MetaGxData: Clinically annotated breast, ovarian and pancreatic cancer datasets and their use in generating a multi-cancer gene signature

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MetaGxData : Clinically annotated breast, ovarian and pancreatic cancer datasets and their use in generating a multi-cancer gene signature. / Gendoo, Deena M A; Zon, Michael; Sandhu, Vandana; Manem, Venkata S K; Ratanasirigulchai, Natchar; Chen, Gregory M; Waldron, Levi; Haibe-Kains, Benjamin.

In: Scientific Reports, Vol. 9, No. 1, 8770, 19.06.2019.

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Gendoo, Deena M A ; Zon, Michael ; Sandhu, Vandana ; Manem, Venkata S K ; Ratanasirigulchai, Natchar ; Chen, Gregory M ; Waldron, Levi ; Haibe-Kains, Benjamin. / MetaGxData : Clinically annotated breast, ovarian and pancreatic cancer datasets and their use in generating a multi-cancer gene signature. In: Scientific Reports. 2019 ; Vol. 9, No. 1.

Bibtex

@article{18ac60bc028944588581490800efcaa8,
title = "MetaGxData: Clinically annotated breast, ovarian and pancreatic cancer datasets and their use in generating a multi-cancer gene signature",
abstract = "A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.",
author = "Gendoo, {Deena M A} and Michael Zon and Vandana Sandhu and Manem, {Venkata S K} and Natchar Ratanasirigulchai and Chen, {Gregory M} and Levi Waldron and Benjamin Haibe-Kains",
year = "2019",
month = jun,
day = "19",
doi = "10.1038/s41598-019-45165-4",
language = "English",
volume = "9",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

TY - JOUR

T1 - MetaGxData

T2 - Clinically annotated breast, ovarian and pancreatic cancer datasets and their use in generating a multi-cancer gene signature

AU - Gendoo, Deena M A

AU - Zon, Michael

AU - Sandhu, Vandana

AU - Manem, Venkata S K

AU - Ratanasirigulchai, Natchar

AU - Chen, Gregory M

AU - Waldron, Levi

AU - Haibe-Kains, Benjamin

PY - 2019/6/19

Y1 - 2019/6/19

N2 - A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.

AB - A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.

U2 - 10.1038/s41598-019-45165-4

DO - 10.1038/s41598-019-45165-4

M3 - Article

C2 - 31217513

VL - 9

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 8770

ER -