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Edsjö A, Russnes HG, Lehtiö J, Tamborero D, Hovig E, Stenzinger A, Rosenquist R. High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond. J Intern Med 2024; 295:785-803. [PMID: 38698538 DOI: 10.1111/joim.13785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.
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Affiliation(s)
- Anders Edsjö
- Department of Clinical Genetics, Pathology and Molecular Diagnostics, Office for Medical Services, Region Skåne, Lund, Sweden
- Division of Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Hege G Russnes
- Department of Pathology, Oslo University Hospital, Oslo, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Janne Lehtiö
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
- Cancer genomics and proteomics, Karolinska University Hospital, Solna, Sweden
| | - David Tamborero
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Eivind Hovig
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Albrecht Stenzinger
- Institute of Pathology, Division of Molecular Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Richard Rosenquist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Clinical Genetics and Genomics, Karolinska University Hospital, Solna, Sweden
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Crawford J, Chikina M, Greene CS. Optimizer's dilemma: optimization strongly influences model selection in transcriptomic prediction. BIOINFORMATICS ADVANCES 2024; 4:vbae004. [PMID: 38282973 PMCID: PMC10822580 DOI: 10.1093/bioadv/vbae004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/09/2023] [Accepted: 01/13/2024] [Indexed: 01/30/2024]
Abstract
Motivation Most models can be fit to data using various optimization approaches. While model choice is frequently reported in machine-learning-based research, optimizers are not often noted. We applied two different implementations of LASSO logistic regression implemented in Python's scikit-learn package, using two different optimization approaches (coordinate descent, implemented in the liblinear library, and stochastic gradient descent, or SGD), to predict mutation status and gene essentiality from gene expression across a variety of pan-cancer driver genes. For varying levels of regularization, we compared performance and model sparsity between optimizers. Results After model selection and tuning, we found that liblinear and SGD tended to perform comparably. liblinear models required more extensive tuning of regularization strength, performing best for high model sparsities (more nonzero coefficients), but did not require selection of a learning rate parameter. SGD models required tuning of the learning rate to perform well, but generally performed more robustly across different model sparsities as regularization strength decreased. Given these tradeoffs, we believe that the choice of optimizers should be clearly reported as a part of the model selection and validation process, to allow readers and reviewers to better understand the context in which results have been generated. Availability and implementation The code used to carry out the analyses in this study is available at https://github.com/greenelab/pancancer-evaluation/tree/master/01_stratified_classification. Performance/regularization strength curves for all genes in the Vogelstein et al. (2013) dataset are available at https://doi.org/10.6084/m9.figshare.22728644.
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Affiliation(s)
- Jake Crawford
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Maria Chikina
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, United States
| | - Casey S Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045, United States
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO 80045, United States
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