Evidence map›Paper›PMID 29187174›Full record

ArticleBMC cancer2017

Intra-tumor heterogeneity in breast cancer has limited impact on transcriptomic-based molecular profiling.

Govindasamy-Muralidharan Karthik, Mattias Rantalainen, Gustav Stålhammar, John Lövrot, Ikram Ullah, Amjad Alkodsi, Ran Ma, Lena Wedlund, Johan Lindberg, Jan Frisell and 2 more

Abstract read
In one paragraph

Article in BMC cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Prognostic significance of pretreatmentBreast cancer research and treatment · 2023
    Article
  7. Article
  8. Article
  9. Article
  10. Neoadjuvant Therapy for Breast Cancer as a Model for Translational Research.Breast cancer : basic and clinical research · 2019
    Review
  11. Article
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

12 authors.

Govindasamy-Muralidharan KarthikDepartment of Oncology and Pathology, Karolinska Institutet, CCK, 17176, Stockholm, Sweden.
Mattias RantalainenDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177, Stockholm, Sweden.
Gustav StålhammarDepartment of Oncology and Pathology, Karolinska Institutet, CCK, 17176, Stockholm, Sweden.
John LövrotDepartment of Oncology and Pathology, Karolinska Institutet, CCK, 17176, Stockholm, Sweden.
Ikram UllahDepartment of Oncology and Pathology, Karolinska Institutet, CCK, 17176, Stockholm, Sweden.
Amjad AlkodsiGenome-Scale Biology Research Program Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Ran MaDepartment of Oncology and Pathology, Karolinska Institutet, CCK, 17176, Stockholm, Sweden.
Lena WedlundDepartment of Clinical Pathology and Cytology, Karolinska University Laboratory, Stockholm, Sweden.
Johan LindbergDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177, Stockholm, Sweden.
Jan FrisellDepartment of Molecular Medicine and Surgery, Karolinska University Hospital, 17176, Stockholm, Sweden.
Jonas BerghDepartment of Oncology and Pathology, Karolinska Institutet, CCK, 17176, Stockholm, Sweden.
Johan HartmanDepartment of Oncology and Pathology, Karolinska Institutet, CCK, 17176, Stockholm, Sweden. Johan.Hartman@ki.se.ORCID http://orcid.org/0000-0002-6500-8527

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTranscriptomic profiling of breast tumors provides opportunity for subtyping and molecular-based patient stratification. In diagnostic applications the specimen profiled should be representative of the expression profile of the whole tumor and ideally capture properties of the most aggressive part of the tumor. However, breast cancers commonly exhibit intra-tumor heterogeneity at molecular, genomic and in phenotypic level, which can arise during tumor evolution. Currently it is not established to what extent a random sampling approach may influence molecular breast cancer diagnostics.

methodsIn this study we applied RNA-sequencing to quantify gene expression in 43 pieces (2-5 pieces per tumor) from 12 breast tumors (Cohort 1). We determined molecular subtype and transcriptomic grade for all tumor pieces and analysed to what extent pieces originating from the same tumors are concordant or discordant with each other. Additionally, we validated our finding in an independent cohort consisting of 19 pieces (2-6 pieces per tumor) from 6 breast tumors (Cohort 2) profiled using microarray technique. Exome sequencing was also performed on this cohort, to investigate the extent of intra-tumor genomic heterogeneity versus the intra-tumor molecular subtype classifications.

resultsMolecular subtyping was consistent in 11 out of 12 tumors and transcriptomic grade assignments were consistent in 11 out of 12 tumors as well. Molecular subtype predictions revealed consistent subtypes in four out of six patients in this cohort 2. Interestingly, we observed extensive intra-tumor genomic heterogeneity in these tumor pieces but not in their molecular subtype classifications.

conclusionsOur results suggest that macroscopic intra-tumoral transcriptomic heterogeneity is limited and unlikely to have an impact on molecular diagnostics for most patients.

Indexed as

Genetic HeterogeneityBiomarkers, TumorBreast NeoplasmsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansSequence Analysis, RNABiomarkers, TumorBreast cancerIntra-tumor transcriptomic heterogeneityMolecular diagnosticsMolecular subtypes

Identifiers

PMID29187174
PMCPMC5708109

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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.