👤 Gina L Mazza

The PRO-CTCAE provides patient-reported data on symptomatic AEs. A summary metric-the ACS-reflecting total AE burden can be calculated by averaging AE-level composite scores at a given timepoint for each participant. This study investigated the psychometric properties and interpretability of this PRO-CTCAE ACS in patients with breast, lung, or head/neck cancers. We conducted a secondary analysis of a PRO-CTCAE validation dataset comprising 940 adults undergoing chemotherapy or radiation therapy (clinicaltrials.gov: NCT02158637). We focused on empirically recommended symptom terms for three cancer sites. Analyses included Spearman's correlations, coefficient alpha, and eigenvalues from the correlation matrices, confirmatory factor analysis (CFA), and principal component analysis (PCA). Latent profile analysis (LPA) was used to assess ACS interpretability in the lung cohort. Mean composite score inter-correlations were moderate (0.30-0.35), and coefficient alphas were high (0.81-0.91). Eigenvalue ratios and CFA supported retention of a single factor/component, with suitable model fit indices. ACS correlated highly with factor scores and the first principal component from the PCA. Reduced sets of terms produced reliable scores that closely approximated the full set scores and aligned with external criteria. LPA in the lung subgroup identified four latent classes; ACS differentiated high vs. low symptom burden groups but did not distinguish the two groups expressing distinct symptom profiles. The ACS demonstrated structural validity through adequately fitting linear factor models and effectively summarized symptomatic AE burden. However, similar ACS values may mask clinically distinct symptomatic AE profiles, underscoring the value of both summary metrics and profile-based approaches. Show less

Cardiomyopathies caused by double gene mutations are rare but conferred a remarkably increased risk of end-stage progression, arrhythmias, and poor outcome. Compound genetic mutations leading to complex phenotype in the setting of cardiomyopathies represent an important challenge in clinical practice, and genetic tests allow risk stratification and personalized clinical management of patients. We report a case of a 50-year-old woman with congestive heart failure characterized by dilated cardiomyopathy, diffuse coronary disease, complete atrioventricular block, and missense mutations in cardiac myosin-binding protein C (MYBPC3) and myopalladin (MYPN). We discuss the plausible role of genetic profile in phenotype determination. Show less

To orchestrate the genomic response to cellular stress signals, p53 recognizes and binds to DNA containing specific and well-characterized p53-responsive elements (REs). Differences in RE sequences can strongly affect the p53 transactivation capacity and occur even between closely related species. Therefore, the identification and characterization of a species-specific p53 Binding sistes (BS) consensus sequence and of the associated target genes may help to provide new insights into the evolution of the p53 regulatory networks across different species. Although p53 functions were studied in a wide range of species, little is known about the p53-mediated transcriptional signature in Danio rerio. Here, we designed and biochemically validated a computational approach to identify novel p53 target genes in Danio rerio genome. Screening all the Danio rerio genome by pattern-matching-based analysis, we found p53 RE-like patterns proximal to 979 annotated Danio rerio genes. Prioritization analysis identified a subset of 134 candidate pattern-related genes, 31 of which have been investigated in further biochemical assays. Our study identified runx1, axin1, traf4a, hspa8, col4a5, necab2, and dnajc9 genes as novel direct p53 targets and 12 additional p53-controlled genes in Danio rerio genome. The proposed combinatorial approach resulted to be highly sensitive and robust for identifying new p53 target genes also in additional animal species. Show less