Quantifying intraleukemic heterogeneity through single-cell RNA sequencing

Abstract

Chronic myelomonocytic leukemia (CMML) is a heterogenous, lethal adult leukemia with a median survival of 34 months. This clonal hematopoietic malignancy is characterized persistent monocytosis and a limited number of genetic clonal abnormalities. The median age of diagnosis is 72 years of age and the only disease-modifying, curative treatment is an allogeneic stem cell transplant, which is often forgone due to age-related co-morbidities of CMML patients. Furthermore, CMML has a propensity to transform into acute myeloid leukemia in between 15% and 30% of cases. Intraleukemic heterogeneity (ILH) serves as a reservoir for resistance evolution, which often occurs due to selection of minor cellular sub-clones. However, little is known regarding whether single or combination targeted therapies against myeloproliferative leukemias alone can ever be successful and if left untreated, the median survival can be measured in weeks. There exists a critical need to better understand resistance evolution during mutationally-directed therapy. Outcome and time-scale of this evolutionary process are hallmarked by ILH. CMML patients offer a unique opportunity to better identify molecular and cell-phenotypic predictors of evolution and therapeutic consequences of ILH in hematologic malignancies, because most patients can be longitudinally followed in a treatment naïve state before they progress. To develop the right toolkit to quantify this evolutionary process and use it for clinical impact, here we focused on individual leukemia patients’ bone marrow mononuclear cell samples, in comparison to healthy subjects, to statistically describe ILH. We use a generalized diversity measure was to quantify ILH, and find that it can characterize disease stage at the level of sub-population structures derived from single cell RNA sequencing. We thus hypothesize that this measure can be used to quantify leukemic tumor evolution. We developed a pipeline that can be used to analyze single cell RNA sequencing data, via multi-sample normalization, clustering and mathematical interpretation. We then verified this platform with clinical data. This approach enabled us to distinguish between leukemic states based on the high-dimensional single cell patient samples. Our analysis shows how structured single cell RNA sequencing data can become useful when clinical sampling is combined with computational analyses and mathematical modeling.