ArticleOncoTargets and therapy2022
Genomic Alteration Spectrum of Non-Small Cell Lung Cancer Patients in East-China Characterized by Tumor Tissue DNA and Cell-Free DNA.
Article in OncoTargets and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- The continually evolving landscape of novel therapies in oncogene-driven advanced non-small-cell lung cancer.Therapeutic advances in medical oncology · 2025Review
- Advanced non-small-cell lung cancer: how to manageDrugs in context · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: From an oncologic perspective, genetic detection is becoming a frontline clinical test, used to identify actionable alterations for targeted therapy, monitor molecular clonal tumor evolution, indicate disease progression and prognosis, and predict medication efficacy and resistance. From analysis of both tumor tissue and cell-free DNA from a large cohort of non-small cell lung cancer patients in East-China, we characterized the full spectrum of genomic alterations. Methods: The study comprised 3000 unpaired samples including 1351 tumor tissue DNA (tDNA) and 1649 cell-free circulating tumor DNA (cfDNA) samples, from which 67 cancer-related genes were sequenced and the genetic alteration profiles were depicted. Integrative molecular analyses identified the frequently mutated genes, uncovered co-occurring somatic alterations, described the distribution of hotspot variants, analyzed the frequency of variant alleles, and notably distinguished actionable, novel variants. Results: The most commonly affected genes were EGFR, TP53, KRAS, CDKN2A, and PIK3CA in both tDNA and cfDNA samples. EGFR and CTNNB1, PIK3CA and PTEN, ERBB2 and SMO were found to have frequent co-occurring alterations in tDNA samples, while EGFR and SMO, KRAS and PDGFRA, PIK3CA and KDR were in cfDNA samples. A large number of primary druggable variants were identified in tDNA samples, while numerous drug-resistance variants, rare actionable variants, and non-EGFR actionable variants were detected in cfDNA samples. Novel variants were enriched in KDR, KIT, TP53, ABL1, FGFR1 in tDNA samples while the majority of novel variants were distributed in PDGFRA, EGFR, KIT, ROS1, BRCA2 in cfDNA samples. Variant allele frequency in tDNA samples was significantly (P < 0.001) higher than that in cfDNA samples. Conclusion: The results revealed considerable differences in the alteration characteristics between the two kinds of specimens. To date, this study represents the largest real-world investigation of its kind, derived from the largest number of patients in East-China. It reinforced and expanded the mechanism of molecular analysis of neoplastic genetic profiles.
Indexed as
Identifiers
What OpenQuestion holds
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.