In this seminar, we study current research publications in biomedical data science. Master students either present a research article, or their master thesis, or they present about their research internship. Credit points can only be earned for the presentation of research articles.

In this semester’s edition of the Journal Club, we focus on research papers that address the topic of intra-tumor heterogeneity, one of the leading determinants of therapeutic resistance and a major reason for poor overall survival in cancer patients with metastatic disease.

News

The master thesis presentation by Will Rieger on Oct 22 will be in online only format. Please use the following link to join the seminar:

https://fu-berlin.webex.com/fu-berlin/j.php?MTID=me489e62abc8c6c2bda691fc8cb9f7c7f

List of papers

Dentro, Stefan C., et al. "Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes." Cell 184.8 (2021): 2239-2254.

Takagi, K., Takagi, M., Hiyama, G. et al. A deep-learning model for characterizing tumor heterogeneity using patient-derived organoids. Sci Rep 14, 22769 (2024). https://doi.org/10.1038/s41598-024-73725-w

Qiao Y, Huang X, Moos PJ, Ahmann JM, Pomicter AD, Deininger MW, Byrd JC, Woyach JA, Stephens DM, Marth GT. A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data. Genome Res. 2024 Feb 7;34(1):94-105. doi: 10.1101/gr.278234.123. PMID: 38195207; PMCID: PMC10903947.

Zuo, C., Zhang, Y., Cao, C. et al. Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning. Nat Commun 13, 5962 (2022). https://doi.org/10.1038/s41467-022-33619-9

Levy-Jurgenson, A., Tekpli, X., Kristensen, V.N. et al. Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer. Sci Rep 10, 18802 (2020). https://doi.org/10.1038/s41598-020-75708-z

Park, S., Silva, E., Singhal, A. et al. A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors. Nat Cancer 5, 996–1009 (2024). https://doi.org/10.1038/s43018-024-00740-1

De Falco, A., Caruso, F., Su, XD. et al. A variational algorithm to detect the clonal copy number substructure of tumors from scRNA-seq data. Nat Commun 14, 1074 (2023). https://doi.org/10.1038/s41467-023-36790-9

Liu, X., Griffiths, J.I., Bishara, I. et al. Phylogenetic inference from single-cell RNA-seq data. Sci Rep 13, 12854 (2023). https://doi.org/10.1038/s41598-023-39995-6