Evidence map›Paper›PMID 28000726›Full record

ArticleScientific reports2016

Heterogeneity of paclitaxel distribution in different tumor models assessed by MALDI mass spectrometry imaging.

Silvia Giordano, Massimo Zucchetti, Alessandra Decio, Marta Cesca, Ilaria Fuso Nerini, Marika Maiezza, Mariella Ferrari, Simonetta Andrea Licandro, Roberta Frapolli, Raffaella Giavazzi and 3 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
5.5field-weighted citation impact, top 4% 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

32 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.

  1. Pooled it
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  10. Pseudo-resistance to anticancer drugs.Therapeutic advances in medical oncology · 2022
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  15. Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence.Liver international : official journal of the International Association for the Study of the Liver · 2020
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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

13 authors at 2 institutions in 1 country.

Silvia GiordanoMass Spectrometry Laboratory, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Massimo ZucchettiDepartment of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Alessandra DecioDepartment of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Marta CescaDepartment of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Ilaria Fuso NeriniDepartment of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Marika MaiezzaDepartment of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Mariella FerrariDepartment of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Simonetta Andrea LicandroDepartment of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Roberta FrapolliDepartment of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Raffaella GiavazziDepartment of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
D'Incalci MaurizioDepartment of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Enrico DavoliMass Spectrometry Laboratory, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Lavinia MorosiDepartment of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19 - 20156 Milan, Italy.
Mario Negri Institute for Pharmacological Research · ITIstituti di Ricovero e Cura a Carattere Scientifico · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The penetration of anticancer drugs in solid tumors is important to ensure the therapeutic effect, so methods are needed to understand drug distribution in different parts of the tumor. Mass spectrometry imaging (MSI) has great potential in this field to visualize drug distribution in organs and tumor tissues with good spatial resolution and superior specificity. We present an accurate and reproducible imaging method to investigate the variation of drug distribution in different parts of solid tumors. The method was applied to study the distribution of paclitaxel in three ovarian cancer models with different histopathological characteristics and in colon cancer (HCT116), breast cancer (MDA-MB-231) and malignant pleural mesothelioma (MPM487). The heterogeneous drug penetration in the tumors is evident from the MALDI imaging results and from the images analysis. The differences between the various models do not always relate to significant changes in drug content in tumor homogenate examined by classical HPLC analysis. The specificity of the method clarifies the heterogeneity of the drug distribution that is analyzed from a quantitative point of view too, highlighting how marked are the variations of paclitaxel amounts in different part of solid tumors.

Indexed as

AnimalsAntineoplastic Agents, PhytogenicDisease Models, AnimalFemaleHeterograftsHumansImage Processing, Computer-AssistedMaleMiceNeoplasmsNeoplasm TransplantationPaclitaxelSensitivity and SpecificitySpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationAntineoplastic Agents, PhytogenicPaclitaxel

Identifiers

PMID28000726
PMCPMC5175283
OpenAlexW2566364225

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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.