Relationship between biomarkers of cartilage in serum and degenerative joint disease in Lusitano horses

Introduction: Cartilage degradation biomarkers are a potential tool for early diagnosis of degenerative joint disease (DJD). In young horses, Coll2-1 and Coll2-1NO2 have been studied in serum and reported to be useful in the assessment of joint disease. Fib3-2 has been described to be higher in serum of humans with osteoarthritis but was never assessed in horses. The aim of the current study was to evaluate biomarkers’ changes with age, sex and exercise and correlate them with DJD. Material and Methods: Blood collection and radiographic examination were performed in 51 Lusitanian horses. Moreover, inertial sensor-based detection of lameness was used to assess pain together with subjective examination. Results: Females presented significantly higher concentrations of Coll2-1 (p = 0.015) and Coll2-1NO2 (p = 0.014) compared to males. We have found significant influence of high level of work in lower concentration of Coll2-1 (p = 0.001) and significant influence of sex in concentration of Coll2-1NO2 (p = 0.030). There was no influence of sex, age and work on Fib3-2. All biomarkers were increased in the DJD group (n= 35) compared to healthy controls (n = 16). This difference was significant for Coll2-1 (p = 0.015). When sorted by sex and age groups, significant difference in Coll2-1 between disease and healthy controls disappeared in old horses and females. Discussion/ Conclusion: Coll2-1 is a good marker of cartilage degradation in horses with DJD, being more specific in young horses and males. Fib3-2 may be further explored to help identify disease in particular cases.

“Biomarkers in a One Health perspective: current knowledge of their ability to merge human and animal health

Biomarkers are nowadays essential tools to be one step ahead for fighting disease, enabling an enhanced focus on disease prevention and on the probability of its occurrence. Research in a multidisciplinary approach has been an important step towards the repeated discovery of new biomarkers. Biomarkers are defined as biochemical measurable indicators of the presence of disease or as indicators for monitoring disease progression. Currently, biomarkers have been used in several domains such as oncology, neurology, cardiovascular, inflammatory and respiratory disease, and several endocrinopathies. Bridging biomarkers in a One Health perspective has been proven useful in almost all of these domains. In oncology, humans and animals are found to be subject to the same environmental and genetic predisposing factors: examples include the existence of mutations in BR-CA1 gene predisposing to breast cancer, both in human and dogs, with increased prevalence in certain dog breeds and human ethnic groups. Also, breast feeding frequency and duration has been related to a decreased risk of breast cancer in women and bitches. When it comes to infectious diseases, this parallelism is prone to be even more important, for as much as 75% of all emerging diseases are believed to be zoonotic. Examples of successful use of biomarkers have been found in several zoonotic diseases such as Ebola, dengue, leptospirosis or West Nile virus infections. Acute Phase Proteins (APPs) have been used for quite some time as biomarkers of inflammatory conditions. These have been used in human health but also in the veterinary field such as in mastitis evaluation and PRRS (porcine respiratory and reproductive syndrome) diagnosis. Advantages rely on the fact that these biomarkers can be much easier to assess than other conventional disease diagnostic approaches (example: measured in easy to collect saliva samples). Another domain in which biomarkers have been essential is food safety: the possibility to measure exposure to chemical contaminants or other biohazards present in the food chain, which are sometimes analytical challenges due to their low bioavailability in body fluids, is nowadays a major breakthrough. Finally, biomarkers are considered the key to provide more personalized therapies, with more efficient outcomes and fewer side effects. This approach is expected to be the correct path to follow also in veterinary medicine, in the near future.

New methodologies in CT perfusion and MRI analysis to develop cancer imaging biomarkers

Quantitative imaging in oncology aims at developing imaging biomarkers for diagnosis and prediction of cancer aggressiveness and therapy response before any morphological change become visible. This Thesis exploits Computed Tomography perfusion (CTp) and multiparametric Magnetic Resonance Imaging (mpMRI) for investigating diverse cancer features on different organs. I developed a voxel-based image analysis methodology in CTp and extended its use to mpMRI, for performing precise and accurate analyses at single-voxel level. This is expected to improve reproducibility of measurements and cancer mechanisms’ comprehension and clinical interpretability. CTp has not entered the clinical routine yet, although its usefulness in the monitoring of cancer angiogenesis, due to different perfusion computing methods yielding unreproducible results. Instead, machine learning applications in mpMRI, useful to detect imaging features representative of cancer heterogeneity, are mostly limited to clinical research, because of results’ variability and difficult interpretability, which make clinicians not confident in clinical applications. In hepatic CTp, I investigated whether, and under what conditions, two widely adopted perfusion methods, Maximum Slope (MS) and Deconvolution (DV), could yield reproducible parameters. To this end, I developed signal processing methods to model the first pass kinetics and remove any numerical cause hampering the reproducibility. In mpMRI, I proposed a new approach to extract local first-order features, aiming at preserving spatial reference and making their interpretation easier. In CTp, I found out the cause of MS and DV non-reproducibility: MS and DV represent two different states of the system. Transport delays invalidate MS assumptions and, by correcting MS formulation, I have obtained the voxel-based equivalence of the two methods. In mpMRI, the developed predictive models allowed (i) detecting rectal cancers responding to neoadjuvant chemoradiation showing, at pre-therapy, sparse coarse subregions with altered density, and (ii) predicting clinically significant prostate cancers stemming from the disproportion between high- and low- diffusivity gland components.

