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Hypertension is a common toxicity induced by bevacizumab and other antiangiogenic drugs. There are no biomarkers to predict the risk of bevacizumab-induced hypertension. This study aimed to identify plasma proteins related to the function of the vasculature to predict the risk of severe bevacizumab-induced hypertension. Using pretreated plasma samples from 398 bevacizumab-treated patients in two clinical trials (CALGB 80303 and 90401), the levels of 17 proteins were measured via ELISA. The association between proteins and grade 3 bevacizumab-induced hypertension was performed by calculating the odds ratio (OR) from logistic regression adjusting for age, sex, and clinical trial. Using the optimal cut-point of each protein, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for hypertension were estimated. Five proteins showed no difference in levels between clinical trials and were used for analyses. Lower levels of angiopoietin-2 (p = 0.0013, OR 3.41, 95% CI 1.67–7.55), VEGF-A (p = 0.0008, OR 4.25, 95% CI 1.93–10.72), and VCAM-1 (p = 0.0067, OR 2.68, 95% CI 1.34–5.63) were associated with an increased risk of grade 3 hypertension. The multivariable model suggests independent effects of angiopoietin-2 (p = 0.0111, OR 2.71, 95% CI 1.29–6.10), VEGF-A (p = 0.0051, OR 3.66, 95% CI 1.54–9.73), and VCAM-1 (p = 0.0308, OR 2.27, 95% CI 1.10–4.92). The presence of low levels of 2–3 proteins had an OR of 10.06 (95% CI 3.92–34.18, p = 1.80 × 10–5) for the risk of hypertension, with sensitivity of 89.7%, specificity of 53.5%, PPV of 17.3%, and NPV of 97.9%. This is the first study providing evidence of plasma proteins with potential value to predict patients at risk of developing bevacizumab-induced hypertension. Clinical trial registration: ClinicalTrials.gov Identifier: NCT00088894 (CALGB 80303); and NCT00110214 (CALGB 90401).

Objective: The liver is the second most common site of distant metastasis from breast cancer that is usually associated with poor prognosis and low quality of life in breast cancer patients. Therefore, the primary diagnosis of liver metastatic lesions in breast cancer patients is very important. In this study, the ability of biochemical markers CA153, CEA, and ALP to be used for prognostic liver metastasis in women with breast cancer was investigated. Methods: 306 women with breast cancer recorded between 2008 and 2012 were included. Serum concentrations of alkaline phosphatase (ALP), carcinogenicity antigen (CEA), cancer antigen (CA-153), age, menopausal status, histologic type, tumor size and number of cancerous axillary lymph nodes in two groups of breast cancer women with liver metastases and without it were studied. To identify independent liver metastasis prognostic factors, logistic regression method was applied. Results: The independent prognostic factors of liver metastases in women with breast cancer are ALP, CEA, age, menopausal status, number of cancerous axillary lymph nodes and tumor size. Sensitivity and specificity analysis showed that CEA with a cutoff value of 1.1 was the most accurate predictive factor. Conclusion: The increase in the levels of CEA and ALP can be diagnostic markers for liver metastases from breast cancer.

The Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.

Background Common and complex traits are the consequence of the interaction and regulation of multiple genes simultaneously, therefore characterizing the interconnectivity of genes is essential to unravel the underlying biological networks. However, the focus of many studies is on the differential expression of individual genes or on co-expression analysis. Methods Going beyond analysis of one gene at a time, we systematically integrated transcriptomics, genotypes and Hi-C data to identify interconnectivities among individual genes as a causal network. We utilized different machine learning techniques to extract information from the network and identify differential regulatory pattern between cases and controls. We used data from the Allen Brain Atlas for replication. Results Employing the integrative systems approach on the data from CommonMind Consortium showed that gene transcription is controlled by genetic variants proximal to the gene (cis-regulatory factors), and transcribed distal genes (trans-regulatory factors). We identified differential gene regulatory patterns in SCZ-cases versus controls and novel SCZ-associated genes that may play roles in the disorder since some of them are primary expressed in human brain. In addition, we observed genes known associated with SCZ are not likely (OR = 0.59) to have high impacts (degree > 3) on the network. Conclusions Causal networks could reveal underlying patterns and the role of genes individually and as a group. Establishing principles that govern relationships between genes provides a mechanistic understanding of the dysregulated gene transcription patterns in SCZ and creates more efficient experimental designs for further studies. This information cannot be obtained by studying a single gene at the time.

