Chimeric antigen receptor-based immunotherapy in breast cancer: Recent progress in China.

Breast cancer (BC) is the fourth most prevalent cancer in China. Despite conventional treatment strategies, BC patients often have poor therapeutic outcomes, leading to significant global cancer mortality rates. Chimeric antigen receptor (CAR)-based immunotherapy is a promising and innovative approach for cancer treatment that redirects immune cells to attack tumor cells expressing selected tumor antigens (TAs). T cells, natural killer (NK) cells, and macrophages, key components of the immune system, are used in CAR-based immunotherapies. Although remarkable progress has been made with CAR-T cells in hematologic malignancies, the application of CAR-based immunotherapy to BC has lagged. This is partly due to obstacles such as tumor heterogeneity, which is further associated with the TA and BC subtypes, and the immunosuppressive tumor microenvironment (TME). Several combinatorial approaches, including the use of immune checkpoint inhibitors, oncolytic viruses, and antitumor drugs, have been proposed to overcome these obstacles in BC treatment. Furthermore, several CAR-based immunotherapies for BC have been translated into clinical trials. This review provides an overview of the recent progress in CAR-based immunotherapy for BC treatment, including targeting of TAs, consideration of BC subtypes, assessment of the TME, and exploration of combinatorial therapies. The authors focused on preclinical studies and clinical trials of CAR-T cells, CAR-NK cells, and CAR-macrophages especially conducted in China, followed by an internal comparison and discussion of current limits. In conclusion, this review elucidates China's contribution to CAR-based immunotherapies for BC and provides inspiration for further research. PLAIN LANGUAGE SUMMARY: Despite conventional treatment strategies, breast cancer (BC) patients in China often have poor therapeutic outcomes. Chimeric antigen receptor (CAR)-based immunotherapy, a promising approach, can redirect immune cells to kill tumor cells expressing selected tumor antigens (TAs). However, obstacles such as TA selection, BC subtypes, and immunosuppressive tumor microenvironment still exist. Therefore, various combinatorial approaches have been proposed. This article elucidates several Chinese CAR-based preclinical and clinical studies in BC treatment with comparisons of foreign research, and CAR-immune cells are analyzed, providing inspiration for further research.

Predicting thermostability difference between cellular protein orthologs.

MOTIVATION: Protein thermostability is of great interest, both in theory and in practice. RESULTS: This study compared orthologous proteins with different cellular thermostability. A large number of physicochemical properties of protein were calculated and used to develop a series of machine learning models for predicting cellular thermostability differences between orthologous proteins. Most of the important features in these models are also highly correlated to relative cellular thermostability. A comparison between the present study with previous comparison of orthologous proteins from thermophilic and mesophilic organisms found that most highly correlated features are consistent in these studies, suggesting they may be important to protein thermostability. AVAILABILITY AND IMPLEMENTATION: Data freely available for download at https://github.com/fangj3/cellular-protein-thermostability-dataset.

Relations between anxiety sensitivity's cognitive concerns and anxiety severity: brooding and reflection as serial multiple mediators.

The outbreak of COVID-19 has caused a major impact on productivity and life functioning, and also led to adverse emotional reactions. In the face of this public health event, increased anxiety is one of the most common emotional reactions. Previous studies have shown that anxiety sensitivity, rumination and anxiety are closely related. Various dimensions of anxiety sensitivity have different effects on anxiety. Also, rumination can be divided into brooding and reflection. To explore the relationships among anxiety sensitivity's cognitive concerns, anxiety and different types of rumination, we conducted an online survey during the outbreak of coronavirus (February 17-25, 2020), using the Anxiety Sensitivity Scale-3 (ASI-3), Ruminative Responses Scale (RSS), and Depression Anxiety Stress Scale-21 (DASS-21). The results showed significant positive correlations among anxiety sensitivity's cognitive concerns, anxiety, brooding and reflection. Furthermore, brooding and reflection had a chain mediation effect between cognitive concerns and anxiety, and the mediation effect of reflection was even stronger. Results suggest that anxiety sensitivity's cognitive concerns may not only affect anxiety directly, but also affect anxiety through rumination, especially reflection.

