Evidence map›Paper›PMID 27540472›Full record

ArticleF1000Research2016

A multi-site cutting device implements efficiently the divide-and-conquer strategy in tumor sampling.

Jose I Lopez, Jesus M Cortes

Open access · goldAbstract read
In one paragraph

Article in F1000Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.2field-weighted citation impact, top 23% of its field
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

5 citing papers in PubMed, 11 citations in OpenAlex.

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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

2 authors at 2 institutions in 1 country.

Jose I LopezDepartment of Pathology, Cruces University Hospital, Barakaldo, Spain; Biomarkers in Cancer Unit, Biocruces Research Institute, Barakaldo, Spain; University of the Basque Country, Leioa, Spain.
Jesus M CortesQuantitative Biomedicine Unit, Biocruces Research Institute, Barakaldo, Spain; Ikerbasque: The Basque Foundation for Science, Bilbao, Spain; Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain.
Ikerbasque · ESUniversity of the Basque Country · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We recently showed that in order to detect intra-tumor heterogeneity a Divide-and-Conquer (DAC) strategy of tumor sampling outperforms current routine protocols. This paper is a continuation of this work, but here we focus on DAC implementation in the Pathology Laboratory. In particular, we describe a new simple method that makes use of a cutting grid device and is applied to clear cell renal cell carcinomas for DAC implementation. This method assures a thorough sampling of large surgical specimens, facilitates the demonstration of intratumor heterogeneity, and saves time to pathologists in the daily practice. The method involves the following steps: 1. Thin slicing of the tumor (by hand or machine), 2. Application of a cutting grid to the slices ( e.g., a French fry cutter), resulting in multiple tissue cubes with fixed position within the slice, 3. Selection of tissue cubes for analysis, and finally, 4. Inclusion of selected cubes into a cassette for histological processing (with about eight tissue fragments within each cassette). Thus, using our approach in a 10 cm in-diameter-tumor we generate 80 tumor tissue fragments placed in 10 cassettes and, notably, in a tenth of time. Eighty samples obtained across all the regions of the tumor will assure a much higher performance in detecting intratumor heterogeneity, as proved recently with synthetic data.

Indexed as

clear cell renal cell carcinomacutting griddivide and conquerintratumor heterogeneitypathology routineTumor sampling

Identifiers

PMID27540472
PMCPMC4965694
OpenAlexW2464184516

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.