Extracellular Vesicle Analysis Allows for Identification of Invasive IPMN.

BACKGROUND AND AIMS: Advances in cross-sectional imaging have resulted in increased detection of intraductal papillary mucinous neoplasms (IPMNs), and their management remains controversial. At present, there is no reliable noninvasive method to distinguish between indolent and high risk IPMNs. We performed extracellular vesicle (EV) analysis to identify markers of malignancy in an attempt to better stratify these lesions. METHODS: Using a novel ultrasensitive digital extracellular vesicle screening technique (DEST), we measured putative biomarkers of malignancy (MUC1, MUC2, MUC4, MUC5AC, MUC6, Das-1, STMN1, TSP1, TSP2, EGFR, EpCAM, GPC1, WNT-2, EphA2, S100A4, PSCA, MUC13, ZEB1, PLEC1, HOOK1, PTPN6, and FBN1) in EV from patient-derived cell lines and then on circulating EV obtained from peripheral blood drawn from patients with IPMNs. We enrolled a total of 133 patients in two separate cohorts: a clinical discovery cohort (n = 86) and a validation cohort (n = 47). RESULTS: From 16 validated EV proteins in plasma samples collected from the discovery cohort, only MUC5AC showed significantly higher levels in high-grade lesions. Of the 11 patients with invasive IPMN (inv/HG), 9 had high MUC5AC expression in plasma EV of the 11 patients with high-grade dysplasia alone, only 1 had high MUC5AC expression (sensitivity of 82%, specificity of 100%). These findings were corroborated in a separate validation cohort. The addition of MUC5AC as a biomarker to imaging and high-riskstigmata allowed detection of all cases requiring surgery, whereas imaging and high-risk stigmata alone would have missed 5 of 14 cases (36%). CONCLUSIONS: MUC5AC in circulating EV can predict the presence of invasive carcinoma within IPMN. This approach has the potential to improve the management and follow-up of patients with IPMN including avoiding unnecessary surgery.

Missing data and prediction: the pattern submodel.

Missing data are a common problem for both the construction and implementation of a prediction algorithm. Pattern submodels (PS)-a set of submodels for every missing data pattern that are fit using only data from that pattern-are a computationally efficient remedy for handling missing data at both stages. Here, we show that PS (i) retain their predictive accuracy even when the missing data mechanism is not missing at random (MAR) and (ii) yield an algorithm that is the most predictive among all standard missing data strategies. Specifically, we show that the expected loss of a forecasting algorithm is minimized when each pattern-specific loss is minimized. Simulations and a re-analysis of the SUPPORT study confirms that PS generally outperforms zero-imputation, mean-imputation, complete-case analysis, complete-case submodels, and even multiple imputation (MI). The degree of improvement is highly dependent on the missingness mechanism and the effect size of missing predictors. When the data are MAR, MI can yield comparable forecasting performance but generally requires a larger computational cost. We also show that predictions from the PS approach are equivalent to the limiting predictions for a MI procedure that is dependent on missingness indicators (the MIMI model). The focus of this article is on out-of-sample prediction; implications for model inference are only briefly explored.