Big Data for Cancer Research William C. Cho Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong.
Citation: Cho. Big Data for Cancer Research. Clinical Medicine Insights: Oncology 2015:9 135–136 doi: 10.4137/CMO.S38279. TYPE: Editorial Funding: Author discloses no external funding sources. Competing Interests: Author discloses no potential conflicts of interest. Correspondence: [email protected]
rior to publication the author has given signed confirmation of agreement to article P publication and compliance with all applicable ethical and legal requirements, including the accuracy of author and contributor information, disclosure of competing interests and funding sources, compliance with ethical requirements relating to human and animal study participants, and compliance with any copyright requirements of third parties. This journal is a member of the Committee on Publication Ethics (COPE). Published by Libertas Academica. Learn more about this journal.
Copyright: © the authors, publisher and licensee Libertas Academica Limited. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.
From the rise in data-mining and the flood of big data has emerged a new era, an informatics age that will probably be the next revolution for humankind in terms of the management of information and generation of new knowledge in diverse fields. A special issue discussing big data storage, management and analyses published by Nature opened the curtain of the big data era in 2008.1 The big data era demonstrated its utility in the successful influenza prediction via Google in 2009.2 Cancer is a heterogeneous disease per se, though through clonal expansion, it is also known that heterogeneity is present within a single patient. It is understandable that somatic mutations accumulate among different single cancer cells. Our current technology for cancer biomarker discovery is usually capable of detecting a snapshot, but not the dynamic and longitudinal changes of the cancer landscape. Big data can tackle this hurdle by gathering different molecular features at the DNA, RNA, protein and metabolite levels. Biodata-mining has led those working in the life sciences to embrace the informatics field, in order to accommodate the deluge of big data generated by next-generation biotechnologies, such as next-generation sequencing, proteomics and metabolomics, as well as the structured and unstructured biomedical and healthcare data from electronic health records. Life scientists are bringing together tremendous volumes of information from various high-throughput studies. The holistic approach on data informatics may facilitate the emergence of an era of precision medicine, which ultimately provides treatments tailored to each individual’s needs. Big data certainly challenges cancer research in various aspects. The trends of integrating different platforms and their findings create an intersection among different areas and dimensionalities, which is ultimately a step towards a holistic understanding of the disease. An omics approach to the study of cancer is keeping its fast pace. 3 Nowadays, next-generation sequencing, proteomics and metabolomics develop rapidly in various directions. However, they also pose a difficult question regarding the existing platform of using a single dimension to measure cancer. Multi-dimensional approach to cancer research is urgently needed.
On the other hand, there is a sea of data, not only huge in volume and complex in structure, but also vast in dynamic scale and depth. We foresee that a large amount of loosely connected, inherently noisy and heterogeneous data may also be gathered in the databases. Do all the data contain useful information? How can we judge whether the information is useful or junk? Thus, certain standards, guidelines or harmanizations may be needed to consolidate useful data in the same database. Some international consortia and institutions have made efforts to set rules to guarantee transparency and traceability so as to ensure the usefulness of the data.4 Electronic health records are becoming a better resource for databases. The information can be exchanged and gathered via telemedicine and mobile connectivity. Patients’ information and data security become an issue and more security measures need to be in place. We are in the precision medicine era, and meticulous decision making based on individual, detailed molecular profiles is a pressing need. Tumor molecular profiling has enabled the subclassification of cancer to redefine treatment regimes. Big data are certainly sailing in the same boat with precision medicine.5 Nowadays, more and more databases have been established, eg, The Catalogue of Somatic Mutations in Cancer (COSMIC), which is the largest database of somatic mutations and their effects on human cancer.4 Another example is the DREAM (Dialogue for Reverse Engineering Assessments and Methods) Consortium, which uses open innovation crowd sourcing to identify top-performing computational methods for inferring genetic heterogeneity from next-generation sequencing data provided by a large multiinstitutional community of cancer genomics projects, including the International Cancer Genome Consortium and The Cancer Genome Atlas.7 These huge databases may lead to a paradigm shift in cancer research, researchers from any corner of the world can share and utilize the data for further research and analysis. It is envisioned that in the years to come, we will step into a revolutionized era of utilizing big data to benefit our cancer patients. Clinical Medicine Insights: Oncology 2015:9
135
Cho
Author Contributions
Wrote the first draft of the manuscript: WC. Made critical revisions and approved final version: WC. The author reviewed and approved of the final manuscript. References
1. Community cleverness required. Nature. 2008;455(7209):1. 2. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009; 457(7232):1012–4.
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3. Cho WC. Omics Approaches in Cancer Research. In: Cho WC, ed. An Omics Perspective on Cancer Research. Heidelberg, Germany: Springer; 2010:1–9. 4. Adams JU. Genetics: big hopes for big data. Nature. 2015;527(7578):S108–9. 5. Roychowdhury S, Chinnaiyan AM. Translating cancer genomes and transcriptomes for precision oncology. CA Cancer J. Clin. 2015;doi:10.3322/caac.21329. 6. Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2015;43:D805–11. 7. Sage Bionetworks. ICGC-TCGA DREAM somatic mutation calling challenge– tumor heterogeneity and evolution. Available from: https://www.synapse. org/#!Synapse:syn2813581. Accessed December 15, 2015.
