For December 2016, the journal Genome Medicine has published a special open access collection discussing the forefronts of precision cancer medicine (here). What struck me is that most of the articles feature an extremely bioinformatics-intensive component, such as predictive cellular modeling in silico. This goes far beyond sequencing 50, or 500 genes, and noting that the ALK mutation is associated with a clinical crizotinib response and the EGFR mutation with a clinical erlotinib response. Rather, the complexity of the genome is leveraged through analytics that a human couldn't see by reviewing a gene-drug table or by just digitizing it into a pathology report.
The new horizons may also create new policy and regulatory challenges. (Is a genomic test incorporating remotely leased or operated SAAS software still an "LDT" for the FDA? Is this "medical software" vended across state lines? How would you know if something went wrong? What does a career laboratorian CLIA inspector inspect? And to be viable over the long run, is the development and management of such software supposed to fit within a lab's falling fee schedule payments for genomics?)
The open access articles in the special collection are accessible at Genome Medicine, here. I've clipped abstracts of articles illustrating the informatics theme after the break.
Note: Although already somewhat dated at just one year old, my February 2016 white paper on "Digital Genomics" is still online here. Since then, multiple additional companies have come to my attention, such as Farsight (here) and innovative alliances have popped up, such as IBM Watson and Quest (here). I'll be chairing a panel on this topic at the NextGenDx conference in Washington in August 2017.
Selected articles in the Genome Medicine collection highlighted here. Publications dates span from October to December, 2016.
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METHOD
iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes
Cancer results from the acquisition of somatic driver mutations. Several computational tools can predict driver genes from population-scale genomic data, but tools for analyzing personal cancer genomes are und...
Genome Medicine 2016 8:135
Cancer network activity associated with therapeutic response and synergism
Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer c...
Genome Medicine 2016 8:88
Combinations of therapies are being actively pursued to expand therapeutic options and deal with cancer’s pervasive resistance to treatment. Research efforts to discover effective combination treatments have f...
Genome Medicine 2016 8:125
Rational design of cancer gene panels with OncoPaD
Profiling the somatic mutations of genes which may inform about tumor evolution, prognostics and treatment is becoming a standard tool in clinical oncology. Commercially available cancer gene panels rely on ma...
Genome Medicine 2016 8:98
Somatic cancer variant curation and harmonization through consensus minimum variant level data
To truly achieve personalized medicine in oncology, it is critical to catalog and curate cancer sequence variants for their clinical relevance. The Somatic Working Group (WG) of the Clinical Genome Resource (C...
Genome Medicine 2016 8:117
Integrating cancer genomic data into electronic health records
The rise of genomically targeted therapies and immunotherapy has revolutionized the practice of oncology in the last 10–15 years. At the same time, new technologies and the electronic health record (EHR) in pa...
Genome Medicine 2016 8:113
Technological considerations for genome-guided diagnosis and management of cancer
Technological, methodological, and analytical advances continue to improve the resolution of our view into the cancer genome, even as we discover ways to carry out analyses at greater distances from the primar...
Genome Medicine 2016 8:112
Germline, hematopoietic, mosaic, and somatic variation: interplay between inherited and acquired genetic alterations in disease assessment
Advances in genetic analysis have revealed new complexities in the interpretation of genetic variants. Correct assessment of a genetic variant relies on the clinical context and knowledge of the underlying bio...
Genome Medicine 2016 8:100
