Researchers say they have developed a new computational technique that allows the identification of molecular alterations associated with prognosis and resistance to therapy of different types of cancer. The team published its study (“psichomics: graphical application for alternative splicing quantification and analysis”) in Nucleic Acids Research.

“Alternative pre-mRNA splicing generates functionally distinct transcripts from the same gene and is involved in the control of multiple cellular processes, with its dysregulation being associated with a variety of pathologies. The advent of next-generation sequencing has enabled global studies of alternative splicing in different physiological and disease contexts. However, current bioinformatics tools for alternative splicing analysis from RNA-seq data are not user-friendly, disregard available exon-exon junction quantification, or have limited downstream analysis features,” write the investigators.

“To overcome such limitations, we have developed psichomics, an R package with an intuitive graphical interface for alternative splicing quantification and downstream dimensionality reduction, differential splicing and gene expression, and survival analyses based on The Cancer Genome Atlas, the Genotype-Tissue Expression project, the Sequence Read Archive project, and user-provided data. These integrative analyses can also incorporate clinical and molecular sample-associated features. We successfully used psichomics in a laptop to reveal alternative splicing signatures specific to stage I breast cancer and associated novel putative prognostic factors.”

Cancer cells are characterized by perturbations in the regulation of genes and, in particular, by alterations in alternative splicing. Some of those alterations are associated with different malignant features of cancer and its resistance to treatment but vary from tumor to tumor. “Each patient hosts a different cancer, so that scientists and clinicians need molecular information about many individuals to, supported by data, understand disease mechanisms, assess prognosis, and make predictions on the best treatment for each patient based on their tumor's molecular profile,” explains Nuno Barbosa Morais, Ph.D., group leader at the Instituto de Medicina Molecular (iMM) João Lobo Antunes in Portugal.

“We have created a software that, by analyzing large databases with clinical and splicing information for thousands of tumors, detects patterns of similarities between different cases and allows, for instance, to identify the relation of each molecular alteration with patient survival, for more than thirty types of cancer. In practice, the program allows us to quickly convert a lot of genome-wide data into biological information with clinical potential,” adds Nuno Saraiva Agostinho, first author of the study and a Ph.D. student at the Centro Académico de Medicina de Lisboa, da Faculdade de Medicina da Universidade Lisboa (CAML).

“Thanks to this approach, we have already identified a possible mechanism of resistance to chemotherapy in colorectal cancer that we are now investigating, in an international collaboration that we are leading. We have also identified a new prognostic marker in breast cancer that we will now study, teaming up with other iMM colleagues,” says Dr. Barbosa Morais, who supervised the study. 

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