TSNAD: an integrated software for cancer somatic mutation and tumour-specific neoantigen detection

Zhou, Zhan; Lyu, Xingzheng; Wu, Jingcheng; Yang, Xiaoyue; Wu, Shanshan; Zhou, Jié; Gu, Xun; Su, Zhixi; Chen, Shuqing

doi:10.1098/rsos.170050

Cited by 65 publications

(63 citation statements)

References 38 publications

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“…It is still a major challenge to accurately predict the interaction between neoantigens and immune cells. There are currently several publicly available neoantigen prediction pipelines, including pVAC‐Seq, INTEGRATE‐neo, TSNAD . pVAC‐Seq combines the tumor mutation and expression data to predict neoantigens by invoking NetMHC 3.4; INTEGRATE‐neo was designed to predict neoantigens from fusion genes based on the pipeline INTEGRATE and NetMHC 4.0.…”

Section: Neoantigen Predictionmentioning

confidence: 99%

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Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens

2018

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Utilizing the host immune system to eradicate cancer cells has been the most investigated subject in the cancer research field in recent years. However, most of the studies have focused on highly variable responses from immunotherapies such as immune checkpoint inhibitors, from which the majority of patients experienced no or minimum clinical benefit. Advances in genomic sequencing technologies have improved our understanding of immunopharmacogenomics and allowed us to identify novel cancer‐specific immune targets. Highly tumor‐specific antigens, neoantigens, are generated by somatic mutations that are not present in normal cells. It is plausible that by targeting antigens with high tumor‐specificity, such as neoantigens, the likelihood of toxic effects is very limited. However, understanding the interaction between neoantigens and the host immune system remains a significant challenge. This review focuses on the potential use of neoantigen‐targeted immunotherapies in cancer treatment and the recent progress of different strategies in predicting, identifying, and validating neoantigens. Successful identification of highly tumor‐specific antigens accelerates the development of personalized immunotherapy with no or minimum adverse effects and with a broader coverage of patients.

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Section: Neoantigen Predictionmentioning

confidence: 99%

“…There are currently several publicly available neoantigen prediction pipelines, including pVAC-Seq, 28 INTEGRATE-neo, 29 TSNAD. 30…”

Section: Neoantigen Pred Ictionmentioning

confidence: 99%

Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens

2018

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“…A feature comparison of pVACseq and pVACfuse [6] , INTEGRATE-neo [4] , Epi-Seq [2] , CloudNeo [58] , TSNAD [5] , MuPeXI [7] , neoantigenR [59] , NeoepitopePred [60] , neoantigen_calling_pipeline (Van Allen laboratory) [27] , retained-intron-neoantigen-pipeline (Van Allen laboratory) [52] , Epidisco [61] , antigen.garnish [62] , NeoPredPipe [63] , Neopepsee [64] , and neoepiscope . Each column corresponds to a software tool, with software features listed by row.…”

Section: Figurementioning

confidence: 99%

`neoepiscope`improves neoepitope prediction with multi-variant phasing

Wood

Nguyen

Struck

et al. 2018

Preprint

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The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false positive and false negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels), and herein illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to ~5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible, and supports several major histocompatibility complex binding affinity prediction tools. We have released neoepiscope as open-source software (MIT license, https://github.com/pdxgx/neoepiscope ) for broad use. KEY POINTS• Germline context and somatic variant phasing are important for neoepitope prediction • Many popular neoepitope prediction tools have issues of performance and reproducibility • We describe and provide performant software for accurate neoepitope prediction from DNA-seq data

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“…In the last years, advances in next-generation sequencing have provided an accessible way to generate patient-specific data, which allows the prediction of tumor neoantigens in a rapid and comprehensive manner (Liu;Mardis, 2017). Several approaches have been developed, such as pVAC-Seq (Hundal et al, 2016), MuPeXI (Bjerregaard et al, 2017), TIminer (Tappeiner et al, 2017) and TSNAD (Zhou et al, 2017), which performs prediction of potential neoantigens produced by nonsynonymous mutations. However, none of these proposed tools considers tumor transcriptome sequencing data (RNA-seq) for identifying somatic mutations.…”

Section: Introductionmentioning

confidence: 99%

neoANT-HILL: an integrated tool for identification of potential neoantigens

Coelho

Fonseca

Martins

et al. 2019

Preprint

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Cancer neoantigens have attracted great interest in immunotherapy due to their ability to elicit antitumoral immune responses. These antigens are formed due to somatic mutations in the cancer genome that result in alterations of the original protein.Although current technological advances in neoantigen identification, it remains a challenging and a large number of false-positive continue to exist. In the current work, we present neoANT-HILL, an automatized user-friendly tool that integrates several immunogenomic analysis to improve neoantigens detection from NGS data. The program input can be a file with somatic mutations called and/or RNA-seq data. Our tool was applied on somatic mutations of melanoma dataset from TCGA and found that neoANT-HILL was able to predicted potential neoantigens. The software is available on github at https://github.com/neoanthill/neoANT-HILL. Author ContributionsACMFC, DLM and PRBL designed and carried out the implementation of the computational pipeline. LMC contributed to design the computational pipeline.ACMFC and ALF analyzed the data. ACMFC wrote the manuscript in consultation with SJS. SJS supervised the project.

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TSNAD: an integrated software for cancer somatic mutation and tumour-specific neoantigen detection

Cited by 65 publications

References 38 publications

Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens

Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens

`neoepiscope`improves neoepitope prediction with multi-variant phasing

neoANT-HILL: an integrated tool for identification of potential neoantigens

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TSNAD: an integrated software for cancer somatic mutation and tumour-specific neoantigen detection

Cited by 65 publications

References 38 publications

Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens

Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens

neoepiscopeimproves neoepitope prediction with multi-variant phasing

neoANT-HILL: an integrated tool for identification of potential neoantigens

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Product

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`neoepiscope`improves neoepitope prediction with multi-variant phasing