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Genome Biology | Full text | Organizing knowledge to enable personalization of medicine in cancer

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Genome Biology | Full text | Organizing knowledge to enable personalization of medicine in cancer

Genome Biology


Organizing knowledge to enable personalization of medicine in cancer

Benjamin M Good1Benjamin J Ainscough23Josh F McMichael2Andrew I Su1* and Obi L Griffith24*
1Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla 92037, CA, USA
2The Genome Institute, Washington University School of Medicine, 4444 Forest Park Ave, St Louis 63108, MO, USA
3Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, St Louis 63110, MO, USA
4Department of Medicine, Washington University School of Medicine, 660 S. Euclid Ave, St Louis 63110, MO, USA
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Genome Biology 2014, 15:438  doi:10.1186/s13059-014-0438-7

The electronic version of this article is the complete one and can be found online at:

Published:27 August 2014
© 2014 Good et al.; licensee BioMed Central Ltd. 
The licensee has exclusive rights to distribute this article, in any medium, for 12 months following its publication. After this time, the article is available under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.


Interpretation of the clinical significance of genomic alterations remains the most severe bottleneck preventing the realization of personalized medicine in cancer. We propose a knowledge commons to facilitate collaborative contributions and open discussion of clinical decision-making based on genomic events in cancer.

The bottleneck for realizing personalized medicine is now interpretation

The landscape of the genomics of tumor progression and heterogeneity has seen incredible advancements in recent years with the maturation of The Cancer Genome Atlas (TCGA) [1], International Cancer Genome Consortium (ICGC) [2] and other large-scale tumor sequencing efforts. Software and workflow systems for predicting and annotating genomic changes have proliferated and continue to improve [3]. Caregivers in the healthcare system will soon be faced with a large number of genomic alterations that are potentially relevant to understanding cancer progression and improving clinical decision making for each individual patient. However, there are few resources to help with the prioritization and interpretation of these alterations in a clinical context. Genomic events and the genes or pathways that they affect must be placed in the context of drug-gene or drug-variant interactions and associations with diagnostic or prognostic endpoints. The evidence for these associations must also be captured and characterized to allow risk-benefit analysis for any proposed clinical action. The bulk of this information remains trapped in the masses of published data, clinical trial records, and domain-specific databases. Sifting through this mountain of information is now the most critical bottleneck to making personalized medicine a reality in cancer. In this Opinion article, we propose the creation of a comprehensive, current, and community-based knowledge base to connect cancer genome events with the necessary evidence to evaluate their biological and clinical significance. Such a framework will allow the harnessing of collaborative contributions and open discussion needed to empower the most informed genomics-based clinical decision-making in a rapidly changing landscape.
Cancer genomics promises to revolutionize medicine by identifying tumor-specific alterations that can guide clinical decision-making. To list just two groundbreaking examples, activating mutations in the epidermal growth factor receptor gene EGFR were linked to gefitinib response [4],[5] and amplification or overexpression of the related gene ERBB2 was shown to predict response to anti-ERBB2 therapies such as lapatinib [6]. Tests for these markers that guide therapy decisions are now part of the standard of care in non-small-cell lung cancer and breast cancer. Since these and other early single-gene findings, large-scale sequencing studies have systematically mapped the landscape of the most common alterations for most common tumor types [1],[2]. Increasingly, these alterations are being linked to diagnostic, prognostic, and drug-response outcomes. As the number of these associations increases and sequencing costs decrease, targeted panels are being replaced by genome- and transcriptome-wide approaches. Several proof-of-principle studies have recently demonstrated the potential for use of such data to identify clinically actionable findings [7]–[9]. In a prototypical study, Jones et al.[10] sequenced an oral adenocarcinoma by whole-genome and whole-transcriptome sequencing, identified upregulation of the mitogen activating protein kinase pathways through overexpression of receptor tyrosine kinase (RET) RNA and deletion of the Phosphatase and tensin homolog (PTEN) gene. They proposed a therapeutic intervention by RET inhibition with sunitinib, a therapy that might not otherwise be considered for this disease type. Most recently, Van Allen et al.[11] described an exome sequencing approach that, when applied prospectively, identified clinically relevant alterations in 15 of 16 cancer patients analyzed.
These anecdotal examples hint at the promise of personalized (‘N-of-one’) medicine to target therapies to the specific genomic alterations of each cancer patient. A typical cancer genomics workflow is depicted in Figure 1. This process has been reviewed elsewhere extensively [11]–[13] and is arguably converging on some level of standardization and automation. The major bottleneck in the process currently lies in the final steps of interpretation and report generation. The challenge is to determine the significance of tumor-specific genomic changes in both a biological and clinical context. A large number of algorithms have been developed to predict the biological effects of single nucleotide variants (SNVs) and to a lesser degree insertions and deletions (indels). The overall accuracy of these methods is generally low [14] and very little has been done for other event types such as chimeric transcripts and copy number variants (CNVs).

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