BoBafit : A copy number clustering tool designed to refit and recalibrate the baseline region of tumors' profiles.

Human cancer arises from a population of cells that have acquired a wide range of genetic alterations, most of which are targets of therapeutic treatments or are used as prognostic factors for patient's risk stratification. Among these, copy number alterations (CNAs) are quite frequent. Currently, several molecular biology technologies, such as microarrays, NGS and single-cell approaches are used to define the genomic profile of tumor samples. Output data need to be analyzed with bioinformatic approaches and particularly by employing computational algorithms. Molecular biology tools estimate the baseline region by comparing either the mean probe signals, or the number of reads to the reference genome. However, when tumors display complex karyotypes, this type of approach could fail the baseline region estimation and consequently cause errors in the CNAs call. To overcome this issue, we designed an R-package, BoBafit , able to check and, eventually, to adjust the baseline region, according to both the tumor-specific alterations' context and the sample-specific clustered genomic lesions. Several databases have been chosen to set up and validate the designed package, thus demonstrating the potential of BoBafit to adjust copy number (CN) data from different tumors and analysis techniques. Relevantly, the analysis highlighted that up to 25% of samples need a baseline region adjustment and a redefinition of CNAs calls, thus causing a change in the prognostic risk classification of the patients. We support the implementation of BoBafit within CN analysis bioinformatics pipelines to ensure a correct patient's stratification in risk categories, regardless of the tumor type.

Clonal and subclonal TP53 molecular impairment is associated with prognosis and progression in multiple myeloma.

Aberrations on TP53, either as deletions of chromosome 17p (del17p) or mutations, are associated with poor outcome in multiple myeloma (MM), but conventional detection methods currently in use underestimate their incidence, hindering an optimal risk assessment and prognostication of MM patients. We have investigated the altered status of TP53 gene by SNPs array and sequencing techniques in a homogenous cohort of 143 newly diagnosed MM patients, evaluated both at diagnosis and at first relapse: single-hit on TP53 gene, either deletion or mutation, detected both at clonal and sub-clonal level, had a minor effect on outcomes. Conversely, the coexistence of both TP53 deletion and mutation, which defined the so-called double-hit patients, was associated with the worst clinical outcome (PFS: HR 3.34 [95% CI: 1.37-8.12] p = 0.008; OS: HR 3.47 [95% CI: 1.18-10.24] p = 0.02). Moreover, the analysis of longitudinal samples pointed out that TP53 allelic status might increase during the disease course. Notably, the acquisition of TP53 alterations at relapse dramatically worsened the clinical course of patients. Overall, our analyses showed these techniques to be highly sensitive to identify TP53 aberrations at sub-clonal level, emphasizing the poor prognosis associated with double-hit MM patients.

The induction of α-helical structure in partially unfolded HypF-N does not affect its aggregation propensity.

The conversion of proteins into structured fibrillar aggregates is a central problem in protein chemistry, biotechnology, biology and medicine. It is generally accepted that aggregation takes place from partially structured states of proteins. However, the role of the residual structure present in such conformational states is not yet understood. In particular, it is not yet clear as to whether the α-helical structure represents a productive or counteracting structural element for protein aggregation. We have addressed this issue by studying the aggregation of pH-unfolded HypF-N. It has previously been shown that the two native α-helices of HypF-N retain a partial α-helical structure in the pH-unfolded state and that these regions are also involved in the formation of the cross-ß structure of the aggregates. We have introduced mutations in such stretches of the sequence, with the aim of increasing the α-helical structure in the key regions of the pH-unfolded state, while minimizing the changes of other factors known to influence protein aggregation, such as hydrophobicity, ß-Sheet propensity, etc. The resulting HypF-N mutants have higher contents of α-helical structure at the site(s) of mutation in their pH-unfolded states, but such an increase does not correlate with a change of aggregation rate. The results suggest that stabilisation of α-helical structure in amyloidogenic regions of the sequence of highly dynamic states does not have remarkable effects on the rate of protein aggregation from such conformational states. Comparison with other protein systems indicate that the effect of increasing α-helical propensity can vary if the stabilised helices are in non-amyloidogenic stretches of initially unstructured peptides (accelerating effect), in amyloidogenic stretches of initially unstructured peptides (no effect) or in amyloidogenic stretches of initially stable helices (decelerating effect).