scholarly journals Machine learning-aided quantification of antibody-based cancer immunotherapy by natural killer cells in microfluidic droplets

Lab on a Chip ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 2317-2327 ◽  
Author(s):  
Saheli Sarkar ◽  
Wenjing Kang ◽  
Songyao Jiang ◽  
Kunpeng Li ◽  
Somak Ray ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Natural Killer Cells ◽  
Nk Cells ◽  
Cancer Immunotherapy ◽  
Natural Killer ◽  
Proteomic Profiling ◽  
Target Interaction ◽  
Microfluidic Droplets ◽  
Neural Network Algorithm

Comparative proteomic profiling and development of convolution neural network algorithm for quantifying discrete target interaction by engineered NK cells in microfluidic droplets.

scholarly journals Nano-loaded natural killer cells as carriers of indocyanine green for synergetic cancer immunotherapy and phototherapy

2019 ◽  
Vol 12 (03) ◽  
pp. 1941002 ◽  
Author(s):  
Shigao Huang ◽  
Chi Ian Fong ◽  
Mengze Xu ◽  
Bing-nan Han ◽  
Zhen Yuan ◽  
...  
Keyword(s):  
Side Effects ◽  
Natural Killer Cells ◽  
Indocyanine Green ◽  
Cancer Treatment ◽  
Nk Cells ◽  
Cancer Immunotherapy ◽  
Natural Killer ◽  
Therapeutic Options ◽  
Theranostic Agent ◽  
Cancer Theranostics

To improve the efficacy of traditional chemotherapy and radiotherapy and reduce their serious side effects, further efforts need to be exerted to identify better cancer therapeutic options that are effective, affordable, and acceptable to patients. In this study, a novel theranostic agent was produced to perform synergetic cancer immunotherapy and phototherapy. The theranostic agent, named natural killer (NK) cells carrying indocyanine green loaded liposomes was synthesized NK cells with ICG nanoparticles to serve as the agent for a newly-established cancer treatment. It is expected that the developed synergistic therapy can pave a new avenue for improved efficacy of cancer theranostics.

scholarly journals Natural Killer Cells for Cancer Immunotherapy: Pluripotent Stem Cells-Derived NK Cells as an Immunotherapeutic Perspective

2014 ◽  
Vol 5 ◽  
Author(s):  
Cristina Eguizabal ◽  
Olatz Zenarruzabeitia ◽  
Jorge Monge ◽  
Silvia Santos ◽  
Miguel Angel Vesga ◽  
...  
Keyword(s):  
Stem Cells ◽  
Natural Killer Cells ◽  
Nk Cells ◽  
Cancer Immunotherapy ◽  
Natural Killer ◽  
Pluripotent Stem Cells ◽  
Killer Cells

scholarly journals Tumor Marker B7-H6 Bound to the Coiled Coil Peptide-Polymer Conjugate Enables Targeted Therapy by Activating Human Natural Killer Cells

Biomedicines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1597
Author(s):  
Barbora Kalousková ◽  
Ondřej Skořepa ◽  
Denis Cmunt ◽  
Celeste Abreu ◽  
Kateřina Krejčová ◽  
...  
Keyword(s):  
Natural Killer Cells ◽  
Tumor Cell ◽  
Nk Cells ◽  
Cancer Immunotherapy ◽  
Natural Killer ◽  
Fas Ligand ◽  
Coiled Coil ◽  
Biologically Active ◽  
Killer Cells ◽  
Polymer Conjugate

Targeted cancer immunotherapy is a promising tool for restoring immune surveillance and eradicating cancer cells. Hydrophilic polymers modified with coiled coil peptide tags can be used as universal carriers designed for cell-specific delivery of such biologically active proteins. Here, we describe the preparation of pHPMA-based copolymer conjugated with immunologically active protein B7-H6 via complementary coiled coil VAALEKE (peptide E) and VAALKEK (peptide K) sequences. Receptor B7-H6 was described as a binding partner of NKp30, and its expression has been proven for various tumor cell lines. The binding of B7-H6 to NKp30 activates NK cells and results in Fas ligand or granzyme-mediated apoptosis of target tumor cells. In this work, we optimized the expression of coiled coil tagged B7-H6, its ability to bind activating receptor NKp30 has been confirmed by isothermal titration calorimetry, and the binding stoichiometry of prepared chimeric biopolymer has been characterized by analytical ultracentrifugation. Furthermore, this coiled coil B7-H6-loaded polymer conjugate activates NK cells in vitro and, in combination with coiled coil scFv, enables their targeting towards a model tumor cell line. Prepared chimeric biopolymer represents a promising precursor for targeted cancer immunotherapy by activating the cytotoxic activity of natural killer cells.

