A research published in Cell has shown that patient-derived cancer cell lines harbour most of the same genetic changes found in patients' tumours, and could be used to learn how tumours are likely to respond to new drugs, increasing the success rate for developing new personalised cancer treatments.

David Tamborero and Núria López-Bigas, members of the Biomedical Genomics research group of the Research Programme on Biomedical Informatics (GRIB), joint programme of UPF and Hospital del Mar Research Institute (IMIM) have participated on this international study led by scientists from the Wellcome Trust Sanger Institute, the European Bioinformatics Institute (EMBL-EBI) and the Netherlands Cancer Institute (NKI), discovering a strong link between many mutations in patient cancer samples, and the sensitivity to particular drugs. This could advance personalised cancer medicine by leading to results that help doctors predict the best available drugs, or the most suitable clinical trials for each individual patient.

In the first systematic, large-scale study to combine molecular data from patients, laboratory cancer cell lines and drug sensitivity, David Tamborero says: "one of the main difficulties of the study was to interpret the large amount of genomic alterations observed in the tumor cells', and continue: "our task was to identify the mutations that are important for the development of each of the cancers in order to associate them with the observed drug responses".

The researchers made two significant discoveries. Firstly, that the majority of molecular abnormalities found in patient's cancers are also found in cancer cells in the laboratory. This means that cell lines are indeed useful models to identify which drugs would work best for patients. Secondly, many of the molecular abnormalities detected in the thousands of patient cancer samples can, both individually but also in combination, have a strong effect on whether a particular drug affects a cancer cell's survival. The results suggest cancer cell lines could be better exploited to learn which drugs offer the most effective treatment to which patients.

Article reference: Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, Aben N, Gonçalves E, Barthorpe S, Lightfoot H, Cokelaer T,  Greninger P, van Dyk E,  Chang H, de Silva H, Heyn H, Deng X, Egan RK, Liu Q, Mironenko T, Mitropoulos X, Richardson L, Wang J, Zhang T, Moran S, Sayols S, Soleimani M, Tamborero D, Lopez-Bigas N, Ross-Macdonald P, Esteller M, Gray NS, Haber DA, Stratton MR, Benes CH, Wessels LFA, Saez-Rodriguez J, McDermott U, Garnett MJ. A Landscape of Pharmacogenomic Interactions in Cancer. Cell, 2016;DOI 10.1016/j.cell.2016.06.017