Performance evaluation method for read mapping tool in clinical panel sequencing

Lee, Hojun; Lee, Kiwook; Lee, Taeseob; Park, Donghyun; Chung, Jongsuk; Lee, Chung; Park, Woong-Yang; Son, Dae‐Soon

doi:10.1007/s13258-017-0621-9

Cited by 12 publications

(11 citation statements)

References 24 publications

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“…Although, BWA-MEM and Bowtie2 demonstrated highly comparable mapping efficiencies, implementation of BWA-MEM mapping possessed the highest mapping efficiency. Our results agree with studies reporting that BWA-MEM possessed a lower number of misaligned reads compared to Bowtie2 [ 6 , 9 , 28 ]. Opposed to results reported by others [ 29 ], Stampy demonstrated the lowest mapping efficiency with over than 5% of unmapped reads.…”

Section: Discussionsupporting

confidence: 92%

Performance evaluation of pipelines for mapping, variant calling and interval padding, for the analysis of NGS germline panels

et al. 2021

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Background Next-generation sequencing (NGS) represents a significant advancement in clinical genetics. However, its use creates several technical, data interpretation and management challenges. It is essential to follow a consistent data analysis pipeline to achieve the highest possible accuracy and avoid false variant calls. Herein, we aimed to compare the performance of twenty-eight combinations of NGS data analysis pipeline compartments, including short-read mapping (BWA-MEM, Bowtie2, Stampy), variant calling (GATK-HaplotypeCaller, GATK-UnifiedGenotyper, SAMtools) and interval padding (null, 50 bp, 100 bp) methods, along with a commercially available pipeline (BWA Enrichment, Illumina®). Fourteen germline DNA samples from breast cancer patients were sequenced using a targeted NGS panel approach and subjected to data analysis. Results We highlight that interval padding is required for the accurate detection of intronic variants including spliceogenic pathogenic variants (PVs). In addition, using nearly default parameters, the BWA Enrichment algorithm, failed to detect these spliceogenic PVs and a missense PV in the TP53 gene. We also recommend the BWA-MEM algorithm for sequence alignment, whereas variant calling should be performed using a combination of variant calling algorithms; GATK-HaplotypeCaller and SAMtools for the accurate detection of insertions/deletions and GATK-UnifiedGenotyper for the efficient detection of single nucleotide variant calls. Conclusions These findings have important implications towards the identification of clinically actionable variants through panel testing in a clinical laboratory setting, when dedicated bioinformatics personnel might not always be available. The results also reveal the necessity of improving the existing tools and/or at the same time developing new pipelines to generate more reliable and more consistent data.

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Section: Discussionsupporting

confidence: 92%

Performance evaluation of pipelines for mapping, variant calling and interval padding, for the analysis of NGS germline panels

et al. 2021

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“…In addition, mutation in a single gene may not be enough to guarantee an alteration in the specific pathway it belongs to, which may have led to misinterpretation in the present study. However, the selected target genes are associated with targeted cancer therapies or response to therapy in the literature and public databases 22,23 , Our data are, therefore, sufficient to identify clinically meaningful associations between genetic alterations and image features.…”

Section: Discussionmentioning

confidence: 92%

“…targeted-sequencing platform designed at our institution. The reliability of this assay was proved by a robust analytic validation in previous studies, where the details of experimental procedures were described 22,23,44 .…”

Section: Cancerscan and Classification Of Oncogenic Signaling Pathwaymentioning

confidence: 90%

Metabolic radiogenomics in lung cancer: associations between FDG PET image features and oncogenic signaling pathway alterations

