Biology subjects

Wu, A.

Publications and source records attributed to Wu, A..

At least 91 records · Page 5Linked to original sources

bioRxiv2021

BehaviorDEPOT: a tool for automated behavior classification and analysis in rodents

Quantitative descriptions of animal behavior are essential to understand the underlying neural substrates. Many behavioral analyses are performed by hand or with expensive and inflexible commercial software that often fail on animals with attached head implants, such as those used for in vivo optogenetics and calcium imaging. With the development of machine learning algorithms that can estimate animal positions across time and space, it is becoming easier for users with no prior coding experience to perform automated animal tracking in behavioral video recordings. Yet classifying discrete behaviors based on positional tracking data remains a significant challenge. To achieve this, we must start with reliable ground truth definitions of behavior, a process that is hindered by unreliable human annotations. To overcome these barriers, we developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a MATLAB-based application comprising six independent modules and a graphical user interface. In the Analysis Module we provide hard-coded classifiers for freezing and rearing. Optionally applied spatiotemporal filters allow users to analyze behaviors in varied experimental designs (e.g. cued tasks or optogenetic manipulations). Even inexperienced users can generate organized behavioral data arrays that can be seamlessly aligned with neurophysiological recordings for detailed analyses of the neural substrates. Four additional modules create an easy-to-use pipeline for establishing reliable ground-truth definitions of behaviors as well as custom behavioral classifiers. Finally, our Experiment Module runs fear conditioning experiments using an Arduino-based design that interfaces with commercialhardware and significantly reduces associated costs. We demonstrate the utility and flexibility of BehaviorDEPOT in widely used behavioral assays including fear conditioning, avoidance, and decision-making tasks. We also demonstrate the robustness of the BehaviorDEPOT freezing classifier across multiple camera types and in mice and rats wearing optogenetic patch cables and head-mounted Miniscopes. BehaviorDEPOT provides a simple, flexible, automated pipeline to move from pose tracking to reliably quantifying a wide variety of task-relevant behaviors.

neuroscience↗

bioRxiv2021

Semi-supervised sequence modeling for improved behavioral segmentation

A popular approach to quantifying animal behavior from video data is through discrete behavioral segmentation, wherein video frames are labeled as containing one or more behavior classes such as walking or grooming. Sequence models learn to map behavioral features extracted from video frames to discrete behaviors, and both supervised and unsupervised methods are common. However, each approach has its drawbacks: supervised models require a time-consuming annotation step where humans must hand label the desired behaviors; unsupervised models may fail to accurately segment particular behaviors of interest. We introduce a semi-supervised approach that addresses these challenges by constructing a sequence model loss function with (1) a standard supervised loss that classifies a sparse set of hand labels; (2) a weakly supervised loss that classifies a set of easy-to-compute heuristic labels; and (3) a self-supervised loss that predicts the evolution of the behavioral features. With this approach, we show that a large number of unlabeled frames can improve supervised segmentation in the regime of sparse hand labels and also show that a small number of hand labeled frames can increase the precision of unsupervised segmentation.

animal behavior and cognition↗

bioRxiv2021

Protein detection in blood with single-molecule imaging

The ability to identify and characterize individual biomarker protein molecules in patient blood samples could enable diagnosis of diseases at an earlier stage, when treatment is typically more effective. Single-molecule imaging offers a promising approach to accomplish this goal. However, thus far single-molecule imaging methods have only been used to monitor protein molecules in solutions or cell lysates, and have not been translated into the clinical arena. Furthermore, the detection limit of these methods has been confined to the picomolar (10-12 M) range. In many diseases, the circulating concentrations of biomarker proteins fall several orders of magnitude below this range. Here we describe Single-Molecule Augmented Capture (SMAC), a single-molecule imaging technique to visualize, quantify, and characterize individual protein molecules of interest down to the subfemtomolar (<10-15 M) range, even in complex biologic fluids. We demonstrate SMAC in a wide variety of applications with human blood samples, including the analysis of disease-associated secreted proteins, membrane proteins, and rare intracellular proteins. Using ovarian cancer as a model, a lethal malignancy in which high-grade disease is driven almost universally by alterations in the TP53 gene and frequently only diagnosed at a late, incurable stage, we found that mutant pattern p53 proteins are released into the blood in patients at an early stage in disease progression. SMAC opens the door to the application of single-molecule imaging in non-invasive disease profiling and allows for the analysis of circulating mutant proteins as a new class of highly specific disease biomarkers. The SMAC platform can be adapted to multiplex or high-throughput formats to characterize heterogeneous biochemical and structural features of circulating proteins-of-interest. One Sentence SummaryA single-molecule imaging approach detects individual disease-specific protein molecules, including mutant intracellular proteins, in blood samples.