A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

A number of machine learning (ML)-based algorithms have been proposed for predicting mutation-induced stability changes in proteins. In this critical review, we used hypothetical reverse mutations to evaluate the performance of five representative algorithms and found all of them suffer from the problem of overfitting. This approach is based on the fact that if a wild-type protein is more stable than a mutant protein, then the same mutant is less stable than the wild-type protein. We analyzed the underlying issues and suggest that the main causes of the overfitting problem include that the numbers of training cases were too small, and the features used in the models were not sufficiently informative for the task. We make recommendations on how to avoid overfitting in this important research area and improve the reliability and robustness of ML-based algorithms in general.

Tightly integrated genomic and epigenomic data mining using tensor decomposition.

Motivation: Complex diseases such as cancers often involve multiple types of genomic and/or epigenomic abnormalities. Rapid accumulation of multiple types of omics data demands methods for integrating the multidimensional data in order to elucidate complex relationships among different types of genomic and epigenomic abnormalities. Results: In the present study, we propose a tightly integrated approach based on tensor decomposition. Multiple types of data, including mRNA, methylation, copy number variations and somatic mutations, are merged into a high-order tensor which is used to develop predictive models for overall survival. The weight tensors of the models are constrained using CANDECOMP/PARAFAC (CP) tensor decomposition and learned using support tensor machine regression (STR) and ridge tensor regression (RTR). The results demonstrate that the tensor decomposition based approaches can achieve better performance than the models based individual data type and the concatenation approach. Supplementary information: Supplementary data are available at Bioinformatics online.

Abnormal Fractional Amplitude of Low-Frequency Fluctuation Changes in Patients with Monocular Blindness: A Functional Magnetic Resonance Imaging (MRI) Study.

BACKGROUND We used fractional amplitude of low-frequency fluctuation (fALFF) technology to investigate spontaneous cerebral activity in patients with monocular blindness (MB) and in healthy controls (HCs). MATERIAL AND METHODS Thirty MB patient and 15 HCs were included in this study. All subjects were scanned by resting-state functional magnetic resonance imaging (rs-fMRI). The independent sample t test and chi-squared test were applied to analyze demographics of MB patients and HCs. The 2-sample t test and receiver operating characteristic (ROC) curves were applied to identify the difference in average fALFF values between MB patients and HCs. Pearson's correlation analysis was applied to explore the relationship between the average fALFF values of brain areas and clinical behavior in the MB group. RESULTS MB patients had lower fALFF values in the left anterior cingulate and higher fALFF values in the left precuneus and right and left inferior parietal lobes than in HCs. Moreover, the mean fALFF values of MB patients in the left anterior cingulate had negative correlations with the anxiety scale score (r=-0.825, P<0.001) and the depression scale score (r=-0.871, P<0.001). CONCLUSIONS Our study found that MB patients had abnormal spontaneous activities in the visual and vision-related regions. The finding of abnormal neuronal activity helps to reveal the underlying neuropathologic mechanisms of vision loss.

13 J direct-liquid-cooled solid-state disk laser with an unstable cavity.

A 13 J direct-liquid-cooled solid-state disk laser with an unstable cavity is developed and demonstrated in this paper. The output energy of the resonant cavity is first analyzed according to the gain level in a stable cavity. At the optimum gain level, a magnification of 1.3 in the unstable cavity is achieved. Experimentally, a concave-convex mirror is used as the cavity mirror. At a magnification of 1.3, a repetition frequency of 100 Hz, and a pulse width of 350 µs, a single-pulse energy output of 13.2 J is obtained, corresponding to an optical-optical efficiency of 22% and a slope efficiency of 27.6%. The x-axis beam quality factor ß is 4.7, and the y-axis beam quality factorß is 16.6.

Exome sequencing reveals a thrombopoietin ligand mutation in a Micronesian family with autosomal recessive aplastic anemia.

