Author

N. Lallous

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 Research Focus:cancer & tumor microenvironment· 2 Research Focus:drug response / drug discovery· 2 Approach:experimental assay· 2 Assay:bulk RNA-seq· 1 Research Focus:chromatin & epigenetic regulation· 1 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

A. Cherkasov· 2 shared A. K. Parra-Nunez· 1 shared B. Sun· 1 shared B. Zhai· 1 shared C. C. Collins· 1 shared C. Sanchez· 1 shared C. Wells· 1 shared D. Brown· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersCancer Biology

A. Cherkasov

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Author profile →

3 papers

Recent·Most cited

A bi-functional PARP-HDAC inhibitor with activity in Ewing sarcoma

L. Ramos, S. Truong, B. Zhai, J. Joshi, F. Ghaidi, M. M. Lizardo, T. Shyp, H. Adomat, S. LeBihan, C. C. Collins, J. Bacha, D. Brown, J. Langlands, W. Shen, N. Lallous, , M. Daugaard

This paper develops a dual PARP and HDAC inhibitor, kt-3283, showing enhanced cytotoxicity and efficacy in Ewing sarcoma models compared to single-agent inhibitors.

bioRxiv · Nov 10, 2022 · PreprintCancer Biology

Dynamic phase separation of the androgen receptor and its coactivators to regulate gene expression.

F. Zhang, S. Wong, J. Lee, S. Lingadahalli, C. Wells, N. Saxena, C. Sanchez, B. Sun, A. K. Parra-Nunez, N. Chan, J. M. Bui, Y. Wang, P. S. Rennie, N. A. Lack, A. Cherkasov, , N. Lallous

This study shows that androgen receptor (AR) and its co-activator MED1 form dynamic, AR-rich condensates at superenhancers in prostate cancer, which are crucial for oncogenic gene expression.

bioRxiv · Mar 27, 2021Cancer Biology

Deep Learning Modelling of Androgen Receptor Responses to Prostate Cancer Therapies

O. Snow, N. Lallous, M. Ester, A. Cherkasov

This study develops a deep neural network to predict Androgen Receptor mutant responses to prostate cancer therapies, showing improved accuracy over other machine learning methods.

bioRxiv · Jan 15, 2020Cancer Biology