Evaluation of alpha-synuclein RT-QuIC for an early and differential diagnosis of Lewy body disease

Synucleinopathies are a group of neurodegenerative diseases characterized by tissue deposition of insoluble aggregates of the protein α-synuclein. Currently, the clinical diagnosis of these diseases, including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA), is very challenging, especially at an early disease stage, due to the heterogeneous and often non-specific clinical manifestations. Therefore, identifying specific biomarkers to aid the diagnosis and improve the clinical management of patients with these disorders represents a primary goal in the field. Pursuing this aim, we applied the α-Syn Real-Time Quaking-Induced Conversion (RT-QuIC), an ultrasensitive technique able to detect minute amounts of amyloidogenic proteins, to a large cohort of 953 CSF samples from clinically well-characterized (“clinical” group), or neuropathologically verified (“NP” group) patients with parkinsonism or dementia. Of significance, we also studied patients with prodromal synucleinopathies (“prodromal” group), such as pure autonomic failure (PAF) (n = 28), isolated REM sleep behavior disorder (iRBD) (n = 18), and mild cognitive impairment due to probable Lewy body (LB) disease (MCI-LB) (n = 81). Our findings show that α-syn RT-QuIC can accurately detect α-Syn seeding activity across the whole spectrum of LB-related disorders (LBD), exhibiting a mean sensitivity of 95.2% in the “clinical” and “NP” group, while ranging between 89.3% (PAF) and 100% (RBD) in the “prodromal group”. Moreover, we observed 95.1% sensitivity and 96.6% specificity in the distinction between MCI-LB patients and cognitively unimpaired controls, demonstrating the solid diagnostic potential of α-Syn RT-QuIC in the early phase of the disease. Finally, 13.3% of MCI-AD patients also had a positive test, suggesting an underlying LB co-pathology. This work demonstrated that α-Syn RT-QuIC is an efficient assay for accurate and early diagnosis of LBD, which should be implemented for clinical management and recruitment for clinical trials in memory clinics.

Research of predictive biomarkers to anti-CD22 antibody-drug conjugate treatment in B-cell acute lymphoblastic leukemia

Background: The treatment of B-cell acute lymphoblastic leukemia (B-ALL) has been enriched by novel agents targeting surface markers CD19 and CD22. Inotuzumab ozogamicin (INO) is a CD22-calicheamicin conjugated monoclonal antibody approved in the setting of relapse/refractory (R/R) B-ALL able to induce a high rate of deep responses, not durable over time. Aims: This study aims to identify predictive biomarkers to INO treatment in B- ALL by flow cytometric analysis of CD22 expression and gene expression profile. Materials and methods: Firstly, the impact on patient outcome in 30 R/R B-ALL patients of baseline CD22 expression in terms of CD22 blast percentage and CD22 fluorescent intensity (CD22-FI) was explored. Secondly, baseline gene expression profile of 18 R/R B-ALL patient samples was analyzed. For statistical analysis of differentially expressed genes (DEGs) patients were divided in non-responders (NR), defined as either INO-refractory or with duration of response (DoR) < 3 months, and responders (R). Gene expression results were analyzed with Ingenuity pathway analysis (IPA). Results: In our patient set higher CD22-FI, defined as higher quartiles (Q2-Q4), correlated with better patient outcome in terms of CR rate, OS and DoR, compared to lower CD22-FI (Q1). CD22 blast percentage was less able to discriminate patients’ outcome, although a trend for better outcome in patients with CD22 ≥ 90% could be appreciated. Concerning gene expression profile, 32 genes with corrected p value <0.05 and absolute FC ≥2 were differentially expressed in NR as compared to R. IPA upstream regulator and regulator effect analysis individuated the inhibition of tumor suppressor HIPK2 as causal upstream condition of the downregulation of 6 DEGs. Conclusions: CD22-FI integrates CD22-percentage on leukemic blasts for a more comprehensive target pre-treatment evaluation. Moreover, a unique pattern of gene expression signature based on HIPK2 downregulation was identified, providing important insights in mechanisms of resistance to INO.

