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figshare: https://api.figshare.com/v2/articles/29923646
version: 2.2.0
datasets:
beataml:
description: Beat acute myeloid leukemia (BeatAML) focuses on acute myeloid leukemia
tumor data. Data includes drug response, proteomics, and transcriptomics datasets.
references:
- citation: Bottomly D, Long N, Schultz AR, et al. Integrative analysis of drug
response and clinical outcome in acute myeloid leukemia. Cancer Cell. 2022;40(8):850-864.e9.
doi: https://doi.org/10.1016/j.ccell.2022.07.002
- citation: Pino JC, Posso C, Joshi SK, et al. Mapping the proteogenomic landscape
enables prediction of drug response in acute myeloid leukemia. Cell Rep Med.
2024;5(1):101359.
doi: https://doi.org/10.1016/j.xcrm.2023.101359
bladder:
description: Tumor Evolution and Drug Response in Patient-Derived Organoid Models of Bladder Cancer
Data includes transcriptomics, mutations, copy number, and drug response data.
references:
- citation: Suk Hyung Lee, Wenhuo Hu, Justin T. Matulay, Mark V. Silva,
Tomasz B. Owczarek, Kwanghee Kim, Chee Wai Chua, LaMont J. Barlow,
Cyriac Kandoth, Alanna B. Williams, Sarah K. Bergren, Eugene J. Pietzak,
Christopher B. Anderson, Mitchell C. Benson, Jonathan A. Coleman, Barry S. Taylor,
Cory Abate-Shen, James M. McKiernan, Hikmat Al-Ahmadie, David B. Solit, Michael M. Shen.
Tumor Evolution and Drug Response in Patient-Derived Organoid Models of Bladder Cancer.
Cell. 2018;173(2):515-528.e17.
doi: https://doi.org/10.1016/j.cell.2018.03.017
ccle:
description: Cancer Cell Line Encyclopedia (CCLE).
references:
- citation: Barretina J, Caponigro G, Stransky N, et al. The Cancer Cell Line
Encyclopedia enables predictive modelling of anticancer drug sensitivity Nature.
2012;483(7391):603-607. Published 2012 Mar 28.
doi: https://doi.org/10.1038/nature11003
ctrpv2:
description: Cancer Therapeutics Response Portal version 2 (CTRPv2)
references:
- citation: Rees MG, Seashore-Ludlow B, Cheah JH, et al. Correlating chemical
sensitivity and basal gene expression reveals mechanism of action. Nat Chem
Biol. 2016;12(2):109-116.
doi: doi:10.1038/nchembio.1986
- citation: Seashore-Ludlow B, Rees MG, Cheah JH, et al. Harnessing Connectivity
in a Large-Scale Small-Molecule Sensitivity Dataset. Cancer Discov. 2015;5(11):1210-1223.
doi: doi:10.1158/2159-8290.CD-15-0235
- citation: Basu A, Bodycombe NE, Cheah JH, et al. An interactive resource to
identify cancer genetic and lineage dependencies targeted by small molecules.
Cell. 2013;154(5):1151-1161.
doi: doi:10.1016/j.cell.2013.08.003
fimm:
description: Institute for Molecular Medicine Finland (FIMM) dataset.
references:
- citation: "Mpindi JP, Yadav B, \xD6stling P, et al. Consistency in drug response\
\ profiling. Nature. 2016;540(7631):E5-E6."
doi: doi:10.1038/nature20171
- citation: Pemovska T, Kontro M, Yadav B, et al. Individualized systems medicine
strategy to tailor treatments for patients with chemorefractory acute myeloid
leukemia. Cancer Discov. 2013;3(12):1416-1429.
doi: doi:10.1158/2159-8290.CD-13-0350
gcsi:
description: The Genentech Cell Line Screening Initiative (gCSI)
references:
- citation: Haverty PM, Lin E, Tan J, et al. Reproducible pharmacogenomic profiling
of cancer cell line panels. Nature. 2016;533(7603):333-337.
doi: https://doi.org/10.1038/nature17987
- citation: Klijn C, Durinck S, Stawiski EW, et al. A comprehensive transcriptional
portrait of human cancer cell lines. Nat Biotechnol. 2015;33(3):306-312.
doi: https://doi.org/10.1038/nbt.3080
gdscv1:
description: Genomics of Drug Sensitivity in Cancer (GDSC) v1
references:
- citation: Garnett MJ, Edelman EJ, Heidorn SJ, et al. Systematic identification
of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483(7391):570-575.