We recently identified 2 siblings afflicted with idiopathic, autosomal recessive aplastic anemia. Whole-exome sequencing identified a novel homozygous missense mutation in thrombopoietin (THPO, c.112C>T) in both affected siblings. This mutation encodes an arginine to cysteine substitution at residue 38 or residue 17 excluding the 21-amino acid signal peptide of THPO receptor binding domain (RBD). THPO has 4 conserved cysteines in its RBD that form 2 disulfide bonds. Our in silico modeling predicts that introduction of a fifth cysteine may disrupt normal disulfide bonding to cause poor receptor binding. In functional assays, the mutant-THPO-containing media shows two- to threefold reduced ability to sustain UT7-TPO cells, which require THPO for proliferation. Both parents and a sibling with heterozygous R17C change have reduced platelet counts, whereas a sibling with wild-type sequence has normal platelet count. Thus, the R17C partial loss-of-function allele results in aplastic anemia in the homozygous state and mild thrombocytopenia in the heterozygous state in our family. Together with the recent identification of THPO receptor (MPL) mutations and the effects of THPO agonists in aplastic anemia, our results have clinical implications in the diagnosis and treatment of patients with aplastic anemia and highlight a role for the THPO-MPL pathway in hematopoiesis in vivo.

ChemCom: A Software Program for Searching and Comparing Chemical Libraries.

An efficient chemical comparator, a computer application facilitating searching and comparing chemical libraries, is useful in drug discovery and other relevant areas. The need for an efficient and user-friendly chemical comparator prompted us to develop ChemCom (Chemical Comparator) based on Java Web Start (JavaWS) technology. ChemCom provides a user-friendly graphical interface to a number of fast algorithms including a novel algorithm termed UnionBit Tree Algorithm. It utilizes an intuitive stepwise mechanism for selecting chemical comparison parameters before starting the comparison process. UnionBit has shown approximately an 165% speedup on average compared to its closest competitive algorithm implemented in ChemCom over real data. It is approximately 11 times faster than the Open Babel FastSearch algorithm in our tests. ChemCom can be accessed free-of-charge via a user-friendly website at http://bioinformatics.org/chemcom/.

Molecular cancer classification using a meta-sample-based regularized robust coding method.

MOTIVATION: Previous studies have demonstrated that machine learning based molecular cancer classification using gene expression profiling (GEP) data is promising for the clinic diagnosis and treatment of cancer. Novel classification methods with high efficiency and prediction accuracy are still needed to deal with high dimensionality and small sample size of typical GEP data. Recently the sparse representation (SR) method has been successfully applied to the cancer classification. Nevertheless, its efficiency needs to be improved when analyzing large-scale GEP data. RESULTS: In this paper we present the meta-sample-based regularized robust coding classification (MRRCC), a novel effective cancer classification technique that combines the idea of meta-sample-based cluster method with regularized robust coding (RRC) method. It assumes that the coding residual and the coding coefficient are respectively independent and identically distributed. Similar to meta-sample-based SR classification (MSRC), MRRCC extracts a set of meta-samples from the training samples, and then encodes a testing sample as the sparse linear combination of these meta-samples. The representation fidelity is measured by the l2-norm or l1-norm of the coding residual. CONCLUSIONS: Extensive experiments on publicly available GEP datasets demonstrate that the proposed method is more efficient while its prediction accuracy is equivalent to existing MSRC-based methods and better than other state-of-the-art dimension reduction based methods.

NURBS: a database of experimental and predicted nuclear receptor binding sites of mouse.

SUMMARY: Nuclear receptors (NRs) are a class of transcription factors playing important roles in various biological processes. An NR often impacts numerous genes and different NRs share overlapped target networks. To fulfil the need for a database incorporating binding sites of different NRs at various conditions for easy comparison and visualization to improve our understanding of NR binding mechanisms, we have developed NURBS, a database for experimental and predicted nuclear receptor binding sites of mouse (NURBS). NURBS currently contains binding sites across the whole-mouse genome of 8 NRs identified in 40 chromatin immunoprecipitation with massively parallel DNA sequencing experiments. All datasets are processed using a widely used procedure and same statistical criteria to ensure the binding sites derived from different datasets are comparable. NURBS also provides predicted binding sites using NR-HMM, a Hidden Markov Model (HMM) model. AVAILABILITY: The GBrowse-based user interface of NURBS is freely accessible at http://shark.abl.ku.edu/nurbs/. NR-HMM and all results can be downloaded for free at the website. CONTACT: jwfang@ku.edu

Prognostic value of EIF5A2 in solid tumors: A meta-analysis and bioinformatics analysis.

