BIOMEDICAL ONTOLOGIES: EXAMINING ASPECTS OF INTEGRATION ACROSS BREAST CANCER KNOWLEDGE DOMAINS

The key ideas developed in this thesis lie at the intersection of epistemology, philosophy of molecular biology, medicine, and computer science. I examine how the epistemic and pragmatic needs of agents distributed across particular scientific disciplines influence the domain-specific reasoning, classification, and representation of breast cancer. The motivation to undertake an interdisciplinary approach, while addressing the problems of knowledge integration, originates in the peculiarity of the integrative endeavour of sciences that is fostered by information technologies and ontology engineering methods. I analyse what knowledge integration in this new field means and how it is possible to integrate diverse knowledge domains, such as clinical and molecular. I examine the extent and character of the integration achieved through the application of biomedical ontologies. While particular disciplines target certain aspects of breast cancer-related phenomena, biomedical ontologies target biomedical knowledge about phenomena that is often captured within diverse classificatory systems and domain-specific representations. In order to integrate dispersed pieces of knowledge, which is distributed across assorted research domains and knowledgebases, ontology engineers need to deal with the heterogeneity of terminological, conceptual, and practical aims that are not always shared among the domains. Accordingly, I analyse the specificities, similarities, and diversities across the clinical and biomedical domain conceptualisations and classifications of breast cancer. Instead of favouring a unifying approach to knowledge integration, my analysis shows that heterogeneous classifications and representations originate from different epistemic and pragmatic needs, each of which brings a fruitful insight into the problem. Thus, while embracing a pluralistic view on the ontologies that are capturing various aspects of knowledge, I argue that the resulting integration should be understood in terms of a coordinated social effort to bring knowledge together as needed and when needed, rather than in terms of a unity that represents domain-specific knowledge in a uniform manner. Furthermore, I characterise biomedical ontologies and knowledgebases as a novel socio-technological medium that allows representational interoperability across the domains. As an example, which also marks my own contribution to the collaborative efforts, I present an ontology for HER2+ breast cancer phenotypes that integrates clinical and molecular knowledge in an explicit way. Through this and a number of other examples, I specify how biomedical ontologies support a mutual enrichment of knowledge across the domains, thereby enabling the application of molecular knowledge into the clinics

Open biomedical pluralism : formalising knowledge about breast cancer phenotypes

We demonstrate a heterogeneity of representation types for breast cancer phenotypes and stress that the characterisation of a tumour phenotype often includes parameters that go beyond the representation of a corresponding empirically observed tumour, thus reflecting significant functional features of the phenotypes as well as epistemic interests that drive the modes of representation. Accordingly, the represented features of cancer phenotypes function as epistemic vehicles aiding various classifications, explanations, and predictions. In order to clarify how the plurality of epistemic motivations can be integrated on a formal level, we give a distinction between six categories of human agents as individuals and groups focused around particular epistemic interests. We analyse the corresponding impact of these groups and individuals on representation types, mapping and reasoning scenarios. Respecting the plurality of representations, related formalisms, expressivities and aims, as they are found across diverse scientific communities, we argue for a pluralistic ontology integration. Moreover, we discuss and illustrate to what extent such a pluralistic integration is supported by the distributed ontology language DOL, a meta-language for heterogeneous ontology representation that is currently under standardisation as ISO WD 17347 within the OntoIOp (Ontology Integration and Interoperability) activity of ISO/TC 37/SC 3. We particularly illustrate how DOL supports representations of parthood on various levels of logical expressivity, mapping of terms, merging of ontologies, as well as non-monotonic extensions based on circumscription allowing a transparent formal modelling of the normal/abnormal distinction in phenotypes

Candidate Genes and MiRNAs Linked to the Inverse Relationship Between Cancer and Alzheimer’s Disease: Insights From Data Mining and Enrichment Analysis

The incidence of cancer and Alzheimer\u2019s disease (AD) increases exponentially with age. A growing body of epidemiological evidence and molecular investigations inspired the hypothesis of an inverse relationship between these two pathologies. It has been proposed that the two diseases might utilize the same proteins and pathways that are, however, modulated differently and sometimes in opposite directions. Investigation of the common processes underlying these diseases may enhance the understanding of their pathogenesis and may also guide novel therapeutic strategies. Starting from a text-mining approach, our in silico study integrated the dispersed biological evidence by combining data mining, gene set enrichment, and protein-protein interaction (PPI) analyses while searching for common biological hallmarks linked to AD and cancer. We retrieved 138 genes (ALZCAN gene set), computed a significant number of enriched gene ontology clusters, and identified four PPI modules. The investigation confirmed the relevance of autophagy, ubiquitin proteasome system, and cell death as common biological hallmarks shared by cancer and AD. Then, from a closer investigation of the PPI modules and of the miRNAs enrichment data, several genes (SQSTM1, UCHL1, STUB1, BECN1, CDKN2A, TP53, EGFR, GSK3B, and HSPA9) and miRNAs (miR-146a-5p, MiR-34a-5p, miR-21-5p, miR-9-5p, and miR-16-5p) emerged as promising candidates. The integrative approach uncovered novel miRNA-gene networks (e.g., miR-146 and miR-34 regulating p62 and Beclin1 in autophagy) that might give new insights into the complex regulatory mechanisms of gene expression in AD and cancer

