Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67

Breast cancer molecular subtypes classification plays an import role to sort patients with divergent prognosis. The biomarkers used are Estrogen Receptor (ER), Progesterone Receptor (PR), HER2, and Ki67. Based on these biomarkers expression levels, subtypes are classified as Luminal A (LA), Luminal B (LB), HER2 subtype, and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is used to classify subtypes, although interlaboratory and interobserver variations can affect its accuracy, besides being a time-consuming technique. The Fourier transform infrared micro-spectroscopy may be coupled with deep learning for cancer evaluation, where there is still a lack of studies for subtypes and biomarker levels prediction. This study presents a novel 2D deep learning approach to achieve these predictions. Sixty micro-FTIR images of 320x320 pixels were collected from a human breast biopsies microarray. Data were clustered by K-means, preprocessed and 32x32 patches were generated using a fully automated approach. CaReNet-V2, a novel convolutional neural network, was developed to classify breast cancer (CA) vs adjacent tissue (AT) and molecular subtypes, and to predict biomarkers level. The clustering method enabled to remove non-tissue pixels. Test accuracies for CA vs AT and subtype were above 0.84. The model enabled the prediction of ER, PR, and HER2 levels, where borderline values showed lower performance (minimum accuracy of 0.54). Ki67 percentage regression demonstrated a mean error of 3.6%. Thus, CaReNet-V2 is a potential technique for breast cancer biopsies evaluation, standing out as a screening analysis technique and helping to prioritize patients.Comment: 32 pages, 3 figures, 6 table

One-dimensional convolutional neural network model for breast cancer subtypes classification and biochemical content evaluation using micro-FTIR hyperspectral images

Breast cancer treatment still remains a challenge, where molecular subtypes classification plays a crucial role in selecting appropriate and specific therapy. The four subtypes are Luminal A (LA), Luminal B (LB), HER2 subtype, and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is the gold-standard evaluation, although interobserver variations are reported and molecular signatures identification is time-consuming. Fourier transform infrared micro-spectroscopy with machine learning approaches have been used to evaluate cancer samples, presenting biochemical-related explainability. However, this explainability is harder when using deep learning. This study created a 1D deep learning tool for breast cancer subtype evaluation and biochemical contribution. Sixty hyperspectral images were acquired from a human breast cancer microarray. K-Means clustering was applied to select tissue and paraffin spectra. CaReNet-V1, a novel 1D convolutional neural network, was developed to classify breast cancer (CA) and adjacent tissue (AT), and molecular subtypes. A 1D adaptation of Grad-CAM was applied to assess the biochemical impact to the classifications. CaReNet-V1 effectively classified CA and AT (test accuracy of 0.89), as well as HER2 and TNBC subtypes (0.83 and 0.86), with greater difficulty for LA and LB (0.74 and 0.68). The model enabled the evaluation of the most contributing wavenumbers to the predictions, providing a direct relationship with the biochemical content. Therefore, CaReNet-V1 and hyperspectral images is a potential approach for breast cancer biopsies assessment, providing additional information to the pathology report. Biochemical content impact feature may be used for other studies, such as treatment efficacy evaluation and development new diagnostics and therapeutic methods.Comment: 23 pages, 5 figures, 2 table

Selection of aptamers against the Jagged-1 protein

The breast cancer is one of the biggest public health problems in the world, being the main female type of cancer that affects the population. The Jagged-1 protein plays an important role in the biology and development of cancer, influencing angiogenesis, growth of neoplastic cells, tumor stem cells, epithelial mesenchymal transition, metastatic processes and resistance to therapies in various types of cancer. In this project, our aim was to select an aptamer for JAG1 ligand using an aptamer library, that could be used as a radiopharmaceutical for PET / SPECT / CT diagnosis of tumors that express JAG1.  Our work showed that MDA-MB-231-JAG1 cells overexpress more mRNA and JAG1 protein than control cells (MDA-MB-231-Control). We also selected aptamers with high affinity for MDA-MB-231-JAG1 cells that could be a useful tool for the development of new radiopharmaceuticals for the diagnosis and treatment of tumors that overexpress JAG1.  

