Dynamics of circulating tumor DNA represented by the activating and resistant mutations in epidermal growth factor receptor tyrosine kinase inhibitor treatment.

Circulating tumor DNA (ctDNA) is an emerging field of cancer research. For lung cancer, non-invasive genotyping of EGFR is the foremost application. The activating mutations represent the ctDNA from all cancer cells, and the T790M-resistant mutation represents that from resistant cells. We examined the ctDNA dynamics of EGFR mutations by using deep sequencing with a massively parallel DNA sequencer. We obtained 190 plasma samples from 57 patients at various times during the treatment course and classified them according to treatment status. The mutation detection rate of exon 19 deletion/L858R in plasma was high at the initiation of treatment with epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI; P = 0.001), suppressed during EGFR-TKI treatment before disease progression, and elevated after the onset of disease progression (P = 0.023). The mutation detection rate of T790M was low until the onset of disease progression and elevated thereafter (P = 0.01). Samples across the development of disease progression were obtained from 10 patients and showed a correlation between increased ctDNA level and disease progression. Decreased ctDNA level in response to the initiation of EGFR-TKI was observed in 4 of 6 eligible patients. In two patients, the ctDNA dynamics suggested the presence of cancer cell populations only with the T790M mutation. In another patient, the T790M ctDNA represented cell subpopulations that respond to cytotoxic agents differently from the major population. Considering the high incidence, ctDNA could be a clinical parameter to complement information from image analyses.

Empirical Bayesian significance measure of neuronal spike response.

BACKGROUND: Functional connectivity analyses of multiple neurons provide a powerful bottom-up approach to reveal functions of local neuronal circuits by using simultaneous recording of neuronal activity. A statistical methodology, generalized linear modeling (GLM) of the spike response function, is one of the most promising methodologies to reduce false link discoveries arising from pseudo-correlation based on common inputs. Although recent advancement of fluorescent imaging techniques has increased the number of simultaneously recoded neurons up to the hundreds or thousands, the amount of information per pair of neurons has not correspondingly increased, partly because of the instruments' limitations, and partly because the number of neuron pairs increase in a quadratic manner. Consequently, the estimation of GLM suffers from large statistical uncertainty caused by the shortage in effective information. RESULTS: In this study, we propose a new combination of GLM and empirical Bayesian testing for the estimation of spike response functions that enables both conservative false discovery control and powerful functional connectivity detection. We compared our proposed method's performance with those of sparse estimation of GLM and classical Granger causality testing. Our method achieved high detection performance of functional connectivity with conservative estimation of false discovery rate and q values in case of information shortage due to short observation time. We also showed that empirical Bayesian testing on arbitrary statistics in place of likelihood-ratio statistics reduce the computational cost without decreasing the detection performance. When our proposed method was applied to a functional multi-neuron calcium imaging dataset from the rat hippocampal region, we found significant functional connections that are possibly mediated by AMPA and NMDA receptors. CONCLUSIONS: The proposed empirical Bayesian testing framework with GLM is promising especially when the amount of information per a neuron pair is small because of growing size of observed network.

Diagnostic Accuracy of Noninvasive Genotyping of EGFR in Lung Cancer Patients by Deep Sequencing of Plasma Cell-Free DNA.