Magnetic Resonance Imaging-Derived biomarkers of Isocitrate-Dehydrogenase mutation in diffuse gliomas: a conventional MR and Diffusion Weighted Imaging study.

The introduction of molecular criteria into the classification of diffuse gliomas has added interesting practical implications to glioma management. This has created a new clinical need for correlating imaging characteristics with glioma genotypes, also known as radiogenomics or imaging genomics. Whilst many studies have primarily focused on the use of advanced magnetic resonance imaging (MRI) techniques for radiogenomics purposes, conventional MRI sequences still remain the reference point in the study and characterization of brain tumours. Moreover, a different approach may rely on diffusion-weighted imaging (DWI) usage, which is considered a “conventional” sequence in line with recently published directions on glioma imaging. In a non-invasive way, it can provide direct insight into the microscopic physical properties of tissues. Considering that Isocitrate-Dehydrogenase gene mutations may reflect alterations in metabolism, cellularity, and angiogenesis, which may manifest characteristic features on an MRI, the identification of specific MRI biomarkers could be of great interest in managing patients with brain gliomas. My study aimed to evaluate the presence of specific MRI-derived biomarkers of IDH molecular status through conventional MRI and DWI sequences.

Longitudinal Micro CT-driven biomarkers as a tool to evaluate lung fibrosis progression and antifibrotic efficacy in a refined Bleomycin mouse model

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease with no curative pharmacological treatment. Animal models play an essential role in revealing molecular mechanisms involved in the pathogenesis of the disease. Bleomycin (BLM)-induced lung fibrosis is the most widely used and characterized model for anti-fibrotic drugs screening. However, several issues have been reported, such as the identification of an optimal BLM dose and administration scheme as well as gender-specificity. Moreover, the balance between disease resolution, an appropriate time window for therapeutic intervention and animal welfare remains critical aspects yet to be fully elucidated. In this thesis, Micro CT imaging has been used as a tool to identify the ideal BLM dose regimen to induce sustained lung fibrosis in mice as well as to assess the anti-fibrotic effect of Nintedanib (NINT) treatment upon this BLM administration regimen. In order to select the optimal BLM dose scheme, C57bl/6 male mice were treated with BLM via oropharyngeal aspiration (OA), following either double or triple BLM administration. The triple BLM administration resulted in the most promising scheme, able to balance disease resolution, appropriate time-window for therapeutic intervention and animal welfare. The fibrosis progression was longitudinally assessed by micro-CT every 7 days for 5 weeks after BLM administration and 5 animals were sacrificed at each timepoint for the BALF and histological evaluation. The antifibrotic effect of NINT was assessed following different treatment regimens in this model. Herein, we have developed an optimized mouse model of pulmonary fibrosis, enabling three weeks of the therapeutic window to screen putative anti-fibrotic drugs. micro-CT scanning, allowed us to monitor the progression of lung fibrosis and the therapeutical response longitudinally in the same subject, drastically reducing the number of animals involved in the experiment.

Identification of molecular biomarkers in esophageal adenocarcinoma

Esophageal adenocarcinoma (EAC) is a severe cancer that has been on the rise in Western nations over the past few decades. It has a high mortality rate and the 5-year survival rate is only 35%–45%. EAC has been included in a group of tumors with one of the highest rates of copy number alterations (CNAs), somatic structural rearrangements, high mutation frequency, with different mutational signatures, and with epigenetic mechanisms. The vast heterogeneity of EAC mutations makes it challenging to comprehend the biology that underlies tumor onset and development, identify prognostic biomarkers, and define a molecular classification to stratify patients. The only way to resolve the current disagreements is through an exhaustive molecular analysis of EAC. We examined the genetic profile of 164 patients' esophageal adenocarcinoma samples (without chemo-radiotherapy). The included patients did not receive neoadjuvant therapies, which can change the genetic and molecular composition of the tumor. Using next-generation sequencing technologies (NGS) at high coverage, we examined a custom panel of 26 cancer-related genes. Over the entire cohort, 337 variants were found, with the TP53 gene showing the most frequent alteration (67.27%). Poorer cancer-specific survival was associated with missense mutations in the TP53 gene (Log Rank P=0.0197). We discovered HNF1alpha gene disruptive mutations in 7 cases that were also affected by other gene changes. We started to investigate its role in EAC cell lines by silencing HNF1alpha to mimic our EAC cohort and we use Seahorse technique to analyze its role in the metabolism in esophageal cell. No significant changes were found in transfected cell lines. We conclude by finding that a particular class of TP53 mutations (missense changes) adversely impacted cancer-specific survival in EAC. HNF1alpha, a new EAC-mutated gene, was found, but more research is required to fully understand its function as a tumor suppressor gene.

