HER2 copy number determination in breast cancer using the highly sensitive droplet digital PCR method

Human epidermal growth factor receptor 2 (HER)-positive breast cancer (BC) is characterized by an aggressive clinical course. In the case of HER2 overexpression/amplification, patients benefit from HER2-targeting therapies. Standardized diagnostic HER2 assessment includes immunohistochemistry (IHC) and/or in situ hybridization (ISH). The aim of this study was to compare this "gold standard" with the Droplet Digital™ polymerase chain reaction (ddPCR), a method that allows sensitive and precise detection of copy number variations (CNV) in FFPE (formalin-fixed, paraffin-embedded) DNA samples. Partitioning of the PCR reaction into 20,000 droplets enables a precise quantitative "CN" discrimination also in heterogeneous samples. FFPE breast cancer samples (n = 170) with routinely assessed HER2 status by IHC/ISH were retrospectively analyzed using the ddPCR CNV ERBB2 assay. Comparison of HER2 status assessment by the two methods revealed concordant results in 92.9% (158/170) of the cases. Discrepant cases were verified and interpreted. For ddPCR, a cut off value of 3 HER2 copies was set to distinguish between HER2-negative and HER2-positive BC. Results obtained with the ddPCR CNV ERBB2 assay were consistent and reproducible, and serial dilutions demonstrated a high stability and sensitivity of the method. The ddPCR CNV ERBB2 assay may be a specific and convenient tool to quantify HER2 copy numbers in BC samples. In our study, this method showed high reproducibility in accuracy of HER2 assessment compared to IHC/ISH analysis.

Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study

Background

It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking.

Objectives

We aimed to characterize neurooncological patients' network topology by analyzing glial brain tumors (GBTs) and brain metastases (BMs) with respect to the presence of structural epilepsy.

Methods

Network topology derived from resting state magnetoencephalography was compared between (1) patients and controls, (2) GBTs and BMs, and (3) patients with (PSEs) and without structural epilepsy (PNSEs). Eligible patients were investigated from February 2019 to March 2021. We calculated whole brain (WB) connectivity in six frequency bands, network topological parameters (node degree, average shortest path length, local clustering coefficient) and performed a stratification, where differences in power were identified. For data analysis, we used Fieldtrip, Brain Connectivity MATLAB toolboxes, and in-house built scripts.

Results

We included 41 patients (21 men), with a mean age of 60.1 years (range 23-82), of those were: GBTs (n = 23), BMs (n = 14), and other histologies (n = 4). Statistical analysis revealed a significantly decreased WB node degree in patients versus controls in every frequency range at the corrected level (p1-30Hz = 0.002, pγ = 0.002, pβ = 0.002, pα = 0.002, pθ = 0.024, and pδ = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p1-30Hz = 0.031, pδ = 0.013) in patients compared to controls, which did not persist the false discovery rate correction. No differences regarding networks of GBTs compared to BMs were identified. However, we found a significant increase in WB local clustering coefficient (pθ = 0.048) and decrease in WB node degree (pα = 0.039) in PSEs versus PNSEs at the uncorrected level.

Conclusion

Our data suggest that network topology is altered in brain tumor patients. Histology per se might not, however, tumor-related epilepsy seems to influence the brain's functional network. Longitudinal studies and analysis of possible confounders are required to substantiate these findings.