biophysics↗

bioRxiv2021

In vitro models of the human esophagus reveal ancestrally diverse response to injury

European Americans (EA) are more susceptible to esophageal tissue damage and inflammation when exposed to gastric acid and bile acid reflux and have a higher incidence of esophageal adenocarcinoma when compared to African Americans (AA). Population studies have implicated specific genes for these differences; however, the underlying cause for these differences is not well understood. We describe a robust long-term culture system to grow primary human esophagus in vitro, use single cell RNA sequencing to compare primary human biopsies to their in vitro counterparts, identify known and new molecular markers of basal cell types, and demonstrate that in vivo cellular heterogeneity is maintained in vitro. We further developed an ancestrally diverse biobank and a high-content, image based, screening assay to interrogate bile-acid injury response. These results demonstrated that AA esophageal cells responded significantly differently than EA-derived cells, mirroring clinical findings, having important implications for addressing disparities in early drug development pipelines.

cell biology↗

bioRxiv2021

Ultrasound stimulation of the motor cortex during tonic muscle contraction

AO_SCPLOWBSTRACTC_SCPLOWTranscranial ultrasound stimulation (tUS) shows potential as a noninvasive brain stimulation (NIBS) technique, offering increased spatial precision compared to other NIBS techniques. However, its reported effects on primary motor cortex (M1) are limited. We aimed to better understand tUS effects in human M1 by performing tUS of the hand area of M1 (M1hand) during tonic muscle contraction of the index finger. Stimulation during muscle contraction was chosen because of the transcranial magnetic stimulation-induced phenomenon known as cortical silent period (cSP), in which transcranial magnetic stimulation (TMS) of M1hand involuntarily suppresses voluntary motor activity. Since cSP is widely considered an inhibitory phenomenon, it presents an ideal parallel for tUS, which has often been proposed to preferentially influence inhibitory interneurons. Recording electromyography (EMG) of the first dorsal interosseous (FDI) muscle, we investigated effects on muscle activity both during and after tUS. We found no change in FDI EMG activity concurrent with tUS stimulation. Using single-pulse TMS, we found no difference in M1 excitability before versus after sparsely repetitive tUS exposure. Using acoustic simulations in models made from structural MRI of the participants that matched the experimental setups, we estimated in-brain pressures and generated an estimate of cumulative tUS exposure experienced by M1hand for each subject. We were unable to find any correlation between cumulative M1hand exposure and M1 excitability change. We also present data that suggest a TMS-induced MEP always preceded a near-threshold cSP.

neuroscience↗

bioRxiv2021

R-SPONDIN2+ Mesenchymal Cells Form the Bud Tip Progenitor Niche During Human Lung Development

Mammalian respiratory system development is regulated by complex reciprocal signaling events that take place between epithelial cells and the surrounding mesenchymal cells; however, mesenchymal heterogeneity and function in the developing human lung is poorly understood. We interrogated single cell RNA sequencing data from multiple human lung specimens and identified a mesenchymal cell population present during development that is highly enriched for expression of the WNT agonist R-SPONDIN2 (RSPO2), and we found that adjacent epithelial bud tip progenitors are enriched for the RSPO2 receptor LGR5. By carrying out functional experiments using organoid models, lung explant cultures, and FACS-isolated RSPO2+ mesenchyme, we show that RSPO2 is a critical niche cue that potentiates WNT signaling in human lung progenitors to maintain their multipotency.

