Whole-genome doubling drives oncogenic loss of chromatin segregation.

Whole-genome doubling (WGD) is a recurrent event in human cancers and it promotes chromosomal instability and acquisition of aneuploidies1-8. However, the three-dimensional organization of chromatin in WGD cells and its contribution to oncogenic phenotypes are currently unknown. Here we show that in p53-deficient cells, WGD induces loss of chromatin segregation (LCS). This event is characterized by reduced segregation between short and long chromosomes, A and B subcompartments and adjacent chromatin domains. LCS is driven by the downregulation of CTCF and H3K9me3 in cells that bypassed activation of the tetraploid checkpoint. Longitudinal analyses revealed that LCS primes genomic regions for subcompartment repositioning in WGD cells. This results in chromatin and epigenetic changes associated with oncogene activation in tumours ensuing from WGD cells. Notably, subcompartment repositioning events were largely independent of chromosomal alterations, which indicates that these were complementary mechanisms contributing to tumour development and progression. Overall, LCS initiates chromatin conformation changes that ultimately result in oncogenic epigenetic and transcriptional modifications, which suggests that chromatin evolution is a hallmark of WGD-driven cancer.

A combined experimental-computational approach uncovers a role for the Golgi matrix protein Giantin in breast cancer progression.

Our understanding of how speed and persistence of cell migration affects the growth rate and size of tumors remains incomplete. To address this, we developed a mathematical model wherein cells migrate in two-dimensional space, divide, die or intravasate into the vasculature. Exploring a wide range of speed and persistence combinations, we find that tumor growth positively correlates with increasing speed and higher persistence. As a biologically relevant example, we focused on Golgi fragmentation, a phenomenon often linked to alterations of cell migration. Golgi fragmentation was induced by depletion of Giantin, a Golgi matrix protein, the downregulation of which correlates with poor patient survival. Applying the experimentally obtained migration and invasion traits of Giantin depleted breast cancer cells to our mathematical model, we predict that loss of Giantin increases the number of intravasating cells. This prediction was validated, by showing that circulating tumor cells express significantly less Giantin than primary tumor cells. Altogether, our computational model identifies cell migration traits that regulate tumor progression and uncovers a role of Giantin in breast cancer progression.

CellAtlasSearch: a scalable search engine for single cells.

Owing to the advent of high throughput single cell transcriptomics, past few years have seen exponential growth in production of gene expression data. Recently efforts have been made by various research groups to homogenize and store single cell expression from a large number of studies. The true value of this ever increasing data deluge can be unlocked by making it searchable. To this end, we propose CellAtlasSearch, a novel search architecture for high dimensional expression data, which is massively parallel as well as light-weight, thus infinitely scalable. In CellAtlasSearch, we use a Graphical Processing Unit (GPU) friendly version of Locality Sensitive Hashing (LSH) for unmatched speedup in data processing and query. Currently, CellAtlasSearch features over 300 000 reference expression profiles including both bulk and single-cell data. It enables the user query individual single cell transcriptomes and finds matching samples from the database along with necessary meta information. CellAtlasSearch aims to assist researchers and clinicians in characterizing unannotated single cells. It also facilitates noise free, low dimensional representation of single-cell expression profiles by projecting them on a wide variety of reference samples. The web-server is accessible at: http://www.cellatlassearch.com.

FlavorDB: a database of flavor molecules.

Flavor is an expression of olfactory and gustatory sensations experienced through a multitude of chemical processes triggered by molecules. Beyond their key role in defining taste and smell, flavor molecules also regulate metabolic processes with consequences to health. Such molecules present in natural sources have been an integral part of human history with limited success in attempts to create synthetic alternatives. Given their utility in various spheres of life such as food and fragrances, it is valuable to have a repository of flavor molecules, their natural sources, physicochemical properties, and sensory responses. FlavorDB (http://cosylab.iiitd.edu.in/flavordb) comprises of 25,595 flavor molecules representing an array of tastes and odors. Among these 2254 molecules are associated with 936 natural ingredients belonging to 34 categories. The dynamic, user-friendly interface of the resource facilitates exploration of flavor molecules for divergent applications: finding molecules matching a desired flavor or structure; exploring molecules of an ingredient; discovering novel food pairings; finding the molecular essence of food ingredients; associating chemical features with a flavor and more. Data-driven studies based on FlavorDB can pave the way for an improved understanding of flavor mechanisms.

The statistics of how natural images drive the responses of neurons.