BACKGROUND: Genotyping of EGFR (epidermal growth factor receptor) mutations is indispensable for making therapeutic decisions regarding whether to use EGFR tyrosine kinase inhibitors (TKIs) for lung cancer. Because some cases might pose challenges for biopsy, noninvasive genotyping of EGFR in circulating tumor DNA (ctDNA) would be beneficial for lung cancer treatment. METHODS: We developed a detection system for EGFR mutations in ctDNA by use of deep sequencing of plasma DNA. Mutations were searched in >100 000 reads obtained from each exon region. Parameters corresponding to the limit of detection and limit of quantification were used as the thresholds for mutation detection. We conducted a multi-institute prospective study to evaluate the detection system, enrolling 288 non-small cell lung cancer (NSCLC) patients. RESULTS: In evaluating the performance of the detection system, we used the genotyping results from biopsy samples as a comparator: diagnostic sensitivity for exon 19 deletions, 50.9% (95% CI 37.9%-63.9%); diagnostic specificity for exon 19 deletions, 98.0% (88.5%-100%); sensitivity for the L858R mutation, 51.9% (38.7%-64.9%); and specificity for L858R, 94.1% (83.5%-98.6%). The overall sensitivities were as follows: all cases, 54.4% (44.8%-63.7%); stages IA-IIIA, 22.2% (11.5%-38.3%); and stages IIIB-IV, 72.7% (60.9%-82.1%). CONCLUSIONS: Deep sequencing of plasma DNA can be used for genotyping of EGFR in lung cancer patients. In particular, the high specificity of the system may enable a direct recommendation for EGFR-TKI on the basis of positive results with plasma DNA. Because sensitivity was low in early-stage NSCLC, the detection system is preferred for stage IIIB-IV NSCLC.

A statistical method of identifying interactions in neuron-glia systems based on functional multicell Ca2+ imaging.

Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron-glia network. We attempted to identify neuron-glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron-glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron-glia systems.

Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information.

For practical brain-machine interfaces (BMIs), electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are the only current methods that are non-invasive and available in non-laboratory environments. However, the use of EEG and NIRS involves certain inherent problems. EEG signals are generally a mixture of neural activity from broad areas, some of which may not be related to the task targeted by BMI, hence impairing BMI performance. NIRS has an inherent time delay as it measures blood flow, which therefore detracts from practical real-time BMI utility. To try to improve real environment EEG-NIRS-based BMIs, we propose here a novel methodology in which the subjects' mental states are decoded from cortical currents estimated from EEG, with the help of information from NIRS. Using a Variational Bayesian Multimodal EncephaloGraphy (VBMEG) methodology, we incorporated a novel form of NIRS-based prior to capture event related desynchronization from isolated current sources on the cortical surface. Then, we applied a Bayesian logistic regression technique to decode subjects' mental states from further sparsified current sources. Applying our methodology to a spatial attention task, we found our EEG-NIRS-based decoder exhibited significant performance improvement over decoding methods based on EEG sensor signals alone. The advancement of our methodology, decoding from current sources sparsely isolated on the cortex, was also supported by neuroscientific considerations; intraparietal sulcus, a region known to be involved in spatial attention, was a key responsible region in our task. These results suggest that our methodology is not only a practical option for EEG-NIRS-based BMI applications, but also a potential tool to investigate brain activity in non-laboratory and naturalistic environments.

Expression profiling using a tumor-specific cDNA microarray predicts the prognosis of intermediate risk neuroblastomas.

To predict the prognosis of neuroblastoma patients and choose a better therapeutic protocol, we developed a cDNA microarray carrying 5340 genes obtained from primary neuroblastomas and examined 136 tumor samples. We made a probabilistic output statistical classifier that provided a high accuracy in prognosis prediction (89% at 5 years) and a highly reliable method to validate it. Kaplan-Meier analysis indicated that the patients in an intermediate group defined by existing markers are divided by microarray into two further groups with 5 year survivals for 36% and 89% of patients (p < 10(-4)), i.e., with unfavorably and favorably predicted neuroblastomas, respectively. According to these results, we developed a gene subset chip for a clinical tool, for which our classifier exhibited 88% prediction accuracy.

A Generalized Encoding System for Alpha Oscillations Through Visual Saliency Analysis.

