast Asia. For effective management of CCA, treatment must be tailored to each patient, individually, for which an assortment of targeted therapies must be available. Despite the increasing numbers of clinical studies in CCA, targeted therapy drugs rarely get approved for clinical use. In this review, we discuss the shortcomings of the conventional clinical trial process and propose the implementation of a novel concept, co-clinical trials to expedite drug development for CCA patients. In co-clinical trials, the preclinical studies and clinical trials are conducted simultaneously, thus enabling real-time data integration to accurately stratify and customize treatment for patients, individually. Hence, co-clinical trials are expected to improve the outcomes of clinical trials and consequently, encourage the approval of targeted therapy drugs. The increased availability of targeted therapy drugs for treatment is expected to facilitate the application of precision medicine in CCA.

pathCHEMO, a generalizable computational framework uncovers molecular pathways of chemoresistance in lung adenocarcinoma

Despite recent advances in discovering a wide array of novel chemotherapy agents, identification of patients with poor and favorable chemotherapy response prior to treatment administration remains a major challenge in clinical oncology. To tackle this challenge, we present a generalizable genome-wide computational framework pathCHEMO that uncovers interplay between transcriptomic and epigenomic mechanisms altered in biological pathways that govern chemotherapy response in cancer patients. Our approach is tested on patients with lung adenocarcinoma who received adjuvant standard-of-care doublet chemotherapy (i.e., carboplatin-paclitaxel), identifying seven molecular pathway markers of primary treatment response and demonstrating their ability to predict patients at risk of carboplatin-paclitaxel resistance in an independent patient cohort (log-rank p-value = 0.008, HR = 10). Furthermore, we extend our method to additional chemotherapy-regimens and cancer types to demonstrate its accuracy and generalizability. We propose that our model can be utilized to prioritize patients for specific chemotherapy-regimens as a part of treatment planning.

Nusrat Epsi et al. present pathCHEMO, a computational framework for uncovering transcriptomic and epigenomic pathways of chemoresistance in cancer that has the potential to improve clinical decision-making. They apply pathCHEMO to lung adenocarcinoma data from public databases, and identify seven molecular pathways implicated in carboplatin-paclitaxel resistance.

Patient-Derived Xenograft Models of Breast Cancer and Their Application

Recently, patient-derived xenograft (PDX) models of many types of tumors including breast cancer have emerged as a powerful tool for predicting drug efficacy and for understanding tumor characteristics. PDXs are established by the direct transfer of human tumors into highly immunodeficient mice and then maintained by passaging from mouse to mouse. The ability of PDX models to maintain the original features of patient tumors and to reflect drug sensitivity has greatly improved both basic and clinical study outcomes. However, current PDX models cannot completely predict drug efficacy because they do not recapitulate the tumor microenvironment of origin, a failure which puts emphasis on the necessity for the development of the next generation PDX models. In this article, we summarize the advantages and limitations of current PDX models and discuss the future directions of this field.

Genetically Engineered Mouse Models of Prostate Cancer in the Postgenomic Era

Recent genomic sequencing analyses have unveiled the spectrum of genomic alterations that occur in primary and advanced prostate cancer, raising the question of whether the corresponding genes are functionally relevant for prostate tumorigenesis, and whether such functions are associated with particular disease stages. In this review, we describe genetically engineered mouse models (GEMMs) of prostate cancer, focusing on those that model genomic alterations known to occur in human prostate cancer. We consider whether the phenotypes of GEMMs based on gain or loss of function of the relevant genes provide reliable counterparts to study the predicted consequences of the corresponding genomic alterations as occur in human prostate cancer, and we discuss exceptions in which the GEMMs do not fully emulate the expected phenotypes. Last, we highlight future directions for the generation of new GEMMs of prostate cancer and consider how we can use GEMMs most effectively to decipher the biological and molecular mechanisms of disease progression, as well as to tackle clinically relevant questions.

Cooperation of loss of NKX3.1 and inflammation in prostate cancer initiation

Although it is known that inflammation plays a critical role in prostate tumorigenesis, the underlying processes are not well understood. Based on analysis of genetically engineered mouse models combined with correlative analysis of expression profiling data from human prostate tumors, we demonstrate a reciprocal relationship between inflammation and the status of the NKX3.1 homeobox gene associated with prostate cancer initiation. We find that cancer initiation in aged Nkx3.1 mutant mice correlates with enrichment of specific immune populations and increased expression of immunoregulatory genes. Furthermore, expression of these immunoregulatory genes is similarly increased in human prostate tumors having low levels of NKX3.1 expression. We further show that induction of prostatitis in Nkx3.1 mutant mice accelerates prostate cancer initiation, which is coincident with aberrant cellular plasticity and differentiation. Correspondingly, human prostate tumors having low levels of NKX3.1 have de-regulated expression of genes associated with these cellular processes. We propose that loss of function of NKX3.1 accelerates inflammation-driven prostate cancer initiation potentially via aberrant cellular plasticity and impairment of cellular differentiation.This article has an associated First Person interview with the first author of the paper.

Preventing clinical progression and need for treatment in patients on active surveillance for prostate cancer

PURPOSE OF REVIEW

Active surveillance is an established treatment option for men with localized, low-risk prostate cancer (CaP). It entails the postponement of immediate therapy with the option of delayed intervention upon disease progression. The rate of clinical progression and need for treatment on active surveillance is approximately 50% over 15 years. The present review summarizes recent data on current methods, attempting to prevent clinical progression.

RECENT FINDINGS

Patient selection for active surveillance is the first mandatory step required to lower progression. Adherence to active surveillance protocols is critical in making sure patients are monitored well and treated early when progression occurs. Before active surveillance allocation and during active surveillance follow-up, methods involving multiparametric MRI, prostate specific antigen derivatives, biopsy factors, urinary, tissue and genetic markers can be used to prevent clinical progression and/or identify those at risk for progression. Medications such as 5α-reductase inhibitors and others might inhibit disease progression in patients on active surveillance.

SUMMARY

Active surveillance is required because of overdiagnosis, along with our inability to accurately predict individual CaP behavior. Several methods can potentially reduce the risk of CaP progression in patients with active surveillance. However, a measure of uncertainty and fear of progression will always accompany patients with active surveillance and the physicians treating them.