Evidence map›Paper›PMID 38321573›Full record

ArticleGenome medicine2024

Rare subclonal sequencing of breast cancers indicates putative metastatic driver mutations are predominately acquired after dissemination.

Matthew R Lawrence-Paul, Tien-Chi Pan, Dhruv K Pant, Natalie N C Shih, Yan Chen, George K Belka, Michael Feldman, Angela DeMichele, Lewis A Chodosh

Abstract read
In one paragraph

Article in Genome medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. Liquid biopsy in solid tumours: expert opinion paper of the European society of pathology.Virchows Archiv : an international journal of pathology · 2026
    Review
  3. Article
  4. Genetic landscape of cancer: mechanisms, key genes, and therapeutic implications.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
  5. Article
  6. Review
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Matthew R Lawrence-Paul2-PREVENT Translational Center of Excellence, Philadelphia, USA.
Tien-Chi Pan2-PREVENT Translational Center of Excellence, Philadelphia, USA.
Dhruv K Pant2-PREVENT Translational Center of Excellence, Philadelphia, USA.
Natalie N C Shih2-PREVENT Translational Center of Excellence, Philadelphia, USA.
Yan Chen2-PREVENT Translational Center of Excellence, Philadelphia, USA.
George K Belka2-PREVENT Translational Center of Excellence, Philadelphia, USA.
Michael Feldman2-PREVENT Translational Center of Excellence, Philadelphia, USA.
Angela DeMichele2-PREVENT Translational Center of Excellence, Philadelphia, USA. Angela.DeMichele@pennmedicine.upenn.edu.
Lewis A Chodosh2-PREVENT Translational Center of Excellence, Philadelphia, USA. chodosh@pennmedicine.upenn.edu.ORCID 0000-0003-1027-8674

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEvolutionary models of breast cancer progression differ on the extent to which metastatic potential is pre-encoded within primary tumors. Although metastatic recurrences often harbor putative driver mutations that are not detected in their antecedent primary tumor using standard sequencing technologies, whether these mutations were acquired before or after dissemination remains unclear.

methodsTo ascertain whether putative metastatic driver mutations initially deemed specific to the metastasis by whole exome sequencing were, in actuality, present within rare ancestral subclones of the primary tumors from which they arose, we employed error-controlled ultra-deep sequencing (UDS-UMI) coupled with FFPE artifact mitigation by uracil-DNA glycosylase (UDG) to assess the presence of 132 "metastasis-specific" mutations within antecedent primary tumors from 21 patients. Maximum mutation detection sensitivity was ~1% of primary tumor cells. A conceptual framework was developed to estimate relative likelihoods of alternative models of mutation acquisition.

resultsThe ancestral primary tumor subclone responsible for seeding the metastasis was identified in 29% of patients, implicating several putative drivers in metastatic seeding including LRP5 A65V and PEAK1 K140Q. Despite this, 93% of metastasis-specific mutations in putative metastatic driver genes remained undetected within primary tumors, as did 96% of metastasis-specific mutations in known breast cancer drivers, including ERRB2 V777L, ESR1 D538G, and AKT1 D323H. Strikingly, even in those cases in which the rare ancestral subclone was identified, 87% of metastasis-specific putative driver mutations remained undetected. Modeling indicated that the sequential acquisition of multiple metastasis-specific driver or passenger mutations within the same rare subclonal lineage of the primary tumor was highly improbable.

conclusionsOur results strongly suggest that metastatic driver mutations are sequentially acquired and selected within the same clonal lineage both before, but more commonly after, dissemination from the primary tumor, and that these mutations are biologically consequential. Despite inherent limitations in sampling archival primary tumors, our findings indicate that tumor cells in most patients continue to undergo clinically relevant genomic evolution after their dissemination from the primary tumor. This provides further evidence that metastatic recurrence is a multi-step, mutation-driven process that extends beyond primary tumor dissemination and underscores the importance of longitudinal tumor assessment to help guide clinical decisions.

Indexed as

Breast NeoplasmsExome SequencingFemaleHumansMutationBreast cancerGenomicsMetastasisMetastatic driver mutationRare subclonal sequencingUltra-deep sequencing

Identifiers

PMID38321573
PMCPMC10848417

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.