By learning how the brain reacts to external visual stimuli and examining possible triggered brain statuses, we conduct a systematic study on an encoding problem that estimates ongoing EEG dynamics from visual information. A novel generalized system is proposed to encode the alpha oscillations modulated during video viewing by employing the visual saliency involved in the presented natural video stimuli. Focusing on the parietal and occipital lobes, the encoding effects at different alpha frequency bins and brain locations are examined by a real-valued genetic algorithm (GA), and possible links between alpha features and saliency patterns are constructed. The robustness and reliability of the proposed system are demonstrated in a 10-fold cross-validation. The results show that stimuli with different saliency levels can induce significant changes in occipito-parietal alpha oscillations and that alpha at higher frequency bins responded the most in involuntary attention related to bottom-up-based visual processing. This study provides a novel approach to understand the processing of involuntary attention in the brain dynamics and would further be beneficial to the development of brain-computer interfaces and visual design.

Using gene expression profiling to identify a prognostic molecular spectrum in gliomas.

Histopathological classification of gliomas is often clinically inadequate due to the diversity of tumors that fall within the same class. The goal of the present study was to identify prognostic molecular features in diffusely infiltrating gliomas using gene expression profiling. We selected 3456 genes expressed in gliomas, including 3012 genes found in a gliomal expressed sequence tag collection. The expression levels of these genes in 152 gliomas (100 glioblastomas, 21 anaplastic astrocytomas, 19 diffuse astrocytomas, and 12 anaplastic oligodendrogliomas) were measured using adapter-tagged competitive polymerase chain reaction, a high-throughput reverse transcription-polymerase chain reaction technique. We applied unsupervised and supervised principal component analyses to elucidate the prognostic molecular features of the gliomas. The gene expression data matrix was significantly correlated with the histological grades, oligo-astro histology, and prognosis. Using 110 gliomas, we constructed a prediction model based on the expression profile of 58 genes, resulting in a scheme that reliably classified the glioblastomas into two distinct prognostic subgroups. The model was then tested with another 42 tissues. Multivariate Cox analysis of the glioblastoma patients using other clinical prognostic factors, including age and the extent of surgical resection, indicated that the gene expression profile was a strong and independent prognostic parameter. The gene expression profiling identified clinically informative prognostic molecular features in astrocytic and oligodendroglial tumors that were more reliable than the traditional histological classification scheme.

High expression of ncRAN, a novel non-coding RNA mapped to chromosome 17q25.1, is associated with poor prognosis in neuroblastoma.

Neuroblastoma shows complex patterns of genetic aberrations including MYCN amplification, deletion of chromosome 1p or 11q, and gain of chromosome 17q. The 17q gain is frequently observed in high-risk neuroblastomas, however, the candidate genes still remain elusive. In the present study, we integrated the data of comparative genomic hybridization of 236 tumors by BAC array and expression profiling of 136 tumors by using the in-house cDNA microarray carrying 5,340 genes derived from primary neuroblastomas. A novel candidate gene mapped to chromosome 17q25.1 with two splicing variants, Nbla10727 and Nbla12061, was identified. The transcript size appeared to be 2.3 kb by Northern blot, however, the cDNA sequences had no obvious open reading frame. The protein product was undetectable by both in vivo and in vitro translation assays, suggesting that the transcript might not encode any protein product. Therefore, we named it as ncRAN (non-coding RNA expressed in aggressive neuroblastoma). In analysis of 70 patients with sporadic neuroblastoma, the high levels of ncRAN mRNA expression were significantly associated with poor outcome of the patients (p<0.001). The multivariate analysis showed that expression of ncRAN mRNA was an independent prognostic factor among age, stage, origin and MYCN expression. Ectopic expression of ncRAN induced transformation of NIH3T3 cells in soft agar, while knockdown of endogenous ncRAN with RNA interference significantly inhibited cell growth in SH-SY5Y cells. Collectively, our results suggest that ncRAN may be a novel non-coding RNA mapped to the region of 17q gain and act like an oncogene in aggressive neuroblastomas.

An unsupervised EEG decoding system for human emotion recognition.

