The GA4GH Variation Representation Specification: A computational framework for variation representation and federated identification

Maximizing the personal, public, research, and clinical value of genomic information will require the reliable exchange of genetic variation data. We report here the Variation Representation Specification (VRS, pronounced verse ), an extensible framework for the computable representation of variation that complements contemporary human-readable and flat file standards for genomic variation representation. VRS provides semantically precise representations of variation and leverages this design to enable federated identification of biomolecular variation with globally consistent and unique computed identifiers. The VRS framework includes a terminology and information model, machine-readable schema, data sharing conventions, and a reference implementation, each of which is intended to be broadly useful and freely available for community use. VRS was developed by a partnership among national information resource providers, public initiatives, and diagnostic testing laboratories under the auspices of the Global Alliance for Genomics and Health (GA4GH)

Estimation of intrafamilial DNA contamination in family trio genome sequencing using deviation from Mendelian inheritance

With the increasing number of sequencing projects involving families, quality control tools optimized for family genome sequencing are needed. However, accurately quantifying contamination in a DNA mixture is particularly difficult when genetically related family members are the sources. We developed TrioMix, a maximum likelihood estimation (MLE) framework based on Mendel\u27s law of inheritance, to quantify DNA mixture between family members in genome sequencing data of parent-offspring trios. TrioMix can accurately deconvolute any intrafamilial DNA contamination, including parent-offspring, sibling-sibling, parent-parent, and even multiple familial sources. In addition, TrioMix can be applied to detect genomic abnormalities that deviate from Mendelian inheritance patterns, such as uniparental disomy (UPD) and chimerism. A genome-wide depth and variant allele frequency plot generated by TrioMix facilitates tracing the origin of Mendelian inheritance deviations. We showed that TrioMix could accurately deconvolute genomes in both simulated and real data sets

Checkpoint blockade-induced CD8+ T cell differentiation in head and neck cancer responders

BACKGROUND: Immune checkpoint blockade (ICB) response in recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) is limited to 15%-20% of patients and underpinnings of resistance remain undefined. METHODS: Starting with an anti-PD1 sensitive murine HNSCC cell line, we generated an isogenic anti-PD1 resistant model. Mass cytometry was used to delineate tumor microenvironments of both sensitive parental murine oral carcinoma (MOC1) and resistant MOC1esc1 tumors. To examine heterogeneity and clonal dynamics of tumor infiltrating lymphocytes (TILs), we applied paired single-cell RNA and TCR sequencing in three HNSCC models. RESULTS: Anti-PD1 resistant MOC1esc1 line displayed a conserved cell intrinsic immune evasion signature. Immunoprofiling showed distinct baseline tumor microenvironments of MOC1 and MOC1esc1, as well as the remodeling of immune compartments on ICB in MOC1esc1 tumors. Single cell sequencing analysis identified several CD8 +TIL subsets including Tcf7 +Pd1- (naïve/memory-like), Tcf7 +Pd1+ (progenitor), and Tcf7-Pd1+ (differentiated effector). Mapping TCR shared fractions identified that successful anti-PD1 or anti-CTLA4 therapy-induced higher post-treatment T cell lineage transitions. CONCLUSIONS: These data highlight critical aspects of CD8 +TIL heterogeneity and differentiation and suggest facilitation of CD8 +TIL differentiation as a strategy to improve HNSCC ICB response

The Cure: Making a game of gene selection for breast cancer survival prediction

Motivation: Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but these signatures leave much to be desired in terms of accuracy, reproducibility and biological interpretability. Methods that take advantage of structured prior knowledge (e.g. protein interaction networks) show promise in helping to define better signatures but most knowledge remains unstructured. Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes previously unheard of. Here, we developed and evaluated a game called The Cure on the task of gene selection for breast cancer survival prediction. Our central hypothesis was that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured from game players. We envisioned capturing knowledge both from the players prior experience and from their ability to interpret text related to candidate genes presented to them in the context of the game. Results: Between its launch in Sept. 2012 and Sept. 2013, The Cure attracted more than 1,000 registered players who collectively played nearly 10,000 games. Gene sets assembled through aggregation of the collected data clearly demonstrated the accumulation of relevant expert knowledge. In terms of predictive accuracy, these gene sets provided comparable performance to gene sets generated using other methods including those used in commercial tests. The Cure is available at http://genegames.org/cure