Determining Cost-Optimal Next-Generation Sequencing Panels for Rare Disease and Pharmacogenomics Testing.

BACKGROUND: Multi-gene panel sequencing using next-generation sequencing (NGS) methods is a key tool for genomic medicine. However, with an estimated 140 000 genomic tests available, current system inefficiencies result in high genetic-testing costs. Reduced testing costs are needed to expand the availability of genomic medicine. One solution to improve efficiency and lower costs is to calculate the most cost-effective set of panels for a typical pattern of test requests. METHODS: We compiled rare diseases, associated genes, point prevalence, and test-order frequencies from a representative laboratory. We then modeled the costs of the relevant steps in the NGS process in detail. Using a simulated annealing-based optimization procedure, we determined panel sets that were more cost-optimal than whole exome sequencing (WES) or clinical exome sequencing (CES). Finally, we repeated this methodology to cost-optimize pharmacogenomics (PGx) testing. RESULTS: For rare disease testing, we show that an optimal choice of 4-6 panels, uniquely covering genes that comprise 95% of the total prevalence of monogenic diseases, saves $257-304 per sample compared with WES, and $66-135 per sample compared with CES. For PGx, we show that the optimal multipanel solution saves $6-7 (27%-40%) over a single panel covering all relevant gene-drug associations. CONCLUSIONS: Laboratories can reduce costs using the proposed method to obtain and run a cost-optimal set of panels for specific test requests. In addition, payers can use this method to inform reimbursement policy.

Ultrasensitive detection of tumor-specific mutations in saliva of patients with oral cavity squamous cell carcinoma.

BACKGROUND: Oral cavity squamous cell carcinoma (OCSCC) is the most common head and neck malignancy. Although the survival rate of patients with advanced-stage disease remains approximately 20% to 60%, when detected at an early stage, the survival rate approaches 80%, posing a pressing need for a well validated profiling method to assess patients who have a high risk of developing OCSCC. Tumor DNA detection in saliva may provide a robust biomarker platform that overcomes the limitations of current diagnostic tests. However, there is no routine saliva-based screening method for patients with OCSCC. METHODS: The authors designed a custom next-generation sequencing panel with unique molecular identifiers that covers coding regions of 7 frequently mutated genes in OCSCC and applied it on DNA extracted from 121 treatment-naive OCSCC tumors and matched preoperative saliva specimens. RESULTS: By using stringent variant-calling criteria, mutations were detected in 106 tumors, consistent with a predicted detection rate ≥88%. Moreover, mutations identified in primary malignancies were also detected in 93% of saliva samples. To ensure that variants are not errors resulting in false-positive calls, a multistep analytical validation of this approach was performed: 1) re-sequencing of 46 saliva samples confirmed 88% of somatic variants; 2) no functionally relevant mutations were detected in saliva samples from 11 healthy individuals without a history of tobacco or alcohol; and 3) using a panel of 7 synthetic loci across 8 sequencing runs, it was confirmed that the platform developed is reproducible and provides sensitivity on par with droplet digital polymerase chain reaction. CONCLUSIONS: The current data highlight the feasibility of somatic mutation identification in driver genes in saliva collected at the time of OCSCC diagnosis.

X-ray, Cryo-EM, and computationally predicted protein structures used in integrative modeling of HIV Env glycoprotein gp120 in complex with CD4 and 17b.

We present the data used for an integrative approach to computational modeling of proteins with large variable domains, specifically applied in this context to model HIV Env glycoprotein gp120 in its CD4 and 17b bound state. The initial data involved X-ray structure PDBID:1GC1 and electron microscopy image EMD:5020. Other existing X-ray structures were used as controls to validate and hierarchically refine partial and complete computational models. A summary of the experiment protocol and data was published (Rasheed et al., 2015) [26], along with detailed analysis of the final model (PDBID:3J70) and its implications.

PF2fit: Polar Fast Fourier Matched Alignment of Atomistic Structures with 3D Electron Microscopy Maps.