Background: Benson’s relaxation (BR) technique is a suitable non-pharmacological approach to reduce preoperative anxiety (PA). Objectives: This study aimed to investigate the effect of BR therapy on PA and the induction and maintenance dose of propofol during cataract surgery (CS). Methods: Seventy-two patients were randomly divided into two experiments or BR and control groups. The Amsterdam and Spielberger State-Trait Anxiety inventory (STAI) scores were used to assess PA directly two days and a half-hour before the CS. The control group did not receive any preoperation intervention or relaxation. Benson’s relaxation method was performed three times, each time for 20 minutes, including two days before surgery, a night before surgery, and an hour before the surgery in the presence of a researcher by an audio file. The induction and maintenance dose of anesthetic drug was recorded and compared between the two groups. Results: The mean propofol consumption was significantly reduced during the induction of anesthesia in the intervention group compared to the control group (0.99 ± 0.29 versus 1.29 ± 0.49; P = 0.005) as well as the maintenance of anesthesia (84.66 ± 17.98 versus 108.33 ± 34.38, P = 0.001). The results of the post-intervention Amsterdam anxiety score showed a significant decrease in the intervention group compared to the control group (P = 0.032, F = 9.61, Eta2 = 0.12). The control group showed a higher Spielberger state score compared to the intervention group as well as the Spielberger trait (P < 0.001, F = 14.78, Eta2 = 0.18). Conclusions: The BR method effectively reduces the level of PA in patients undergoing CS. Moreover, it reduces the need for anesthetic drug, propofol, during surgery.

3515 Background: CALGB/SWOG 80405 was a randomized phase III trial in first-line mCRC patients treated with Bev, Cet, or both, plus chemotherapy. No difference in OS was found between Bev and Cet. We tested the effect of immune signatures on OS in all the three arms of the study and analyzed differences in OS between the Cet and Bev arms. Methods: 578 primary tumors were profiled by RNAseq. Immune signatures of TGF-β, cytotoxic T cells, wound healing, macrophages, lymphocytes, and INF-γ, as well as relative frequencies of CD8+ T-cells, memory resting CD4+ T cells, memory activated CD4+ T cells, macrophages M1 and M2, and activated mast cells were measured. Multivariate Cox proportional hazard models were applied using elastic-net penalization with covariates (age, race, gender, all RAS and BRAF V600E mutations). For relevant signatures, optimal cut-offs for OS were calculated. Results: In all the three arms of the study, high expression of macrophages M2 (HR 6.81, 95% CI 3.56-30.16) and TGF-β (HR 1.37, 95% CI 1.03-2.10) conferred reduced OS compared to low expression; high expression of plasma cells (HR 0.52, 95% CI 0.27-0.83) and memory-activated CD4+ T cells (HR 0.34, 95% CI 0.10-0.65) conferred increased OS compared to low expression. Using optimal cut-offs from these 4 signatures, pts have been categorized as to whether they had either 4, 3, 2, 1, or 0 beneficial signatures associated with increased OS. In all arms of the study (N = 469, after accounting for covariates), the median (95% CI) OS decreased from 42.5 (35.8-47.8; N = 79), to 31.0 (28.8-34.4; N = 177), 25.2 (20.6-27.9; N = 144), and 17.0 (13.5-20.4; N = 69) months when the number of beneficial signatures decreased from 4, to 3, 2, and 0-1 (combined due to a low number of pts), respectively (p = 3.48e-11). In the Bev arm (N = 205), high expression of macrophages M2 conferred reduced OS compared to low expression (HR 6.6, 95% CI 2.7-67.1). In the Cet arm (N = 165), high expression of macrophages M2 conferred reduced OS compared to low expression (HR 4.3, 95% CI 2.1-79.8); high expression of plasma cells (HR 0.36, 95% CI 0.06-0.55) and memory activated CD4+ T cells (HR 0.37, 95% CI 0.03-0.98) conferred increased OS compared to low expression of either signatures. The plasma cell signature interacted with Bev and Cet on the OS of pts (interaction p = 0.009). Conclusions: Tumor immune signatures in mCRC pts are determinants of survival. In pts treated with Bev- and Cet-combination therapies that are standard of care, immune signatures affect response to therapy. These results, provide new markers for treatment selection and for the development of novel active combinations including immune checkpoint inhibitors. Support: U10CA180821, U10CA180882, U24CA196171; https://acknowledgments.alliancefound.org

Heart failure is a major cause for premature death. Given the heterogeneity of the heart failure syndrome, identifying genetic determinants of cardiac function and structure may provide greater insights into heart failure. Despite progress in understanding the genetic basis of heart failure through genome wide association studies, the heritability of heart failure is not well understood. Gaining further insights into mechanisms that contribute to heart failure requires systematic approaches that go beyond single trait analysis. We integrated a Bayesian multi-trait approach and a Bayesian networks for the analysis of 10 correlated traits of cardiac structure and function measured across 3387 individuals with whole exome sequence data. While using single-trait based approaches did not find any significant genetic variant, applying the integrative Bayesian multi-trait approach, we identified 3 novel variants located in genes, RGS3, CHD3, and MRPL38 with significant impact on the cardiac traits such as left ventricular volume index, parasternal long axis interventricular septum thickness, and mean left ventricular wall thickness. Among these, the rare variant NC_000009.11:g.116346115C > A (rs144636307) in RGS3 showed pleiotropic effect on left ventricular mass index, left ventricular volume index and maximal left atrial anterior-posterior diameter while RGS3 can inhibit TGF-beta signaling associated with left ventricle dilation and systolic dysfunction.

