Proteogenomic data and resources for pan-cancer analysis

Clinical Proteomic Tumor Analysis Consortium

doi:10.1016/j.ccell.2023.06.009

Proteogenomic data and resources for pan-cancer analysis

Clinical Proteomic Tumor Analysis Consortium

Research output: Contribution to journal › Review article › peer-review

12 Scopus citations

Abstract

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Original language	English (US)
Pages (from-to)	1397-1406
Number of pages	10
Journal	Cancer Cell
Volume	41
Issue number	8
DOIs	https://doi.org/10.1016/j.ccell.2023.06.009
State	Published - Aug 14 2023

Keywords

CPTAC
data harmonization
multi-omics
open data
pan-cancer
proteogenomics

ASJC Scopus subject areas

Oncology
Cancer Research

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10.1016/j.ccell.2023.06.009

Cite this

@article{8b9ea60e82fb47de83778b0011200a66,

title = "Proteogenomic data and resources for pan-cancer analysis",

abstract = "The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.",

keywords = "CPTAC, data harmonization, multi-omics, open data, pan-cancer, proteogenomics",

author = "{Clinical Proteomic Tumor Analysis Consortium} and Yize Li and Yongchao Dou and {Da Veiga Leprevost}, Felipe and Yifat Geffen and Calinawan, {Anna P.} and Fran{\c c}ois Aguet and Yo Akiyama and Shankara Anand and Chet Birger and Song Cao and Rekha Chaudhary and Padmini Chilappagari and Marcin Cieslik and Antonio Colaprico and Zhou, {Daniel Cui} and Corbin Day and Domagalski, {Marcin J.} and {Esai Selvan}, Myvizhi and David Feny{\"o} and Foltz, {Steven M.} and Alicia Francis and Tania Gonzalez-Robles and G{\"u}m{\"u}{\c s}, {Zeynep H.} and David Heiman and Michael Holck and Runyu Hong and Yingwei Hu and Jaehnig, {Eric J.} and Jiayi Ji and Wen Jiang and Lizabeth Katsnelson and Ketchum, {Karen A.} and Klein, {Robert J.} and Lei, {Jonathan T.} and Liang, {Wen Wei} and Yuxing Liao and Lindgren, {Caleb M.} and Weiping Ma and Lei Ma and MacCoss, {Michael J.} and {Martins Rodrigues}, Fernanda and Wilson McKerrow and Ngoc Nguyen and Robert Oldroyd and Alexander Pilozzi and Pietro Pugliese and Boris Reva and Paul Rudnick and Druker, {Brian J.} and Ozgun Babur",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

month = aug,

day = "14",

doi = "10.1016/j.ccell.2023.06.009",

language = "English (US)",

volume = "41",

pages = "1397--1406",

journal = "Cancer Cell",

issn = "1535-6108",

publisher = "Cell Press",

number = "8",

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T1 - Proteogenomic data and resources for pan-cancer analysis

AU - Clinical Proteomic Tumor Analysis Consortium

AU - Li, Yize

AU - Dou, Yongchao

AU - Da Veiga Leprevost, Felipe

AU - Geffen, Yifat

AU - Calinawan, Anna P.

AU - Aguet, François

AU - Akiyama, Yo

AU - Anand, Shankara

AU - Birger, Chet

AU - Cao, Song

AU - Chaudhary, Rekha

AU - Chilappagari, Padmini

AU - Cieslik, Marcin

AU - Colaprico, Antonio

AU - Zhou, Daniel Cui

AU - Day, Corbin

AU - Domagalski, Marcin J.

AU - Esai Selvan, Myvizhi

AU - Fenyö, David

AU - Foltz, Steven M.

AU - Francis, Alicia

AU - Gonzalez-Robles, Tania

AU - Gümüş, Zeynep H.

AU - Heiman, David

AU - Holck, Michael

AU - Hong, Runyu

AU - Hu, Yingwei

AU - Jaehnig, Eric J.

AU - Ji, Jiayi

AU - Jiang, Wen

AU - Katsnelson, Lizabeth

AU - Ketchum, Karen A.

AU - Klein, Robert J.

AU - Lei, Jonathan T.

AU - Liang, Wen Wei

AU - Liao, Yuxing

AU - Lindgren, Caleb M.

AU - Ma, Weiping

AU - Ma, Lei

AU - MacCoss, Michael J.

AU - Martins Rodrigues, Fernanda

AU - McKerrow, Wilson

AU - Nguyen, Ngoc

AU - Oldroyd, Robert

AU - Pilozzi, Alexander

AU - Pugliese, Pietro

AU - Reva, Boris

AU - Rudnick, Paul

AU - Druker, Brian J.

AU - Babur, Ozgun

PY - 2023/8/14

Y1 - 2023/8/14

N2 - The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

AB - The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

KW - CPTAC

KW - data harmonization

KW - multi-omics

KW - open data

KW - pan-cancer

KW - proteogenomics

UR - http://www.scopus.com/inward/record.url?scp=85168062422&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85168062422&partnerID=8YFLogxK

U2 - 10.1016/j.ccell.2023.06.009

DO - 10.1016/j.ccell.2023.06.009

M3 - Review article

C2 - 37582339

AN - SCOPUS:85168062422

SN - 1535-6108

VL - 41

SP - 1397

EP - 1406

JO - Cancer Cell

JF - Cancer Cell

IS - 8

ER -

Proteogenomic data and resources for pan-cancer analysis

Abstract

Keywords

ASJC Scopus subject areas

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