HER2-enriched subtype and novel molecular subgroups drive aromatase inhibitor resistance and an increased risk of relapse in early ER+/HER2+ breast cancer

BACKGROUND: Oestrogen receptor positive/ human epidermal growth factor receptor positive (ER+/HER2+) breast cancers (BCs) are less responsive to endocrine therapy than ER+/HER2- tumours. Mechanisms underpinning the differential behaviour of ER+HER2+ tumours are poorly characterised. Our aim was to identify biomarkers of response to 2 weeks’ presurgical AI treatment in ER+/HER2+ BCs. METHODS: All available ER+/HER2+ BC baseline tumours (n=342) in the POETIC trial were gene expression profiled using BC360™ (NanoString) covering intrinsic subtypes and 46 key biological signatures. Early response to AI was assessed by changes in Ki67 expression and residual Ki67 at 2 weeks (Ki672wk). Time-To-Recurrence (TTR) was estimated using Kaplan-Meier methods and Cox models adjusted for standard clinicopathological variables. New molecular subgroups (MS) were identified using consensus clustering. FINDINGS: HER2-enriched (HER2-E) subtype BCs (44.7% of the total) showed poorer Ki67 response and higher Ki672wk (p<0.0001) than non-HER2-E BCs. High expression of ERBB2 expression, homologous recombination deficiency (HRD) and TP53 mutational score were associated with poor response and immune-related signatures with High Ki672wk. Five new MS that were associated with differential response to AI were identified. HER2-E had significantly poorer TTR compared to Luminal BCs (HR 2.55, 95% CI 1.14–5.69; p=0.0222). The new MS were independent predictors of TTR, adding significant value beyond intrinsic subtypes. INTERPRETATION: Our results show HER2-E as a standardised biomarker associated with poor response to AI and worse outcome in ER+/HER2+. HRD, TP53 mutational score and immune-tumour tolerance are predictive biomarkers for poor response to AI. Lastly, novel MS identify additional non-HER2-E tumours not responding to AI with an increased risk of relapse

On the nature of time-varying functional connectivity in resting fMRI

The brain is a complex dynamical system composed of many interacting sub-regions. Knowledge of how these interactions reconfigure over time is critical to a full understanding of the brain’s functional architecture, the neural basis of flexible cognition and behavior, and how neural systems are disrupted in psychiatric and neurological illness. The idea that we might be able to study neural and cognitive dynamics through analysis of neuroimaging data has catalyzed substantial interest in methods which seek to estimate moment-to-moment fluctuations in functional connectivity (often referred to as “dynamic” or time-varying connectivity; TVC). At the same time, debates have emerged regarding the application of TVC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive relevance of resting TVC. These and other unresolved issues complicate the interpretation of resting TVC findings and limit the insights which can be gained from this otherwise promising research area. This article reviews the current resting TVC literature in light of these issues. We introduce core concepts, define key terms, summarize current controversies and open questions, and present a forward-looking perspective on how resting TVC analyses can be rigorously applied to investigate a wide range of questions in cognitive and systems neuroscience