Tumori Foundation

Early relapse in multiple myeloma (MM) is a major predictor of poor prognosis, regardless of cytogenetic risk or treatment intensity. Here we analyzed 1026 MM patients treated across 12 Italian hematology centers. FHR was defined as progression-free survival (PFS) ≤18 months in transplant-eligible (TE) and ≤12 months in non-transplant-eligible (NTE) patients. Logistic regression and ROC analysis were used to identify significant predictors of FHR and build a risk score. FHR status was identified in 175 patients (17%). These patients had significantly shorter PFS (7 vs. 57.5 months) and overall survival (19 months vs. not reached; p < 0.001). FHR status was associated with higher median LDH, lower Hb level, higher creatinine level and lower platelets count. Modified EASIX formula was built by these significant continuous variables, to be tested in a logistic analysis: [(LDH × creatinine)/(Hb × PLT) × 100]. A significantly higher rate of FHR was found with a score > 2.0 (89% vs. 11%, p < 0.001). Multivariate logistic analysis selected the above formula, ECOG PS ≥ 2 and ISS III as factors associated with FHR. Scoring these variables according to OR, three groups of patients were segregated with a rate of FHR patients of 7%, 29.5%, and 63.5%, respectively. Treatment with anti-CD38 monoclonal antibodies was associated with lower FHR frequency. This study proposes a simple, clinically applicable model to identify FHR MM patients early in their disease course. However, very in-depth biological tools, not available in clinical practice, are needed to identify singularly risk of becoming FHR.

Introduction The microbiota includes the collection of microorganisms that colonize our body, playing a pivotal role in maintaining homeostasis and modulating immune responses. An imbalance in the composition of these microbes is associated with various diseases, including oncological conditions [1]. From a methodological perspective, several aspects should be considered during microbiome analysis, such as the compositional nature of the data, its high dimensionality, overdispersion, and the prevalence of zero-inflated count data [2]. The institutional microbiome project currently active at the Fondazione IRCCS Istituto Nazionale dei Tumori was designed to assess the feasibility of investigating the microbiota in different tumors settings and biological matrices, with the final aim of identifying microbial communities able of distinguishing patients characterized by worse prognosis from those with less aggressive disease. The cancer settings included in the projects are lung, breast and prostate cancer, as well as pseudomyxoma peritonei. Objectives Given the unique characteristics of microbiome data, we propose a unified workflow designed for analysing data from various cancer settings to ensure consistency and comparability across the different cancer types. Methods Biological samples analysed in this study were disease-specific and include, tumor tissues and matched normal counterpart for lung cancer, tumor tissues for breast and prostate cancer, matched tumor tissue, feces and mucin for pseudomyxoma peritonei. Samples were processed using the same standardized analytical pipeline across all pathologies. This process includes bacterial DNA extraction, enrichment, library preparation and sequencing of the variable regions of the bacterial 16S rRNA gene. Microbial communities were firstly characterized in terms of alpha- and beta-diversity: the first one quantifies the diversity within a given sample in terms of richness or eveness [3], whereas second one assesses diversity differences between sample-groups. Specifically, for alpha-diversity we used a set of different indices: Chao1, Hill and Observed for richness, Gini-Simpson and Shannon for eveness [3]; for beta-diversity the Principal Coordinate Analysis (PCoA) analysis [4,5] was adopted together with the PERmutational Multivariate ANalysis of VAriance (PERMANOVA) [4] and PERmutational Multivariate analysis of DISPersion (PERMDISP) tests [6] by using the Bray-Curtis distance metrics [5]. Then, the workflow incorporates tests and statistical models for both continuous and categorical data to identify bacterial taxa that are differentially expressed or present in distinct biological matrices or associated with clinical-pathological characteristics investigated in each cancer context. Test for paired or unpaired groups comparison were included for both continuous and categorical data analysis. Results Using lung cancer as a model setting, matched tumor and normal counterpart of 155 lung cancer patients (stage I-III), were profiled using 16S rRNA gene sequencing for a total of 310 observation and 63 identified bacteria taxa (i.e. genus level). The analysis revealed distinct microbial compositions, in terms of beta-diversity, according to histology. Moreover, by modelling the count data and its presence/absence, a specific subset of bacteria was significantly associated with tumor progression and aggressiveness; specific bacteria were also found differentially expressed between tissue types (tumor or normal counterpart). We are now applying this workflow at the other settings, with the final aim of better understanding the bacteria that characterize tumor aggressiveness. Conclusion The developed workflow allows: (i) the use of a shared pre-analytical and analytical workflow of analysis among the different tumor settings under investigation, (ii) the characterization of the microbial community within/between samples and (iii) the evaluation of associations between specific taxa bacteria and tumor characteristics.

