ISOWN: accurate somatic mutation identification in the absence of normal tissue controls

Kalatskaya, Irina; Trinh, Quang; Spears, Melanie; Mcpherson, John Douglas; Bartlett, John M.S.; Stein, Lincoln

doi:10.1186/s13073-017-0446-9

Cited by 51 publications

(42 citation statements)

References 40 publications

(48 reference statements)

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“…The called somatic mutations were randomly split to cross validation set and holdout test set based on a 7:3 ratio. Tools predicting somatic mutations in matched normal free samples have been developed 39,82 . However, the developed tools attempted to predict somatic mutations in tumor-only samples.…”

Section: Predictive Model For Somatic Mutationsmentioning

confidence: 99%

Somatic Genetic Aberrations in Benign Breast Disease and the Risk of Subsequent Breast Cancer

Zeng¹,

et al. 2019

Preprint

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One Sentence Summary: Genetic aberrations in benign breast lesions distinguish breast cancer cases from controls and predict cancer risk.Abstract: It is largely unknown how the risk of development of breast cancer is transduced by somatic genetic alterations. To address this lacuna of knowledge and acknowledging that benign breast disease (BBD) is an established risk factor for breast cancer, we established a case-control study: The Benign Breast & Cancer Risk (BBCAR) Study. Cases are women with BBD who developed subsequent invasive breast cancer (IBC) at least 3 years after the biopsy and controls are women with BBD who did not develop IBC (median follow-up 16.6 years). We selected 135 cases and individually matched controls (1:2) to cases based on age and type of benign disease: non-proliferative or proliferation without atypia. Whole exome sequencing was performed on DNA from the benign lesions and from subsets with available germline DNA or tumor DNA. Although the number of cases and controls with copy number variation data is limited, several amplifications and deletions are exclusive to the cases. In addition to two known mutational signatures, a novel signature was identified that is significantly (p=0.007) associated with triple negative breast cancer. The somatic mutation rate in benign lesions is similar to that of invasive breast cancer and does not differ between cases and controls. Two mutated genes are significantly associated with time to the diagnosis of breast cancer, and mutations shared between the benign biopsy tissue and the breast malignancy for the ten cases for which we had matched pairs were identified. BBD tissue is a rich source of clues to breast oncogenesis.

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Section: Predictive Model For Somatic Mutationsmentioning

confidence: 99%

Somatic Genetic Aberrations in Benign Breast Disease and the Risk of Subsequent Breast Cancer

Zeng¹,

et al. 2019

Preprint

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“…Without matched normal samples, it is necessary to distinguish algorithmically between somatic mutations and germline variants. Existing approaches commonly involve machine learning using public germline and somatic databases, in silico predictions of the functional impact of mutations, as well as allelic fractions (the ratios of non-reference to total sequencing reads) of mutations and their neighboring SNPs (Kalatskaya et al, 2017;Smith et al, 2016) .…”

Section: Introductionmentioning

confidence: 99%

Reliable analysis of clinical tumor-only whole exome sequencing data

Ramos

Morgan

et al. 2019

Preprint

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AbstractBackgroundAllele-specific copy number alteration (CNA) analysis is essential to study the functional impact of single nucleotide variants (SNV) and the process of tumorigenesis. Most commonly used tools in the field rely on high quality genome-wide data with matched normal profiles, limiting their applicability in clinical settings.MethodsWe propose a workflow, based on the open-source PureCN R/Bioconductor package in conjunction with widely used variant-calling and copy number segmentation algorithms, for allele-specific CNA analysis from whole exome sequencing (WES) without matched normals. We use The Cancer Genome Atlas (TCGA) ovarian carcinoma (OV) and lung adenocarcinoma (LUAD) datasets to benchmark its performance against gold standard SNP6 microarray and WES datasets with matched normal samples. Our workflow further classifies SNVs by somatic status and then uses this information to infer somatic mutational signatures and tumor mutational burden (TMB).ResultsApplication of our workflow to tumor-only WES data produces tumor purity and ploidy estimates that are highly concordant with estimates from SNP6 microarray data and matched-normal WES data. The presence of cancer type-specific somatic mutational signatures was inferred with high accuracy. We also demonstrate high concordance of TMB between our tumor-only workflow and matched normal pipelines.ConclusionThe proposed workflow provides, to our knowledge, the only open-source option for comprehensive allele-specific CNA analysis and SNV classification of tumor-only WES with demonstrated high accuracy.

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“…Sites whose AFs fail to pass a threshold would be ruled out from consideration even though alternative reads were detected. The criteria are usually stringent to rule out the noise caused by the impurity of tumors, the impact of copy number alterations in tumors, sequencing errors, PCR errors and so on . A threshold of 1% signifies that the sites with ≤10 alternative reads would be removed at the sequencing depth of 1,000 × .…”

Section: Introductionmentioning

confidence: 99%

“…The criteria are usually stringent to rule out the noise caused by the impurity of tumors, the impact of copy number alterations in tumors, sequencing errors, PCR errors and so on. 18 A threshold of 1% signifies that the sites with ≤10 alternative reads would be removed at the sequencing depth of 1,000×. This strategy ensures the high percentage of true positives in the called mutations, but, at the same time, unfortunately, leads to nonconsideration of high amounts of true mutations.…”

Section: Introductionmentioning

confidence: 99%

Using plasma cell‐free DNA to monitor the chemoradiotherapy course of cervical cancer

Tian

Geng

et al. 2019

Intl Journal of Cancer

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The liquid biopsy is being integrated into cancer diagnostics and surveillance. However, critical questions still remain, such as how to precisely evaluate cancer mutation burden and interpret the corresponding clinical implications. Herein, we evaluated the role of peripheral blood cell‐free DNA (cfDNA) in characterizing the dynamic mutation alterations of 48 cancer driver genes from cervical cancer patients. We performed targeted deep sequencing on 93 plasma cfDNA from 57 cervical cancer patients and from this developed an algorithm, allele fraction deviation (AFD), to monitor in an unbiased manner the dynamic changes of genomic aberrations. Differing treatments, including chemotherapy (n = 22), radiotherapy (n = 14) and surgery (n = 15), led to a significant decrease in AFD values (Wilcoxon, p = 0.029). The decrease of cfDNA AFD values was accompanied by shrinkage in the size of the tumor in most patients. However, in a subgroup of patients where cfDNA AFD values did not reflect a reduction in tumor size, there was a detection of progressive disease (metastasis). Furthermore, a low AFD value at diagnosis followed a later increase of AFD value also successfully predicted relapse. These results show that plasma cfDNA, together with targeted deep sequencing, may help predict treatment response and disease development in cervical cancer.

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ISOWN: accurate somatic mutation identification in the absence of normal tissue controls

Cited by 51 publications

References 40 publications

Somatic Genetic Aberrations in Benign Breast Disease and the Risk of Subsequent Breast Cancer

Somatic Genetic Aberrations in Benign Breast Disease and the Risk of Subsequent Breast Cancer

Reliable analysis of clinical tumor-only whole exome sequencing data

Using plasma cell‐free DNA to monitor the chemoradiotherapy course of cervical cancer

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