Maria Sirenko

Memorial Sloan Kettering Cancer Center

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Elli Papaemmanuil

Memorial Sloan Kettering Cancer Center

Elsa Bernard

Memorial Sloan Kettering Cancer Center

Juan Arango Ossa

Memorial Sloan Kettering Cancer Center

Dylan Domenico

Memorial Sloan Kettering Cancer Center

Virginia M. Klimek

Memorial Sloan Kettering Cancer Center

Maria Creignou

Karolinska Institutet

Uwe Platzbecker

University Hospital Leipzig

Peter L. Greenberg

Stanford University

Carlo Finelli

Istituti di Ricovero e Cura a Carattere Scientifico

Olivier Kosmider

Assistance Publique – Hôpitaux de Paris

Cooperative Institutions

Memorial Sloan Kettering Cancer Center

Kettering University

Cornell University

New York University

NYU Langone’s Laura and Isaac Perlmutter Cancer Center

NYU Langone Health

Icahn School of Medicine at Mount Sinai

Karolinska Institutet

Molecular Oncology (United States)

Istituti di Ricovero e Cura a Carattere Scientifico

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

RAS-mutant leukaemia stem cells drive clinical resistance to venetoclax

Nature (2024)

Junya Sango Saul Carcamo Maria Sirenko Abhishek Maiti Hager Mansour

Cancer driver mutations often show distinct temporal acquisition patterns, but the biological basis for this, if any, remains unknown. RAS mutations occur invariably late in the course of acute myeloid leukaemia, upon progression or relapsed/refractory disease1–6. Here, by using human leukaemogenesis models, we first show that RAS mutations are obligatory late events that need to succeed earlier cooperating mutations. We provide the mechanistic explanation for this in a requirement for mutant RAS to specifically transform committed progenitors of the myelomonocytic lineage (granulocyte–monocyte progenitors) harbouring previously acquired driver mutations, showing that advanced leukaemic clones can originate from a different cell type in the haematopoietic hierarchy than ancestral clones. Furthermore, we demonstrate that RAS-mutant leukaemia stem cells (LSCs) give rise to monocytic disease, as observed frequently in patients with poor responses to treatment with the BCL2 inhibitor venetoclax. We show that this is because RAS-mutant LSCs, in contrast to RAS-wild-type LSCs, have altered BCL2 family gene expression and are resistant to venetoclax, driving clinical resistance and relapse with monocytic features. Our findings demonstrate that a specific genetic driver shapes the non-genetic cellular hierarchy of acute myeloid leukaemia by imposing a specific LSC target cell restriction and critically affects therapeutic outcomes in patients. We find that RAS-mutant leukaemia stem cells are resistant to venetoclax, driving clinical resistance and relapse with monocytic features.

Venetoclax

10.1038/s41586-024-08137-x

Cite

Citations (4)

MPN Transformation Is Characterized By Heterogeneous Shifts in Lineage Character

Blood (2023)

Deepika Dilip Kamal Menghrajani Jacob L. Glass Raajit K. Rampal Christopher Famulare

Hierarchical clustering

Lineage (genetic)

10.1182/blood-2023-189767

Cite

Citations (1)

2012 – N/KRAS-MUTANT AML LSCS ORIGINATE FROM COMMITTED MYELOMONOCYTIC PROGENITORS AND DRIVE CLINICAL RESISTANCE TO VENETOCLAX

Experimental Hematology (2024)

Junya Sango Saul Carcamo Maria Sirenko Abhishek Maiti Hager Mansour

Venetoclax

10.1016/j.exphem.2024.104569

Cite

Citations (0)

An Improved Single Molecule Imaging Vivo Method for In Vivo Stoichiometric and Functional Analysis of Protein Complexes

Biophysical Journal (2017)

Warren R. Zipfel Avtar Singh Maria Sirenko Alexander Song Paul J. Kammermeier

Stoichiometry

Photobleaching

Fluorescence Correlation Spectroscopy

10.1016/j.bpj.2016.11.821

Cite

Citations (0)

Incidence, Clinical Associations, and Co-Mutation Patterns of UBA1 Mutations in MDS

Blood (2022)

