Clonal evolution in cancer - the selection for and emergence of increasingly malignant clones during progression and therapy, resulting in cancer metastasis and relapse - has been highlighted as an important phenomenon in the biology of leukemia and other cancers. While the role of clonal evolution during leukemia development and therapy has been a focus for a number of avenues of research, our abilities to deduce the clonal composition of individual samples have been limited by the use of bulk leukemia samples and mainly mutation data (limiting our analyses to only a fraction of all leukemias). In part, because of this caveat, deconvolution of the clonal structure from bulk sequencing data requires a model of the cancer, specifically regarding the heterozygosity of mutations in single cells, the order of acquisition of mutations, and the requirement for unique mutational events. Our work at the single cell level in acute myeloid leukemia (AML) suggests an underlying tumor heterogeneity beyond what is currently understood in the disease and precludes us from using bulk techniques to assess diversity accurately. The ability to describe and track clonal diversity over the course of therapy would allow us determine what impact diversity has on outcomes and would be a useful tool in designing therapies informed by the possibility of cancer cell evolution. This proposal seeks to expand our preliminary work in single cell genetics to create a defined approach for generating single cell and population sampling data that can refine our model of tumor heterogeneity, allowing for accurate reconstruction of clonal diversity for all types of AML. The work will address a number of challenges in the detection of clonal diversity and the possibility of evolution impacting therapy, as well as provide additional experience in bioinformatics, evolutionary biology, high?performance computing, statistics and clinical research. An excellent mentoring and collaborative team has assembled at Fred Hutch to support both the training and scientific efforts proposed including Dr. Jerald Radich (clinical research), Dr. Justin Guinney (bioinformatics), Dr. Brent Wood (clinical flow cytometry), and Dr. Ted Gooley (clinical statistics. With this team, along with the extensive resources available at Fred Hutch such as clinical access/repositories, genomics facilities, as well as high performance computing infrastructure, we are uniquely poised to move the lessons learned at the single cell level from select research settings to the clinic. The resulting approaches for describing clonal diversity and its role in therapeutic responses will be informative not only in the setting of leukemia, but for other heterogeneous cancer types as well. With a better understanding of how clonal evolution can be described and monitored, the potential to design therapeutic strategies meant to manage it provides a valuable opportunity to refine cancer treatments.
Emerging evidence shows that tumors often consist of multiple cancer cell clones and suggests that variability in outcomes for patients may be due in part to intra?tumor heterogeneity and the resulting clonal evolution during therapy. This proposal seeks to develop single cell and population sampling methods that serve to define patient?specific models of tumor heterogeneity beyond bulk Big Data analyses. An approach to describe cancer heterogeneity that is feasible to use clinically over the course of therapy would allow for a better understandin of its impact on outcomes and be valuable for developing effective and long?lasting therapeutic strategies, regardless of cancer type.
| Thompson, Alison M; Smith, Jordan L; Monroe, Luke D et al. (2018) Self-digitization chip for single-cell genotyping of cancer-related mutations. PLoS One 13:e0196801 |
| Smith, Catherine C; Paguirigan, Amy; Jeschke, Grace R et al. (2017) Heterogeneous resistance to quizartinib in acute myeloid leukemia revealed by single-cell analysis. Blood 130:48-58 |