developmental biology↗

bioRxiv2021

Deep neural network analysis employing diffusion basis spectrum imaging metrics as classifiers improves prostate cancer detection and grading

Structural and cellular complexity of prostatic histopathology limits the accuracy of noninvasive detection and grading of prostate cancer (PCa). We addressed this limitation by employing a novel diffusion basis spectrum imaging (DBSI) to derive structurally-specific diffusion fingerprints reflecting various underlying prostatic structural and cellular components. We further developed diffusion histology imaging (DHI) by combining DBSI-derived structural fingerprints with a deep neural network (DNN) algorithm to more accurately classify different histopathological features and predict tumor grade in PCa. We examined 243 patients suspected with PCa using in vivo DBSI. The in vivo DBSI-derived diffusion metrics detected coexisting prostatic pathologies distinguishing inflammation, PCa, and benign prostatic hyperplasia. DHI distinguished PCa from benign peripheral and transition zone tissues with over 95% sensitivity and specificity. DHI also demonstrated over 90% sensitivity and specificity for Gleason score noninvasively. We present DHI as a novel diagnostic tool capable of noninvasive detection and grading of PCa. One sentence summaryDiffusion histology imaging noninvasively and accurately detects and grades prostate cancer.

bioinformatics↗

bioRxiv2021

Brain kernel: a new spatial covariance function for fMRI data

A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore the fact that correlations in neural activity may fall off at different rates in different brain areas, or exhibit discontinuities across anatomical or functional boundaries. Moreover, such methods do not exploit the fact that widely separated brain regions may exhibit strong correlations due to bilateral symmetry or the network organization of brain regions. To capture this non-stationary spatial correlation structure, we introduce the brain kernel, a continuous covariance function for whole-brain activity patterns. We define the brain kernel in terms of a continuous nonlinear mapping from 3D brain coordinates to a latent embedding space, parametrized with a Gaussian process (GP). The brain kernel specifies the prior covariance between voxels as a function of the distance between their locations in embedding space. The GP mapping warps the brain nonlinearly so that highly correlated voxels are close together in latent space, and uncorrelated voxels are far apart. We estimate the brain kernel using resting-state fMRI data, and we develop an exact, scalable inference method based on block coordinate descent to overcome the challenges of high dimensionality (10-100K voxels). Finally, we illustrate the brain kernels usefulness with applications to brain decoding and factor analysis with multiple task-based fMRI datasets.

neuroscience↗

bioRxiv2021

Single-cell transcriptome profiling simulation reveals the impact of sequencing parameters and algorithms on clustering

Despite of scRNA-seq analytic algorithms developed, their performance for cell clustering cannot be quantified due to the unknown "true" clusters. Referencing the transcriptomic heterogeneity of cell clusters, a "true" mRNA number matrix of cell individuals was defined as ground truth. Based on the matrix and real data generation procedure, a simulation program (SSCRNA) for raw data was developed. Subsequently, the consistence between simulated data and real data was evaluated. Furthermore, the impact of sequencing depth, and algorithms for analyses on cluster accuracy was quantified. As a result, the simulation result is highly consistent with that of the real data. It is found that mis-classification rate can be attributed to multiple reasons on current scRNA platforms, and clustering accuracy is not only sensitive to sequencing depth increasement, but can also be reflected by the position of the cluster on TSNE plot. Among the clustering algorithms, Gaussian normalization method is more appropriate for current workflows. In the clustering algorithms, k-means&louvain clustering method performs better in dimension reduced data than full data, while k-means clustering method is stable under both situations. In conclusion, the scRNA simulation algorithm developed restores the real data generation process, discovered impact of parameters on mis-clustering, compared the normalization/clustering algorithms and provided novel insight into scRNA analyses.

bioinformatics↗

bioRxiv2021

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.

neuroscience↗

bioRxiv2021

Targeting fibrosis in the Duchenne Muscular Dystrophy mice model: an uphill battle

