Gerlinger: a single biopsy misses most of the mutations in a kidney tumour
Sequencing multiple regions of four kidney cancers and their metastases showed that around two-thirds of mutations were not shared across the whole tumour, that good- and poor-prognosis gene signatures coexisted in the same tumour, and that different regions had independently hit the same genes.
Swanton's group performed exome sequencing, chromosome aberration analysis and ploidy profiling on multiple spatially separated samples from primary clear-cell renal carcinomas and associated metastases in four patients, reconstructing phylogenetic trees for each tumour.
Branched rather than linear evolution was the rule: 63-69% of all somatic mutations in the index case were not detectable in every region. Prognostic gene expression signatures classified different regions of the same tumour as favourable or unfavourable. Convergent evolution was seen, with distinct inactivating mutations in SETD2, KDM5C and PTEN arising in different regions, indicating strong selection on those pathways.
The paper made intratumour heterogeneity a central problem for biomarkers, drug resistance and trial design, and led directly to the TRACERx programme.
- 63-69% of somatic mutations in the index tumour were not present in every region sampled
- A single biopsy captured only a minority of the tumour's mutational landscape
- Good- and poor-prognosis expression signatures were found in different regions of the same tumour
- Convergent evolution: distinct loss-of-function mutations in SETD2, KDM5C and PTEN in separate regions of the same tumour
A single biopsy is an incomplete picture of a patient's cancer. Truncal mutations shared by all cells (in kidney cancer, VHL) are the most reliable drug targets, whereas mutations in only some branches predict resistance. This is why liquid biopsy and multi-region sampling matter.
- Four patients; the extent of heterogeneity varies across cancer types
- Clear-cell renal carcinoma may be unusually heterogeneous
- Exome sequencing depth of the time limited detection of low-frequency subclones
- Clinical consequences (whether targeting truncal alterations improves outcomes) were not tested
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not linked directly; found by shared links- Key paperTRACERx 421: the full-cohort picture of how lung cancer evolves and which subclones drive relapse
Shares Charles Swanton, TRACERx first 100: tracking how lung cancers evolve, and how chromosomal chaos predicts relapse, The Francis Crick Institute, Clonal evolution & minimal residual disease.
- IdeaBank three spatially separate tumour blocks from every resection
Shares The Francis Crick Institute, Tumour heterogeneity and clonal evolution, Whole-exome & whole-genome sequencing, Cancer Research UK.
- IdeaPool every multi-sample tumour genome into one open evolution atlas
Shares The Francis Crick Institute, Tumour heterogeneity and clonal evolution, Whole-exome & whole-genome sequencing, Cancer Research UK.
- IdeaA national rapid research autopsy network for end-stage cancer
Shares The Francis Crick Institute, Tumour heterogeneity and clonal evolution, Whole-exome & whole-genome sequencing, Cancer Research UK.
- Key paperMartincorena: normal sun-exposed skin is a patchwork of cancer-mutation clones
Shares Variant allele frequency (VAF), Clonal evolution & minimal residual disease, Whole-exome & whole-genome sequencing, Biomarkers are not validated or standardised.
- InstitutionWellcome
Shares Charles Swanton, The Francis Crick Institute, Whole-exome & whole-genome sequencing, Cancer Research UK.
- IdeaCheck whether a tumour can still show itself to the immune system
Shares Charles Swanton, Drug resistance (primary and acquired), Whole-exome & whole-genome sequencing, Acquired resistance to every therapy.
- IdeaLook for the resistant sub-population before the first dose
Shares Variant allele frequency (VAF), Tumour heterogeneity and clonal evolution, Acquired resistance to every therapy, Liquid biopsy (ctDNA).