To model the responses of neurons in the early visual system, at least three basic components are required: a receptive field, a normalization term, and a specification of encoding noise. Here, we examine how the receptive field, the normalization factor, and the encoding noise affect the drive to model-neuron responses when stimulated with natural images. We show that when these components are modeled appropriately, the response drives elicited by natural stimuli are Gaussian-distributed and scale invariant, and very nearly maximize the sensitivity (d') for natural-image discrimination. We discuss the statistical models of natural stimuli that can account for these response statistics, and we show how some commonly used modeling practices may distort these results. Finally, we show that normalization can equalize important properties of neural response across different stimulus types. Specifically, narrowband (stimulus- and feature-specific) normalization causes model neurons to yield Gaussian response-drive statistics when stimulated with natural stimuli, 1/f noise stimuli, and white-noise stimuli. The current work makes recommendations for best practices and lays a foundation, grounded in the response statistics to natural stimuli, upon which to build principled models of more complex visual tasks.

Depth variation and stereo processing tasks in natural scenes.

Local depth variation is a distinctive property of natural scenes, but its effects on perception have only recently begun to be investigated. Depth variation in natural scenes is due to depth edges between objects and surface nonuniformities within objects. Here, we demonstrate how natural depth variation impacts performance in two fundamental tasks related to stereopsis: half-occlusion detection and disparity detection. We report the results of a computational study that uses a large database of natural stereo-images and coregistered laser-based distance measurements. First, we develop a procedure for precisely sampling stereo-image patches from the stereo-images and then quantify the local depth variation in each patch by its disparity contrast. Next, we show that increased disparity contrast degrades half-occlusion detection and disparity detection performance and changes the size and shape of the spatial integration areas ("receptive fields") that optimize performance. Then, we show that a simple image-computable binocular statistic predicts disparity contrast in natural scenes. Finally, we report the most likely spatial patterns of disparity variation and disparity discontinuities (half-occlusions) in natural scenes. Our findings motivate computational and psychophysical investigations of the mechanisms that underlie stereo processing tasks in local regions of natural scenes.

Epistasis and evolutionary dependencies in human cancers.

Cancer evolution is driven by the concerted action of multiple molecular alterations, which emerge and are selected during tumor progression. An alteration is selected when it provides an advantage to the tumor cell. However, the advantage provided by a specific alteration depends on the tumor lineage, cell epigenetic state, and presence of additional alterations. In this case, we say that an evolutionary dependency exists between an alteration and what influences its selection. Epistatic interactions between altered genes lead to evolutionary dependencies (EDs), by favoring or vetoing specific combinations of events. Large-scale cancer genomics studies have discovered examples of such dependencies, and showed that they influence tumor progression, disease phenotypes, and therapeutic response. In the past decade, several algorithmic approaches have been proposed to infer EDs from large-scale genomics datasets. These methods adopt diverse strategies to address common challenges and shed new light on cancer evolutionary trajectories. Here, we review these efforts starting from a simple conceptualization of the problem, presenting the tackled and still unmet needs in the field, and discussing the implications of EDs in cancer biology and precision oncology.

Erratum: Iyer, A., et al. Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells. J. Clin. Med. 2020, 9, 1206.

In the published manuscript [...].

Discovering functional evolutionary dependencies in human cancers.

Cancer cells retain genomic alterations that provide a selective advantage. The prediction and validation of advantageous alterations are major challenges in cancer genomics. Moreover, it is crucial to understand how the coexistence of specific alterations alters response to genetic and therapeutic perturbations. In the present study, we inferred functional alterations and preferentially selected combinations of events in >9,000 human tumors. Using a Bayesian inference framework, we validated computational predictions with high-throughput readouts from genetic and pharmacological screenings on 2,000 cancer cell lines. Mutually exclusive and co-occurring cancer alterations reflected, respectively, functional redundancies able to rescue the phenotype of individual target inhibition, or synergistic interactions, increasing oncogene addiction. Among the top scoring dependencies, co-alteration of the phosphoinositide 3-kinase (PI3K) subunit PIK3CA and the nuclear factor NFE2L2 was a synergistic evolutionary trajectory in squamous cell carcinomas. By integrating computational, experimental and clinical evidence, we provide a framework to study the combinatorial functional effects of cancer genomic alterations.

Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells.

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic system for label-free enrichment of CTCs.

Neurocognitive and psychophysiological analysis of human performance within virtual reality environments.

