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A biopsy samples one place at one time; the tumour is many places changing over time. Subclones with different drivers coexist, and the one that survives treatment was often a minority nobody sequenced.
Pick a product above a diagram to see the nodes it hits and the escape routes below the block. Hover or tap any node or arrow for what it is; every node opens its target, glossary entry or the pathway page. Violet boxes are druggable targets.
Clonal evolution is like weeding a field with one herbicide year after year: the field fills with the one weed that shrugs it off. Rotating herbicides and leaving some susceptible weeds to crowd out the resistant ones is the evolutionary alternative.
Chromosomal instability is a library that reshuffles and duplicates random shelves every night. Most rearrangements are useless, some ruin the building, but occasionally one yields a book the librarian needs to survive a new rule, and the mess itself keeps the fire alarms twitching.
The genome is the book; epigenetics is the highlighting and the pages stapled shut. Cancer staples shut the safety chapters and highlights the growth chapters. Epigenetic drugs pull staples.
In plain words, then the glossary entries the stage rests on. Chapter 9, Why treatments fail: Every cancer drug eventually meets resistance.
A biopsy samples one place at one time; the tumour is many places changing over time. Subclones with different drivers coexist, and the one that survives treatment was often a minority nobody sequenced.
Clonal evolution & minimal residual disease. A tumour is a population that evolves by natural selection. Treatment kills the sensitive cells and selects the rest, which is why resistance is the rule; measuring the surviving population (MRD) and adapting therapy is the counter-strategy.
Chromosomal instability & aneuploidy. Most cancers have the wrong number of chromosomes and keep shuffling them at every division. This chaos fuels evolution and drug resistance, but it also stresses the cell and can trigger immune alarms, a double edge that researchers are trying to exploit.
Epigenetic reprogramming. Cancer changes not just its genes but how they are read: chemical tags on DNA and histones silence guardians and awaken growth programmes. Unlike mutations, these changes are reversible, which is the hope behind epigenetic drugs.
The proteins and genes at this stage, with their role and how many products act on each. Listed players come from the atlas; drawn players sit as nodes in the diagrams above.
A growth receptor that is mutated in some lung cancers and overproduced in others; the first great success of targeted pills.
A metabolic enzyme whose mutant form produces a molecule that scrambles how genes are read; blocking it slows brain tumours and leukaemias.
TP53 is the 'guardian of the genome', broken in half of all cancers. Fixing it directly has so far defeated every attempt, so drugs exploit what its loss makes cancers depend on.
EZH2 is an enzyme that silences genes. The first drug against it treated a rare sarcoma and some lymphomas until it was withdrawn in 2026 for causing second blood cancers.
A scaffold protein that certain leukaemias need to keep their genes switched on; the first drug against it was approved in 2024.
Products grouped by the node they hit, most advanced first, with the cancers an approved product is linked to. Pick one above the diagram to see it light up.
Records tied to this stage that describe resistance, evasion or tolerance. Resistance: how tumours escape each drug class lists the routes class by class.
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.
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.
When a targeted drug stops working, the tumour has usually changed in a way you can read. Most patients still move to the next treatment on a protocol rather than on a test of what actually happened.
Resistant cancer cells can become dependent on the drug they resisted, as shown for BRAF-inhibitor-resistant melanoma in mice. Stopping the drug for a defined washout and then rechallenging, while tracking the resistance allele in blood tumour DNA, could make the tumour vulnerable to it once more.
Brain tumours release little DNA into blood because of the blood-brain barrier. Sonobiopsy briefly opens the barrier with focused ultrasound and microbubbles, raising circulating tumour DNA severalfold so a blood sample can replace repeat surgical biopsy for diagnosis and resistance monitoring in glioma and brain metastases.
Personal cancer vaccines target a list of mutations, some present in only part of the tumour, so the tumour can escape by losing them. Restricting vaccines and T-cell products to clonal mutations shared by every tumour cell, identified by multi-region sequencing, should close that escape route.
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.
Aggressive glioblastomas, sarcomas and gastric cancers keep amplified cancer genes such as EGFR, MYC, MDM2 and CDK4 on free-floating DNA circles (ecDNA) whose copy number rises and falls quickly, letting the tumour dial resistance up and down. Cells carrying ecDNA depend on CHK1, giving a first drug target.
Biomarkers, tests and assays in the corpus that read this stage in a patient.
What is not known at this stage: the atlas's own questions, the bottlenecks it bears on, and the ideas in the corpus that try to answer them.
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.
Once a year, send the same blinded blood samples to every company selling a tumour-DNA test and publish how each performed.
Dozens of companies sell blood tests for tumour DNA and they report different results on the same sample. Government-issued reference samples with known amounts of tumour DNA would expose the differences.
Rare cancers often share a broken cellular machine even when they arise in different organs. Grouping patients by that shared fault makes trials possible.
Whole regions of tissue carry cancer mutations long before a tumour exists. Detecting and treating the field, not the tumour, could prevent cancers rather than cure them.
Most cancers have the wrong number of chromosomes; normal cells do not. If that difference creates a specific weakness, a drug against it would spare normal tissue by definition.
Current blood tests for leftover cancer track a few dozen mutations and miss low-level disease. Whole-genome and error-corrected methods integrate signal across thousands of tumour-specific sites plus methylation and fragment features, reaching detection near one part per million in research settings; cost, turnaround and reproducibility are the barriers.
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.
Chromosomally unstable, often whole-genome-doubled tumours survive constant chromosome mistakes by depending on the motor protein KIF18A, which diploid cells do not need. Blocking it kills unstable cancer cells while sparing normal ones; inhibitors are in early trials in ovarian and other cancers.
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.
24 more ideas are linked to this stage's pathways, targets and terms; see the rankings →
Papers in the corpus tied to this stage's pathways, targets and terms, newest first.
src/data/mechanics-atlas.ts). Players, medicines, escape routes, tests, ideas and papers are resolved from the knowledge graph at build time through the stage's pathways, targets and terms, so every item here has its own page and sources. Where a section is missing, the corpus has no record tied to the stage yet. Nothing here is medical advice; see about and methodology. Stage 9.2 of 56.