MutaSeq reveals the transcriptomic consequences of clonal evolution in acute myeloid leukemia

The step-wise acquisition of genetic abnormalities in cancer is thought to represent a major driver of disease initiation, relapse and therapy resistance. Acute myeloid leukemia (AML) represents a prime example of an aggressive cancer that develops in a multi-step manner from multipotent hematopoietic progenitors via pre-leukemic intermediates to leukemic cells. While bulk and single-cell genomics provide powerful tools to study the phylogenetics of cancer evolution, the specific transcriptomic changes induced by the accumulation of mutations remain largely unexplored. Here, we introduce MutaSeq, a combined single-cell genetic and transcriptomics platform for the identification of molecular consequences of cancer evolution. Through in-depth profiling of an AML patient, we demonstrate that MutaSeq is capable of: (1) fine-mapping clonal and developmental hierarchies (2) quantifying the ability of leukemic and pre-leukemic clones to give rise to mature lineages and (3) identifying surface markers and mRNA transcripts specific to pre-leukemic, leukemic, and residual healthy cells. The experimental and analytical approach presented here is broadly applicable to other types of cancer, and can help identify targets for eradicating both pre-cancerous and cancerous reservoirs of relapse.

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Computational reconstruction of clonal hierarchies from bulk sequencing data of acute myeloid leukemia samples

10.1101/2021.01.23.427897 ◽

2021 ◽

Author(s):

Thomas Stiehl ◽

Anna Marciniak-Czochra

Keyword(s):

Acute Myeloid Leukemia ◽

Single Cell ◽

Myeloid Leukemia ◽

Clonal Evolution ◽

Malignant Cell ◽

Leukemic Cells ◽

Sequencing Data ◽

Single Cell Sequencing ◽

Computational Reconstruction ◽

Acute Myeloid

AbstractAcute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution.

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Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples

Frontiers in Physiology ◽

10.3389/fphys.2021.596194 ◽

2021 ◽

Vol 12 ◽

Author(s):

Thomas Stiehl ◽

Anna Marciniak-Czochra

Keyword(s):

Acute Myeloid Leukemia ◽

Single Cell ◽

Myeloid Leukemia ◽

Clonal Evolution ◽

Malignant Cell ◽

Leukemic Cells ◽

Sequencing Data ◽

Single Cell Sequencing ◽

Computational Reconstruction ◽

Acute Myeloid

Acute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution.

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Clinical Implications of Inflammation in Acute Myeloid Leukemia

Frontiers in Oncology ◽

10.3389/fonc.2021.623952 ◽

2021 ◽

Vol 11 ◽

Cited By ~ 1

Author(s):

Christian Récher

Keyword(s):

Acute Myeloid Leukemia ◽

Disease Progression ◽

Myeloid Leukemia ◽

Kinase Inhibitors ◽

Lymphoblastic Leukemia ◽

Clonal Evolution ◽

Leukemic Cells ◽

Prognostic Impact ◽

Acute Myeloid

Recent advances in the description of the tumor microenvironment of acute myeloid leukemia, including the comprehensive analysis of the leukemic stem cell niche and clonal evolution, indicate that inflammation may play a major role in many aspects of acute myeloid leukemia (AML) such as disease progression, chemoresistance, and myelosuppression. Studies on the mechanisms of resistance to chemotherapy or tyrosine kinase inhibitors along with high-throughput drug screening have underpinned the potential role of glucocorticoids in this disease classically described as steroid-resistant in contrast to acute lymphoblastic leukemia. Moreover, some mutated oncogenes such as RUNX1, NPM1, or SRSF2 transcriptionally modulate cell state in a manner that primes leukemic cells for glucocorticoid sensitivity. In clinical practice, inflammatory markers such as serum ferritin or IL-6 have a strong prognostic impact and may directly affect disease progression, whereas interesting preliminary data suggested that dexamethasone may improve the outcome for AML patients with a high white blood cell count, which paves the way to develop prospective clinical trials that evaluate the role of glucocorticoids in AML.

