ArticleOncotarget2015
A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation.
Article in Oncotarget, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 54 citations in OpenAlex.
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- Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments.BMC medical genomics · 2020Article
- Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology.International journal of molecular sciences · 2020Article
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- Analysis of the autophagy gene expression profile of pancreatic cancer based on autophagy-related protein microtubule-associated protein 1A/1B-light chain 3.World journal of gastroenterology · 2019Article
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- Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs.Frontiers in pharmacology · 2019Article
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- Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare.Oncotarget · 2018Article
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- A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency.Cell cycle (Georgetown, Tex.) · 2018Article
- The expression of CCL18 in diffuse large B cell lymphoma and its mechanism research.Cancer biomarkers : section A of Disease markers · 2018Article
- Drug Repositioning in Glioblastoma: A Pathway Perspective.Frontiers in pharmacology · 2018Review
- Personalized prescription of tyrosine kinase inhibitors in unresectable metastatic cholangiocarcinoma.Experimental hematology & oncology · 2018Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.