The CRAPome: a contaminant repository for affinity purification-mass spectrometry data.

Affinity purification coupled with mass spectrometry (AP-MS) is a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. Fortunately, negative controls are largely bait independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the contaminant repository for affinity purification (the CRAPome) and describe its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/.

Personalized Cancer Monitoring Assay for the Detection of ctDNA in Patients with Solid Tumors.

BACKGROUND: Highly sensitive molecular assays have been developed to detect plasma-based circulating tumor DNA (ctDNA), and emerging evidence suggests their clinical utility for monitoring minimal residual disease and recurrent disease, providing prognostic information, and monitoring therapy responses in patients with solid tumors. The Invitae Personalized Cancer Monitoring™ assay uses a patient-specific, tumor-informed variant signature identified through whole exome sequencing to detect ctDNA in peripheral blood of patients with solid tumors. METHODS: The assay's tumor whole exome sequencing and ctDNA detection components were analytically validated using 250 unique human specimens and nine commercial reference samples that generated 1349 whole exome sequencing and cell-free DNA (cfDNA)-derived libraries. A comparison of tumor and germline whole exome sequencing was used to identify patient-specific tumor variant signatures and generate patient-specific panels, followed by targeted next-generation sequencing of plasma-derived cfDNA using the patient-specific panels with anchored multiplex polymerase chain reaction chemistry leveraging unique molecular identifiers. RESULTS: Whole exome sequencing resulted in overall sensitivity of 99.8% and specificity of > 99.9%. Patient-specific panels were successfully designed for all 63 samples (100%) with ≥ 20% tumor content and 24 (80%) of 30 samples with ≥ 10% tumor content. Limit of blank studies using 30 histologically normal, formalin-fixed paraffin-embedded specimens resulted in 100% expected panel design failure. The ctDNA detection component demonstrated specificity of > 99.9% and sensitivity of 96.3% for a combination of 10 ng of cfDNA input, 0.008% allele frequency, 50 variants on the patient-specific panels, and a baseline threshold. Limit of detection ranged from 0.008% allele frequency when utilizing 60 ng of cfDNA input with 18-50 variants in the patient-specific panels (> 99.9% sensitivity) with a baseline threshold, to 0.05% allele frequency when using 10 ng of cfDNA input with an 18-variant panel with a monitoring threshold (> 99.9% sensitivity). CONCLUSIONS: The Invitae Personalized Cancer Monitoring assay, featuring a flexible patient-specific panel design with 18-50 variants, demonstrated high sensitivity and specificity for detecting ctDNA at variant allele frequencies as low as 0.008%. This assay may support patient prognostic stratification, provide real-time data on therapy responses, and enable early detection of residual/recurrent disease.

From 2D Ultrasound to Patient-Specific 3D Surface Models for Interventional Guidance.

Ultrasound exam output of large organs like liver has traditionally been limited to still images or 2D cine loops of key structures, without 3D context. Having 3D context for follow up studies makes ultrasound scanning much easier and for interventional applications such as biopsy. 3D context will reduce wrong sample selection thereby increasing patient comfort. As of today, there is no existing solution which provides 3D anatomical context to users during scanning for large organs like liver. Even for routine measurements like liver volume, patients have to undergo CT or MR scan. In this paper, we propose a novel approach to build-patient specific 3D anatomical surface models from B-mode ultrasound images and tracking information from position sensors. The complexity of the problem stems from the fact that liver boundaries are often not very clear in ultrasound images, in addition to large variability in liver size and shape across patients. Our work uses state-of-the-art deep learning algorithms to detect surface landmarks of liver followed by registering a geometric model to surface point cloud to build patient specific 3D liver model. Further, the developed models will be used to guide users to right lesion locations during the interventional procedure. Our proposed semi -automated workflow ensures the accuracy of the developed models are within acceptable limits for the targeted problem.

Drosophila ELMO/CED-12 interacts with Myoblast city to direct myoblast fusion and ommatidial organization.

Members of the CDM (CED-5, Dock180, Myoblast city) superfamily of guanine nucleotide exchange factors function in diverse processes that include cell migration and myoblast fusion. Previous studies have shown that the SH3, DHR1 and DHR2 domains of Myoblast city (MBC) are essential for it to direct myoblast fusion in the Drosophila embryo, while the conserved DCrk-binding proline rich region is expendable. Herein, we describe the isolation of Drosophila ELMO/CED-12, an approximately 82 kDa protein with a pleckstrin homology (PH) and proline-rich domain, by interaction with the MBC SH3 domain. Mass spectrometry confirms the presence of an MBC/ELMO complex within the embryonic musculature at the time of myoblast fusion and embryos maternally and/or zygotically mutant for elmo exhibit defects in myoblast fusion. Overexpression of MBC and ELMO in the embryonic mesoderm causes defects in myoblast fusion reminiscent of those seen with constitutively-activated Rac1, supporting the previous finding that both the absence of and an excess of Rac activity are deleterious to myoblast fusion. Overexpression of MBC and ELMO/CED-12 in the eye causes perturbations in ommatidial organization that are suppressed by mutations in Rac1 and Rac2, demonstrating genetically that MBC and ELMO/CED-12 cooperate to activate these small GTPases in Drosophila.