Dataset Information

A systematic analysis of signaling reactivation and drug resistance.

ABSTRACT: Increasing evidence suggests that the reactivation of initially inhibited signaling pathways causes drug resistance. Here, we analyze how network topologies affect signaling responses to drug treatment. Network-dependent drug resistance is commonly attributed to negative and positive feedback loops. However, feedback loops by themselves cannot completely reactivate steady-state signaling. Newly synthesized negative feedback regulators can induce a transient overshoot but cannot fully restore output signaling. Complete signaling reactivation can only occur when at least two routes, an activating and inhibitory, connect an inhibited upstream protein to a downstream output. Irrespective of the network topology, drug-induced overexpression or increase in target dimerization can restore or even paradoxically increase downstream pathway activity. Kinase dimerization cooperates with inhibitor-mediated alleviation of negative feedback. Our findings inform drug development by considering network context and optimizing the design drug combinations. As an example, we predict and experimentally confirm specific combinations of RAF inhibitors that block mutant NRAS signaling.

SUBMITTER: Kholodenko BN

PROVIDER: S-EPMC8202068 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A systematic analysis of signaling reactivation and drug resistance.

Kholodenko Boris N BN Rauch Nora N Kolch Walter W Rukhlenko Oleksii S OS

Cell reports 20210501 8

Increasing evidence suggests that the reactivation of initially inhibited signaling pathways causes drug resistance. Here, we analyze how network topologies affect signaling responses to drug treatment. Network-dependent drug resistance is commonly attributed to negative and positive feedback loops. However, feedback loops by themselves cannot completely reactivate steady-state signaling. Newly synthesized negative feedback regulators can induce a transient overshoot but cannot fully restore out ...[more]

PMID: 34038718

Similar Datasets

Project description:BackgroundCancer chemotherapy resistance is one of the most critical obstacles in cancer therapy. Since Warburg O first observed alterations in cancer metabolism in the 1950s, people gradually found tumor metabolism pathways play a fundamental role in regulating the response to chemotherapeutic drugs, and the attempts of targeting tumor energetics have shown promising preclinical outcomes in recent years. This study aimed to summarize the knowledge structure and identify emerging trends and potential hotspots in metabolic signaling pathways of tumor drug resistance research.MethodsPublications related to metabolic signaling pathways of tumor drug resistance published from 1992 to 2022 were retrieved from the Web of Science Core Collection database. The document type was set to articles or reviews with language restriction to English. Two different scientometric software including Citespace and VOS viewer were used to conduct this scientometric analysis.ResultsA total of 2,537 publications including 1,704 articles and 833 reviews were retrieved in the final analysis. The USA made the most contributions to this field. The leading institution was the University of Texas MD Anderson Cancer Center. Avan A was the most productive author, and Hanahan D was the key researcher with the most co-citations, but there is no leader in this field yet. Cancers was the most influential academic journal, and Oncology was the most popular research field. Based on keywords occurrence analysis, these selected keywords could be roughly divided into five main topics: cluster 1 (study of cancer cell apoptosis pathway); cluster 2 (study of resistance mechanisms of different cancer types); cluster 3 (study of cancer stem cells); cluster 4 (study of tumor oxidative stress and inflammation signaling pathways); and cluster 5 (study of autophagy). The keywords burst detection identified several keywords as new research hotspots, including "tumor microenvironment," "invasion," and "target".ConclusionTumor metabolic reprogramming of drug resistance research is advancing rapidly. This study serves as a starting point, providing a thorough overview, the development landscape, and future opportunities in this field.

Project description:Gene Expression Profiling of Breast Cancer Patients with Brain Metastases Brain metastases confer the worst prognosis of breast cancer as no therapy exists that prevents or eliminates the cancer from spreading to the brain. We developed a new computational modeling method to derive specific downstream signaling pathways that reveal unknown target-disease connections and new mechanisms for specific cancer subtypes. The model enables us to reposition drugs based on available gene expression data of patients. We applied this model to repurpose known or shelved drugs for brain, lung, and bone metastases of breast cancer with the hypothesis that cancer subtypes have their own specific signaling mechanisms. To test the hypothesis, we addressed the specific CSBs for each metastasis that satisfy that (1) CSB proteins are activated by the maximal number of enriched signaling pathways specific to this metastasis, and (2) CSB proteins involve in the most differential expressed coding-genes specific to the specific breast cancer metastasis. The identified signaling networks for the three types of metastases contain 31, 15, and 18 proteins, respectively, and are used to reposition 15, 9, and 2 drug candidates for the brain, lung, and bone metastases of breast cancer. We performed in vitro and in vivo preclinical experiments as well as analysis on patient tumor specimens to evaluate the targets and repositioned drugs. Two known drugs, Sunitinib (FDA approved for renal cell carcinoma and imatinib-resistant gastrointestinal stromal tumor) and Dasatinib (FDA approved for chronic myelogenous leukemia (CML) after imatinib treatment and Philadelphia chromosome-positive acute lymphoblastic leukemia), were shown to prohibit the metastatic colonization in brain. The TMH-52 cohort includes 11 patients who were examined with brain metastasis at the time of breast cancer diagnosis. The other 41 patients were examined with other organ metastasis at the time of breast cancer diagnosis.

Dataset Information

A systematic analysis of signaling reactivation and drug resistance.

Publications

A systematic analysis of signaling reactivation and drug resistance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets