Computational Biology

The Computational Biology Laboratory at the Saint John’s Cancer Institute transforms data into biological insights. We empower researchers through our collaborative efforts to discover patterns and predictors of cancer that ultimately aid clinical and preventive practice.

Computational Biology - Saint John's Cancer Institute

Mission and Vision

Computational Biology Services – Saint John’s Cancer Institute

Within the Computational Biology department of Saint John’s Cancer institute, we are focused on understanding whole system of cancer evolution and progression. Our mission is to transform data into clear biological insights that will ultimately influence clinical practice and improve outcomes.

The Computational Biology Lab also collaborates with key investigators at the Saint John’s Cancer Institute, including our oncology clinicals at Saint John’s Health Center to support ongoing research, projects, and patient care.  Areas of focus include urologic, melanoma, endocrine, thoracic, breast, gynecologic, gastrointestinal and epigenetics.

Our function includes gene expression and regulation, disease gene mapping, molecular evolution of tumors, as well as quantitative and analytical modeling.

Current Research Topics

The Tumor Epi-Transcriptome

How do modifications to RNA molecules influence tumor biology?

We are currently using third-generation sequencing technologies to explore the diversity of RNA modifications in tumors.

With breast cancer epi-transcriptome in focus, we are actively working to address the questions:

  • How do RNA modifications differ among tumor subtypes?
  • What role do RNA modifications play in the development of treatment resistance?
  • What role do RNA modifications play in metastasis?
  • Can we identify therapeutic targets within the epi-transcriptome?

Secondary Cancers

Computational Biology - Pathways - Persistence vs Undetectable
Understanding the pathways of primary tumors can help identify attributes of cancer persistence against metastasis

Do the tumors of patients with a history of cancer differ from those with no history?

Breast cancer patients with a prior cancer history are of particular interest, drawing the following unknowns:

  • What are the differences at the molecular level between patients with and without a prior cancer?
  • What can we learn about these tumors be integrating multiple “omics” data?
  • Can we identify molecular alterations that are clinically relevant?


What can we learn from reconstructing the evolutionary history from primary tumors to metastases?

Following lymph node metastasis in prostate cancer offers data that can support recurrence prediction, including addressing the following questions:

  • Does the spread of tumor cells from the primary tumor to the lymph nodes follow a specific pattern?
  • Are there specific regions of the primary tumor that are responsible for the metastasis?
  • Can we predict who will develop lymph node metastasis?
computational biology publications