ArticleScientific reports2017
Detection of Somatic Mutations in Exome Sequencing of Tumor-only Samples.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Validation and Implementation of a Somatic-Only Tumor Exome for Routine Clinical Application.The Journal of molecular diagnostics : JMD · 2024Article
- Exploring the Role of Non-synonymous and Deleterious Variants Identified in Colorectal Cancer: A Multi-dimensional Computational Scrutiny of Exomes.Current genomics · 2024Article
- Article
- BDL-SP: A Bio-inspired DL model for the identification of altered Signaling Pathways in Multiple Myeloma using WES data.American journal of cancer research · 2023Article
- Detection of Potential Mutated Genes Associated with Common Immunotherapy Biomarkers in Non-Small-Cell Lung Cancer Patients.Current oncology (Toronto, Ont.) · 2022Article
- UNMASC: tumor-only variant calling with unmatched normal controls.NAR cancer · 2021Article
- Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): challenges and guidelines.BMC medical genomics · 2019Article
- Comprehensive genomic transcriptomic tumor-normal gene panel analysis for enhanced precision in patients with lung cancer.Oncotarget · 2018Article
- Identification of Single Nucleotide Non-coding Driver Mutations in Cancer.Frontiers in genetics · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to lack of normal samples in clinical diagnosis and to reduce costs, detection of small-scale mutations from tumor-only samples is required but remains relatively unexplored. We developed an algorithm (GATKcan) augmenting GATK with two statistics and machine learning to detect mutations in cancer. The averaged performance of GATKcan in ten experiments outperformed GATK in detecting mutations of randomly sampled 231 from 241 TCGA endometrial tumors (EC). In external validations, GATKcan outperformed GATK in TCGA breast cancer (BC), ovarian cancer (OC) and melanoma tumors, in terms of Matthews correlation coefficient (MCC) and precision, where MCC takes both sensitivity and specificity into account. Further, GATKcan reduced high fractions of false positives detected by GATK. In mutation detection of somatic variants, classified commonly by VarScan 2 and MuTect from the called variants in BC, OC and melanoma, ranked by adjusted MCC (adjusted precision) GATKcan was the top 1, followed by MuTect, VarScan 2 and GATK. Importantly, GATKcan enables detection of mutations when alternate alleles exist in normal samples. These results suggest that GATKcan trained by a cancer is able to detect mutations in future patients with the same type of cancer and is likely applicable to other cancers with similar mutations.
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.