Cancer immunotherapy design and analysis through discrete optimization, positive-unlabeled learning, and semi-structured regression models

From ideation to market availability, developing new drugs and therapies can take more than a billion dollars and a decade of work. Clinical testing in human subjects is a particularly time-consuming phase of the development process, and nine out of ten clinical trials fail to demonstrate safety and/or efficacy of the treatments. This delays the introduction to the market by years, and makes the treatment more expensive for end consumers. The safety and efficacy of any given treatment is determined by characteristics of patients and diseases, but our limited ability to identify such factors inevitably leads to reduced success rates of clinical trials, because of overly broad categorization of diseases and patients. Cancer treatments in particular are plagued by low response rates, with therapies often failing to clear the tumor. The recent introduction of novel computational and experimental tools in clinical practice, mostly enabled by artificial intelligence techniques, led to the discovery of a large number of previously unknown biomarkers, i.e., chemical factors that differentiate sub-populations of patients and sub-types of diseases, leading to an improved understanding of the variables that drive the efficacy of therapies. At the same time, advances in experimental techniques generated an exponential increase in the amount of available data characterizing the molecular landscape of patients, making computational tools a necessity to recognize patterns and identify promising directions to develop new therapies, in an approach known as precision medicine. This thesis contributes to the precision medicine revolution by introducing an expert opinion paper about potential uses of artificial intelligence in this practice, novel computational tools to aid the development of cancer immunotherapies, and methodological advances to confront some challenges arising from the complex data modalities frequently found in this field. From an applied perspective, this thesis introduces two frameworks for cancer vaccine design based on discrete optimization, complemented by a benchmark of machine learning predictors that are used in conjunction with such frameworks. Then, recognizing the frequent absence of negative examples with which to train machine learning models for such biological problems, this thesis introduces two methods to learn from this type of data with a particular focus on imbalanced distributions. Finally, enabling practitioners to interpret the effect of tabular data such as clinical variables of a patient, modeled jointly with non-tabular data including radiology and histopathology images, this thesis presents a method to perform correct statistical inference in semi-structured regression models. One application of such models, predicting the spread of COVID-19 in Germany, highlights the advantage of such hybrid modeling.Von der Idee bis zur Marktreife kann die Entwicklung neuer Medikamente und Therapien mehr als eine Milliarde Dollar und ein Jahrzehnt Arbeit in Anspruch nehmen. Dabei stellen klinische Studien am Menschen eine besonders zeitaufwändige Phase des Entwicklungsprozesses dar, und in neun von zehn Fällen gelingt es nicht, die Sicherheit und/oder Wirksamkeit der Behandlungen nachzuweisen. Dadurch verzögert sich die Markteinführung um Jahre, und die Behandlung wird für die Endverbraucher teurer. Die Sicherheit und Wirksamkeit einer bestimmten Behandlung hängt von den Charakteristika der Patienten und Krankheiten ab. Aber unsere begrenzte Fähigkeit, solche Faktoren zu identifizieren, führt unweigerlich zu geringeren Erfolgsquoten bei klinischen Versuchen, weil Krankheiten und Patienten zu breit kategorisiert werden. Insbesondere Krebsbehandlungen haben mit niedrigen Ansprechraten zu kämpfen, da die Therapien den Tumor oft nicht beseitigen können. Die jüngste Einführung neuartiger computergestützter Instrumente in der klinischen Praxis, die größtenteils durch Techniken der künstlichen Intelligenz ermöglicht werden, führte zur Entdeckung einer großen Zahl bisher unbekannter Biomarker.Das sind Faktoren, die Subpopulationen von Patienten und Subtypen von Krankheiten unterscheiden, was zu einem besseren Verständnis der Variablen führt, die die Wirksamkeit von Therapien bestimmen. Gleichzeitig haben Fortschritte bei den experimentellen Techniken zu einem exponentiellen Anstieg der verfügbaren Datenmenge geführt, die die molekulare Landschaft der Patienten charakterisiert. Dies führt dazu, dass computergestützte Werkzeuge eine Notwendigkeit geworden sind, um Muster zu erkennen und vielversprechende Richtungen für die Entwicklung neuer Therapien zu identifizieren. Diese Arbeit leistet einen Beitrag zur Revolution der Präzisionsmedizin, indem sie ein Expertengutachten über den möglichen Einsatz künstlicher Intelligenz in dieser Praxis vorstellt. Dabei werden auch neuartige computergestützte Werkzeuge zur Unterstützung der Entwicklung von Krebsimmuntherapien und methodische Fortschritte zur Bewältigung einiger Herausforderungen beleuchtet, die sich aus den komplexen Datenmodalitäten ergeben, die in diesem Bereich häufig anzutreffen sind. Desweiteren, werden in der Arbeit aus einer angewandten Perspektive zwei Rahmenwerke für die Entwicklung von Krebsimpfstoffen vorgestellt, die auf diskreter Optimierung beruhen, ergänzt durch einen Benchmark von Prädiktoren für maschinelles Lernen, die in Verbindung mit solchen Rahmenwerken verwendet werden. Ferner, in Anbetracht des häufigen Fehlens von Negativbeispielen, mit denen maschinelle Lernmodelle für solche biologischen Probleme trainiert werden können, werden in dieser Arbeit zwei Methoden zum Lernen aus dieser Art von Daten mit besonderem Schwerpunkt auf unausgewogenen Verteilungen vorgestellt. Schließlich wird eine Methode zur korrekten statistischen Inferenz in semi-strukturierten Regressionsmodellen vorgestellt, die es Praktikern ermöglicht, die Auswirkungen von tabellarischen Daten wie klinischen Variablen eines Patienten zu interpretieren, die gemeinsam mit nicht-tabellarischen Daten wie radiologischen und histopathologischen Bildern modelliert werden. Eine Anwendung solcher Modelle, die Vorhersage der Ausbreitung von COVID-19 in Deutschland, verdeutlicht den Vorteil einer solchen hybriden Modellierung