Emotion plays a vital role in human health and many aspects of life, including relationships, behaviors and decision-making. An intelligent emotion recognition system may provide a flexible method to monitor emotion changes in daily life and send warning information when unusual/unhealthy emotional states occur. Here, we proposed a novel unsupervised learning-based emotion recognition system in an attempt to decode emotional states from electroencephalography (EEG) signals. Four dimensions of human emotions were examined: arousal, valence, dominance and liking. To better characterize the trials in terms of EEG features, we used hypergraph theory. Emotion recognition was realized through hypergraph partitioning, which divided the EEG-based hypergraph into a specific number of clusters, with each cluster indicating one of the emotion classes and vertices (trials) in the same cluster sharing similar emotion properties. Comparison of the proposed unsupervised learning-based emotion recognition system with other recognition systems using a well-known public emotion database clearly demonstrated the validity of the proposed system.

UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images.

Recently, there has been rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from stacks of two-dimensional (2D) electron microscopy (EM) images. The spatial scale of the 3D reconstruction increases rapidly owing to deep convolutional neural networks (CNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for CNN-based segmentation. However, the complexity of such pipelines makes their use difficult even for computer experts and impossible for non-experts. In this study, we developed a new software program, called UNI-EM, for 2D and 3D CNN-based segmentation. UNI-EM is a software collection for CNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM incorporates a set of 2D CNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D CNN-based neuron segmentation algorithm. The 2D- and 3D-CNNs are known to demonstrate state-of-the-art level segmentation performance. We then provided two example workflows: mitochondria segmentation using a 2D CNN and neuron segmentation using FFNs. By following these example workflows, users can benefit from CNN-based segmentation without possessing knowledge of Python programming or CNN frameworks.

Logical design of oral glucose ingestion pattern minimizing blood glucose in humans.

Excessive increase in blood glucose level after eating increases the risk of macroangiopathy, and a method for not increasing the postprandial blood glucose level is desired. However, a logical design method of the dietary ingestion pattern controlling the postprandial blood glucose level has not yet been established. We constructed a mathematical model of blood glucose control by oral glucose ingestion in three healthy human subjects, and predicted that intermittent ingestion 30 min apart was the optimal glucose ingestion patterns that minimized the peak value of blood glucose level. We confirmed with subjects that this intermittent pattern consistently decreased the peak value of blood glucose level. We also predicted insulin minimization pattern, and found that the intermittent ingestion 30 min apart was optimal, which is similar to that of glucose minimization pattern. Taken together, these results suggest that the glucose minimization is achieved by suppressing the peak value of insulin concentration, rather than by enhancing insulin concentration. This approach could be applied to design optimal dietary ingestion patterns.

Characterization of electroencephalography signals for estimating saliency features in videos.

Understanding the functions of the visual system has been one of the major targets in neuroscience for many years. However, the relation between spontaneous brain activities and visual saliency in natural stimuli has yet to be elucidated. In this study, we developed an optimized machine learning-based decoding model to explore the possible relationships between the electroencephalography (EEG) characteristics and visual saliency. The optimal features were extracted from the EEG signals and saliency map which was computed according to an unsupervised saliency model (Tavakoli and Laaksonen, 2017). Subsequently, various unsupervised feature selection/extraction techniques were examined using different supervised regression models. The robustness of the presented model was fully verified by means of ten-fold or nested cross validation procedure, and promising results were achieved in the reconstruction of saliency features based on the selected EEG characteristics. Through the successful demonstration of using EEG characteristics to predict the real-time saliency distribution in natural videos, we suggest the feasibility of quantifying visual content through measuring brain activities (EEG signals) in real environments, which would facilitate the understanding of cortical involvement in the processing of natural visual stimuli and application developments motivated by human visual processing.

Computational Methods for Estimating Molecular System from Membrane Potential Recordings in Nerve Growth Cone.

