Structured testing of genetic association with mixed clinical outcomes.

Genetic factors play a fundamental role in disease development. Studying the genetic association with clinical outcomes is critical for understanding disease biology and devising novel treatment targets. However, the frequencies of genetic variations are often low, making it difficult to examine the variants one-by-one. Moreover, the clinical outcomes are complex, including patients' survival time and other binary or continuous outcomes such as recurrences and lymph node count, and how to effectively analyze genetic association with these outcomes remains unclear. In this article, we proposed a structured test statistic for testing genetic association with mixed types of survival, binary, and continuous outcomes. The structured testing incorporates known biological information of variants while allowing for their heterogeneous effects and is a powerful strategy for analyzing infrequent genetic factors. Simulation studies show that the proposed test statistic has correct type I error and is highly effective in detecting significant genetic variants. We applied our approach to a uterine corpus endometrial carcinoma study and identified several genetic pathways associated with the clinical outcomes.

TCRpred: incorporating T-cell receptor repertoire for clinical outcome prediction.

T-cell receptor (TCR) plays critical roles in recognizing antigen peptides and mediating adaptive immune response against disease. High-throughput technologies have enabled the sequencing of TCR repertoire at the single nucleotide level, allowing researchers to characterize TCR sequences with high resolutions. The TCR sequences provide important information about patients' adaptive immune system, and have the potential to improve clinical outcome prediction. However, it is challenging to incorporate the TCR repertoire data for prediction, because the data is unstructured, highly complex, and TCR sequences vary widely in their compositions and abundances across different individuals. We introduce TCRpred, an analytic tool for incorporating TCR repertoire for clinical outcome prediction. The TCRpred is able to utilize features that can be extracted from the TCR amino acid sequences, as well as features that are hidden in the TCR amino acid sequences and are hard to extract. Simulation studies show that the proposed approach has a good performance in predicting clinical outcome and tends to be more powerful than potential alternative approaches. We apply the TCRpred to real cancer datasets and demonstrate its practical utility in clinical outcome prediction.

Dietary Factors Differ Between Young-Onset and Older-Onset Colorectal Cancer Patients.

We aimed to evaluate differences in dietary factors between young-onset (diagnosed at ages <50) and older-onset colorectal cancer (CRC). CRC patients diagnosed from 1998 to 2018 reported to the Puget Sound Surveillance, Epidemiology, and End Results registry were recruited using mail and telephone. Consented patients completed questionnaires assessing demographics, medical history, and CRC risk factors, including dietary factors. We used multi-variable logistic regression to calculate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) comparing dietary intake in young-onset vs. older-onset CRC. Analyses included 1,087 young- and 2,554 older-onset CRC patients. Compared to older-onset CRC, young-onset CRC patients had lower intake of vegetables (OR for highest intake vs. lowest = 0.59 CI: 0.55, 0.64) and fruit (OR for highest intake vs. lowest = 0.94 CI: 0.88, 0.99) and higher intake of processed meat (OR for highest intake vs. lowest = 1.82 CI: 1.11, 2.99) and spicy food (OR for highest intake vs. lowest = 1.69 CI: 1.09, 2.61). There was no statistically significant difference between young- and older-onset CRC patients for red meat consumption. Dietary patterns differed between young- and older-onset CRC; young-onset CRC patients had lower intake of vegetables and fruit and higher intakes of processed meat and spicy food.

Estimation for the bivariate quantile varying coefficient model with application to diffusion tensor imaging data analysis.

Despite interest in the joint modeling of multiple functional responses such as diffusion properties in neuroimaging, robust statistical methods appropriate for this task are lacking. To address this need, we propose a varying coefficient quantile regression model able to handle bivariate functional responses. Our work supports innovative insights into biomedical data by modeling the joint distribution of functional variables over their domains and across clinical covariates. We propose an estimation procedure based on the alternating direction method of multipliers and propagation separation algorithms to estimate varying coefficients using a B-spline basis and an $L_2$ smoothness penalty that encourages interpretability. A simulation study and an application to a real-world neurodevelopmental data set demonstrates the performance of our model and the insights provided by modeling functional fractional anisotropy and mean diffusivity jointly and their association with gestational age and sex.

