MicroRNA in diagnosis and therapy monitoring of early-stage triple-negative breast cancer

Abstract

Breast cancer is a heterogeneous disease with distinct molecular subtypes including the aggressive subtype triple-negative breast cancer (TNBC). We compared blood-borne miRNA signatures of early-stage basal-like (cytokeratin-CK5-positive) TNBC patients to age-matched controls. The miRNAs of TNBC patients were assessed prior to and following platinum-based neoadjuvant chemotherapy (NCT). After an exploratory genome-wide study on 21 cases and 21 controls using microarrays, the identified signatures were verified independently in two laboratories on the same and a new cohort by RT-qPCR. We differentiated the blood of TNBC patients before NCT from controls with 84% sensitivity. The most significant miRNA for this diagnostic classification was miR-126-5p (two tailed t-test p-value of 1.4 × 10−5). Validation confirmed the microarray results for all tested miRNAs. Comparing cancer patients prior to and post NCT highlighted 321 significant miRNAs (among them miR-34a, p-value of 1.2 × 10−23). Our results also suggest that changes in miRNA expression during NCT may have predictive potential to predict pathological complete response (pCR). In conclusion we report that miRNA expression measured from blood facilitates early and minimally-invasive diagnosis of basal-like TNBC. We also demonstrate that NCT has a significant influence on miRNA expression. Finally, we show that blood-borne miRNA profiles monitored over time have potential to predict pCR.

MiR-144: A New Possible Therapeutic Target and Diagnostic/Prognostic Tool in Cancers

“…Kahraman M et al showed the overexpression of hsa-miR-144-3p and hsa-miR-144-5p in the aggressive subtype triple-negative breast cancer (TNBC). These miRNAs seem to be potential candidates for invasive detection of basal-like TNBC, prediction of progression-free survival (PFS), and therapy monitoring [149,150].…”

Identifying a miRNA signature for predicting the stage of breast cancer

“…In addition, other well-known machine learning methods, such as the feed forward neural network algorithm 21 , the C4.5 decision tree method 22 , the linear discreet analysis method 23 and the neuron-fuzzy technique 24 , have been developed for breast cancer diagnosis. Most machine learning methods developed for breast cancer classification using breast tumor images 25 and gene/miRNA expression profiles 26 to distinguish molecular subtypes 27 . Shimomura et al .…”

Membrane morphologies induced by mixtures of arc-shaped particles with opposite curvature

“…[1][2][3][4]55 Arc-shaped scaffolding proteins impose curvature on the membrane by binding to the lipid bilayer, 7,8 transmembrane proteins with a conical or wedged shape induce a curvature on the lipid bilayer that surrounds the proteins, 26,56 and hydrophobic protein motifs that are partially inserted into the lipid bilayer can act as wedges to generate membrane curvature. [57][58][59] A central parameter for membrane shaping is the induced curvature angle of the particles or proteins. 23,60 For our arc-shaped particles, the induced angle of the curved membrane segments to which the particles are bound is close to the arc angle of the particles, 23 which varies here from 60 • to 120 • .…”