Defining the KRAS- and ERK-dependent transcriptome in KRAS-mutant cancers.

How the KRAS oncogene drives cancer growth remains poorly understood. Therefore, we established a systemwide portrait of KRAS- and extracellular signal-regulated kinase (ERK)-dependent gene transcription in KRAS-mutant cancer to delineate the molecular mechanisms of growth and of inhibitor resistance. Unexpectedly, our KRAS-dependent gene signature diverges substantially from the frequently cited Hallmark KRAS signaling gene signature, is driven predominantly through the ERK mitogen-activated protein kinase (MAPK) cascade, and accurately reflects KRAS- and ERK-regulated gene transcription in KRAS-mutant cancer patients. Integration with our ERK-regulated phospho- and total proteome highlights ERK deregulation of the anaphase promoting complex/cyclosome (APC/C) and other components of the cell cycle machinery as key processes that drive pancreatic ductal adenocarcinoma (PDAC) growth. Our findings elucidate mechanistically the critical role of ERK in driving KRAS-mutant tumor growth and in resistance to KRAS-ERK MAPK targeted therapies.

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm.

BACKGROUND AND OBJECTIVE: Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcome. METHODS: We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. Classification accuracy of each gene were compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. RESULTS: WSO-SVM achieved 0.80 accuracy, 0.79 sensitivity, and 0.81 specificity for classifying EGFR; 0.71 accuracy, 0.70 sensitivity, and 0.72 specificity for classifying PDGFRA; 0.80 accuracy, 0.78 sensitivity, and 0.83 specificity for classifying PTEN; these results significantly outperformed the existing ML algorithms. Using SHAP, we found that the relative contributions of the five contrast images differ between genes, which are consistent with findings in the literature. The prediction maps revealed extensive intra-tumoral region-to-region heterogeneity within each individual tumor in terms of the alteration status of the three genes. CONCLUSIONS: This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma.

Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse Magnetic Resonance Imaging (MRI) with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically-informed neural network model, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet offers valuable insights into the integration of multiple implicit and qualitative biological domain knowledge, which are challenging to describe in mathematical formulations. BioNet performs significantly better than a range of existing methods on cross-validation and blind test datasets. Voxel-level prediction maps of the gene modules by BioNet help reveal intratumoral heterogeneity, which can improve surgical targeting of confirmatory biopsies and evaluation of neuro-oncological treatment effectiveness. The non-invasive nature of the approach can potentially facilitate regular monitoring of the gene modules over time, and making timely therapeutic adjustment. These results also highlight the emerging role of ML in precision medicine.

Reaching High Accuracy for Energetic Properties at Second-Order Perturbation Cost by Merging Self-Consistency and Spin-Opposite Scaling.

Quantum chemical methods dealing with challenging systems while retaining low computational costs have attracted attention. In particular, many efforts have been devoted to developing new methods based on second-order perturbation that may be the simplest correlated method beyond Hartree-Fock. We have recently developed a self-consistent perturbation theory named one-body Møller-Plesset second-order perturbation theory (OBMP2) and shown that it can resolve issues caused by the noniterative nature of standard perturbation theory. In this work, we extend the method by introducing spin-opposite scaling to the double-excitation amplitudes, resulting in the O2BMP2 method. We assess the O2BMP2 performance on the triple-bond N2 dissociation, singlet-triplet gaps, and ionization potentials. O2BMP2 performs much better than standard MP2 and reaches the accuracy of coupled-cluster methods in all cases considered in this work.

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol.

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.

Corrigendum: Applications and techniques for fast machine learning in science.

[This corrects the article DOI: 10.3389/fdata.2022.787421.].

Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing.