AimFibrosis is the most common complication from chronic diseases, and yet no therapy capable of mitigating its effects is available. Our goal is to unveil specific signallings regulating the fibrogenic process and to identify potential small molecule candidates that block fibrogenic differentiation of fibro/adipogenic progenitors. MethodWe performed a large-scale drug screen using muscle-resident fibro/adipogenic progenitors from a mouse model expressing EGFP under the Collagen1a1 promotor. We first confirmed that the EGFP was expressed in response to TGF{beta}1 stimulation in vitro. Then we treated cells with TGF{beta}1 alone or with drugs from two libraries of known compounds. The drugs ability to block the fibrogenic differentiation was quantified by imaging and flow cytometry. From a two-rounds screening, positive hits were tested in vivo in the mice model for the Duchenne muscular dystrophy (mdx mice). The histopathology of the muscles was assessed with picrosirius red (fibrosis) and laminin staining (myofiber size). Key findingsFrom the in vitro drug screening, we identified 21 drugs and tested 3 in vivo on the mdx mice. None of the three drugs significantly improved muscle histopathology. SignificanceThe in vitro drug screen identified various efficient compounds, none of them strongly inhibited fibrosis in skeletal muscle of mdx mice. To explain these observations, we hypothesize that in Duchenne Muscular Dystrophy, in which fibrosis is a secondary event due to chronic degeneration and inflammation, the drugs tested could have adverse effect on regeneration or inflammation, balancing off any positive effects and leading to the absence of significant results.

cell biology↗

bioRxiv2020

DOT1L Complex Regulates Transcriptional Initiation 1 in Human Cells

DOT1L, the only H3K79 methyltransferase in human cells and a homolog of the yeast Dot1, normally forms a complex with AF10, AF17 and ENL/AF9, is dysregulated in most of the cases of mixed lineage leukemia (MLL) and is believed to regulate transcriptional elongation without much evidence. Here we show that the depletion of DOT1L reduced the global occupancy without affecting the traveling ratio or the elongation rate of Pol II, suggesting it not a major elongation factor. An examination of general transcription factors (GTFs) binding revealed globally reduced TBP and TFIIA occupancies near promoters after DOT1L loss, pointing to a role in transcriptional initiation. Proteomic studies uncovered that DOT1L regulates transcriptional initiation likely by facilitating the recruitment of TFIID. Moreover, ENL, a DOT1L complex subunit with a known role in DOT1L recruitment, also regulates transcriptional initiation. Furthermore, DOT1L stimulates H2B monoubiquitination by limiting the recruitment of human SAGA complex, and the connection between Dot1/DOT1L and SAGA complex is conserved between yeast and human. These results advanced current understanding of roles of DOT1L complex in transcriptional regulation and MLL.

molecular biology↗

bioRxiv2020

Prokaryotic virus Host Predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics

BackgroundViruses are ubiquitous biological entities, estimated to be the largest reservoirs of unexplored genetic diversity on Earth. Full functional characterization and annotation of newly-discovered viruses requires tools to enable taxonomic assignment, the range of hosts, and biological properties of the virus. Here we focus on prokaryotic viruses, which include phages and archaeal viruses, and for which identifying the viral host is an essential step in characterizing the virus, as the virus relies on the host for survival. Currently, the method for determining the viral host is either to culture the virus, which is low-throughput, time-consuming, and expensive, or to computationally predict the viral hosts, which needs improvements at both accuracy and usability. Here we develop a Gaussian model to predict hosts for prokaryotic viruses with better performances than previous computational methods. ResultsWe present here Prokaryotic virus Host Predictor (PHP), a software tool using a Gaussian model, to predict hosts for prokaryotic viruses using the differences of Ae-mer frequencies between viral and host genomic sequences as features. PHP gave a host prediction accuracy of 34% (genus level) on the VirHostMatcher benchmark dataset and a host prediction accuracy of 35% (genus level) on a new dataset containing 671 viruses and 60,105 prokaryotic genomes. The prediction accuracy exceeded that of two alignment-free methods (VirHostMatcher and WIsH, 28%-34%, genus level). PHP also outperformed these two alignment-free methods much (24%-38%vs 18%-20%, genus level) when predicting hosts for prokaryotic viruses which cannot be predicted by the BLAST-based or the CRISPR-spacer-based methods alone. Requiring a minimal score for making predictions (thresholding) and taking the consensus of the top 30 predictions further improved the host prediction accuracy of PHP. ConclusionsThe Prokaryotic virus Host Predictor software tool provides an intuitive and user-friendly API for the Gaussian model described herein. This work will facilitate the rapid identification of hosts for newly-identified prokaryotic viruses in metagenomic studies. Author SummaryProkaryotic viruses which include phages and archaeal viruses play an important role in balancing the global ecosystem by regulating the composition of bacteria and archaea in water and soil. Identifying the viral host is essential for characterizing the virus, as the virus relies on the host for survival. Currently, the method for determining the viral host is either to culture the virus which is low-throughput, time-consuming, and expensive, or to computationally predict the viral hosts which needs improvements at both accuracy and usability. This study developed a Gaussian model to predict hosts for prokaryotic viruses with better performances than previous computational methods. It will contribute to the rapid identification of hosts for prokaryotic viruses in metagenomic studies, and will extend our knowledge of virus-host interactions.

