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Time Period: Fall 2020 – Present


Cancer phylogenetics is an evolutionary-based approach for understanding how healthy cells become cancerous, and how cancerous cells spread to other sites of the body (metastasis). By following the genetic changes that cells undergo as they divide, researchers can begin to make predictions on clinical outcomes such as tumor progression, resistance and treatment response. These genetic changes are unique to each tumor, and therefore can help guide clinical implications specific to each patient (individualized medicine).


Since cancer phylogenetics is a relatively new field, researchers and clinicians still need standardized, reproducible workflows for interpreting phylogenetic data from their patient studies. Current analysis methods makeup a piecemeal process that requires the use of separate computational tools and multiple data formats. Additionally, the end output is a static image that is difficult to pull meaningful information from.


Our experience in data analytics, visualization and user interface design were used to develop software for targeted use cases identified by our lung, breast and colorectal cancer research collaborators.

Data analytics: build phylogeny tree, predict tumor progression and other clinical factors

Visualization: develop an interactive tool to visualize and explore relationships between cell populations and therapeutic implications

User interface design: condense complex computational workflow into a standardized and reproducible software platform tailored to clinical researchers


The VA Group is developing a tumor phylogeny analysis software that provides end-to-end workflow steps, from data input to interactive visualization for identification of clinical correlations.

FAVE-Phylo makes new approaches in individualized medicine broadly accessible to researchers and clinicians. With FAVE-Phylo, cancer investigators can begin to use phylogenetic analyses principles in order to understand how tumors develop and progress, and how these factors might affect a patient’s prognosis and treatment outcomes.

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VA Participants

Charles Blatti, PhD, Principal Investigator

Peter Groves, MS, Backend Development

Matt Berry, MS, Frontend Development

Colleen Bushell, MFA, Data Visualization and Information Design

Lisa Gatzke, BFA, UI/UX DesignAHO Collaborating Center on Information Systems for Health

Our Collaborators

Mohammed El-Kebir, PhD, Assistant Professor, Illinois Department of Computer Science - Website

Chuanyi Zhang, Graduate Student, El-Kebir Lab - Linkedin

Nicholas Chia, PhD, Professor, Mayo Clinic Center for Individualized Medicine - Website 

Zeynep Madak-Erdogan, PhD, Associate Professor, Illinois Department of Food Science & Human Nutrition - Website

Collaborative Approach

The El-Kebir lab has a long history of developing tumor phylogeny methods. As we construct the software analysis workflow, the El-Kebir lab works with us to accurately integrate their methodologies and other available tools into a single platform.

The Chia and Madak-Erdogan labs are cancer research groups that study colorectal and metastatic breast cancer, respectively.They provide insights for the problems that cancer researchers face today, and together, we develop the use cases and software solutions. We design around their targeted use cases, building software with their datasets so they can help us iteratively refine FAVE-Phylo throughout the development process.

Publications and Presentations:

FAVE-Phylo: Tools Designed for Discovery from Tumor Phylogenies. Mayo Clinic Individualizing Medicine Conference. Virtual; October 8-9, 2021.

Link to Poster

Project Resources

PhyloDiver Phylogeny Visualization Application - Prototype, GitHub


PhyloFlow Analysis Workflow Software - Dockstore, Container Repo, GitHub


NCSA Project Highlight - Link


Project Contact:

Charles Blatti
FAVE-Phylo Principle Investigator

Funding agency:
Cancer Center at Illinois (CCIL) Seed Grant Program

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