We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we attempt to reprocess the data by running several thousand concurrent grid jobs, a rate we expect to be typical of current and future neutrino physics experiments. We process most of the dataset with the GPU version of our processing algorithm and the remainder with the CPU version for timing comparisons. We find that a 100-GPU cloud-based server is able to easily meet the processing demand, and that using the GPU version of the event processing algorithm is two times faster than processing these data with the CPU version when comparing to the newest CPUs in our sample. The amount of data transferred to the inference server during the GPU runs can overwhelm even the highest-bandwidth network switches, however, unless care is taken to observe network facility limits or otherwise distribute the jobs to multiple sites. We discuss the lessons learned from this processing campaign and several avenues for future improvements.

Radiosensitizing Effect of Dextran-Coated Iron Oxide Nanoparticles on Malignant Glioma Cells.

The potential of standard methods of radiation therapy is limited by the dose that can be safely delivered to the tumor, which could be too low for radical treatment. The dose efficiency can be increased by using radiosensitizers. In this study, we evaluated the sensitizing potential of biocompatible iron oxide nanoparticles coated with a dextran shell in A172 and Gl-Tr glioblastoma cells in vitro. The cells preincubated with nanoparticles for 24 h were exposed to ionizing radiation (X-ray, gamma, or proton) at doses of 0.5-6 Gy, and their viability was assessed by the Resazurin assay and by staining of the surviving cells with crystal violet. A statistically significant effect of radiosensitization by nanoparticles was observed in both cell lines when cells were exposed to 35 keV X-rays. A weak radiosensitizing effect was found only in the Gl-Tr line for the 1.2 MeV gamma irradiation and there was no radiosensitizing effect in both lines for the 200 MeV proton irradiation at the Bragg peak. A slight (ca. 10%) increase in the formation of additional reactive oxygen species after X-ray irradiation was found when nanoparticles were present. These results suggest that the nanoparticles absorbed by glioma cells can produce a significant radiosensitizing effect, probably due to the action of secondary electrons generated by the magnetite core, whereas the dextran shell of the nanoparticles used in these experiments appears to be rather stable under radiation exposure.

Sialic acid-decorated liposomes enhance the anti-cancer efficacy of docetaxel in tumor-associated macrophages.

Tumor-associated macrophages (TAMs) in the tumor microenvironment potentially enhance tumor growth and invasion through various mechanisms and are thus an essential factor in tumor immunity. The highly expressed siglec-1 receptors on the surfaces of TAMs are potential targets for cancer drug delivery systems. Sialic acid (SA) is a specific ligand for siglec-1. In this study, the sialic acid-polyethylene glycol conjugate (DSPE-PEG2000-SA) was synthesized to modify the surface of liposomes and target TAMs by interacting with the siglec-1 receptor. Three docetaxel (DTX)-loaded liposomes, conventional (DTX-CL), DSPE-PEG2000-coated (DTX-PL), and DSPE-PEG2000-SA-coated (DTX-SAPL) liposomes, were prepared, with a particle size of <100 nm, uniform polydispersity index (PDI) values, negative zeta potential, and % encapsulation efficiency (EE) exceeding 95 %. Liposomes showed high stability after 3 months of storage at 4 °C without significant changes in particle size, PDI, zeta potential, or % EE. DTX was released from liposomes according to the Weibull model, and DTX-SAPL exhibited more rapid drug release than other liposomes. In vitro studies demonstrated that DTX-SAPL liposome exhibited a higher uptake and cytotoxicity on RAW 264.7 cells (TAM model) and lower toxicity on NIH3T3 cells (normal cell model) than other formulations. The high cell uptake ability was demonstrated by the role of the SA-SA receptor. Biodistribution studies indicated a high tumor accumulation of surface-modified liposomal formulations, particularly SA-modified liposomes, showing high signal accumulation at the tumor periphery, where TAMs were highly concentrated. Ex vivo imaging showed a significantly higher accumulation of SA-modified liposomes in the tumor, kidney, and heart than conventional liposomes. In the anti-cancer efficacy study, DTX-SAPL liposomes showed effective inhibition of tumor growth and relatively low systemic toxicity, as evidenced by the tumor volume, tumor weight, body weight values, and histopathological analysis. Therefore, DSPE-PEG2000-SA-coated liposomes could be promising carriers for DTX delivery targeting TAMs in cancer therapy.

