التفاصيل البيبلوغرافية
| العنوان: |
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 |
| المؤلفون: |
Yackoubov, Dima, Pato, Aviad, Rifman, Julia, Cohen, Sherri, Hailu, Astar, Brycman, Nurit, Berhani-Zipori, Orit, Edri, Avishay, Buhandler, Boaz, Shahor, Moshe, Dinowitz, Nathan, David, Amram Ben, Izraeli, Avi, Chalifa-Caspi, Vered, Levin, Liron, Rabinowitz, Joshua, Lu, Wenyun, Lodie, Tracey, Adams, Julian, Geffen, Yona |
| المصدر: |
Journal for ImmunoTherapy of Cancer ; volume 9, issue Suppl 2, page A230-A230 ; ISSN 2051-1426 |
| بيانات النشر: |
BMJ |
| سنة النشر: |
2021 |
| مصطلحات موضوعية: |
Cancer Research, Pharmacology, Oncology, Molecular Medicine, Immunology, Immunology and Allergy |
| الوصف: |
Background Nicotinamide (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. Methods In 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. Results Omics 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 ... |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
English |
| DOI: |
10.1136/jitc-2021-sitc2021.217 |
| DOI: |
10.1136/jitc-2021-SITC2021.217 |
| الإتاحة: |
https://doi.org/10.1136/jitc-2021-sitc2021.217Test |
| رقم الانضمام: |
edsbas.95363084 |
| قاعدة البيانات: |
BASE |
