Orchestration of multiscale model for computational oncology

Cancer is a challenging disease that involves multiple types of biological interactions in different time and space scales. Often computational modelling has been facing problems that, in the current technology level, is impracticable to represent in a single space-time continuum. To handle this sort of problems, complex orchestrations of multiscale models is frequently done. PRIMAGE is a large EU project that aims to support personalized childhood cancer diagnosis and prognosis. The goal is to do so predicting the growth of the solid tumour using multiscale in-silico technologies. The project proposes an open cloud-based platform to support decision making in the clinical management of paediatric cancers. The orchestration of predictive models is in general complex and would require a software framework that support and facilitate such task. The present work, proposes the development of an updated framework, referred herein as the VPH-HFv3, as a part of the PRIMAGE project. This framework, a complete re-writing with respect to the previous versions, aims to orchestrate several models, which are in concurrent development, using an architecture as simple as possible, easy to maintain and with high reusability. This sort of problem generally requires unfeasible execution times. To overcome this problem was developed a strategy of particularisation, which maps the upper-scale model results into a smaller number and homogenisation which does the inverse way and analysed the accuracy of this approach.

Urine-derived Renal Epithelial Cells from kidney transplanted patients: phenotype, immunomodulatory properties, and effect of the exposition to NGAL

During kidney transplant procedure transplanted organs can undergo ischaemia reperfusion phenomena, often associated with the onset of acute kidney damage, loss of kidney function and rejection. These events promote cell turnover to replace damaged cells and preserve kidney function, thus cells deriving from nephrons structures are highly voided in urine. Urine derived cells represents a promising cell source since they can be easily isolated and cultured. The aim of this project was to characterise Urine-derived Renal Epithelial Cells (URECs) from transplanted kidney and to evaluate how these cells react to the co-culture with immune cells. URECs expressed typical markers of kidney tubule epithelial cells (Cytokeratin and CD13), and a subpopulation of these cells expressed CD24 and CD133, which are markers of kidney epithelial progenitor cells. The expression of immunosuppressive molecules as HLA-G and CD73 was also observed. As matter of fact, during the co-culture with PBMCs, UREC suppressed the proliferation of CD4 and CD8 Lymphocytes and reduce the T helper 1 subset, while increasing the T regulatory counterpart. Also, preliminary data observed in this study indicated that the exposition to kidney damage associated molecule, such as NGAL, could significantly affect UREC viability and immunomodulatory capacity. These results add new information about the phenotype of urine cells obtained after kidney transplant and reveal that these cells show promising immunomodulatory properties, suggesting their potential application in personalized cell therapy approaches.

Plasma-activated liquids as promising tools for non-invasive selective treatment of epithelial ovarian cancer

Plasma medicine is a branch of plasma-promising biomedical applications that uses cold atmospheric plasma (CAP) as a therapeutic agent in treating a wide range of medical conditions including cancer. Epithelial ovarian cancer (EOC) is a highly malignant and aggressive form of ovarian cancer, and most patients are diagnosed at advanced stages which significantly reduces the chances of successful treatment. Treatment resistance is also common, highlighting the need for novel therapies to be developed to treat EOC. Research in Plasma Medicine has revealed that plasma has unique properties suitable for biomedical applications and medical therapies, including responses to hormetic stimuli. However, the exact mechanisms by which CAP works at the molecular level are not yet fully understood. In this regard, the main goal of this thesis is to identify a possible adjuvant therapy for cancer, which could exert a cytotoxic effect, without damaging the surrounding healthy cells. An examination of different plasma-activated liquids (PALs) revealed their potential as effective tools for significantly inhibiting the growth of EOC. The dose-response profile between PALs and their targeted cytotoxic effects on EOC cells without affecting healthy cells was established. Additionally, it was validated that PALs exert distinct effects on different subtypes of EOC, possibly linked to the cells' metabolism. This suggests the potential for developing new, personalized anticancer strategies. Furthermore, it was observed that CAP treatment can alter the chemistry of a biomolecule present in PAL, impacting its cytotoxic activity. The effectiveness of the treatment was also preliminarily evaluated in 3D cultures, opening the door for further investigation of a possible correlation between the tumor microenvironment and PALs' resistance. These findings shed light on the intricate interplay between CAP and the liquid substrate and cell behaviour, providing valuable insights for the development of a novel and promising CAP-based cancer treatment for clinical application.

