  Cancer has become one of the most widespread diseases in the world.
Specifically, breast cancer is diagnosed more often than any other type of
cancer. However, breast cancer patients and their individual tumors are often
unique. Identifying the underlying genetic phenotype can lead to precision
(personalized) medicine. Tailoring medical treatment strategies to best fit the
needs of individual patients can dramatically improve their health. Such an
approach requires sufficient knowledge of the patients and the diseases, which
is currently unavailable to practitioners. This study focuses on breast cancer
and proposes a novel two-stage clustering method to partition patients into
hierarchical groups. The first stage is broad grouping, which is based on
phenotypes such as demographic information and clinical features. The second
stage is fine grouping based on genomic characteristics, such as copy number
variation and somatic mutation, of patients in a subgroup resulting from the
first stage. Generally, this framework offers a mechanism to mix multiple forms
of data, both phenotypic and genomic, to most effectively define individual
patients for personalized predictions. This method provides the ability to
detect correlation among all factors.