Published 2012 Mar 28.
doi: doi:10.1038/nature11005
- citation: Iorio F, Knijnenburg TA, Vis DJ, et al. A Landscape of Pharmacogenomic
Interactions in Cancer. Cell. 2016;166(3):740-754.
doi: doi:10.1016/j.cell.2016.06.017
- citation: 'Yang W, Soares J, Greninger P, et al. Genomics of Drug Sensitivity
in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer
cells. Nucleic Acids Res. 2013;41(Database issue):D955-D961.'
doi: doi:10.1093/nar/gks1111
gdscv2:
description: Genomics of Drug Sensitivity in Cancer (GDSC) v2
references:
- citation: Garnett MJ, Edelman EJ, Heidorn SJ, et al. Systematic identification
of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483(7391):570-575.
Published 2012 Mar 28.
doi: doi:10.1038/nature11005
- citation: Iorio F, Knijnenburg TA, Vis DJ, et al. A Landscape of Pharmacogenomic
Interactions in Cancer. Cell. 2016;166(3):740-754.
doi: doi:10.1016/j.cell.2016.06.017
- citation: 'Yang W, Soares J, Greninger P, et al. Genomics of Drug Sensitivity
in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer
cells. Nucleic Acids Res. 2013;41(Database issue):D955-D961.'
doi: doi:10.1093/nar/gks1111
hcmi:
description: Human Cancer Models Initiative (HCMI) encompasses numerous cancer
types and includes cell line, organoid, and tumor data. Data includes the transcriptomics,
somatic mutation, and copy number datasets.
mpnst:
description: Malignant Peripheral Nerve Sheath Tumor is a rare, agressive sarcoma
that affects peripheral nerves throughout the body.
references:
- citation: Dehner C, Moon CI, Zhang X, et al. Chromosome 8 gain is associated
with high-grade transformation in MPNST. JCI Insight. 2021;6(6):e146351. Published
2021 Mar 22.
doi: https://doi.org/10.1172/jci.insight.146351
nci60:
description: National Cancer Institute 60
references:
- citation: Shoemaker RH. The NCI60 human tumour cell line anticancer drug screen.
Nat Rev Cancer. 2006;6(10):813-823.
doi: doi:10.1038/nrc1951
pancreatic:
description: Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer
Data includes transcriptomics, mutations, copy number, and drug response data.
references:
- citation: Hervé Tiriac, Pascal Belleau, Dannielle D. Engle, Dennis Plenker, Astrid Deschênes,
Tim D. D. Somerville, Fieke E. M. Froeling, Richard A. Burkhart, Robert E. Denroche, Gun-Ho Jang,
Koji Miyabayashi, C. Megan Young, Hardik Patel, Michelle Ma, Joseph F. LaComb, Randze L. D. Palmaira,
Ammar A. Javed, Jasmine C. Huynh, Molly Johnson, Kanika Arora, Nicolas Robine, Minita Shah,
Rashesh Sanghvi, Austin B. Goetz, Cinthya Y. Lowder, Laura Martello, Else Driehuis, Nicolas LeComte,
Gokce Askan, Christine A. Iacobuzio-Donahue, Hans Clevers, Laura D. Wood, Ralph H. Hruban, Elizabeth Thompson,
Andrew J. Aguirre, Brian M. Wolpin, Aaron Sasson, Joseph Kim, Maoxin Wu, Juan C. Bucobo, Peter Allen,
Divyesh V. Sejpal, William Nealon, James D. Sullivan, Jordan M. Winter, Phyllis A. Gimotty, Jean L. Grem,
Dominick J. DiMaio, Jonathan M. Buscaglia, Paul M. Grandgenett, Jonathan R. Brody, Michael A. Hollingsworth,
Grainne M. O'Kane, Faiyaz Notta, Edward Kim, James M. Crawford, Craig Devoe, Allyson Ocean, Christopher L. Wolfgang,
Kenneth H. Yu, Ellen Li, Christopher R. Vakoc, Benjamin Hubert, Sandra E. Fischer, Julie M. Wilson, Richard Moffitt,
Jennifer Knox, Alexander Krasnitz, Steven Gallinger, David A. Tuveson. Organoid Profiling Identifies Common
Responders to Chemotherapy in Pancreatic Cancer. Cancer Discov. 2018;8(9):1112-1129.
doi: https://doi.org/10.1158/2159-8290.CD-18-0349
prism:
description: Profiling Relative Inhibition Simultaneously in Mixtures
references:
- citation: Corsello SM, Nagari RT, Spangler RD, et al. Discovering the anti-cancer
potential of non-oncology drugs by systematic viability profiling. Nat Cancer.
