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.
Chromosomal instability (CIN) arises from mitotic errors, whole-genome doubling, replication stress, and centrosome amplification; it produces aneuploidy, micronuclei, chromothripsis, and ecDNA (extrachromosomal oncogene amplicons). Consequences: intratumour heterogeneity and rapid adaptation (TRACERx), cytosolic DNA that activates cGAS-STING (immunogenic in bursts, tolerated chronically via non-canonical NF-κB), and proteotoxic and metabolic stress that creates dependencies (KIF18A, spindle assembly checkpoint, BCL-XL). CIN is a poor-prognosis marker across cancers; ecDNA drives resistance to targeted therapy in glioblastoma and others.
In one picture
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.
Diagram
top- KIF18A inhibitors (sovilnesib) selectively kill CIN-high cells; phase 1/2 in ovarian and TNBC
- ecDNA-directed strategies (CHK1 inhibition, transcription–replication conflict) from the Cancer Grand Challenges eDyNAmiC team
- STING pathway modulation; radiation exploits CIN
- Aneuploidy scores (TRACERx) as prognostic biomarkers
Notes
top- Leading programmes: Swanton (Crick/UCL, TRACERx, CIN and immune evasion); Bakhoum (MSK, CIN–STING); Mischel (Stanford) and the eDyNAmiC Cancer Grand Challenge on ecDNA; Sheltzer (Yale) on aneuploidy dependencies.
Relapse after surgery is driven by particular subclones that can be identified in the primary tumour and tracked in blood, which argues for evolution-aware adjuvant strategies. The pollution finding reframes carcinogenesis: some agents promote already-mutant cells rather than causing mutations.
Lung cancers keep evolving after they form, and it is ongoing chromosomal instability rather than the number of mutations that best predicts who will relapse. This gives a rationale for targeting the earliest (clonal) drivers and neoantigens and for tracking evolution in blood after surgery.