Preclinical evaluation of a MYCN-specific inhibitor for the treatment of small cell lung cancer

Small cell lung cancer (SCLC) is the most aggressive form of lung cancer, characterized by rapid growth, early metastasis and acquired drug resistance. SCLC is usually sensitive to initial treatment, however, most patients relapse within few months; thus more effective therapies are urgently needed. Key genetic alterations very frequently observed in SCLC include loss of TP53 and RB1 and mutations in the MYC family genes (MYC, MYCL or MYCN). One of them is amplified and overexpressed in a mutually exclusive manner and represents the most prominent activating oncogene alteration in this malignancy. In particular, MYCN amplification is associated with tumor progression, treatment failure and poor prognosis. Given the role of MYCN in SCLC and its restricted expression profile, MYCN represents a promising therapeutic target; although it is considered undruggable by traditional approaches. An innovative approach to target the oncogene concerns specific MYCN expression inhibition, acting directly at the level of DNA, through an antigene peptide nucleic acid (agPNA) oligonucleotide, called BGA002. This thesis focused on the study of BGA002, as a possible targeted therapeutic strategy for the treatment of MYCN-related SCLC. In this context, BGA002 proved to be a specific and highly effective inhibitor. Furthermore, MYCN silencing induced alterations in many downstream pathways and led to apoptosis, in concomitance with autophagy reactivation. Moreover, systemic administration of BGA002 was effective in vivo as well, significantly increasing survival in MNA mouse models, even in the scenario of multidrug-resistance. In addition, BGA002 treatment successfully reduced N-Myc protein expression and, more importantly, caused a massive diminishment in tumor vascularization in the multidrug-resistant model. Overall, these results proved that MYCN inhibition by BGA002 may represent a new promising precision medicine approach, to treat MYCN-related SCLC.

Developing a radiomic based machine learning model to identify patients with resected non-small-cell lung cancer at high risk of relapse

Background There is a wide variation of recurrence risk of Non-small-cell lung cancer (NSCLC) within the same Tumor Node Metastasis (TNM) stage, suggesting that other parameters are involved in determining this probability. Radiomics allows extraction of quantitative information from images that can be used for clinical purposes. The primary objective of this study is to develop a radiomic prognostic model that predicts a 3 year disease free-survival (DFS) of resected Early Stage (ES) NSCLC patients. Material and Methods 56 pre-surgery non contrast Computed Tomography (CT) scans were retrieved from the PACS of our institution and anonymized. Then they were automatically segmented with an open access deep learning pipeline and reviewed by an experienced radiologist to obtain 3D masks of the NSCLC. Images and masks underwent to resampling normalization and discretization. From the masks hundreds Radiomic Features (RF) were extracted using Py-Radiomics. Hence, RF were reduced to select the most representative features. The remaining RF were used in combination with Clinical parameters to build a DFS prediction model using Leave-one-out cross-validation (LOOCV) with Random Forest. Results and Conclusion A poor agreement between the radiologist and the automatic segmentation algorithm (DICE score of 0.37) was found. Therefore, another experienced radiologist manually segmented the lesions and only stable and reproducible RF were kept. 50 RF demonstrated a high correlation with the DFS but only one was confirmed when clinicopathological covariates were added: Busyness a Neighbouring Gray Tone Difference Matrix (HR 9.610). 16 clinical variables (which comprised TNM) were used to build the LOOCV model demonstrating a higher Area Under the Curve (AUC) when RF were included in the analysis (0.67 vs 0.60) but the difference was not statistically significant (p=0,5147).