Colic surgery in the equine patient: novel biomarkers, postoperative management, microbiotal influence and epidemiological analysis on the italian territory

The aim of this work was to investigate novel diagnostic and prognostic tools, postoperative treatments and epidemiologic factors impacting the outcome of surgical cases of colic. To make a more accurate diagnosis and establish a prognosis, several biomarkers have been investigated in colic patients. In this study we evaluated peritoneal PCT and blood ADMA and SDMA in SIRS positive and negative colic patients to be used as prognostic biomarkers. Our results highlighted the limits of these biomarkers in detection and the lack of specificity. In fact PCT was not detectable and even if ADMA and SDMA significantly increased in colic horses, they are not diagnostic nor prognostic markers for SIRS. Fluid therapy has been described to be crucial for the outcome of colic patients, nevertheless no guidelines have been established. Overhydration was the common practice in post surgical management. We compared cases with an extended fluid therapy protocol and cases with a restricted protocol. Results showed that survival rate and postoperative complications were similar between the groups, despite costs being significantly lower in the restricted group. The possible correlation between intestinal microbiota and colics has gained interest. In this study, cecal and colonic content from horses undergoing laparotomy were collected, and the microbiota analized. Results showed some differences in microbiota between discharged and non discharged patients, and between strangulating and non strangulating types of colic, that might suggest some influence of hind gut microbiota on the disease. A multicentric study involving three veterinary teaching hospitals on the italian territory was conducted investigating factors affecting postoperative survival and complications in colics. Results showed that the influence of age, PCV, TPP, blood lactate, reflux, type of disease, type of lesion, presence of anastomosis, duration of surgery and surgeons, were in line with literature. Amount of crystalloids used could affected the outcome.

[In search of] Digital biomarkers for objective pain assessment

Pain is a highly complex phenomenon involving intricate neural systems, whose interactions with other physiological mechanisms are not fully understood. Standard pain assessment methods, relying on verbal communication, often fail to provide reliable and accurate information, which poses a critical challenge in the clinical context. In the era of ubiquitous and inexpensive physiological monitoring, coupled with the advancement of artificial intelligence, these new tools appear as the natural candidates to be tested to address such a challenge. This thesis aims to conduct experimental research to develop digital biomarkers for pain assessment. After providing an overview of the state-of-the-art regarding pain neurophysiology and assessment tools, methods for appropriately conditioning physiological signals and controlling confounding factors are presented. The thesis focuses on three different pain conditions: cancer pain, chronic low back pain, and pain experienced by patients undergoing neurorehabilitation. The approach presented in this thesis has shown promise, but further studies are needed to confirm and strengthen these results. Prior to developing any models, a preliminary signal quality check is essential, along with the inclusion of personal and health information in the models to limit their confounding effects. A multimodal approach is preferred for better performance, although unimodal analysis has revealed interesting aspects of the pain experience. This approach can enrich the routine clinical pain assessment procedure by enabling pain to be monitored when and where it is actually experienced, and without the involvement of explicit communication,. This would improve the characterization of the pain experience, aid in antalgic therapy personalization, and bring timely relief, with the ultimate goal of improving the quality of life of patients suffering from pain.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.

Anatomical biomarkers of body composition in oncological population: application and development of artificial intelligences

In the Era of precision medicine and big medical data sharing, it is necessary to solve the work-flow of digital radiological big data in a productive and effective way. In particular, nowadays, it is possible to extract information “hidden” in digital images, in order to create diagnostic algorithms helping clinicians to set up more personalized therapies, which are in particular targets of modern oncological medicine. Digital images generated by the patient have a “texture” structure that is not visible but encrypted; it is “hidden” because it cannot be recognized by sight alone. Thanks to artificial intelligence, pre- and post-processing software and generation of mathematical calculation algorithms, we could perform a classification based on non-visible data contained in radiological images. Being able to calculate the volume of tissue body composition could lead to creating clasterized classes of patients inserted in standard morphological reference tables, based on human anatomy distinguished by gender and age, and maybe in future also by race. Furthermore, the branch of “morpho-radiology" is a useful modality to solve problems regarding personalized therapies, which is particularly needed in the oncological field. Actually oncological therapies are no longer based on generic drugs but on target personalized therapy. The lack of gender and age therapies table could be filled thanks to morpho-radiology data analysis application.

