Targeted mutation detection in breast cancer using MammaSeq™

Smith, Nicholas G.; Gyanchandani, Rekha; Gurda, Grzegorz T.; Lucas, Peter C.; Hartmaier, Ryan J.; Brufsky, Adam; Puhalla, Shannon; Bahreini, Amir; Kota, Karthik; Wald, Abigail I.; Nikiforov, Yuri E.; Nikiforova, Marina N.; Oesterrich, Steffi; Lee, Adrian V.

doi:10.1101/264267

Cited by 7 publications

(8 citation statements)

References 33 publications

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“…In comparison with other whole exome and whole genome studies 9 , 10 , 60 our capture is definitely smaller, as it is less than an exome, but in some incidences the number of our targeted genes is larger 10 . Also, our array contained genes that were absent from clinical panels 61 e.g. KMT2C , that was mutated in 23% of our cohort .…”

Section: Discussionmentioning

confidence: 99%

Targeted sequencing reveals the somatic mutation landscape in a Swedish breast cancer cohort

Mathioudaki

Ljungström

Melin

et al. 2020

Sci Rep

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Breast cancer (BC) is a genetically heterogeneous disease with high prevalence in Northern Europe. However, there has been no detailed investigation into the Scandinavian somatic landscape. Here, in a homogeneous Swedish cohort, we describe the somatic events underlying BC, leveraging a targeted next-generation sequencing approach. We designed a 20.5 Mb array targeting coding and regulatory regions of genes with a known role in BC (n = 765). The selected genes were either from human BC studies (n = 294) or from within canine mammary tumor associated regions (n = 471). A set of predominantly estrogen receptor positive tumors (ER + 85%) and their normal tissue counterparts (n= 61) were sequenced to ~ 140 × and 85 × mean target coverage, respectively. MuTect2 and VarScan2 were employed to detect single nucleotide variants (SNVs) and copy number aberrations (CNAs), while MutSigCV (SNVs) and GISTIC (CNAs) algorithms estimated the significance of recurrent somatic events. The significantly mutated genes (q ≤ 0.01) were PIK3CA (28% of patients), TP53 (21%) and CDH1 (11%). However, histone modifying genes contained the largest number of variants (KMT2C and ARID1A, together 28%). Mutations in KMT2C were mutually exclusive with PI3KCA mutations (p ≤ 0. 001) and half of these affect the formation of a functional PHD domain. The tumor suppressor CDK10 was deleted in 80% of the cohort while the oncogene MDM4 was amplified. Mutational signature analyses pointed towards APOBEC deaminase activity (COSMIC signature 2) and DNA mismatch repair (COSMIC signature 6). We noticed two significantly distinct patterns related to patient age; TP53 being more mutated in the younger group (29% vs 9% of patients) and CDH23 mutations were absent from the older group. The increased somatic mutation prevalence in the histone modifying genes KMT2C and ARID1A distinguishes the Swedish cohort from previous studies. KMT2C regulates enhancer activation and assists tumor proliferation in a hormone-rich environment, possibly pointing to a role in ER + BC, especially in older cases. Finally, age of onset appears to affect the mutational landscape suggesting that a larger age-diverse population incorporating more molecular subtypes should be studied to elucidate the underlying mechanisms.

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Section: Discussionmentioning

confidence: 99%

Targeted sequencing reveals the somatic mutation landscape in a Swedish breast cancer cohort

Mathioudaki

Ljungström

Melin

et al. 2020

Sci Rep

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“…Samples with adequate DNA were sent for Ion Torrent Sequencing at the University of Pittsburgh Genomics Core following established methods 3 . In summary, we collected 20ng of DNA (divided equally between two pools of primers) for library preparation using Ion AmpliSeq TM library kit (ThermoFisher Scientific) and MammaSeq TM primer panel.…”

