Citations

Numerical laboratory data at admission have been proposed for enhancement of inpatient predictive modeling from administrative claims. In this study, predictive models for inpatient/30-day postdischarge mortality and for risk-adjusted prolonged length of stay, as a surrogate for severe inpatient complications of care, were designed with administrative data only and with administrative data plus numerical laboratory variables. A comparison of resulting inpatient models for acute myocardial infarction, congestive heart failure, coronary artery bypass grafting, and percutaneous cardiac interventions demonstrated improved discrimination and calibration with administrative data plus laboratory values compared to administrative data only for both mortality and prolonged length of stay. Improved goodness of fit was most apparent in acute myocardial infarction and percutaneous cardiac intervention. The emergence of electronic medical records should make the addition of laboratory variables to administrative data an efficient and practical method to clinically enhance predictive modeling of inpatient outcomes of care.

Predictive modeling for postdischarge outcomes of inpatient care has been suboptimal. This study evaluated whether admission numerical laboratory data added to administrative models from New York and Minnesota hospitals would enhance the prediction accuracy for 90-day postdischarge deaths without readmission (PD-90) and 90-day readmissions (RA-90) following inpatient care for cardiac patients. Risk-adjustment models for the prediction of PD-90 and RA-90 were designed for acute myocardial infarction, percutaneous cardiac intervention, coronary artery bypass grafting, and congestive heart failure. Models were derived from hospital claims data and were then enhanced with admission laboratory predictive results. Case-level discrimination, goodness of fit, and calibration were used to compare administrative models (ADM) and laboratory predictive models (LAB). LAB models for the prediction of PD-90 were modestly enhanced over ADM, but negligible benefit was seen for RA-90. A consistent predictor of PD-90 and RA-90 was prolonged length of stay outliers from the index hospitalization.

Abstract Purpose: PI3K/AKT pathway activation is an important endocrine resistance mechanism in estrogen receptor–positive (ER+) breast cancer. After promising preclinical modeling of MK-2206, an allosteric pan-AKT inhibitor, with either estrogen deprivation or fulvestrant, we conducted a phase I trial in patients with metastatic ER+HER2− breast cancer to determine the recommended phase II treatment dose (RPTD) of MK-2206 when combined with either anastrozole, fulvestrant, or anastrozole/fulvestrant. Experimental Design: ER+ breast cancer cell lines were exposed in vitro to MK-2206 plus estrogen deprivation with or without fulvestrant and monitored for apoptosis. A standard 3+3 design was employed to first determine the maximum tolerated dose (MTD) of MK-2206 plus anastrozole based on cycle 1 toxicity. Each cycle was 28 days. The RPTD was determined on the basis of toxicities observed at MTD level during the first 3 cycles. Subsequent patients received MK-2206, at the RPTD determined above, plus fulvestrant or anastrozole/fulvestrant to define RPTD for these additional regimens. Results: MK-2206 induced apoptosis in parental ER+ but not in long-term estrogen-deprived cell lines, for which fulvestrant was required for apoptosis induction. Thirty-one patients enrolled. The RPTD was defined as MK-2206 150 mg orally weekly with prednisone prophylaxis for each combination. Grade 3 rash was dose limiting. 42% (95% CI, 23%–63%) patients derived clinical benefit without progression within 6 months. Response was not associated with tumor PIK3CA mutation. Conclusions: MK-2206 plus endocrine treatments were tolerable. MK-2206 in combination with anastrozole is being further evaluated in a phase II neoadjuvant trial for newly diagnosed ER+HER2− breast cancer. Clin Cancer Res; 22(11); 2650–8. ©2016 AACR. See related commentary by Jansen et al., p. 2599

Abstract Purpose: There is a clear need to improve risk stratification and to identify novel therapeutic targets in aggressive prostate cancer. The goal of this study was to investigate genes with outlier expression with prognostic association in high-risk prostate cancer patients as potential biomarkers and drug targets. Experimental Design: We interrogated microarray gene expression data from prostatectomy samples from 545 high-risk prostate cancer patients with long-term follow-up (mean 13.4 years). Three independent clinical datasets totaling an additional 545 patients were used for validation. Novel prognostic outlier genes were interrogated for impact on oncogenic phenotypes in vitro using siRNA-based knockdown. Association with clinical outcomes and comparison with existing prognostic instruments was assessed with multivariable models using a prognostic outlier score. Results: Analysis of the discovery cohort identified 20 prognostic outlier genes. Three top prognostic outlier genes were novel prostate cancer genes; NVL, SMC4, or SQLE knockdown reduced migration and/or invasion and outlier expression was significantly associated with poor prognosis. Increased prognostic outlier score was significantly associated with poor prognosis independent of standard clinicopathologic variables. Finally, the prognostic outlier score prognostic association is independent of, and adds to existing genomic and clinical tools for prognostication in prostate cancer (Decipher, the cell-cycle progression signature, and CAPRA-S). Conclusions: To our knowledge, this study represents the first unbiased high-throughput investigation of prognostic outlier genes in prostate cancer and demonstrates the potential biomarker and therapeutic importance of this previously unstudied class of cancer genes. Clin Cancer Res; 22(7); 1777–86. ©2015 AACR.

