Science & Enterprise subscription

Please share Science & Enterprise

RSS
Follow by Email
Facebook
Facebook
Google+
Twitter
Visit Us
LinkedIn
INSTAGRAM

Project to Catalog Precision Lung Cancer Immunotherapies

Lung cancer development

Computer-generated model of lung cancer development (Jeroen Claus, Francis Crick Institute)

30 Nov. 2018. A new research initiative in the U.K. is collecting and mapping precise combinations of therapies that invoke the immune system to treat lung cancer. The Rubicon study is a project of the Francis Crick Institute and advocacy and fund-raising organization Cancer Research UK, both in London, funded by a £2.4 million ($US 3.1 million) grant from drug maker Bristol Myers Squibb.

The Rubicon project — short for rule book and immune atlas for combination therapy — aims to develop guidelines for lung cancer treatments with immunotherapies, by digging deeper into data collected by Francis Crick Institute researchers and others. One of the main providers of data to Rubicon is a current study known as TracerX, for Tracking Cancer Evolution through therapy (Rx), that follows the evolution of lung cancer in patients from initial diagnosis through treatment and cure or relapse.

The study, underway for 9 years, is recruiting 842 lung cancer patients, collecting genomic and clinical data during the time of their cancer diagnosis and treatments. TracerX aims to discover the relationships between different types of lung cancer, the various stages of those cancers, and various types of treatments including immunotherapies.

Another study providing data to Rubicon and supported by Cancer Research UK is gathering detailed data on cancer development from people who died from the disease. The study called Posthumous Evaluation of Advanced Cancer Environment, or Peace, is collecting data from people who have all types of cancer, with the researchers looking for insights into the cancer’s development and spread, treatment failures, and reactions of the body in the final stages of the disease.

The Rubicon project is expected to apply artificial intelligence tools to analyze data from these studies. The study plans to apply deep learning to analyze tumor samples and clinical outcomes to establish relationships between immune cell activity and different regions of lung tumors, as well as development of the tumor’s supportive, yet complex, microenvironment. From those inter-relationships, the researchers expect to highlight treatments, and combinations of treatments, that address those specific conditions.

Deep learning is a form of machine learning and artificial intelligence that makes it possible for systems to discern underlying patterns in relationships, and build those relationships into knowledge bases. The technique forms layers of neural networks, with each layer adding to the knowledge derived from previous layers.

Francis Crick Institute cancer researcher Charles Swanton is leading the Rubicon project. Swanton, also chief clinician at Cancer Research UK, says in a joint statement, “When we see patients with hard to treat cancers like lung, we struggle to keep up with the speed at which tumors evolve, become aggressive and resistant to treatment.” Swanton adds, “By learning more about immune suppressive cell types – the molecules they express and how stable they are during disease evolution – we hope researchers can start to develop molecularly targeted immunotherapy combination strategies.”

Bristol Myers Squibb, a developer of cancer drugs and sponsor of Rubicon, has a long-standing interest in the cancer drug combinations, as well as immune system mechanisms, the tumor microenvironment, and bioinformatics. The company says it’s studying the factors responsible for cancer immunotherapy’s inability to successfully treat more than half of the people with cancer.

More from Science & Enterprise:

*     *     *

Please share Science & Enterprise ...

4 comments to Project to Catalog Precision Lung Cancer Immunotherapies

Leave a Reply

You can use these HTML tags

<a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>

  

  

  

This site uses Akismet to reduce spam. Learn how your comment data is processed.