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Breast cancer (BC) is the most common cancer in women, highly heterogeneous at both the clinical and molecular level. Radiation therapy (RT) represents an efficient modality to treat localized tumor in BC care, although the choice of a unique treatment plan for all BC patients, including RT, may not be the best option. Technological advances in RT are evolving with the use of charged particle beams (i.e. protons) which, due to a more localized delivery of the radiation dose, reduce the dose administered to the heart compared with conventional RT. However, few data regarding proton-induced molecular changes are currently available. The aim of this study was to investigate and describe the production of immunological molecules and gene expression profiles induced by proton irradiation. We performed Luminex assay and cDNA microarray analyses to study the biological processes activated following irradiation with proton beams, both in the non-tumorigenic MCF10A cell line and in two tumorigenic BC cell lines, MCF7 and MDA-MB-231. The immunological signatures were dose dependent in MCF10A and MCF7 cell lines, whereas MDA-MB-231 cells show a strong pro-inflammatory profile regardless of the dose delivered. Clonogenic assay revealed different surviving fractions according to the breast cell lines analyzed. We found the involvement of genes related to cell response to proton irradiation and reported specific cell line- and dose-dependent gene signatures, able to drive cell fate after radiation exposure. Our data could represent a useful tool to better understand the molecular mechanisms elicited by proton irradiation and to predict treatment outcome

BackgroundPositron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure.This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between patients who respond to treatment or not. For these purposes, 31 brain metastases, for predictive evaluation, and 25 ones, for follow-up evaluation after treatment, were delineated using the proposed method. Successively, 11C-MET PET studies and related volumetric segmentations were used to extract 108 features to investigate the potential application of radiomics analysis in patients with brain metastases. A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification.ResultsFor predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUVmean, SULpeak, SUVmin, SULpeakprod-surface-area, SUVmeanprod-sphericity, surface mean SUV 3, SULpeakprod-sphericity, and second angular moment) were selected with optimal performance in discriminant analysis classification (sensitivity 86.28%, specificity 87.75%, and accuracy 86.57%) outperforming the use of all features.ConclusionsThe proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.

Background: There is extensive scientific evidence that radiation therapy (RT) is a crucial treatment, either alone or in combination with other treatment modalities, for many types of cancer, including breast cancer (BC). BC is a heterogeneous disease at both clinical and molecular levels, presenting distinct subtypes linked to the hormone receptor (HR) status and associated with different clinical outcomes. The aim of this study was to assess the molecular changes induced by high doses of ionizing radiation (IR) on immortalized and primary BC cell lines grouped according to Human epidermal growth factor receptor (HER2), estrogen, and progesterone receptors, to study how HR status influences the radiation response. Our genomic approach using in vitro and ex-vivo models (e.g., primary cells) is a necessary first step for a translational study to describe the common driven radio-resistance features associated with HR status. This information will eventually allow clinicians to prescribe more personalized total doses or associated targeted therapies for specific tumor subtypes, thus enhancing cancer radio-sensitivity. Methods: Nontumorigenic (MCF10A) and BC (MCF7 and MDA-MB-231) immortalized cell lines, as well as healthy (HMEC) and BC (BCpc7 and BCpcEMT) primary cultures, were divided into low grade, high grade, and healthy groups according to their HR status. At 24 h post-treatment, the gene expression profiles induced by two doses of IR treatment with 9 and 23 Gy were analyzed by cDNA microarray technology to select and compare the differential gene and pathway expressions among the experimental groups. Results: We present a descriptive report of the substantial alterations in gene expression levels and pathways after IR treatment in both immortalized and primary cell cultures. Overall, the IR-induced gene expression profiles and pathways appear to be cell-line dependent. The data suggest that some specific gene and pathway signatures seem to be linked to HR status. Conclusions: Genomic biomarkers and gene-signatures of specific tumor subtypes, selected according to their HR status and molecular features, could facilitate personalized biological-driven RT treatment planning alone and in combination with targeted therapies.

Background: Osteoarthritis (OA) is a degenerative joints disorder influenced by genetic predisposition. We reported that rs11718863 DVWA SNP was represented in Sicilian with a more severe Kellgren and Lawrence (KL) radiographic grade, displaying its predictive role as OA marker progression. Here, we describe the DVWA SNPs: rs11718863, rs7639618, rs7651842, rs7639807 and rs17040821 probably able to induce protein functional changes. Findings: Sixty-one Sicilian patients with knee OA and 100 healthy subjects were enrolled. Clinical and radiographic evaluation was performed using AKSS scores and KL. Linkage Disequilibrium (LD) analyses were performed in order to verify whether the SNPs segregate as haplotype. All DVWA SNPs'MinorAllele Frequencies (MAF) were greater than in the European. The rs7639618 SNP showed a statistical association with KL. Our analyses show that a LD exists among rs11718863 and rs7639618, as well as between rs7651842, rs7639807 and rs17040821 SNPs. We also observed that three out of the 161 individuals investigated were simultaneously homozygous carriers of the rs7651842, rs7639807 and rs17040821 MAF alleles. Conclusions: In summary, the purpose of this preliminary research was to highlight possible associations between DVWA SNPs and OA clinical and radiographic data. This work represents a multidisciplinary medicine approach to study OA where clinical, radiological and genetic evaluation could contribute to better define OA grading.

Brain-derived neurotrophic factor (BDNF) is a neurotrophin that influences neuronal plasticity throughout life. Emergence from a vegetative state (VS) after a traumatic brain injury (TBI) implies that the brain undergoes plastic changes. A common polymorphism in the BDNF gene—BDNF Val66Met (referred to herein as BDNFMet)—impairs cognitive function in healthy subjects. The aim of this study was to determine whether the BDNFMet polymorphism plays a role in the recovery of consciousness and cognitive functions in patients in a VS after a TBI. Fifty-three patients in a VS 1 month after a TBI were included in the study and genotyped for the BDNFMet polymorphism. Scores of levels of cognitive functioning (LCF) at 1, 3, 6, and 12 months post-TBI were retrospectively compared in patients without (Val group), and with (Met group), the BDNFMet polymorphism. The BDNFMet polymorphism was detected in 20 out of the 53 patients. The mean LCF scores in the Val and Met groups were 1.6 – 0.5 and 1.4 – 0.5 at 1 month, 2.3 – 0.7 and 2.5 – 1.2 at 3 months, 3.3 – 1.7 and 3.5 – 1.7 at 6 months, and 4 – 1.9 and 3.9 – 1.8 at 12 months, respectively ( p > 0.05). The percentages of patients in the Val and Met groups who emerged from the VS were 36.4% and 30% at 3 months, 66.3% and 70% at 6 months, and 70% and 87.5% at 12 months ( p > 0.05), respectively. These findings provide evidence that the BDNFMet polymorphism is not involved in cognitive improvement in patients with a VS following TBI. Future studies should focus on the role of other BDNF polymorphisms in the recovery from a VS.