Establishment of a Biorepository for Down Syndrome: Experience of the Inter-Institutional Multidisciplinary BioBank - BioBIM.

BACKGROUND: Down syndrome, or Trisomy 21, is the leading genetic cause of cognitive disability in children and is associated with a high risk of several comorbidities, particularly congenital heart defects, early onset Alzheimer's disease, leukaemia, and autoimmune disorders. OBJECTIVE: This study describes the design, methods, and operational procedures employed to establish a biobank dedicated to Down syndrome that can support research projects investigating the effects of various genetic and environmental factors on this complex disease. METHODS: Blood was collected from all recruited subjects, processed, aliquoted and immediately frozen at -80 °C in the Interinstitutional Multidisciplinary BioBank (BioBIM) facilities. A small aliquot of the sample was used to perform blood tests for which analysis would not be feasible at a later date, such as blood cell counts. Each biological sample was coded, assigned a Standard PREanalytical Code, and registered in the oloBIOBANK software connected to a medical card containing all the donor's anamnestic data. All samples were stored under continuous real-time temperature recording using a freezer connected to a T-GUARD alarm system. In addition, a radiofrequency identification tracking system strictly monitored each cryopreservation operation performed throughout the sample lifecycle. RESULTS: Biological samples were collected from 454 individuals with Down syndrome from 2007 to 2023. A total of 2233 biological samples were available for research purposes, including whole blood in different anticoagulants, serum, plasma, and frozen peripheral blood mononuclear cells. The quality of the nucleic acids obtained through specific standard operating procedures demonstrated that these samples were appropriate for clinical and basic research. CONCLUSION: By establishing this biobank, we have gathered a significant number of biological samples and clinical data from individuals with Down syndrome, thereby fostering collaboration between different research groups in an open and transparent manner. Sharing expertise and resources among scientists will ultimately facilitate the transfer of knowledge to clinical practice, leading to the development of more effective therapeutic treatments to improve the outcomes and quality of life of patients with Down syndrome.

Biospecimen Digital Twins: Moving from a "High Quality" to a "Fit-for-Purpose" Concept in the Era of Omics Sciences.

The growing demand for personalized medicine we are currently witnessing has given rise to more in-depth research in the field of biomarker discovery and, thus, in biological banks that hold the ability to process, collect, store, and distribute "high-quality" biological specimens. However, the notion of "specimen quality" is subject to change with technological advancements. In this perspective, we propose that the notion of sample quality should shift from a broad definition of "high-quality" to a "fit-for-purpose" concept more suitable for precision medicine studies. Digital twins are a digital replica of real entities. These are largely adopted in any digitalized domain and are currently finding applications in biomedicine. The adoption of digital twins for biosamples, proposed in this paper, can provide prompt information about the whole lifecycle of the physical twin (i.e., the biosample) and substantially extend the possible matching criteria between the available samples and the researchers' and physicians' requests. This fine-tuning matching could greatly contribute to improving the "fit-for-purpose" quality, not only for studies based on current needs, but also to improve the identification of the best available samples in future situations, determined by the evolution of technologies and biosciences. Assuming and exploiting a data-science view in our biobank perspective, the more (accurate) data there are available, the more information can be extrapolated from them, the more opportunities there are for matching future, currently unknown, needs. This should be a mandatory principle that the 'time machines' called biobanks should follow.

Association of LTA and SOD Gene Polymorphisms with Cerebral White Matter Hyperintensities in Migraine Patients.

