Fong Chun Chan

I am currently the Head of Bioinformatics in the Department of Lymphoid Cancer Research, British Columbia Cancer Research Centre, Vancouver, Canada where I spearhead the overall bioinformatics strategic direction of the department.

I am also concurrently undertaking a PhD in Bioinformatics under the co-supervision of Dr. Sohrab Shah and Dr. Christian Steidl. My main research interest surrounds the implications of tumour diversity on disease progression in B-cell lymphomas. In particular, how tumour and microenvironment evolution plays a role in treatment resistance as understanding this process may aid in the determination of relevant and precise therapeutic approaches for each cancer patient.

I have a passion for applying statistical and machine learning approaches to big data, in particular genomics data (e.g. sequencing data). In terms of technical skills:

  1. I have expertise in dealing with big data in the R programming language (e.g. data.table, tidyr, dplyr) and a thorough understanding of the core principles around R
  2. I have extensive experience in doing reproducible research through interactive applications and D3 web reporting (e.g. Rmarkdown, knitr, pandoc, git, shiny)
  3. I have expertise in developing/managing big data processing pipelines through the Make engine with experience in using ruffus, bpipe, snakemake, and nextflow.

Selected Publications [Google Scholar]

Histological Transformation and Progression in Follicular Lymphoma: a Clonal Evolution Study

Robert Kridel*, Fong Chun Chan*, Anja Mottok, Merrill Boyle, et al. 2016. PLOS Medicine.
* Equal Contribution

An RCOR1 loss-associated gene expression signature identifies a prognostically significant DLBCL subgroup

Fong Chun Chan, Adele Telenius, Shannon Healy, Susana Ben-Neriah, et al. 2015. Blood.

Recurrent somatic mutations of PTPN1 in primary mediastinal B cell lymphoma and Hodgkin lymphoma

Jay Gunawardana, Fong Chun Chan, Adele Telenius, Bruce Woolcock, et al. 2014. Nature Genetics.

Genomic rearrangements involving programmed death ligands are recurrent in primary mediastinal large B-cell lymphoma

David D W Twa, Fong Chun Chan, Susana Ben-Neriah, Bruce W Woolcock, et al. 2014. Blood.

Gene expression-based model using formalin-fixed paraffin-embedded biopsies predicts overall survival in advanced-stage classical Hodgkin lymphoma

David W Scott, Fong Chun Chan, Fangxin Hong, Sanja Rogic, et al. 2013. Journal of Clinical Oncology.



An R package for generating cofeature (feature by sample) matrices. The package utilizies ggplot2::geom_tile to generate the matrix allowing for easy additions from the base matrix.


This package uses functional programming principles to iteratively run Cox regression and plot its results. The results are reported in tidy data frames. Additional utility functions are available for working with other aspects of survival analysis such as survival curves, C-statistics, etc.


An R Package for the Classical Hodgkin Lymphoma (CHL) 26 Gene Overall Survival Predictor. This is the companion R package for the predictor that has been published.


Variant Calling in Cancer Genomes

Workshop on how calling somatic single nucleotide variants in cancer genome data

Generating Heatmaps in R - Workshop

UBC R Study Group Workshop on generating Heatmaps in R


Clinical Implications of inter-tumour, intra-tumour, and tumour microenvironment heterogeneity in B-cell lymphomas

PhD Thesis. 2012 - 2017.

Detection of differentially expressed alternative transcripts using conventional microarrays : with application to diffuse large B-cell lymphoma

MSc Thesis. 2009 - 2011.