Summary
Objective: The aim is to provide a comprehensive review of state-of-the art omics approaches,
including proteomics, metabolomics, cell-free DNA, and patient cohort matching algorithms
in precision oncology.
Methods: In the past several years, the cancer informatics revolution has been the beneficiary
of a data explosion. Different complementary omics technologies have begun coming
into their own to provide a more nuanced view of the patient-tumor interaction beyond
that of DNA alterations. A combined approach is beneficial to the patient as nearly
all new cancer therapeutics are designed with an omics biomarker in mind. Proteomics
and metabolomics provide us with a means of assaying in real-time the response of
the tumor to treatment. Circulating cell-free DNA may allow us to better understand
tumor heterogeneity and interactions with the host genome.
Results: Integration of increasingly available omics data increases our ability to segment
patients into smaller and smaller cohorts, thereby prompting a shift in our thinking
about how to use these omics data. With large repositories of patient omics-outcomes
data being generated, patient cohort matching algorithms have become a dominant player.
Conclusions: The continued promise of precision oncology is to select patients who are most likely
to benefit from treatment and to avoid toxicity for those who will not. The increased
public availability of omics and outcomes data in patients, along with improved computational
methods and resources, are making precision oncology a reality.
Keywords
Circulating tumor DNA - proteomics - metabolomics - decision support techniques -
algorithms - omics integration
