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Science Papers Examine Reidentification Risks, Patient Similarity Network for Multiple Myeloma

The privacy risks related to reidentifying genomic data by linking it to matched images of human faces are substantially lower than many believe, according to a new report appearing in this week's Science Advances. Direct-to-consumer DNA testing has resulted in an abundance of genomic data for millions of people who may choose to share their sequenced genomes in the public domain through services such as OpenSNP, raising a number of privacy concerns. One particularly acute concern raised in recent literature the ability to link a genome to a photograph of an individual's face. To assess this risk, a group of Washington University in St. Louis and Vanderbilt University scientists developed a computational reidentification approach that they applied to the OpenSNP database and a new dataset of face images collected from an online setting and paired with a select subset of 126 genomes. They find that, for most individuals, the actual risk posed by linkage attacks to typical face images is substantially smaller than reported in prior investigations. Moreover, they show that a small amount of well-calibrated noise, imperceptible to humans, can be added to images to markedly reduce such risk. "The results of this investigation create an opportunity to create image filters that enable individuals to have better control over reidentification risk based on linkage," they write. GenomeWeb has more on this, here.

A multi-omics patient similarity network for multiple myeloma (MM), which can be used to classify co-occurring primary and secondary genomic aberrations and assess their clinical significance, is described in this week's Science Advances. Patient similarity networks, which connect patients with similar biological profiles, have emerged as a powerful tool to capture the complexity and diversity of clinical, genetic, and molecular information across a patient population. Aiming to leverage this approach for MM, a mostly incurable cancer with remarkable genetic heterogeneity, a group led by scientists from Icahn School of Medicine at Mount Sinai developed MM-PSN, the first multi-omics patient similarity network of myeloma. They use the resource to uncover 12 distinct subgroups defined by five data types generated from whole-genome sequencing, whole-exome sequencing, and RNA sequencing of 655 patients, as well as to identify novel patient subgroups defined by specific patterns of alterations, enriched for specific gene vulnerabilities, and associated with potential therapeutic options. "Our study confirms the advantages of using multiple features to dissect cancer heterogeneity and the ability of [patient similarity networks] to handle multiple data types to generate clear and interpretable disease models," they write. "The MM-PSN classification is a valuable and accessible resource that can be used in most clinical settings, because the features defining high risk can be easily detected by fluorescence in situ hybridization/cytogenetics." GenomeWeb also covers this, here.