Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse

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Abstract

<p class="first" id="P1">Improved insight into cancer cell populations responsible for relapsed disease will lead to better outcomes for patients. Here, we report a single-cell study of B-cell precursor acute lymphoblastic leukemia at diagnosis that revealed hidden developmentally dependent cell signaling states uniquely associated with relapse. With mass cytometry, we simultaneously quantified 35 B-cell developmental proteins in 60 primary diagnostic samples. Each leukemia cell was then matched to it’s nearest healthy B-cell population by a developmental classifier that operated at the single-cell level. Machine learning identified 6 features of expanded leukemic populations sufficient to predict patient relapse at diagnosis. These features implicated pro-BII cells with activated mTOR signaling, and pre-BI cells with activated and unresponsive pre-B-cell receptor signaling, to be associated with relapse. This model, termed Developmentally Dependent Predictor of Relapse (DDPR), significantly improves currently established risk stratification methods. DDPR features exist at diagnosis and persist at relapse. Leveraging a data-driven approach, we demonstrate the predictive value of single-cell ‘omics’ for patient stratification in a translational setting and provide a framework for application in human cancers. </p>

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Clonal architecture of secondary acute myeloid leukemia.

Matthew J. Walter, Dong Shen, Li Ding … (2012)

The myelodysplastic syndromes are a group of hematologic disorders that often evolve into secondary acute myeloid leukemia (AML). The genetic changes that underlie progression from the myelodysplastic syndromes to secondary AML are not well understood. We performed whole-genome sequencing of seven paired samples of skin and bone marrow in seven subjects with secondary AML to identify somatic mutations specific to secondary AML. We then genotyped a bone marrow sample obtained during the antecedent myelodysplastic-syndrome stage from each subject to determine the presence or absence of the specific somatic mutations. We identified recurrent mutations in coding genes and defined the clonal architecture of each pair of samples from the myelodysplastic-syndrome stage and the secondary-AML stage, using the allele burden of hundreds of mutations. Approximately 85% of bone marrow cells were clonal in the myelodysplastic-syndrome and secondary-AML samples, regardless of the myeloblast count. The secondary-AML samples contained mutations in 11 recurrently mutated genes, including 4 genes that have not been previously implicated in the myelodysplastic syndromes or AML. In every case, progression to acute leukemia was defined by the persistence of an antecedent founding clone containing 182 to 660 somatic mutations and the outgrowth or emergence of at least one subclone, harboring dozens to hundreds of new mutations. All founding clones and subclones contained at least one mutation in a coding gene. Nearly all the bone marrow cells in patients with myelodysplastic syndromes and secondary AML are clonally derived. Genetic evolution of secondary AML is a dynamic process shaped by multiple cycles of mutation acquisition and clonal selection. Recurrent gene mutations are found in both founding clones and daughter subclones. (Funded by the National Institutes of Health and others.).

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Clinical significance of minimal residual disease in childhood acute lymphoblastic leukemia and its relationship to other prognostic factors: a Children's Oncology Group study.

Michael J. Borowitz, Meenakshi Devidas, Stephen P. Hunger … (2008)

Minimal residual disease (MRD) is an important predictor of relapse in acute lymphoblastic leukemia (ALL), but its relationship to other prognostic variables has not been fully assessed. The Children's Oncology Group studied the prognostic impact of MRD measured by flow cytometry in the peripheral blood at day 8, and in end-induction (day 29) and end-consolidation marrows in 2143 children with precursor B-cell ALL (B-ALL). The presence of MRD in day-8 blood and day-29 marrow MRD was associated with shorter event-free survival (EFS) in all risk groups; even patients with 0.01% to 0.1% day-29 MRD had poor outcome compared with patients negative for MRD patients (59% +/- 5% vs 88% +/- 1% 5-year EFS). Presence of good prognostic markers TEL-AML1 or trisomies of chromosomes 4 and 10 still provided additional prognostic information, but not in National Cancer Institute high-risk (NCI HR) patients who were MRD(+). The few patients with detectable MRD at end of consolidation fared especially poorly, with only a 43% plus or minus 7% 5-year EFS. Day-29 marrow MRD was the most important prognostic variable in multi-variate analysis. The 12% of patients with all favorable risk factors, including NCI risk group, genetics, and absence of days 8 and 29 MRD, had a 97% plus or minus 1% 5-year EFS with nonintensive therapy. These studies are registered at www.clinicaltrials.gov as NCT00005585, NCT00005596, and NCT00005603.

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Automated Mapping of Phenotype Space with Single-Cell Data

Nikolay Samusik, Zinaida Good, Matthew Spitzer … (2016)

Accurate and rapid identification of cell populations is key to discovering novelty in multidimensional single cell experiments. We present a population finding algorithm X-shift that can process large datasets using fast KNN estimation of cell event density and automatically arranges populations by a marker-based classification system. X-shift analysis of mouse bone marrow data resolved the majority of known and several previously undescribed cell populations. Interestingly, previously known cell populations, as well as intermediate cell populations in early hematopoietic development, were described via novel marker combinations that were defined via routes to their locations in expressed marker space. X-shift provides a rapid, reliable approach to managed cell subset analysis that maximizes automation that not only best mimics human intuition, but as we show provides access to novel insights that “prior knowledge” might prevent the researcher from visualizing.