Summary We analyzed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing and reverse phase protein arrays. Our ability to integrate information across platforms provided key insights into previously-defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at > 10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the Luminal A subtype. We identified two novel protein expression-defined subgroups, possibly contributed by stromal/microenvironmental elements, and integrated analyses identified specific signaling pathways dominant in each molecular subtype including a HER2/p-HER2/HER1/p-HER1 signature within the HER2-Enriched expression subtype. Comparison of Basal-like breast tumors with high-grade Serous Ovarian tumors showed many molecular commonalities, suggesting a related etiology and similar therapeutic opportunities. The biologic finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biologic subtypes of breast cancer.

Scanner and reconstruction parameters can significantly affect SUV measurements. When using serial SUV measurements to assess early response to therapy, imaging should be performed on the same scanner using the same image acquisition and reconstruction protocols. In addition, attention to detail is required for accurate determination of the administered radiopharmaceutical dose.

Purpose Next-generation sequencing (NGS) has transformed genetic research and is poised to revolutionize clinical diagnosis. However, the vast amount of data and inevitable discovery of incidental findings require novel analytic approaches. We therefore implemented for the first time a strategy that utilizes an a priori structured framework and a conservative threshold for selecting clinically relevant incidental findings. Methods We categorized 2016 genes linked with Mendelian diseases into “bins” based on clinical utility and validity, and used a computational algorithm to analyze 80 whole genome sequences in order to explore the use of such an approach in a simulated real-world setting. Results The algorithm effectively reduced the number of variants requiring human review and identified incidental variants with likely clinical relevance. Incorporation of the Human Gene Mutation Database (HGMD) improved the yield for missense mutations, but also revealed that a substantial proportion of purported disease-causing mutations were misleading. Conclusions This approach is adaptable to any clinically relevant bin structure, scalable to the demands of a clinical laboratory workflow, and flexible with respect to advances in genomics. We anticipate that application of this strategy will facilitate pre-test informed consent, laboratory analysis, and post-test return of results in a clinical context.

PurposeUtilization of sequencing to screen the general population for preventable monogenic conditions is receiving substantial attention due to its potential to decrease morbidity and mortality. However, the selection of which variants to return is a serious implementation challenge. Procedures must be investigated to ensure optimal test characteristics and avoidance of harm from false positive test results.MethodsWe scanned exome sequences from 478 well-phenotyped individuals for potentially pathogenic variants in 17 genes representing 11 conditions that are among the most medically actionable Mendelian disorders in adults. We developed 5 variant selection algorithms with increasing sensitivity and measured their specificity in these 17 genes.ResultsVariant selection algorithms with increasing sensitivity exhibited decreased specificity, and performance was highly dependent on the genes analyzed. The most sensitive algorithm ranged from 88.8% to 99.6% specificity among the 17 genes.ConclusionFor very low prevalence conditions, small reductions in specificity greatly increase false positives. This inescapable test characteristic governs the predictive value of genomic sequencing in the general population. To address this issue, test performance must be evaluated systematically for each condition so that the false negatives and false positives can be tailored for optimal outcomes, depending on the downstream clinical consequences.

ObjectiveTo evaluate the diagnostic yield and workflow of genome-scale sequencing in patients with neuromuscular disorders (NMDs).MethodsWe performed exome sequencing in 93 undiagnosed patients with various NMDs for whom a molecular diagnosis was not yet established. Variants on both targeted and broad diagnostic gene lists were identified. Prior diagnostic tests were extracted from the patient's medical record to evaluate the use of exome sequencing in the context of their prior diagnostic workup.ResultsThe overall diagnostic yield of exome sequencing in our cohort was 12.9%, with one or more pathogenic or likely pathogenic variants identified in a causative gene associated with the patient's disorder. Targeted gene lists had the same diagnostic yield as a broad NMD gene list in patients with clear neuropathy or myopathy phenotypes, but evaluation of a broader set of disease genes was needed for patients with complex NMD phenotypes. Most patients with NMD had undergone prior testing, but only 10/16 (63%) of these procedures, such as muscle biopsy, were informative in pointing to a final molecular diagnosis.ConclusionsGenome-scale sequencing or analysis of a panel of relevant genes used early in the evaluation of patients with NMDs can provide or clarify a diagnosis and minimize invasive testing in many cases.