Interconversion between Tumorigenic and Differentiated States in Acute Myeloid Leukemia

McKenzie, Mark; Ghisi, Margherita; Oxley, Ethan P; Ngo, Steven; Cimmino, Luisa; Esnault, Cécile; Liu, Ruijie; Salmon, Jessica M.; Bell, Charles C.; Ahmed, Nouraiz; Erlichster, Michael; Witkowski, Matthew T; Liu, Grace J.; Chopin, Michaël; Dakic, Aleksandar; Simankowicz, Emilia; Pomilio, Giovanna; Vu, Tina; Krsmanovic, Pavle; Su, Shian; Tian, Luyi; Baldwin, Tracey M.; Zalcenstein, Daniela; DiRago, Ladina; Wang, Shu; Metcalf, Donald; Johnstone, Ricky W.; Croker, Ben A.; Lancaster, Graeme I.; Murphy, Andrew; Naik, Shalin H.; Nutt, Stephen L.; Pospíšil, Vít; Schroeder, Timm; Wall, Meaghan; Dawson, Mark A.; Wei, Andrew H.; Thé, Hugues de; Ritchie, Matthew E.; Zuber, Johannes; Dickins, Ross A.

doi:10.1016/j.stem.2019.07.001

Cited by 71 publications

(66 citation statements)

References 88 publications

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“…However, differentiation is clearly not sufficient for the success of APL therapy [21]. Even AML dedifferentiation may occur and explain clinical relapses [150]. Apart from differentiation, a mechanistically distinct consequence of therapy is loss of APL self-renewal [33][34][35].…”

Section: Available Data For Therapy Response In Variant Aplsmentioning

confidence: 99%

Classic and Variants APLs, as Viewed from a Therapy Response

Geoffroy

2020

Cancers

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Most acute promyelocytic leukemia (APL) are caused by PML-RARA, a translocation-driven fusion oncoprotein discovered three decades ago. Over the years, several other types of rare X-RARA fusions have been described, while recently, oncogenic fusion proteins involving other retinoic acid receptors (RARB or RARG) have been associated to very rare cases of acute promyelocytic leukemia. PML-RARA driven pathogenesis and the molecular basis for therapy response have been the focus of many studies, which have now converged into an integrated physio-pathological model. The latter is well supported by clinical and molecular studies on patients, making APL one of the rare hematological disorder cured by targeted therapies. Here we review recent data on APL-like diseases not driven by the PML-RARA fusion and discuss these in view of current understanding of “classic” APL pathogenesis and therapy response.

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Section: Available Data For Therapy Response In Variant Aplsmentioning

confidence: 99%

Classic and Variants APLs, as Viewed from a Therapy Response

Geoffroy

2020

Cancers

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“…These data are consistent with the overall rarity of hematological malignancy amongst individuals with chronic inflammatory phenotypes, despite the relative increase in risk (Anderson et al, 2009;Ganan-Gomez et al, 2015). On the other hand, loss-of function mutations in PU.1 itself are rarely observed in hematological malignancy, though a wide array of myeloid leukemia-associated oncogenic lesions can interfere with PU.1 expression or function, including PML/RARA, AML1-ETO, NPM1c, and mitogenic kinase mutations in BCR/ABL and Flt3 ITD (Gerloff et al, 2015;McKenzie et al, 2019;Mueller et al, 2006;Noguera et al, 2016;Vangala et al, 2003;Yang et al, 2012). In addition, TET2 and/or DNMT3A mutations associated with early oncogenesis may interfere with PU.1 function due to aberrant methylation of PU.1 binding sites (Kaasinen et al, 2019).…”

Section: Discussionmentioning

confidence: 57%

PU.1 enforces quiescence and limits hematopoietic stem cell expansion during inflammatory stress

Rabe

Loeffler

Higa

et al. 2020

Preprint

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Loss of hematopoietic stem cell (HSC) quiescence and resulting clonal expansion are common initiating events in the development of hematological malignancy. Likewise, chronic inflammation related to aging, disease and/or tissue damage is associated with leukemia progression, though its role in oncogenesis is not clearly defined. Here, we show that PU.1-dependent repression of protein synthesis and cell cycle genes in HSC enforces homeostatic protein synthesis levels and HSC quiescence in response to IL-1 stimulation. These genes are constitutively de-repressed in PU.1-deficient HSC, leading to activation of protein synthesis, loss of quiescence and aberrant expansion of HSC. Taken together, our data identify a mechanism whereby HSC regulate their cell cycle activity and pool size in response to chronic inflammatory stress.

