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Abstract Background Although the role of viral agents, such as human papillomavirus (e.g. HPV16, HPV18) in colorectal cancer (CRC) has been previously investigated, results remain inconclusive. Methods To further evaluate the involvement of oncogenic HPV types in CRC, 40 frozen neoplastic and 40 adjacent colonic tissues collected from Italian patients were analyzed by Luminex-based assays that detect a broad spectrum of HPV types, i.e. Alpha (n = 21), Beta (n = 46) and Gamma HPVs (n = 52). In addition, 125 frozen CRC samples and 70 surrounding mucosal tissues were collected from Czech patients and analyzed by broad spectrum PCR protocols: (i) FAP59/64, (ii) FAPM1 and (iii) CUT combined with Next Generation Sequencing (NGS). Results Using Luminex-basedassays, DNA from HPV16 was detected in 5% (2/40) CRC tissues from Italian patients. One HPV16 DNA-positive CRC case was subsequently confirmed positive for E6*I mRNA. Cutaneous beta HPV types were detected in 10% (4/40) adjacent tissues only, namely HPV111 (n = 3) and HPV120 (n = 1), while gamma HPV168 (n = 1) and HPV199 (n = 1) types were detected in adjacent and in tumor tissues, respectively. The NGS analysis of the CRC Czech samples identified HPV sequences from mucosal alpha-3 (HPV89), alpha-7 (HPV18, 39, 68 and 70) and alpha-10 species (HPV11), as well as cutaneous beta-1 (HPV20, 24, 93, 98, 105,124) beta-2 (HPV23), beta-3 (HPV49) and gamma-1 species (HPV205). Conclusions Our findings indicate that HPV types belonging to the mucosal alpha, and the ‘cutaneous’ beta and gamma genera can be detected in the colonic mucosal samples with a low prevalence rate and a low number of HPV reads by Luminex and NGS, respectively. However, additional studies are required to corroborate these findings.

Cancer treatment related infertility (CTRI) affects more than one third of young women undergoing anti-cancer protocols, inducing a premature exhaustion of the ovarian reserve. In addition to ovarian suppression by GnRHa, oocyte and cortex cryopreservation has gained interest in patients with estrogen-sensitive tumors for whom the hormonal burst to prompt the multiple follicular growth could provide a further pro-life tumor pulsing. On the other hand, cortex reimplantation implies a few drawbacks due to the unknown consistency of the follicles to be reimplanted or the risk of reintroducing malignant cells. The capability of ovarian stem cells (OCSs) from fresh ovarian cortex fragments to differentiate in vitro to mature oocytes provides a tool to overcome these drawbacks. In fact, since ovarian cortex sampling and cryopreservation is practicable before gonadotoxic treatments, the recruitment of OSCs from defrosted fragments could provide a novel opportunity to verify their suitability to be expanded in vitro as oocyte like cells (OLCs). Here, we describe in very preliminary experiments the consistency of an OSC population from a single cryopreserved ovarian cortex after thawing as well as both their viability and their suitability to be further explored in their property to differentiate in OLCs, thus reinforcing interest in stemness studies in the treatment of female CTRI.

IntroductionRecently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable.MethodsThus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis.ResultsAge, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames.DiscussionThus, our framework aims at shortening the distance between AI and clinical practice

Abstract The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the prediction of the final pathological complete response (pCR). In this study, we proposed a transfer learning approach to predict if a patient achieved pCR (pCR) or did not (non-pCR) by exploiting, separately or in combination, pre-treatment and early-treatment exams from I-SPY1 TRIAL public database. First, low-level features, i.e., related to local structure of the image, were automatically extracted by a pre-trained convolutional neural network (CNN) overcoming manual feature extraction. Next, an optimal set of most stable features was detected and then used to design an SVM classifier. A first subset of patients, called fine-tuning dataset (30 pCR; 78 non-pCR), was used to perform the optimal choice of features. A second subset not involved in the feature selection process was employed as an independent test (7 pCR; 19 non-pCR) to validate the model. By combining the optimal features extracted from both pre-treatment and early-treatment exams with some clinical features, i.e., ER, PgR, HER2 and molecular subtype, an accuracy of 91.4% and 92.3%, and an AUC value of 0.93 and 0.90, were returned on the fine-tuning dataset and the independent test, respectively. Overall, the low-level CNN features have an important role in the early evaluation of the NAC efficacy by predicting pCR. The proposed model represents a first effort towards the development of a clinical support tool for an early prediction of pCR to NAC.

