Evidence map›Paper›PMID 35406417›Full record

ArticleCancers2022

Simulating the Dynamic Intra-Tumor Heterogeneity and Therapeutic Responses.

Yongjing Liu, Cong Feng, Yincong Zhou, Xiaotian Shao, Ming Chen

Abstract read
In one paragraph

Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Yongjing LiuDepartment of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
Cong FengDepartment of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.
Yincong ZhouDepartment of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0002-4798-8357
Xiaotian ShaoJoint Research Centre for Engineering Biology, Zhejiang University-University of Edinburgh Institute, Zhejiang University, Haining 314400, China.ORCID 0000-0001-7241-7698
Ming ChenDepartment of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0002-9677-1699

Funding

National Natural Sciences Foundation of China 31771477; 32070677the 151 Talent Project of Zhejiang Province (first level) Not applicablethe National Key Research and Development Program of China 2018YFC0310602; 2016YFA0501704
6 · The paper itself

Abstract

A tumor is a complex tissue comprised of heterogeneous cell subpopulations which exhibit substantial diversity at morphological, genetic and epigenetic levels. Under the selective pressure of cancer therapies, a minor treatment-resistant subpopulation could survive and repopulate. Therefore, the intra-tumor heterogeneity is recognized as a major obstacle to effective treatment. In this paper, we propose a stochastic clonal expansion model to simulate the dynamic evolution of tumor subpopulations and the therapeutic effect at different times during tumor progression. The model is incorporated in the CES webserver, for the convenience of simulation according to initial user input. Based on this model, we investigate the influence of various factors on tumor progression and treatment consequences and present conclusions drawn from observations, highlighting the importance of treatment timing. The model provides an intuitive illustration to deepen the understanding of temporal intra-tumor heterogeneity dynamics and treatment responses, thus helping the improvement of personalized diagnostic and therapeutic strategies.

Indexed as

clonal evolutionsimulation modeltargeted therapytreatment outcomestumor heterogeneity

Identifiers

PMID35406417
PMCPMC8996855

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.