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Cancer and Deep Phenotyping

Almost all of us have been impacted by cancer in some significant way. After a certain age, we've likely all had run-ins with this complex disease, either personally or through a family member or loved ones. 

In our pre-emptive fight against the disease, we schedule yearly wellness visits, including certain screenings designed to catch cancer in its earliest stages. But as we move into the future, the scientific community is exploring new ways to stay one step ahead of the disease, and one of our best tools is a "systems approach" to clinical oncology, which relies on deep phenotyping. We call this the N-of-1 approach. In scientific research, "N" represents the number of people in a study. In an N-of-1 study, we compare everything to an individual's own baseline, instead of population averages. [link to blog post that I’ve yet to write]

One promising outcome of this novel approach is that it allows us to understand the earliest transitions from wellness to disease–that critical time period that enables detection before cancer manifests. Another advantage of this approach is that it creates a context for optimized, personal treatment therapies, creating a roadmap for actionable therapeutic alternatives when tackling the complex nature of this disease. 

Results roundup:

  • Cancer medicine in the 21st Century requires a holistic view of disease mechanisms that captures the unique contributions of genetics, lifestyle, environment, and healthcare to each individual’s disease
  • A systems approach to cancer medicine embodies this holistic view of pathophysiology by using longitudinal deep phenotyping to take a multiscale snapshot of the various networks that comprise human physiology
  • New therapies will depend on characterizing networks spanning the informational hierarchy of biology: single molecules, single cells, tissues/organs, and organism
  • New technologies—particularly high-throughput measurements of analytes in blood and relevant tissue, and single-cell -omics platforms—will empower the quantification and reconstruction of relevant cancer-related biological networks
  • Understanding how perturbations to biological networks lead to disease will allow for the development of diagnostics, treatments, and preventative therapies
  • Longitudinal deep phenotyping of individuals has the potential to profoundly transform how we study disease by enabling a detailed characterization of how the earliest transitions from wellness to disease occur—providing opportunity to reverse the disease at this earliest state rather than after it has manifested itself phenotypically
  • The knowledge gained from deep phenotyping will result in the development of multimodal therapies that will, in time, help move cancer from a fatal to a chronic disease
  • An N-of-1 approach will become the standard for cancer treatment in the future because of the individual complexities of each patient

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