Tumour heterogeneity and clonal evolution

Blood tests can already detect tumour DNA. Reporting which sub-populations of the tumour are growing or shrinking, cycle by cycle, would turn the test into an evolution monitor.

Instead of hitting a tumour with the maximum dose until it stops working, adjust the dose to keep the tumour small and let drug-sensitive cells suppress resistant ones. Test this properly across several cancers.

When patients who agreed in advance die of cancer, sampling every tumour within hours reveals how the disease evolved and escaped every drug. Few hospitals can do this today.

Some tumours change fast and escape drugs quickly; others are stable. A single validated score for how evolvable a tumour is would tell doctors how aggressively to combine treatments.

When a tumour evolves resistance to one drug, it sometimes becomes weaker against another. Map these trade-offs systematically so doctors can pick the next drug to exploit them.

Map every state a cancer cell can be in, and how drugs and the surrounding tissue move it between states, into an open computational model anyone can query and improve.

Hospitals usually keep one piece of a removed tumour. Keeping three pieces from different parts would show how varied the tumour is, at almost no extra cost.

Tag every cell in a patient's lab-grown tumour with a unique DNA label, give it a drug, and read the labels to see which cells survive. This predicts which resistant clone will emerge.

When a scan shows most tumours shrinking but one growing, that odd lesion holds the escape mechanism. Sampling it, and treating it locally, should be routine.

Use tumour DNA in the blood as the signal to pause and restart a lung cancer pill, keeping the tumour in check while slowing the rise of resistant cells.

Choose two treatments so that whatever the tumour does to escape the first, it becomes easier to kill with the second. The immune system is a good candidate partner.

Some cancers escape treatment by changing into a different kind of cell that the drug no longer affects. Tumour RNA in blood could show this shift months before a biopsy would.

Build a computer model of each patient's cancer that forecasts how it will respond to each treatment option, and prove it by writing the forecast down before the real result is known.

Cancer is an evolving population, but treatment decisions are rarely made with an evolutionary biologist present. Add one to the weekly meeting and see whether decisions change.

Cells that receive only a small amount of a drug survive and adapt. Measuring where inside a tumour the drug actually reaches would show where resistance is being bred.

Flu vaccines are chosen by predicting which virus strains will dominate next season. The same forecasting maths could predict which resistance mutation a patient's tumour will develop next.

Rare cancers often share a broken cellular machine even when they arise in different organs. Grouping patients by that shared fault makes trials possible.

A drug aimed at a mutation present in every tumour cell works differently from one aimed at a mutation in only some cells. Test reports should say which is which.

Resistance mutations often exist in a tiny fraction of cells before treatment starts. A very sensitive test at diagnosis could find them and prompt a combination from day one.

A drug that shrinks a tumour by half but leaves the resistant sub-population untouched will fail. Trials should measure whether every sub-population is cleared, not just overall size.

New imaging shows where every cell type sits in a tumour slice. Using it to see which sub-populations are hidden from immune cells could explain why immunotherapy fails in parts of a tumour.

Blood tests tell you which tumour sub-populations are growing; scans tell you which lesions are growing. Joining the two would tell you where to biopsy or irradiate.

Resistant cancer cells sometimes come to depend on the very drug they resisted. Stopping the drug for a while can make them vulnerable to it once more.

Several big projects have sequenced the same tumours at different times and places, but their data sit apart. Bringing them together with common analysis would show general rules of how cancers evolve.

Antibody drugs need their target to still be present. After one fails, checking which surface markers remain would guide the choice of the next one instead of guessing.

Treatment is often chosen from a biopsy taken years earlier from the original tumour. The spread disease may now look different. Test it again before switching drugs.

Many tumours carry an enzyme that keeps creating new mutations, feeding resistance. Blocking that enzyme while a targeted drug works could make resistance arrive later.

Species go extinct when a second disaster hits a population already shrunk by a first one. Apply the same logic: hit the tumour with a different kind of drug when it is smallest, rather than waiting for it to grow back.

Tumour DNA in blood can be told apart by chemical marks as well as mutations. Marks are more numerous and cheaper to read, so they could track more sub-populations for less money.

Highly unstable tumours survive constant chromosome mistakes by leaning on a motor protein. Blocking it kills unstable cancer cells while sparing normal ones.

If a drug relies on one marker, the tumour can survive by dropping it. A drug that recognises two markers at once makes that escape harder.

Brain tumours shed very little DNA into blood, so a sonobiopsy briefly opens the brain's barrier with focused ultrasound to release enough to read from a blood sample.

Personal cancer vaccines target many mutations, some of which are present in only part of the tumour. Aiming only at mutations every cell shares should stop the tumour escaping by losing them.