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.

Although research biobanks are among the most promising tools to fight disease and improve public health, there are a range of risks biobanks may face that mainly need to be assessed in an attempt to be relieved. We conducted a strategic insurance review of an institutional cancer biobank with the aim of both identifying the insurable risks of our own Biobank and gathering useful evidence of primary exposure to insurable risks. In this practical scenario, risks have been outlined and categorized into inherent and residual risks, along with their possible impact on biobank maintenance. Results at the Biobank of the Cancer Institute of Bari showed evidence of potentially significant and intrinsic risk due to highly relevant threats, along with already implemented improvements that significantly reduce risks to a range of relative acceptability.

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.

BRCA-associated hereditary breast and ovarian cancer syndrome (HBOC) is characterized by an increased risk of developing other malignancies including cholangiocarcinoma (CCA). Somatic BRCA mutations have been reported in CCA, but they have yet to be utilized in a proband case to identify HBOC in families. Two healthy daughters of a deceased female patient who had had metachronous breast cancer and CCA received genetic counseling to assess their cancer risk. Somatic BRCA1/2 mutation analysis was performed by next-generation sequencing on the DNA extracted from a formalin-fixed, paraffin-embedded CCA biopsy specimen of their mother. A pathogenic variant was identified (c.6468_6469delTC in a BRCA2 gene mutation). Germline BRCA mutation analysis of the two daughters detected the same pathogenic variant in one of them. For the first time, a CCA somatic BRCA mutation has been used to identify a family with HBOC.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also identified as Corona virus disease 19 (COVID-19), has recently produced a dramatic and widespread sanitary emergency. However, despite the necessity to assist a substantial number of affected patients, it is also essential to, at the same time, guarantee the usual clinical care, particularly to cancer patients, including fertility preservation (FP) strategies before the beginning of the anti-cancer treatments. The FP techniques for adult female patients include oocyte and embryo cryopreservation, which require both adequate ovarian reserve (OR) and controlled ovarian stimulation (COS) to promote multiple follicular growth. However, ovarian tissue cryopreservation is an additional FP practice suitable when an anti-cancer treatment is urgently required, whereas, for male patients, sperm cryopreservation is a simple and well-adopted procedure. Here, we focus on the current conditions in terms of agreements and rules of FP procedures during this COVID-19 pandemic to achieve and provide useful recommendations for the adoption of these techniques in patients with cancer.

Background: In the Italian Breast Cancer Intergroup Studies (IBIS) 3 phase III trial, we compared cyclophosphamide, methotrexate, 5-fluorouracil (CMF) alone to sequential epirubicin/CMF regimens in patients with rapidly proliferating early breast cancer (RPEBC). We performed a post hoc analysis in the subgroup of patients with hormone-receptor-positive RPEBC on the prognostic role of progesterone receptor (PgR) status. Methods: RPEBC was defined by thymidine labeling index (TLI) >3% or grade 3 or S-phase >10% or Ki67 >20%. We analyzed 466 patients with hormone-receptor-positive RPEBC receiving sequential epirubicin/CMF regimens followed by tamoxifen, and for whom the status of ER and PgR was available. Results: Considering both cut-off values of 10% and 20%, PgR expression was significantly associated with age, menopausal status, and ER expression; HER2 status was associated with PgR status only at a cutoff value of 20% PgR. Upon univariate analysis, tumor size, nodal status, and PgR were significantly associated with disease-free survival (DFS) and overall survival (OS), while age class and local treatment type were associated only with DFS. Patients with PgR

The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.