Monitoring the neurocognitive and psychophysiological activity of persons operating within a complex environment poses exacting measurement challenges. Three experiments are reported in this paper. In these experiments we made use of VRCPAT to assess persons' neurocognitive and psychophysiological responses to high-fidelity, immersive virtual environments. The first experiment provided continued support for the validity of the VRCPAT as a measure of learning and memory through the use of an increased sample size. In the second experiment we aimed at assessing whether an increase in stimulus complexity would result in a significant decrease in performance on attentional tasks. We also wanted to see whether an increase in stimulus intensity would result in a significant decrease in performance on attentional tasks. The third experiment looked at participants' psychophysiological responses in both low and high immersion virtual environments.

A virtual human agent for assessing bias in novice therapists.

Monitoring the psychological and physiological activity of persons interacting with virtual humans poses exacting measurement challenges. Three experiments are reported in this paper. In these experiments we made use of Virtual Human Agent technology to assess persons' psychological and physiological responses to Virtual Standardized Patients. The first experiment provided support for the usability of the Virtual Standardized Patients through the use of a virtual character emulating an adolescent male with conduct disorder. In the second experiment we further developed the technology and aimed at assessing whether novice mental health clinicians could conduct an interview with a virtual character that emulates an adolescent female who has recently been physically traumatized. The third experiment looked at the usability of Virtual Standardized Patients for eliciting psychophysiological responses following exposure to virtual humans representing different ethnicities.

Persistent reduction in global longitudinal strain in the longer term after radiation therapy in patients with breast cancer.

BACKGROUND: More than 80% of breast cancer patients receive radiotherapy (RT). However, RT can lead to cardiotoxicity, which usually develops insidiously over years, making diagnosis difficult. It is also unknown whether early identification of at-risk patients might improve long-term outcome. We have previously described subclinical alterations, detected by two-dimensional speckle tracking strain echocardiography, in left ventricular (LV) function immediately following RT in breast cancer. HYPOTHESIS: Subclinical myocardial alterations in LV function consequent to RT cardiotoxicity, observed early, persist at 12 months. METHODS: 40 chemotherapy naive women with left-sided breast cancer, treated with surgery and adjuvant breast RT, were prospectively recruited from two tertiary hospitals. Transthoracic echocardiography was performed at baseline (pre-RT), 6 weeks post-RT, and 12 months post-RT. RESULTS: An increase in LV end diastolic and end systolic volumes was seen from baseline, consistent with persistent LV remodelling; however, due to the increase in both systolic and diastolic volumes over time, no change in LV ejection fraction (EF) was observed. Global longitudinal strain (GLS) and S' velocity remained significantly lower at 12 months post-RT. GLS dropped by >10% in 16 patients and by >20% in 4 patients compared to baseline. CONCLUSIONS: Subclinical cardiac dysfunction using strain analysis, evident early, persists one year after RT, despite unchanged conventional indices such as LVEF. Persistent GLS reduction may be of particular importance in breast cancer patients receiving concomitant chemotherapy. Longer term prospective studies are required to determine if reductions in strain post-RT are associated with future adverse cardiovascular events.

Better than the real thing: eliciting fear with moving and static computer-generated stimuli.

As the popularity of virtual reality as an exposure therapy increases, it is important to validate the use of computer-generated stimuli in comparison to standardized images of "real" phobic objects, such as those of the International Affective Picture System (IAPS). The present study examined physiological and subjective measures of negative affect when viewing static IAPS images, static computer-generated images and moving videos of computer-generated images of feared stimuli and other negative stimuli which were not specifically feared. For example, a picture of a spider would be a "feared" stimulus for a spider fearful participant, whereas a picture of a snake would be categorized as a "negative" stimulus for that participant. Eighteen participants scoring high (high fear (HF) cohort) on questionnaires assessing specific fears of spiders or snakes and 20 participants scoring low (low fear (LF) cohort) on the questionnaires viewed the stimuli. The computer-generated videos elicited greater physiological (skin conductance and startle eyeblink potentiation) and self-report arousal responses than the IAPS images and the computer-generated static images. Computer-generated stills and IAPS images did not differ in eliciting emotional responses. Additionally, HF participants showed greater heart rate acceleration and larger skin conductance responses to their feared stimulus than to the negative stimulus, especially when viewing computer-generated moving videos. The results demonstrate the importance of motion in eliciting fear and the usefulness of computer-generated stimuli in the study of emotion.