Regorafenib induces damage-associated molecular patterns, cancer cell death and immune modulatory effects in a murine triple negative breast cancer model.

Damage associated molecular patterns (DAMPs), including calreticulin (CRT) exposure, high-mobility group box 1 protein (HMGB1) elevation, and ATP release, characterize immunogenic cell death (ICD) and may play a role in cancer immunotherapy. Triple negative breast cancer (TNBC) is an immunogenic subtype of breast cancer with higher lymphocyte infiltration. Here, we found that regorafenib, a multi-target angiokinase inhibitor previously known to suppress STAT3 signaling, induced DAMPs and cell death in TNBC cells. Regorafenib induced the expression of HMGB1 and CRT, and the release of ATP. Regorafenib-induced HMGB1 and CRT were attenuated following STAT3 overexpression. In a 4T1 syngeneic murine model, regorafenib treatment increased HMGB1 and CRT expression in xenografts, and effectively suppressed 4T1 tumor growth. Immunohistochemical staining revealed increased CD4+ and CD8+ tumor-infiltrating T cells in 4T1 xenografts following regorafenib treatment. Regorafenib treatment or programmed death-1 (PD-1) blockade using anti-PD-1 monoclonal antibody reduced lung metastasis of 4T1 cells in immunocompetent mice. While regorafenib increases the proportion of MHC II high expression on dendritic cells in mice with smaller tumors, the combination of regorafenib and PD-1 blockade did not show a synergistic effect on anti-tumor activity. These results suggest that regorafenib induces ICD and suppresses tumor progression in TNBC. It should be carefully evaluated when developing a combination therapy with an anti-PD-1 antibody and a STAT3 inhibitor.

Interfering B cell receptor signaling via SHP-1/p-Lyn axis shows therapeutic potential in diffuse large B-cell lymphoma.

BACKGROUND: Diffuse large B cell lymphoma (DLBCL) is an aggressive and molecularly heterogeneous non-Hodgkin's lymphoma. The B cell receptor (BCR) signaling pathway in DLBCL emerges as a new drug target. Protein phosphatase SHP-1 negatively regulates several oncogenic tyrosine kinases and plays a tumor suppressive role. METHODS: The direct SHP-1 agonists were used to evaluate the potential therapeutic implication of SHP-1 in DLBCL. Immunohistochemical staining for SHP-1 was quantified by H-score. The SHP-1 phosphatase activity was determined using tyrosine phosphatase assay. In vitro studies, including MTT, western blot analysis and cell apoptosis, were utilized to examined biological functions of SHP-1. RESULTS: Oral administration of SHP-1 agonist showed the potent anti-tumor effects compared to a selective Bruton's tyrosine kinase (BTK) inhibitor ibrutinib in mice bearing U2932 xenografts. SHP-1 agonist increased SHP-1 activity as well as downregulated p-Lyn in vivo. Here, we demonstrated that immunohistochemical staining for SHP-1 expression was positive in 76% of DLBCL samples. SHP-1 agonist exerted anti-proliferative and apoptotic effects compared with ibrutinib in DLBCL cells. Mechanistically, SHP-1 agonist decreased BCR signaling, especially p-Lyn, and led to apoptosis. CONCLUSIONS: These data suggest that SHP-1 negatively regulates phosphorylation of Lyn, and targeting SHP-1/p-Lyn using SHP-1 agonist has therapeutic potential for treatment of DLBCL.

LncRNA Gm16638-201 Inhibits the 14-3-3Ɛ Pathway in the Murine Prefrontal Cortex to Induce Depressive Behaviors.

