BoBafit: a copy number clustering tool designed to refit and recalibrate the baseline region of tumors’ profiles

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Tools generating CN profiles derive the baseline region from samples’ median CN signal.

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This canonical approach might cause wrongly estimated CN profile in complex karyotypes.

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CNAs call is crucial for patients’ risk stratification aimed at personalized treatment.

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BoBafit computes the correct baseline region and the CN profile, taking into account tumor genomic complexity and samples-specific alterations.

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BoBafit should be implemented within CN analysis pipelines especially for clinical aims.

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.

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-β 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, β-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).