The impact of intraclonal heterogeneity on the outcomes of Multiple Myeloma patients treated with new drugs

Understanding the biology of Multiple Myeloma (MM) is of primary importance in the struggle to achieve a cure for this yet incurable neoplasm. A better knowledge of the mechanism underlying the development of MM can guide us in the development of new treatment strategies. Studies both on solid and haematological tumours have shown that cancer comprises a collection of related but subtly different clones, a feature that has been termed “intra-clonal heterogeneity”. This intra-clonal heterogeneity is likely, from a “Darwinian” natural selection perspective, to be the essential substrate for cancer evolution, disease progression and relapse. In this context the critical mechanism for tumour progression is competition between individual clones (and cancer stem cells) for the same microenvironmental “niche”, combined with the process of adaptation and natural selection. The Darwinian behavioural characteristics of cancer stem cells are applicable to MM. The knowledge that intra-clonal heterogeneity is an important feature of tumours’ biology has changed our way to addressing cancer, now considered as a composite mixture of clones and not as a linear evolving disease. In this variable therapeutic landscape it is important for clinicians and researchers to consider the impact that evolutionary biology and intra-clonal heterogeneity have on the treatment of myeloma and the emergence of treatment resistance. It is clear that if we want to effectively cure myeloma it is of primarily importance to understand disease biology and evolution. Only by doing so will we be able to effectively use all of the new tools we have at our disposal to cure myeloma and to use treatment in the most effective way possible. The aim of the present research project was to investigate at different levels the presence of intra-clonal heterogeneity in MM patients, and to evaluate the impact of treatment on clonal evolution and on patients’ outcomes.

Role of gender in transthyretin-related amyloidosis: an international perspective based on the Transthyretin Amyloid Outcomes Survey (THAOS)

Background. Transthyretin amyloidosis (ATTR) is an underdiagnosed disease caused by destabilization of transthyretin (TTR) due to pathogenic mutations (ATTRm) or aging (ATTRwt). We explored the role of gender in determining clinical picture using the largest available database on ATTR, the ongoing Transthyretin Amyloid Outcomes Survey (THAOS) international registry. Methods. Data through 1st April 2019 were explored. Symptomatic ATTRm (n=3737), asymptomatic ATTRm (n=644) and ATTRwt (n=874) patients were studied. Results. Male prevalence was 61% in the entire registry, 53% in ATTRm and 95% in ATTRwt. In the overall cohort, cardiac phenotype was more frequent in males (30.7% vs 10.5%, p<0.001). Among ATTRm, 72.3% of patients with amyloidotic cardiomyopathy (ATTR-CM) were males (p<0.001) but echocardiographic features showed no substantial gender differences. Sensory abnormalities (70.1% vs 64.1%, p<0.001), autonomic abnormalities (60% vs 48.5%, p<0.001) and walking disabilities were more frequent among ATTRm males. Carpal tunnel syndrome was more frequent in ATTRm males (18.6% vs 15.5%, p=0.014). In ATTRwt cohort, females had a more pronounced (but anyhow mild) walking disability. Male-to-female ratio varied within genotype, from 0.61 in Val30Met to 11.11 in ATTRwt; furthermore, males’ imbalance was more evident among symptomatic patients rather than in asymptomatic ones. Male gender, age at presentation and specific genotype were independently associated with the presence of ATTR-CM. Conclusions. In ATTR, cardiac involvement is more frequent in men, supporting the hypothesis that some biologic characteristics may “protect” from myocardial amyloid infiltration in women. Further investigations are needed to identify possible underlying protective mechanism and orient the research for innovative, gender-tailored therapeutic approaches.

The human gut microbiome in disease: Role in chemotherapy treatments and relationship with clinical outcomes

