Cancer is a highly complex disease triggered by the accumulation of so-called driver genomic alterations that empower cells with tumorigenic capabilities. The genomic profiling of each patient’s tumor represents powerful weaponry in our current arsenal of novel approaches, technologies and techniques aimed at rendering anti-cancer therapies more precise. Importantly, better establishing the role and relevance of these genomic drivers — particularly those inducing oncogenic addiction, represents the Achilles’ heel in matching targeted therapies to these alterations.
Despite the tremendous and undeniable progress marked in this direction to-date, the tailoring of treatments according to the genomic profile of each patient’s tumor is currently not happening as effectively or as rapidly as it should. The two major obstacles that are hampering progress are firstly, elucidating how each of these alterations fuels cancer growth and second, the vast amount of data being generated through preclinical and clinical studies, is scattered and largely remains unharnessed. While there are a number of resources aimed at reversing this tremendous loss of opportunity, unfortunately these databases incorporate diverse approaches and do not generally constitute optimal frameworks through which to pair data to alterations observed in individual patient tumors.
Recently published online and open access in the journal Genome Medicine*, co-author Rodrigo Dienstmann, Principal Investigator of VHIO’s Oncology Data Science Group (ODysSey), in collaboration with the Biomedical Genomics Group of the Institute for Research in Biomedicine, IRB – Barcelona, led by first author and ICREA Professor Nuria López-Bigas, describe the Cancer Genome Interpreter (CGI) platform. This versatile and freely accessible resource has been designed to advance precision medicine in oncology by better determining which alterations identified in tumors might be driver events towards better targeting their respective genomic vulnerabilities.
Rodrigo Dienstmann has pioneered the design of several open access online tools to help guide and better inform physicians and investigators in the clinical interpretation of genomic data. The CGI represents an important step in providing the scientific community with access to this data in a more structured, consolidated, and user-friendly way. “The implementation of large-scale molecular profiling and the standardized reporting of predictive biomarkers are fundamental in our ambitions to render precision cancer medicine more precise,” explains Rodrigo.
Obtaining the full genomic landscape of each tumor is therefore critical to more accurately match anti-cancer therapies to their individual molecular make-up. While there is a tremendous amount of genomic data available, sorting the meaningful and clinically significant data from that with an unclear translational value represents an important challenge.
“Many mutations in known driver genes have uncertain functional importance, exposing a wide gap in knowledge concerning their potential in developing and potentiating novel anti-cancer therapies. Identifying these rare mutations and predicting their functional significance could better guide treatment decisions and facilitate access to targeted agents in early clinical trials,” he observes. Novel tools and platforms must therefore be designed and developed following common, global criteria and a unified format so that investigators and oncologists can access and exchange these insights in a user-friendly manner.
Multiple undertakings across the international scientific community currently aim to better harness, store and more effectively share and exchange existing findings. As an example, launched back in 2015, MedBioinformatics is a project supported by Horizon 2020’s European Union funding for Research and Innovation. Incorporating 13 groups from nine renowned research entities, Rodrigo’s team has developed integrative bioinformatics tools to analyze the huge amount of data and knowledge generated and the project’s Cancer Biomarkers database is publicly available.
The first database created by VHIO researchers defined the indispensable variables for interpreting genomic data and the standardized terminology through which to classify the targetability of molecular mutations linked to PubMed identifiers. In 2015, Rodrigo devised and published the first open access database which was reported in Cancer Discovery** – the Gene-Drug Knowledge Database and Clinical Targetability Index (GDKD), and Clinical Interpretation of Variants in Cancer (CIVic) validation study, also co-authored by Rodrigo, published online and open access in Nature Genetics***.
“Each database and portal has its advantages and limitations and can therefore be considered as complementary. For example, unlike CIViC, CGI can review thousands of mutations simultaneously, even complete exomes, and prioritise which are to be analysed and be given more attention to when it comes to defining the targeted therapies. On the other hand, while CIViC only enables the one by one interpretation of mutations, the data is presented in extensive detail. Both tools are easy to use and extremely intuitive for clinical oncologists,” he concludes.
The significance and benefits of being able to effectively and publicly share these insights are invaluable. These databases and tools, along with others, have been discussed in an open access Perspective piece in Cell**** co-authored by José Baselga, Physician-in-Chief, Memorial Sloan Kettering Cancer Center, MSKCC – New York, and President of VHIO’s Internal Scientific Committee. These different solutions aimed at better storing and exchanging this trove of knowledge have translated into both the design of new clinical trials, different therapeutic approaches, and are significantly impacting precision cancer medicine.
* David Tamborero, Carlota Rubio-Perez, Jordi Deu-Pons, Michael P. Schroeder, Ana Vivancos, Ana Rovira, Ignasi Tusquets, Joan Albanell, Jordi Rodon, Josep Tabernero, Carmen de Torres, Rodrigo Dienstmann, Abel Gonzalez-Perez and Nuria Lopez-Bigas. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Medicine 201810:25.
** Dienstmann R, Jang IS, Bot B, Friend S, Guinney J. Database of genomic biomarkers for cancer drugs and clinical targetability in solid tumors. Cancer Discov. 2015 Feb;5(2):118-23
*** Griffith M, Spies NC, Krysiak K, McMichael JF, Coffman AC, Danos AM, Ainscough BJ, Ramirez CA, Rieke DT, Kujan L, Barnell EK, Wagner AH, Skidmore ZL, Wollam A, Liu CJ, Jones MR, Bilski RL, Lesurf R, Feng YY, Shah NM, Bonakdar M, Trani L, Matlock M, Ramu A, Campbell KM, Spies GC, Graubert AP, Gangavarapu K, Eldred JM, Larson DE, Walker JR, Good BM, Wu C, Su AI, Dienstmann R, Margolin AA, Tamborero D, Lopez-Bigas N, Jones SJ, Bose R, Spencer DH, Wartman LD, Wilson RK, Mardis ER, Griffith OL.CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet. 2017 Jan 31;49(2):170-174.
**** Hyman DM, Taylor BS, Baselga J. Implementing Genome-Driven Oncology. Cell. 2017 Feb 9;168(4):584-599.