Biological cells express intracellular biomolecular information to the extracellular environment as various physical responses. We show a novel computational approach to estimate intracellular biomolecular pathways from growth cone electrophysiological responses. Previously, it was shown that cGMP signaling regulates membrane potential (MP) shifts that control the growth cone turning direction during neuronal development. We present here an integrated deterministic mathematical model and Bayesian reversed-engineering framework that enables estimation of the molecular signaling pathway from electrical recordings and considers both the system uncertainty and cell-to-cell variability. Our computational method selects the most plausible molecular pathway from multiple candidates while satisfying model simplicity and considering all possible parameter ranges. The model quantitatively reproduces MP shifts depending on cGMP levels and MP variability potential in different experimental conditions. Lastly, our model predicts that chloride channel inhibition by cGMP-dependent protein kinase (PKG) is essential in the core system for regulation of the MP shifts.

Constructing a meta-tracker using Dropout to imitate the behavior of an arbitrary black-box tracker.

Imitating the behaviors of an arbitrary visual tracking algorithm enables many higher level tasks such as tracker identification and efficient tracker-fusion. It is also useful for discovering the features essential in a black-box tracker or learning from several trackers to form a super-tracker. In this study, we propose a non-linear feature fusion framework, "MIMIC" that imitates many popular trackers by mixing a pool of heterogeneous features. The MIMIC framework consists of two subtasks, feature selection and feature weight tuning. These subtasks, however, tended to suffer from an overfitting problem when the number of videos available for training is limited. To address this issue, we incorporated Dropout algorithm into the training, which grants the trained MIMIC tracker a high degree of generalization. Extensive experiments testified the effectiveness of the proposed framework so that its applications would be promoted into different related tasks in visual tracking.

Model-based control of the temporal patterns of intracellular signaling in silico.

The functions of intracellular signal transduction systems are determined by the temporal behavior of intracellular molecules and their interactions. Of the many dynamical properties of the system, the relationship between the dynamics of upstream molecules and downstream molecules is particularly important. A useful tool in understanding this relationship is a methodology to control the dynamics of intracellular molecules with an extracellular stimulus. However, this is a difficult task because the relationship between the levels of upstream molecules and those of downstream molecules is often not only stochastic, but also time-inhomogeneous, nonlinear, and not one-to-one. In this paper, we present an easy-to-implement model-based control method that makes the target downstream molecule to trace a desired time course by changing the concentration of a controllable upstream molecule. Our method uses predictions from Monte Carlo simulations of the model to decide the strength of the stimulus, while using a particle-based approach to make inferences regarding unobservable states. We applied our method to in silico control problems of insulin-dependent AKT pathway model and EGF-dependent Akt pathway model with system noise. We show that our method can robustly control the dynamics of the intracellular molecules against unknown system noise of various strengths, even in the absence of complete knowledge of the true model of the target system.

Semi-supervised discovery of differential genes.

BACKGROUND: Various statistical scores have been proposed for evaluating the significance of genes that may exhibit differential expression between two or more controlled conditions. However, in many clinical studies to detect clinical marker genes for example, the conditions have not necessarily been controlled well, thus condition labels are sometimes hard to obtain due to physical, financial, and time costs. In such a situation, we can consider an unsupervised case where labels are not available or a semi-supervised case where labels are available for a part of the whole sample set, rather than a well-studied supervised case where all samples have their labels. RESULTS: We assume a latent variable model for the expression of active genes and apply the optimal discovery procedure (ODP) proposed by Storey (2005) to the model. Our latent variable model allows gene significance scores to be applied to unsupervised and semi-supervised cases. The ODP framework improves detectability by sharing the estimated parameters of null and alternative models of multiple tests over multiple genes. A theoretical consideration leads to two different interpretations of the latent variable, i.e., it only implicitly affects the alternative model through the model parameters, or it is explicitly included in the alternative model, so that the interpretations correspond to two different implementations of ODP. By comparing the two implementations through experiments with simulation data, we have found that sharing the latent variable estimation is effective for increasing the detectability of truly active genes. We also show that the unsupervised and semi-supervised rating of genes, which takes into account the samples without condition labels, can improve detection of active genes in real gene discovery problems. CONCLUSION: The experimental results indicate that the ODP framework is effective for hypotheses including latent variables and is further improved by sharing the estimations of hidden variables over multiple tests.