Image-based models of T-cell distribution identify a clinically meaningful response to a dendritic cell vaccine in patients with glioblastoma.

BACKGROUND: Glioblastoma is an extraordinarily heterogeneous tumor, yet the current treatment paradigm is a "one size fits all" approach. Hundreds of glioblastoma clinical trials have been deemed failures because they did not extend median survival, but these cohorts are comprised of patients with diverse tumors. Current methods of assessing treatment efficacy fail to fully account for this heterogeneity. METHODS: Using an image-based modeling approach, we predicted T-cell abundance from serial MRIs of patients enrolled in the dendritic cell (DC) vaccine clinical trial. T-cell predictions were quantified in both the contrast-enhancing and non-enhancing regions of the imageable tumor, and changes over time were assessed. RESULTS: A subset of patients in a DC vaccine clinical trial, who had previously gone undetected, were identified as treatment responsive and benefited from prolonged survival. A mere two months after initial vaccine administration, responsive patients had a decrease in model-predicted T-cells within the contrast-enhancing region, with a simultaneous increase in the T2/FLAIR region. CONCLUSIONS: In a field that has yet to see breakthrough therapies, these results highlight the value of machine learning in enhancing clinical trial assessment, improving our ability to prospectively prognosticate patient outcomes, and advancing the pursuit towards individualized medicine.

WSD-0922, a novel brain-penetrant inhibitor of epidermal growth factor receptor, promotes survival in glioblastoma mouse models.

Background: Although the epidermal growth factor receptor (EGFR) is a frequent oncogenic driver in glioblastoma (GBM), efforts to therapeutically target this protein have been largely unsuccessful. The present preclinical study evaluated the novel EGFR inhibitor WSD-0922. Methods: We employed flank and orthotopic patient-derived xenograft models to characterize WSD-0922 and compare its efficacy to erlotinib, a potent EGFR inhibitor that failed to provide benefit for GBM patients. We performed long-term survival studies and collected short-term tumor, plasma, and whole-brain samples from mice treated with each drug. We utilized mass spectrometry to measure drug concentrations and spatial distribution and to assess the impact of each drug on receptor activity and cellular signaling networks. Results: WSD-0922 inhibited EGFR signaling as effectively as erlotinib in in vitro and in vivo models. While WSD-0922 was more CNS penetrant than erlotinib in terms of total concentration, comparable concentrations of both drugs were measured at the tumor site in orthotopic models, and the concentration of free WSD-0922 in the brain was significantly less than the concentration of free erlotinib. WSD-0922 treatment provided a clear survival advantage compared to erlotinib in the GBM39 model, with marked suppression of tumor growth and most mice surviving until the end of the study. WSD-0922 treatment preferentially inhibited phosphorylation of several proteins, including those associated with EGFR inhibitor resistance and cell metabolism. Conclusions: WSD-0922 is a highly potent inhibitor of EGFR in GBM, and warrants further evaluation in clinical studies.

Current State and Prospectives for Proton Boron Capture Therapy.

The development of new methods increasing the biological effectiveness of proton therapy (PT) is of high interest in radiation oncology. The use of binary technologies, in which the damaging effect of proton radiation is further enhanced by the selective accumulation of the radiosensitizer in the target tissue, can significantly increase the effectiveness of radiation therapy. To increase the absorbed dose in a tumor target, proton boron capture therapy (PBCT) was proposed based on the reaction of proton capture on the 11B isotope with the formation of three α-particles. This review summarizes data on theoretical and experimental studies on the effectiveness and prospects of proton boron capture therapy.

Income, coal consumption, and the environmental Kuznets curve in Vietnam.