Study of the urinary biomarkers of synthetic cannabinoid receptor agonists (SCRAs) for the forensic unequivocal proof of consumption: medico-legal implications for drug abuse prevention

Introduction. Synthetic cannabinoid receptor agonists (SCRAs) represent the widest group of New Psychoactive Substances (NPS) and, around 2021-2022, new compounds emerged on the market. The aims of the present research were to identify suitable urinary markers of Cumyl-CB-MEGACLONE, Cumyl-NB-MEGACLONE, Cumyl-NB-MINACA, 5F-EDMB-PICA, EDMB-PINACA and ADB-HEXINACA, to present data on their prevalence and to adapt the methodology from the University of Freiburg to the University of Bologna. Materials and methods. Human phase-I metabolites detected in 46 authentic urine samples were confirmed in vitro with pooled human liver microsomes (pHLM) assays, analyzed by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-qToF-MS). Prevalence data were obtained from urines collected for abstinence control programs. The method to study SCRAs metabolism in use at the University of Freiburg was adapted to the local facilities, tested in vitro with 5F-EDMB-PICA and applied to the study of ADB-HEXINACA metabolism. Results. Metabolites built by mono, di- and tri-hydroxylation were recommended as specific urinary biomarkers to monitor the consumption of SCRAs bearing a cumyl moiety. Monohydroxylated and defluorinated metabolites were suitable proof of 5F-EDMB-PICA consumption. Products of monohydroxylation and amide or ester hydrolysis, coupled to monohydroxylation or ketone formation, were recognized as specific markers for EDMB-PINACA and ADB-HEXINACA. The LC-qToF-MS method was successfully adapted to the University of Bologna, as tested with 5F-EDMB-PICA in vitro metabolites. Prevalence data showed that 5F-EDMB-PINACA and EDMB-PINACA were more prevalent than ADB-HEXINACA, but for a limited period. Conclusion. Due to undetectability of parent compounds in urines and to shared metabolites among structurally related compounds, the identification of specific urinary biomarkers as unequivocal proofs of SCRAs consumption remains challenging for forensic laboratories. Urinary biomarkers are necessary to monitor SCRAs abuse and prevalence data could help in establishing tailored strategies to prevent their spreading, highlighting the role for legal medicine as a service to public health.

Identification of outcome-oriented cut-offs for copy number alterations in multiple myeloma: predictive biomarkers with improved prognostic accuracy

Aim of the present study was to develop a statistical approach to define the best cut-off Copy number alterations (CNAs) calling from genomic data provided by high throughput experiments, able to predict a specific clinical end-point (early relapse, 18 months) in the context of Multiple Myeloma (MM). 743 newly diagnosed MM patients with SNPs array-derived genomic and clinical data were included in the study. CNAs were called both by a conventional (classic, CL) and an outcome-oriented (OO) method, and Progression Free Survival (PFS) hazard ratios of CNAs called by the two approaches were compared. The OO approach successfully identified patients at higher risk of relapse and the univariate survival analysis showed stronger prognostic effects for OO-defined high-risk alterations, as compared to that defined by CL approach, statistically significant for 12 CNAs. Overall, 155/743 patients relapsed within 18 months from the therapy start. A small number of OO-defined CNAs were significantly recurrent in early-relapsed patients (ER-CNAs) - amp1q, amp2p, del2p, del12p, del17p, del19p -. Two groups of patients were identified either carrying or not ≥1 ER-CNAs (249 vs. 494, respectively), the first one with significantly shorter PFS and overall survivals (OS) (PFS HR 2.15, p<0001; OS HR 2.37, p<0.0001). The risk of relapse defined by the presence of ≥1 ER-CNAs was independent from those conferred both by R-IIS 3 (HR=1.51; p=0.01) and by low quality (< stable disease) clinical response (HR=2.59 p=0.004). Notably, the type of induction therapy was not descriptive, suggesting that ER is strongly related to patients’ baseline genomic architecture. In conclusion, the OO- approach employed allowed to define CNAs-specific dynamic clonality cut-offs, improving the CNAs calls’ accuracy to identify MM patients with the highest probability to ER. As being outcome-dependent, the OO-approach is dynamic and might be adjusted according to the selected outcome variable of interest.