A Novel Mouse Model of Diffuse Intrinsic Pontine Glioma Initiated in Pax3-Expressing Cells.

Diffuse intrinsic pontine glioma (DIPG) is a rare and incurable brain tumor that arises predominately in children and involves the pons, a structure that along with the midbrain and medulla makes up the brainstem. We have previously developed genetically engineered mouse models of brainstem glioma using the RCAS/Tv-a system by targeting PDGF-B overexpression, p53 loss, and H3.3K27M mutation to Nestin-expressing brainstem progenitor cells of the neonatal mouse. Here we describe a novel mouse model targeting these same genetic alterations to Pax3-expressing cells, which in the neonatal mouse pons consist of a Pax3+/Nestin+/Sox2+ population lining the fourth ventricle and a Pax3+/NeuN+ parenchymal population. Injection of RCAS-PDGF-B into the brainstem of Pax3-Tv-a mice at postnatal day 3 results in 40% of mice developing asymptomatic low-grade glioma. A mixture of low- and high-grade glioma results from injection of Pax3-Tv-a;p53(fl/fl) mice with RCAS-PDGF-B and RCAS-Cre, with or without RCAS-H3.3K27M. These tumors are Ki67+, Nestin+, Olig2+, and largely GFAP- and can arise anywhere within the brainstem, including the classic DIPG location of the ventral pons. Expression of the H3.3K27M mutation reduces overall H3K27me3 as compared with tumors without the mutation, similar to what has been previously shown in human and mouse tumors. Thus, we have generated a novel genetically engineered mouse model of DIPG, which faithfully recapitulates the human disease and represents a novel platform with which to study the biology and treatment of this deadly disease.

Cervical cancer precursors and hormonal contraceptive use in HIV-positive women: application of a causal model and semi-parametric estimation methods.

OBJECTIVE: To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND: Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. METHODS: We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. RESULTS: We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. CONCLUSION: Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased prevalence of CIN2+ associated with COC, evidence is insufficient to conclude causality. Priority areas for future studies to better satisfy causal criteria are identified.

Scaling limits of a model for selection at two scales

The dynamics of a population undergoing selection is a central topic in evolutionary biology. This question is particularly intriguing in the case where selective forces act in opposing directions at two population scales. For example, a fast-replicating virus strain outcompetes slower-replicating strains at the within-host scale. However, if the fast-replicating strain causes host morbidity and is less frequently transmitted, it can be outcompeted by slower-replicating strains at the between-host scale. Here we consider a stochastic ball-and-urn process which models this type of phenomenon. We prove the weak convergence of this process under two natural scalings. The first scaling leads to a deterministic nonlinear integro-partial differential equation on the interval $[0,1]$ with dependence on a single parameter, $\lambda$. We show that the fixed points of this differential equation are Beta distributions and that their stability depends on $\lambda$ and the behavior of the initial data around $1$. The second scaling leads to a measure-valued Fleming-Viot process, an infinite dimensional stochastic process that is frequently associated with a population genetics.