German China

Spain: Cancer Risk Analysis

Publicly Available Data Helped Identify New Cancer Genes

| Editor: Alexander Stark

Three researchers at the CRG identified new cancer genes only using publically available data.
Three researchers at the CRG identified new cancer genes only using publically available data. (Source: Pixabay / CC0)

Three researchers at the Centre for Genomic Regulation (CRG) in Barcelona, led by the Icrea Research Professor Ben Lehner, have developed a new statistical method to identify cancer predisposition genes from tumour sequencing data. Their computational method uses an old idea that cancer genes often require ‘two hits’ before they cause cancer.

Barcelona/Spain — There are many genetic causes of cancer: while some mutations are inherited from your parents, others are acquired all throughout your life due to external factors or due to mistakes in copying DNA. Large-scale genome sequencing has revolutionised the identification of cancers driven by the latter group of mutations — somatic mutations — but it has not been as effective in the identification of the inherited genetic variants that predispose to cancer. The main source for identifying these inherited mutations is still family studies.

The scientists at the CRG developed a method that allowed them to systematically identify cancer genes from existing cancer genome datasets. The method allows researchers to find risk variants without a control sample, meaning that they do not need to compare cancer patients to groups of healthy people. Solip Park, first author of the study and Juan de la Cierva postdoctoral researcher at the CRG, explained that now they had a powerful tool to detect new cancer predisposition genes and, consequently, to contribute to improving cancer diagnosis and prevention in the future.

The work, which is published in Nature Communications, presents their statistical method 'Alfred' and identifies 13 candidate cancer predisposition genes, of which 10 are new. The team applied their method to the genome sequences of more than 10,000 cancer patients with 30 different tumour types and identified known and new possible cancer predisposition genes that have the potential to contribute substantially to cancer risk.

According to Ben Lehner, principal investigator of the study, their results showed that the new cancer predisposition genes might have an important role in many types of cancer. For example, they were associated with 14 % of ovarian tumours, 7 % of breast tumours and to about 1 in 50 of all cancers. For example, inherited variants in one of the newly-proposed risk genes — NSD1 — may be implicated in at least 3 out of 1,000 cancer patients.

Melatonin Helps Burn Calories and Reduces Weight Gain

Spain: Hormone Treatment

Melatonin Helps Burn Calories and Reduces Weight Gain

15/05/2018 - Researchers from the University of Granada, University Hospital La Paz (Madrid) and the University of Texas (USA) have identified a new molecular mechanism underlying the anti-obesity effects of the chronic administration of melatonin, a naturally occurring hormone released by the pineal gland overnight. read...

When Sharing is Key to Advance Knowledge

The researchers worked with genome data from several cancer studies from around the world, including The Cancer Genome Atlas (TCGA) project and also from several projects having nothing to do with cancer research. They managed to develop and test a new method that hopefully will improve their understanding of cancer genomics and will contribute to cancer research, diagnostics and prevention just by using public data.

Ben Lehner adds that their work highlighted how important it was to share genomic data. It was a success story for how being open was far more efficient and had a multiplier effect, he said. The researchers combined data from many different projects and by applying a new computational method were able to identify important cancer genes that were not identified by the original studies. Many patient groups lobby for better sharing of genomic data because it was only by comparing data across hospitals, countries and diseases that they could obtain a deep understanding of many rare and common diseases. Unfortunately, many researchers still would not share their data and this was something we needed to actively change as a society, said Lehner.

K Computer Predicts Exotic Di-Omega Particle

Japan: Quantum Chromodynamics

K Computer Predicts Exotic Di-Omega Particle

27/05/2018 - Japanes scientists used one of the most powerful computers in the world to predicted a new type of dibaryon — a particle that contains six quarks instead of the usual three. Studying how these elements form could help scientists understand the interactions among elementary particles in extreme environments such as the interiors of neutron stars or the early universe moments after the Big Bang. read...

Postdoctoral researcher Solip Park joined the CRG from South Korea with a Novartis Postdoctoral Fellowship and, more recently has been awarded a Juan de la Cierva Fellowship and the CRG “Women Scientists Support (Woss)” grant. This Woss special grant is an internal initiative of the CRG Gender Balance Committee to support female scientists who have the ambition and potential to reach a leading position in research.

Reference: Solip Park, Fran Supek, and Ben Lehner. ‘Systematic discovery of germline cancer predisposition genes through the identification of somatic second hits’ Nature Communications (2018) 9:2601 | DOI: 10.1038/s41467-018-04900-7

Comments are being loaded ....

Leave a comment

The comment is checked by an editor and will be released soon.

  1. Avatar
    Avatar
    Edited by at
    Edited by at
    1. Avatar
      Avatar
      Edited by at
      Edited by at

Comments are being loaded ....

Report comment

Kommentar Freigeben

Der untenstehende Text wird an den Kommentator gesendet, falls dieser eine Email-hinterlegt hat.

Freigabe entfernen

Der untenstehende Text wird an den Kommentator gesendet, falls dieser eine Email-hinterlegt hat.

copyright

This article is protected by copyright. You want to use it for your own purpose? Contact us via: support.vogel.de/ (ID: 45401277 / Laborpraxis Worldwide)