Author

M. Quesnel-Vallieres

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 3

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Approach:experimental assay· 1

Frequent Co-Authors

A. Thomas-Tikhonenko· 2 shared K. W. Lynch· 2 shared Y. Barash· 2 shared A. A. Moverley· 1 shared A. Castro· 1 shared A. Ferrando· 1 shared A. Jha· 1 shared C. D. Falkenstein· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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2 shared papersCancer Biology

A. Thomas-Tikhonenko

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Author profile →

3 papers

Recent·Most cited

An Alternatively Spliced Gain-of-Function NT5C2 Isoform Contributes to Chemoresistance in Acute Lymphoblastic Leukemia

M. T. Diz, C. Reglero, C. D. Falkenstein, A. Castro, K. E. Hayer, C. Radens, M. Quesnel-Vallieres, Z. Ang, P. Sehgal, M. M. Li, Y. Barash, S. K. Tasian, A. Ferrando, A. Thomas-Tikhonenko

The paper identifies an alternatively spliced NT5C2 isoform contributing to chemoresistance in B-ALL by enhancing nucleosidase activity and 6-MP resistance.

bioRxiv · Sep 14, 2023Cancer Biology

A mitochondrial surveillance mechanism activated by SRSF2 mutations in hematologic malignancies

X. Liu, S. A. Devadiga, R. F. Stanley, R. Moroow, K. Janssen, M. Quesnel-Vallieres, O. Pomp, A. A. Moverley, C. Li, N. Skuli, M. P. Carroll, J. Huang, D. C. Wallace, K. W. Lynch, O. Abdel-Wahab, P. S. Klein

This study shows that SRSF2 mutations disrupt mitochondrial mRNA splicing, impair complex I function, and increase mitophagy through a PINK1 splicing mechanism, identifying mitophagy as a therapeutic target in MDS and AML.

bioRxiv · Jun 25, 2023 · PreprintCancer Biology

Identifying common transcriptome signatures of cancer by interpreting deep learning models

A. Jha, M. Quesnel-Vallieres, A. Thomas-Tikhonenko, K. W. Lynch, Y. Barash

This study identifies common transcriptome signatures across cancer types using deep learning models trained on RNA-Seq data, revealing genes and splicing variations associated with cancer.

bioRxiv · Nov 11, 2021Cancer Biology