Characterization of tumoricidal activities mediated by a novel immune cell regimen composing interferon-producing killer dendritic cells and tumor-specific cytotoxic T lymphocytes.

BACKGROUND: Although immune cell therapy has long been used for treating solid cancer, its efficacy remains limited. Interferon (IFN)-producing killer dendritic cells (IKDCs) exhibit cytotoxicity and present antigens to relevant cells; thus, they can selectively induce tumor-associated antigen (TAA)-specific CD8 T cells and may be useful in cancer treatment. Various protocols have been used to amplify human IKDCs from peripheral sources, but the complexity of the process has prevented their widespread clinical application. Additionally, the induction of TAA-specific CD8 T cells through the adoptive transfer of IKDCs to immunocompromised patients with cancer may be insufficient. Therefore, we developed a method for generating an immune cell-based regimen, Phyduxon-T, comprising a human IKDC counterpart (Phyduxon) and expanded TAA-specific CD8 T cells. METHODS: Peripheral blood mononuclear cells from ovarian cancer patients were cultured with human interleukin (hIL)-15, hIL-12, and hIL-18 to generate Phyduxon-T. Then, its phenotype, cytotoxicity, and antigen-presenting function were evaluated through flow cytometry using specific monoclonal antibodies. RESULTS: Phyduxon exhibited the characteristics of both natural killer and dendritic cells. This regimen also exhibited cytotoxicity against primary ovarian cancer cells and presented TAAs, thereby inducing TAA-specific CD8 T cells, as evidenced by the expression of 4-1BB and IFN-γ. Notably, the Phyduxon-T manufacturing protocol effectively expanded IFN-γ-producing 4-1BB+ TAA-specific CD8 T cells from peripheral sources; these cells exhibited cytotoxic activities against ovarian cancer cells. CONCLUSIONS: Phyduxon-T, which is a combination of natural killer cells, dendritic cells, and TAA-specific CD8 T cells, may enhance the efficacy of cancer immunotherapy.

May I see what you see? Predicting visual features from neuronal activity.

Understanding brain response to audiovisual stimuli is a key challenge in understanding neuronal processes. In this paper, we describe our effort aimed at reconstructing video frames from observed functional MRI images. We also demonstrate that our model can predict visual objects. Our method constructs an autoencoder model for a set of training video segments to code video streams into their corresponding latent representations. Next, we learn a mapping from the observed fMRI response to the corresponding latent video frame representation. Finally, we pass the latent vectors computed using the fMRI response through the decoder to reconstruct the predicted image. We show that the representations of video frames and those constructed from corresponding fMRI images are highly clustered, the latent representations can be used to predict objects in video frames using just the fMRI frames, and fMRI responses can be used to reconstruct the inputs to predict the presence of faces.

Identifying and analyzing sepsis states: A retrospective study on patients with sepsis in ICUs.

Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the highest among hospital admissions in the US. Improved understanding of disease states, progression, severity, and clinical markers has the potential to significantly improve patient outcomes and reduce cost. We develop a computational framework that identifies disease states in sepsis and models disease progression using clinical variables and samples in the MIMIC-III database. We identify six distinct patient states in sepsis, each associated with different manifestations of organ dysfunction. We find that patients in different sepsis states are statistically significantly composed of distinct populations with disparate demographic and comorbidity profiles. Our progression model accurately characterizes the severity level of each pathological trajectory and identifies significant changes in clinical variables and treatment actions during sepsis state transitions. Collectively, our framework provides a holistic view of sepsis, and our findings provide the basis for future development of clinical trials, prevention, and therapeutic strategies for sepsis.

AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU.

BACKGROUND: The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model's complexity. In our system AIKYATAN, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications' combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes). RESULTS: We develop a suite of ML models, under the banner AIKYATAN, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-'a-vis the competition. Specifically, AIKYATAN-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS. CONCLUSIONS: Our exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our AIKYATAN suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions.