Concerns over adverse environmental effects have been raised due to Vietnam's reliance on fossil fuels like coal. At the same time, efforts are being made to boost the usage of renewable energy while simultaneously lowering greenhouse gas emissions. This study examines whether there is an environmental Kuznets curve (EKC) relationship between gross domestic product (GDP) and coal consumption in Vietnam by controlling for renewable energy consumption and oil prices from 1984 to 2021. We adopt the autoregressive distributed lag (ARDL) framework to explore a long-run level relationship between the study variables. We find that the GDP elasticity of coal demand has been greater than one since the 1990s and about 3.5 in recent years, indicating that the coal intensity of GDP has increased with economic growth. Thus, the GDP-coal consumption relationship resembles an upward-sloping curve instead of an inverted U-shaped EKC. This relationship is robust when we use other estimation methods and account for two additional independent variables. While a 1% rise in renewable energy consumption results in a 0.4% reduction in coal consumption, the impact of oil prices on coal consumption is negative but insignificant. The findings allow us to provide policy implications for the sustainable development of Vietnam: (1) more stringent policies, for example, enacting a carbon pricing scheme, are needed to reduce coal consumption; (2) policies should be implemented to make renewable energy sources more affordable; and (3) as facing high oil prices, the country should diversify its energy mix by expanding the usage of renewable energy.

Genome-wide analysis of aberrant position and sequence of plasma DNA fragment ends in patients with cancer.

Genome-wide fragmentation patterns in cell-free DNA (cfDNA) in plasma are strongly influenced by cellular origin due to variation in chromatin accessibility across cell types. Such differences between healthy and cancer cells provide the opportunity for development of novel cancer diagnostics. Here, we investigated whether analysis of cfDNA fragment end positions and their surrounding DNA sequences reveals the presence of tumor-derived DNA in blood. We performed genome-wide analysis of cfDNA from 521 samples and analyzed sequencing data from an additional 2147 samples, including healthy individuals and patients with 11 different cancer types. We developed a metric based on genome-wide differences in fragment positioning, weighted by fragment length and GC content [information-weighted fraction of aberrant fragments (iwFAF)]. We observed that iwFAF strongly correlated with tumor fraction, was higher for DNA fragments carrying somatic mutations, and was higher within genomic regions affected by copy number amplifications. We also calculated sample-level means of nucleotide frequencies observed at genomic positions spanning fragment ends. Using a combination of iwFAF and nine nucleotide frequencies from three positions surrounding fragment ends, we developed a machine learning model to differentiate healthy individuals from patients with cancer. We observed an area under the receiver operative characteristic curve (AUC) of 0.91 for detection of cancer at any stage and an AUC of 0.87 for detection of stage I cancer. Our findings remained robust with as few as 1 million fragments analyzed per sample, demonstrating that analysis of fragment ends can become a cost-effective and accessible approach for cancer detection and monitoring.

Experimental validation of proton boron capture therapy for glioma cells.

Proton boron capture therapy (PBCT) has emerged from particle acceleration research for enhancing the biological effectiveness of proton therapy. The mechanism responsible for the dose increase was supposed to be related to proton-boron fusion reactions (11B + p → 3α + 8.7 MeV). There has been some experimental evidence that the biological efficiency of protons is significantly higher for boron-11-containing prostate or breast cancer cells. The aim of this study was to evaluate the sensitizing potential of sodium borocaptate (BSH) under proton irradiation at the Bragg peak of cultured glioma cells. To address this problem, cells of two glioma lines were preincubated with 80 or 160 ppm boron-11, irradiated both at the middle of 200 MeV beam Spread-Out Bragg Peak (SOBP) and at the distal end of the 89.7 MeV beam SOBP and assessed for the viability, as well as their ability to form colonies. Our results clearly show that BSH provides for only a slight, if any, enhancement of the effect of proton radiation on the glioma cells in vitro. In addition, we repeated the experiments using the Du145 prostate cancer cell line, for which an increase in the biological efficiency of proton irradiation in the presence of sodium borocaptate was demonstrated previously. The data presented add new argument against the efficiency of proton boron capture therapy when based solely on direct dose-enhancement effect by the proton capture nuclear reaction, underlining the need to investigate the indirect effects of the secondary alpha irradiation depending on the state and treatment conditions of the irradiated tissue.