Alpha-Momorcharin Inhibits Proinflammatory Cytokine Expression by M1 Macrophages but Not Anti-Inflammatory Cytokine Expression by M2 Macrophages.

Background: Alpha-momorcharin (α-MMC) is a natural medicine derived from bitter melon and has been found to exert immunomodulatory effects. Our previous study indicated that α-MMC can regulate cytokine release from monocytes, but it remains unknown about its regulatory effect on different types of cytokines, such as inflammatory cytokines or anti-inflammatory cytokines. Methods: LPS-induced M1-type macrophages model and IL-4-induced M2-type macrophages model were established, and the expression of proinflammatory cytokines and anti-inflammatory cytokines were assessed by ELISA after α-MMC was administered. Then, a LPS-induced acute pneumonia mouse model was established, the proinflammatory cytokines levels and inflammatory lesions in lung tissues were examined by ELISA or H&E staining. Furthermore, omics screening analysis and Western blotting verification were performed on TLR4 and JAK1-STAT6 signalling pathway-related proteins to elucidate the regulatory mechanism of α-MMC in those M1 macrophages and M2 macrophages. Results: At a noncytotoxic dose of 0.3 µg/mL, α-MMC significantly inhibited the LPS-induced expression of inflammatory cytokines, such as TNF-α, IL-1ß, IL-6, IL-8, MIP-1α and MCP-1, by M1 macrophages in a time-dependent manner, but α-MMC did not inhibit the IL-4-induced synthesis of anti-inflammatory cytokines, such as IL-10, IL-1RA, EGF, VEGF, TGF-ß and CCL22, by M2 macrophages. Moreover, α-MMC also inhibited inflammatory cytokine expression in an LPS-induced acute pneumonia mouse model and alleviated inflammation in lung tissues. Furthermore, omics screening and Western blotting analysis confirmed that α-MMC inhibited TAK1/p-TAK1 and subsequently blocked the downstream MAPK and NF-κB pathways, thus inhibiting the LPS-induced inflammatory cytokine expression. Conclusion: Our results reveal that α-MMC inhibits proinflammatory cytokine expression by M1 macrophages but not anti-inflammatory cytokine expression by M2 macrophages. The efficacy of α-MMC in selectively inhibiting proinflammatory cytokine expression renders it particularly suitable for the treatment of severe inflammation and autoimmune diseases characterized by cytokine storms.

Combining FIB-4 and Liver Stiffness Into the FIB-5, a Single Model that Accurately Predicts Complications of Portal Hypertension.

INTRODUCTION: We aimed to combine the fibrosis (FIB)-4 score and fibroscan-derived liver stiffness (LS) into a single score (FIB-5) that predicts incident complications of portal hypertension (PH) in persons with compensated liver disease. METHODS: In this retrospective cohort study, we identified 5849 US veterans who underwent LS measurement from May 01, 2014 to June 30, 2019, and laboratory tests enabling FIB-4 calculation within 6 months of LS measurement. Patients were followed up from the LS measurement date until February 05, 2020, for incident complications of PH. We combined LS values and the individual components of the FIB-4 score (i.e. age, aspartate aminotransferase, alanine aminotransferase, and platelet count) using multivariable Cox proportional hazards modeling and the machine learning algorithm eXtreme gradient boosting to develop the C-FIB-5 and X-FIB-5 models, respectively. Models were internally validated using optimism-corrected measures. RESULTS: Among 5,849 patients, the mean age was 62.8 years, 95.9% were men, and the mean follow-up time was 2.14 ± 1.21 years. Within 3 years after LS measurement date, 116 (2.0%) patients developed complications of PH. The X-FIB-5 (area under the receiver operating characteristic [AUROC] 0.845) and C-FIB-5 scores (AUROC 0.868) demonstrated superior discrimination over LS (AUROC 0.688) and FIB-4 (AUROC 0.672) for predicting incident complications of PH. Both the X-FIB-5 and C-FIB-5 models demonstrated higher classification accuracy across all sensitivity cutoffs when compared with LS or FIB-4 alone. DISCUSSION: We combined LS and the individual components of the FIB-4 into a single scoring system (FIB-5, www.fib5.net ), which can help identify patients with compensated liver disease at risk of developing complications of PH.