Section: Ion Torrent Sequencingmentioning

confidence: 99%

“…The variants were prefiltered for allele frequency cutoff of 5%. For further variant filtering we followed established methods as described in Smith et al 2019 3 . Briefly, we annotated the variants using Cravat CHASM v4.3 6 removed variations which were: 1) synonymous, 2) present in 30% or more of the samples, 3) having high strand bias, 4) found in gnomAD or 1000 genomes database with an allele frequency of more than 1%, 5) having allele frequency of more than 90% and 6) having a read depth of 50 or lower.…”

Section: Variant Callingmentioning

confidence: 99%

Identifying Genomic Alterations in Patients With Stage IV Breast Cancer Using MammaSeq: An International Collaborative Study

Shah

Soran

Şahi̇n

et al. 2021

Clinical Breast Cancer

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Highlights-41 stage IV stage breast cancer patients of Turkish descent were sequenced using MammaSeq TM -49 single nucleotide variations and 10 copy number variations identified -PIK3CA and TP53 mutations were present in 24% and 17% of the samples respectively -37% of the samples had ERBB2/GRB7 gains and 7% had loss of BRCA2/RB1 locus -Eight clinically significant alterations were identified . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is madeThe copyright holder for this preprint this version posted March 2, 2020. ;

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“…The analysis of this massive data requires the use of robust computational approaches to exploit the information effectively. Precision oncology by focusing on targeted clinical panel sequencing can be helpful, i.e., a breast cancer-speci c NGS panel, including 79 genes has been validated for use in primary and metastatic breast cancer (4). In this way, the advent of bioinformatics tools in parallel with the development of molecular techniques could lead to discovering biomarkers that are e cient in cancer diagnosis and prognosis (5).…”

Section: Introductionmentioning

confidence: 99%

EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

Mirsadeghi

Hosseini

Banaei-Moghaddam

et al. 2020

Preprint

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BackgroundToday, there are a lot of markers on the prognosis and diagnosis of complex diseases such as primary breast cancer. However, our understanding of the drivers that influence cancer aggression is limited.MethodsIn this work, we study somatic mutation data consists of 450 metastatic breast tumor samples from cBio Cancer Genomics Portal. We use four software tools to extract features from this data. Then, an ensemble classifier (EC) learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). ResultsThis study is an attempt to focus on the findings in several aspects of MBCA prognosis and diagnosis. First, drivers and passengers predicted by SVM, ANN, RF, and EARN are introduced. Second, biological inferences of predictions based on gene set enrichment analysis are discussed. Third, statistical validation and comparison of all learning methods based on evaluation metrics are done. Finally, the pathway enrichment analysis (PEA) using ReactomeFIVIz tool (FDR<0.03) for the top 100 genes predicted by EARN leads us to propose a new gene set panel for MBCA, including HDAC3, ABAT, GRIN1, PLCB1, and KPNA2 as well as NCOR1, TBL1XR1, SIRT4, KRAS, CACNA1E, PRKCG, GPS2, SIN3A, ACTB, KDM6B, and PRMT1. Furthermore, we compare results for MBCA to other outputs regarding 983 primary tumor samples of breast invasive carcinoma (BRCA) obtained from the Cancer Genome Atlas (TCGA). The comparison between outputs shows that ROC-AUC reached 99.24% using EARN for MBCA and 99.79% for BRCA. This statistical result is better than three individual classifiers in each case.ConclusionsThis research using an integrative approach assists precision oncologists to design compact targeted panels that eliminate the need for whole-genome/exome sequencing.

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Targeted mutation detection in breast cancer using MammaSeq™

Abstract: 24Background: Breast cancer is the most common invasive cancer among women 25

Cited by 7 publications

References 33 publications

Targeted sequencing reveals the somatic mutation landscape in a Swedish breast cancer cohort

Targeted sequencing reveals the somatic mutation landscape in a Swedish breast cancer cohort

Identifying Genomic Alterations in Patients With Stage IV Breast Cancer Using MammaSeq: An International Collaborative Study

EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

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