Aims: In cancer biology, the aberrant overexpression of eukaryotic translation initiation factor 5A2 (EIF5A2) has been correlative with an ominous prognosis, thereby underscoring its pivotal role in fostering metastatic progression. Consequently, EIF5A2 has garnered significant attention as a compelling prognostic biomarker for various malignancies. Our research endeavors were thus aimed at elucidating the utility and significance of EIF5A2 as a robust indicator of cancer outcome prediction. Method: An exhaustive search of the PubMed, EMBASE, and Web of Science databases found relevant studies. The link between EIF5A2 and survival prognosis was examined using hazard ratios and 95% confidence intervals. Subsequently, The Cancer Genome Atlas (TCGA) and the Gene Expression Profiling Interactive Analysis (GEPIA) databases were employed to validate EIF5A2 expression across various cancer types. Results: Through pooled analysis, we found that increased EIF5A2 expression was significantly associated with decreased overall survival (OS) and disease-free survival/progression-free survival/relapse-free survival (DFS/PFS/RFS). Moreover, TCGA analysis revealed that EIF5A2 was significantly upregulated in 27 types of cancer, with overexpression being linked to shorter OS in three, worse DFS in two, and worse PFS in six types of cancer. GEPIA showed that patients with EIF5A2 overexpression had reduced OS and DFS. Conclusions: In solid tumors, EIF5A2 emerges as a reliable prognostic marker. Our meta-analysis comprehensively analyzed the prognostic value of EIF5A2 in solid tumors and assessed its efficacy as a predictive marker.

Differential long-term bivalent HPV vaccine cross-protection by variants in the Costa Rica HPV vaccine trial.

The AS04-adjuvanted human papillomavirus (HPV)16/18 vaccine, an L1-based vaccine, provides strong vaccine efficacy (VE) against vaccine-targeted type infections, and partial cross-protection to phylogenetically-related types, which may be affected by variant-level heterogeneity. We compared VE against incident HPV31, 33, 35, and 45 detections between lineages and SNPs in the L1 region among 2846 HPV-vaccinated and 5465 HPV-unvaccinated women through 11-years of follow-up in the Costa Rica HPV Vaccine Trial. VE was lower against HPV31-lineage-B (VE=60.7%;95%CI = 23.4%,82.8%) compared to HPV31-lineage-A (VE=94.3%;95%CI = 83.7%,100.0%) (VE-ratio = 0.64;95%CI = 0.25,0.90). Differential VE was observed at several lineage-associated HPV31-L1-SNPs, including a nonsynonymous substitution at position 6372 on the FG-loop, an important neutralization domain. For HPV35, the only SNP-level difference was at position 5939 on the DE-loop, with significant VE against nucleotide-G (VE=65.0%;95%CI = 28.0,87.8) but not for more the common nucleotide-A (VE=7.4%;95%CI = -34.1,36.7). Because of the known heterogeneity in precancer/cancer risk across cross-protected HPV genotype variants by race and region, our results of differential variant-level AS04-adjuvanted HPV16/18 vaccine efficacy has global health implications.

Diagnostic prediction of complex diseases using phase-only correlation based on virtual sample template.