TCR-L: an analysis tool for evaluating the association between the T-cell receptor repertoire and clinical phenotypes.

BACKGROUND: T cell receptors (TCRs) play critical roles in adaptive immune responses, and recent advances in genome technology have made it possible to examine the T cell receptor (TCR) repertoire at the individual sequence level. The analysis of the TCR repertoire with respect to clinical phenotypes can yield novel insights into the etiology and progression of immune-mediated diseases. However, methods for association analysis of the TCR repertoire have not been well developed. METHODS: We introduce an analysis tool, TCR-L, for evaluating the association between the TCR repertoire and disease outcomes. Our approach is developed under a mixed effect modeling, where the fixed effect represents features that can be explicitly extracted from TCR sequences while the random effect represents features that are hidden in TCR sequences and are difficult to be extracted. Statistical tests are developed to examine the two types of effects independently, and then the p values are combined. RESULTS: Simulation studies demonstrate that (1) the proposed approach can control the type I error well; and (2) the power of the proposed approach is greater than approaches that consider fixed effect only or random effect only. The analysis of real data from a skin cutaneous melanoma study identifies an association between the TCR repertoire and the short/long-term survival of patients. CONCLUSION: The TCR-L can accommodate features that can be extracted as well as features that are hidden in TCR sequences. TCR-L provides a powerful approach for identifying association between TCR repertoire and disease outcomes.

A Phase 1/2 Trial Combining Avelumab and Trabectedin for Advanced Liposarcoma and Leiomyosarcoma.

PURPOSE: Leiomyosarcoma and liposarcoma frequently express PD-L1 but are generally resistant to PD-1/PD-L1 inhibition (immune checkpoint inhibitor). Trabectedin is FDA approved for leiomyosarcoma and liposarcoma. This study aimed to evaluate the safety and efficacy of trabectedin with anti-PD-L1 antibody avelumab in patients with advanced leiomyosarcoma and liposarcoma. PATIENTS AND METHODS: A single-arm, open-label, Phase 1/2 study tested avelumab with trabectedin for advanced leiomyosarcoma and liposarcoma. The phase I portion evaluated safety and feasibility of trabectedin (1, 1.2, and 1.5 mg/m2) with avelumab at standard dosing. Primary endpoint of the phase II portion was objective response rate (ORR) by RECIST 1.1. Correlative studies included T-cell receptor sequencing (TCRseq), multiplex IHC, and tumor gene expression. RESULTS: 33 patients were evaluable: 24 with leiomyosarcoma (6 uterine and 18 non-uterine) and 11 with liposarcoma. In Phase 1, dose-limiting toxicities (DLT) were observed in 2 of 6 patients at both trabectedin 1.2 and 1.5 mg/m2. The recommended Phase 2 dose (RP2D) was 1.0 mg/m2 trabectedin and 800-mg avelumab. Of 23 patients evaluable at RP2D, 3 (13%) had partial response (PR) and 10 (43%) had stable disease (SD) as best response. Six-month PFS was 52%; median PFS was 8.3 months. Patients with PR had higher Simpson Clonality score on TCRseq from peripheral blood mononuclear cells versus those with SD (0.182 vs. 0.067, P = 0.02) or progressive disease (0.182 vs. 0.064, P = 0.01). CONCLUSIONS: Although the trial did not meet the primary objective response rate endpoint, PFS compared favorably with prior studies of trabectedin warranting further investigation.

Immunologic Gene Signature Analysis Correlates Myeloid Cells and M2 Macrophages with Time to Trabectedin Failure in Sarcoma Patients.

