Breast Cancer Plasticity after Chemotherapy Highlights the Need for Re-Evaluation of Subtyping in Residual Cancer and Metastatic Tissues.

This research paper presents a novel approach to identifying biomarkers that can be used to prognosticate patients with triple-negative breast cancer (TNBC) eligible for neoadjuvant therapy. The study utilized survival and RNA sequencing data from a cohort of TNBC patients and identified 276 genes whose expression was related to survival in such patients. The gene expression data were then used to classify patients into two major groups based on the presence or absence of Wingless/Integrated-pathway (Wnt-pathway) and mesenchymal (Mes) markers (Wnt/Mes). Patients with a low expression of Wnt/Mes-related genes had a favorable outcome, with no deaths observed during follow-up, while patients with a high expression of Wnt/Mes genes had a higher mortality rate of 50% within 19 months. The identified gene list could be validated and potentially used to shape treatment options for TNBC patients eligible for neoadjuvant therapy providing valuable insights into the development of more effective treatments for TNBC. Our data also showed significant variation in gene expression profiles before and after chemotherapy, with most tumors switching to a more mesenchymal/stem cell-like profile. To verify this observation, we performed an in silico analysis to classify breast cancer tumors in Prediction Analysis of Microarray 50 (PAM50) molecular classes before treatment and after treatment using gene expression data. Our findings demonstrate that following drug intervention and metastasis, certain tumors undergo a transition to alternative subtypes, resulting in diminished therapeutic efficacy. This underscores the necessity for reevaluation of patients who have experienced relapse or metastasis post-chemotherapy, with a focus on molecular subtyping. Tailoring treatment strategies based on these refined subtypes is imperative to optimize therapeutic outcomes for affected individuals.

Methods gold standard in clinic millifluidics multiplexed extended gate field-effect transistor biosensor with gold nanoantennae as signal amplifiers.

We present a portable multiplexed biosensor platform based on the extended gate field-effect transistor and demonstrate its amplified response thanks to gold nanoparticle-based bioconjugates introduced as a part of the immunoassay. The platform comprises a disposable chip hosting an array of 32 extended gate electrodes, a readout module based on a single transistor operating in constant charge mode, and a multiplexer to scan sensing electrodes one-by-one. Although employing only off-the-shelf electronic components, our platform achieves sensitivities comparable to fully customized nanofabricated potentiometric sensors. In particular, it reaches a detection limit of 0.2 fM for the pure molecular assay when sensing horseradish peroxidase-linked secondary antibody (∼0.4 nM reached by standard microplate methods). Furthermore, with the gold nanoparticle bioconjugation format, we demonstrate ca. 5-fold amplification of the potentiometric response compared to a pure molecular assay, at the detection limit of 13.3 fM. Finally, we elaborate on the mechanism of this amplification and propose that nanoparticle-mediated disruption of the diffusion barrier layer is the main contributor to the potentiometric signal enhancement. These results show the great potential of our portable, sensitive, and cost-efficient biosensor for multidimensional diagnostics in the clinical and laboratory settings, including e.g., serological tests or pathogen screening.

The impacts of 13 novel mutations of SARS-CoV-2 on protein dynamics: In silico analysis from Turkey.

SARS-CoV-2 inherits a high rate of mutations making it better suited to the host since its fundamental role in evolution is to provide diversity into the genome. This research aims to identify variations in Turkish isolates and predict their impacts on proteins. To identify novel variations and predict their impacts on protein dynamics, in silico methodology was used. The 411 sequences from Turkey were analysed. Secondary structure prediction by Garnier-Osguthorpe-Robson (GOR) was used. To find the effects of identified Spike mutations on protein dynamics, the SARS-CoV-2 structures (PDB:6VYB, 6M0J) were uploaded and predicted by Cutoff Scanning Matrix (mCSM), DynaMut and MutaBind2. To understand the effects of these mutations on Spike protein molecular dynamics (MD) simulation was employed. Turkish sequences were aligned with sequences worldwide by MUSCLE, and phylogenetic analysis was performed via MegaX. The 13 novel mutations were identified, and six of them belong to spike glycoprotein. Ten of these variations revealed alteration in the secondary structure of the protein. Differences of free energy between the reference sequence and six mutants were found below zero for each of six isolates, demonstrating these variations have stabilizing effects on protein structure. Differences in vibrational entropy calculation revealed that three variants have rigidification, while the other three have a flexibility effect. MD simulation revealed that point mutations in spike glycoprotein and RBD:ACE-2 complex cause changes in protein dynamics compared to the wild-type, suggesting possible alterations in binding affinity. The phylogenetic analysis showed Turkish sequences distributed throughout the tree, revealing multiple entrances to Turkey.