Arizona Cancer Evolution Center (ACE)

Neoplastic Cell Evolution Investigators

Trevor Graham

Trevor Graham

Professor, Barts Cancer Institute, QMUL

Professor Trevor Graham leads the Evolution and Cancer laboratory at the Barts Cancer Institute, QMUL in London, UK. The lab focuses on measuring the dynamics and drivers of somatic evolution in human tissues, particular in the gastrointestinal tract, and tries to use these measures to better predict cancer development risk in premalignant disease, and determine prognosis and optimise treatment regimes in established cancers.

His multidisciplinary lab combines expertise in both theory (maths, physics, computer science, evolutionary biology) together with empirical measurement (molecular genetics, histopathology, bioinformatics). Trevor’s background is similarly multidisciplinary. His undergraduate training was in mathematics (Imperial College London, 2002) and he has a PhD in Mathematical Biology (University College London, 2009, supervised by Ian Tomlinson and Karen Page). It was during his PhD he had his first experience of the wet-lab as part of a (fantastically inspiring) sabbatical placement with Darryl Shibata. Post-PhD, Trevor was a postdoc in Nick Wright’s Histopathology laboratory (London Research Institute, 2008-2011 the forerunner of the Crick Institute) before spending two more years as a postdoc with Carlo Maley (UCSF, 2011-2013). He joined the Barts Cancer Institute as a lecturer (assistant professor) in late 2013. His lab is principally funded by Cancer Research UK and the Wellcome Trust.

Yinyin Yuan

Yinyin Yuan

Team Leader, The Institute of Cancer Research, London

Yinyin Yuan joined the ICR in 2012 as the leader of the Computational Pathology and Integrative Genomics team. Currently, her team is part of the Centre for Evolution and Cancer and the Division of Molecular Pathology. Her team develops computational approaches to study tumours as evolving ecosystems by fusing digital pathology, bioinformatics and ecological statistics.

Her research focuses on the emerging concept that tumours are complex, evolving ecosystems with dynamic crosstalk among cancer, immune and normal cells. Studying the complex relationships between cancer cells and their natural habitats allows for development of new and effective therapeutic interventions, analogous to draining the swamps to help eradicate malaria.

By combining high-throughput pathological image analysis, machine learning and spatial statistics, her team studies how genetically different cancers grow and spread under selective pressures from the tumour microenvironment. Her team was among the first to demonstrate the use of spatial statistics in large-scale inference of microenvironmental spatial heterogeneity, which led to the development of new immune scoring methods and mechanistic studies of immunosuppression in solid tumours.

Yinyin was trained in computer science and bioinformatics. She obtained her academic degrees in computer science during her education at the University of Science and Technology of China (BSc 2003) and University of Warwick (MSc by research 2005, computer vision and steganography; PhD 2009, machine learning and bioinformatics).

Her postdoctoral work in cancer bioinformatics at the Cancer Research UK Cambridge Institute focused on the discovery of new subtypes in 2,000 breast cancers using large-scale machine learning and image processing on molecular data and pathological images. From 2010-2012 she served as a member of the governing body at Wolfson College, University of Cambridge. Outside work, she is a keen hiker and rock climber.
Darryl Shibata Co-PI

Darryl Shibata Co-PI

Professor, University of Southern California

After attending UCLA for his undergraduate degree, Dr. Shibata obtained his medical degree from the Keck School of Medicine of USC. After completing his internship training in pediatrics from UC San Diego, Dr. Shibata returned to USC for his residency and fellowship at LAC+USC Medical Center. Currently, Dr. Shibata has clinical appointments at both LAC+USC Medical Center and USC/Norris Comprehensive Cancer Center. In addition to his wide array of responsibilities in the research, education and practitioner capacities, Dr. Shibata sits on the editorial board of the BMC Cancer Journal and the American Journal of Pathology.
Andrea Sottoriva

Andrea Sottoriva

Team Leader, The Institute of Cancer Research, London

Dr Andrea Sottoriva obtained his BSc in computer science from the University of Bologna in 2006 and his MSc in computational modelling from the University of Amsterdam in 2008. While attending his BSc and master’s he worked in neutrino physics at the Department of Physics of the University of Bologna and at the Institute for Nuclear and High Energy Physics (NIKHEF) in the Netherlands as a research assistant. During his master’s he specialised in computational biology and bioinformatics and became interested in mathematical modelling of cancer. This emerging field employs rigorous mechanistic modelling and simulations to understand complex biological systems such as cancer.

In 2012 he completed his PhD in cancer genomics and modelling at the University of Cambridge within the CRUK Cambridge Research Institute, focusing on the integration of computational models with cancer genomic data. After his PhD he conducted postdoctoral research at the University of Southern California within the Norris Comprehensive Cancer Centre, investigating the use of multiple sampling genomic data from human malignancies to understand tumour evolution.

Dr Sottoriva joined the Centre for Evolution and Cancer at The Institute of Cancer Research, London, in 2013, where his research focuses on using multi-disciplinary approaches based on high-throughput genomics and mathematical modelling to understand cancer as a complex system driven by evolutionary principles. The goal of his group is to identify those patient-specific rules that regulate the development and progression of the disease, to inform prognosis and novel therapeutic options that are tailored to the need of the individual cancer patient.

