Author

Q. D. Morris

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

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Cancer Biology· 5

Signature Assays, Organisms & Methods

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Organism:human· 4 Assay:whole-genome sequencing· 4 Research Focus:clustering· 2 Research Focus:differential expression· 2 Approach:statistical model· 2 Research Focus:variant calling· 2 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

F. Markowetz· 3 shared G. Getz· 3 shared M. Gerstung· 3 shared M. Tarabichi· 3 shared P. C. Boutros· 3 shared C. Jolly· 2 shared D. G. Livitz· 2 shared D. Rosebrock· 2 shared

Activity by Year

2018-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersCancer Biology

F. Markowetz

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5 papers

Recent·Most cited

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

S. C. Dentro, I. Leshchiner, K. Haase, M. Tarabichi, J. Wintersinger, A. G. Deshwar, K. Yu, Y. Rubanova, G. Macintyre, J. Demeulemeester, I. Vazquez-Garcia, K. Kleinheinz, D. G. Livitz, S. Malikic, N. Donmez, , Q. D. Morris

This study characterizes intra-tumor heterogeneity across 2,658 human cancer genomes, identifying subclonal mutations and evolutionary patterns across 38 cancer types.

Cell · Apr 1, 2021Peer-reviewedCancer Biology

Quantifying the Influence of Mutation Detection on Tumour Subclonal Reconstruction

L. Y. Liu, V. Bhandari, A. Salcedo, S. M. Espiritu, Q. D. Morris, T. R. Kislinger, P. C. Boutros

This study evaluates subclonal reconstruction pipelines for prostate cancer, showing significant variability and biases in predicting subclonal architecture and mutation timing.

Nature Communications · Dec 7, 2020Peer-reviewedCancer Biology

A deep learning system can accurately classify primary and metastatic cancers based on patterns of passenger mutations

W. Jiao, G. Atwal, P. Polak, R. Karlic, E. Cuppen, A. Danyi, J. de Ridder, C. van Herpen, M. P. Lolkema, N. Steeghs, G. Getz, Q. D. Morris, L. D. Stein, PCAWG Pathology & Clinical Correlates Working Grp, ICGC/TCGA Pan-cancer Analysis of Whole Genomes Net

This paper develops a deep learning classifier using whole genome sequencing data to distinguish cancer types and identify metastatic origins.

bioRxiv · Jan 22, 2019 · PreprintCancer Biology

The evolutionary history of 2,658 cancers

M. Gerstung, C. Jolly, I. Leshchiner, S. C. Dentro, S. Gonzalez Rosado, D. Rosebrock, T. J. Mitchell, Y. Rubanova, P. Anur, K. Yu, M. Tarabichi, A. Deshwar, J. Wintersinger, K. Kleinheinz, I. Vazquez-Garcia, , Spellman

This study uses whole-genome sequencing of cancer samples to reconstruct the evolutionary history of 39 cancer types, identifying early and late-stage mutational patterns and driver genes.

bioRxiv · Sep 12, 2018Cancer Biology

Neutral tumor evolution?

M. Tarabichi, I. Martincorena, M. Gerstung, A. M. Leroi, F. Markowetz, P. T. Spellman, Q. D. Morris, O. C. Lingjaerde, D. C. Wedge, P. Van Loo, PCAWG Evolution and Heterogeneity Working Group

The paper explores tumor evolution through subclonal analysis and discusses the role of driver and passenger mutations in cancer progression.

bioRxiv · Jul 23, 2018Cancer Biology