A mathematical model for assessing KRAS mutation effect on monoclonal antibody treatment of colorectal cancer

N/ACitations
Citations of this article
3Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The most challenging task in colorectal cancer research nowadays is to understand the development of acquired resistance to anti-EGFR drugs. The key reason for this problem is the KRAS mutations produced after the treatment with monoclonal antibodies (mAb). KRAS screening tests done before the start of the treatment are not very sensitive to identify minute quantity of the mutated cells, which can produce resistance to the therapy after the beginning of the treatment. Here we present a mathematical model for the analysis of KRAS mutations behavior in colorectal cancer with respect to mAb treatments. To evaluate the drug performance we have developed equations for two types of tumors cells, i.e. KRAS mutated and KRAS wildtype. Both tumor cell populations were treated with a combination of mAb and chemotherapy drugs. It was observed that even the minimal initial concentration of KRAS mutation before the treatment has the ability to make the tumor refractory to the treatment. Patient’s immune responses are specifically taken into considerations and it is found that, in case of KRAS mutations, the immune strength does not affect medication efficacy. Finally, Cetuximab (mAb) and Irinotecan (chemotherapy) drugs are analyzed as firstline treatment of colorectal cancer with few KRAS mutated cells. Results show that this combined treatment is only effective for patients with high immune strengths and it should not be recommended as first-line therapy for patients with moderate immune strengths or weak immune systems because of a potential risk of relapse, with KRAS mutant cells acquired resistance involved with them.

Cite

CITATION STYLE

APA

Sameen, S., Barbuti, R., Milazzo, P., & Cerone, A. (2015). A mathematical model for assessing KRAS mutation effect on monoclonal antibody treatment of colorectal cancer. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8938, pp. 243–258). Springer Verlag. https://doi.org/10.1007/978-3-319-15201-1_16

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free