The dysregulation of certain long non-coding RNAs (lncRNAs) has been considered to be involved in neuropsychiatric disorders such as depression, implying the vital role of these transcripts. We have previously identified many differentially expressed lncRNAs in chronic unpredictable mild stress (CUMS) induced mice. Among them, lncRNA Gm16638-201 was highly expressed in the hippocampus (HIP) of CUMS, but the specific role and the underlying mechanisms remain unclear. Here, we reported that lncRNA Gm16638-201 was highly expressed in the prefrontal cortex (PFC) of CUMS induced depressive mice. Bioinformatic analysis shows that Gm16638-201 is mainly located in the cytoplasm. Nine neurological-related genes (Elmo2, Satb1, Hnrnpul1, Sipa1l3, Mapt, Tada3, Sgip1, IL-16, and StarD5) were predicted to be regulated in cis or trans by Gm16638-201 and involved into the 14-3-3Ɛ neurotrophic signaling pathway. We further confirmed the down-regulation of 14-3-3Ɛ and the nine predicted target genes in the PFC of CUMS mice except for Sgip1 and IL-16. In addition, they were also down-regulated in the primary cortical cell cultures with overexpression of Gm16638-201 constructed using an adenoviral-medicated gene expression system. In conclusion, we found that overexpression of Gm16638-201 negatively regulated several target genes and inhibited the 14-3-3Ɛ pathway in the PFC of CUMS induced depressive mice. This promising result suggests that Gm16638-201 may be a potential novel therapeutic target for depression.

Multivariate linear mixed models with censored and nonignorable missing outcomes, with application to AIDS studies.

The analysis of multivariate longitudinal data could encounter some complications due to censorship induced by detection limits of the assay and nonresponse occurring when participants missed scheduled visits intermittently or discontinued participation. This paper establishes a generalization of the multivariate linear mixed model that can accommodate censored responses and nonignorable missing outcomes simultaneously. To account for the nonignorable missingness, the selection approach which decomposes the joint distribution as a marginal distribution for the primary outcome variables and a model describing the missing process conditional on the hypothetical complete data is used. A computationally feasible Monte Carlo expectation conditional maximization algorithm is developed for parameter estimation with the maximum likelihood (ML) method. Furthermore, a general information-based approach is presented to assess the variability of ML estimators. The techniques for the prediction of censored responses and imputation of missing outcomes are also discussed. The methodology is motivated and exemplified by a real dataset concerning HIV-AIDS clinical trials. A simulation study is conducted to examine the performance of the proposed method compared with other traditional approaches.

[Research Progress on Reproductive System Damage in Male Coronavirus Disease 2019 Patients].

The incidence and severity of coronavirus disease 2019(COVID-19) have significant gender differences.Males are more likely to contract severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) than the age-matched females.The virus uses angiotensin-converting enzyme 2(ACE2) receptors to enter human cells.In addition to infecting the respiratory system,ACE2 can also attack the digestive system,nervous system,immune system and so on,due to the various levels of expression in multiple human organs.The testes are one of the ACE2-rich organs.SARS-CoV-2 has been detected in the semen of some COVID-19 patients,which suggests that SARS-CoV-2 may damage the male reproductive system.However,the damage mechanism remains to be studied.The available studies focus on the short-term effect of SARS-CoV-2 on male reproduction and increasing attention has been paid to the long-term effect.This paper briefly describes the possible mechanisms of reproductive cell damage,hypogonadism,and testicular inflammation mediated by SARS-CoV-2 in male COVID-19 patients and points out the existing problems in the current studies,which will broaden the thinking for deciphering the mechanism of reproductive system damage in male COVID-19 patients.

Bayesian analysis of multivariate linear mixed models with censored and intermittent missing responses.

Multivariate longitudinal data usually exhibit complex features such as the presence of censored responses due to detection limits of the assay and unavoidable missing values arising when participants make irregular visits that lead to intermittently recorded characteristics. A generalization of the multivariate linear mixed model constructed by taking into account impacts of censored and intermittent missing responses simultaneously, which is named as the MLMM-CM, has been recently proposed for more precisely analyzing such kinds of data. This paper aims at presenting a fully Bayesian sampling-based approach to the MLMM-CM for addressing the uncertainties of censored and missing responses as well as unknown parameters. Two widely accepted Bayesian computational techniques based on the Markov chain Monte Carlo and the inverse Bayes formulas coupled with the Gibbs (IBF-Gibbs) schemes are developed for carrying out posterior inference of the model. The proposed methodology is illustrated through a simulation study and a real-data example from the Adult AIDS Clinical Trials Group 388 study. Numerical results show empirically that the proposed Bayesian methodology performs satisfactorily and offers reliable posterior inference.

Multivariate-$t$ nonlinear mixed models with application to censored multi-outcome AIDS studies.