Clinical impact of next-generation sequencing multi-gene panel highlighting the landscape of germline alterations in ovarian cancer patients

BRCA1 and BRCA2 are the most frequently mutated genes in ovarian cancer (OC), crucial both for the identification of cancer predisposition and therapeutic choices. However, germline variants in other genes could be involved in OC susceptibility. We characterized OC patients to detect mutations in genes other than BRCA1/2 that could be associated with a high risk to develop OC, and that could permit patients to enter the most appropriate treatment and surveillance program. Next-Generation Sequencing analysis with a 94-gene panel was performed on germline DNA of 219 OC patients. We identified 34 pathogenic/likely-pathogenic variants in BRCA1/2 and 38 in other 21 genes. Patients with pathogenic/likely-pathogenic variants in non-BRCA1/2 genes developed mainly OC alone compared to the other groups that developed also breast cancer or other tumors (p=0.001). Clinical correlation analysis showed that low-risk patients were significantly associated with platinum sensitivity (p<0.001). Regarding PARP inhibitors (PARPi) response, patients with pathogenic mutations in non-BRCA1/2 genes had significantly worse PFS and OS. Moreover, a statistically significant worse PFS was found for every increase of one thousand platelets before PARPi treatment. To conclude, knowledge about molecular alterations in genes beyond BRCA1/2 in OC could allow for more personalized diagnostic, predictive, prognostic, and therapeutic strategies for OC patients.

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.

Combining molecular alterations and functional imaging in metastatic castration resistant prostate cancer treated with taxanes

The treatment of metastatic castration-resistant prostate cancer (mCRPC) is currently characterized by several drugs with different mechanisms of action, such as new generation hormonal agents (abiraterone, enzalutamide), chemotherapy (docetaxel, cabazitaxel), PARP inhibitors (olaparib) and radiometabolic therapies (radium-223, LuPSMA). There is an urgent need to identify biomarkers to guide personalized therapy in mCRPC. In recent years, the status of androgen receptor (AR) gene detected in liquid biopsy has been associated with outcomes in patients treated with abiraterone or enzalutamide. More recently, plasma tumor DNA (ptDNA) and its changes during treatment have been identified as early indicators of response to anticancer treatments. Recent works also suggested a potential role of tumor-related metabolic parameters of 18Fluoro-Choline Positron Emission Tomography (F18CH-PET)-computed tomography (CT) as a prognostic tool in mCRCP. Other clinical features, such as the presence of visceral metastases, have been correlated with outcome in mCRPC patients. Recent studies conducted by our research group have designed and validated a prognostic model based on the combination of molecular characteristics (ptDNA levels), metabolic features found in basal FCH PET scans (metabolic tumor volume values, MTV), clinical parameters (absence or presence of visceral metastases), and laboratory tests (serum lactate dehydrogenase levels, LDH). Within this PhD project, 30 patients affected by mCRPC, pre-treated with abiraterone or enzalutamide, candidate for taxane-based treatments (docetaxel or cabazitaxel), have been prospectively evaluated. The prognostic model previously described was applied to this population, to interrogate its prognostic power in a more advanced cohort of patients, resulting in a further external validation of the tool.

Response times in computerized adaptive testing: a method for cheating detection

In the field of educational and psychological measurement, the shift from paper-based to computerized tests has become a prominent trend in recent years. Computerized tests allow for more complex and personalized test administration procedures, like Computerized Adaptive Testing (CAT). CAT, following the Item Response Theory (IRT) models, dynamically generates tests based on test-taker responses, driven by complex statistical algorithms. Even if CAT structures are complex, they are flexible and convenient, but concerns about test security should be addressed. Frequent item administration can lead to item exposure and cheating, necessitating preventive and diagnostic measures. In this thesis a method called "CHeater identification using Interim Person fit Statistic" (CHIPS) is developed, designed to identify and limit cheaters in real-time during test administration. CHIPS utilizes response times (RTs) to calculate an Interim Person fit Statistic (IPS), allowing for on-the-fly intervention using a more secret item bank. Also, a slight modification is proposed to overcome situations with constant speed, called Modified-CHIPS (M-CHIPS). A simulation study assesses CHIPS, highlighting its effectiveness in identifying and controlling cheaters. However, it reveals limitations when cheaters possess all correct answers. The M-CHIPS overcame this limitation. Furthermore, the method has shown not to be influenced by the cheaters’ ability distribution or the level of correlation between ability and speed of test-takers. Finally, the method has demonstrated flexibility for the choice of significance level and the transition from fixed-length tests to variable-length ones. The thesis discusses potential applications, including the suitability of the method for multiple-choice tests, assumptions about RT distribution and level of item pre-knowledge. Also limitations are discussed to explore future developments such as different RT distributions, unusual honest respondent behaviors, and field testing in real-world scenarios. In summary, CHIPS and M-CHIPS offer real-time cheating detection in CAT, enhancing test security and ability estimation while not penalizing test respondents.