2020;1(2):235-248.
doi: doi:10.1038/s43018-019-0018-6
- citation: Yu C, Mannan AM, Yvone GM, et al. High-throughput identification of
genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell
lines. Nat Biotechnol. 2016;34(4):419-423.
doi: doi:10.1038/nbt.3460
sarcoma:
description: The landscape of drug sensitivity and resistance in sarcoma
Data includes transcriptomics, mutations, and drug response data.
references:
- citation: Ahmad Al Shihabi, Peyton J. Tebon, Huyen T. L. Nguyen,
Jomjit Chantharasamee, Sara Sartini, Ardalan Davarifar, Alexandra Y. Jensen,
Miranda Diaz-Infante, Hannah Cox, Alfredo E. Gonzalez, Summer Norris, Jantzen Sperry,
Jonathan Nakashima, Nasrin Tavanaie, Helena Winata, Sorel T. Fitz-Gibbon,
Takafumi N. Yamaguchi, Jae H. Jeong, Sarah Dry, Arun S. Singh, Bartosz Chmielowski,
Joseph G. Crompton, Anusha K. Kalbasi, Fritz C. Eilber, Francis Hornicek,
Nicholas M. Bernthal, Scott D. Nelson, Paul C. Boutros, Noah C. Federman, Jane Yanagawa,
Alice Soragni. The landscape of drug sensitivity and resistance in sarcoma.
Cell Stem Cell. 2024;31(10):1524-1542.e4.
doi: https://doi.org/10.1016/j.stem.2024.08.010
colorectal:
description: Prospective Derivation of a Living Organoid Biobank of Colorectal Cancer Patients
references:
- citation: Marc van de Wetering, Hayley E. Francies, Joshua M. Francis, Gergana Bounova,
Francesco Iorio, Apollo Pronk, Winan van Houdt, Joost van Gorp, Amaro Taylor-Weiner,
Lennart Kester, Anne McLaren-Douglas, Joyce Blokker, Sridevi Jaksani, Sina Bartfeld,
Richard Volckman, Peter van Sluis, Vivian S.W. Li, Sara Seepo, Chandra Sekhar Pedamallu,
Kristian Cibulskis, Scott L. Carter, Aaron McKenna, Michael S. Lawrence,
Lee Lichtenstein, Chip Stewart, Jan Koster, Rogier Versteeg, Alexander van Oudenaarden,
Julio Saez-Rodriguez, Robert G.J. Vries, Gad Getz, Lodewyk Wessels, Michael R. Stratton,
Ultan McDermott, Matthew Meyerson, Mathew J. Garnett, Hans Clevers, Prospective Derivation
of a Living Organoid Biobank of Colorectal Cancer Patients, Cell, Volume 161, Issue 4, 2015,
Pages 933-945, ISSN 0092-8674
doi: https://doi.org/10.1016/j.cell.2015.03.053
liver:
description: Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology
references:
- citation: Ji S, Feng L, Fu Z, Wu G, Wu Y, Lin Y, Lu D, Song Y, Cui P, Yang Z, Sang C, Song G,
Cai S, Li Y, Lin H, Zhang S, Wang X, Qiu S, Zhang X, Hua G, Li J, Zhou J, Dai Z, Wang X,
Ding L, Wang P, Gao D, Zhang B, Rodriguez H, Fan J, Clevers H, Zhou H, Sun Y, Gao Q.
Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology.
doi: https://doi.org/10.1126/scitranslmed.adg3358
novartis:
description: High-throughput screening using patient-derived tumor xenografts to predict clinical trial
drug response
references:
- citation: Hui Gao, Joshua M. Korn, Stéphane Ferretti, John E. Monahan, Youzhen Wang, Mallika Singh,
Chao Zhang, Christian Schnell, Guizhi Yang, Yun Zhang, O. Alejandro Balbin, Stéphanie Barbe,
Hongbo Cai, Fergal Casey, Susmita Chatterjee, Derek Y. Chiang, Shannon Chuai, Shawn M. Cogan,
Scott D. Collins, Ernesta Dammassa, Nicolas Ebel, Millicent Embry, John Green, Audrey Kauffmann,
Colleen Kowal, Rebecca J. Leary, Joseph Lehar, Ying Liang, Alice Loo, Edward Lorenzana,
E. Robert McDonald III, Margaret E. McLaughlin, Jason Merkin, Ronald Meyer, Tara L. Naylor,
Montesa Patawaran, Anupama Reddy, Claudia Röelli, David A. Ruddy, Fernando Salangsang,
Francesca Santacroce, Angad P. Singh, Yan Tang, Walter Tinetto, Sonja Tobler, Roberto Velazquez,
Kavitha Venkatesan, Fabian Von Arx, Hui Qin Wang, Zongyao Wang, Marion Wiesmann, Daniel Wyss,
Fiona Xu, Hans Bitter, Peter Atadja, Emma Lees, Francesco Hofmann, En Li, Nicholas Keen,
Robert Cozens, Michael Rugaard Jensen, Nancy K. Pryer, Juliet A. Williams, William R. Sellers,
High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug
response, Nature Medicine, Volume 21, 2015, Pages 1318–1325.
doi: https://doi.org/10.1038/nm.3954