In multivariate longitudinal HIV/AIDS studies, multi-outcome repeated measures on each patient over time may contain outliers, and the viral loads are often subject to a upper or lower limit of detection depending on the quantification assays. In this article, we consider an extension of the multivariate nonlinear mixed-effects model by adopting a joint multivariate-$t$ distribution for random effects and within-subject errors and taking the censoring information of multiple responses into account. The proposed model is called the multivariate-$t$ nonlinear mixed-effects model with censored responses (MtNLMMC), allowing for analyzing multi-outcome longitudinal data exhibiting nonlinear growth patterns with censorship and fat-tailed behavior. Utilizing the Taylor-series linearization method, a pseudo-data version of expectation conditional maximization either (ECME) algorithm is developed for iteratively carrying out maximum likelihood estimation. We illustrate our techniques with two data examples from HIV/AIDS studies. Experimental results signify that the MtNLMMC performs favorably compared to its Gaussian analogue and some existing approaches.

Multivariate longitudinal data analysis with censored and intermittent missing responses.

The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements because of a detection limit of the assay in combination with unavoidable missing values arising when subjects miss some of their scheduled visits intermittently. This paper presents a generalization of the MLMM approach, called the MLMM-CM, for a joint analysis of the multivariate longitudinal data with censored and intermittent missing responses. A computationally feasible expectation maximization-based procedure is developed to carry out maximum likelihood estimation within the MLMM-CM framework. Moreover, the asymptotic standard errors of fixed effects are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and a case study from an AIDS clinical trial. Experimental results reveal that the proposed method is able to provide more satisfactory performance as compared with the traditional MLMM approach.

Carfilzomib induces leukaemia cell apoptosis via inhibiting ELK1/KIAA1524 (Elk-1/CIP2A) and activating PP2A not related to proteasome inhibition.

Enhancing the tumour suppressive activity of protein phosphatase 2A (PP2A) has been suggested to be an anti-leukaemic strategy. KIAA1524 (also termed CIP2A), an oncoprotein inhibiting PP2A, is associated with disease progression in chronic myeloid leukaemia and may be prognostic in cytogenetically normal acute myeloid leukaemia. Here we demonstrated that the selective proteasome inhibitor, carfilzomib, induced apoptosis in sensitive primary leukaemia cells and in sensitive leukaemia cell lines, associated with KIAA1524 protein downregulation, increased PP2A activity and decreased p-Akt, but not with the proteasome inhibition effect of carfilzomib. Ectopic expression of KIAA1524, or pretreatment with the PP2A inhibitor, okadaic acid, suppressed carfilzomib-induced apoptosis and KIAA1524 downregulation in sensitive cells, whereas co-treatment with the PP2A agonist, forskolin, enhanced carfilzomib-induced apoptosis in resistant cells. Mechanistically, carfilzomib affected KIAA1524 transcription through disturbing ELK1 (Elk-1) binding to the KIAA1524 promoter. Moreover, the drug sensitivity and mechanism of carfilzomib in xenograft mouse models correlated well with the effects of carfilzomib on KIAA1524 and p-Akt expression, as well as PP2A activity. Our data disclosed a novel drug mechanism of carfilzomib in leukaemia cells and suggests the potential therapeutic implication of KIAA1524 in leukaemia treatment.

Multivariate t nonlinear mixed-effects models for multi-outcome longitudinal data with missing values.

The multivariate nonlinear mixed-effects model (MNLMM) has emerged as an effective tool for modeling multi-outcome longitudinal data following nonlinear growth patterns. In the framework of MNLMM, the random effects and within-subject errors are assumed to be normally distributed for mathematical tractability and computational simplicity. However, a serious departure from normality may cause lack of robustness and subsequently make invalid inference. This paper presents a robust extension of the MNLMM by considering a joint multivariate t distribution for the random effects and within-subject errors, called the multivariate t nonlinear mixed-effects model. Moreover, a damped exponential correlation structure is employed to capture the extra serial correlation among irregularly observed multiple repeated measures. An efficient expectation conditional maximization algorithm coupled with the first-order Taylor approximation is developed for maximizing the complete pseudo-data likelihood function. The techniques for the estimation of random effects, imputation of missing responses and identification of potential outliers are also investigated. The methodology is motivated by a real data example on 161 pregnant women coming from a study in a private fertilization obstetrics clinic in Santiago, Chile and used to analyze these data.

Mixtures of t $$ t $$ factor analysers with censored responses and external covariates: An application to educational data from Peru.

