Modern cancer research is increasingly driven by high-volume molecular patient data, such as multi-omics and single-cell data. This type of data provides unprecedented insights into tumour biology and disease trajectories, and can be utilized to optimise targeted cancer therapies. The analysis of such complex molecular data requires specialised computational and statistical methods that are geared towards its unique technical and medical challenges. In this course, we study original research papers and discuss the current state-of-the-art of computational cancer research and its contributions to the clinical practice.

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Following popular request, we shifted the course to 8:30-10:00.

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Schedule

Research papers:

Deep Learning in Biomedicine

Wainberg M, Merico D, Delong A, Frey BJ. Deep learning in biomedicine. Nat Biotechnol. 2018;36:829–38.

Microscopy-based assessment of cancer

Hägele, M., Seegerer, P., Lapuschkin, S. et al. Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. Sci Rep 10, 6423 (2020). https://doi.org/10.1038/s41598-020-62724-2

Molecular subtyping

Gao, Feng, et al. "DeepCC: a novel deep learning-based framework for cancer molecular subtype classification." Oncogenesis 8.9 (2019): 1-12.

Islam, Md Mohaiminul, et al. "An integrative deep learning framework for classifying molecular subtypes of breast cancer." Computational and structural biotechnology journal 18 (2020): 2185-2199.

Joshi, P., Dhar, R. EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer. Sci Rep 12, 14628 (2022). https://doi.org/10.1038/s41598-022-18874-6

Cancers of unknown primary

Jiao, W., Atwal, G., Polak, P. et al. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nat Commun 11, 728 (2020). https://doi.org/10.1038/s41467-019-13825-8

Lu, M.Y., Chen, T.Y., Williamson, D.F.K. et al. AI-based pathology predicts origins for cancers of unknown primary. Nature 594, 106–110 (2021). https://doi.org/10.1038/s41586-021-03512-4

Prognosis prediction

Chereda, Hryhorii, et al. "Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer." Genome medicine 13.1 (2021): 1-16.

Tumor microenvironment

Menden K, Marouf M, Oller S, Dalmia A, Magruder DS, Kloiber K, Heutink P, Bonn S. Deep learning-based cell composition analysis from tissue expression profiles. Sci Adv. 2020 Jul 22;6(30):eaba2619. doi: 10.1126/sciadv.aba2619. PMID: 32832661; PMCID: PMC7439569.

Immunotherapy

Jiang, P., Gu, S., Pan, D. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 24, 1550–1558 (2018). https://doi.org/10.1038/s41591-018-0136-1

Spatial transcriptomics

He, B., Bergenstråhle, L., Stenbeck, L. et al. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat Biomed Eng 4, 827–834 (2020). https://doi.org/10.1038/s41551-020-0578-x

Tumor cell identification

Dohmen, J., Baranovskii, A., Ronen, J. et al. Identifying tumor cells at the single-cell level using machine learning. Genome Biol 23, 123 (2022). https://doi.org/10.1186/s13059-022-02683-1

Pharmacogenomics

Kristina Preuer, Richard P I Lewis, Sepp Hochreiter, Andreas Bender, Krishna C Bulusu, Günter Klambauer, DeepSynergy: predicting anti-cancer drug synergy with Deep Learning, Bioinformatics, Volume 34, Issue 9, 01 May 2018, Pages 1538–1546, https://doi.org/10.1093/bioinformatics/btx806