| Abstract: |
| Cancer immunotherapy, which utilizes antibodies targeting immune checkpoints, has yielded
remarkable results. Despite these successes, positive responses are observed only in a specific
subgroup of patients, and the underlying biological processes determining efficacy remain
incompletely understood. This lack of comprehension poses a challenge to the development of
well-informed combination treatments. Mouse clinical experiments were conducted to
compare the cellular composition and gene expression profiles between responsive and
nonresponsive tumors in mice prior to Immune Checkpoint Blockade treatment. A
computational mathematical approach is proposed to transform the genes profiles using
Quadrant Scan and construct a differential expression network to gain a detailed insight into
the genes` dynamics through the treatment time-course and identify whether a patient will
respond to the treatment or additional drugs are needed to advance the response. |
|