Study of dose-dependent combination immunotherapy using engineered T cells and IL-2 in cervical cancer
Clicks: 87
ID: 281552
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
25.8
/100
87 views
27 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #187 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Adoptive T cell based immunotherapy is gaining significant traction in cancer
treatment. Despite its limited success, so far, in treating solid cancers, it
is increasingly successful, demonstrating to have a broader therapeutic
potential. In this paper we develop a mathematical model to study the efficacy
of engineered T cell receptor (TCR) T cell therapy targeting the E7 antigen in
cervical cancer cell lines. We consider a dynamical system that follows the
population of cancer cells, TCR T cells, and IL-2. We demonstrate that there
exists a TCR T cell dosage window for a successful cancer elimination that can
be expressed in terms of the initial tumor size. We obtain the TCR T cell dose
for two cervical cancer cell lines: 4050 and CaSki. Finally, a combination
therapy of TCR T cell and IL-2 treatment is studied. We show that certain
treatment protocols can improve therapy responses in the 4050 cell line, but
not in the CaSki cell line.
| Reference Key |
levy2020study
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Heyrim Cho; Zuping Wang; Doron Levy |
| Journal | arXiv |
| Year | 2020 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.