Biological differences between healthy and disease states are the sum of contributions between physiologically normal and abnormal cells. Recent data suggests that intra-tumor cellular differences may be key to understanding the varied responses to therapy between patients. A major limitation in our ability to effectively treat cance is the innate variability of the response to therapy between patients, i.e., predicting those who will relapse/be refractory and those who will achieve remission for a given therapy. The development of robust and rapid assays of intercellular heterogeneity is thus essential for our understanding of both normal and abnormal biology and how cancer phenotypes develop and evolve during therapy. In this proposal we will refine robust methods to assay and quantify cellular heterogeneity using the model of the hematopoietic system. We will compare the heterogeneity in normal stem and progenitor cells to that seen in acute myeloid leukemia (AML), a leukemia that derives from normal hematopoietic stem cells and in myelodysplastic syndrome (MDS), a myeloid malignancy that can progress to AML. To do so, we will 1) Develop robust single-cell assays and analysis methods. We will expand our current single cell molecular biology methods to accurately measure and quantify heterogeneity in genetics and gene expression of single cells. Statistical, and technological validation of these assays will be performed alongside development of computational tools to handle and compare single-cell genetic data. 2) Measure clonal variation in normal and malignant hematopoietic cells. We will assess cell-to-cell heterogeneity in normal and malignant hematopoietic stem and progenitor cells to tease apart what combinations of alterations represent neoplastic hits and what are neutral changes. By comparing the patterns of heterogeneity seen in MDS to that in AML will allow us to identify features common in myeloid malignancies, and those that are disease-specific and those related to relapse after therapy. 3) Design high-throughput microfluidic methods to capture and interrogate single cells. Current macroscale methods for single-cell analysis are by definition limited by scale, and thus cost and sample handling challenges, whereas these bottlenecks can be remedied via microscale methods. We will design and implement a microfluidic platform that will capture single cells, and efficiently quantify genetic markers and expression levels, making further single cell of analyses on additional AML and MDS clinical samples realistic, rapid and cost-effective. The implications of current therapeutic strategies for cancer treatment with respect to clonal evolution are substantial and broadly applicable to many cancer types. Understanding the clonal structure within a tumor and its change with chemotherapy would allow us to 1) develop diagnostics to quickly quantify clones at diagnosis and during therapy and 2) apply adaptive therapies responsive to clonal evolution. The development of the molecular tools required is a crucial first step to understanding the role of clonal evolution in cancer.
This work applies emerging techniques from several disciplines to make it feasible to study how differences between otherwise identical blood stem cells impact cancer initiation and response to therapy. A better understanding of how heterogeneity between single cells can be measured and applied to basic and clinical research will allow for a completely new perspective on how cancer develops and how we should endeavor to treat it more effectively.
|Schmitt, Michael W; Pritchard, Justin R; Leighow, Scott M et al. (2018) Single-Molecule Sequencing Reveals Patterns of Preexisting Drug Resistance That Suggest Treatment Strategies in Philadelphia-Positive Leukemias. Clin Cancer Res 24:5321-5334|
|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|
|Zheng, Grace X Y; Terry, Jessica M; Belgrader, Phillip et al. (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049|
|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|
|Kuo, Chun-Ting; Thompson, Alison M; Gallina, Maria Elena et al. (2016) Optical painting and fluorescence activated sorting of single adherent cells labelled with photoswitchable Pdots. Nat Commun 7:11468|
|Ye, Fangmao; White, Collin C; Jin, Yuhui et al. (2015) Toxicity and oxidative stress induced by semiconducting polymer dots in RAW264.7 mouse macrophages. Nanoscale 7:10085-10093|
|Paguirigan, Amy L; Smith, Jordan; Meshinchi, Soheil et al. (2015) Single-cell genotyping demonstrates complex clonal diversity in acute myeloid leukemia. Sci Transl Med 7:281re2|
|Peng, Hong-Shang; Chiu, Daniel T (2015) Soft fluorescent nanomaterials for biological and biomedical imaging. Chem Soc Rev 44:4699-722|
|Schmitt, Michael W; Fox, Edward J; Prindle, Marc J et al. (2015) Sequencing small genomic targets with high efficiency and extreme accuracy. Nat Methods 12:423-5|
|Thompson, Alison M; Gansen, Alexander; Paguirigan, Amy L et al. (2014) Self-digitization microfluidic chip for absolute quantification of mRNA in single cells. Anal Chem 86:12308-14|
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