scholarly journals Structural plasticity of KIR2DL2 and KIR2DL3 enables altered docking geometries atop HLA-C

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shoeib Moradi ◽  
Sanda Stankovic ◽  
Geraldine M. O’Connor ◽  
Phillip Pymm ◽  
Bruce J. MacLachlan ◽  
...  
Keyword(s):  
Natural Killer Cells ◽  
Human Leukocyte Antigen ◽  
Nk Cells ◽  
Natural Killer ◽  
Crystal Structures ◽  
Killer Cell ◽  
Human Leukocyte ◽  
Leukocyte Antigen ◽  
Functional Differences ◽  
Molecular Bases

AbstractThe closely related inhibitory killer-cell immunoglobulin-like receptors (KIR), KIR2DL2 and KIR2DL3, regulate the activation of natural killer cells (NK) by interacting with the human leukocyte antigen-C1 (HLA-C1) group of molecules. KIR2DL2, KIR2DL3 and HLA-C1 are highly polymorphic, with this variation being associated with differences in the onset and progression of some human diseases. However, the molecular bases underlying these associations remain unresolved. Here, we determined the crystal structures of KIR2DL2 and KIR2DL3 in complex with HLA-C*07:02 presenting a self-epitope. KIR2DL2 differed from KIR2DL3 in docking modality over HLA-C*07:02 that correlates with variabilty of recognition of HLA-C1 allotypes. Mutagenesis assays indicated differences in the mechanism of HLA-C1 allotype recognition by KIR2DL2 and KIR2DL3. Similarly, HLA-C1 allotypes differed markedly in their capacity to inhibit activation of primary NK cells. These functional differences derive, in part, from KIR2DS2 suggesting KIR2DL2 and KIR2DL3 binding geometries combine with other factors to distinguish HLA-C1 functional recognition.

scholarly journals Potent ex vivo expanded, human CD34+ cord blood-derived natural killer cells for cancer immunotherapy

2015 ◽  
Vol 3 (S2) ◽  
Author(s):  
Xiaokui Zhang ◽  
Lin Kang ◽  
Ivana Djuretic ◽  
Eric Law ◽  
Vanessa Voskinarian-Berse ◽  
...  
Keyword(s):  
Natural Killer Cells ◽  
Cord Blood ◽  
Cancer Immunotherapy ◽  
Natural Killer ◽  
Ex Vivo ◽  
Killer Cells ◽  
Human Cd34

Natural Killer Cells: Prospects in Cancer Immunotherapy

2018 ◽  
Vol 14 (2) ◽  
pp. 100-104
Author(s):  
Bianca Dorana de Oliveira Souza ◽  
Brenda Francisconi Diaz ◽  
Gabriela Salvador Guidugli ◽  
Laura Socio Ferraz ◽  
Marla Karine Amarante ◽  
...  
Keyword(s):  
Natural Killer Cells ◽  
Cancer Immunotherapy ◽  
Natural Killer ◽  
Killer Cells

scholarly journals 217 Cytotoxicity of nicotinamide enhanced natural killer cells GDA201 is based on metabolic modulation as demonstrated by artificial intelligence assisted analysis of NK cell transcriptome and metabolome

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A230-A230
Author(s):  
Dima Yackoubov ◽  
Aviad Pato ◽  
Julia Rifman ◽  
Sherri Cohen ◽  
Astar Hailu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Informed Consent ◽  
Nk Cells ◽  
Natural Killer ◽  
Cell Function ◽  
Nk Cell ◽  
Medical Center ◽  
Enzymatic Reactions ◽  
Combined Algorithm

