The Society of Cancer Management
  • Home
    • An After Life
    • News Archive
  • About
    • Terms & Conditions
    • Privacy Policy
    • Copyright Notice
  • Contact

. . . supporting research that improves cancer survival.

 
Please contact us if you would like to contribute a news item. We are keen to publish more articles from UK-based research and findings that relate to microbial infections during therapy.

Predicting the risk of severe side effects of cancer treatment

13/11/2020

0 Comments

 
PicturePhoto by Marcelo Leal on Unsplash
The risk of serious adverse effects on the blood status and bone marrow of patients during chemotherapy can be predicted by a model developed at Linköping University, Sweden. This research may make it possible to use genetic analysis to identify patients with a high probability of side effects. 

It is often difficult during cancer treatment to achieve a balance between getting rid of as many tumour cells as possible, while at the same time not causing serious side effects.

One of the common properties of tumour cells is that they grow rapidly and in an uncontrolled manner. The chemotherapy drugs that are used to treat cancer have for this reason been designed to kill rapidly growing cells. But the treatment also kills normal cells that grow rapidly. One of the more sensitive tissues is the bone marrow, where various types of blood cell are formed at a rapid rate. Approximately 25% of lung cancer patients who receive combination treatment with the drugs gemcitabine and carboplatin experience life-threatening side effects on the bone marrow during standard treatment. In many cases, the treatment must be discontinued.

We know that genetic factors play a role in the response of an individual to these treatments. Complicated interactions between many genes are probably involved. The scientists who carried out the study have therefore investigated whether genetic signatures exist that can be used to identify the patients at a high risk of experiencing severe side effects from the treatment. This would enable them to adapt treatment to the individual more accurately from the start: those with a low risk of side effects can be given higher doses, with a stronger effect on the cancer, while those with highest risk can be given another treatment. 
​
The study, published in npj Systems Biology and Applications, is a collaboration between researchers in pharmacogenetics and bioinformatics. They determined the complete DNA sequences of 96 patients with non-small cell lung cancer who had been treated with gemcitabine/carboplatin. Sequencing of the whole genome in this way provides information about millions of genetic variants that may be interesting. The researchers wanted to see whether they could find in this huge amount of data functional groups of genes that were linked to the degree of toxicity that the treatment had had on the bone marrow of the different patients

The researchers in a first step identified a network of 215 genes that were tightly linked to each other. This network was particularly rich in genes that have been associated with these drugs in previous studies. The next step was to reduce the number of genetic variants in the gene network to the 62 that are included in the final model. The researchers demonstrate that the model can be used to classify patients into one of two groups, with high or low probability of experiencing severe side effects.

"It's extremely interesting that the genes involved are associated with cell division, in particular in bone marrow. We managed not only to predict side effects for the patients, but also show that the model is biologically relevant", says Henrik Gréen, professor at the Department of Biomedical and Clinical Sciences, Linköping University.

The prediction model must be tested in further studies before it can be used in the clinic. Increasingly advanced methods of genetic analysis are being introduced into the Swedish medical care system, which makes it possible in the long term to introduce this type of method, built on an analysis of many genes at the same time.
​
"We want to work towards establishing a standard within translational bioinformatics, and show that the same type of method can be applied in several medical situations. The patient material here may appear to be small, but we have even so demonstrated that this approach can be used to predict the severity of side effects for patients", says Mika Gustafsson, senior lecturer in the Department of Physics, Chemistry and Biology at Linköping University, and, together with Henrik Gréen, leader of the study.

Björn et al. Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients. NPJ Syst Biol Appl. 2020;6:25. doi: 10.1038/s41540-020-00146-6 [Article]
0 Comments

Your comment will be posted after it is approved.


Leave a Reply.

    Cancer Therapy & Palliative Care News

    This feed features recent developments in cancer therapy and palliative care. Views in these articles do not necessarily represent those of the Cancer Management Society.

    Archives

    January 2021
    December 2020
    November 2020
    October 2020
    September 2020
    August 2020
    July 2020
    June 2020
    May 2020
    April 2020
    March 2020
    February 2020
    January 2020
    December 2019
    November 2019
    October 2019
    September 2019
    August 2019
    July 2019
    June 2019
    May 2019
    April 2019
    March 2019
    February 2019
    January 2019
    December 2018
    November 2018
    October 2018
    September 2018
    August 2018
    July 2018
    June 2018
    May 2018
    April 2018
    March 2018
    February 2018
    January 2018
    December 2017
    November 2017
    October 2017
    September 2017
    August 2017
    July 2017
    June 2017
    May 2017
    April 2017
    March 2017
    February 2017
    January 2017
    December 2016
    November 2016
    October 2016
    September 2016
    August 2016
    July 2016
    June 2016
    May 2016
    April 2016
    March 2016
    February 2016
    January 2016
    December 2015
    November 2015
    October 2015
    September 2015
    August 2015
    July 2015
    June 2015
    May 2015
    April 2015
    March 2015
    February 2015
    January 2015
    December 2014
    November 2014
    October 2014
    September 2014
    August 2014
    July 2014
    June 2014
    May 2014
    April 2014
    March 2014
    February 2014
    January 2014
    December 2013
    November 2013

    Categories

    All
    General
    Presentation
    Research
    Review

    RSS Feed

Home

About

Contact Us

Terms & Conditions

Privacy Policy

Copyright Notice

RSS Feed

Proudly powered by Weebly
© The Society of Cancer Management 2017