Analysing data from educational tests allows governments to make decisions for improving the quality of life of individuals in a society. One of the key responsibilities of statisticians is to develop models that provide decision-makers with pertinent information about the latent process that educational tests seek to represent. Mixtures of t $$ t $$ factor analysers (MtFA) have emerged as a powerful device for model-based clustering and classification of high-dimensional data containing one or several groups of observations with fatter tails or anomalous outliers. This paper considers an extension of MtFA for robust clustering of censored data, referred to as the MtFAC model, by incorporating external covariates. The enhanced flexibility of including covariates in MtFAC enables cluster-specific multivariate regression analysis of dependent variables with censored responses arising from upper and/or lower detection limits of experimental equipment. An alternating expectation conditional maximization (AECM) algorithm is developed for maximum likelihood estimation of the proposed model. Two simulation experiments are conducted to examine the effectiveness of the techniques presented. Furthermore, the proposed methodology is applied to Peruvian data from the 2007 Early Grade Reading Assessment, and the results obtained from the analysis provide new insights regarding the reading skills of Peruvian students.

Multivariate t linear mixed models for irregularly observed multiple repeated measures with missing outcomes.

Missing outcomes or irregularly timed multivariate longitudinal data frequently occur in clinical trials or biomedical studies. The multivariate t linear mixed model (MtLMM) has been shown to be a robust approach to modeling multioutcome continuous repeated measures in the presence of outliers or heavy-tailed noises. This paper presents a framework for fitting the MtLMM with an arbitrary missing data pattern embodied within multiple outcome variables recorded at irregular occasions. To address the serial correlation among the within-subject errors, a damped exponential correlation structure is considered in the model. Under the missing at random mechanism, an efficient alternating expectation-conditional maximization (AECM) algorithm is used to carry out estimation of parameters and imputation of missing values. The techniques for the estimation of random effects and the prediction of future responses are also investigated. Applications to an HIV-AIDS study and a pregnancy study involving analysis of multivariate longitudinal data with missing outcomes as well as a simulation study have highlighted the superiority of MtLMMs on the provision of more adequate estimation, imputation and prediction performances.

Flexible modeling of multiple nonlinear longitudinal trajectories with censored and non-ignorable missing outcomes.

Multivariate nonlinear mixed-effects models (MNLMMs) have become a promising tool for analyzing multi-outcome longitudinal data following nonlinear trajectory patterns. However, such a classical analysis can be challenging due to censorship induced by detection limits of the quantification assay or non-response occurring when participants missed scheduled visits intermittently or discontinued participation. This article proposes an extension of the MNLMM approach, called the MNLMM-CM, by taking the censored and non-ignorable missing responses into account simultaneously. The non-ignorable missingness is described by the selection-modeling factorization to tackle the missing not at random mechanism. A Monte Carlo expectation conditional maximization algorithm coupled with the first-order Taylor approximation is developed for parameter estimation. The techniques for the calculation of standard errors of fixed effects, estimation of unobservable random effects, imputation of censored and missing responses and prediction of future values are also provided. The proposed methodology is motivated and illustrated by the analysis of a clinical HIV/AIDS dataset with censored RNA viral loads and the presence of missing CD4 and CD8 cell counts. The superiority of our method on the provision of more adequate estimation is validated by a simulation study.

Identification of hub genes associated with spermatogenesis by bioinformatics analysis.

Spermatogenesis is a complex process related to male infertility. Till now, the critical genes and specific mechanisms have not been elucidated clearly. Our objective was to determine the hub genes that play a crucial role in spermatogenesis by analyzing the differentially expressed genes (DEGs) present in non-obstructive azoospermia (NOA) compared to OA and normal samples using bioinformatics analysis. Four datasets, namely GSE45885, GSE45887, GSE9210 and GSE145467 were used. Functional enrichment analyses were performed on the DEGs. Hub genes were identified based on protein-protein interactions between DEGs. The expression of the hub genes was further examined in the testicular germ cell tumors from the TCGA by the GEPIA and validated by qRT-PCR in the testes of lipopolysaccharide-induced acute orchitis mice with impaired spermatogenesis. A total of 203 DEGs including 34 up-regulated and 169 down-regulated were identified. Functional enrichment analysis showed DEGs were mainly involved in microtubule motility, the process of cell growth and protein transport. PRM2, TEKT2, FSCN3, UBQLN3, SPATS1 and GTSF1L were identified and validated as hub genes for spermatogenesis. Three of them (PRM2, FSCN3 and TEKT2) were significantly down-regulated in the testicular germ cell tumors and their methylation levels were associated with the pathogenesis. In summary, the hub genes identified may be related to spermatogenesis and may act as potential therapeutic targets for NOA and testicular germ cell tumors.

