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Translational Medicine / 转化医学molecular pathology

Matthew Meyerson

马修·迈耶森

MD, PhD

🏢Dana-Farber Cancer Institute / Broad Institute of MIT and Harvard(达纳-法伯癌症研究所 / 麻省理工学院与哈佛大学博德研究所)🌐USA

Professor of Pathology; Senior Associate Member, Broad Institute病理学教授;博德研究所高级成员

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Key Papers
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Awards
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Key Contributions

👥Biography 个人简介

Matthew Meyerson MD PhD is Professor of Pathology at Harvard Medical School, a Senior Physician at Dana-Farber Cancer Institute, and a Senior Associate Member of the Broad Institute. He is one of the architects of large-scale cancer genomics, having served as a co-leader of the Genome Data Analysis Center (GDAC) and analytical lead for multiple cancer types within The Cancer Genome Atlas (TCGA). His laboratory developed GISTIC, one of the most widely used algorithms for identifying significant somatic copy number alterations in cancer, and contributed SNP array and sequencing analysis pipelines foundational to TCGA and ICGC. Meyerson's group made the landmark discovery that FGFR1 amplification and NKX2-1 amplification drive distinct subtypes of squamous and adenocarcinoma of the lung, and characterized the genomic landscape of small-cell lung cancer. He also contributed to the discovery of ALK rearrangements in lung adenocarcinoma and the characterization of EML4-ALK fusions. His work on telomerase and cancer cell immortalisation contributed foundational understanding of cancer biology. Meyerson has received numerous honours including election to the National Academy of Medicine and the American Academy of Arts and Sciences.

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🧪Research Fields 研究领域

Cancer Genome Sequencing癌症基因组测序
Lung Cancer Genomics肺癌基因组学
TCGA LeadershipTCGA项目领导
Somatic Copy Number Alterations体细胞拷贝数变异
Oncogene Discovery癌基因发现

🎓Key Contributions 主要贡献

GISTIC Algorithm for Somatic Copy Number Alterations

Co-developed GISTIC (Genomic Identification of Significant Targets in Cancer), the standard computational tool for identifying regions of recurrent somatic copy number gain and loss across cancer cohorts, enabling discovery of amplified oncogenes and deleted tumour suppressors genome-wide.

TCGA Lung Cancer Genomics

Led genomic characterisation of lung squamous cell carcinoma and lung adenocarcinoma through TCGA, revealing recurrent alterations in NKX2-1, FGFR1, SOX2, and other drivers, and defining molecular subtypes that stratify prognosis and therapeutic response.

ALK Rearrangements in Lung Adenocarcinoma

Contributed to the identification and characterisation of EML4-ALK and other ALK fusion oncogenes in non-small-cell lung cancer, establishing ALK as a major druggable target and enabling the development of crizotinib and subsequent ALK inhibitors.

Telomerase and Cancer Immortalisation

Made early foundational discoveries demonstrating that ectopic TERT expression is sufficient to immortalise human cells, establishing telomerase reactivation as a hallmark mechanism of cancer cell immortalisation.

Representative Works 代表性著作

[1]

Comprehensive genomic characterization of squamous cell lung cancers

Nature (2012)

TCGA study defining the somatic mutation landscape, copy number alterations, and molecular subtypes of lung squamous cell carcinoma across 178 tumours.

[2]

GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

Genome Biology (2011)

Presentation of GISTIC2.0 algorithm for identifying significant copy number alterations, which has become the standard tool used across TCGA and international cancer genomics studies.

[3]

Identification of a lung cancer oncogene, TITF1, using computational analysis of amplified sequences

Science (2007)

Discovery of NKX2-1 (TITF1) as an amplified lineage survival oncogene in lung adenocarcinoma, establishing a paradigm for identifying tissue-lineage oncogenes through copy number analysis.

🏆Awards & Recognition 奖项与荣誉

🏆Elected Member, National Academy of Medicine
🏆Elected Member, American Academy of Arts and Sciences
🏆AACR Team Science Award (TCGA)
🏆Howard Hughes Medical Institute Investigator

📄Data Sources 数据来源

Last updated: 2026-01-15 | All information from publicly available academic sources

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