Uveal melanoma (UM) is an aggressive cancer of the eye that frequently results in metastatic death. UMs are most likely to metastasize when they are small, at a time when they are difficult to distinguish from benign nevi and often observed without treatment. Unfortunately, little is known about the early genetic evolution of UM or potential biomarkers to indicate small tumors undergoing malignant transformation. Here, we performed targeted next generation sequencing for the 7 canonical UM driver mutations in 1140 primary UMs, including 131 small early-stage tumors. We found that the evolutionary burst of genetic aberrations that determines the archetypal UM subtypes and metastatic propensity has already occurred by the time most small tumors are biopsied, although a significantly larger proportion of small tumors are still evolving compared to larger tumors. We found that the 15-gene expression profile (15-GEP) support vector machine discriminant score was the best indicator of tumors in transition from low-risk Class 1 to high-risk Class 2 signature. While BAP1, SF3B1 and EIF1AX mutations were associated with poor, intermediate and good prognosis, respectively, mutation analysis was inferior to the prospectively validated 15-GEP + PRAME expression classifier for predicting metastasis-free and overall survival. These results provide a more complete picture of genetic evolution in UM, and they move us closer to a molecular definition of malignant transformation in this cancer type.

90Y glass microspheres have a shelf life of 12days from the calibration date, allowing flexible administration after a variable decay interval. For a fixed intended activity, a longer interval results in a higher number of administered microspheres per GBqand in a lower activity per sphere as. This study aimed to demonstrate that, for a fixed Tumour Absorbed Dose (TAD), Tumour Control Probability (TCP) is higher when the decay interval is shorter (4days vs. 8days). In the second part of the study, we focused on fully perfused lesions, i.e. those showing matching perfused and radiological volumes, where calculating mean microsphere spatial density (ρ) is meaningful. We investigated which variable was associated with radiological response. We retrospectively analysed lesion-by-lesion response at the best response time using the mRECIST criterion. Two chronologically sequential cohorts of patients were compared. Both cohorts were planned and treated with lobar administration, with exactly the same methodology, except for the post-calibration decay interval (Administration day: day 4 vs day 8). Infiltrative lesions and those with additional treatments post-radioembolization were excluded. To compare efficacy at the same TAD, we plotted TCP curves as a function of TAD and stratified TCP by lesion mass (M), using an arbitrary 50g cut-off (equivalent to a 4.6cm diameter). On fully perfused lesions, TCP was analyzed also as function of ρ and mass. We then conducted univariate ROC and multivariate analysed to assess response impact based on TAD, ρ, as, and M, grouping CR + PR (Objective Response, OR) versus SD + PD, as well as CR versus PR + SD + PD. A total of 94 patients with 150 lesions were analyzed. TCP (for TAD up to 600Gy) was significantly higher for the 59 lesions treated on Day 4 compared to the 91 lesions treated on Day 8, though the difference diminished at higher TAD values. In fully perfused lesions, TCP plateaued at 344Gy and 160Gy for lesions treated on Day 8 and Day 4 respectively. ROC analysis for fully perfused lesions showed poor AUC values for CR + PR versus SD + PD: 0.62, p = 0.01 for as, 0.63, p = 0.01 for TAD, and 0.60 p = 0.01 for M, with AUC forρ being non-significant. When comparing CR versus PR + SD + PD classes, only M was significant, with a fair AUC value of 0.71, p = 0.01. Multivariate analysis showed that CR + PR was significantly associated only to as, with 79% higher response probability for administration on Day 4. When considering CR alone, significance was confirmed only for M, with an odd ratio of 0.19. Our findings on TCP confirmed our preliminary unpublished studies from a different lesion cohort assessed by two independent radiologists. Additionally, our results align with recent experimental histological studies on complete pathological necrosis (CPN) in explanted liver samples after neoadjuvant segmentectomy prior to liver transplantation. However, our data and the CPN findings contrast with previously published simulations based purely on physical parameters (dose-volume histograms at microscopic scale). No explanation is currently available for this discrepancy. When considering OR probability, administration of 90Y glass microsphere on Day 8 is not recommended, favouring Day 4 instead. For tumours < 50g treated on Day 4, increasing TAD beyond 160Gy does not improve response probability but may increase toxicity risk in lobar administrations. Conversely, larger lesions benefit from a maximal tolerable activity approach. TRIAL REGISTRATION: NumberDose in TARE, INT 154/19. Registered on 8 August 2019.