Citation: Cho. Big Data for Cancer Research. Clinical Medicine Insights: Oncology 2015:9 135–136 doi: 10.4137/CMO.S38279. TYPE: Editorial Funding: Author discloses no external funding sources. Competing Interests: Author discloses no potential conflicts of interest. Correspondence: [email protected]
rior to publication the author has given signed confirmation of agreement to article P publication and compliance with all applicable ethical and legal requirements, including the accuracy of author and contributor information, disclosure of competing interests and funding sources, compliance with ethical requirements relating to human and animal study participants, and compliance with any copyright requirements of third parties. This journal is a member of the Committee on Publication Ethics (COPE). Published by Libertas Academica. Learn more about this journal.
Copyright: © the authors, publisher and licensee Libertas Academica Limited. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.
From the rise in data-mining and the flood of big data has emerged a new era, an informatics age that will probably be the next revolution for humankind in terms of the management of information and generation of new knowledge in diverse fields. A special issue discussing big data storage, management and analyses published by Nature opened the curtain of the big data era in 2008.1 The big data era demonstrated its utility in the successful influenza prediction via Google in 2009.2 Cancer is a heterogeneous disease per se, though through clonal expansion, it is also known that heterogeneity is present within a single patient. It is understandable that somatic mutations accumulate among different single cancer cells. Our current technology for cancer biomarker discovery is usually capable of detecting a snapshot, but not the dynamic and longitudinal changes of the cancer landscape. Big data can tackle this hurdle by gathering different molecular features at the DNA, RNA, protein and metabolite levels. Biodata-mining has led those working in the life sciences to embrace the informatics field, in order to accommodate the deluge of big data generated by next-generation biotechnologies, such as next-generation sequencing, proteomics and metabolomics, as well as the structured and unstructured biomedical and healthcare data from electronic health records. Life scientists are bringing together tremendous volumes of information from various high-throughput studies. The holistic approach on data informatics may facilitate the emergence of an era of precision medicine, which ultimately provides treatments tailored to each individual’s needs. Big data certainly challenges cancer research in various aspects. The trends of integrating different platforms and their findings create an intersection among different areas and dimensionalities, which is ultimately a step towards a holistic understanding of the disease. An omics approach to the study of cancer is keeping its fast pace. 3 Nowadays, next-generation sequencing, proteomics and metabolomics develop rapidly in various directions. However, they also pose a difficult question regarding the existing platform of using a single dimension to measure cancer. Multi-dimensional approach to cancer research is urgently needed.
On the other hand, there is a sea of data, not only huge in volume and complex in structure, but also vast in dynamic scale and depth. We foresee that a large amount of loosely connected, inherently noisy and heterogeneous data may also be gathered in the databases. Do all the data contain useful information? How can we judge whether the information is useful or junk? Thus, certain standards, guidelines or harmanizations may be needed to consolidate useful data in the same database. Some international consortia and institutions have made efforts to set rules to guarantee transparency and traceability so as to ensure the usefulness of the data.4 Electronic health records are becoming a better resource for databases. The information can be exchanged and gathered via telemedicine and mobile connectivity. Patients’ information and data security become an issue and more security measures need to be in place. We are in the precision medicine era, and meticulous decision making based on individual, detailed molecular profiles is a pressing need. Tumor molecular profiling has enabled the subclassification of cancer to redefine treatment regimes. Big data are certainly sailing in the same boat with precision medicine.5 Nowadays, more and more databases have been established, eg, The Catalogue of Somatic Mutations in Cancer (COSMIC), which is the largest database of somatic mutations and their effects on human cancer.4 Another example is the DREAM (Dialogue for Reverse Engineering Assessments and Methods) Consortium, which uses open innovation crowd sourcing to identify top-performing computational methods for inferring genetic heterogeneity from next-generation sequencing data provided by a large multiinstitutional community of cancer genomics projects, including the International Cancer Genome Consortium and The Cancer Genome Atlas.7 These huge databases may lead to a paradigm shift in cancer research, researchers from any corner of the world can share and utilize the data for further research and analysis. It is envisioned that in the years to come, we will step into a revolutionized era of utilizing big data to benefit our cancer patients. Clinical Medicine Insights: Oncology 2015:9
135
Cho
Author Contributions
Wrote the first draft of the manuscript: WC. Made critical revisions and approved final version: WC. The author reviewed and approved of the final manuscript. References
1. Community cleverness required. Nature. 2008;455(7209):1. 2. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009; 457(7232):1012–4.
136
Clinical Medicine Insights: Oncology 2015:9
3. Cho WC. Omics Approaches in Cancer Research. In: Cho WC, ed. An Omics Perspective on Cancer Research. Heidelberg, Germany: Springer; 2010:1–9. 4. Adams JU. Genetics: big hopes for big data. Nature. 2015;527(7578):S108–9. 5. Roychowdhury S, Chinnaiyan AM. Translating cancer genomes and transcriptomes for precision oncology. CA Cancer J. Clin. 2015;doi:10.3322/caac.21329. 6. Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2015;43:D805–11. 7. Sage Bionetworks. ICGC-TCGA DREAM somatic mutation calling challenge– tumor heterogeneity and evolution. Available from: https://www.synapse. org/#!Synapse:syn2813581. Accessed December 15, 2015.