White matter hyperintensities (WMHs) in migraine could be related to inflammatory and antioxidant events. The aim of this study is to verify whether migraine patients with WMHs carry a genetic pro-inflammatory/pro-oxidative status. To test this hypothesis, we analyzed lymphotoxin alpha (LTA; rs2071590T and rs2844482G) and superoxide dismutase 1 (SOD1; rs2234694C) and 2 (SOD2; rs4880T) gene polymorphisms (SNPs) in 370 consecutive patients affected by episodic (EM; n = 251) and chronic (CM; n = 119) migraine and in unrelated healthy controls (n = 100). Brain magnetic resonance was available in 183/370 patients. The results obtained show that genotypes and allele frequencies for all tested SNPs did not differ between patients and controls. No association was found between single SNPs or haplotypes and sex, migraine type, cardiovascular risk factors or disorders. Conversely, the LTA rs2071590T (OR = 2.2) and the SOD1 rs2234694C (OR = 4.9) alleles were both associated with WMHs. A four-loci haplotype (TGCT haplotype: rs2071590T/rs2844482G/rs2234694C/rs4880T) was significantly more frequent in migraineurs with WMHs (7 of 38) compared to those without WMHs (4 of 134; OR = 8.7). We may, therefore, conclude by suggesting that that an imbalance between pro-inflammatory/pro-oxidative and antioxidant events in genetically predisposed individuals may influence the development of WMHs.

Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis.

Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set.

Risk Prediction and New Prophylaxis Strategies for Thromboembolism in Cancer.

Venous thromboembolism (VTE) is a compelling challenge across all phases of cancer care as it may result in treatment delays, impaired quality of life (QoL), and increased mortality [...].

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine-specifically, to cancer research-and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors' predictive capacity and achieve individualised therapies in the near future.

Irinotecan or Oxaliplatin: Which is the First Move for the Mate?

OBJECTIVES: The aim of the present review is to discuss the potential link between RAS, BRAF and microsatellite instability (MSI) mutational patterns and chemotherapeutic agent efficacy [Irinotecan (IRI) vs. Oxaliplatin (OXA)], and how this can potentially influence the choice of the chemotherapy backbone. METHODS: Following a review of the research literature, all pertinent articles published in the core journals were selected for the study. The inclusion criteria regarded relevant clinical and pre-clinical studies on the topic of interest (Relationship of OXA and IRI to KRAS/BRAF mutations and MSI). RESULTS: Excision repair cross complementation group 1 (ERCC1) expression is inhibited by KRAS mutation, making tumor cells more sensitive to OXA. Results from OPUS, COIN and PRIME trials support that no conclusive data are available for BRAF mutant population because of the small number of patients. Enhanced IRI cytotoxicity to MSI cell lines is due to the participation of some of the mismatch repair (MMR) components in various DNA repair processes and their role in the maintenance of the pro-apoptotic effect of IRI and G2/M cell arrest. CONCLUSION: OXA and IRI are indispensable drugs for mCRC treatment and their selection must be as careful as that of targeted agents. We suggest taking into consideration the interaction between known genomic alterations and OXA and IRI activity to personalize chemotherapy in mCRC patients.

Machine learning approach to predict medication overuse in migraine patients.

Machine learning (ML) is largely used to develop automatic predictors in migraine classification but automatic predictors for medication overuse (MO) in migraine are still in their infancy. Thus, to understand the benefits of ML in MO prediction, we explored an automated predictor to estimate MO risk in migraine. To achieve this objective, a study was designed to analyze the performance of a customized ML-based decision support system that combines support vector machines and Random Optimization (RO-MO). We used RO-MO to extract prognostic information from demographic, clinical and biochemical data. Using a dataset of 777 consecutive migraine patients we derived a set of predictors with discriminatory power for MO higher than that observed for baseline SVM. The best four were incorporated into the final RO-MO decision support system and risk evaluation on a five-level stratification was performed. ROC analysis resulted in a c-statistic of 0.83 with a sensitivity and specificity of 0.69 and 0.87, respectively, and an accuracy of 0.87 when MO was predicted by at least three RO-MO models. Logistic regression analysis confirmed that the derived RO-MO system could effectively predict MO with ORs of 5.7 and 21.0 for patients classified as probably (3 predictors positive), or definitely at risk of MO (4 predictors positive), respectively. In conclusion, a combination of ML and RO - taking into consideration clinical/biochemical features, drug exposure and lifestyle - might represent a valuable approach to MO prediction in migraine and holds the potential for improving model precision through weighting the relative importance of attributes.