bioinformatics↗

bioRxiv2020

Controlling T cells shape, mechanics and activation by micropatterning.

We designed a strategy, based on a careful examination of the activation capabilities of proteins and antibodies used as substrates for adhering T cells, coupled to protein microstamping. This allowed us to control at the same time the position, shape, mechanics and activation state of T cells. Once adhered on shaped patterns we examined the capacities of T cells to be activated with soluble aCD3, in comparison to T cells adhered to a continuously decorated substrate with the same density of ligands. We show that, in our hand, adhering onto an anti CD45 (aCD45) antibody decorated surface is not affecting T cell calcium fluxes, even adhered on variable size micro-patterns. We further demonstrate this by expressing MEGF10 as a non immune adhesion receptor in T cells to obtain the very same spreading area on PLL substrates and Young modulus than immobilized cells on aCD45, while retaining similar activation capabilities using soluble aCD3 or through model APC contacts. We propose that our system is a way to test activation or anergy of T cells with defined adhesion and mechanical characteristics, and may allow to dissect fine details of these mechanisms since it allows to observe homogenised populations in standardized T cell activation assays.

biophysics↗

bioRxiv2020

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking

Noninvasive behavioral tracking of animals is crucial for many scientific investigations. Recent transfer learning approaches for behavioral tracking have considerably advanced the state of the art. Typically these methods treat each video frame and each object to be tracked independently. In this work, we improve on these methods (particularly in the regime of few training labels) by leveraging the rich spatiotemporal structures pervasive in behavioral video -- specifically, the spatial statistics imposed by physical constraints (e.g., paw to elbow distance), and the temporal statistics imposed by smoothness from frame to frame. We propose a probabilistic graphical model built on top of deep neural networks, Deep Graph Pose (DGP), to leverage these useful spatial and temporal constraints, and develop an efficient structured variational approach to perform inference in this model. The resulting semi-supervised model exploits both labeled and unlabeled frames to achieve significantly more accurate and robust tracking while requiring users to label fewer training frames. In turn, these tracking improvements enhance performance on downstream applications, including robust unsupervised segmentation of behavioral "syllables," and estimation of interpretable "disentangled" low-dimensional representations of the full behavioral video. Open source code is available at https://github.com/paninski-lab/deepgraphpose.

animal behavior and cognition↗

bioRxiv2020

Single-Strand DNA Breaks Cause Replisome Disassembly

DNA damage impedes replication fork progression and threatens genome stability. Upon encounter with most DNA adducts, the replicative CMG helicase (CDC45-MCM2-7-GINS) stalls or uncouples from the point of synthesis, yet CMG eventually resumes replication. However, little is known about the effect on replication of single-strand breaks or "nicks", which are abundant in mammalian cells. Using Xenopus egg extracts, we reveal that CMG collision with a nick in the leading strand template generates a blunt-ended double-strand break (DSB). Moreover, CMG, which encircles the leading strand template, "runs off" the end of the DSB. In contrast, CMG collision with a lagging strand nick generates a broken end with a single-stranded overhang. In this setting, CMG translocates beyond the nick on double-stranded DNA and is then actively removed from chromatin by the p97 ATPase. Our results show that nicks are uniquely dangerous DNA lesions that invariably cause replisome disassembly, and they argue that CMG cannot be deposited on dsDNA while cells resolve replication stress. HighlightsO_LIThe structures of leading and lagging strand collapsed forks are different C_LIO_LICMG passively "runs off" the broken DNA end during leading strand fork collapse C_LIO_LICMG is unloaded from duplex DNA after lag collapse in a p97-dependent manner C_LIO_LINicks are uniquely toxic lesions that cause fork collapse and replisome disassembly C_LI