Estimating transmission probability in schools for the 2009 H1N1 influenza pandemic in Italy

BACKGROUND: Epidemic models are being extensively used to understand the main pathways of spread of infectious diseases, and thus to assess control methods. Schools are well known to represent hot spots for epidemic spread; hence, understanding typical patterns of infection transmission within schools is crucial for designing adequate control strategies. The attention that was given to the 2009 A/H1N1pdm09 flu pandemic has made it possible to collect detailed data on the occurrence of influenza-like illness (ILI) symptoms in two primary schools of Trento, Italy. RESULTS: The data collected in the two schools were used to calibrate a discrete-time SIR model, which was designed to estimate the probabilities of influenza transmission within the classes, grades and schools using Markov Chain Monte Carlo (MCMC) methods. We found that the virus was mainly transmitted within class, with lower levels of transmission between students in the same grade and even lower, though not significantly so, among different grades within the schools. We estimated median values of R 0 from the epidemic curves in the two schools of 1.16 and 1.40; on the other hand, we estimated the average number of students infected by the first school case to be 0.85 and 1.09 in the two schools. CONCLUSIONS: The discrepancy between the values of R 0 estimated from the epidemic curve or from the within-school transmission probabilities suggests that household and community transmission played an important role in sustaining the school epidemics. The high probability of infection between students in the same class confirms that targeting within-class transmission is key to controlling the spread of influenza in school settings and, as a consequence, in the general population

On Counting L-Convex Polyominoes

A convex polyomino P is L-convex if any two cells of P can be joined by a monotone path inside P with at most one change of direction. In this paper we show that the problem of computing the number of L-convex polyominoes of area n can be solved in polynomial time using O(n^4) space. We designed a C++ program to significantly extend the counting sequence of L-convex polyominoes and to improve the estimate of the associated growth constant

Explaining divergent bargaining outcomes for agency workers: the role of labour divides and labour market reforms

