Author

S. Tavare

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 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Assay:bulk RNA-seq· 2 Research Focus:cell type annotation· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Assay:whole-genome sequencing· 2 Research Focus:chromatin & epigenetic regulation· 1 Research Focus:clustering· 1

Frequent Co-Authors

A. Dariush· 1 shared A. Fatemi· 1 shared A. Küpcü Yoldas· 1 shared A. Lambert· 1 shared A. M. Frankell· 1 shared A. Roth· 1 shared B. Bodenmiller· 1 shared C. Caldas· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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1 shared paperCancer Biology

A. Dariush

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

The Imaging and Molecular Annotation of Xenografts and Tumours (IMAXT) High Throughput Data and Analysis Infrastructure

E. A. Gonzalez-Solares, A. Dariush, C. Gonzalez-Fernandez, A. Küpcü Yoldas, M. Al Sa'd, N. Millar, T. Whitmarsh, N. Chornay, I. Falciatori, A. Fatemi, D. Goodwin, L. Kuett, C. M. Mulvey, M. Paez Ribes, F. Qosaj, , N. A. Walton

The paper presents an infrastructure for high-throughput data and analysis of tumour and xenograft samples using multimodal imaging and molecular annotation.

bioRxiv · Jun 22, 2021Cancer Biology

Fibroblastic reticular cells predict response to immune checkpoint inhibitors

D. Biasci, J. Thaventhiran, S. Tavare

The paper shows that pre-treatment expression of B cell genes predicts response to immune checkpoint inhibitors, but this association is explained by FDCSP in fibroblastic reticular cells.

bioRxiv · Feb 19, 2020 · PreprintImmunology

Statistical inference for the evolutionary history of cancer genomes

K. N. Đinh, R. Jaksik, M. Kimmel, A. Lambert, S. Tavare

This paper compares SFS derived from birth-death processes and coalescent models in cancer evolution, showing they can be made comparable and presents a selective sweep model for tumor history analysis.

bioRxiv · Aug 1, 2019Cancer Biology

The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic

A. M. Frankell, S. Jammula, X. Li, G. Contino, S. S. Killcoyne, S. Abbas, J. Perner, L. Bower, G. Devonshire, E. Ococks, N. Grehan, J. Mok, M. O'Donovan, S. Macrae, M. Eldridge, , The Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium

This study identifies 77 driver genes and 21 non-coding driver elements in 551 Esophageal Adenocarcinomas, linking them to clinical outcomes and therapeutic targets.

Nature Genetics · Feb 4, 2019Peer-reviewedCancer Biology