安捷伦科技

We developed and benchmarked Exome Cancer Test v.2.0 (EXaCT-2), a novel whole-exome sequencing (WES) assay based on Agilent’s SureSelect hybrid-capture technology and expanded with custom probes targeting cancer-informative genomic regions. EXaCT-2 provides ~1,400 cancer genes with the depth of coverage typical of targeted panels, while achieving the genomic breadth to detect somatic copy number alterations (SCNAs), common cancer-related rearrangements, oncogenic viruses and B-cell receptor (BCR) clonotypes. Evaluated with a cancer patient cohort of 244 matched tumor/normal pairs and compared with clinically-validated results, EXaCT-2 achieved a mean sequencing depth of ~400× for critical cancer genes and ~100× for the remainder of the exome, with SCNA characterization showing improved boundary detection and overall segmentation. The assay demonstrated enhanced sensitivity for detecting sub-clonal, low-allele-frequency mutations missed by standard exome assays, such as mutations in GC-rich genes like KRAS. Analysis is performed by a modular, bespoke pipeline that leverages a workflow manager (Nextflow), in combination with containerized open-source tools. In addition to mutations and SCNAs, the pipeline reports common cancer rearrangements, hematologic oncogenic viruses, BCR clonotypes, and global molecular metrics, such as tumor mutational burden (TMB) and microsatellite instability (MSI). Collectively, these results establish EXaCT-2 as a comprehensive platform for integrated cancer genome profiling.

Abstract Major depressive disorder (MDD) is a severe psychiatric disorder that affects more than 350 million people worldwide, yet its biomolecular mechanisms are incompletely understood, and clinically applicable markers remain elusive. To shed new light on the underlying pathophysiology of MDD across multiple research disciplines, we first used a biochemical fingerprinting approach with human hair (the first 3 cm cut from the scalp) to identify changes in the total set of detectable metabolites and lipids (metabolipidomics) using quadrupole time-of-flight mass spectrometry (qToF-MS). In this study, we focused on endocannabinoid (ECB)-related lipid compounds and identified 7 candidate markers that differed between depressed and non-depressed female participants. Two phosphatidylinositols, namely PI 24:0 and PI 37:4, showed dose-dependent associations with the severity of depressive symptoms. Finally, to bridge hair findings with previously reported results in blood, we tested associations between changes in identified ECB-related compounds and parameters of mitochondrial respiratory activity in peripheral blood mononuclear cells. We found 17 significant associations, with the strongest effects for the lipids PI 24:0, MGDG-O 16:3, PG 12:0, and PI 37:4. Our approach not only identified novel associations between endocannabinoid (ECB)-related lipid dysregulation and impaired mitochondrial energy metabolism in MDD but also revealed ECB-related lipids as a possible surrogate marker of impaired bioenergetic metabolism in MDD, at least in immune cells. More research is needed to replicate these findings, ideally by testing reversibility in longitudinal intervention studies and by including both sexes in larger cohorts.

Liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS) is commonly used for the analysis of per- and polyfluoroalkyl substances (PFAS). Targeted approaches with LC-HRMS/MS often cover less than 30% of PFAS across various matrices and hence nontargeted strategies are necessary to enhance identification coverage. We expanded FluoroMatch Suite, a nontargeted PFAS data-processing software, to leverage full-scan (MS1) data for highly accurate formula prediction and Kaufmann analysis. Software features include Kaufmann analysis with isoline cutoffs determined using kernel density based on an EPA PFAS data set and an 11-step formula prediction algorithm. Application of the FluoroMatch Suite with the MS1 extension to AFFF contaminated soil revealed 179 PFAS-confirmed features. Kauffman 95% isoline cutoffs captured 94% of the confirmed PFAS and removed 96% of features assigned as likely non-PFAS. The PFAS-formula prediction introduced in this manuscript had a false positive rate of 26% and a false negative rate (no predicted formula) of 30%. Using a novel homologous series voting algorithm, where the predominant subclass from formula prediction were used to predict all formulas for the homologous series, we achieved a 0% false positive rate and 6% false negative rate in formula prediction. The novel nontargeted algorithms developed in this study proved to be highly accurate and by leveraging MS1 data enhances the capacity to identify unknown PFAS in complex environmental matrices.