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Survival Stratification In Acute Myeloid Leukemia By Single Cell Signal Profiling

Blood ◽

10.1182/blood.v122.21.2625.2625 ◽

2013 ◽

Vol 122 (21) ◽

pp. 2625-2625 ◽

Cited By ~ 1

Author(s):

Jørn Skavland ◽

Håkon Reikvam ◽

Øystein Bruserud ◽

Bjorn T. Gjertsen

Keyword(s):

Principal Component Analysis ◽

Acute Myeloid Leukemia ◽

Ribosomal Protein ◽

Single Cell ◽

Myeloid Leukemia ◽

Principal Component ◽

Leukemic Cells ◽

Ribosomal Protein S6 ◽

Phosphorylation Level ◽

Acute Myeloid

Abstract Introduction Dysregulation and mutations in signaling genes of cancer cells characterize more than half of the acute myeloid leukemia (AML) patients, and contribute to chemoresistance through regulation of cellular processes including apoptosis and DNA repair. We investigated if determination of single cell basal phosphorylation of signaling proteins reflected mutation of FLT3 and NPM1, cytogenetics, response to first course of chemotherapy or overall survival. Methods We employed flow cytometric single cell analysis of phosphoproteins central to signal transduction pathways in myeloid cancer cells and analyzed peripheral blood leukocytes from 93 acute myeloid leukemia (AML) patients. Blood samples from consecutively diagnosed AML patients with high peripheral blood blast counts (>7x109/L, >70% blasts) were collected after informed consent was given and biobanked by cryo-preservation. AML samples was thawed and equilibrated for one hour in a defined serum-free medium (StemSpan SFEM medium, Stem Cell Technologies) which include insulin and transferrin. All samples were viability controlled and validated for growth factor response. Basal phosphorylation was determined using 17 phosphorylation specific antibodies: All staining panels contain the same 4 surface antibodies and a live dead discriminator; CD33(P67.6) PerCP-Cy5.5, CD38(HB7) PE-Cy7, CD34(581) PE, CD45(MEM-28) PE-Dynamics590 and phospho c-PARP(Asp214) Alexa Flour 700. Two phosphospecific antibodies were added to each panel with the respective direct conjugated dye Alexa Flour 488 and Alexa Flour 647; p38(pT180/pY182) and ERK2(pT202/pY204), SRC(pY418) and Akt(pT308), PDK1(pS241) and Akt(pS473), STAT1(pY701) and ribosomal protein S6(pS235/36), STAT3(pY705) and STAT5(pY694), CREB(pS133) and STAT3(pS727), ribosomal protein S6(pS240) and NFkB(pS529), 4EBP(pT37/pT45) and STAT6(Y641), without p-antibody and JNK(pT183/pY185). The lowest median signal for each phosphoprotein in AML cells and lymphocytes, respectively, were used as reference value for calculation of basal phosphorylation. Hierarchical clustering with the use of complete linkage were created using TM4, and Principal Component Analysis was carried out using Unscrambler X (CAMO Software). Results Unsupervised clustering revealed two distinct signature clusters based on low or elevated phosphorylation level among AML cells. A similar cluster signature was absent in endogenous non-leukemic lymphocytes from the same patients. No correlations between basal phosphorylation and prognostic mutations (FLT3 or NPM1), cytogenetics or response to first course of chemotherapy were found. In AML patients treated with intensive chemotherapy (n=45) the cluster with low phosphorylation level (n=19) correlated with significant (p=0.007) shorter overall survival. Principal component analysis verified the cluster analysis and guided a reduction to only three phosphoproteins (STAT3, 4EBP1, ribosomal protein S6) with statistically significant (p=0.014) stratification of survival. Conclusion Leukemic cells demonstrated a phosphorylation profile that reflected survival of the intensively treated patients, but surprisingly not correlated with mutational status of FLT3, NPM1, cytogenetics or first course remission status. This suggests that phosphoprotein determination in leukemic cells provide prognostic information so far not available with current diagnostics. The robust and relatively simple method of single cell signal profiling should be tested in clinical trials to examine its feasibility in therapy response prediction. More extensive mutational and epigenetic analyses are needed in search for the molecular origin of the low/high signal profiles. (A) Unsupervised hierarchical cluster analysis based on three phospho protein analysis stratified in two distinct clusters of patients receiving standard intensive induction chemotherapy (n=45; daunorubicin + cytarabine or idarubicin + cytarabine (3+7) similar to HOVON AML protocols 103 and 102, respectively). (B) Kaplan-Meier plot was performed calculating P value with the use mantel-Cox log-rank test. Disclosures: No relevant conflicts of interest to declare.

