Single cell mutational profiling delineates clonal trajectories in myeloid malignancies

Abstract

1Myeloid malignancies, including acute myeloid leukemia (AML), arise from the proliferation and 2 expansion of hematopoietic stem and progenitor cells which acquire somatic mutations. Bulk 3 molecular profiling studies on patient samples have suggested that somatic mutations are obtained 4 in a step-wise fashion, where mutant genes with high variant allele frequencies (VAFs) are 5 proposed to occur early in disease development and mutations with lower VAFs are thought to be 6 acquired later in disease progression 1-3 . Although bulk sequencing informs leukemia biology and 7 prognostication, it cannot distinguish which mutations occur in the same clone(s), accurately 8 measure clonal complexity and clone size, or offer definitive evidence of mutational order. To 9 elucidate the clonal framework of myeloid malignancies, we performed single cell mutational 10 profiling on 146 samples from 123 patients. We found AML is most commonly comprised of a 11 small number of dominant clones, which in many cases harbor co-occurring mutations in 12 epigenetic regulators. Conversely, mutations in signaling genes often occur more than once in 13 distinct subclones consistent with increasing clonal diversity. We also used these data to map the 14 clonal trajectory of each patient and found that specific mutation combinations (FLT3-ITD + 15 NPM1 c ) synergize to promote clonal expansion and dominance. We combined cell surface protein 16 expression with single cell mutational analysis to map somatic genotype and clonal architecture 17 with immunophenotype. Our studies of clonal architecture at a single cell level provide novel 18 insights into the pathogenesis of myeloid transformation and how clonal complexity contributes 19 to disease progression. 20 21 22 23 2 Results 24The genomic landscape of myeloid malignancies has been well described, with a near complete 25 catalogue of putative mutant driver genes [3][4][5][6][7] . While combinations of mutations have been 26 functionally investigated in mouse models of disease, there remains uncertainty about the co-27 occurrence of somatic mutations within the same clone. The composition and combinatorial 28 distribution of mutations across cell types and states continues to be poorly understood. To analyze 29 the clonal architecture in clinical isolates from patients with myeloid malignancies, we performed 30 single cell DNA sequencing on the Tapestri platform using a custom 109 amplicon panel covering 31 31 of the most frequently mutated genes in myeloid malignancies (Extended Table 1) 8 . We 32 sequenced 740,529 cells from 146 samples from 123 patients with myeloid malignancies, 33 including clonal hematopoiesis (CH), myeloproliferative neoplasms (MPN), and AML (Figure 341A). Samples from patients at diagnosis and at relapse were queried, with the majority of samples 35 being from patients with relapsed/refractory disease (Figure 1B; Extended Table 2). The most 36 common mutations identified in single cell profiling were found in DNMT3A (n=62 patients), 37 TET2 (n=58 patients), NPM1 (...

State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia

“…Recent studies have interrogated the clonal architecture of AML over time (45), and shown that somatic mutations may precede diagnosis of AML by months or years (46) and that deep sequencing of mutations can be used to differentiate age-related clonal hematopoiesis from pre-AML and predict AML risk in otherwise healthy individuals (47). Our approach detects system-wide perturbation before any leukemic blasts are seen in the blood, or differential expression of known leukemogenic marker genes, suggesting the signal detected by bulk RNA-seq is not driven solely by the presence of leukemic cells.…”

Single-cell DNA amplicon sequencing reveals clonal heterogeneity and evolution in T-cell acute lymphoblastic leukemia

“…mutational data to individual cells. 22,28,29 In this study, we used the Tapestri technology to dissect T-ALL heterogeneity at diagnosis and clonal evolution during chemotherapy treatment. Tapestri is amplicon based, and we designed a panel to interrogate 305 genomic regions that are frequently mutated in ALL.…”

Pancreatic Cancer Molecular Classifications: From Bulk Genomics to Single Cell Analysis

“…Moreover, although DNA bulk sequencing can greatly inform cancer biology, it cannot distinguish which mutations occur in the same clone(s) as well as it cannot measure in deep clonal complexity. Single-cell DNA sequencing (sc-DNAseq) could overcome this issue, especially if associated to sc-RNAseq: preliminary and unpublished evidences in acute myeloid leukemia [67] suggest that scDNA-seq could precisely map the clonal trajectory of each patient, identifying the specific mutation combination that synergize to promote clonal expansion and dominance. Therefore, a combination of single cell genomic and transcriptomic tools applied to pancreatic cancer is highly desirable, and probably the only way to reveal the complete PDAC sub-clonal structure.…”