The gut microbiome (GM) is a plastic entity, capable of adapting in response to intrinsic and extrinsic factors. However, several circumstances can disrupt this homeostatic balance, forcing the GM to shift from a health-associated mutualistic configuration to a disease-associated profile. Nowadays, a new frontier of microbiome research is understanding the GM role in chemo-immunotherapies and clinical outcomes. Here, the role of the genotoxin‐producing pathogen Salmonella in colorectal carcinogenesis was characterized by in-vitro models. A synergistic effect of Salmonella and the CRC-associated mutation (APC gene) promoted a tumorigenic microenvironment by increasing cellular genomic instability. Subsequently, the GM involvement in anti-cancer therapies was investigated via next-generation sequencing in different patient cohorts. The GM trajectory during treatments was characterized for women with epithelial ovarian cancer and pediatric patients undergoing hematopoietic stem cell transplantation (HSCT). The results highlighted the loss of GM homeostasis, with diversity reduction, decrease in health-associated microorganisms and pathobiont bloom. Interestingly, a distinctive GM profile was identified in ovarian cancer patients with a poor response to chemotherapy compared to patients in remission. Moreover, maintenance of GM homeostasis through enteral feeding in pediatric HSCT patients highlighted a better prognosis, with reduced risk of clinical complications. In this context, the gut resistome – the pattern of GM antibiotic-resistance genes (ARGs) – was evaluated longitudinally in HSCT patients. The results showed new acquisitions and consolidation of ARGs already present in patients developing clinical complications. Antibiotic exposure was also evaluated in infants under low-dose antibiotic prophylaxis for vesico-ureteral reflux showing an impairment of the GM configuration with possible long-term health implications. Dramatic GM dysbiosis was finally observed in critically ill patients with COVID-19 (undergoing multiple drug therapies) and correlated with increased risk of bloodstream infection. All these findings pointed out the importance of maintaining GM homeostasis during chemotherapy treatments for improving patients’ clinical outcomes.

Digital 3D reconstruction as a research environment in art and architecture history: uncertainty classification and visualisation

The dissertation addresses the still not solved challenges concerned with the source-based digital 3D reconstruction, visualisation and documentation in the domain of archaeology, art and architecture history. The emerging BIM methodology and the exchange data format IFC are changing the way of collaboration, visualisation and documentation in the planning, construction and facility management process. The introduction and development of the Semantic Web (Web 3.0), spreading the idea of structured, formalised and linked data, offers semantically enriched human- and machine-readable data. In contrast to civil engineering and cultural heritage, academic object-oriented disciplines, like archaeology, art and architecture history, are acting as outside spectators. Since the 1990s, it has been argued that a 3D model is not likely to be considered a scientific reconstruction unless it is grounded on accurate documentation and visualisation. However, these standards are still missing and the validation of the outcomes is not fulfilled. Meanwhile, the digital research data remain ephemeral and continue to fill the growing digital cemeteries. This study focuses, therefore, on the evaluation of the source-based digital 3D reconstructions and, especially, on uncertainty assessment in the case of hypothetical reconstructions of destroyed or never built artefacts according to scientific principles, making the models shareable and reusable by a potentially wide audience. The work initially focuses on terminology and on the definition of a workflow especially related to the classification and visualisation of uncertainty. The workflow is then applied to specific cases of 3D models uploaded to the DFG repository of the AI Mainz. In this way, the available methods of documenting, visualising and communicating uncertainty are analysed. In the end, this process will lead to a validation or a correction of the workflow and the initial assumptions, but also (dealing with different hypotheses) to a better definition of the levels of uncertainty.

Advancing digital healthcare research and machine learning-aided knowledge extraction amidst the COVID-19 pandemic

The rapid progression of biomedical research coupled with the explosion of scientific literature has generated an exigent need for efficient and reliable systems of knowledge extraction. This dissertation contends with this challenge through a concentrated investigation of digital health, Artificial Intelligence, and specifically Machine Learning and Natural Language Processing's (NLP) potential to expedite systematic literature reviews and refine the knowledge extraction process. The surge of COVID-19 complicated the efforts of scientists, policymakers, and medical professionals in identifying pertinent articles and assessing their scientific validity. This thesis presents a substantial solution in the form of the COKE Project, an initiative that interlaces machine reading with the rigorous protocols of Evidence-Based Medicine to streamline knowledge extraction. In the framework of the COKE (“COVID-19 Knowledge Extraction framework for next-generation discovery science”) Project, this thesis aims to underscore the capacity of machine reading to create knowledge graphs from scientific texts. The project is remarkable for its innovative use of NLP techniques such as a BERT + bi-LSTM language model. This combination is employed to detect and categorize elements within medical abstracts, thereby enhancing the systematic literature review process. The COKE project's outcomes show that NLP, when used in a judiciously structured manner, can significantly reduce the time and effort required to produce medical guidelines. These findings are particularly salient during times of medical emergency, like the COVID-19 pandemic, when quick and accurate research results are critical.