Losartán y sus derivados como radiotrazadores del receptor tipo 1 de agiotensina II

El receptor tipo 1 de angiotensina II (AT1R) juega un papel fundamental en las enfermedades cardiovasculares y se ha reconocido, en los últimos diez años, por su activa expresión en varios tipos de células y tejidos tumorales propiciando la progresión del cáncer. Los fármacos antagonistas de este receptor, como el losartán, utilizados para el control de la hipertensión arterial han demostrado un potencial efecto antitumoral al ser capaces de reducir la progresión del tumor, la vascularización y la metastásis. El objetivo de este trabajo es brindar una panorámica sobre los estudios más recientes de los derivados marcados del losartán, destacando su posible utilización como radiotrazadores del AT1R en tumores que lo expresen. Derivados del losartán marcados con tecnecio-99 metaestable, carbono-11 y fluor-18 permiten detectar y cuantificar el AT1Ren algunos órganos como el corazón y los riñones. Se demuestra la relación existente entre el losartán y el cáncer según los resultados de estudios preclínicos, se resumen diferentes vías de obtención de sus derivados marcados y los resultados de su evaluación como radiotrazadores biológicos del AT1R. En las conclusiones se señala que los derivados de losartán marcados con 99mTc, 11C y 18F pudieran ser utilizados como radiotrazadores para detectar y cuantificar el AT1R en células y tejidos tumorales de pacientes utilizando la imagen molecular como técnica no invasiva.  Existe un amplio campo de investigación relacionado con el desarrollo de nuevos radiotrazadores SPECT o PET del AT1R con fines diagnósticos tanto en patologías como la hipertensión y enfermedades cardiovasculares, como en oncología

Synthesis and Evaluation of [F-18]FEtLos and [F-18]AMBF(3)Los as Novel F-18-Labelled Losartan Derivatives for Molecular Imaging of Angiotensin II Type 1 Receptors

Losartan is widely used in clinics to treat cardiovascular related diseases by selectively blocking the angiotensin II type 1 receptors (AT(1)Rs), which regulate the renin-angiotensin system (RAS). Therefore, monitoring the physiological and pathological biodistribution of AT(1)R using positron emission tomography (PET) might be a valuable tool to assess the functionality of RAS. Herein, we describe the synthesis and characterization of two novel losartan derivatives PET tracers, [F-18]fluoroethyl-losartan ([F-18]FEtLos) and [F-18]ammoniomethyltrifluoroborate-losartan ([F-18]AMBF(3)Los). [F-18]FEtLos was radiolabeled by F-18-fluoroalkylation of losartan potassium using the prosthetic group 2-[F-18]fluoroethyl tosylate; whereas [F-18]AMBF(3)Los was prepared following an one-step F-18-F-19 isotopic exchange reaction, in an overall yield of 2.7 +/- 0.9% and 11 +/- 4%, respectively, with high radiochemical purity (>95%). Binding competition assays in AT(1)R-expressing membranes showed that AMBF(3)Los presented an almost equivalent binding affinity (K-i 7.9 nM) as the cold reference Losartan (K-i 1.5 nM), unlike FEtLos (K-i 2000 nM). In vitro and in vivo assays showed that [F-18]AMBF(3)Los displayed a good binding affinity for AT(1)R-overexpressing CHO cells and was able to specifically bind to renal AT(1)R. Hence, our data demonstrate [F-18]AMBF(3)Los as a new tool for PET imaging of AT(1)R with possible applications for the diagnosis of cardiovascular, inflammatory and cancer diseases

Lack of Galectin-3 Drives Response to Paracoccidioides brasiliensis toward a Th2-Biased Immunity

There is recent evidence that galectin-3 participates in immunity to infections, mostly by tuning cytokine production. We studied the balance of Th1/Th2 responses to P. brasiliensis experimental infection in the absence of galectin-3. The intermediate resistance to the fungal infection presented by C57BL/6 mice, associated with the development of a mixed type of immunity, was replaced with susceptibility to infection and a Th2-polarized immune response, in galectin-3-deficient (gal3−/−) mice. Such a response was associated with defective inflammatory and delayed type hypersensitivity (DTH) reactions, high IL-4 and GATA-3 expression and low nitric oxide production in the organs of infected animals. Gal3−/− macrophages exhibited higher TLR2 transcript levels and IL-10 production compared to wild-type macrophages after stimulation with P. brasiliensis antigens. We hypothesize that, during an in vivo P. brasiliensis infection, galectin-3 exerts its tuning role on immunity by interfering with the generation of regulatory macrophages, thus hindering the consequent Th2-polarized type of response