He is currently the Chris Rokos Fellow in Evolution and Cancer at the ICR.
Li Liu

Li Liu

Assistant Professor, Arizona State University

Dr. Liu is an assistant professor of Biomedical Informatics and the director of the Bioinformatics Core Facility at Arizona State University. She holds an M.D. degree in Medicine and an M.S. degree in Information System. As a trained clinician and a bioinformatics researcher, she fully appreciates the critical roles genomic medicine and bioinformatics play in advancing precision medicine. By integrating genomic, phylogenetic, population genetic, statistical and machine-learning techniques, Dr. Liu and her research team investigate clinical and molecular signatures of human diseases, and develop novel computational methods to discover biomarkers for early diagnosis and accurate prediction of therapeutic responses for individual patients. Before joining ASU, Dr. Liu helped build and directed the bioinformatics core facility at University of Florida.
Christina Curtis

Christina Curtis

Assistant Professor, Stanford University

Christina Curtis, PhD, MSc is an Assistant Professor in the Departments of Medicine (Oncology) and Genetics in the School of Medicine at Stanford University where she leads the Cancer Systems Biology Group and serves as Co-Director of the Molecular Tumor Board at the Stanford Cancer Institute. Trained in molecular and computational biology, she received her doctorate from the University of Southern California in 2007 advised by Simon Tavaré, and holds Masters degrees in Bioinformatics and Computational Biology from the University of Southern California and in Molecular and Cellular Biology from the University of Heidelberg, Germany. She has been the recipient of several young investigator awards, including the 2012 V Foundation for Cancer V Scholar Award, the 2012 STOP Cancer Research Career Development Award, a 2016 AACR Career Development Award and was named a Kavli Fellow of the National Academy of Sciences in 2016. Dr. Curtis is the principal investigator on grants from the NIH/NCI, Department of Defense, American Association for Cancer Research, Breast Cancer Research Foundation, Susan G. Komen Foundation, V Foundation for Cancer Research and Emerson Collective. She also serves on the Editorial Boards of Breast Cancer Research, Carcinogenesis: Integrative Biology, the Journal of Computational Biology and JCO Precision Oncology.

Sathya Muralidhar

Postdoctoral Training Fellow 

Dr Sathya Muralidhar is a cell biologist in the Division of Molecular Pathology. She works in Dr Yinyin Yuan’s Computer Pathology and Integrated Genomics team, and uses machine learning to identify cancer cells from tissue samples. 

“I use deep learning-based pipelines to analyse histological images of colorectal cancer. The aim is to create an algorithm, which can efficiently ‘learn’ histological features (such as the cell shape, size etc) defined by the pathologist. Such an algorithm would be able to identify the various cell types (such as tumour cell, normal cell, lymphocyte etc), using the knowledge it was trained on. This tool can quantify the cellular architecture of any given histological sample and help us understand how tumour cells physically interact with its environment. One of my objectives is also to understand these interactions on the genomic level, for which I will perform integrated analyses. Importantly, these tools help answer questions of clinical relevance such as: are there certain cell types and/or histological features which are detrimental/beneficial to patient prognosis and/or response to therapy?”- Dr. Sathya Muralidhar

Luis Cisneros

Postdoctoral Researcher

My overall research interest is studying the mechanisms of emergence of collective behavior in complex systems. I have engaged this subject from different angles going from theoretical, to computational to data-driven approaches.

I was originally formed as a theoretical physicist that moved into topics of nonlinear dynamics, information theory and complexity. My doctorate research was on bio-fluid dynamics and collective phenomena of swimming bacteria.

My postdoctoral research focused on different aspects of biological organization by taking a systems-biology perspective of the evolution of multicellularity. In particular, my work focused in looking at cancer as a breakdown of the multicellular structure. As a member of Dr. Paul Davies’ team in the ASU-PSOC program, I developed a model of metastasis revealing that early stages of organ invasion could be driven by rare event dynamics rather than selective advantage of the invasive tumor cells. I also produced a metabolic switching agent-based model that connects the evolution of multicellularity with the glycolytic cancer phenotype. I also did extensive data-driven studies in collaboration with biologist Kimberly Bussey at ASU to test Dr. Davies’ atavistic theory of cancer (which presents cancer as an ancient biological program). In these studies, we correlated gene evolutionary ages with patterns of mutations and expression changes in cancer, demonstrating that ancient genes tend to be robust and that their genomic modifications are associated to advanced stages in cancer. My collaboration with Dr. Bussey yielded a collection of (patented) bioinformatics methodologies that revealed that the mutational patterns observed in whole-genome sequencing of cancer samples are a signature of stress- induced mutagenesis, and thus related to ancestral (pre-metazoan) biological mechanisms of evolvability and diversification.

Additionally I have participated in a diverse set of other lines of research, most notably modeling of urban dynamics in collaboration with anthropologist Thomas Park from the University of Arizona, and development of bioinformatic measures of RNAseq transcript integrity and co-expression patterns of genomic elements while working in NantOmics, a biotech company form California.

My current research goals as part of ACE are the implementation of landscape ecology methods to multicellular systems in order to assess the onset and progression of cancer, and modeling of spatial heterogeneity and evolutionary dynamics of populations of cancer cells in order to assess disease recurrence and clonal expansion of resistant strains in adaptive cancer therapies.

Neoplastic Cell Evolution

This project takes into account both the evolution of cancer cell mutations and the environment surrounding a tumor in order to develop a better predictive test for the invasiveness of a tumor.