The impact of polymorphisms in P-gp, DNA repair and folic acid metabolism genes in newly diagnosed multiple myeloma patients treated with thalidomide plus dexamethasone, with or without bortezomib

The principle aim of this study was to investigate biological predictors of response and resistance to multiple myeloma treatment. Two hypothesis had been proposed as responsible of responsiveness: SNPs in DNA repair and Folate pathway, and P-gp dependent efflux. As a first objective, panel of SNPs in DNA repair and Folate pathway genes, were analyzed. It was a retrospective study in a group of 454, previously untreated, MM patients enrolled in a randomized phase III open-label study. Results show that some SNPs in Folate pathway are correlated with response to MM treatment. MTR genotype was associated with favorable response in the overall population of MM patients. However, this relation, disappear after adjustment for treatment response. When poor responder includes very good partial response, partial response and stable/progressive disease MTFHR rs1801131 genotype was associated with poor response to therapy. This relation - unlike in MTR – was still significant after adjustment for treatment response. Identification of this genetic variant in MM patients could be used as an independent prognostic factor for therapeutic outcome in the clinical practice. In the second objective, basic disposition characteristics of bortezomib was investigated. We demonstrated that bortezomib is a P-gp substrate in a bi-directional transport study. We obtain apparent permeability rate values that together with solubility values can have a crucial implication in better understanding of bortezomib pharmacokinetics with respect to the importance of membrane transporters. Subsequently, in view of the importance of P-gp for bortezomib responsiveness a panel of SNPs in ABCB1 gene - coding for P-gp - were analyzed. In particular we analyzed five SNPs, none of them however correlated with treatment responsiveness. However, we found a significant association between ABCB1 variants and cytogenetic abnormalities. In particular, deletion of chromosome 17 and t(4;14) translocation were present in patients harboring rs60023214 and rs2038502 variants respectively.

Serum anti-interferon alpha antibodies in chronic hepatitis C patients treated with pegylated interferon alpha containing therapy

The development of anti-IFNα antibodies is an occurrence described in chronic hepatitis C patients during treatment with Interferonα/PEG-Interferonα. However, its relevance, especially in difficult-to treat patients, has not been defined. Methods: We retrospectively measured the serum levels of anti-IFNα antibodies (baseline and week 12) and IFNα levels (week 12) by ELISA in 76 previous non-responders, and in 14 naive patients treated with Pegylated-IFNα and Ribavirin. A group of 57 healthy donors (HD) was also assessed as control. Positivity to anti-IFNα antibodies was established on the values of HD. Results: Baseline anti-IFNα antibodies were detected in 15.5% of patients and in 7% of HD, with significantly higher concentrations in patients than HD (181.5±389.9 vs 95.9±143.0 ng mL−1, p=0.0023). All positive patients were IFNα-experienced. At week 12, the prevalence of positivity increased to 22.3 and 28.5% in experienced and naïve patients, respectively, and the levels of anti-IFNα antibodies did not differ between the two groups (391±792.3 vs 384.7±662.6 ng mL−1, respectively). IFNα concentrations were significantly lower in antibody-positive patients than in antibody-negatives (988.2±1402 vs 3462±830.8 pg mL−1, p≤0.0001) and the levels of antibodies and IFNα were inversely correlated (r=-0.405, p=0.0001). The antibody-positive population clustered in null responders (67%) and 19/21 patients (90%) did not achieve SVR. Conclusions: The development of anti-IFNα antibodies is a non-negligible occurrence in patients treated with PEG-IFNα, is stable over time, and has a relevant clinical impact when associated with low levels of circulating PEG-IFNα. It should be considered in patients undergoing treatments including PEG-IFNα.

Exploratory analysis of the role of circulating microRNA in metastatic melanoma patients

MicroRNAs act as oncogene or tumor suppressor gene regulators and are actively released from tumor cells in the circulation. Specific microRNAs can be isolated and quantified in the blood, usually in serum or plasma fractions, where they are uncommonly stable. Cell-free microRNAs serve many, and possibly yet unexplored, functional roles and microRNA levels reflect underlying conditions and have been associated with skin cancer presence, stage and evolution. However, the clinical potential of circulating miRNAs in metastatic melanoma remains largely undefined. From May 2020 to September 2022, we conducted a spontaneous, monocentric, exploratory study on human tissues in vitro, which aimed to evaluate the prognostic and predictive role of circulating miRNAs in metastatic melanoma patients. At the Medical Oncology Unit of Policlinico Sant’Orsola-Malpighi of Bologna, peripheral venous blood samples from patients with metastatic melanoma treated with checkpoint inhibitors (CPI) were collected before the start of CPI (baseline, T0) and longitudinally, approximately every 3 months (T1, T2, etc). Circulating miRNA quantification was performed by droplet digital PCR (Biorad) using an EvaGreen and LNA primer-based assays. QuantaSoft Program (Biorad) calculated the absolute quantifications of each miRNA, indicated as copies/µL. After analysis of the literature, we chose to analyze miR-155-5p, miR-320a and miR-424-5p level. All miRNAs except miR-424-5p show a significantly higher level in plasma of patients who are alive after 1 year of follow-up. High/low levels of baseline miR-155-5p, miR-320a and miR-424-5p are significantly associated with overall survival and progression-free survival. Furthermore, a preliminary analysis on the group of patients who received first-line with anti-PD-1 (N=7), baseline miR-155-5p shows higher levels in responder vs. non responder patients (p 0.06). These data, though promising, are preliminary and need to be further investigated in a larger cohort of patients.

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).