The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug–target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.

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.

Health-care workers (HCW) are at high risk for SARS-CoV-2 infection and, if asymptomatic, for transmitting the virus to fragile cancer patients. We monitored all asymptomatic HCWs of a cancer institute (94% of all employees agreed to enter the study) with the rapid serological test, VivaDiagTM, identifying SARS-CoV-2 associated-IgM/IgG. The tests were performed at time 0 (n = 606) and after 14 days (n = 393). Overall, the VivaDiagTM results of nine HCWs (1.5%) were positive, with one confirmed to be SARS-CoV-2-positive after oropharyngeal swab testing by RT-PCR. At time 0, all nine cases showed IgM expression while IgG was detected in only one. After 14 days, IgM persisted in all the cases, while IgG became evident in four. A chemiluminescence immunoassay (CLIA) confirmed IgM positivity in 5/13 VivaDiagTM positive cases and IgG positivity in 4/5 VivaDiagTM positive cases. Our study suggests that the VivaDiagTM test can be of help in identifying SARS-CoV-2 infected people in cohorts of subjects with a high prevalence.

Abstract 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.

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.

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.

Gynecologic cancers account for approximately 11% of the newly diagnosed cancers in women in the United States and for 18% globally. The presence of tumor-infiltrating lymphocytes (TILs) influences the clinical outcome of cancer patients and immune checkpoint inhibitors (ICIs), including anti programmed cell death protein-1 (anti-PD-1), anti-programmed death-ligand 1 (anti-PD-L1), and anticytotoxic T-lymphocyte antigen 4 (anti-CTLA-4), which have been approved for treating different types of malignancies. Antibodies targeting the PD-1/PD-L1 checkpoint have shown dynamic and durable tumor regressions, suggesting a rebalancing of the host–tumor interaction. There are several the US food and drug administration (FDA)-approved ICIs targeting PD-1, including pembrolizumab and nivolumab, as well as those targeting PD-L1, including avelumab, atezolizumab, and durvalumab for melanoma, renal cell cancer, colorectal cancer, head and neck cancer, cervix cancer, urothelial cancer, and lung cancer. Current pre-clinical and clinical studies assessing PD-1/PD-L1 inhibitors in several gynecologic cancers have reported significant antitumor activity. In this review, we investigate pre-clinical and clinical studies that describe the safety and efficacy of anti-PD-1/PD-L1 antibodies, with a particular focus on ongoing clinical trials, analyzing the oncological outcome and adverse effects of ICIs in gynecologic cancers.

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.

Novel anti-cancer treatments have improved the survival rates of female young patients, reopening pregnancy issues for female cancer survivors affected by the tumor treatment-related infertility. This condition occurs in approximately one third of women of fertile age and is mainly dependent on gonadotoxic protocols, including radiation treatments. Besides routine procedures such as the hormonal induction of follicular growth and subsequent cryopreservation of oocytes or embryos, the ovarian protection by gonadotropin-releasing hormone (GnRH) agonists during chemotherapy as well as even gonadal shielding during radiotherapy, other innovative techniques are available today and need to be optimized to support their introduction into the clinical practice. These novel methods are hormone stimulation-free and include the ovarian cortex cryopreservation before anti-cancer treatments and its subsequent autologous reimplantation and a regenerative medicine approach using oocytes derived in vitro from ovarian stem cells (OSCs). For both procedures, the major benefit is related to the prompt recruitment and processing of the ovarian cortex fragments before gonadotoxic treatments. However, while the functional competence of oocytes within the cryopreserved cortex is not assessable, the in vitro maturation of OSCs to oocytes, allows to select the most competent eggs to be cryopreserved for fertility restoration.