Missed opportunities of flu vaccination in Italian target categories: Insights from the online EPICOVID 19 survey

We aimed to assess the reported rate of flu vaccination in the 2019/2020 season for respondents to the Italian nationwide online EPICOVID 19 survey. A national convenience sample of volunteers aged 18 or older was assessed between 13 April and 2 June 2020. Flu vaccine rates were calculated for all classes of age. The association between the independent variables and the flu vaccine was assessed by applying a multivariable binary logistic regression model. Of the 198,822 respondents, 41,818 (21.0%) reported having received a flu vaccination shot during the last influenza season. In particular, 15,009 (53.4%) subjects aged 65 years or older received a flu vaccination shot. Being 65 years aged or older (Adjusted Odds Ratios (aOR) 3.06, 95% Confidence Interval (CI) 2.92-3.20) and having a high education level (aOR 1.34. 95%CI 1.28-1.41) were independently associated to flu vaccination. Heart and lung diseases were the morbidities associated with the higher odds of being vaccinated (aOR 1.97 (95%CI 1.86-2.09) and aOR 1.92 (95%CI 1.84-2.01), respectively). Nursing home residents aged ≥ 65 years showed lower odds of being vaccinated (aOR 0.39 (95%CI 0.28-0.54)). Our data indicate the need for an urgent public heath effort to fill the gap of missed vaccination opportunities reported in the past flu seasons

Rapid COVID-19 screening based on self-reported symptoms: Psychometric assessment and validation of the EPICOVID19 short diagnostic scale

Background: Confirmed COVID-19 cases have been registered in more than 200 countries, and as of July 28, 2020, over 16 million cases have been reported to the World Health Organization. This study was conducted during the epidemic peak of COVID-19 in Italy. The early identification of individuals with suspected COVID-19 is critical in immediately quarantining such individuals. Although surveys are widely used for identifying COVID-19 cases, outcomes, and associated risks, no validated epidemiological tool exists for surveying SARS-CoV-2 infection in the general population. Objective: We evaluated the capability of self-reported symptoms in discriminating COVID-19 to identify individuals who need to undergo instrumental measurements. We defined and validated a method for identifying a cutoff score. Methods: Our study is phase II of the EPICOVID19 Italian national survey, which launched in April 2020 and included a convenience sample of 201,121 adults who completed the EPICOVID19 questionnaire. The Phase II questionnaire, which focused on the results of nasopharyngeal swab (NPS) and serological tests, was mailed to all subjects who previously underwent NPS tests. Results: Of 2703 subjects who completed the Phase II questionnaire, 694 (25.7%) were NPS positive. Of the 472 subjects who underwent the immunoglobulin G (IgG) test and 421 who underwent the immunoglobulin M test, 22.9% (108/472) and 11.6% (49/421) tested positive, respectively. Compared to NPS-negative subjects, NPS-positive subjects had a higher incidence of fever (421/694, 60.7% vs 391/2009, 19.5%; P<.001), loss of taste and smell (365/694, 52.6% vs 239/2009, 11.9%; P<.001), and cough (352/694, 50.7% vs 580/2009, 28.9%; P<.001). With regard to subjects who underwent serological tests, IgG-positive subjects had a higher incidence of fever (65/108, 60.2% vs 43/364, 11.8%; P<.001) and pain in muscles/bones/joints (73/108, 67.6% vs 71/364, 19.5%; P<.001) than IgG-negative subjects. An analysis of self-reported COVID-19 symptom items revealed a 1-factor solution, the EPICOVID19 diagnostic scale. The following optimal scores were identified: 1.03 for respiratory problems, 1.07 for chest pain, 0.97 for loss of taste and smell 0.97, and 1.05 for tachycardia (ie, heart palpitations). These were the most important symptoms. For adults aged 18-84 years, the cutoff score was 2.56 (sensitivity: 76.56%; specificity: 68.24%) for NPS-positive subjects and 2.59 (sensitivity: 80.37%; specificity: 80.17%) for IgG-positive subjects. For subjects aged ≥60 years, the cutoff score was 1.28, and accuracy based on the presence of IgG antibodies improved (sensitivity: 88.00%; specificity: 89.58%). Conclusions: We developed a short diagnostic scale to detect subjects with symptoms that were potentially associated with COVID-19 from a wide population. Our results support the potential of self-reported symptoms in identifying individuals who require immediate clinical evaluations. Although these results come from the Italian pandemic period, this short diagnostic scale could be optimized and tested as a screening tool for future similar pandemics