Patients with metastatic soft tissue sarcoma (STS) have a poor prognosis and few available systemic treatment options. Trabectedin is currently being investigated as a potential adjunct to immunotherapy as it has been previously shown to kill tumor-associated macrophages. In this retrospective study, we sought to identify biomarkers that would be relevant to trials combining trabectedin with immunotherapy. We performed a single-center retrospective study of sarcoma patients treated with trabectedin with long-term follow-up. Multiplex gene expression analysis using the NanoString platform was assessed, and an exploratory analysis using the lasso-penalized Cox regression and kernel association test for survival (MiRKAT-S) methods investigated tumor-associated immune cells and correlated their gene signatures to patient survival. In total, 147 sarcoma patients treated with trabectedin were analyzed, with a mean follow-up time of 5 years. Patients with fewer prior chemotherapy regimens were more likely to stay on trabectedin longer (pairwise correlation = -0.17, p = 0.04). At 5 years, increased PD-L1 expression corresponded to worse outcomes (HR = 1.87, p = 0.04, q = 0.199). Additionally, six immunologic gene signatures were associated with up to 7-year survival by MiRKAT-S, notably myeloid-derived suppressor cells (p = 0.023, q = 0.058) and M2 macrophages (p = 0.03, q = 0.058). We found that the number of chemotherapy regimens prior to trabectedin negatively correlated with the number of trabectedin cycles received, suggesting that patients may benefit from receiving trabectedin earlier in their therapy course. The correlation of trabectedin outcomes with immune cell infiltrates supports the hypothesis that trabectedin may function as an immune modulator and supports ongoing efforts to study trabectedin in combination with immunotherapy. Furthermore, tumors with an immunosuppressive microenvironment characterized by macrophage infiltration and high PD-L1 expression were less likely to benefit from trabectedin, which could guide clinicians in future treatment decisions.

Statistical Inference for High-Dimensional Pathway Analysis with Multiple Responses.

Pathway analysis, i.e., grouping analysis, has important applications in genomic studies. Existing pathway analysis approaches are mostly focused on a single response and are not suitable for analyzing complex diseases that are often related with multiple response variables. Although a handful of approaches have been developed for multiple responses, these methods are mainly designed for pathways with a moderate number of features. A multi-response pathway analysis approach that is able to conduct statistical inference when the dimension is potentially higher than sample size is introduced. Asymptotical properties of the test statistic are established and theoretical investigation of the statistical power is conducted. Simulation studies and real data analysis show that the proposed approach performs well in identifying important pathways that influence multiple expression quantitative trait loci (eQTL).

Neoadjuvant Therapy Induces a Potent Immune Response to Sarcoma, Dominated by Myeloid and B Cells.

PURPOSE: To characterize changes in the soft-tissue sarcoma (STS) tumor immune microenvironment induced by standard neoadjuvant therapy with the goal of informing neoadjuvant immunotherapy trial design. EXPERIMENTAL DESIGN: Paired pre- and postneoadjuvant therapy specimens were retrospectively identified for 32 patients with STSs and analyzed by three modalities: multiplexed IHC, NanoString, and RNA sequencing with ImmunoPrism analysis. RESULTS: All 32 patients, representing a variety of STS histologic subtypes, received neoadjuvant radiotherapy and 21 (66%) received chemotherapy prior to radiotherapy. The most prevalent immune cells in the tumor before neoadjuvant therapy were myeloid cells (45% of all immune cells) and B cells (37%), with T (13%) and natural killer (NK) cells (5%) also present. Neoadjuvant therapy significantly increased the total immune cells infiltrating the tumors across all histologic subtypes for patients receiving neoadjuvant radiotherapy with or without chemotherapy. An increase in the percentage of monocytes and macrophages, particularly M2 macrophages, B cells, and CD4+ T cells was observed postneoadjuvant therapy. Upregulation of genes and cytokines associated with antigen presentation was also observed, and a favorable pathologic response (≥90% necrosis postneoadjuvant therapy) was associated with an increase in monocytic infiltrate. Upregulation of the T-cell checkpoint TIM3 and downregulation of OX40 were observed posttreatment. CONCLUSIONS: Standard neoadjuvant therapy induces both immunostimulatory and immunosuppressive effects within a complex sarcoma microenvironment dominated by myeloid and B cells. This work informs ongoing efforts to incorporate immune checkpoint inhibitors and novel immunotherapies into the neoadjuvant setting for STSs.

Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types.

Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types, we integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six biomarkers, our approach can easily discriminate any individual cancer type vs. normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can simultaneously multi-classify common cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions. This detection system provides a promising practical framework for automatic cancer screening at population level. Key points: (1) We developed a practical cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify cancers vs. normal with >96% AUC. (3) In total, 26 individual cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple cancer types simultaneously with >82% accuracy.

Home Spirometry Telemonitoring for Early Detection of Bronchiolitis Obliterans Syndrome in Patients with Chronic Graft-versus-Host Disease.

Early detection of bronchiolitis obliterans syndrome (BOS) after allogeneic hematopoietic cell transplantation (HCT) depends on recognition of subclinical spirometric changes, which is possible only with frequent interval spirometry. We evaluated the feasibility of home monitoring of weekly spirometry via a wireless handheld device and a web monitoring portal in a cohort of high-risk patients for the detection of lung function changes preceding BOS diagnosis. In this observational study, 46 patients with chronic graft-versus-host disease or a decline in forced expiratory volume in 1 second (FEV1) of unclear etiology after allogeneic HCT were enrolled to perform weekly home spirometry with a wireless portable spirometer for a period of 1 year. Measurements were transmitted wirelessly to a Cloud-based monitoring portal. Feasibility evaluation included adherence with study procedures and an assessment of the home spirometry measurements compared with laboratory pulmonary function tests. Thirty-six patients (78%) completed 1 year of weekly monitoring. Overall adherence with weekly home spirometry measurements was 72% (interquartile range, 47% to 90%), which did not meet the predetermined threshold of 75% for high adherence. Correlation of home FEV1 with laboratory FEV1 was high, with a bias of 0.123 L (lower limit, -0.294 L; upper limit, 0.541 L), which is within acceptable limits for reliability. Of the 12 patients who were diagnosed with BOS or suspected BOS during the study period, 9 had an antecedent FEV1 decline detected by home spirometry. Our data indicate that wireless handheld spirometry performed at home in a high-risk HCT cohort is feasible for close monitoring of pulmonary function and appears to facilitate early detection of BOS.

A method for subtype analysis with somatic mutations.

MOTIVATION: Cancer is a highly heterogeneous disease, and virtually all types of cancer have subtypes. Understanding the association between cancer subtypes and genetic variations is fundamental to the development of targeted therapies for patients. Somatic mutation plays important roles in tumor development and has emerged as a new type of genetic variations for studying the association with cancer subtypes. However, the low prevalence of individual mutations poses a tremendous challenge to the related statistical analysis. RESULTS: In this article, we propose an approach, subtype analysis with somatic mutations (SASOM), for the association analysis of cancer subtypes with somatic mutations. Our approach tests the association between a set of somatic mutations (from a genetic pathway) and subtypes, while incorporating functional information of the mutations into the analysis. We further propose a robust p-value combination procedure, DAPC, to synthesize statistical significance from different sources. Simulation studies show that the proposed approach has correct type I error and tends to be more powerful than possible alternative methods. In a real data application, we examine the somatic mutations from a cutaneous melanoma dataset, and identify a genetic pathway that is associated with immune-related subtypes. AVAILABILITY AND IMPLEMENTATION: The SASOM R package is available at https://github.com/rksyouyou/SASOM-pkg. R scripts and data are available at https://github.com/rksyouyou/SASOM-analysis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Germline variation in the insulin-like growth factor pathway and risk of Barrett's esophagus and esophageal adenocarcinoma.