Under what conditions can unions successfully regulate precarious employment? We compare the divergent trajectories of collective bargaining on agency work in the Italian and German metal sectors from the late 1990s. We explain the differences by the interaction between trade unions’ institutional and associational power resources, mediated by employers’ divide-and-rule strategies and by union strategies to (re)build a unitary front. In both countries, the liberalization of agency work allowed employers to exploit labour divides, undermining unions’ associational power and preventing labour from negotiating effectively. However, while Italian unions remained ‘trapped’ in the vicious circle between weak legislation and fragmented labour, German unions were able to overcome their internal divides. The different degree of success depended on the nature of the divides within the labour movements

Improved proteasomal cleavage prediction with positive-unlabeled learning

Accurate in silico modeling of the antigen processing pathway is crucial to enable personalized epitope vaccine design for cancer. An important step of such pathway is the degradation of the vaccine into smaller peptides by the proteasome, some of which are going to be presented to T cells by the MHC complex. While predicting MHC-peptide presentation has received a lot of attention recently, proteasomal cleavage prediction remains a relatively unexplored area in light of recent advancesin high-throughput mass spectrometry-based MHC ligandomics. Moreover, as such experimental techniques do not allow to identify regions that cannot be cleaved, the latest predictors generate decoy negative samples and treat them as true negatives when training, even though some of them could actually be positives. In this work, we thus present a new predictor trained with an expanded dataset and the solid theoretical underpinning of positive-unlabeled learning, achieving a new state-of-the-art in proteasomal cleavage prediction. The improved predictive capabilities will in turn enable more precise vaccine development improving the efficacy of epitope-based vaccines. Pretrained models are available on GitHubComment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual, http://www.ml4h.cc, 8 page

Refining reproduction number estimates to account for unobserved generations of infection in emerging epidemics

Background: Estimating the transmissibility of infectious diseases is key to inform situational awareness and for response planning. Several methods tend to overestimate the basic (R0) and effective (Rt) reproduction numbers during the initial phases of an epidemic. The reasons driving the observed bias are unknown. In this work we explore the impact of incomplete observations and underreporting of the first generations of infections during the initial epidemic phase. Methods: We propose a debiasing procedure which utilises a linear exponential growth model to infer unobserved initial generations of infections and apply it to EpiEstim. We assess the performance of our adjustment using simulated data, considering different levels of transmissibility and reporting rates. We also apply the proposed correction to SARS-CoV-2 incidence data reported in Italy, Sweden, the United Kingdom and the United States of America. Results: In all simulation scenarios, our adjustment outperforms the original EpiEstim method. The proposed correction reduces the systematic bias and the quantification of uncertainty is more precise, as better coverage of the true R0 values is achieved with tighter credible intervals. When applied to real world data, the proposed adjustment produces basic reproduction number estimates which closely match the estimates obtained in other studies while making use of a minimal amount of data. Conclusions: The proposed adjustment refines the reproduction number estimates obtained with the current EpiEstim implementation by producing improved, more precise estimates earlier than with the original method. This has relevant public health implications

Systems for Conflict Resolution in Comparative Perspective

A cornerstone of industrial relations theory is the idea that the potential for conflict is inherent in the employment relationship. Across countries, forms of workplace conflict and methods of conflict resolution take a range of different forms. Yet aside from attempts to understand cross-national variation in strikes, little research has examined systemic differences in the manifestation and management of workplace conflict. The authors seek to fill this void by analyzing through a comparative lens practices for addressing employment-related conflict in four countries: Germany, the United States, Italy, and Australia. In contrast to the unidimensional varieties of capitalism approach, they analyze workplace conflict resolution systems across two dimensions: collective-individual and regulated-voluntarist. The analysis also emphasizes the importance of within-country variation and interactions between different conflict resolution subsystems

