Targeting MAPK Signaling in Cancer: Mechanisms of Drug Resistance and Sensitivity

Abstract

Mitogen-activated protein kinase (MAPK) pathways represent ubiquitous signal transduction pathways that regulate all aspects of life and are frequently altered in disease. Here, we focus on the role of MAPK pathways in modulating drug sensitivity and resistance in cancer. We briefly discuss new findings in the extracellular signaling-regulated kinase (ERK) pathway, but mainly focus on the mechanisms how stress activated MAPK pathways, such as p38 MAPK and the Jun N-terminal kinases (JNK), impact the response of cancer cells to chemotherapies and targeted therapies. In this context, we also discuss the role of metabolic and epigenetic aberrations and new therapeutic opportunities arising from these changes.

Keywords: ERK; JNK; MAPK; cancer; combination therapy; drug resistance; epigenetics; metabolism; p38.

Figure 1

Organization of MAPK pathways. The MAPK core consists of three kinases (MAPKKK, MAPKK, and MAPK), which form a signal transduction cascade that receives input from G-proteins and produces different biological outputs.

Figure 2

The ERK pathway functions as a negative feedback amplifier (NFA). (A) Schematic representation of the ERK pathway with approximate stoichiometries of pathway components typically found in cells and negative feedbacks indicated. (B) Comparison of a standard amplifier and NFA. The formula relating input (u) to output (y) shows that the NFA output is dominated by the strength of feedback (F) rather than the amplification (A). (C) Comparison of the standard amplifier (blue) and NFA (red). Figure adapted from [2].

Figure 3

Resistance mechanisms to RAF inhibitor. (A) Different types of signaling network adaptations restore ERK activation. No mutations that compromise drug binding to the target (RAF) have been observed. (B) Mechanisms that enhance RAF protein dimerization account for ~60% of drug resistance [17].

Figure 4

Targeting RAF dimerization. (A) X-ray structures showing the two conformations (in/out) of the Cα helix and DFG motif and their distribution in available RAF structures deposited in the Protein Data Bank. (B) Frequency of in/out distributions and RAF inhibitors targeting such conformations. (C) Depiction of the computational modeling approach developed by Rukhlenko et al. [57].

Figure 5

The Digital Twins concept. Integrating molecular, clinical, and imaging data through computational models will allow us to generate a digital twin of each patient. Treatments can be first tried on the digital twin and the best option selected for the treatment of the real patient. Likewise, personal prevention pathways can be designed. Both approaches optimize patient outcomes while minimizing costs as patients will not receive unnecessary treatments.