A multi-class predictor based on a probabilistic model: application to gene expression profiling-based diagnosis of thyroid tumors.

BACKGROUND: Although microscopic diagnosis has been playing the decisive role in cancer diagnostics, there have been cases in which it does not satisfy the clinical need. Differential diagnosis of malignant and benign thyroid tissues is one such case, and supplementary diagnosis such as that by gene expression profile is expected. RESULTS: With four thyroid tissue types, i.e., papillary carcinoma, follicular carcinoma, follicular adenoma, and normal thyroid, we performed gene expression profiling with adaptor-tagged competitive PCR, a high-throughput RT-PCR technique. For differential diagnosis, we applied a novel multi-class predictor, introducing probabilistic outputs. Multi-class predictors were constructed using various combinations of binary classifiers. The learning set included 119 samples, and the predictors were evaluated by strict leave-one-out cross validation. Trials included classical combinations, i.e., one-to-one, one-to-the-rest, but the predictor using more combination exhibited the better prediction accuracy. This characteristic was consistent with other gene expression data sets. The performance of the selected predictor was then tested with an independent set consisting of 49 samples. The resulting test prediction accuracy was 85.7%. CONCLUSION: Molecular diagnosis of thyroid tissues is feasible by gene expression profiling, and the current level is promising towards the automatic diagnostic tool to complement the present medical procedures. A multi-class predictor with an exhaustive combination of binary classifiers could achieve a higher prediction accuracy than those with classical combinations and other predictors such as multi-class SVM. The probabilistic outputs of the predictor offer more detailed information for each sample, which enables visualization of each sample in low-dimensional classification spaces. These new concepts should help to improve the multi-class classification including that of cancer tissues.

Prediction of peritoneal metastasis in advanced gastric cancer by gene expression profiling of the primary site.

Peritoneal metastasis is the most common cause of tumour progression in advanced gastric cancer. Clinicopathological findings including cytologic examination of peritoneal lavage have been applied to assess the risk of peritoneal metastasis, but are sometimes inadequate for predicting peritoneal metastasis in individuals. Hence, we tried to construct a new prediction system for peritoneal metastasis by using a PCR-based high throughput array with 2304 genes. The prediction system, constructed from the learning set comprised of 30 patients with the most informative 18 genes, classified each case into a 'good signature group' or 'poor signature group'. Then, we confirmed the predictive performance in an additional validation set comprised of 24 patients, and the prediction accuracy for peritoneal metastasis was 75%. Kaplan-Meier analysis with peritoneal metastasis revealed significant difference between these two groups (P=0.0225). By combining our system with conventional clinicopathological factors, we can identify high risk cases for peritoneal metastasis more accurately.

Numerical indices based on circulating tumor DNA for the evaluation of therapeutic response and disease progression in lung cancer patients.

Monitoring of disease/therapeutic conditions is an important application of circulating tumor DNA (ctDNA). We devised numerical indices, based on ctDNA dynamics, for therapeutic response and disease progression. 52 lung cancer patients subjected to the EGFR-TKI treatment were prospectively collected, and ctDNA levels represented by the activating and T790M mutations were measured using deep sequencing. Typically, ctDNA levels decreased sharply upon initiation of EGFR-TKI, however this did not occur in progressive disease (PD) cases. All 3 PD cases at initiation of EGFR-TKI were separated from other 27 cases in a two-dimensional space generated by the ratio of the ctDNA levels before and after therapy initiation (mutation allele ratio in therapy, MART) and the average ctDNA level. For responses to various agents after disease progression, PD/stable disease cases were separated from partial response cases using MART (accuracy, 94.7%; 95% CI, 73.5-100). For disease progression, the initiation of ctDNA elevation (initial positive point) was compared with the onset of objective disease progression. In 11 out of 28 eligible patients, both occurred within ±100 day range, suggesting a detection of the same change in disease condition. Our numerical indices have potential applicability in clinical practice, pending confirmation with designed prospective studies.