The greatest knowledge of cancer genetics yet will help advance the field of precision cancer medicine – in which the genome of a patient’s tumour is sequenced, and drugs are designed to target its vulnerabilities.

Cancer is a genetic disease that occurs when mutations in DNA cause cells to divide and grow uncontrollably. Some cancer treatments already target specific genetic mutations – Herceptin for HER2-positive breast cancer is a well-known example. But the majority do not, and the mainstays of cancer treatment, chemotherapy and radiotherapy, can damage healthy cells in the body as well as the cancerous ones. In many cases, a person’s cancer may not respond to a particular treatment at all.

There has been a huge effort over the last decade to sequence the genomes of tumours, and vast amounts of data are now available. The challenge for next year will be using that data in the clinic.

Researchers at the Wellcome Sanger Institute, alongside the Broad Institute at MIT and Harvard, are leading an international project called the Cancer Dependency Map (DepMap), which aims to map out weaknesses in every type of cancer cell.

The first element of DepMap is to create new cancer models – clusters of cells that recreate patient tumours, but can be grown in a dish and studied in the laboratory. There are about 1,600 such models in existence already, but this isn’t enough: certain cancer types and mutations are not represented. In 2020, we will begin to fill in those gaps with over 1,000 new cancer models. We will do this by using CRISPR technology to disrupt each of the 20,000 genes in a genome, one at a time. If cell growth is affected, the gene that has been deleted is considered essential for that cancer and becomes a potential drug target.

These models can then be used to accelerate the search for new precision treatments. Cancer drugs, both licensed and experimental, can now be tested against hundreds of cell models. In the latest release of freely available data, 453 drugs were tested on 989 cell models, representing 30 types of cancer, resulting in hundreds of millions of data points and the largest dataset of its kind in the world. In 2020, we can expect even larger datasets, as the work is expanded to test nearly 3,000 drug combinations on the models.

Read the rest of Alison Cranage’s article at Wired