ArticleBriefings in bioinformatics2021
Identification of genetic variations associated with drug resistance in non-small cell lung cancer patients undergoing systemic treatment.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Multi-omics integration in deciphering non-small cell lung cancer drug resistance: current status, challenges, and future prospects.Hereditas · 2026Review
- Circulating tumor DNA in lung cancer: a bibliometric analysis of studies published from 2001 to 2024.Journal of thoracic disease · 2025Article
- Development and validation of hierarchical signature for precision individualized therapy based on the landscape associated with necroptosis in clear cell renal cell carcinoma.Frontiers in pharmacology · 2025Article
- Circulating tumor DNA methylation detection as biomarker and its application in tumor liquid biopsy: advances and challenges.MedComm · 2024Review
- Effects of ABCB1 DNA methylation in donors on tacrolimus blood concentrations in recipients following liver transplantation.British journal of clinical pharmacology · 2022Article
- FCGR3A: A new biomarker with potential prognostic value for prostate cancer.Frontiers in oncology · 2022Article
- Development and Validation of an ADME-Related Gene Signature for Survival, Treatment Outcome and Immune Cell Infiltration in Head and Neck Squamous Cell Carcinoma.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-small cell lung cancer (NSCLC) is characterized by relatively rapid response to systemic treatments yet inevitable resistance and predisposed to distant metastasis. We thus aimed at performing sequencing analysis to determine genomic events and underlying mechanisms concerning drug resistance in NSCLC. We performed targeted sequencing of 40 medication-relevant genes on plasma samples from 98 NSCLC patients and analyzed impact of genetic alterations on clinical presentation as well as response to systemic treatments. Profiling of multi-omics data from 1024 NSCLC tissues in public datasets was carried out for comparison and validation of identified molecular events implicated in resistance. A genetic association of CYP2D6 deletion with drug resistance was identified through circulating tumor DNA (ctDNA) profiling and response assessment. FCGR3A amplification was potentially involved in resistance to EGFR inhibitors. We further verified our findings in tissue samples and focused on potential resistance mechanisms, which uncovered that depleted CYP2D6 affected a set of genes involved in EMT, oncogenic signaling as well as inflammatory pathways. Tumor microenvironment analysis revealed that NSCLC with CYP2D6 loss manifested increased levels of immunomodulatory gene expressions, PD-L1 expression, relatively high mutational burden and lymphocyte infiltration. DNA methylation alterations were also found to be correlated with mRNA expressions and copy numbers of CYP2D6. Finally, MEK inhibitors were identified by CMap as the prospective therapeutic drugs for CYP2D6 deletion. These analyses identified novel resistance mechanisms to systemic NSCLC treatments and had significant implications for the development of new treatment strategies.
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Registered trials
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.