Details

Autor(en) / Beteiligte

• Kolmar, Leonie
• Autour, Alexis
• Ma, Xiaoli
• Vergier, Blandine
• Eduati, Federica
• Merten, Christoph A.

Titel

Technological and computational advances driving high-throughput oncology

Ist Teil von

• Trends in cell biology, 2022-11, Vol.32 (11), p.947-961

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2022

Quelle

Elsevier ScienceDirect Journals

Beschreibungen/Notizen

• Engineering and computational advances have opened many new avenues in cancer research, particularly when being exploited in interdisciplinary approaches. For example, the combination of microfluidics, novel sequencing technologies, and computational analyses has been crucial to enable single-cell assays, giving a detailed picture of tumor heterogeneity for the very first time. In a similar way, these ‘tech’ disciplines have been elementary for generating large data sets in multidimensional cancer ‘omics’ approaches, cell–cell interaction screens, 3D tumor models, and tissue level analyses. In this review we summarize the most important technology and computational developments that have been or will be instrumental for transitioning classical cancer research to a large data-driven, high-throughput, high-content discipline across all biological scales. Advanced sequencing and barcoding technologies allow the analysis of tumor heterogeneity and evolution by combining multi-omics data sets and including spatial information.Miniaturized microfluidic assay formats can be exploited to obtain detectable concentrations of analytes from single cells and to perform large-scale drug screens on very limited patient material.Microfluidics allows the analysis of the tumor microenvironment (TME) by pairing cells for interaction studies and by generating highly complex and geometrically defined tumor models in vitro.Advances in computational analysis provide essential tools to integrate different data modalities, allowing a holistic description of the tumor.Machine learning facilitates the identification of relevant patterns in high-throughput data, thus enabling their use for cancer diagnosis and prognosis.