Towards Quantifying Tumor Progression: from Genomic Insights to Predictive Modeling

Christina Curtis, Ph.D., MSc

Associate Professor of Medicine & Genetics

Endowed Faculty Scholar, Stanford Cancer Institute

Director, Breast Cancer Translational Research

Co-Director, Molecular Tumor Board

Stanford University


Seminar Information

Seminar Date
May 21, 2021 - 2:00 PM


Photo Image of Christina Curtis, Ph.D., MSc


Metastasis is the most lethal and insidious aspect of cancer. Despite significant therapeutic advances, metastatic disease is generally incurable. To date, the natural history, clonal evolution and patterns of systemic spread are poorly understood, hindering effective treatment and prevention efforts. In this talk, I will outline a suite of computational tools to infer the evolutionary dynamics of tumor progression and metastasis from patient genomic data by coupling population genetic theory, spatial computational modeling and approximate Bayesian computation. In particular, I will describe methods to infer the timing of metastasis based on patterns of genomic divergence between paired primary tumors and distant metastases and show how application of this approach yields quantitative evidence for early systemic spread in common epithelial tumors, while uncovering the drivers of this lethal process. These studies illuminate evolutionary constraints during tumor progression and opportunities for earlier intervention. Building on these discoveries, I will outline our ongoing efforts to interrogate the earliest events during tumor progression by studying pre-cancerous lesions and through oncogene engineering of human organoids. Throughout, I will discuss the context dependencies that underlie disease progression and how this may inform strategies for patient stratification and therapeutic targeting.

Speaker Bio

Dr. Christina Curtis, PhD, MSc, is an Associate Professor of Medicine and Genetics and an Endowed Faculty Scholar at Stanford University, where she leads the Cancer Computational and Systems Biology group. She is also the Director of Breast Cancer Translational Research and Co-Director of the Molecular Tumor Board at the Stanford Cancer Institute. Dr. Curtis leverages computational modeling, machine learning and iterative experimentation to establish a quantitative and mechanistic understanding of tumor progression. By pioneered techniques to measure the dynamics of tumor growth and metastasis from genomic data, her research has led to new paradigms in understanding how tumors evolve. For example, her team’s description of a Big Bang model of tumor growth refines the decades-old model of sequential clonal evolution and was highlighted by Nature as a cancer research milestone over the past two decades. Her research has also redefined the molecular map of breast cancer, identifying novel subgroups with distinct relapse trajectories, and motivating new therapeutic approaches that are being evaluated in clinical trials.

In recognition of her achievements, Dr. Curtis has been the recipient of career development awards from the V Foundation for Cancer Research, STOP Cancer, and the American Association for Cancer Research. She was named a Kavli Fellow of the National Academy of Sciences in 2016 and received the National Institutes of Health (NIH) Director's Pioneer Award in 2018. In 2020, Dr. Curtis was named a Susan G. Komen Scholar and received the Stanford Prize in Population Genetics and Society. She was named an In vivo Rising Leader in the Life Sciences in 2021.

Dr. Curtis is the principal investigator of grants from the Department of Defense (DOD), National Cancer Institute (NCI), National Human Genome Research Institute (NHGRI), Breast Cancer Research Foundation and Susan G. Komen Foundation amongst others. She serves on the scientific advisory boards of major cancer centers, including the Herbert Irving Comprehensive Cancer Center at Columbia University, the Adaptive Oncology Program at the Ontario Institute for Cancer Research as well as for international funding bodies and the biotech sector. Additionally, she is an editorial board member for multiple journals spanning the fields of computational biology to precision oncology.