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Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics

Nature Communications ◽

10.1038/s41467-020-19119-8 ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 3

Author(s):

Kiyomi Morita ◽

Feng Wang ◽

Katharina Jahn ◽

Tianyuan Hu ◽

Tomoyuki Tanaka ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Single Cell ◽

Myeloid Leukemia ◽

Single Cell Analysis ◽

Clonal Evolution ◽

Evolutionary Process ◽

Treatment Resistance ◽

Clonal Diversity ◽

Clonal Architecture ◽

Acute Myeloid

AbstractClonal diversity is a consequence of cancer cell evolution driven by Darwinian selection. Precise characterization of clonal architecture is essential to understand the evolutionary history of tumor development and its association with treatment resistance. Here, using a single-cell DNA sequencing, we report the clonal architecture and mutational histories of 123 acute myeloid leukemia (AML) patients. The single-cell data reveals cell-level mutation co-occurrence and enables reconstruction of mutational histories characterized by linear and branching patterns of clonal evolution, with the latter including convergent evolution. Through xenotransplantion, we show leukemia initiating capabilities of individual subclones evolving in parallel. Also, by simultaneous single-cell DNA and cell surface protein analysis, we illustrate both genetic and phenotypic evolution in AML. Lastly, single-cell analysis of longitudinal samples reveals underlying evolutionary process of therapeutic resistance. Together, these data unravel clonal diversity and evolution patterns of AML, and highlight their clinical relevance in the era of precision medicine.

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Single Cell Whole Exome Sequencing in NPM1/Flt3 Positive Pediatric Acute Myeloid Leukemia

Blood ◽

10.1182/blood.v124.21.1043.1043 ◽

2014 ◽

Vol 124 (21) ◽

pp. 1043-1043

Author(s):

Christiane Walter ◽

Winfried Hofmann ◽

Katarina Reinhardt ◽

Dirk Reinhardt ◽

Nils von Neuhoff

Keyword(s):

