Title |
Development of a semi-conductor sequencing-based panel for genotyping of colon and lung cancer by the Onconetwork consortium
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Published in |
BMC Cancer, January 2015
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DOI | 10.1186/s12885-015-1015-5 |
Pubmed ID | |
Authors |
Bastiaan BJ Tops, Nicola Normanno, Henriette Kurth, Eliana Amato, Andrea Mafficini, Nora Rieber, Delphine Le Corre, Anna Maria Rachiglio, Anne Reiman, Orla Sheils, Christoph Noppen, Ludovic Lacroix, Ian A Cree, Aldo Scarpa, Marjolijn JL Ligtenberg, Pierre Laurent-Puig |
Abstract |
BackgroundThe number of predictive biomarkers that will be necessary to assess in clinical practice will increase with the availability of drugs that target specific molecular alterations. Therefore, diagnostic laboratories are confronted with new challenges: costs, turn-around-time and the amount of material required for testing will increase with the number of tests performed on a sample. Our consortium of European clinical research laboratories set out to test if semi-conductor sequencing provides a solution for these challenges.MethodsWe designed a multiplex PCR targeting 87 hotspot regions in 22 genes that are of clinical interest for lung and/or colorectal cancer. The gene-panel was tested by 7 different labs in their own clinical setting using ion-semiconductor sequencing.ResultsWe analyzed 155 samples containing 112 previously identified mutations in the KRAS, EGFR en BRAF genes. Only 1 sample failed analysis due to poor quality of the DNA. All other samples were correctly genotyped for the known mutations, even as low as 2%, but also revealed other mutations. Optimization of the primers used in the multiplex PCR resulted in a uniform coverage distribution over the amplicons that allows for efficient pooling of samples in a sequencing run.ConclusionsWe show that a semi-conductor based sequencing approach to stratify colon and lung cancer patients is feasible in a clinical setting. |
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Spain | 1 | 11% |
Italy | 1 | 11% |
Belgium | 1 | 11% |
Unknown | 4 | 44% |
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Scientists | 2 | 22% |
Mendeley readers
Geographical breakdown
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Japan | 1 | 1% |
Netherlands | 1 | 1% |
Germany | 1 | 1% |
Unknown | 78 | 95% |
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Researcher | 16 | 20% |
Other | 10 | 12% |
Student > Postgraduate | 7 | 9% |
Student > Bachelor | 7 | 9% |
Student > Master | 7 | 9% |
Other | 14 | 17% |
Unknown | 21 | 26% |
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Agricultural and Biological Sciences | 17 | 21% |
Medicine and Dentistry | 15 | 18% |
Engineering | 5 | 6% |
Computer Science | 2 | 2% |
Other | 3 | 4% |
Unknown | 21 | 26% |