Kim¹,

Kim²,

Cha³

et al. 2020

Sci Rep

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this study investigated the associations between image features extracted from tumor 18 F-fluorodeoxyglucose (FDG) uptake and genetic alterations in patients with lung cancer. A total of 137 patients (age, 62.7 ± 10.2 years) who underwent FDG positron emission tomography/computed tomography (PET/CT) and targeted deep sequencing analysis for a tumor lesion, comprising 61 adenocarcinoma (ADC), 31 squamous cell carcinoma (SQCC), and 45 small cell lung cancer (SCLC) patients, were enrolled in this study. From the tumor lesions, 86 image features were extracted, and 381 genes were assessed. PET features were associated with genetic mutations: 41 genes with 24 features in ADC; 35 genes with 22 features in SQCC; and 43 genes with 25 features in SCLC (FDR < 0.05). Clusters based on PET features showed an association with alterations in oncogenic signaling pathways: Cell cycle and WNT signaling pathways in ADC (p = 0.023, p = 0.035, respectively); Cell cycle, p53, and WNT in SQCC (p = 0.045, 0.009, and 0.029, respectively); and TGFβ in SCLC (p = 0.030). In addition, SUV peak and SUV max were associated with a mutation of the TGFβ signaling pathway in ADC (FDR = 0.001, < 0.001). In this study, PET image features had significant associations with alterations in genes and oncogenic signaling pathways in patients with lung cancer. Radiogenomics, merging medical imaging data and genomic information, has great potential in the era of personalized medicine 1-4. Genomic information could enable physicians to select appropriate management strategies according to the genetic alterations in an individual patient with cancer 5. Nevertheless, the application of genomic medicine in the field of oncology has shortcomings because tumors have genetic and phenotypic diversities even within a single mass 5-7. Such intra-tumor heterogeneity eventually drives treatment failure and disease progression 7-9. To overcome it, multiple and sequential biopsies should be conducted to identify all the genetic alterations within the whole tumor throughout the course of disease progression within a patient, which is not always feasible in routine clinical practice. Therefore, the search for noninvasive techniques that reflect genetic alterations in multiple sites and at multiple time points is important because such techniques could improve patient care. Various features from medical images of a tumor could be surrogate markers that predict the alteration of particular genetic pathways. A known association between image features and genetic alterations has a potential as a useful additional information to improve decision making of biopsies, which could create new, accessible management strategies for patients with cancer 4,6,10 .

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“…We performed least absolute shrinkage and selection operator (LASSO) to remove redundancy within the radiomic and genetic information by selecting the most prognostic characteristics in radiomic and genetic analysis. 18,26 LASSO is a method of selecting a few suitable features using L1-norm regularization and the method is frequently used for the feature selection in radiomics. 27,28 We performed multivariate Cox proportional hazards analysis to determine predictors of DFS.…”

Section: Discussionmentioning

confidence: 99%

“…This customized platform provides the researchers and clinicians the flexibility to include selected genes in the literature required. Single nucleotide variation (SNV), small indels, copy number variation, and gene fusion were detected using both existing 18 and custom algorithms. 19 Samples were typically sequenced without paired normal tissue.…”

Section: Genetic Analysismentioning

confidence: 99%

Parallel comparison and combining effect of radiomic and emerging genomic data for prognostic stratification of non‐small cell lung carcinoma patients

Kim

Park

et al. 2020

Thoracic Cancer

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Background A single institution retrospective analysis of 124 non‐small cell lung carcinoma (NSCLC) patients was performed to identify whether disease‐free survival (DFS) achieves incremental values when radiomic and genomic data are combined with clinical information. Methods Using the least absolute shrinkage and selection operator (LASSO) Cox regression method, radiomic and genetic features were reduced in number for selection of the most useful prognostic feature. We created four models using only baseline clinical data, clinical data with selected genetic features, clinical data with selected radiomic features, and clinical data with selected genetic and radiomic features together. Multivariate Cox proportional hazards analysis was performed to determine predictors of DFS. Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for DFS prediction by four constructed models at the five‐year time point. Results On precontrast scan, improved discrimination performance was obtained in a merging of selected radiomics and genetics (AUC = 0.8638), compared with clinical data only (AUC = 0.7990), selected genetic features (AUC = 0.8497), and selected radiomic features (AUC = 0.8355). On post‐contrast scan, discrimination performance was improved (AUC = 0.8672) compared with the clinical variables (AUC = 0.7913), and selected genetic features (AUC = 0.8376) and selected radiomic features (AUC = 0.8399) were considered. Conclusions The combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating clinicopathologic model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone. Key points Significant findings of the study Receiver operating characteristic (ROC) calculation was made to compare the discriminative performance for disease‐free survival (DFS). The discriminative performance for DFS was better when combining radiomic and genetic features compared to clinical data only, selected genetic features, and selected radiomic features. What this study adds The combination of selected radiomic and genomic features improved stratification of NSCLC patients upon survival. Thus, integrating a clinicopathological model with radiomic and genomic features may lead to improved prognostic accuracy compared to conventional clinicopathological data alone.

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Performance evaluation method for read mapping tool in clinical panel sequencing

Cited by 12 publications

References 24 publications

Performance evaluation of pipelines for mapping, variant calling and interval padding, for the analysis of NGS germline panels

Performance evaluation of pipelines for mapping, variant calling and interval padding, for the analysis of NGS germline panels

Metabolic radiogenomics in lung cancer: associations between FDG PET image features and oncogenic signaling pathway alterations

Parallel comparison and combining effect of radiomic and emerging genomic data for prognostic stratification of non‐small cell lung carcinoma patients

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