Author

M. P. Lolkema

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· 4

Signature Assays, Organisms & Methods

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Organism:human· 4 Approach:statistical model· 3 Assay:whole-genome sequencing· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1

Frequent Co-Authors

E. Cuppen· 3 shared P. Hamberg· 3 shared A. A. M. van der Veldt· 2 shared G. J. L. H. van Leenders· 2 shared H. J. G. van de Werken· 2 shared H. M. Westgeest· 2 shared M. S. van der Heijden· 2 shared N. Mehra· 2 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 42 mutual co-author links

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3 shared papersCancer Biology

E. Cuppen

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Author profile →

4 papers

Recent·Most cited

Genomic and transcriptomic landscape of advanced renal cell cancer to individualize treatment strategy

K. de Joode, W. S. van de Geer, G. J. L. H. van Leenders, P. Hamberg, H. M. Westgeest, A. Beeker, S. F. Oosting, J. M. van Rooijen, L. V. Beerepoot, M. Labots, R. H. J. Mathijssen, M. P. Lolkema, E. Cuppen, S. Sleijfer, H. J. G. van de Werken, A. A. M. van der Veldt

This study uses genomic and transcriptomic data from advanced renal cell cancer patients to identify actionable mutations and immune signatures to guide personalized treatment strategies.

Scientific Reports · Jul 3, 2023Peer-reviewedCancer Biology

T Cell-to-Stroma Enrichment (TSE) score: a gene expression metric that predicts response to immune checkpoint inhibitors in patients with urothelial cancer

M. M. Rijnders, J. A. Nakauma-Gonzalez, D. G. J. Robbrecht, A. Gil-Jimenez, H. E. Balcioglu, A. A. M. Oostvogels, M. J. B. Aarts, J. L. Boormans, P. Hamberg, M. S. van der Heijden, B. E. Szabados, G. J. L. H. van Leenders, N. Mehra, J. Voortman, H. M. Westgeest, , M. P. Lolkema

bioRxiv · May 30, 2022 · PreprintCancer Biology

The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact

L. F. van Dessel, J. van Riet, M. Smits, Y. Zhu, P. Hamberg, M. S. van der Heijden, A. M. Bergman, I. M. van Oort, R. de Wit, E. E. Voest, N. Steeghs, T. N. Yamaguchi, J. Livingstone, P. C. Boutros, J. W. M. Martens, , M. P. Lolkema

This study uses whole-genome sequencing to identify four distinct genotypes in metastatic castration-resistant prostate cancers, with potential clinical implications for patient stratification.

bioRxiv · Oct 7, 2019Cancer 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