CYP17A1 polymorphisms and clinical outcome of patients with metastatic castration-resistant prostate cancer treated with abiraterone

Background. Abiraterone acetate is a potent inhibitor of cytochrome P450 17 α-hydrolase (CYP17A1) that causes a reduction in the synthesis of testosterone in the adrenal glands, testes and tumor microenvironment. Blocking androgen production, abiraterone has been shown to prolong progression-free survival (PFS) and overall survival (OS) in patients with metastatic castration-resistant prostate cancer (CRPC) previously submitted to chemotherapy. The aim of our study was to verify the role of single nucleotide polymorphisms (SNPs) in predicting clinical outcome in CRPC patients treated with abiraterone after chemotherapy. Methods. We analyzed 48 CRPC consecutive patients treated with abiraterone after at least one chemotherapeutic regimen with docetaxel. DNA was extracted from peripheral blood and genotyped for four polymorphisms in the CYP17A1 gene (rs743572, rs10883783, rs17115100, rs284849). PFS and OS survival curves were used to identify statistical associations between haplotypes and clinical outcome. Results. Forty-eight Caucasian patients with metastatic CRPC treated with abiraterone were genotyped for polymorphisms in the CYP17A1 gene. All samples were evaluable for both sequencing and TaqMan Genotyping assay. The CRPC patients treated with abiraterone had a median PFS and OS of 7.6 months (95% CI: 4.3-10.5) and 17.6 months (95% CI: 10.5-19.0), respectively Statistical analyses highlighted a difference approaching statistical significance (log-rank test p = 0.0534) between rs10883783 and PFS. Other polymorphisms were not associated with a benefit from treatment with abiraterone. Conclusions. In our case series of 48 treated patients, rs10883783 only was identified as a possible predictive marker, results showing a trend toward statistical significance. Further analysis of this polymorphism is needed in larger series of patients to confirm our findings.

Clinical outcome and biological characteristics of Chronic Myeloid Leukemia patients treated with nilotinib front-line

The present work reports the outcome of the GIMEMA CML WP study CML0811, an independent trial investigating nilotinib as front-line treatment in chronic phase chronic myeloid leukemia (CML). Moreover, the results of the proteomic analysis of the CD34+ cells collected at CML diagnosis, compared to the counterpart from healthy donors, are reported. Our study confirmed that nilotinib is highly effective in the prevention of the progression to accelerated/blast phase, a condition that today is still associated with high mortality rates. Despite the relatively short follow-up, cardiovascular issues, particularly atherosclerotic adverse events (AE), have emerged, and the frequency of these AEs may counterbalance the anti-leukemic efficacy. The deep molecular response rates in our study compare favorably to those obtained with imatinib, in historic cohorts, and confirm the findings of the Company-sponsored ENESTnd study. Considering the increasing rates of deep MR over time we observed, a significant proportion of patients will be candidate to treatment discontinuation in the next years, with higher probability of remaining disease-free in the long term. The presence of the additional and complex changes we found at the proteomic level in CML CD34+ cells should be taken into account for the investigation on novel targeted therapies, aimed at the eradication of the disease.

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.