Dopaminergic symptoms in migraine: A cross-sectional study on 1148 consecutive headache center-based patients.

BACKGROUND: Dopaminergic symptoms may be extremely pronounced in some migraine patients during the attack, representing a major source of disability. OBJECTIVES: We aimed to carefully characterize the clinical picture of migraine patients with dopaminergic symptoms in a large patients' population as a putative migraine endophenotype, allowing more precise disease management, treatment and outcome prediction. METHODS: We screened 1148 consecutive tertiary care episodic and chronic migraine patients with face-to-face interviews collecting thorough data on lifestyle, socio-demographic factors, and clinical migraine features. RESULTS: We identified 374 patients with migraine with dopaminergic symptoms (32.6%). The most frequent dopaminergic symptom was yawning followed by somnolence, nausea, vomiting, fatigue, mood changes and diuresis. Migraine patients with dopaminergic symptoms had longer attack duration (OR: 1.82; 95% CI: 1.41-2.36, p < 0.0001), more frequent osmophobia (OR: 2.01; 95% CI: 1.50-2.69, p < 0.0001), allodynia (OR: 1.43; 95% CI: 1.10-1.85, p = 0.0071) and unilateral cranial autonomic symptoms (OR: 1.31; 95% CI: 1.01-1.68, p = 0.045), but used less preventative treatments (OR: 0.74; 95% CI: 0.57-0.98, p = 0.033) than patients without dopaminergic symptoms. CONCLUSIONS: Migraine patients with dopaminergic symptoms are characterized by a full-blown, more disabling migraine. Dopaminergic system modulation should be carefully considered in individuals with migraine with dopaminergic symptoms for both acute and preventative treatments in future ad hoc designed studies.

Venous Thromboembolism in Cancer Patients on Simultaneous and Palliative Care.

Simultaneous care represents the ideal integration between early supportive and palliative care in cancer patients under active antineoplastic treatment. Cancer patients require a composite clinical, social and psychological management that can be effective only if care continuity from hospital to home is guaranteed and if such a care takes place early in the course of the disease, combining standard oncology care and palliative care. In these settings, venous thromboembolism (VTE) represents a difficult medical challenge, for the requirement of acute treatments and for the strong impact on anticancer therapies that might be delayed or, even, totally discontinued. Moreover, cancer patients not only display high rates of VTE occurrence/recurrence but are also more prone to bleeding and this forces clinicians to optimize treatment strategies, balancing between hemorrhages and thrombus formation. VTE prevention is, therefore, regarded as a double-edged sword. Indeed, while on one hand the appropriate use of antithrombotic agents can reduce VTE occurrence, on the other it significantly increases the bleeding risk, especially in the frail patients who present with multiple co-morbidities and poly-therapy that can interact with anticoagulant drugs. For these reasons, thromboprophylaxis should start while active cancer treatment is ongoing, according to a simultaneous care model in a patient-centered perspective.

Clinical Utility of Plasma KRAS, NRAS and BRAF Mutational Analysis with Real Time PCR in Metastatic Colorectal Cancer Patients-The Importance of Tissue/Plasma Discordant Cases.