Multivariate-t linear mixed models with censored responses, intermittent missing values and heavy tails.

Multivariate longitudinal data arisen in medical studies often exhibit complex features such as censored responses, intermittent missing values, and atypical or outlying observations. The multivariate-t linear mixed model (MtLMM) has been recognized as a powerful tool for robust modeling of multivariate longitudinal data in the presence of potential outliers or fat-tailed noises. This paper presents a generalization of MtLMM, called the MtLMM-CM, to properly adjust for censorship due to detection limits of the assay and missingness embodied within multiple outcome variables recorded at irregular occasions. An expectation conditional maximization either (ECME) algorithm is developed to compute parameter estimates using the maximum likelihood (ML) approach. The asymptotic standard errors of the ML estimators of fixed effects are obtained by inverting the empirical information matrix according to Louis' method. The techniques for the estimation of random effects and imputation of missing responses are also investigated. The proposed methodology is illustrated on two real-world examples from HIV-AIDS studies and a simulation study under a variety of scenarios.

Kynurenine 3-monooxygenase upregulates pluripotent genes through ß-catenin and promotes triple-negative breast cancer progression.

BACKGROUND: Triple-negative breast cancer (TNBC) is aggressive and has a poor prognosis. Kynurenine 3-monooxygenase (KMO), a crucial kynurenine metabolic enzyme, is involved in inflammation, immune response and tumorigenesis. We aimed to study the role of KMO in TNBC. METHODS: KMO alteration and expression data from public databases were analyzed. KMO expression levels in TNBC samples were analyzed using immunohistochemistry. Knockdown of KMO in TNBC cells was achieved by RNAi and CRISPR/Cas9. KMO functions were examined by MTT, colony-forming, transwell migration/invasion, and mammosphere assays. The molecular events were analyzed by cDNA microarrays, Western blot, quantitative real-time PCR and luciferase reporter assays. Tumor growth and metastasis were detected by orthotopic xenograft and tail vein metastasis mouse models, respectively. FINDINGS: KMO was amplified and associated with worse survival in breast cancer patients. KMO expression levels were higher in TNBC tumors compared to adjacent normal mammary tissues. In vitro ectopic KMO expression increased cell growth, colony and mammosphere formation, migration, invasion as well as mesenchymal marker expression levels in TNBC cells. In addition, KMO increased pluripotent gene expression levels and promoter activities in vitro. Mechanistically, KMO was associated with ß-catenin and prevented ß-catenin degradation, thereby enhancing the transcription of pluripotent genes. KMO knockdown suppressed tumor growth and the expression levels of ß-catenin, CD44 and Nanog. Furthermore, mutant KMO (known with suppressed enzymatic activity) could still promote TNBC cell migration/invasion. Importantly, mice bearing CRISPR KMO-knockdown TNBC tumors showed decreased lung metastasis and prolonged survival. INTERPRETATION: KMO regulates pluripotent genes via ß-catenin and plays an oncogenic role in TNBC progression.

Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness.

In biomedical studies, the analysis of longitudinal data based on Gaussian assumptions is common practice. Nevertheless, more often than not, the observed responses are naturally skewed, rendering the use of symmetric mixed effects models inadequate. In addition, it is also common in clinical assays that the patient's responses are subject to some upper and/or lower quantification limit, depending on the diagnostic assays used for their detection. Furthermore, responses may also often present a nonlinear relation with some covariates, such as time. To address the aforementioned three issues, we consider a Bayesian semiparametric longitudinal censored model based on a combination of splines, wavelets, and the skew-normal distribution. Specifically, we focus on the use of splines to approximate the general mean, wavelets for modeling the individual subject trajectories, and on the skew-normal distribution for modeling the random effects. The newly developed method is illustrated through simulated data and real data concerning AIDS/HIV viral loads.

