Modeling Immunotherapy in Ex Vivo Organ Culture of Non-Small Cell Lung Cancer (Nsclc)
Cancer Research(2016)SCI 1区
Sheba Medical Ctr. Inst. of Oncology
Abstract
Abstract PD1-PDL1 interaction is one mechanism of tumor evasion of the immune system, and inhibitors of this interaction can allow cancer cell killing by cytotoxic T cells. In NSCLC as well as in most other cancers, the clinical benefit from such inhibitors is far from universal; around 20% of tumors respond to this treatment. Response of tumors to anti-cancer agents depends on interactions of epithelial tumor cells and the microenvironment, including stromal cells such as fibroblasts, immune cells and extracellular matrix. Studying cell signaling and drug-sensitivity of cancer should take into consideration the different compartments of an individual tumor. Regarding immunotherapy, manipulating regulators of PDL1 expression might augment the activity of these drugs, or possibly be active as an immunotherapy on its own. Specifically, little is known about the impact of chemotherapy, radiotherapy or targeted agents on the expression and activity of the PD1-PDL1 signaling in human cancer. Combining such treatments with immunotherapy is a potentially promising approach that is currently investigated clinically. However, the number of potential combinations is vast, and no valid and convenient experimental model exists to test candidate treatments and combinations. Experimental procedures: Ex vivo organ cultures (EVOC) were directly established from fresh NSCLC tissues, as a model that recapitulates real tumor and its microenvironment, including immune cells. Tissue elements were mechanistically dispersed to cell clumps (30-100 cells per clump), or cut to one cubic mm pieces and placed in culture. LDH release was used as a surrogate of cell death. Samples were analyzed by formalin fixation and paraffin embedment, sectioning and hematoxilin and eosin visualization of cells. PDL1 mRNA and protein levels were measured by RT-PCR and western blots. Results: Cell viability of NSCLC EVOC is maintained over a time window of at least 4-7 days. Cytotoxic drugs evoke cell death. PDL1 mRNA and protein levels are elevated in NSCLC EVOC in response to inflammation signals as Interferon gamma. Glucocorticoid steroidal drugs causes reduction in PDL1 mRNA and protein in NSCLC EVOC. Cisplatin treatment causes elevation in PDL1 protein. Variability in basal and induced PDL1 protein levels was detected in response to inflammation signals in EVOCs generated from different patients. Conclusions: Our results indicate the feasibility of EVOC for NSCLC and the potential to use it as a model to study the impact of immunotherapy agents, alone or in combination with other therapeutic tools such as chemotherapy or radiotherapy. Citation Format: Jair Bar, Inbal Daniel-Meshulam, Amir Onn, Alon Ben-Nun, David Simansky, Nona Zeitlin, Nir Golan, Meirav Rokah, Ronni Ben-Avi, Iris Kamer. Modeling immunotherapy in ex vivo organ culture of non-small cell lung cancer (NSCLC). [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4910.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper