TY - JOUR
T1 - Integrated approaches for the use of large datasets to identify rational therapies for the treatment of lung cancers
AU - Cardnell, Robert J.
AU - Byers, Lauren Averett
AU - Wang, Jing
N1 - Funding Information:
This research was supported by the NIH/NCI CCSG P30-CA01667 (L.A.B., J.W.), NIH/NCI R01-CA207295 (L.A.B.), NIH/NCI SPORE P5-CA070907 (L.A.B., J.W.), an MD Andersen Cancer Center Physician Scientist Award (L.A.B.), and through generous philanthropic contributions to The University of Texas MD Anderson Cancer Center Lung Cancer Moonshot Program. The authors would like to acknowledge the key members of the Lung Cancer Multidisciplinary Program, without who these studies could not have been conducted, including John V. Heymach, Ignacio I. Wistuba, Jack Roth, Bonnie S. Glisson, Don L. Gibbons, Vali Papadimitrakopoulou, Junya Fujimoto, Lixia Diao, Pan Tong, Lerong Li, C. Allison Stewart, and Carl M. Gay.
Funding Information:
Funding: This research was supported by the NIH/NCI CCSG P30-CA01667 (L.A.B., J.W.), NIH/NCI R01-CA207295 (L.A.B.), NIH/NCI SPORE P5-CA070907 (L.A.B., J.W.), an MD Andersen Cancer Center Physician Scientist Award (L.A.B.), and through generous philanthropic contributions to The University of Texas MD Anderson Cancer Center Lung Cancer Moonshot Program.
Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019/2
Y1 - 2019/2
N2 - The benefit and burden of contemporary techniques for the molecular characterization of samples is the vast amount of data generated. In the era of “big data”, it has become imperative that we develop multi-disciplinary teams combining scientists, clinicians, and data analysts. In this review, we discuss a number of approaches developed by our University of Texas MD Anderson Lung Cancer Multidisciplinary Program to process and utilize such large datasets with the goal of identifying rational therapeutic options for biomarker-driven patient subsets. Large integrated datasets such as the The Cancer Genome Atlas (TCGA) for patient samples and the Cancer Cell Line Encyclopedia (CCLE) for tumor derived cell lines include genomic, transcriptomic, methylation, miRNA, and proteomic profiling alongside clinical data. To best use these datasets to address urgent questions such as whether we can define molecular subtypes of disease with specific therapeutic vulnerabilities, to quantify states such as epithelial-to-mesenchymal transition that are associated with resistance to treatment, or to identify potential therapeutic agents in models of cancer that are resistant to standard treatments required the development of tools for systematic, unbiased high-throughput analysis. Together, such tools, used in a multi-disciplinary environment, can be leveraged to identify novel treatments for molecularly defined subsets of cancer patients, which can be easily and rapidly translated from benchtop to bedside.
AB - The benefit and burden of contemporary techniques for the molecular characterization of samples is the vast amount of data generated. In the era of “big data”, it has become imperative that we develop multi-disciplinary teams combining scientists, clinicians, and data analysts. In this review, we discuss a number of approaches developed by our University of Texas MD Anderson Lung Cancer Multidisciplinary Program to process and utilize such large datasets with the goal of identifying rational therapeutic options for biomarker-driven patient subsets. Large integrated datasets such as the The Cancer Genome Atlas (TCGA) for patient samples and the Cancer Cell Line Encyclopedia (CCLE) for tumor derived cell lines include genomic, transcriptomic, methylation, miRNA, and proteomic profiling alongside clinical data. To best use these datasets to address urgent questions such as whether we can define molecular subtypes of disease with specific therapeutic vulnerabilities, to quantify states such as epithelial-to-mesenchymal transition that are associated with resistance to treatment, or to identify potential therapeutic agents in models of cancer that are resistant to standard treatments required the development of tools for systematic, unbiased high-throughput analysis. Together, such tools, used in a multi-disciplinary environment, can be leveraged to identify novel treatments for molecularly defined subsets of cancer patients, which can be easily and rapidly translated from benchtop to bedside.
KW - Bioinformatics
KW - Integrated approaches
KW - Lung cancer
KW - Rational therapy
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U2 - 10.3390/cancers11020239
DO - 10.3390/cancers11020239
M3 - Review article
C2 - 30791396
AN - SCOPUS:85063570720
SN - 2072-6694
VL - 11
JO - Cancers
JF - Cancers
IS - 2
M1 - 239
ER -