MOTIVATION: Complex diseases induce perturbations to interaction and regulation networks in living systems, resulting in dynamic equilibrium states that differ for different diseases and also normal states. Thus identifying gene expression patterns corresponding to different equilibrium states is of great benefit to the diagnosis and treatment of complex diseases. However, it remains a major challenge to deal with the high dimensionality and small size of available complex disease gene expression datasets currently used for discovering gene expression patterns. RESULTS: Here we present a phase-only correlation (POC) based classification method for recognizing the type of complex diseases. First, a virtual sample template is constructed for each subclass by averaging all samples of each subclass in a training dataset. Then the label of a test sample is determined by measuring the similarity between the test sample and each template. This novel method can detect the similarity of overall patterns emerged from the differentially expressed genes or proteins while ignoring small mismatches. CONCLUSIONS: The experimental results obtained on seven publicly available complex disease datasets including microarray and protein array data demonstrate that the proposed POC-based disease classification method is effective and robust for diagnosing complex diseases with regard to the number of initially selected features, and its recognition accuracy is better than or comparable to other state-of-the-art machine learning methods. In addition, the proposed method does not require parameter tuning and data scaling, which can effectively reduce the occurrence of over-fitting and bias.

Identification of properties important to protein aggregation using feature selection.

BACKGROUND: Protein aggregation is a significant problem in the biopharmaceutical industry (protein drug stability) and is associated medically with over 40 human diseases. Although a number of computational models have been developed for predicting aggregation propensity and identifying aggregation-prone regions in proteins, little systematic research has been done to determine physicochemical properties relevant to aggregation and their relative importance to this important process. Such studies may result in not only accurately predicting peptide aggregation propensities and identifying aggregation prone regions in proteins, but also aid in discovering additional underlying mechanisms governing this process. RESULTS: We use two feature selection algorithms to identify 16 features, out of a total of 560 physicochemical properties, presumably important to protein aggregation. Two predictors (ProA-SVM and ProA-RF) using selected features are built for predicting peptide aggregation propensity and identifying aggregation prone regions in proteins. Both methods are compared favourably to other state-of-the-art algorithms in cross validation. The identified important properties are fairly consistent with previous studies and bring some new insights into protein and peptide aggregation. One interesting new finding is that aggregation prone peptide sequences have similar properties to signal peptide and signal anchor sequences. CONCLUSIONS: Both predictors are implemented in a freely available web application (http://www.abl.ku.edu/ProA/). We suggest that the quaternary structure of protein aggregates, especially soluble oligomers, may allow the formation of new molecular recognition signals that guide aggregate targeting to specific cellular sites.

The role of retinoic acid in hepatic lipid homeostasis defined by genomic binding and transcriptome profiling.

BACKGROUND: The eyes and skin are obvious retinoid target organs. Vitamin A deficiency causes night blindness and retinoids are widely used to treat acne and psoriasis. However, more than 90% of total body retinol is stored in liver stellate cells. In addition, hepatocytes produce the largest amount of retinol binding protein and cellular retinoic acid binding protein to mobilize retinol from the hepatic storage pool and deliver retinol to its receptors, respectively. Furthermore, hepatocytes express the highest amount of retinoid x receptor alpha (RXRα) among all the cell types. Surprisingly, the function of endogenous retinoids in the liver has received very little attention. RESULTS: Based on the data generated from chromatin immunoprecipitation followed by sequencing, the global DNA binding of transcription factors including retinoid x receptor α (RXRα) along with its partners i.e. retinoic acid receptor α (RARα), pregnane x receptor (PXR), liver x receptor (LXR), farnesoid x receptor (FXR), and peroxisome proliferator-activated receptor α (PPARα) has been established. Based on the binding, functional annotation illustrated the role of those receptors in regulating hepatic lipid homeostasis. To correlate the DNA binding data with gene expression data, the expression patterns of 576 genes that regulate lipid homeostasis were studied in wild type and liver RXRα-null mice treated with and without RA. The data showed that RA treatment and RXRα-deficiency had opposite effects in regulating lipid homeostasis. A subset of genes (114), which could clearly differentiate the effect of ligand treatment and receptor deficiency, were selected for further functional analysis. The expression data suggested that RA treatment could produce unsaturated fatty acids and induce triglyceride breakdown, bile acid secretion, lipolysis, and retinoids elimination. In contrast, RXRα deficiency might induce the synthesis of saturated fatty acids, triglyceride, cholesterol, bile acids, and retinoids. In addition, DNA binding data indicated extensive cross-talk among RARα, PXR, LXR, FXR, and PPARα in regulating those RA/RXRα-dependent gene expression levels. Moreover, RA reduced serum cholesterol, triglyceride, and bile acid levels in mice. CONCLUSIONS: We have characterized the role of hepatic RA for the first time. Hepatic RA mediated through RXRα and its partners regulates lipid homeostasis.

