Search

SignificanceTriple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Despite extensive cancer genome-sequencing efforts, there is still an incomplete understanding of the genetic networks driving TNBC. Here, we usedSleeping Beautytransposon mutagenesis to identify genes that cooperate with mutantPtenin the induction of TNBC. We identified 12 candidate TNBC trunk drivers and a larger number of progression genes. Subsequent functional validation studies identified eight human TNBC tumor suppressor genes, including the GATA-like transcriptional repressorTRPS1, which was shown to inhibit lung metastasis by deregulating the expression of multiple serpin and epithelial-to-mesenchymal transition (EMT) pathway genes. Our study provides a better understanding of the genetic forces driving TNBC and discovered genes with clinical importance in TNBC.

With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention.We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak.We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore.Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore's dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response.Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue.Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369-1375; http://dx.doi.org/10.1289/ehp.1509981.