There continue to be increasing occurrences of both atomistic structure models in the PDB (possibly reconstructed from X-ray diffraction or NMR data), and 3D reconstructed cryo-electron microscopy (3D EM) maps (albeit at coarser resolution) of the same or homologous molecule or molecular assembly, deposited in the EMDB. To obtain the best possible structural model of the molecule at the best achievable resolution, and without any missing gaps, one typically aligns (match and fits) the atomistic structure model with the 3D EM map. We discuss a new algorithm and generalized framework, named PF(2) fit (Polar Fast Fourier Fitting) for the best possible structural alignment of atomistic structures with 3D EM. While PF(2) fit enables only a rigid, six dimensional (6D) alignment method, it augments prior work on 6D X-ray structure and 3D EM alignment in multiple ways: Scoring. PF(2) fit includes a new scoring scheme that, in addition to rewarding overlaps between the volumes occupied by the atomistic structure and 3D EM map, rewards overlaps between the volumes complementary to them. We quantitatively demonstrate how this new complementary scoring scheme improves upon existing approaches. PF(2) fit also includes two scoring functions, the non-uniform exterior penalty and the skeleton-secondary structure score, and implements the scattering potential score as an alternative to traditional Gaussian blurring. Search. PF(2) fit utilizes a fast polar Fourier search scheme, whose main advantage is the ability to search over uniformly and adaptively sampled subsets of the space of rigid-body motions. PF(2) fit also implements a new reranking search and scoring methodology that considerably improves alignment metrics in results obtained from the initial search.

Computational Refinement and Validation Protocol for Proteins with Large Variable Regions Applied to Model HIV Env Spike in CD4 and 17b Bound State.

Envelope glycoprotein gp120 of HIV-1 possesses several variable regions; their precise structure has been difficult to establish. We report a new model of gp120, in complex with antibodies CD4 and 17b, complete with its variable regions. The model was produced by a computational protocol that uses cryo-electron microscopy (EM) maps, atomic-resolution structures of the core, and information on binding interactions. Our model has excellent fit with EMD: 5020, is stereochemically and energetically favorable, and has the expected binding interfaces. Comparison of the ternary arrangement of the loops in this model with those bound to PGT122 and PGV04 suggested a possible motion of the V1V2 away from the CCR5 binding site and toward CD4. Our study also revealed that the CD4-bound state of the V1V2 loop is not optimal for gp120 bound with several neutralizing antibodies.

NONUNIFORM FOURIER TRANSFORMS FOR RIGID-BODY AND MULTI-DIMENSIONAL ROTATIONAL CORRELATIONS.

The task of evaluating correlations is central to computational structural biology. The rigid-body correlation problem seeks the rigid-body transformation (R, t), R ∈ SO(3), t ∈ ℝ3 that maximizes the correlation between a pair of input scalar-valued functions representing molecular structures. Exhaustive solutions to the rigid-body correlation problem take advantage of the fast Fourier transform to achieve a speedup either with respect to the sought translation or rotation. We present PFcorr, a new exhaustive solution, based on the non-equispaced SO(3) Fourier transform, to the rigid-body correlation problem; unlike previous solutions, ours achieves a combination of translational and rotational speedups without requiring equispaced grids. PFcorr can be straightforwardly applied to a variety of problems in protein structure prediction and refinement that involve correlations under rigid-body motions of the protein. Additionally, we show how it applies, along with an appropriate flexibility model, to analogs of the above problems in which the flexibility of the protein is relevant.

Macromolecular structure modeling from 3D EM using VolRover 2.0.

We review tools for structure identification and model-based refinement from three-dimensional electron microscopy implemented in our in-house software package, VOLROVER 2.0. For viral density maps with icosahedral symmetry, we segment the capsid, polymeric, and monomeric subunits using techniques based on automatic symmetry detection and multidomain fast marching. For large biomolecules without symmetry information, we again use our multidomain fast-marching method with manual or fit-based multiseeding to segment meaningful substructures. In either case, we subject the resulting segmented subunit to secondary structure detection when the EM resolution is sufficiently high, and rigid-body structure fitting when the corresponding X-ray structure is available. Secondary structure elements are identified by three techniques: our earlier volume-based and boundary-based skeletonization methods as well as a new method, currently in development, based on solving the grassfire flow equation. For rigid-body fitting, we adapt our earlier fast Fourier-based correlation scheme F2Dock. Our reported segmentation, secondary structure elements identification, and rigid-body fitting techniques, implemented in VOLROVER 2.0 are applied to the PSB 2011 cryo-EM modeling challenge data, and our results are briefly compared to similar results submitted from other research groups. The comparisons show that our techniques are equally capable of segmenting relatively accurate subunits from a viral or protein assembly, and that high segmentation quality leads in turn to higher-quality results of secondary structure elements identification and correlation-based rigid-body fitting. © 2012 Wiley Periodicals, Inc. Biopolymers 97: 709-731, 2012.