Evidence map›Paper›PMID 34199298›Full record

ArticleInternational journal of molecular sciences2021

Inter-Metastatic Heterogeneity of Tumor Marker Expression and Microenvironment Architecture in a Preclinical Cancer Model.

Jessica Kalra, Jennifer Baker, Justin Song, Alastair Kyle, Andrew Minchinton, Marcel Bally

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 34% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

6 authors at 3 institutions in 2 countries.

Jessica KalraExperimental Therapeutics, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada.
Jennifer BakerIntegrative Oncology, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada.
Justin SongChemical and Biomolecular Engineering Department, Vanderbilt University, Nashville, TN 37235, USA.
Alastair KyleIntegrative Oncology, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada.
Andrew MinchintonFaculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Marcel BallyExperimental Therapeutics, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada.
University of British Columbia · CABC Cancer Agency · CAVanderbilt University · US

Funding

Canadian Cancer Society and Canadian Institute for Health Rersearch CCS 702491 and 705290; CIHR MOP-102611
6 · The paper itself

Abstract

backgroundPreclinical drug development studies rarely consider the impact of a candidate drug on established metastatic disease. This may explain why agents that are successful in subcutaneous and even orthotopic preclinical models often fail to demonstrate efficacy in clinical trials. It is reasonable to anticipate that sites of metastasis will be phenotypically unique, as each tumor will have evolved heterogeneously with respect to gene expression as well as the associated phenotypic outcome of that expression. The objective for the studies described here was to gain an understanding of the tumor heterogeneity that exists in established metastatic disease and use this information to define a preclinical model that is more predictive of treatment outcome when testing novel drug candidates clinically.

methodsFemale NCr nude mice were inoculated with fluorescent (mKate), Her2/neu-positive human breast cancer cells (JIMT-mKate), either in the mammary fat pad (orthotopic; OT) to replicate a primary tumor, or directly into the left ventricle (intracardiac; IC), where cells eventually localize in multiple sites to create a model of established metastasis. Tumor development was monitored by in vivo fluorescence imaging (IVFI). Subsequently, animals were sacrificed, and tumor tissues were isolated and imaged ex vivo. Tumors within organ tissues were further analyzed via multiplex immunohistochemistry (mIHC) for Her2/neu expression, blood vessels (CD31), as well as a nuclear marker (Hoechst) and fluorescence (mKate) expressed by the tumor cells.

resultsFollowing IC injection, JIMT-1mKate cells consistently formed tumors in the lung, liver, brain, kidney, ovaries, and adrenal glands. Disseminated tumors were highly variable when assessing vessel density (CD31) and tumor marker expression (mkate, Her2/neu). Interestingly, tumors which developed within an organ did not adopt a vessel microarchitecture that mimicked the organ where growth occurred, nor did the vessel microarchitecture appear comparable to the primary tumor. Rather, metastatic lesions showed considerable variability, suggesting that each secondary tumor is a distinct disease entity from a microenvironmental perspective.

conclusionsThe data indicate that more phenotypic heterogeneity in the tumor microenvironment exists in models of metastatic disease than has been previously appreciated, and this heterogeneity may better reflect the metastatic cancer in patients typically enrolled in early-stage Phase I/II clinical trials. Similar to the suggestion of others in the past, the use of models of established metastasis preclinically should be required as part of the anticancer drug candidate development process, and this may be particularly important for targeted therapeutics and/or nanotherapeutics.

Indexed as

Tumor MicroenvironmentAnimalsBiomarkers, TumorBrainCell Line, TumorDisease Models, AnimalFemaleLungMiceMice, NudeNeoplasm MetastasisBiomarkers, Tumorchemoresistancedrug developmentinter-metastatic heterogeneitymetastasismultiplex immunohistochemistrypreclinical studiestranslational medicine

Identifiers

PMID34199298
PMCPMC8231937
OpenAlexW3166368708

What OpenQuestion holds

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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.