BACKGROUND: Gene expression is mediated by specialized cis-regulatory modules (CRMs), the most prominent of which are called enhancers. Early experiments indicated that enhancers located far from the gene promoters are often responsible for mediating gene transcription. Knowing their properties, regulatory activity, and genomic targets is crucial to the functional understanding of cellular events, ranging from cellular homeostasis to differentiation. Recent genome-wide investigation of epigenomic marks has indicated that enhancer elements could be enriched for certain epigenomic marks, such as, combinatorial patterns of histone modifications. METHODS: Our efforts in this paper are motivated by these recent advances in epigenomic profiling methods, which have uncovered enhancer-associated chromatin features in different cell types and organisms. Specifically, in this paper, we use recent state-of-the-art Deep Learning methods and develop a deep neural network (DNN)-based architecture, called EP-DNN, to predict the presence and types of enhancers in the human genome. It uses as features, the expression levels of the histone modifications at the peaks of the functional sites as well as in its adjacent regions. We apply EP-DNN to four different cell types: H1, IMR90, HepG2, and HeLa S3. We train EP-DNN using p300 binding sites as enhancers, and TSS and random non-DHS sites as non-enhancers. We perform EP-DNN predictions to quantify the validation rate for different levels of confidence in the predictions and also perform comparisons against two state-of-the-art computational models for enhancer predictions, DEEP-ENCODE and RFECS. RESULTS: We find that EP-DNN has superior accuracy and takes less time to make predictions. Next, we develop methods to make EP-DNN interpretable by computing the importance of each input feature in the classification task. This analysis indicates that the important histone modifications were distinct for different cell types, with some overlaps, e.g., H3K27ac was important in cell type H1 but less so in HeLa S3, while H3K4me1 was relatively important in all four cell types. We finally use the feature importance analysis to reduce the number of input features needed to train the DNN, thus reducing training time, which is often the computational bottleneck in the use of a DNN. CONCLUSIONS: In this paper, we developed EP-DNN, which has high accuracy of prediction, with validation rates above 90 % for the operational region of enhancer prediction for all four cell lines that we studied, outperforming DEEP-ENCODE and RFECS. Then, we developed a method to analyze a trained DNN and determine which histone modifications are important, and within that, which features proximal or distal to the enhancer site, are important.

The efficacy of a novel vaccine approach using tumor cells that ectopically express a codon-optimized murine GM-CSF in a murine tumor model.

Granulocyte macrophage-colony stimulating factor (GM-CSF) is a potent immunomodulatory cytokine that is known to facilitate vaccine efficacy by promoting the development and prolongation of both humoral and cellular immunity. Here, we investigated a novel vaccine approach using a human papillomavirus (HPV)-16 E6/E7-transformed cell line, TC-1, that ectopically expresses a codon-optimized 26-11-2015 murine GM-CSF (cGM-CSF). Ectopically expressing cGM-CSF in TC-1 (TC-1/cGM) cells significantly increased expression of a GM-CSF that was functionally identical to wt GM-CSF by 9-fold compared with ectopically expressed wild type GM-CSF in TC-1 cells (TC-1/wt). Mice vaccinated with irradiated TC-1/cGM cells exhibited enhanced survival compared with mice vaccinated with TC-1/wt cells when both groups were subsequently injected with live TC-1. Consistently, mice vaccinated with irradiated TC-1/cGM cells exhibited stronger IFN-γ production in HPV E7-specific CD8(+) T cells. More dendritic cells were recruited to the draining lymph nodes (dLNs) of mice vaccinated with TC-1/cGM cells than C-1/wt cells. Regarding dLN cell recall responses, both proliferation and IFN-γ production in the HPV E7-specific CD8(+) T cells were enhanced in mice that were vaccinated with TC-1/cGM cells. Our results demonstrate that a novel practical molecular strategy utilizing a codon-optimized GM-CSF gene overcomes the limitation and improves the efficacy of tumor cell-based vaccines.

Subset selection of high-depth next generation sequencing reads for de novo genome assembly using MapReduce framework.

BACKGROUND: Recent progress in next-generation sequencing technology has afforded several improvements such as ultra-high throughput at low cost, very high read quality, and substantially increased sequencing depth. State-of-the-art high-throughput sequencers, such as the Illumina MiSeq system, can generate ~15 Gbp sequencing data per run, with >80% bases above Q30 and a sequencing depth of up to several 1000x for small genomes. Illumina HiSeq 2500 is capable of generating up to 1 Tbp per run, with >80% bases above Q30 and often >100x sequencing depth for large genomes. To speed up otherwise time-consuming genome assembly and/or to obtain a skeleton of the assembly quickly for scaffolding or progressive assembly, methods for noise removal and reduction of redundancy in the original data, with almost equal or better assembly results, are worth studying. RESULTS: We developed two subset selection methods for single-end reads and a method for paired-end reads based on base quality scores and other read analytic tools using the MapReduce framework. We proposed two strategies to select reads: MinimalQ and ProductQ. MinimalQ selects reads with minimal base-quality above a threshold. ProductQ selects reads with probability of no incorrect base above a threshold. In the single-end experiments, we used Escherichia coli and Bacillus cereus datasets of MiSeq, Velvet assembler for genome assembly, and GAGE benchmark tools for result evaluation. In the paired-end experiments, we used the giant grouper (Epinephelus lanceolatus) dataset of HiSeq, ALLPATHS-LG genome assembler, and QUAST quality assessment tool for comparing genome assemblies of the original set and the subset. The results show that subset selection not only can speed up the genome assembly but also can produce substantially longer scaffolds. AVAILABILITY: The software is freely available at https://github.com/moneycat/QReadSelector.