Literature DB >> 27560719

The Role of Genetic Testing in the Selection of Therapy for Breast Cancer: A Review.

Polly Niravath1, Burcu Cakar1, Matthew Ellis1.   

Abstract

IMPORTANCE: The application of next-generation sequencing (NGS) genomic testing for somatic mutations in breast oncology has been slower than anticipated due to issues with clinical applicability and natural heterogeneity of breast cancer. This review summarizes the state of the field and considers approaches for more effective implementation. OBSERVATIONS: While there is an emerging role for germline genetic testing potentially predicting sensitivity to platinum salts and PARP inhibitors, the data regarding somatic mutation for prediction of drug sensitivity remains controversial. Currently, there are no guidelines or regulatory approvals for genomic somatic tumor mutation testing to direct therapy. However, some small populations show promise, such as those with ERBB2/HER2 mutation who may represent the first population to have a positive drug somatic mutation match. Similarly, those with ESR1 mutation may be the first to emerge for a negative association with the efficacy of aromatase-inhibitor treatment. One of the barriers to progress is the necessary focus on metastatic disease, which is often challenging, expensive, and risky to biopsy. In addition, because of the clonal heterogeneity of advanced disease, a single sample may not contain all the genomic information necessary for treatment. Thus, circulating tumor DNA analysis is perhaps one of the most practical and promising approaches. CONCLUSIONS AND RELEVANCE: Circulating tumor DNA analysis, once sensitive and broad enough, will accelerate progress in the quest to make NGS technologies relevant to breast cancer treatment. A broad and coordinated coalition to systematically connect somatic mutations to clinical and pharmacologic data will be critical for progress. We recommend instituting an open source encyclopedia, which would serve as a reference for NGS sequencing report interpretation and would be available to all clinicians to help direct therapy.

Entities:  

Year:  2017        PMID: 27560719     DOI: 10.1001/jamaoncol.2016.2719

Source DB:  PubMed          Journal:  JAMA Oncol        ISSN: 2374-2437            Impact factor:   31.777


  3 in total

1.  DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.

Authors:  Jiaqi Liu; Hengqiang Zhao; Yu Zheng; Lin Dong; Sen Zhao; Yongxin Yang; Zhihong Wu; Zhihua Liu; Jianming Ying; Xin Wang; Yukuan Huang; Shengkai Huang; Tianyi Qian; Jiali Zou; Shu Liu; Jun Li; Zihui Yan; Yalun Li; Shuo Zhang; Xin Huang; Wenyan Wang; Yiqun Li; Jie Wang; Yue Ming; Xiaoxin Li; Zeyu Xing; Ling Qin; Zhengye Zhao; Ziqi Jia; Jiaxin Li; Gang Liu; Menglu Zhang; Kexin Feng; Jiang Wu; Jianguo Zhang; Jianzhong Su; Xiang Wang; Nan Wu
Journal:  Genome Med       Date:  2022-02-25       Impact factor: 11.117

Review 2.  Nanotechnology-Based Strategies for Early Cancer Diagnosis Using Circulating Tumor Cells as a Liquid Biopsy.

Authors:  Qinqin Huang; Yin Wang; Xingxiang Chen; Yimeng Wang; Zhiqiang Li; Shiming Du; Lianrong Wang; Shi Chen
Journal:  Nanotheranostics       Date:  2018-01-01

3.  Prevalence of BRCA1 and BRCA2 pathogenic variants in a large, unselected breast cancer cohort.

Authors:  Jingmei Li; Wei Xiong Wen; Martin Eklund; Anders Kvist; Mikael Eriksson; Helene Nordahl Christensen; Astrid Torstensson; Svetlana Bajalica-Lagercrantz; Alison M Dunning; Brennan Decker; Jamie Allen; Craig Luccarini; Karen Pooley; Jacques Simard; Leila Dorling; Douglas F Easton; Soo-Hwang Teo; Per Hall; Åke Borg; Henrik Grönberg; Kamila Czene
Journal:  Int J Cancer       Date:  2018-11-09       Impact factor: 7.396

  3 in total

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