BACKGROUND: Tumor tissue (T) mutational analysis represents the standard for metastatic colorectal cancer (mCRC); however, circulating tumor DNA (ctDNA) detected by liquid biopsy in plasma (PL) can better represent tumor heterogeneity. METHODS: mCRC patients undergoing standard first-line chemotherapy with known T-KRAS/NRAS/BRAF status were enrolled in the present prospective study. PL mutations were assessed within 2 weeks before chemotherapy start with real time PCR and correlated with T status and Progression free survival (PFS). Clinical and biochemical variables including also total number of tumor lesions (TNL) and the sum of maximum diameter (SMD) of all lesions were assessed as potential predictors of T/PL discordance. RESULTS: Among 45 enrolled patients, all BRAF mutations were concordant between T and PL and there were 20% of patients RAS discordant: 9% wild type in T and mutated in PL and 11% mutated in T and wild type in PL. T mutations were significantly associated to median PFS (mPFS of 4.5, 8.3 and 22.9 months for T-BRAF mutated, T-RAS mutated, and T-wild type patients, respectively, p for trend 0.00014). PL mutations further refined prognosis: RAS wild type in T and mutated in PL had significantly shorter PFS than concordant RAS wild type in T and PL: mPFS 9.6 vs. 23.3 months, respectively, p = 0.02. Patients RAS mutated in T and wild type in PL had longer PFS than concordant RAS mutated in T and PL: 24.4 vs. 7.8 months, respectively, p = 0.008. At a multivariate cox regression analysis for PFS, PL mutations were independent prognostic factor superior to T analysis (HR 0.13, p = 0.0008). At multivariate logistic regression analysis TNL and SMD were significant predictors of discordant cases. CONCLUSIONS: PL mutational analysis allows a better prognostication than T analysis alone and could help in mCRC treatment management.

Dopamine-beta-hydroxylase 19-bp insertion/deletion polymorphism affects medication overuse in patients with chronic migraine.

Dopamine-beta-hydroxylase (DBH) enzyme activity is modulated at the genetic level by the presence of several polymorphisms. Among these, the 19-bp insertion/deletion (I/D) polymorphism (rs72393728/rs141116007) was investigated in several genetic association studies for its correlation with the susceptibility to develop episodic migraine, but conflicting results were achieved. In the present study we analyzed this genetic variant in a carefully characterized population of migraineurs encompassing both episodic and chronic migraine (with and without medication overuse) with the aim to perform a replication study and verify any possible correlation with migraine endophenotypes. Genotyping of the DBH 19-bp I/D polymorphism was performed on 400 migraine patients and 204 healthy individuals. The associations between genotypic frequencies and the clinical and sociodemographic features of migraineurs were then investigated. The DBH 19-bp I/D polymorphism did not correlate with migraine susceptibility or most clinical variables, with the exception of a statistically significant correlation within the subgroup of patients affected by chronic migraine were the individuals carrying the deleted (D) allele were significantly more prone to abuse in analgesics. As a result of this finding, the DBH 19-bp I/D polymorphism does not influence migraine susceptibility, but it might contribute to the development of medication overuse in patient with chronic migraine.

Breast Cancer Prognosis Using a Machine Learning Approach.

Machine learning (ML) has been recently introduced to develop prognostic classification models that can be used to predict outcomes in individual cancer patients. Here, we report the significance of an ML-based decision support system (DSS), combined with random optimization (RO), to extract prognostic information from routinely collected demographic, clinical and biochemical data of breast cancer (BC) patients. A DSS model was developed in a training set (n = 318), whose performance analysis in the testing set (n = 136) resulted in a C-index for progression-free survival of 0.84, with an accuracy of 86%. Furthermore, the model was capable of stratifying the testing set into two groups of patients with low- or high-risk of progression with a hazard ratio (HR) of 10.9 (p < 0.0001). Validation in multicenter prospective studies and appropriate management of privacy issues in relation to digital electronic health records (EHR) data are presently needed. Nonetheless, we may conclude that the implementation of ML algorithms and RO models into EHR data might help to achieve prognostic information, and has the potential to revolutionize the practice of personalized medicine.

Predicting VTE in Cancer Patients: Candidate Biomarkers and Risk Assessment Models.

Risk prediction of chemotherapy-associated venous thromboembolism (VTE) is a compelling challenge in contemporary oncology, as VTE may result in treatment delays, impaired quality of life, and increased mortality. Current guidelines do not recommend thromboprophylaxis for primary prevention, but assessment of the patient's individual risk of VTE prior to chemotherapy is generally advocated. In recent years, efforts have been devoted to building accurate predictive tools for VTE risk assessment in cancer patients. This review focuses on candidate biomarkers and prediction models currently under investigation, considering their advantages and disadvantages, and discussing their diagnostic performance and potential pitfalls.

