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Tailored therapy aims to cure cancer patients effectively and safely, based on the complex interactions between patients' genomic features, disease pathology and drug metabolism. Thus, the continual increase in scientific literature drives the need for efficient methods of data mining to improve the extraction of useful information from texts based on patients' genomic features. An important application of text mining to tailored therapy in cancer encompasses the use of mutations and cancer fusion genes as moieties that change patients' cellular networks to develop cancer, and also affect drug metabolism. Fusion proteins, which are derived from the slippage of two parental genes, are produced in cancer by chromosomal aberrations and trans-splicing. Given that the two parental proteins for predicted fusion proteins are known, we used our previously developed method for identifying chimeric protein–protein interactions (ChiPPIs) associated with the fusion proteins. Here, we present a validation approach that receives fusion proteins of interest, predicts their cellular network alterations by ChiPPI and validates them by our new method, ProtFus, using an online literature search. This process resulted in a set of 358 fusion proteins and their corresponding protein interactions, as a training set for a Naïve Bayes classifier, to identify predicted fusion proteins that have reliable evidence in the literature and that were confirmed experimentally. Next, for a test group of 1817 fusion proteins, we were able to identify from the literature 2908 PPIs in total, across 18 cancer types. The described method, ProtFus, can be used for screening the literature to identify unique cases of fusion proteins and their PPIs, as means of studying alterations of protein networks in cancers. Availability: http://protfus.md.biu.ac.il/, Author summary Tailored therapy aims to cure cancer patients in a fully personal way. Thus, the continual increase in scientific information and, particularly, in published literature, drives the need for efficient methods of data mining to find unique personal genomic features and their connections. Fusion proteins, which are derived from the slippage of two parental genes or chromosomal translocations, are frequently drivers of cancers. We used our previously developed method for identifying chimeric protein–protein interactions (ChiPPIs) for multiple fusion proteins. In this paper, we present a validation approach, ProtFus, which receives fusion proteins of interest, predicts their cellular network alterations by ChiPPI and validates them by online literature searches. This process resulted in a set of 358 fusion proteins and their corresponding protein interactions, as training set. Next, for a test set of 1817 fusion proteins, we were able to identify 2908 previously published PPIs across 18 different cancer types. The described method can be used for screening the literature to identify unique cases of fusion proteins and their PPI networks, as means of studying alterations of protein networks for personalized approaches in cancers.

“Ethical Challenges of Big Data in Public Health” by Vayena et al. is a timely paper for a number of reasons, all of which are concisely embodied within the title. Ethical challenges are not something we consider often enough in this or most biomedical research journals. Yet, such challenges increasingly confront us as private citizens providing health data and as researchers using these data for public health and research purposes. Big data is an ill-defined term, yet there can be no denying that social media data (which are described in this article for use in digital disease detection [DDD]) cover a large and varied area, measured by both volume and velocity (rate of accumulation) and, as the authors describe, have the potential for important public health uses, but also have risks. And that, as described, is only one potential use of these data; there are many other uses barely introduced by the authors. In short the appeal of such data is huge, but so are the risks. The authors do an excellent job of characterizing the pluses and minuses of DDD. I encourage all of us to contemplate this article and the implications it masterfully elucidates. The underlying message from Vayena et al. is clear: use of these data far outpaces the governance and due diligence of the ethical considerations that need to be addressed. Quoting the authors, stigmatization of particular communities which may adversely affect individual members; and even the infringement of individual freedoms, such as the freedom of movement of an individual falsely identified as a carrier of a particular disease.… the same problems associated with current disease detection and public health approaches are likely to be more pronounced with the advent of social media and big data. In fact, DDD will only exacerbate the challenges of preventing misuse of the data. The authors are to be commended for raising the profile of this important issue and getting us thinking. Interestingly, the framework for addressing the issue has been around for a long time. It is embodied in Article 27 of the 1948 Universal Declaration of Human Rights, which declares it a human right “to share in scientific advancement and its benefits” (including to freely engage in responsible scientific inquiry) and at the same time have “the protection of the moral and material interests resulting from any scientific production of which [a person] is the author” [1]. More in a research context, this has recently been considered further by the global community and has been embodied in the Global Alliance for Genomic Health (GA4GH)’s work on a framework for responsible sharing of genomic and health-related data [2]. Governments and organizations worldwide have their own more in-depth versions. In the United States and within the National Institutes of Health (NIH), there is an update to the Common Rule underway [3], and a more extensive genomics data sharing plan was recently announced [4]. Further measures are under consideration for protecting the rights of the individual whilst ensuring that maximum access to patient data for appropriate research purposes is maintained to accelerate much needed cures and improvements to health care in general. As illustrated in this article, whether for purposes of research and/or public health, social media data require that further attention be paid. The authors are to be commended for raising the profile of these ethical issues.