In some cancers, intriguingly, the same mutation results in drastically different disease phenotypes in different patients. An example is a type of blood cancer, known as myeloproliferative neoplasm (MPN), where a single nucleotide change in the JAK2 gene, may result in either an increase in the number of red blood cells, an increase in the number of platelets, or scarring of bone marrow tissue, in different patients. The disease outcome is just as unpredictable. Some patients show no symptoms for decades whereas others rapidly progress to acute leukemias. This disconnect between genotype and phenotype may be due to the identity of the hematopoietic stem cell (HSC) in which the mutation first occurs. Not all HSCs are equivalent and some may preferentially give rise to certain types of blood cells. Additionally, the subsequent expansion of the population of mutated stem cells may be different in different patients. Therefore, to understand the heterogeneity in disease presentation, we would like to know when and in which cell the cancer mutation first occurred in each patient, how the population of mutated HSCs expanded, and to what extent the differentiation trajectory of the cancer cells deviates from that of the healthy cells. Here, we propose a comprehensive research program to make these measurements in individual MPN patients. To understand the difference between the cancer cells and the healthy cells in each patient, we will profile each cell individually. Bulk measurements average over the cancer cells and healthy cells, and obscure different cell states along the differentiation trajectory. We have recently developed a technology platform to simultaneously read out the full transcriptome and the cancer mutation in single cells. We will apply this platform to cells obtained from bone marrow biopsies of MPN patients. To obtain the history of the expansion of the cancer stem cells in each patient, we will reconstruct the lineage tree of the HSCs by sequencing the somatic mutations in the whole genomes of individual HSCs. Somatic mutations occur randomly at each cell division and are passed on to a cell?s descendants. Critically, we will also trace the differentiation trajectories of the progenies of each HSC by identifying the somatic mutations that uniquely mark each HSC in our single-cell transcriptomic data. Taken together, these measurements will provide the most detailed molecular picture of MPN at a single-cell resolution and the most comprehensive molecular history of cancer progression in individual patients. Finally, to identify and test potential therapies for MPN, we will engineer animal models whereby lineage histories of individual cells can be obtained without whole genome sequencing. We will engineer a mouse model of MPN in which individual cells record their lineage histories in their own DNA by using Cas9 to induce heritable mutations in synthetic target arrays that are transcribed and read out using sequencing. Our proposal will answer some of the most outstanding and fundamental questions about MPNs and blood development. Ultimately, our measurements should reveal patient-specific targeted therapies that preferentially eradicate the cancer stem cells or hinder their differentiation.
More than 20,000 people in the United States are diagnosed with myeloproliferative neoplasms every year. Currently, we lack the tools to identify which patients will progress to acute leukemias and the only existing cure is allogeneic bone marrow transplant, a risky procedure that is feasible in only a small subset of the patients. Here, we propose a comprehensive research program to characterize individual cancer cells and reconstruct the history of disease progression in each patient, ultimately, identifying patient-specific targeted therapies that eradicate the cancer cells or hinder their growth.