Genome-wide association studies (GWAS) of esophageal adenocarcinoma (EAC) and its precursor, Barrett's esophagus (BE), have uncovered significant genetic components of risk, but most heritability remains unexplained. Targeted assessment of genetic variation in biologically relevant pathways using novel analytical approaches may identify missed susceptibility signals. Central obesity, a key BE/EAC risk factor, is linked to systemic inflammation, altered hormonal signaling and insulin-like growth factor (IGF) axis dysfunction. Here, we assessed IGF-related genetic variation and risk of BE and EAC. Principal component analysis was employed to evaluate pathway-level and gene-level associations with BE/EAC, using genotypes for 270 single-nucleotide polymorphisms (SNPs) in or near 12 IGF-related genes, ascertained from 3295 BE cases, 2515 EAC cases and 3207 controls in the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON) GWAS. Gene-level signals were assessed using Multi-marker Analysis of GenoMic Annotation (MAGMA) and SNP summary statistics from BEACON and an expanded GWAS meta-analysis (6167 BE cases, 4112 EAC cases, 17 159 controls). Global variation in the IGF pathway was associated with risk of BE (P = 0.0015). Gene-level associations with BE were observed for GHR (growth hormone receptor; P = 0.00046, false discovery rate q = 0.0056) and IGF1R (IGF1 receptor; P = 0.0090, q = 0.0542). These gene-level signals remained significant at q < 0.1 when assessed using data from the largest available BE/EAC GWAS meta-analysis. No significant associations were observed for EAC. This study represents the most comprehensive evaluation to date of inherited genetic variation in the IGF pathway and BE/EAC risk, providing novel evidence that variation in two genes encoding cell-surface receptors, GHR and IGF1R, may influence risk of BE.

Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.

Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.

A general framework for functionally informed set-based analysis: Application to a large-scale colorectal cancer study.

Genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants associated with various phenotypes, but together they explain only a fraction of heritability, suggesting many variants have yet to be discovered. Recently it has been recognized that incorporating functional information of genetic variants can improve power for identifying novel loci. For example, S-PrediXcan and TWAS tested the association of predicted gene expression with phenotypes based on GWAS summary statistics by leveraging the information on genetic regulation of gene expression and found many novel loci. However, as genetic variants may have effects on more than one gene and through different mechanisms, these methods likely only capture part of the total effects of these variants. In this paper, we propose a summary statistics-based mixed effects score test (sMiST) that tests for the total effect of both the effect of the mediator by imputing genetically predicted gene expression, like S-PrediXcan and TWAS, and the direct effects of individual variants. It allows for multiple functional annotations and multiple genetically predicted mediators. It can also perform conditional association analysis while adjusting for other genetic variants (e.g., known loci for the phenotype). Extensive simulation and real data analyses demonstrate that sMiST yields p-values that agree well with those obtained from individual level data but with substantively improved computational speed. Importantly, a broad application of sMiST to GWAS is possible, as only summary statistics of genetic variant associations are required. We apply sMiST to a large-scale GWAS of colorectal cancer using summary statistics from ∼120, 000 study participants and gene expression data from the Genotype-Tissue Expression (GTEx) project. We identify several novel and secondary independent genetic loci.

In-silico analysis of ligand-receptor binding patterns of α-MMC, TCS and MAP30 protein to LRP1 receptor.

Alpha-momorcharin (α-MMC), trichosanthin (TCS), and momordica anti-HIV protein of 30 kD (MAP30) are potential anti-tumor drug candidates but have cytotoxicity to normal cells. The binding of these proteins to LRP1 receptor and the subsequent endocytosis are essential to their cytotoxicity, but this binding process remains largely unknown. This study, in-silico analysis of the binding patterns, was conducted via the protein-protein docking software, ZDOCK 3.0.2 package, to better understand the binding process. Specifically, α-MMC, TCS and MAP30 were selected and bound to binding subunits CR56 and CR17 of LRP1. After docking, the 10 best docking solutions are retained based on the default ZDOCK scores and used for structural assessment. Our results showed that, α-MMC bound to LRP1 stably at the amino acid residues 1-20, at which 8 residues formed 21 hydrogen bonds with 15 residues of CR56 and 10 residues formed 15 hydrogen bonds with 12 residues of CR17. In contrast, TCS and MAP30 bound mainly to LRP1 at the residues 1-57/79-150 and residues 58-102, respectively, which were functional domains of TCS and MAP30. Since residues 1-20 are outside the functional domain of α-MMC, α-MMC is considered more suitable to attenuate by mutating the receptor binding site. Thus, our analysis lays the foundation for future genetic engineering work on α-MMC, and makes important contributions to its potential clinical use in cancer treatment.