Cancer is a complicated disease. Tumors are made up of many different types of cancer cells, and our current treatment techniques can't always clear them all out. Now, a team of Oxford researchers has developed a way to track the genetic "life histories" of thousands of individual cancer cells at once, which may lead to more effective and personalized cancer treatments.

Cancer cells are basically just normal cells that have accumulated certain mutations that allow them to grow out of control. But even within one tumor, individual cancer cells can mutate in different ways over time, to the point where treatments will work on some, but not all, cancer cells in a patient. Surviving cells could then continue to grow and spread through the body.

"Without knowing what kinds of cells a patient has, it is difficult to predict how the patient will respond to a particular kind of treatment or drug," says Professor Adam Mead, an author of the study. "This means that cancer patients often have relapses, because their treatment only killed off some kinds of cancer cells."

To better track a wider range of cancer cells, the Oxford researchers created a new technique called TARGET-seq. The method is apparently sensitive enough to detect mutations in single cells, as well as the consequences of those mutations – known as the transcriptome. With that, the technique can be used to unravel the entire life history of cells, including which mutations occurred and in what order.

The team tested the TARGET-seq technique on 4,559 single cells collected from 15 patients with blood cancer. Interestingly, although the patients had the same types of cancer and had been treated with the same therapies, the life histories of their cancer cells were very different. Even a patient's own cells had very different back stories.

With this new tool, scientists could better understand which types of cancer cells each patient has, and personalize treatments for them that can wipe out tumors completely. It can also help scientists develop more effective treatments and then track how well they perform in trials.

"Having this information is really important, because it not only gives us unique information about how tumors change over time, but also how they might respond in future to different treatments and drugs,' says Alba Rodriguez-Meira, first author on the study. "No two patients will have exactly the same mixture of cancer cells with exactly the same pattern of evolution, and our technique will allow clinicians to monitor patient progression during clinical trials and ultimately customize the treatment they offer to the unique mixture of cancer cells that every cancer patient has."

The research was published in the journal Molecular Cell.