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.

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Recurrent triple‐negative breast cancer (TNBC) needs new therapeutic targets. Src homology region 2 domain‐containing phosphatase‐1 (SHP‐1) can act as a tumor suppressor by dephosphorylating oncogenic kinases. One major target of SHP‐1 is STAT3, which is highly activated in TNBC. In this study, we tested a sorafenib analogue SC‐60, which lacks angiokinase inhibition activity, but acts as a SHP‐1 agonist, in TNBC cells. SC‐60 inhibited proliferation and induced apoptosis by dephosphorylating STAT3 in both a dose‐ and time‐dependent manner in TNBC cells (MDA‐MB‐231, MDA‐MB‐468, and HCC1937). By contrast, ectopic expression of STAT3 rescued the anticancer effect induced by SC‐60. SC‐60 also increased the SHP‐1 activity, but this effect was inhibited when the N‐SH2 domain (DN1) was deleted or with SHP‐1 point mutation (D61A), implying that SHP‐1 is the major target of SC‐60 in TNBC. The use of SC‐60 in combination with docetaxel synergized the anticancer effect induced by SC‐60 through the SHP‐1/STAT3 pathway in TNBC cells. Importantly, SC‐60 also displayed a significant antitumor effect in an MDA‐MB‐468 xenograft model by modulating the SHP‐1/STAT3 axis, indicating the anticancer potential of SC‐60 in TNBC treatment. Targeting SHP‐1/p‐STAT3 and the potential combination of SHP‐1 agonist with chemotherapeutic docetaxel is a feasible therapeutic strategy for TNBC.

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 b-catenin and prevented b-catenin degradation, thereby enhancing the transcription of pluripotent genes. KMO knockdown suppressed tumor growth and the expression levels of b-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 b-catenin and plays an oncogenic role in TNBC progression.

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.

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BackgroundTriple-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.MethodsData 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.FindingsTCGA 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.InterpretationThese 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.

The multivariate linear mixed model (MLMM) is a frequently used tool for a joint analysis of more than one series of longitudinal data. Motivated by a concern of sensitivity to potential outliers or data with longer-than-normal tails and possible serial correlation, we develop a robust generalization of the MLMM that is constructed by using the multivariate t distribution and a parsimonious AR(p) dependence structure for the within-subject errors. A score test for the inspection of autocorrelation among within-subject errors is derived. A hybrid ECME-scoring procedure is developed for computing the maximum likelihood estimates with standard errors as a by-product. The methodology is illustrated through an application to a set of AIDS data and several simulation studies.