BackgroundNicotinamide (NAM), an allosteric inhibitor of NAD-dependent enzymes, has been shown to preserve cell function and prevent differentiation in ex vivo cell culture. GDA-201 is an investigational natural killer (NK) cell immunotherapy derived from allogeneic donors and expanded using IL-15 and NAM. In previous preclinical studies, NAM led to increased homing and cytotoxicity, preserved proliferation, and enhanced tumor reduction of NK cells. In a phase I clinical trial, treatment with GDA-201 showed tolerability and clinical responses in patients with refractory non-Hodgkin lymphoma (NHL) (Bachanova, et. al., Blood 134:777, 2019). While NAM is known to affect cellular metabolism and participate in 510 enzymatic reactions −in 66 as an inhibitor or activator− its mechanism of action and role in GDA-201 cytotoxicity is unknown.MethodsIn order to define the network of intracellular interactions that leads to the GDA-201 phenotype, flow-cytometry, next generation sequencing (NGS), and liquid chromatography–mass spectrometry (LC-MS)-based metabolite quantification were performed on NK cells cultured for 14 days with IL-15 and human serum in the presence or absence of NAM (7 mM). Artificial Intelligence (AI) machine learning analysis was applied by Pomicell in order to analyze the data using the Pomicell databases supporting data extracted from multiple origins including scientific articles organized using natural language processing tools. AI training was done using a combined algorithm designed to blindly explain and predict the transcriptomic and metabolomic (omics) profile.ResultsOmics analyses defined 1,204 differentially expressed genes, and 100 significantly modified metabolites in the presence of NAM. An in silico model was created that successfully predicted the experimental data in 83% of the cases. Upregulation of TIM-3 expression in GDA-201 was predicted to be mediated by inhibition of IL-10 and SIRT3, via CREB1/HLA-G signaling and adrenoceptor beta 2 (ADRB2) upregulation. Adenosine metabolite reduction supports this and suggests dopaminergic activation of NK cytotoxicity. Upregulation of CD62L in the presence of NAM was predicted to be mediated by transcription factor Dp-1 (TFDP1) via dihydrofolate reductase (DHFR) activation and intracellular folic acid reduction. Interferon-gamma and CASP3 modulation (via JUN and MCL1, respectively), via PPARa inhibition, support that finding.ConclusionsIn conclusion, AI machine learning of transcriptome and metabolome data revealed multiple pleiotropic metabolic pathways modulated by NAM. These data serve to further elucidate the mechanism by which NAM enhances cell function, leading to the observed cytotoxicity and potency of GDA-201.Ethics ApprovalWe hereby declare that the collection of the Apheresis units in the three participating institutes (sites) has been done under an approved clinical study that meets the following requirements:1. Ethics approval has been obtained from the local EC at each of the sites, prior to any study related activities.2. The working procedures of the EC at the sites for conduct of clinical studies are in due compliance with local regulations (Israeli Ministry of Health) and provisions of Harmonized International Guidelines for Good Clinical Practice, namely: ICH-GCP.3. Sites follow EC conditions & requirements in terms of submissions, notifications, and approval renewals. 4. Participants gave Informed Consent (approved by the EC) before taking part in the study.5. Informed Consent has been approved by the ECs. The Israeli template of Informed Consent is in used and it includes study specific information (e.g. study goal, design, method, duration, risks, etc.). Name of the Institute Name of the EC/IRB EC Study No.Hadassah Medical Center Helsinki Committee 0483-16-HMORambam Health Care Campus Helsinki Committee 0641-18-RMBIchilov Sourasky Medical Center Tel-Aviv Helsinki Committee 0025-17-TLV

scholarly journals Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study (Preprint)

2019 ◽  
Author(s):  
Longxiang Su ◽  
Chun Liu ◽  
Dongkai Li ◽  
Jie He ◽  
Fanglan Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Data Set ◽  
Machine Learning Methods ◽  
Heparin Treatment ◽  
Neural Network Algorithm ◽  
Mimic Iii ◽  
Machine Learning Models

BACKGROUND Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. OBJECTIVE The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. METHODS Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. RESULTS Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. CONCLUSIONS The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

scholarly journals In Vivo Survival and Homeostatic Proliferation of Natural Killer Cells

2003 ◽  
Vol 197 (8) ◽  
pp. 967-976 ◽  
Author(s):  
Martin Prlic ◽  
Bruce R. Blazar ◽  
Michael A. Farrar ◽  
Stephen C. Jameson
Keyword(s):  
T Cells ◽  
Natural Killer Cells ◽  
Nk Cells ◽  
Natural Killer ◽  
Cell Sorting ◽  
Functional Activity ◽  
Homeostatic Proliferation ◽  
Killer Cells ◽  
Bystander Cells

While the specificity and development of natural killer (NK) cells have been intensely studied, little is known about homeostasis of the mature NK population. Here we show that mouse NK cells undergo homeostatic proliferation when transferred into NK-deficient Rag−/− γC−/− hosts. Normal NK functional activity is maintained during this process, although there are some changes in NK phenotype. Using cell sorting, we demonstrate that mature (Mac-1hi) NK cells undergo homeostatic proliferation in an NK-deficient environment, yet immature (Mac-1lo) NK cells also proliferate in such hosts. We find that mature NK cells survive but do not proliferate in hosts which possess an endogenous NK pool. However, we go on to show that mature NK survival is critically dependent on interleukin (IL)-15. Surprisingly, NK survival is also compromised after transfer of cells into IL-15Rα−/− mice, implying that IL-15 responsiveness by bystander cells is critical for NK maintenance. These data imply that, similar to T cells, homeostasis of the NK pool is much more dynamic than previously appreciated and this may be relevant to manipulation of NK cells for therapeutic purposes.

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