Swiss-PO: A Web Tool for Personalised Oncology – Upcoming Updates - European Medical Journal

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Swiss-PO: A Web Tool for Personalised Oncology – Upcoming Updates

1 Mins
Oncology
Authors:
Fanny S. Krebs,1 Shakiba Fadaei,1 Olivier Michielin,2,3 Vincent Zoete1,2
  • 1. Computer-Aided Molecular Engineering, Department of Oncology, University of Lausanne, Epalinges, Switzerland
  • 2. Modelling Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
  • 3. Département d’oncologie, Hôpitaux Universitaires de Genève (HUG), Switzerland
*Correspondence to [email protected]
Disclosure:

The authors have declared no conflicts of interest.

Acknowledgements:

The authors would like to thank the University of Lausanne, the Lausanne branch of the Ludwig Institute for Cancer Research, and the Swiss Institute of Bioinformatics, Lausanne, Switzerland, for their support.

Citation:
EMJ Oncol. ;11[1]:44-45. DOI/10.33590/emjoncol/10306640. https://doi.org/10.33590/emjoncol/10306640.
Keywords:
Cancer, modelling, mutation, webtool.

Each article is made available under the terms of the Creative Commons Attribution-Non Commercial 4.0 License.

INTRODUCTION

Next-generation sequencing techniques enable rapid detection of mutations present in a patient’s tumour. The next step is to define which mutations may contribute to the cell’s malignant transformation or to resistance to a treatment. In some cases, this information is available in databases. Very often, however, the variants detected are uncharacterised, and predicting their potential effect requires bioinformatics analysis, such as molecular modelling. This task can be complicated by a wide dispersion of relevant information. Based on the authors’ experience as molecular experts and active participants of the Molecular Tumor Board of the Réseau Romand d’Oncologie, they have developed Swiss-PO1 to help non-specialists in molecular modelling tackle questions regarding uncharacterised mutations detected in patients with cancer.

DISCUSSION

To meet growing demand and adapt to the new next-generation sequencing panels used in hospitals, the authors have updated the oncodriver gene list from 50 to >900, covering among others the FoundationOne®CDx gene panel (Foundation Medicine, Cambridge, Massachusetts, USA). Soon, a new structure-based scoring function to predict potentially damaging mutations in kinases will be added, one capitalising on known structures extracted from the Protein Data Bank (RCSB, Rutgers, The State University of New Jersey, Piscataway, USA). Among others, a prediction tool for BRAF mutation class will be introduced, and a new section dedicated to kinases will be proposed, with useful information concerning the kinase inhibitors, families, and mutations.2 The authors can also mention the addition of new 3D structures, including predicted models for domains not covered by experimental structures (PDB3 [RCSB], AlphaFold4 [DeepMind, London, UK], and SWISS-MODEL5 [ExPASy, Geneva, Switzerland]), leading to a total of >15,000 curated structures, >700 uncharacterised mutations manually analysed by molecular modelling, sequence alignments of human proteins, orthologous organisms, and paralogous to analyse amino acid conservation, etc.

CONCLUSION

The authors are convinced that the expert curation of data available in Swiss-PO, the additional tools to come, and its user-friendliness will make it a crucial web tool for the analysis of newly discovered and uncharacterised mutations, particularly during tumour boards

References
Krebs FS et al. A new tool to analyze the impact of mutations on protein three-dimensional structures for precision oncology. NPJ Precis Oncol. 2021;5(1):19. Krebs FS et al. Structure-based prediction of BRAF mutation classes using machine-learning approaches. Sci Rep. 2022;12(1):12528. Berman HM et al. The protein data bank. Nucleic Acids Res. 2000;28(1):235-42. Jumper J et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583-9. Waterhouse A et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018;46(W1):W296-303.

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