Prediction of core cancer genes using multi-task classification framework.

Cancer is deemed as a highly heterogeneous disease specific to cell type and tissue origin. All cancers, however, share a common pathogenesis. Therefore, it is widely believed that cancers may share common mechanisms. In this study, we introduce a novel strategy based on multi-tasking learning methods to predict core cancer genes shared by multiple cancers in the hope of elucidating common cancer mechanisms. Our strategy uses two multi-tasking learning algorithms, one for feature selection and the other for validation of selected features. The combined use of two methods results in more robust classifiers and reliable selected features. The top 73 significant features, mapped to 72 genes, are selected as core cancer genes. The effectiveness of the 73 features is further demonstrated in a blind test conducted on an independent test data. The biological significance of these genes is evaluated using systems biology analyses. Extensive functional, pathway and network analysis confirms findings in previous studies and brings new insights into common cancer mechanisms. Our strategy can be used as a general method to find important genes from large gene expression datasets on the genomic level. The selected genes can be used to predict cancers.

Hepatocyte nuclear factor 4 alpha and farnesoid X receptor co-regulates gene transcription in mouse livers on a genome-wide scale.

PURPOSE: Farnesoid X receptor (Fxr) is a ligand-activated nuclear receptor critical for liver function. Reports indicate that the functions of Fxr in the liver may overlap with those of hepatocyte nuclear factor 4α (Hnf4α), but studies of their precise genome-wide interaction to regulate gene transcription in the liver are lacking. Thus, we compared the genome-wide binding of Fxr and Hnf4α in the liver of mice and characterized their cooperative activity on binding to and activating target gene transcription. METHODS: Genome-wide ChIP-Seq data of Fxr and Hnf4α in mouse liver were analyzed by MACS, BEDTools, and DAVID. Co-immunoprecipitation, ChIP-qPCR, and luciferase assays were done to test for protein-protein interaction and cooperative binding. RESULTS: ChIP-seq analysis showed nearly 50% binding sites of Fxr and Hnf4α in mouse liver overlap and Hnf4α bound to shared target sites upstream and in close proximity to Fxr. Moreover, genes co-bound by Fxr and Hnf4α are enriched in complement and coagulation cascades and drug metabolism. A direct Fxr-Hnf4α protein interaction dependent on Fxr activity was detected and transcriptional assays suggest that Hnf4α can increase Fxr transcriptional activity. Conversely, binding assays showed Hnf4α can be either Fxr-dependent or -independent at different shared binding sites. CONCLUSION: Our results showed that Fxr cooperates with Hnf4α in the liver to modulate gene transcription. This study provides the first evidence on a genome-wide scale of both cooperative and independent interactions between Fxr and Hnf4α in regulating gene transcription in the liver.

The role of data imbalance bias in the prediction of protein stability change upon mutation.

There is a controversy over what causes the low robustness of some programs for predicting protein stability change upon mutation. Some researchers suggested that low-quality data and insufficiently informative features are the primary reasons, while others attributed the problem largely to a bias caused by data imbalance as there are more destabilizing mutations than stabilizing ones. In this study, a simple approach was developed to construct a balanced dataset that was then conjugated with a leave-one-protein-out approach to illustrate that the bias may not be the primary reason for poor performance. A balanced dataset with some seemly good conventional n-fold CV results should not be used as a proof that a model for predicting protein stability change upon mutations is robust. Thus, some of the existing algorithms need to be re-examined before any practical applications. Also, more emphasis should be put on obtaining high quality and quantity of data and features in future research.