Genetic bases of the nutritional approach to migraine.

Migraine is a common multifactorial and polygenic neurological disabling disorder characterized by a genetic background and associated to environmental, hormonal and food stimulations. A large series of evidence suggest a strong correlation between nutrition and migraine and indicates several commonly foods, food additives and beverages that may be involved in the mechanisms triggering the headache attack in migraine-susceptible persons. There are foods and drinks, or ingredients of the same, that can trigger the migraine crisis as well as some foods play a protective function depending on the specific genetic sensitivity of the subject. The recent biotechnological advances have enhanced the identification of some genetic factors involved in onset diseases and the identification of sequence variants of genes responsible for the individual sensitivity to migraine trigger-foods. Therefore many studies are aimed at the analysis of polymorphisms of genes coding for the enzymes involved in the metabolism of food factors in order to clarify the different ways in which people respond to foods based on their genetic constitution. This review discusses the latest knowledge and scientific evidence of the role of gene variants and nutrients, food additives and nutraceuticals interactions in migraine.

Circulating Biomarkers in Migraine: New Opportunities for Precision Medicine.

BACKGROUND: Migraine is the most common neurological disorder and the second most disabling human condition, whose pathogenesis is favored by a combination of genetic, epigenetic, and environmental factors. In recent years, several efforts have been made to identify reliable biomarker(s) useful to monitor disease activity and/or ascertain the response to a specific treatment. OBJECTIVE: To review the current evidence on the potential biological markers associated with migraine. METHODS: A structured search of peer-reviewed research literature was performed by searching major publications databases up to December 2017. RESULTS: Several circulating biomarkers have been proposed as diagnostic or therapeutic tools in migraine, mostly related to migraine's inflammatory pathophysiological aspects. Nonetheless, their detection is still a challenge for the scientific community, reflecting, at least in part, disease complexity and clinical diagnostic limitations. At the present time, calcitonin generelated peptide (CGRP) represents probably the most promising candidate as a diagnostic and/or therapeutic biomarker, as its plasma levels are elevated during migraine attack and decrease during successful treatment. Other molecules (including some neuropeptides, cytokines, adipokines, or vascular activation markers) despite promising, do not possess the sufficient prerequisites to be considered as migraine biomarkers. CONCLUSION: The characterization of migraine-specific biomarkers would be fundamental in a perspective of precision medicine, enabling risk assessment and tailored treatments. However, speculating on the clinical validity of migraine biomarkers may be premature and controlled clinical trials are presently needed to investigate both the diagnostic and therapeutic value of these biomarkers in migraine.

Prognostic Significance of Neutrophil-to-lymphocyte Ratio in the Framework of the 8th TNM Edition for Breast Cancer.

BACKGROUND/AIM: To investigate whether neutrophil-to-lymphocyte ratio (NLR) might represent an additional biological criterion able to identify patients with worse prognosis within the 8th edition TNM prognostic staging system for breast cancer (BC). PATIENTS AND METHODS: Pre-treatment NLR was retrospectively analyzed in 475 BC women prospectively followed for a mean time of 3.8 years. The optimal NLR cutoff, identified by ROC analysis, was set at 2. RESULTS: Elevated pre-treatment NLR was associated with worse disease-free survival (DFS) (HR=2.28) and overall survival (OS) (HR=3.39). The prognostic value of NLR was mostly evident in stage I BC (HR for DFS=2.89; HR for OS=1.30), in whom NLR significantly stratified patients who developed distant metastasis (HR= 4.62), but not local recurrence. CONCLUSION: NLR might provide important information in risk stratification, especially in stage I BC patients in whom the presence of a high NLR might raise the question as to whether they should be more aggressively managed.