Development and validation of a blood-based genomic mutation signature to predict the clinical outcomes of atezolizumab therapy in NSCLC

Liu, Manjiao; Xia, Sijian; Zhang, Xu; Zhang, Bei; Yan, Linlin; Yang, Meng; Ren, Yong; Guo, Hao; Zhao, Jie

doi:10.1016/j.lungcan.2022.06.016

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…The study of these 'mutational signatures' has become a solid field of research in oncology, and is now seen as a field which has made significant advances over the last years (Alexandrov, Nik-Zainal, Wedge, et al, 2013;Koh et al, 2021). The importance of studying mutational signatures in oncology is irrefragable, as mutation patterns are related to cancer aetiology, diagnosis and prognosis, appear to predict response to therapy (Liu, Xia, et al, 2022;Liu, Lin, et al, 2022) and may echo genomic alterations induced by chemotherapy, making the valuable tools for most aspects of cancer research Koh et al, 2021. The first method to extract mutational signatures from somatic mutation counts was based on non-negative matrix factorisation (NMF) techniques applied to Single Nucleotide Variations (SNVs) counts, see Alexandrov, Nik-Zainal, Wedge, et al, 2013. Since then, several methods for mutational signature extraction have emerged, most of them based on variations of the NMF algorithm; see Kim et al, 2021 for a recent overview and a comparison of current methods.…”

Section: Introductionmentioning

confidence: 99%

Relating mutational signature exposures to clinical data in cancers via signeR 2.0

Drummond

Defelicibus

Meyenberg

et al. 2023

Preprint

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Motivation: Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already became a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: 1) de novo estimation of signatures and their exposures, 2) estimation of the exposures of each one of a set of pre-defined signatures. Our group developed signeR, a Bayesian approach to both these tasks. Results: Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes an user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded on the package for easy access. Availability: signer 2.0 is an open-source R package available through the Bioconductor project at https://doi.org/doi:10.18129/B9.bioc.signeR

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Section: Introductionmentioning

confidence: 99%

Relating mutational signature exposures to clinical data in cancers via signeR 2.0

Drummond

Defelicibus

Meyenberg

et al. 2023

Preprint

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Relating mutational signature exposures to clinical data in cancers via signeR 2.0

Drummond,

Defelicibus,

Meyenberg

et al. 2023

BMC Bioinformatics

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Background Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures. Results Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access. Conclusion signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at (https://doi.org/10.18129/B9.bioc.signeR).

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A gene mutation-based risk model for prognostic prediction in liver metastases

Yu,

Zhang,

Feng

et al. 2023

BMC Genomics

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Background Liver metastasis is the major challenge in the treatment for malignant tumors. Genomic profiling is increasingly used in the diagnosis, treatment and prediction of prognosis in malignancies. In this study, we constructed a gene mutation-based risk model to predict the survival of liver metastases. Method We identified the gene mutations associated with survival and constructed the risk model in the training cohort including 800 patients with liver metastases from Memorial Sloan-Kettering Cancer Center (MSKCC) dataset. Other 794 patients with liver metastases were collected from 4 cohorts for validation. Furthermore, the analyses of tumor microenvironment (TME) and somatic mutations were performed on 51 patients with breast cancer liver metastases (BCLM) who had both somatic mutation data and RNA-sequencing data. Results A gene mutation-based risk model involved 10 genes was constructed to divide patients with liver metastases into the high- and low-risk groups. Patients in the low-risk group had a longer survival time compared to those in the high-risk group, which was observed in both training and validation cohorts. The analyses of TME in BCLM showed that the low-risk group exhibited more immune infiltration than the high-risk group. Furthermore, the mutation signatures of the high-risk group were completely different from those of the low-risk group in patients with BCLM. Conclusions The gene mutation-based risk model constructed in our study exhibited the reliable ability of predicting the prognosis in liver metastases. The difference of TME and somatic mutations among BCLM patients with different risk score can guide the further research and treatment decisions for liver metastases.

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Development and validation of a blood-based genomic mutation signature to predict the clinical outcomes of atezolizumab therapy in NSCLC

Cited by 3 publications

References 32 publications

Relating mutational signature exposures to clinical data in cancers via signeR 2.0

Relating mutational signature exposures to clinical data in cancers via signeR 2.0

Relating mutational signature exposures to clinical data in cancers via signeR 2.0

A gene mutation-based risk model for prognostic prediction in liver metastases

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