We previously reported that the cardiomyocyte-specific leucine-rich repeat containing protein (LRRC)10 has critical functions in the mammalian heart. In the present study, we tested the role of LRRC10 in the response of the heart to biomechanical stress by performing transverse aortic constriction on Lrrc10-null ( Lrrc10−/−) mice. Mild pressure overload induced severe cardiac dysfunction and ventricular dilation in Lrrc10−/−mice compared with control mice. In addition to dilation and cardiomyopathy, Lrrc10−/−mice showed a pronounced increase in heart weight with pressure overload stimulation and a more dramatic loss of cardiac ventricular performance, collectively suggesting that the absence of LRRC10 renders the heart more disease prone with greater hypertrophy and structural remodeling, although rates of cardiac fibrosis and myocyte dropout were not different from control mice. Lrrc10−/−cardiomyocytes also exhibited reduced contractility in response to β-adrenergic stimulation, consistent with loss of cardiac ventricular performance after pressure overload. We have previously shown that LRRC10 interacts with actin in the heart. Here, we show that His150of LRRC10 was required for an interaction with actin, and this interaction was reduced after pressure overload, suggesting an integral role for LRRC10 in the response of the heart to mechanical stress. Importantly, these experiments demonstrated that LRRC10 is required to maintain cardiac performance in response to pressure overload and suggest that dysregulated expression or mutation of LRRC10 may greatly sensitize human patients to more severe cardiac disease in conditions such as chronic hypertension or aortic stenosis.

Abstract Background: Prostate cancer molecular subtypes based on ETS gene fusions and SPINK1 were originally identified through distinct gene expression profiles. Such molecular subtypes may have utility in disease stratification and clonality assessment, complementing available purely prognostic tests. Hence, we determined the analytical validity of molecular subtyping and explored clinical associations using global gene expression profiles in a large cohort of PCa. Methods: We analyzed 1,577 patient Affymetrix Human Exon 1.0ST GeneChip expression profiles from 8 radical prostatectomy (RP) cohorts; 5 of them generated as part of the Decipher® platform for the Decipher® discovery or validation. Multi-feature random forest classifiers and outlier analysis were used to define microarray-based molecular subtypes and characterize clinical associations. Results: A random forest (RF) classifier (m-ERG) was trained and validated to predict ERG fusion status using separate subsets of a single-institution RP cohort (total n=407) with known ERG rearrangement status defined by FISH, achieving >95% sensitivity and specificity in the validation subset. The model was then applied to 7 independent RP cohorts to predict ERG rearrangement status. Less frequent rearrangements involving other ETS genes (ETV1, ETV4, ETV5, FLI1) or SPINK1 over-expression were predicted based on gene expression outlier analysis. Across cohorts, 45%, 9% 8% and 38% of PCa were classified as ERG+, ERG—ETS+, ERG—SPINK+, and Triple Negative (ERG—/ETS—/SPINK1—), respectively. Global gene expression analysis shows that the four subtypes could be collapsed into three entities (ERG+, ERG—ETS+ and SPINK+/Triple Negative) based on expression patterns and clinical characteristics similarity. Based on multivariable analysis, ERG+ is significantly associated with lower pre-PSA (p<0.001), lower Gleason score (p<0.001), the presence of extraprostatic extension (p<0.001) and European Americans (p<0.001), but not associated with outcome. ERG—ETS+ is significantly associated with SVI (p=0.01) and ERG—SPINK+ is enriched in African Americans (p<0.001). Conclusions: The Decipher® platform can accurately determine ERG rearrangement status and PCa molecular subtypes. Inclusion of molecular subtyping, such as m-ERG status, may enable additional precision medicine opportunities in prognostic tests and provides insights into the development of novel therapeutic approaches. Citation Format: Mohammed Alshalalfa, Scott A. Tomlins, Nicholas Erho, Kasra Yousefi, Shuang Zhao, Robert B. Den, Adam P. Dicker, Bruce Trock, Angelo DeMarzo, Edward M. Schaeffer, Ashley Ross, Eric A. Klein, Cristina Magi-Galluzzi, Jeffery R. Karnes, Robert B Jenkins, Elai Davicioni, Felix Feng. Molecular and clinical characterization of 1,577 primary prostate cancer tumors reveals novel clinical and biological insights into its subtypes. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-64.

Abstract The aim of this study was to estimate the effect of exposure to secondhand tobacco smoke on the incidence of lung adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA). Data from seven case–control studies participating in the International Lung Cancer Consortium (ILCCO) were pooled, resulting in 625 cases of AIS/MIA and 7,403 controls, of whom 170 cases and 3,035 controls were never smokers. Unconditional logistic regression was used to estimate adjusted ORs (ORadj) and 95% confidence intervals (CI), controlling for age, sex, race, smoking status (ever/never), and pack-years of smoking. Study center was included in the models as a random-effects intercept term. Ever versus never exposure to secondhand tobacco smoke was positively associated with AIS/MIA incidence in all subjects (ORadj = 1.48; 95% CI, 1.14–1.93) and in never smokers (ORadj = 1.45; 95% CI, 1.00–2.12). There was, however, appreciable heterogeneity of ORadj across studies (P = 0.01), and the pooled estimates were largely influenced by one large study (40% of all cases and 30% of all controls). These findings provide weak evidence for an effect of secondhand tobacco smoke exposure on AIS/MIA incidence. Further studies are needed to assess the impact of secondhand tobacco smoke exposure using the newly recommended classification of subtypes of lung adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 24(12); 1902–6. ©2015 AACR.