World’s largest database for predicting cancer treatment response based on cancer proteins

A team of scientists from Australia and the UK has produced the largest database of its type which will be used to predict the response of an individual patients to treatments based on the protein of the cancer.

A paper published today in the leading international cancer journal, CANCER CELL, lays the foundation for ongoing efforts to predict the response of an individual cancer to treatments based on the proteins the cancer contains. These data will also inform the development of new treatments.

Two research teams, one at the Wellcome Sanger Institute in Cambridge, UK, and another at Children’s Medical Research Institute (CMRI) in Sydney, Australia are jointly announcing the completion of a protein map for 949 cancers of many types grown in the laboratory as cancer cell lines which have been tested with 650 different treatments, and the advanced computational methods they have used to predict the response of cancer cells to treatment.

Every cell in the body contains thousands of different proteins (collectively referred to as the “proteome”), which are responsible for most of the functions of life, such as the behaviour of cancer cells and how they respond to treatment. It has been known by clinical cancer specialists for many decades that for some types of cancer measuring the quantities of a few specific proteins can help guide the choice of the most appropriate treatment. But methods for measuring the thousands of other types of proteins were not readily available for clinical use.

CMRI’s ProCan® team (also known as the Australian Cancer Research Foundation International Centre for the Proteome of Human Cancer) has developed a high-throughput workflow using mass spectrometry to measure thousands of different proteins in very large numbers of cancers. Using this methodology and 10,000 hours of mass spectrometry instrument time, they have generated a proteomic database for the 949 cancer cell lines grown by the Sanger team, who analysed the response of each cell line to up to 650 different drugs, and who have previously deeply analysed the genes (the “genome”) and other key molecules in these cancer lines.

In contrast to clinical trials which can each test only one treatment or treatment combination, there is no limit to the number of drugs that can be tested on cancer cell cultures in the laboratory. Generating data regarding the response of such a large number of cancer cell lines to 650 drugs, and their comprehensive molecular analysis, has required a major investment of resources and effort over many years by the team of Wellcome Sanger Institute researchers led by Dr Mathew Garnett.

Data scientists from the CMRI and Sanger teams worked together to analyse the results with advanced computational methods, developing a new deep learning technique to use proteomic data to predict the response of the cancer cells to treatment. The results also pinpoint vulnerabilities in cancer cells that provide opportunities for developing new treatments.

Professor Roger Reddel, a senior author of the study and a co-founder of ProCan, said “This study has been a collaborative team effort involving proteomics experts, software engineers, data scientists, cancer cell biologists, and oncology researchers that has resulted in important new insights into the interactions among thousands of key molecules within cancer cells, and the response of cancer cells to drug treatments. It is a major step towards ProCan’s goal of using proteogenomic data to help clinicians choose the best treatment for individual cancer patients.”

The cancer database, which is of unprecedented size for this type of data, is now being made available as a resource for cancer researchers and clinicians around the world. The work at ProCan was done under the auspices of a Memorandum of Understanding between CMRI and the U.S. National Cancer Institute’s International Cancer Proteogenomics Consortium (ICPC), that encourages cooperation among institutions and nations in proteogenomic cancer research in which datasets are made available to the public.  Dr Mathew Garnett (WSI), also a senior author, said “In addition to revealing new insights about the biology of cancer, this study is also helping to fulfil the mission of my team to generate reference datasets for widespread use in the international cancer research community. This proteomic map will contribute to our Cancer Dependency Map1 – an effort to systematically identify vulnerabilities in cancer cells to guide drug development.”   

This article was originally published by Scimex. ACRF has backed $12 million of brilliant research at the Children’s Medical Research Institute (CMRI).