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“…AML is a heterogeneous disease where the diversity of malignant cell types partially recapitulates the stages of myeloid development. Mapping the malignant cells in AML to the differentiation stage of their cell of origin strongly impacts tumor prognosis and treatment, as malignant cells that originate from earlier stage progenitors have higher risk of relapse 35, 36 . In the original paper, the authors sequenced 7698 cells from 5 healthy donors to build a reference map of cell types during myeloid development, and then mapped 30712 cells from 16 AML patients across multiple time points to this reference to identify the differentiation stage of the malignant cells.…”

Section: Resultsmentioning

confidence: 99%

Surface protein imputation from single cell transcriptomes by deep neural networks

Zhou

Wang

et al. 2019

Preprint

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11 (215) 898-8007 12 Department of Statistics 13The Wharton School 14University of Pennsylvania 15 16While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell 17 populations, cell-surface proteins are often integral markers of cellular function and 18 serve as primary targets for therapeutic intervention. Here we propose a transfer learning 19 framework, single cell Transcriptome to Protein prediction with deep neural network 20 (cTP-net), to impute surface protein abundances from scRNA-seq data by learning from 21 existing single-cell multi-omic resources. 22 multi-omics 24 through the REAP-seq and CITE-seq protocols 2, 3 . Cell surface proteins can serve as integral 29 markers of specific cellular functions and primary targets for therapeutic intervention. 30Immunophenotyping by cell surface proteins has been an indispensable tool in hematopoiesis, 31 immunology and cancer research during the past 30 years. Yet, due to technological barriers 32 and cost considerations, most single cell studies, including Human Cell Atlas project 6 , quantify 33 the transcriptome only and do not have cell-matched measurements of relevant surface proteins 34 7, 8 . Sometimes, which cell types and corresponding surface proteins are essential become 35 apparent only after exploration by scRNA-seq. This motivates our inquiry of whether protein 36 abundances in individual cells can be accurately imputed by the cell's transcriptome. 37We propose cTP-net (single cell Transcriptome to Protein prediction with deep neural network), 38 a transfer learning approach based on deep neural networks that imputes surface protein 39 abundances for scRNA-seq data. Through comprehensive benchmark evaluations and 40 applications to Human Cell Atlas and acute myeloid leukemia data sets, we show that cTP-net 41 outperform existing methods and can transfer information from training data to accurately 42 impute 24 immunophenotype markers, which achieve a more detailed characterization of 43 cellular state and cellular phenotypes than transcriptome measurements alone. cTP-net relies, 44 for model training, on accumulating public data of cells with paired transcriptome and surface 45 protein measurements. 46 47 Results 48 Method overview 49 An overview of cTP-net is shown in Figure 1a. Studies based on both CITE-seq and REAP-seq 50 have shown that the relative abundance of most surface proteins, at the level of individual cells, 51is only weakly correlated with the relative abundance of the RNA of its corresponding gene 2, 3, 9 . 52 This is due to technical factors such as RNA and protein measurement error 10 , as well as 53 inherent stochasticity in RNA processing, translation and protein transport [11][12][13][14][15] . To accurately 54 impute surface protein abundance from scRNA-seq data, cTP-net employs two steps: (1) 55 denoising of the scRNA-seq count matrix and (2) imputation based on the denoised data 56 through a transcriptome-protein mapping (Figure 1a). The initial denoising, by SAVER-X 16 , 57 produces more accura...

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Interconversion between Tumorigenic and Differentiated States in Acute Myeloid Leukemia

Cited by 71 publications

References 88 publications

Classic and Variants APLs, as Viewed from a Therapy Response

Classic and Variants APLs, as Viewed from a Therapy Response

PU.1 enforces quiescence and limits hematopoietic stem cell expansion during inflammatory stress

Surface protein imputation from single cell transcriptomes by deep neural networks

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