Quantitative susceptibility mapping as biomarker of neurodegeneration: methodological models and clinical applications

Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance technique that can quantify in vivo biomarkers of pathology, such as alteration in iron and myelin concentration. It allows for the comparison of magnetic susceptibility properties within and between different subject groups. In this thesis, QSM acquisition and processing pipeline are discussed, together with clinical and methodological applications of QSM to neurodegeneration. In designing the studies, significant emphasis was placed on results reproducibility and interpretability. The first project focuses on the investigation of cortical regions in amyotrophic lateral sclerosis. By examining various histogram susceptibility properties, a pattern of increased iron content was revealed in patients with amyotrophic lateral sclerosis compared to controls and other neurodegenerative disorders. Moreover, there was a correlation between susceptibility and upper motor neuron impairment, particularly in patients experiencing rapid disease progression. Similarly, in the second application, QSM was used to examine cortical and sub-cortical areas in individuals with myotonic dystrophy type 1. The thalamus and brainstem were identified as structures of interest, with relevant correlations with clinical and laboratory data such as neurological evaluation and sleep records. In the third project, a robust pipeline for assessing radiomic susceptibility-based features reliability was implemented within a cohort of patients with multiple sclerosis and healthy controls. Lastly, a deep learning super-resolution model was applied to QSM images of healthy controls. The employed model demonstrated excellent generalization abilities and outperformed traditional up-sampling methods, without requiring a customized re-training. Across the three disorders investigated, it was evident that QSM is capable of distinguishing between patient groups and healthy controls while establishing correlations between imaging measurements and clinical data. These studies lay the foundation for future research, with the ultimate goal of achieving earlier and less invasive diagnoses of neurodegenerative disorders within the context of personalized medicine.

Cancers of unknown primary site (CUPs) as a model of metastatic process.

Cancers of unknown primary site (CUPs) are a rare group of metastatic tumours, with a frequency of 3-5%, with an overall survival of 6-10 month. The identification of tumour primary site is usually reached by a combination of diagnostic investigations and immunohistochemical testing of the tumour tissue. In CUP patients, these investigations are inconclusive. Since international guidelines for treatment are based on primary site indication, CUP treatment requires a blind approach. As a consequence, CUPs are usually empiric treated with poorly effective. In this study, we applied a set of microRNAs using EvaGreen-based Droplet Digital PCR in a retrospective and prospective collection of formalin-fixed paraffin-embedded tissue samples. We assessed miRNA expression of 155 samples including primary tumours (N=94), metastases of known origin (N=10) and metastases of unknown origin (N=50). Then, we applied the shrunken centroids predictive algorithm to obtain the CUP’s site(s)-of-origin. The molecular test was successfully applied to all CUP samples and provided a site-of-origin identification for all samples, potentially within a one-week time frame from sample inclusion. In the second part of the study we derived two CUP cell lines, and corresponding patient-derived xenografts (PDXs). CUP cell lines and PDXs underwent histological, molecular, and genomic characterization confirming the features of the original tumour. Tissues-of-origin prediction was obtained from the tumour microRNA expression profile and confirmed by single cell RNA sequencing. Genomic testing analysis identified FGFR2 amplification in both models. Drug-screening assays were performed to test the activity of FGFR2-targeting drug and the combination treatment with the MEK inhibitor trametinib, which proved to be synergic and exceptionally active, both in vitro and in vivo. In conclusion, our study demonstrated that miRNA expression profiling could be employed as diagnostic test. Then we successfully derived two CUP models from patients, used for therapy tests, bringing personalized therapy closer to CUP patients.