Acute Myeloid Leukemia ◽

Single Cell ◽

Exome Sequencing ◽

Whole Exome Sequencing ◽

Myeloid Leukemia ◽

Single Cells ◽

Clonal Evolution ◽

Npm1 Mutation ◽

Whole Exome ◽

Acute Myeloid

Abstract Introduction: Acute myeloid leukemia (AML) is one of the most frequent forms of leukemia in children younger than 15 years. The detection of several mutations in a blast population of pediatric AML (pAML) is supposed to be caused by a clonal evolution from a leukemic stem cell (LSC) to leukemic blasts. LSC are believed to be more resistant to chemotherapy, to be able to survive during treatment and to be responsible for the emergence of a relapse due to the persistence in the bone marrow (BM) niche. Since LSC and potential leukemic subclones are only present in small subpopulations, it has been a major technical challenge to particular analyse only the specific population. To acquire a better understanding of the underlying mechanisms of mutagenesis, clonal evolution and leukemogenesis, the aim of this study was to establish methods that allow the analysis and detection of mutations in single cells of a subpopulation known to contain HSC as well as LSC (CD34+CD38-). We especially focused on a pAML subgroup with mutations in Nucleophosmin (NPM1) and/or fms related tyrosine kinase 3 (Flt3). Methods and Results: We established methods to perform single cell sorting, whole genome amplification (WGA) using multiple displacement amplification (MDA) technology (Qiagen) and subsequent whole exome sequencing. The sorting efficiency was checked as Hoechst stained cells were sorted onto glas slides with 48 defined spots and the presence of single cells was checked under an inverse fluorescent microscope. Subsequently, single CD34+CD38- patient derived cells were sorted into 0,5ml low binding tubes containing 4µl PBS followed by WGA and whole exome sequencing. The mutational status of the sorted single cells from three patients suffering from pAML was analysed and compared to mutations detected at initial diagnosis in DNA from a bulk of BM cells. WGA from single CD34+CD38-PI- cells resulted in an amount of 29 to 31.7µg DNA from each of five single cells. The quality of the amplified DNA was sufficient for whole exome sequencing. A 4bp insertion in exon 12 of NPM1 reflecting a common NPM1 mutation (MutA) initially detected from a bulk of cells was identified in amplified DNA from single cells using whole exome sequencing in 2/3 patients. Internal tandem duplications in Flt3 indicated by mismatches in the alignment could be detected in amplified DNA from single cells of two patients. The detected ITD resemble those initially detected in DNA from a bulk of BM cells. Discussion and Conclusion: Single cell sequencing provides a useful tool to amend the detection of genetic aberrations from a bulk of cells and to confirm the presence of specific mutations in single cells from small subpopulations. It therefore helps to get further insights into the clonal evolution in pAML. Disclosures No relevant conflicts of interest to declare.

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Clonal Evolution of Acute Myeloid Leukemia Revealed by High-Throughput Single-Cell Genomics

10.1101/2020.02.07.925743 ◽

2020 ◽

Cited By ~ 5

Author(s):

Kiyomi Morita ◽

Feng Wang ◽

Katharina Jahn ◽

Jack Kuipers ◽

Yuanqing Yan ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Dna Sequencing ◽

Single Cell ◽

High Throughput ◽

Myeloid Leukemia ◽

Clonal Evolution ◽

Treatment Resistance ◽

Clonal Diversity ◽

History Of ◽

Acute Myeloid

SummaryOne of the pervasive features of cancer is the diversity of mutations found in malignant cells within the same tumor; a phenomenon called clonal diversity or intratumor heterogeneity. Clonal diversity allows tumors to adapt to the selective pressure of treatment and likely contributes to the development of treatment resistance and cancer recurrence. Thus, the ability to precisely delineate the clonal substructure of a tumor, including the evolutionary history of its development and the co-occurrence of its mutations, is necessary to understand and overcome treatment resistance. However, DNA sequencing of bulk tumor samples cannot accurately resolve complex clonal architectures. Here, we performed high-throughput single-cell DNA sequencing to quantitatively assess the clonal architecture of acute myeloid leukemia (AML). We sequenced a total of 556,951 cells from 77 patients with AML for 19 genes known to be recurrently mutated in AML. The data revealed clonal relationship among AML driver mutations and identified mutations that often co-occurred (e.g., NPM1/FLT3-ITD, DNMT3A/NPM1, SRSF2/IDH2, and WT1/FLT3-ITD) and those that were mutually exclusive (e.g., NRAS/KRAS, FLT3-D835/ITD, and IDH1/IDH2) at single-cell resolution. Reconstruction of the tumor phylogeny uncovered history of tumor development that is characterized by linear and branching clonal evolution patterns with latter involving functional convergence of separately evolved clones. Analysis of longitudinal samples revealed remodeling of clonal architecture in response to therapeutic pressure that is driven by clonal selection. Furthermore, in this AML cohort, higher clonal diversity (≥4 subclones) was associated with significantly worse overall survival. These data portray clonal relationship, architecture, and evolution of AML driver genes with unprecedented resolution, and illuminate the role of clonal diversity in therapeutic resistance, relapse and clinical outcome in AML.

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