molecular biology↗

bioRxiv2020

An organoid and multi-organ developmental cell atlas reveals multilineage fate specification in the human intestine

Human intestinal organoids (HIOs) generated from pluripotent stem cells provide extraordinary opportunities to explore development and disease. Here, we generate a single-cell transcriptome reference atlas from HIOs and from multiple developing human organs to quantify the specificity of HIO cell fate acquisition, and to explore alternative fates. We identify epithelium-mesenchyme interactions, transcriptional regulators involved in cell fate specification, and stem cell maturation features in the primary tissue that are recapitulated in HIOs. We use an HIO time course to reconstruct the molecular dynamics of intestinal stem cell emergence, as well as the specification of multiple mesenchyme subtypes. We find that the intestinal master regulator CDX2 correlates with distinct phases of epithelial and mesenchymal development, and CDX2 deletion perturbs the differentiation of both intestinal epithelium and mesenchyme. Collectively our data provides a comprehensive and quantitative assessment of HIO development, and illuminates the molecular machinery underlying endodermal and mesodermal cell fate specification.

developmental biology↗

bioRxiv2020

PI3Kγ inhibition suppresses microglia/TAM accumulation in glioblastoma microenvironment to promote exceptional temozolomide response

Precision medicine in oncology leverages clinical observations of exceptional response. Towards an understanding of the molecular features that define this response, we applied an integrated, multi-platform analysis of RNA profiles derived from clinically annotated glioblastoma samples. This analysis suggested that specimens from exceptional responders are characterized by decreased accumulation of microglia/macrophages in the glioblastoma microenvironment. Glioblastoma-associated microglia/macrophages secreted interleukin 11 (IL11) to activate STAT3-MYC signaling in glioblastoma cells. This signaling induced stem cell states that confer enhanced tumorigenicity and resistance to the standard-of-care chemotherapy, temozolomide (TMZ). Targeting a myeloid cell restricted isoform of PI3K, PI3K{gamma}, by pharmacologic inhibition or genetic inactivation, disrupted this signaling axis by suppressing microglia/macrophage accumulation and associated IL11 secretion in the tumor microenvironment. Mirroring the clinical outcomes of exceptional responders, PI3K{gamma} inhibition synergistically enhanced the anti-neoplastic effects of TMZ in orthotopic murine glioblastoma models. Moreover, inhibition or genetic inactivation of PI3K{gamma} in murine glioblastoma models recapitulated expression profiles observed in clinical specimens isolated from exceptional responders. Our results suggest key contributions from tumor-associated microglia/macrophages in exceptional responses and highlight the translational potential for PI3K{gamma} inhibition as a glioblastoma therapy. Significance StatementUnderstanding the basis for exceptional responders represents a key pillar in the framework of precision medicine. In this study, we utilized distinct informatics platforms to analyze the expression profiles of clinically annotated tumor specimens derived from patients afflicted with glioblastoma, the most common form of primary brain cancer. These analyses converged on prognostic contributions from glioblastoma-associated microglia/macrophages. Glioblastoma-associated microglia secreted interleukin 11 (IL11) to activate a STAT3-MYC signaling axis in glioblastoma cells, facilitating resistance to the standard-of-care chemotherapy, temozolomide. Microglia recruitment and IL11 secretion were dependent on the myeloid specific phosphoinositide-3-kinase gamma isoform (PI3K{gamma}). Inhibition or genetic inactivation of PI3K{gamma} in murine glioblastoma models recapitulated expression profiles observed in specimens derived from exceptional responders, suggesting potential for clinical translation.

cancer biology↗