The Goode laboratory combines bioinformatics, genomics, molecular evolution and population genetics to study the evolutionary forces governing the formation of tumours and their responses to therapy, with a focus on the role of genomic instability (mutation) in these processes. We mainly work on prostate cancer, breast cancer and sarcoma, though our approaches can be applied to a range of solid tumours.
Evolutionary genomics is the use of genome-scale analysis to investigate how natural selection has shaped genetic and phenotypic diversity between and within species. As cancer results from strong selection for particular cellular phenotypes, evolutionary genomics has great potential to unlock the secrets of this disease.
We use evolutionary genomics to study all stages of tumour evolution, through in-depth analysis of large genome and RNA sequencing data sets from cancer patients and laboratory models of cancer as well as computational and statistical modeling.
Our research involves assessing genetic, epigenetic and phenotypic changes in individual tumours over time, as well as the impact impacts of mutation, selection and drift at the population and species level on the incidence and manifestation of cancer. We’re particularly interested are the tradeoffs related to elevated genomic instability typical of tumours, which can be both advantageous and deleterious, depending on external and intracellular conditions.
Understanding common hallmarks of cancer through evolutionary network analysis
Tumour cells share many properties with single-celled organisms, suggesting the evolutionary age of a gene is related to its role in cancer. Our recent analysis of data from TCGA showed genes originating in unicellular or multicellular species exhibited different expression patterns (Trigos et al, PNAS, 2017). Furthermore, transcriptional networks shaped during the emergence of multicellularity to control more primitive processes (e.g., cell replication, glycolysis) are frequently disrupted in cancer. We are the biological and therapeutic implications of these signatures via a novel evolutionary network analysis strategy that combines evolutionary sequence conservation, transcriptome and gene-gene interaction data, in collaboration with Prof. Rick Pearson and his group.
Computational models of tumour evolution
Many of the factors influencing a tumour’s response to therapy are established very early on in its development, but such events are difficult to observe directly. My group has designed a sophisticated computational simulation models to reconstruct all stages of tumour evolution. One major goal is to investigate the role of genetic instability in tumour development and response to therapy, with the goal of testing and identifying novel drug dosing strategies. Model predictions are validated using genetic and clinical data from large patient cohorts of breast and other solid cancers, with assistance from Prof. Sherene Loi’s group.
Evolution of castration-resistant prostate cancer
Many prostate tumours develop resistance to androgen blockade, a common first-line therapy for prostate cancer. Patients with castration-resistance prostate cancer (CRPC) have poor long-term survival prospects and few effective options for subsequent treatment. We are combining whole-genome, transcriptome and DNA methylation with clinical and experimental data to undercover the genetic and epigenetic changes driving CRPC. Of particular interest is the role of genetic and spatial intratumoural heterogeneity in the development and evolution of CRPC This work is being jointly conducted with the Prostate Cancer Research group, headed Prof Gail Risbridger, as well as oncologist Dr. Shahneen Sandhu.
Identification of polygenic risk factors for heritable cancers
Cancer with a strong family history and/or early age of onset are driven by inherited genetic risk factors, yet most cases cannot be explained by known cancer risk genes. Iwe are investigating how the damaging effects of multiple genetic variants within the same person’s genome may combine to jointly risk cancer risk. These polygenic risk studies are being conducted in sarcoma and familial breast cancer cohorts, with Prof. Ian Campbell’s Cancer Genetics group at the Peter Mac and Prof. David Thomas at the Garvan Institute of Medical Research in Sydney, respectively.
Trigos AS, Pearson RB, Papenfuss AT, Goode DL (2017). Altered interactions between unicellular and multicellular genes drive hallmarks of transformation in a diverse range of solid tumors. Proceedings of the National Academy of Sciences USA.114(24):6406-6411.
Kader T*, Goode DL* , Wong SQ, Connaughton J, Rowley SM, Devereux L, Byrne D, Fox SB, Mir Arnau G, Tothill RW, Campbell IG, Gorringe KL (2016). Copy number analysis by low coverage whole genome sequencing using ultra low-input DNA from formalin-fixed paraffin embedded tumor tissue. Genome Medicine.8(1):121.
Ballinger ML*, Goode DL*, Ray-Coquard I, James PA, Mitchell G, Niedermayr E, Puri A, Schiffman JD, Dite GS, Cipponi A, Maki RG, Brohl AS, Myklebost O, Stratford EW, Lorenz S, Ahn SM, Ahn JH, Kim JE, Shanley S, Beshay V, Randal RL, Judson I, Seddon B, Campbell IG, Young MA, Sarin R, Blay JY, O’Donoghue SI, and Thomas DM for the International Sarcoma Kindred Study (2016). Monogenic and polygenic determinants of sarcoma risk: an international genetic study. The Lancet Oncology.17(9):1261-71.
Goode DL*, Hunter S*, Doyle MA, Ma T, Rowley SM, Choong D, Ryland GL and Campbell IG (2013). A simple consensus approach improves somatic mutation prediction accuracy. Genome Medicine.5(9):90.
Montgomery SB*, Goode DL*, Kvikstad E*, Albers K, Zhang Z, Gurprasad A, Howie B, Karczewski KJ, Smith KS, Anaya V, Richardson R, Davis J; 1000 Genomes Project Consortium, MacArthur DG, Sidow A, Duret L, Gerstein M, Makova KD, Marchini J, McVean G, Lunter G (2013). The origin, evolution, and functional impact of short insertion-deletion variants identified in 179 human genomes. Genome Research.23(5):749-761.
Goode DL, Cooper GM, Schmutz J, Dickson M, Gonzales E, Tsai M, Karra K, Davydov E, Batzoglou S, Myers RM, Sidow A (2010). Evolutionary constraint facilitates interpretation of genetic variation in resequenced human genomes. Genome Research.20(3):301-310.