For many years, scientists have struggled to understand and cure cancer. The study of the genome of multiple tumors has been fundamental to detect recurrent alterations in several types of cancer, and has facilitated their classification and the development of new therapeutic strategies.

In particular, high-throughput methodologies have been applied in the context of multiple international projects to detect actionable alterations, i.e. genetic changes in the genome of cancer cells that can be used to develop new targeted therapies. These studies have highlighted the heterogeneity of genetic alterations in patients suffering from the same type of cancer, motivating the development of individualized treatments. However, known actionable alterations tend to occur at low frequency, and often a tumor sample has fewer mutations than those seemingly necessary to explain the tumoral process. Thus, there is a need to expand the catalogue of cancer signatures to integrate other molecular alterations for the characterization of individual tumors.

Most of the strategies used in cancer genome projects are based on searching for genetic alterations or changes in the expression of genes. On the other hand, there is more and more evidence that alterations in the splicing regulatory program play an important role in tumor transformation. That has benn demostrated in an study led by Eduardo Eyras, ICREA researcher at the Department of Experimental and Health Sciences (CEXS).

The work has been published in the journal Nucleic Acids Research which explains the results of applying a new computational method developed by these scientists to project data from the Cancer Genome Atlas (TCGA) branch funded by the National Institutes of Health (NIH) US project ICGC (International Cancer Genome Consortium). This work represents the first large-scale analysis published describing alterations in splicing of new cancers using RNA sequencing data from more than 4000 samples.

In this work, they discovered that there exist many splicing switches in patients with the same cancer type and showed that these switches separate with high accuracy tumor and normal samples, and different types of cancer from each other, providing a predictive signature for the disease.

In particular, they provide simple rules based on the expression of a few RNA molecules that would potentially allow determining the cancer type from an RNA sample of a new patient. Additionally, they find similar rules for the triple-negative breast cancer subtype, which is one of the most aggressive subtypes of breast cancer. This new computational method reveals novel signatures of cancer in terms of RNA transcript isoforms specifically expressed in tumors, providing potential novel molecular targets for prognosis and therapy.

Changes in splicing or switches may become cancer

Splicing is a process that takes place in the cell nucleus and by which the long RNA molecule transcribed from the gene in the genome is processed to remove segments called introns, giving rise to an RNA transcript.

Alternative splicing provides a mechanism to generate multiple RNA transcripts from the same gene by eliminating introns in different ways. This process is tightly regulated, and is known to give rise to proteins with cell-type specific or opposing functions, or even provide a way to activate or deactivate gene function. This change in splicing between conditions is generally called a splicing switch.

Splicing switches that are not originally present and regulated by the cell, can induce altered cellular states, leading to disease. Accordingly, the determination of alterations in alternative splicing in tumors can be fundamental for the development of tumor specific molecular targets for prognosis and therapy. However, the study of splicing in cancer is hindered by the heterogeneity of tumors of the same origin from different individuals, as well as by the normal variability between individuals. Sebestyén and colleagues have published a new computational method, robust to biological and technical variability, which identifies significant splicing switches across a large number of tumor samples and  shows high accuracy on held-out datasets.

Sebestyén et al. applied this new method to data from the Cancer Genome Atlas (TCGA) project. The method is capable of identifying complex alternative splicing changes that cannot be described using standard methodologies. Additionally, the method is independent of parameterizations, which is especially relevant currently for the analysis of RNA sequencing data from samples from multiple laboratories and technological platforms.

This new computational method reveals novel signatures of cancer in terms of RNA transcript isoforms specifically expressed in tumors, providing potential novel molecular targets for prognosis and therapy.

Eyras has had the collaboration of Endre Sebestyén, member of his Computational Genomics Lab. and a special collaboration of Michal Zawisza, computer science from the Polytechnic University of Catalonia, recently graduated, who has shown to have a strong interest and willingness to biology.

This work has been funded by the Cancer Foundation Sandra Ibarra and the Spanish network of the splicing RNAREG, funding by the Ministry of Economy and Competitiveness.

Reference article:

Endre Sebestyén, Michał Zawisza, Eduardo Eyras (2015), " Detection of recurrent alternative splicing switches in tumor samples reveals novel signatures of cancer", Nucleic Acids Research, doi: 10.1093 / NAR / gku1392.

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