The Untapped Potential of Genetically Engineered Mouse Models in Chemoprevention Research: Opportunities and Challenges

The past decade has witnessed the unveiling of a powerful new generation of genetically-engineered mouse (GEM) models of human cancer, which are proving to be highly effective for elucidating cancer mechanisms and interrogating novel experimental therapeutics. This new generation of GEM models are well-suited for chemoprevention research, particularly for investigating progressive stages of carcinogenesis, identifying biomarkers for early detection and intervention, and pre-clinical assessment of novel agents or combinations of agents. Here we discuss opportunities and challenges for the application of GEM models in prevention research, as well as strategies to maximize their relevance for human cancer

BRAF Activation Initiates but Does Not Maintain Invasive Prostate Adenocarcinoma

Prostate cancer is the second leading cause of cancer-related deaths in men. Activation of MAP kinase signaling pathway has been implicated in advanced and androgen-independent prostate cancers, although formal genetic proof has been lacking. In the course of modeling malignant melanoma in a tyrosinase promoter transgenic system, we developed a genetically-engineered mouse (GEM) model of invasive prostate cancers, whereby an activating mutation of BRAFV600E–a mutation found in ∼10% of human prostate tumors–was targeted to the epithelial compartment of the prostate gland on the background of Ink4a/Arf deficiency. These GEM mice developed prostate gland hyperplasia with progression to rapidly growing invasive adenocarcinoma without evidence of AKT activation, providing genetic proof that activation of MAP kinase signaling is sufficient to drive prostate tumorigenesis. Importantly, genetic extinction of BRAFV600E in established prostate tumors did not lead to tumor regression, indicating that while sufficient to initiate development of invasive prostate adenocarcinoma, BRAFV600E is not required for its maintenance

DNA-PKcs-Mediated Transcriptional Regulation Drives Prostate Cancer Progression and Metastasis.

Emerging evidence demonstrates that the DNA repair kinase DNA-PKcs exerts divergent roles in transcriptional regulation of unsolved consequence. Here, in vitro and in vivo interrogation demonstrate that DNA-PKcs functions as a selective modulator of transcriptional networks that induce cell migration, invasion, and metastasis. Accordingly, suppression of DNA-PKcs inhibits tumor metastases. Clinical assessment revealed that DNA-PKcs is significantly elevated in advanced disease and independently predicts for metastases, recurrence, and reduced overall survival. Further investigation demonstrated that DNA-PKcs in advanced tumors is highly activated, independent of DNA damage indicators. Combined, these findings reveal unexpected DNA-PKcs functions, identify DNA-PKcs as a potent driver of tumor progression and metastases, and nominate DNA-PKcs as a therapeutic target for advanced malignancies

The Msx1 Homeoprotein Recruits G9a Methyltransferase to Repressed Target Genes in Myoblast Cells

Although the significance of lysine modifications of core histones for regulating gene expression is widely appreciated, the mechanisms by which these modifications are incorporated at specific regulatory elements during cellular differentiation remains largely unknown. In our previous studies, we have shown that in developing myoblasts the Msx1 homeoprotein represses gene expression by influencing the modification status of chromatin at its target genes. We now show that genomic binding by Msx1 promotes enrichment of the H3K9me2 mark on repressed target genes via recruitment of G9a histone methyltransferase, the enzyme responsible for catalyzing this histone mark. Interaction of Msx1 with G9a is mediated via the homeodomain and is required for transcriptional repression and regulation of cellular differentiation, as well as enrichment of the H3K9me2 mark in proximity to Msx1 binding sites on repressed target genes in myoblast cells as well as the developing limb. We propose that regulation of chromatin status by Msx1 recruitment of G9a and other histone modifying enzymes to regulatory regions of target genes represents an important means of regulating the gene expression during development

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Homeobox genes and cancer New OCTaves for an old tune

AbstractIn this issue of Cancer Cell, Gidekel et al. demonstrate that Oct-4, a member of the POU class of homeobox genes, is a critical player in the genesis of testicular germ cell tumors. This study provides further evidence that deregulated expression of homeobox genes, which occurs in many solid tumors, is functionally relevant for carcinogenesis and highlights unique features that distinguish homeobox genes from other cancer-promoting genes

Working model.

<p>As described in the text, we have proposed that binding of the Msx1 homeoprotein to specific target genes brings G9a and/or Ezh2 to the regulatory regions of these genes to influence histone modifications. According G9a and Ezh2 bound status, the Msx1 bound and down-regulated target genes were categorized in 4 categories. (A) Category I, Msx1 brings G9a and Ezh2 to the same site on target genes. (B) Category II, Msx1 brings G9a and Ezh2 to the same target genes but at different sites. (C) Category III, Msx1 only brings Ezh2 to the Msx1 bound site on target genes. (D) Category IV, Msx1 do not brings ether G9a or Ezh2 to the target genes, but Msx1 may brings other factors to Msx1 bound site to repress target genes expression.</p

Msx1 genomic binding associated with enrichment of the H3K9me2 repressive mark in the developing limb.

<p>ChIP-qPCR analyses show relative levels of H3K9me2 at Msx1 genomic binding sites in <i>Msx1; Msx2</i> conditional mutant versus wild-type limb (13.5 <i>dpc</i>). ChIP data are expressed as relative enrichment of the H3K9me2 mark normalized to input. <i>(inset)</i> ChIP data expressed as fold enrichment in wild-type embryonic limb versus <i>Msx1</i>; <i>Msx2</i> conditional mutant embryonic limb (and normalized to input). The * indicate the following: ***<i>P</i><0.0001, **<i>P</i><0.001, *<i>P</i><0.01.</p