Biobanks and scientists: supply and demand

The biobanks, providers of biospecimens, and the scientists, users of biological material, are both strategic actors in translational medicine but the communication about those two subjects seems to be delicate. Recently, biobank managers from US and Europe stressed the danger of underuse of biospecimens stored in their biobanks thus stimulating the debate about innovative ways to collect samples and to communicate their availability. We hypothesize that the already stored collections meet the interest of present scientists only in specific situations. Serial biospecimens from patients with large associated clinical data concerning voluptuary habits, environmental exposure, anthropomorphic information are needed to meet the even more specific projects the scientists are planning. The hypothesis of activation of specific sections in ranked journals aimed to facilitate the communication between partners interested in finding/collecting ad hoc biospecimens is discussed

Fertility preservation techniques in cervical carcinoma

Astract The usefulness of this review is to highlight how a fertility preservation (FP) approach is currently feasible for patients diagnosed with uterine cervical cancer. To this regard, a fertility sparing surgery has just overcome its traditional limits, gained acceptance within the major gynecologic oncology societies thanks to the ability to identify the "ideal" candidates to this conservative treatment. On the other hand, the use of other FPs for oocyte and ovarian cortex cryopreservation is still extremely debated. In fact, the existing risk of tumor spreading during oocyte retrieval necessary for oocyte cryostorage for patients' candidates for neo-adjuvant therapy, as well as the potential hazard of cancer cell dissemination after ovarian tissue replacement in cases of non-squamous type cervical carcinomas should not be underestimated. Therefore, in consideration of the encountered limitations and the need to ensure adequate reproductive health for young uterine cervical cancer survivors, translational research regarding the FP has progressively collected innovative insights into the employment of stemness technology. In this context, the property of ovarian stem cells obtained from the ovarian cortex to generate functional oocytes in women could represent a promising therapeutic alternative to the current procedures for a novel and safer FP approach in cancer survivors

Long Non-Coding RNA Landscape in Prostate Cancer Molecular Subtypes: A Feature Selection Approach

Prostate cancer is one of the most common malignancies in men. It is characterized by a high molecular genomic heterogeneity and, thus, molecular subtypes, that, to date, have not been used in clinical practice. In the present paper, we aimed to better stratify prostate cancer patients through the selection of robust long non-coding RNAs. To fulfill the purpose of the study, a bioinformatic approach focused on feature selection applied to a TCGA dataset was used. In such a way, LINC00668 and long non-coding(lnc)-SAYSD1-1, able to discriminate ERG/not-ERG subtypes, were demonstrated to be positive prognostic biomarkers in ERG-positive patients. Furthermore, we performed a comparison between mutated prostate cancer, identified as “classified”, and a group of patients with no peculiar genomic alteration, named “not-classified”. Moreover, LINC00920 lncRNA overexpression has been linked to a better outcome of the hormone regimen. Through the feature selection approach, it was found that the overexpression of lnc-ZMAT3-3 is related to low-grade patients, and three lncRNAs: lnc-SNX10-87, lnc-AP1S2-2, and ADPGK-AS1 showed, through a co-expression analysis, significant correlation values with potentially druggable pathways. In conclusion, the data mining of publicly available data and robust bioinformatic analyses are able to explore the unknown biology of malignancies

Rapid serological and SARS-CoV-2 RT-PCR assays: comparison performed simultaneously in symptomatic COVID-19 patients

RT-PCR testing for the identification of viral nucleic acid is the current standard diagnostic method for the diagnosis of SARS-CoV-2 infection but technical reasons limit its utilization for large-scale screening. Serological IgM/IgG testing has been proposed as a useful tool to detect SARS-CoV-2 exposure

Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs

Cancer treatment planning benefits from an accurate early prediction of the treatment efficacy. The goal of this study is to give an early prediction of three-year Breast Cancer Recurrence (BCR) for patients who underwent neoadjuvant chemotherapy. We addressed the task from a new perspective based on transfer learning applied to pre-treatment and early-treatment DCE-MRI scans. Firstly, low-level features were automatically extracted from MR images using a pre-trained Convolutional Neural Network (CNN) architecture without human intervention. Subsequently, the prediction model was built with an optimal subset of CNN features and evaluated on two sets of patients from I-SPY1 TRIAL and BREAST-MRI-NACT-Pilot public databases: a fine-tuning dataset (70 not recurrent and 26 recurrent cases), which was primarily used to find the optimal subset of CNN features, and an independent test (45 not recurrent and 17 recurrent cases), whose patients had not been involved in the feature selection process. The best results were achieved when the optimal CNN features were augmented by four clinical variables (age, ER, PgR, HER2+), reaching an accuracy of 91.7% and 85.2%, a sensitivity of 80.8% and 84.6%, a specificity of 95.7% and 85.4%, and an AUC value of 0.93 and 0.83 on the fine-tuning dataset and the independent test, respectively. Finally, the CNN features extracted from pre-treatment and early-treatment exams were revealed to be strong predictors of BCR

A Mediterranean Dietary Intervention in Female Carriers of BRCA Mutations: Results from an Italian Prospective Randomized Controlled Trial

Background: Women carriers of BRCA1/2 mutations face a high lifetime risk (penetrance) of developing breast and/or ovarian cancer. Insulin-like growth factor I (IGF-I), body weight and markers of insulin resistance affect BRCA penetrance. We conducted a multicenter prospective two-armed (1:1) randomized controlled trial (NCT03066856) to investigate whether a Mediterranean dietary intervention with moderate protein restriction reduces IGF-I and other metabolic modulators of BRCA penetrance. Methods: BRCA carriers, with or without a previous cancer, aged 18–70 years and without metastases were randomly assigned to an active dietary intervention group (IG) or to a control group (CG). The primary endpoint of the intervention was the IGF-I reduction. Results: 416 women (216 in the IG and 200 in the CG) concluded the six-month dietary intervention. The IG showed significantly lowered serum levels of IGF-I (−11.3 ng/mL versus −1.3 ng/mL, p = 0.02), weight (−1.5 Kg versus −0.5 Kg, p < 0.001), waist circumference (−2 cm versus −0.7 cm, p = 0.01), hip circumference (−1.6 cm versus −0.5 cm, p = 0.01), total cholesterol (−10.2 mg/dL versus −3.6 mg/dL, p = 0.04) and triglycerides (−8.7 mg/dL versus + 5.5 mg/dL, p = 0.01) with respect to the CG. Conclusions: A Mediterranean dietary intervention with moderate protein restriction is effective in reducing IGF-I and other potential modulators of BRCA penetrance

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy

To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori “Giovanni Paolo II” in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol

The strategic alliance between clinical and molecular science in the war against SARS-CoV-2, with the rapid-diagnostics test as an indispensable weapon for front line doctors

Our work concerns the actual problem of spread of SARS- CoV-2 outbreak which requires fast and correct as possible answer. In current scenario, the need of rapid answer put away the imperative of proper methodology. We focus on the serogical immunoassay for diagnosis of Covid-19 as an important weapon not only for diagnostic purpose, but also for epidemiologic one. The right equilibrium between high speed, low cost and accuracy is obtained with easy-to-use decentralized point-of-care test as the colloidal gold-based immunochromatographic strip assay which detects IgM and IgG antibodies directed against SARS-CoV-2. As our aim is to evaluate the efficacy of Covid-19 rapid tests and of serological assays in real-life settings, we designed a research protocol aimed to establish how to use correctly these diagnostics, taking into account the different possible clinical and epidemiological scenarios