Revealing the biology behind MRI signatures in high grade glioma.

Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.

Optimizing Meropenem in Highly Resistant Klebsiella pneumoniae Environments: Population Pharmacokinetics and Dosing Simulations in Critically Ill Patients.

Critically ill patients are characterized by substantial pathophysiological changes that alter the pharmacokinetics (PK) of hydrophilic antibiotics, including carbapenems. Meropenem is a key antibiotic for multidrug-resistant Gram-negative bacilli, and such pathophysiological alterations can worsen treatment outcomes. This study aimed to determine the population PK of meropenem and to propose optimized dosing regimens for the treatment of multidrug-resistant Klebsiella pneumoniae in critically ill patients. Two plasma samples were collected from eligible patients over a dosing interval. Nonparametric population PK modeling was performed using Pmetrics. Monte Carlo simulations were applied to different dosing regimens to determine the probability of target attainment and the cumulative fraction of response, taking into account the local MIC distribution for K. pneumoniae. The targets of 40% and 100% for the fraction of time that free drug concentrations remained above the MIC (ƒT>MIC) were tested, as suggested for critically ill patients. A one-compartment PK model using data from 27 patients showed high interindividual variability. Significant PK covariates were the 8-h creatinine clearance for meropenem and the presence of an indwelling catheter for pleural, abdominal, or cerebrospinal fluid drainage for the meropenem volume of distribution. The target 100% ƒT>MIC for K. pneumoniae, with a MIC of ≤2 mg/liter, could be attained by the use of a continuous infusion of 2.0 g/day. Meropenem therapy in critically ill patients could be optimized for K. pneumoniae isolates with an MIC of ≤2 mg/liter by using a continuous infusion in settings with more than 50% isolates have a MIC of ≥32mg/L.

CD33-targeting extracellular vesicles deliver antisense oligonucleotides against FLT3-ITD and miR-125b for specific treatment of acute myeloid leukaemia.

INTRODUCTION: Acute Myeloid Leukaemia (AML) is the most common blood cancer in adults. Although 2 out of 3 AML patients go into total remission after chemotherapies and targeted therapies, the disease recurs in 60%-65% of younger adult patients within 3 years after diagnosis with a dramatically decreased survival rate. Therapeutic oligonucleotides are promising treatments under development for AML as they can be designed to silence oncogenes with high specificity and flexibility. However, there are not many well validated approaches for safely and efficiently delivering oligonucleotide drugs. This issue could be resolved by utilizing a new generation of delivery vehicles such as extracellular vesicles (EVs). METHODS: In this study, we harness red blood cell-derived EVs (RBCEVs) and engineer them via exogenous drug loading and surface functionalization to develop an efficient drug delivery system for AML. Particularly, EVs are designed to target CD33, a common surface marker with elevated expression in AML cells via the conjugation of a CD33-binding monoclonal antibody onto the EV surface. RESULTS: The conjugation of RBCEVs with the CD33-binding antibody significantly increases the uptake of RBCEVs by CD33-positive AML cells, but not by CD33-negative cells. We also load CD33-targeting RBCEVs with antisense oligonucleotides (ASOs) targeting FLT3-ITD or miR-125b, 2 common oncogenes in AML, and demonstrate that the engineered EVs improve leukaemia suppression in in vitro and in vivo models of AML. CONCLUSION: Targeted RBCEVs represent an innovative, efficient, and versatile delivery platform for therapeutic ASOs and can expedite the clinical translation of oligonucleotide drugs for AML treatments by overcoming current obstacles in oligonucleotide delivery.