14 Dec Q&A with Mark Kiel, CSO and Co-founder, Genomenon – Speaker at PMWC 2018 Silicon Valley
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[et_pb_accordion_item title=”Q: How did you become interested in the clinical interpretation of next-generation sequencing data?”]
A: During my residency in Clinical Pathology, the power of next-generation DNA sequencing was readily apparent to me. Just as obvious was the limitation of fully utilizing the technology at a large scale due to challenges in efficiently and accurately analyzing these data for clinical purposes. Genomenon – from a Greek word meaning “born out of need” – began as my reaction to a lack of commercially available tools to help facilitate genomic analysis. My research in interpreting the genomes of hematopoietic malignancies to uncover their genetic cause was a perfect testing environment for some of the data-mining and organization techniques being used by Mastermind.
[/et_pb_accordion_item][et_pb_accordion_item title=”Q: What was your experience as a molecular pathologist at the University of Michigan when you had to search for disease, gene and variant relationships?”]
A: Two-fold. In many circumstances, the variant was well-known as a pathogenic variant but more detailed information was needed to address the specific clinical circumstance of the individual patient – things like identifying the most appropriate therapy for a variant in a particular disease context or determining whether the given variant had ever been seen in a specific clinical context. In a second case, when the variant was an unusual or otherwise less well-characterized variant, simply determining whether the variant had ever been published before, and if so where, how many times, in what disease contexts and with what clinical annotations, were the primary questions that needed to be addressed. In both circumstances, this discovery process relied heavily on information in the primary literature and invariably the process of manually finding and organizing the evidence was much more time-consuming and idiosyncratic than actually interpreting the results. I recognized that each time I consulted the literature, the same types of questions were asked. The inspiration behind founding Genomenon was when I recognized that much of this work could be automated and a comprehensive set of questions could be asked and answered up-front and at a massive scale.
[/et_pb_accordion_item][et_pb_accordion_item title=”Q: What are the challenges facing clinical interpretation of NGS data today?”]
A: Reproducibility of interpretation is the biggest challenge we face as clinicians. There are guidelines that are emerging to help compel order and regularity to the process but this is only effective when everyone is working with the same set of evidence. Efforts to organize a comprehensive dataset of clinically meaningful information for each individual variant face the challenge of doing this work at the massive scale demanded by the complexity and quantity of the data. These are the challenges that the Mastermind Genomic Search Engine, Genomenon’s first product, is addressing. It does this by collecting, organizing and presenting information at the variant level through a comprehensive data aggregation process predicated on the primary evidence in the medical literature.
[/et_pb_accordion_item][et_pb_accordion_item title=”Q: What are the benefits of Genomenon’s new evidence-based blood cancer panel?”]
A: One of the interesting facets of using NGS technology in Molecular Diagnostics is the ability to sequence many more genes on a single assay than was previously possible. This has lead to the promulgation of many dozens of disease-focused and more comprehensive diagnostic gene panels with a large number of biomarkers. One unforeseen challenge that arises as a result is the heterogeneity of each of these panels that are purporting to test for variants in the same disease or diseases. Using the data in Mastermind, we were able to automatically develop the first ever evidence-based list of candidate biomarkers for a disease category – hematopoietic malignancy. We published this cancer panel with all the evidence to demonstrate how biomarkers can be automatically identified at scale based on the evidence found in the scientific literature. This way, the inclusion or exclusion of a biomarker – whether a gene or a variant – can be predicated on the evidence supporting the usefulness of the gene or variant from the primary data, eliminating the subjectivity and inefficiency of a previously laborious and manual process.
[/et_pb_accordion_item][et_pb_accordion_item title=”Q: Can you give an example of how Genomenon’s Mastermind knowledge-base can improve cancer patient outcomes?”]
A: There are two ways Mastermind improves patient outcomes – both individual and collectively. For the individual, providing immediate and well-organized access to information in the medical literature will streamline the efficiency of the manual variant curation process. This is useful in bolstering more wide-spread use of defined guidelines for variant interpretation and promoting increased reproducibility of interpretations across institutions and molecular diagnosticians. In terms of improving patient outcomes collectively, having the comprehensive data made available to researchers allows for deeper and more immediate insight into the landscape of disease-gene and disease-gene-variant relationships that are otherwise difficult to see when examining individual studies or individual disease-relationships. Mastermind is being used in research to highlight these affinities and allow cancer researchers to ask questions more globally than previously possible.
[/et_pb_accordion_item][et_pb_accordion_item title=”Q: What are the complex challenges that the field of diagnostic genomics will face in the next five years?”]
A: With the ability to sequence more and more individuals in a greater variety of clinical scenarios, we are poised to controvert some long-held beliefs about genetic variation and the development of disease. These new discoveries will begin to challenge current diagnostic, and treatment paradigms. As an example, the discovery of a number of presumptive pathogenic variants discovered at a low-level in disease-free individuals will make much earlier interventions possible but it will also require clinicians to make a determination of which patients will in fact go on to develop disease and which may require enhanced surveillance.
[/et_pb_accordion_item][et_pb_accordion_item title=”Q: What advice can you give to young scientists considering an academic research career?”]
A: Keep an open mind. During your academic training, there will be the tendency to only see your successes measured by your publication record. In my case, I had successful outcomes from both my graduate training and post-doctoral work. During the course of the latter, I recognized that there were ways to advance a field from outside of academia that had the potential to make bigger and faster change than would typically be afforded from a conventional professorship. I recognized the benefit of effectuating change outside of academia in the private sector. Acceleration of the initial Human Genome Sequencing Project at the hands of Craig Venter and Celera proved that genome sequencing was possible at a much faster pace. This was followed by dramatic advances in efficiency and cost-effectiveness of DNA sequencing at scale afforded by technical revolutions in private companies such as Roche and Illumina which proved that genome sequencing was plausible. Our goal at Genomenon is to make genome sequencing practical by eliminating the final barrier to wide-spread adoption – accurate, efficient and reproducible genome interpretation.
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