Borges, Ariosto e la vita segreta dei personaggi minori

Inteso quale omaggio a Emilio Bigi, sia pure indiretto, lo studio prende spunto da un’intervista, tuttora inedita, rilasciata nel 1984 dallo scrittore argentino Jorge Luis Borges, in cui aveva citato, credendoli ariosteschi, dei versi che per lui, giunto in età ormai avanzata, erano diventati, più che un motto, una metafora di vita. Poco importa che, come verrà chiarito vagliando la loro storia e le relative fonti, quei versi, facenti capo alla misteriosa figura di un personaggio saraceno, Alibante di Toledo, in realtà appartenessero all’officina poetica di Francesco Berni: essi vengono ciò nondimeno assunti quale filo conduttore per un percorso esplorativo all’interno dell’Orlando furioso che ne vaglia la valenza per così dire “ariostesca”, giusta la reminiscenza borgesiana. Approdata infine a Cervantes, la discussione si conclude facendo ritorno a Borges e misurando l’impatto che quei medesimi versi hanno avuto sulla sua propria opera poetica.Intended as a heartfelt tribute to Emilio Bigi, albeit an indirect one, this essay draws on an interview, still unpublished, given by Argentine writer Jorge Luis Borges in 1984, in which he quoted a couple of lines of poetry which he firmly believed to be by Ariosto, and which for him, by then an old man, had become, more than a motto, a metaphor of life. It does not really matter that, as will become apparent retracing their history as well as their sources, these lines, centring on the mysterious figure of a Saracen character called Alibante of Toledo, actually belonged to Francesco Berni: they are here assumed as the main motif to be explored within Orlando furioso, in order to ascertain to what extent their nature may be regarded to be Ariostan, just as Borges thought. Having finally reached Cervantes, the discussion concludes by returning once more to Borges and considering the impact that these lines, which had remained engrained in his memory throughout his life, had on his own poetry

DEFENDING THE CORE? AN ANALYSIS OF TRADE UNION'S BEHAVIOUR TOWARDS OUTSOURCING IN THE GERMAN CHEMICAL AND METAL SECTOR

Over the last decade, the role of trade unions in segmented labour markets has been a relevant and strongly debated topic in the literature. On the one hand, the dualisation literature is portraying trade unions' behaviour in Coordinated Market Economies (CMEs) as segmentalist: Confronted with employers' pressures for cost reduction and increased flexibility, unions in core sectors are allowing for segmentation to take place (through outsourcing and the use of atypical forms of employment) in order to protect their members, which are overwhelmingly represented among core workers. On the other, the power resource approach is arguing that segmentation derives from the weakness of employees' representatives which are no longer able to oppose employers' segmentation strategies. This paper will contribute to this debate through a case-study analysis of trade unions' behaviour towards outsourcing in the German chemical and metal sector. We will show that trade unions have adopted both inclusive and exclusive strategies towards peripheral employees depending on three main factors: the peculiar trade unions' identity characterising the two sectors, how outsourcing processes impact on the core workforce and how they impact on the union's organisational interests

Primary Pancreatic Sarcoma

Primary pancreatic sarcomas are extremely rare entities, accounting for less than 0.1% of all pancreatic neoplasms1. They originate from the mesenchymal tissue of pancreatic support, and the leiomyosarcoma subtype is the most frequently reported2. This neoplasia usually presents poor prognosis due to late diagnosis and accelerated growth relative to other pancreatic neoplasia1,3. The pancreatic head is the most commonly involved site, followed by the tail and body, and it occurs more frequently in younger individuals3. Clinically, patients may present with weight loss, palpable abdominal mass, epigastric pain, nausea and vomiting, similar to other pancreatic diseases, thus being unspecific to sarcomas1-3. On CT scanning, the findings include a bulky, heterogeneous mass and, with peripheral enhancement after contrast injection, pseudocystic masses are also described.CSV is an exclusive marker of sarcoma regardless of its tissue origin4. The prognosis is influenced by the patient’s age, tumor size, the presence of tumor necrosis, and vascular invasion. The curative treatment is surgical – extensive surgical resection should be advocated, even when morphologic results show a low-grade lesion2. The tumor is likely to metastasize to the liver but not to regional lymph nodes. The role of chemot