Targeting SET to restore PP2A activity disrupts an oncogenic CIP2A-feedforward loop and impairs triple negative breast cancer progression.

BACKGROUND: Triple-negative breast cancer (TNBC) remains difficult to be targeted. SET and cancerous inhibitor of protein phosphatase 2A (CIP2A) are intrinsic protein-interacting inhibitors of protein phosphatase 2A (PP2A) and frequently overexpressed in cancers, whereas reactivating PP2A activity has been postulated as an anti-cancer strategy. Here we explored this strategy in TNBC. METHODS: Data from The Cancer Genome Atlas (TCGA) database was analyzed. TNBC cell lines were used for in vitro studies. Cell viability was examined by MTT assay. The apoptotic cells were examined by flow cytometry and Western blot. A SET-PP2A protein-protein interaction antagonist TD19 was used to disrupt signal transduction. In vivo efficacy of TD19 was tested in MDA-MB-468-xenografted animal model. FINDINGS: TCGA data revealed upregulation of SET and CIP2A and positive correlation of these two gene expressions in TNBC tumors. Ectopic SET or CIP2A increased cell viability, migration, and invasion of TNBC cells. Notably ERK inhibition increased PP2A activity. ERK activation is known crucial for Elk-1 activity, a transcriptional factor regulating CIP2A expression, we hypothesized an oncogenic feedforward loop consisting of pERK/pElk-1/CIP2A/PP2A. This loop was validated by knockdown of PP2A and ectopic expression of Elk-1, showing reciprocal changes in loop members. In addition, ectopic expression of SET increased pAkt, pERK, pElk-1 and CIP2A expressions, suggesting a positive linkage between SET and CIP2A signaling. Moreover, TD19 disrupted this CIP2A-feedforward loop by restoring PP2A activity, demonstrating in vitro and in vivo anti-cancer activity. Mechanistically, TD19 downregulated CIP2A mRNA via inhibiting pERK-mediated Elk-1 nuclear translocation thereby decreased Elk-1 binding to the CIP2A promoter. INTERPRETATION: These findings suggested that a novel oncogenic CIP2A-feedforward loop contributes to TNBC progression and targeting SET to disrupt this oncogenic CIP2A loop showed therapeutic potential in TNBC.

Varlitinib Downregulates HER/ERK Signaling and Induces Apoptosis in Triple Negative Breast Cancer Cells.

Triple-negative breast cancer (TNBC) is a complex disease associated with the aggressive phenotype and poor prognosis. TNBC harbors heterogeneous molecular subtypes with no approved specific targeted therapy. It has been reported that HER receptors are overexpressed in breast cancer including TNBC. In this study, we evaluated the efficacy of varlitinib, a reversible small molecule pan-HER inhibitor in TNBC. Our results showed that varlitinib reduced cell viability and induced cell apoptosis in most TNBC cell lines but not in MDA-MB-231 cells. MEK and ERK inhibition overcame resistance to varlitinib in MDA-MB-231 cells. Varlitinib inhibited HER signaling which led to inhibition of migration, invasion and mammosphere formation of TNBC cells as well as significant suppression of tumor growth of MDA-MB-468 xenograft mouse model. In summary, these results suggest that HER signaling plays an important role in TNBC progression and that pan-HER inhibition is potentially an effective treatment for TNBC patients.

Extending multivariate- t linear mixed models for multiple longitudinal data with censored responses and heavy tails.

The analysis of complex longitudinal data is challenging due to several inherent features: (i) more than one series of responses are repeatedly collected on each subject at irregularly occasions over a period of time; (ii) censorship due to limits of quantification of responses arises left- and/or right- censoring effects; (iii) outliers or heavy-tailed noises are possibly embodied within multiple response variables. This article formulates the multivariate- t linear mixed model with censored responses (MtLMMC), which allows the analysts to model such data in the presence of the above described features simultaneously. An efficient expectation conditional maximization either (ECME) algorithm is developed to carry out maximum likelihood estimation of model parameters. The implementation of the E-step relies on the mean and covariance matrix of truncated multivariate- t distributions. To enhance the computational efficiency, two auxiliary permutation matrices are incorporated into the procedure to determine the observed and censored parts of each subject. The proposed methodology is demonstrated via a simulation study and a real application on HIV/AIDS data.