Self-reported symptoms of SARS-CoV-2 infection in a non-hospitalized population : results from the large Italian web-based EPICOVID19 cross-sectional survey. (Preprint)

Background: Understanding the occurrence of Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2)-like symptoms in a large non-hospitalized population, when the epidemic peak was occurring in Italy, is of paramount importance but data are scarce. Objective: Aims of this study were to evaluate the association of self-reported symptoms with SARS-CoV-2 nasopharyngeal swab (NPS) test in non-hospitalized individuals and to estimate the occurrence of COVID-19-like symptoms in a larger non-tested population. Methods: This is an Italian countrywide self-administered cross-sectional web-based survey on voluntary adults who completed an anonymous questionnaire in the period 13-21 April 2020. The associations between symptoms potentially related to SARS-CoV-2 infection and NPS results were calculated as adjusted odds ratios with 95% confidence intervals (aOR, 95%CI) by means of multiple logistic regression analysis controlling for age, sex, education, smoking habits, and the number of co-morbidities. Thereafter, for each symptom and for their combination, we calculated sensitivity, specificity, accuracy and AUC in a ROC analysis to estimate the occurrence of COVID-19-like infections in the non-tested population. Results: A total of 171,310 responded to the survey (59.9% females, mean age 47.4 years). Out of the 4,785 respondents with known NPS test result, 4,392 were not hospitalized. Among them, the NPS positive respondents (n=856) most frequently reported myalgia (61.6%), olfactory and/or taste disorders (OTDs, 59.2%), cough (54.4%), and fever (51.9%) whereas 7.7% were asymptomatic. Multiple regression analysis showed that OTDs (aOR 10.3, [95%CI 8.4-12.7]), fever (2.5, 95%CI 2.0-3.1), myalgia (1.5, 95%CI 1.2-1.8), and cough (1.3, 95%CI 1.0-1.6) were associated with NPS positivity. Having two to four of these symptoms increased the aOR from 7.4 (95%CI, 5.6-9.7) to 35.5 (95%CI, 24.6-52.2). The combination of the four symptoms showed an AUC of 0.810 (95%CI 0.795-0.825) in classifying NPS-P, and was applied to the non-hospitalized and non-tested sample (n=165,782). We found that from 4.4% to 12.1% of respondents had experienced symptoms suggestive of COVID-19 infection. Conclusions: Our results suggest that self-reported symptoms may be reliable indicators of SARS-CoV-2 infection in a pandemic context. A not negligible part (up to 12.1%) of the symptomatic respondents were left undiagnosed and potentially contributed to the spread of the infection

Bridging the explanatory gap between pathology and molecular interactions : a semantic approach for the representation of knowledge about cancer

I explore the way that the representation of knowledge about cancer meets two aspects: explanation and heterogeneous data integration. I will question the possibility of a successful explanation of pathological phenomena on the level of the molecular interactions. By way of an example, I will discuss the case of breast cancer. Finally, I will argue for an integrative approach to pathology, which is not reducible to the molecular level only. For, an explanation of complex multilevel process in terms of molecular interactions alone turns a loss in its explanatory power. Hence, it is deficient in two ways: 1) it is not comprehensible for the human understanding of cancer 2) it hampers a representation of knowledge about cancer in the Life Sciences Knowledge Databases. In my argument I oppose the position according to which problem in the representation of complex biological processes is due to our inability to identify the relevant functional units. I claim that neither a complete knowledge about the components or some functional units involved in the process, nor mere mathematical models of systems biology can offer a fully comprehensible solution for the linkage of the different levels in pathology. The molecular classification of cancer deals with data such as whole genome expression profiles, searching for related irregularities, overexpression, downregulation or mutation of genes and its effects on their products. Seemingly, it is the best candidate for an accurate scientific explanation of cancer. However, if we stay just on the level of gene expression and molecular interactions, without connecting them with other levels of observations that are characterized as relevant for an explanation, we could lose a criterion of significance among billions of genomic data. In order to make sense of data, there is an essential need for an organised representation of existing knowledge. Moreover, there is a need to connect and keep track of different levels of information and reasoning from experimental and clinical data to biomedical claims about disease. Integration of molecular oncology with pathological data is necessary. Only by these means can we have not only a comprehensible representation and explanation of different levels involved in cancer, but also a useful tool that can help us to direct our interest and distinguish what is a relevant unit of explanation. A convenient solution can be found in a semantic approach which uses formal ontologies (*), connecting them into a relational logical structure. It opens a field for a comprehensible representation of pathology. Moreover, the semantic approach can deal with the diverse pragmatic and research interests. In this way, a disordered picture in the field of cancer research could be settled. For, an advantage that the semantics brings in dealing with the complex phenomena, such as cancer, consists in its flexibility and capability to connect different levels of explanation. *)I speak about ontology in an epistemological manner, which has to do with the issue of the classification and representation of knowledge in the biological databases