Maria Sirenko Elsa Bernard David B. Beck Maria Creignou Dylan Domenico

10.1182/blood-2022-162397

Cite

Citations (0)

Biomarker Detection By Single-Walled Carbon Nanotube Optical Bandgap Modulation

Meeting abstracts/Meeting abstracts (Electrochemical Society. CD-ROM) (2016)

Ryan M. Williams Christopher Lee Thomas Vito Galassi Jackson D. Harvey Maria Sirenko

The unique optical properties of single-walled carbon nanotubes (SWCNT) confer ideal sensing properties for biological applications. SWCNT near-infrared photoluminescence exhibits photostability, penetration through biological media, and environmental sensitivity. Nanotube emission can undergo modulation of the optical bandgap, causing a shift in emission wavelength. These characteristics allow for consistent rapid and long-term analyte detection and quantification in complex ex vivo and in vivo environments. We have employed these unique properties to develop biomarker-specific SWCNT-based sensors for translational biomedical applications.

10.1149/ma2016-01/7/607

Cite

Citations (0)

Stoichiometric analysis of protein complexes by cell fusion and single molecule imaging

Scientific Reports (2020)

Avtar Singh Alexander L. Van Slyke Maria Sirenko Alexander Song Paul J. Kammermeier

Abstract The composition, stoichiometry and interactions of supramolecular protein complexes are a critical determinant of biological function. Several techniques have been developed to study molecular interactions and quantify subunit stoichiometry at the single molecule level. However, these typically require artificially low expression levels or detergent isolation to achieve the low fluorophore concentrations required for single molecule imaging, both of which may bias native subunit interactions. Here we present an alternative approach where protein complexes are assembled at physiological concentrations and subsequently diluted in situ for single-molecule level observations while preserving them in a near-native cellular environment. We show that coupling this dilution strategy with fluorescence correlation spectroscopy permits quantitative assessment of cytoplasmic oligomerization, while stepwise photobleaching and single molecule colocalization may be used to study the subunit stoichiometry of membrane receptors. Single protein recovery after dilution (SPReAD) is a simple and versatile means of extending the concentration range of single molecule measurements into the cellular regime while minimizing potential artifacts and perturbations of protein complex stoichiometry.

Stoichiometry

Photobleaching

Colocalization

Fluorescence Correlation Spectroscopy

10.1038/s41598-020-71630-6

Cite

Citations (9)

Predicting single cell genotypes from single cell expression profiles in AML using deep learning

Georgios Asimomitis Maria Sirenko Christos Fotis Dan A. Landau Leonidas G. Alexopoulos

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy composed of a mixture of genotypically, phenotypically and functionally diverse cell populations including wild-type (WT) cells. The generation of high throughput single cell gene expression and mutational profiles in AML enables the deployment of deep learning frameworks for gaining insights on how genotypic changes are associated with disease phenotypes. However, the question if the single cell gene expression patterns together with the computational power of neural networks have the capacity to predict a cell's genotype remains unclear. In this study, we train two supervised deep learning models to predict the cell's malignant or wild-type (WT) status as well as the mutational status of specific genomic abnormalities in a binary and multi-class multi-label setting respectively, based on single cell RNA sequencing data from 6 AML patients and 4 healthy individuals. In the independent test sets, the binary classification model achieved an accuracy of 98% while the multi-class multi-label model achieved a macro-average AUC ROC of 0.84. Moreover, applying black box feature selection on the trained networks identified genes involved in biological processes and pathways of reported significance in AML, such as the IL-2/STAT5 and NF-kB signaling pathways. Overall, this study proposes two deep learning tasks for the prediction of single cell genotypic profiles from single cell expression data and showcases how the trained models can be used for the derivation of biologically related signals.

Cell type

10.1145/3586139.3586140

Cite

Citations (1)

Determining the Stoichiometry of EGFR and ADRβ2 using Cell Fusion and Single Molecule Imaging

Biophysical Journal (2016)

Maria Sirenko Avtar Singh Alexander Song Paul J. Kammermeier Warren R. Zipfel

Stoichiometry

Photobleaching

10.1016/j.bpj.2015.11.3424

Cite

Citations (0)