Toward a Shared Vision for Cancer Genomic Data

Grossman, Robert L.; Heath, Allison P.; Ferretti, Vincent; Varmus, Harold; Lowy, Douglas R.; Kibbe, Warren A.; Staudt, Louis M.

doi:10.1056/nejmp1607591

Cited by 1,422 publications

(1,314 citation statements)

References 6 publications

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“…However, it has been challenging to extend this paradigm to treat all cancers in part because tumor-suppressor mutations play a significant role in driving tumorigenesis, but remain difficult to target therapeutically (8). Nevertheless, there is hope that the broad application of tumor sequencing, even if it fails to identify directly targetable mutations, can further personalize treatment approaches to benefit a broader swath of patients (6,9).…”

Section: Introductionmentioning

confidence: 99%

Nature and Nurture: What Determines Tumor Metabolic Phenotypes?

Mayers

Heiden

2017

Cancer Research

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Understanding the genetic basis of cancer has led to therapies that target driver mutations and has helped match patients with more personalized drugs. Oncogenic mutations influence tumor metabolism, but other tumor characteristics can also contribute to their metabolic phenotypes. Comparison of isogenic lung and pancreas tumor models suggests that use of some metabolic pathways is defined by lineage rather than by driver mutation. Lung tumors catabolize circulating branched chain amino acids (BCAA) to extract nitrogen for nonessential amino acid and nucleotide synthesis, whereas pancreatic cancer obtains amino acids from catabolism of extracellular protein. These differences in amino acid metabolism translate into distinct pathway dependencies, as genetic disruption of the enzymes responsible for utilization of BCAA nitrogen limits the growth of lung tumors, but not pancreatic tumors. These data argue that some cancer metabolic phenotypes are defined by cancer tissue-of-origin and environment and that these features constrain the influence of genetic mutations on metabolism. A better understanding of the factors defining tumor nutrient utilization could be exploited to help improve cancer therapy.

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Section: Introductionmentioning

confidence: 99%

Nature and Nurture: What Determines Tumor Metabolic Phenotypes?

Mayers

Heiden

2017

Cancer Research

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“…"Toward a Shared Vision for Cancer Genomic Data" by Robert L. Grossman and others talks about sharing raw genomic data including diagnostic, histologic, and clinical outcome data through Genomic Data Commons (GDC), a data repository developed by a consortium that includes National Cancer Institute (NCI), University of Chicago, Ontario Institute of Cancer Research, and Leidos Medical Research. (Grossman et al, 2016).…”

Section: Many Of the Recent Studies Published In The New England Jourmentioning

confidence: 99%

Future of Medical Research in Rare Diseases and Cancers: Shift from Pharma to Biotech and the Golden Age of Medical Advancement

Pallegar

2017

CCO

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The objective of the paper is to elucidate how interconnected biological systems can be better mapped and understood using the rapidly growing area of Big Data. We can harness network efficiencies by analyzing diverse medical data and probe how we can effectively lower the economic cost of finding cures for rare diseases. Most rare diseases are due to genetic abnormalities, many forms of cancers develop due to genetic mutations. Finding cures for rare diseases requires us to understand the biology and biological processes of the human body. In this paper, we explore what the historical shift of focus from pharmacology to biotechnology means for accelerating biomedical solutions. With biotechnology playing a leading role in the field of medical research, we explore how network efficiencies can be harnessed by strengthening the existing knowledge base. Studying rare or orphan diseases provides rich observable statistical data that can be leveraged for finding solutions. Network effects can be squeezed from working with diverse data sets that enables us to generate the highest quality medical knowledge with the fewest resources. This paper examines gene manipulation technologies like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) that can prevent diseases of genetic variety. We further explore the role of the emerging field of Big Data in analyzing large quantities of medical data with the rapid growth of computing power and some of the network efficiencies gained from this endeavor.

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“…First, participants have pledged and started to contribute data that underpin the preanalytical and analytical criteria for different liquid biopsy test platforms. The second goal is to develop a prototype BloodPAC Data Commons and analysis cloud, which will be based in part on the open source software developed for the NCI Genomic Data Commons and Cancer Genomics Cloud Pilots 7. Data commons and analysis clouds colocate the data, storage, and computing infrastructure with commonly used tools, applications and services to create interoperable resources for the research and clinical communities 7.…”

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confidence: 99%

“…The second goal is to develop a prototype BloodPAC Data Commons and analysis cloud, which will be based in part on the open source software developed for the NCI Genomic Data Commons and Cancer Genomics Cloud Pilots 7. Data commons and analysis clouds colocate the data, storage, and computing infrastructure with commonly used tools, applications and services to create interoperable resources for the research and clinical communities 7. With a data commons and analysis cloud, researchers can collaboratively analyze data in a scalable and reproducible manner using existing or novel tools, without first downloading it and setting up their own analysis environments 7.…”

mentioning

confidence: 99%

“…Data commons and analysis clouds colocate the data, storage, and computing infrastructure with commonly used tools, applications and services to create interoperable resources for the research and clinical communities 7. With a data commons and analysis cloud, researchers can collaboratively analyze data in a scalable and reproducible manner using existing or novel tools, without first downloading it and setting up their own analysis environments 7. The BloodPAC Data Commons shares a data model with the NCI Genomic Data Commons and employs the same core web services, such as those providing unique digital IDs for data, so that linking the two commons and building applications that access data from both is straightforward.…”

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confidence: 99%

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Collaborating to Compete: Blood Profiling Atlas in Cancer (BloodPAC) Consortium

Grossman

Abel

Angiuoli

et al. 2017

Clin Pharma and Therapeutics

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The cancer community understands the value of blood profiling measurements in assessing and monitoring cancer. We describe an effort among academic, government, biotechnology, diagnostic, and pharmaceutical companies called the Blood Profiling Atlas in Cancer (BloodPAC) Project. BloodPAC will aggregate, make freely available, and harmonize for further analyses, raw datasets, relevant associated clinical data (e.g., clinical diagnosis, treatment history, and outcomes), and sample preparation and handling protocols to accelerate the development of blood profiling assays.

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Toward a Shared Vision for Cancer Genomic Data

Cited by 1,422 publications

References 6 publications

Nature and Nurture: What Determines Tumor Metabolic Phenotypes?

Nature and Nurture: What Determines Tumor Metabolic Phenotypes?

Future of Medical Research in Rare Diseases and Cancers: Shift from Pharma to Biotech and the Golden Age of Medical Advancement

Collaborating to Compete: Blood Profiling Atlas in Cancer (BloodPAC) Consortium

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