The Maley Cancer and Evolution Lab is exploring fundamental concepts in neoplastic progression, the processes by which normal tissue becomes cancerous, for the purposes of developing better methods for cancer prevention and therapy. They are applying evolutionary biology, ecology, computational biology and genetics to the understanding of these problems. We are interested in all aspects of evolution in cancer, including the evolution of cells within tumors and normal tissues (“somatic evolution“) as well as the selective effects of cancer on the evolution of multicellular organisms. We take three, mutually reinforcing, approaches to these problems:

Observational: Evolutionary analysis of the genetics from tumors

We analyze tissue samples from tumors, mainly Barrett’s esophagus and acute myeloid leukemia (AML), and apply evolutionary theory to the genetics and epigenetics of the samples. We are interested in understanding and controlling the dynamics of clonal diversity within those tumors.

Theoretical: Computational modeling of somatic evolution

We use agent-based models to represent the populations of cells in tumors and to track their evolution over time. These models help us explore the implications of current theories in cancer biology and to design experiments aimed at understanding and controlling somatic evolution.

Experimental: Evolutionary experiments in model systems

We use both yeast and human cell lines in tissue culture to study how we might control somatic evolution so as to prevent cancer and therapeutic resistance.

Ongoing Projects

  • Using phylogenetic and other methods to measure the rate of evolution in tumors. These measures should be universal biomarkers applicable to all types of tumors, helping to predict which tumors are most likely to become malignant, and which malignant tumors are most likely be become resistant in response to therapy.

  • Measuring the dynamics of clonal diversity (intra-tumor heterogeneity) in a wide variety of cancers and pre-malignant neoplasms as universal predictors of prognosis and therapeutic resistance.

  • Using our measures of evolution to identify interventions that slow the evolution of tumors and thereby prevent cancer.

  • Developing novel methods to reconstruct the phylogeny of cell lineages within tumors given the different kinds of genetic alterations that accumulate in neoplastic cells.

  • Exploring the constraints (fitness landscapes) on the evolution of tumors by developing methods to detect regularities across the phylogenies that characterize a set of tumors.

  • Testing evolutionary approaches to preventing therapeutic resistance, including collaborating with Robert Gatenby on his adaptive therapy.

  • Applying ecological theory to tumors to measure selective pressures and habitat heterogeneity across a tumor.

  • Predicting and discovering the basis of acquired therapeutic resistance based on genomic analyses and profiling of the genetic diversity with tumors prior to therapy and at relapse.

  • Discovering how large, long-lived animals, such as elephants and whales, are able to prevent cancer better than humans, and testing those discoveries in mice, genetically engineered to be cancer resistant.

  • Developing computational simulations of the evolution of cells in tumors in order to identify novel approaches to preventing cancer and therapeutic resistance.