Pre-diabetes is a strong risk factor for type 2diabetes (T2D). The aim of this study was to explore factors associated with normal glucose maintenance and pre-diabetes prevention or delay. Data of 1016 first-degree relatives of T2D patients were retrieved from the Isfahan Diabetes Prevention Study (IDPS). Association of various variables including nutrients, serum tests and physical activity with the risk of pre-diabetes was assessed using recurrent events approach. Cumulative incidence of diabetes was 8.17, 9.44, and 4.91% for total sample and individuals with and without pre-diabetes experience in the follow-up. Risk of progression to pre-diabetes was higher in women and older people (p

Background: Using data from different health centers can provide more accurate knowledge of the survival prognostic factors and their effect on the patient’s survival. In this multicenter study, we aimed to investigate the role of prognostic factors on breast cancer survival with large data set. Methods: This historical cohort study was carried out using data from 1785 participants with breast cancer. Data were gathered from medical records of patients referring to 4 breast cancer research centers in Tehran, Iran, between 1997 and 2013. Age at diagnosis (year), size of the tumor, involve lymph nodes, tumor grade, type of surgery, auxiliary treatment of chemotherapy, radiotherapy, recurrence, and metastasis were the prognosis factors considered in this study. A shared frailty model with a gamma distribution for frailty term was used. Results: The median follow-up period was 29.71 months with the interquartile range of 19 to 61 months. During the follow-up period, 337 (18.9%) patients died from breast cancer and 1448 (81.1%) survived. The 1-, 3-, 5-, and 10-year survival rates were 96%, 84%, 76%, and 58%, respectively. In the Cox model by centers, in Center A, the type of surgery, number of nodes involved, and the grade 3 tumor; in center B, age, radiotherapy, metastasis, and between 1 and 3 involved nodes; in center C, age, radiotherapy, recurrence, metastasis, tumor size, and grade 3 tumor; and in center D, chemotherapy, metastasis, and lymph nodes involved were significant. Shared frailty model showed that type of surgery, number of lymph nodes involved, metastasis, radiotherapy, and the tumor grade are the prognostic factors survival in breast cancer. The frailty variance was significant, and it affirmed there was significant variability between centers. Conclusions: This study showed it is necessary to consider the frailty term in modeling multicenter survival studies and confirmed the importance of early diagnosis of cancer before the involvement of lymph nodes and the onset of metastasis and timely treatment could lead to longer life and increased quality of life for patients.

Objective: Bone is the most common site of metastasis in breast cancer. Prognostic factors for predicting bone metastases in breast cancer are controversial yet. In this study, we investigated clinical factors associated with secondary bone metastasis of breast cancer. Methods: In total, 1690 patients with breast cancer recorded between 2002 and 2012 in Motamed Cancer Institute, Tehran, Iran entered in the retrospective study. We studied age, menopausal status, histologic type, tumor size, number of cancerous axillary lymph nodes, serum concentrations of alkaline phosphatase (ALP), carcinogenicity antigen (CEA), cancer antigen (CA)-153, and hemoglobin (HB) in 2 groups with bone metastases (n = 123) and without it, respectively. We applied logistic regression to identify bone metastasis prognostic factors in breast cancer patients and calculated the cut-off value, sensitivity, and characteristics of independent prognostic factors using receiver operating characteristic (ROC) curve analysis. Results: Menopause, larger tumor size, and the greater number of cancerous axillary lymph nodes increased the chance of bone metastases significantly ( P .05). Logistic regression showed that age (odds ratio (OR) = 1.021), menopausal status (OR = 1.854), number of cancerous axillary lymph nodes (OR = 1.065), a tumor size between 2 and 5 cm diameter (OR = 2.002) and more than 5 cm diameter (OR = 4.009), and ALP (OR = 1.005) are independent prognostic factors associated with bone metastases. The ROC curve showed that the abovementioned factors have comparable predictive accuracy for bone metastases. Conclusions: Age, menopausal status, number of axillary lymph node metastases, tumor size, and ALP were identified as prognostic factors for bone metastasis in patients with breast cancer. So patients with these characteristics should be monitored more precisely with regular follow-ups.