Single cell RNA sequencing of AML initiating cells reveals RNA-based evolution during disease progression

Balasubramanian, Dheepa; Ribeiro, Susan Pereira; Stefan, Tammy; Xu, Xuan; Fourati, Slim; Roe, Anne; Jackson, Zachary; Schauner, Robert; Sharma, Ashish; Tamilselvan, Banumathi; Li, Samuel; Lima, Marcos de; Hwang, Tae Hyun; Balderas, Robert; Saunthararajah, Yogen; Maciejewski, Jaroslaw P.; LaFramboise, Thomas; Barnholtz‐Sloan, Jill S.; Sékaly, Rafick‐Pierre; Wald, David N.

doi:10.1038/s41375-021-01338-7

Cited by 49 publications

(60 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, the AML chemotherapy dataset [5] consisted of peripheral blood mononuclear cells (PBMCs) collected from a patient with Acute Myeloid Leukemia (AML) at baseline or after two or four days of treatment with chemotherapy agents Venetoclax and Azacitidine. It is hypothesized that the persistence of leukemia stem cells (LSCs) following treatment drives disease severity, relapse, and results in worse clinical outcomes [7, 42]. Here, the authors demonstrate how chemotherapy treatment induces the depletion of LSCs through metabolic reprogramming, where oxidative phosphorylation, a critical pathway for LSC maintenance and survival, is suppressed.…”

Section: Resultsmentioning

confidence: 99%

“…To assess integration performance on classifying cells according to disease status, we considered three case/ control datasets of two disease systems, Acute Myeloid Leukemia (AML) and Multiple Sclerosis (MS). In the first dataset [7], Leukemia stem cells (LSCs) were collected from AML patients at treatment-naive diagnosis ( N = 5) and following relapse after chemotherapy treatment ( N = 5). Here, the authors compared diagnosis from relapse samples to characterize gene expression heterogeneity during AML disease progression and show that differences were largely due to metabolic reprogramming, apoptotic signaling, and chemokine signaling.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

Ranek

Stanley

Purvis

2022

Preprint

View full text Add to dashboard Cite

Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics. Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark eight integration approaches on eight datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods. This work provides users with practical recommendations for task-specific integration of single-cell gene expression modalities.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

Ranek

Stanley

Purvis

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, in FLT3-ITD patient s3432 the clear separation of Dx and Re cells could be caused by de novo mutations in FAT3, ITGB7, UBA2 and SLC4A3. Furthermore, the presence of quiescent LSC's that escape conventional therapeutic interventions could explain recurrence in the absence of clonal rearrangements 14,54,55 . In agreement with this hypothesis, we detected transcriptionally similar LSC-like cells in the Dx and Re samples of the two otherwise distinct AML1-ETO samples.…”

Section: Discussionmentioning

confidence: 99%

“…The advent of single-cell RNA sequencing provides revolutionary opportunities to assess the heterogeneity of cancer populations at the single-cell level and explore the transcriptional features of individual cell types, such as subpopulations contributing to the relapse. However, few longitudinal studies 13,14 focused on analyzing pair-wise samples from AML patients, at first diagnosis and relapse.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal single-cell transcriptomics reveals distinct patterns of recurrence in acute myeloid leukemia

Zhai

Singh

Dolnik

et al. 2022

Preprint

View full text Add to dashboard Cite

The heterogeneity and evolution of AML blasts can render therapeutic interventions ineffective in a yet poorly understood patient-specific manner. To gain insight into the clonal heterogeneity of diagnosis (Dx) and relapse (Re) pairs, we employed whole-exome sequencing and single-cell RNA-seq to longitudinally profile two t(8;21) (AML1-ETO = RUNX1-RUNX1T1), and four FLT3-ITD AML cases. The single cell RNA data underpinned the tumor heterogeneity amongst patient blasts. The Dx-Re transcriptomes of high risk FLT3-ITD pairs formed a continuum from extensively changed in the absence of significantly mutational changes in AML-associated genes to rather similar Dx-Re pair of an intermediate risk FLT3-ITD. In one high risk FLT3-ITD pair, a pathway switched from an AP-1 regulated network in Dx to mTOR signaling in Re. The distinct AML1-ETO pairs comprise clusters that share genes related to hematopoietic stem cell maintenance and cell migration suggesting that the Re leukemic stem cell-like (LSC-like) cells probably evolved from the Dx LSC-like cells. In summary, our study revealed a continuum from drastic transcriptional changes to extensive similarities between respective Dx-Re pairs that are poorly explained by the well-established model of clonal evolution. Our results suggest alternative and currently unappreciated and unexplored mechanisms leading to therapeutic resistance and AML recurrence.

show abstract

“…In line with this, leukemia samples as well as lymphoma cells circulating in the peripheral blood were the subjects in hematology-focused projects of early days [ 26 , 27 ], with acute myeloid leukemia (AML) being the most frequently published oncohematological entity [ 28 ]. To date, SCS datasets have been generated for a wide range of blood cancers, including chronic myeloid leukemia [ 29 ], myeloproliferative neoplasms [ 30 , 31 ], myelodysplastic syndrome/acute myeloid leukemia [ 21 , 27 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ], acute lymphoblastic leukemia (ALL) [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ], chronic lymphocytic leukemia [ 59 , 60 , 61 ], mantle cell lymphoma [ 61 , 62 , 63 ], follicular lymphoma [ 61 , 64 , 65 , 66 ], diffuse large B-cell lymphoma [ 61 ], multiple myeloma [ 26 , 67 ], Hodgkin lymphoma […”

Section: Introductionmentioning

confidence: 99%

Single-Cell Sequencing: Biological Insight and Potential Clinical Implications in Pediatric Leukemia

Alpár

Egyed

Bödör

et al. 2021

Cancers

View full text Add to dashboard Cite

Single-cell sequencing (SCS) provides high-resolution insight into the genomic, epigenomic, and transcriptomic landscape of oncohematological malignancies including pediatric leukemia, the most common type of childhood cancer. Besides broadening our biological understanding of cellular heterogeneity, sub-clonal architecture, and regulatory network of tumor cell populations, SCS can offer clinically relevant, detailed characterization of distinct compartments affected by leukemia and identify therapeutically exploitable vulnerabilities. In this review, we provide an overview of SCS studies focused on the high-resolution genomic and transcriptomic scrutiny of pediatric leukemia. Our aim is to investigate and summarize how different layers of single-cell omics approaches can expectedly support clinical decision making in the future. Although the clinical management of pediatric leukemia underwent a spectacular improvement during the past decades, resistant disease is a major cause of therapy failure. Currently, only a small proportion of childhood leukemia patients benefit from genomics-driven therapy, as 15–20% of them meet the indication criteria of on-label targeted agents, and their overall response rate falls in a relatively wide range (40–85%). The in-depth scrutiny of various cell populations influencing the development, progression, and treatment resistance of different disease subtypes can potentially uncover a wider range of driver mechanisms for innovative therapeutic interventions.

show abstract

Single cell RNA sequencing of AML initiating cells reveals RNA-based evolution during disease progression

Cited by 49 publications

References 83 publications

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

Longitudinal single-cell transcriptomics reveals distinct patterns of recurrence in acute myeloid leukemia

Single-Cell Sequencing: Biological Insight and Potential Clinical Implications in Pediatric Leukemia

Contact Info

Product

Resources

About