Related Experiment Video

Updated: Sep 28, 2025

Establishing Dual Resistance to EGFR-TKI and MET-TKI in Lung Adenocarcinoma Cells In Vitro with a 2-step Dose-escalation Procedure
09:38

Establishing Dual Resistance to EGFR-TKI and MET-TKI in Lung Adenocarcinoma Cells In Vitro with a 2-step Dose-escalation Procedure

Published on: August 11, 2017

8.9K

Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung

Anna Gogleva1, Dimitris Polychronopoulos2, Matthias Pfeifer3

  • 1Biological Insight Knowledge Graph (BIKG), AI Engineering, R&D IT, AstraZeneca, Cambridge, UK.

Nature Communications
|March 30, 2022
PubMed

Insights

Identifying novel drug resistance markers in non-small cell lung cancer (NSCLC) is accelerated by a new AI-powered recommendation system. This tool rapidly pinpoints genetic drivers of resistance to epidermal growth factor receptor inhibitors (EGFRi), significantly reducing analysis time.

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Resistance to epidermal growth factor receptor inhibitors (EGFRi) is a significant challenge in treating non-small cell lung cancer (NSCLC).
  • Current methods for identifying resistance markers, like CRISPR screens followed by manual gene triage, are time-consuming and require specialized expertise.

Purpose of the Study:

  • To develop a rapid and efficient method for identifying key genetic drivers of EGFRi resistance in NSCLC.
  • To leverage a biomedical knowledge graph and recommendation system to accelerate the discovery of resistance markers.

Main Methods:

  • Construction of a heterogeneous biomedical knowledge graph integrating pre-clinical, clinical, and literature data.
  • Development of a recommendation system to rank genes based on integrated evidence related to EGFRi resistance mechanisms.
  • Application of the system to functional genetic screening data (e.g., CRISPR) to identify resistance markers.

Main Results:

  • The recommendation system identified 57 potential EGFRi resistance markers from over 3,000 genes.
  • This approach reduced the time for hit identification from months to minutes.
  • The system successfully reproduced known resistance markers and uncovered novel, previously unexplored resistance mechanisms.

Conclusions:

  • An AI-driven recommendation system, utilizing a biomedical knowledge graph, significantly accelerates the identification of NSCLC EGFRi resistance markers.
  • This approach offers an unbiased and efficient method for discovering novel resistance mechanisms, with potential for prospective validation.
  • The developed system has the potential to expedite drug development and improve treatment strategies for NSCLC patients.

More Related Videos

Establishment and Characterization of Three Afatinib-resistant Lung Adenocarcinoma PC-9 Cell Lines Developed with Increasing Doses of Afatinib09:38

Establishment and Characterization of Three Afatinib-resistant Lung Adenocarcinoma PC-9 Cell Lines Developed with Increasing Doses of Afatinib

Published on: June 26, 2019

8.0K
Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids08:52

Profiling Sensitivity to Targeted Therapies in EGFR-Mutant NSCLC Patient-Derived Organoids

Published on: November 22, 2021

4.2K

Related Concept Videos

Treatment Resistant Cancers02:56

Treatment Resistant Cancers

Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
3.4K
Mitogens and the Cell Cycle02:38

Mitogens and the Cell Cycle

Mitogens and their receptors play a crucial role in controlling the progression of the cell cycle. However, the loss of mitogenic control over cell division leads to tumor formation. Therefore, mitogens and mitogen receptors play an important role in cancer research. For instance, the epidermal growth factor (EGF) - a type of mitogen and its transmembrane receptor (EGFR), decides the fate of the cell's proliferation. When EGF binds to EGFR, a member of the ErbB family of tyrosine kinase...
7.0K
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
7.9K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.1K