Evidence map›Paper›PMID 34708245›Full record

ArticleBioscience reports2021

Capturing tumour heterogeneity in pre- and post-chemotherapy colorectal cancer ascites-derived cells using single-cell RNA-sequencing.

Tiraput Poonpanichakul, Meng-Shin Shiao, Natnicha Jiravejchakul, Ponpan Matangkasombut, Ekaphop Sirachainan, Varodom Charoensawan, Natini Jinawath

Open access · goldAbstract readComparative Study
In one paragraph

Article in Bioscience reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

15 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Single-cell multi-omics in the study of digestive system cancers.Computational and structural biotechnology journal · 2024
    Review
  5. Molecular Targets for the Diagnosis and Treatment of Pancreatic Cancer.International journal of molecular sciences · 2024
    Review
  6. Review
  7. Article
  8. Article
  9. Drug repurposing for cancer therapy.Signal transduction and targeted therapy · 2024
    Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Review
  15. Review
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

7 authors at 1 institution in 1 country.

Tiraput PoonpanichakulChakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand.ORCID 0000-0002-3651-3575
Meng-Shin ShiaoResearch Center, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.ORCID 0000-0001-6655-1700
Natnicha JiravejchakulDepartment of Microbiology, Faculty of Science, Mahidol University, Bangkok, Thailand.
Ponpan MatangkasombutDepartment of Microbiology, Faculty of Science, Mahidol University, Bangkok, Thailand.ORCID 0000-0003-0101-4386
Ekaphop SirachainanDivision of Medical Oncology, Department of Internal Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Varodom CharoensawanSystems Biology of Diseases Research Unit (SyBiD), Faculty of Science, Mahidol University, Bangkok, Thailand.ORCID 0000-0002-2199-4126
Natini JinawathRamathibodi Comprehensive Cancer Center, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.ORCID 0000-0002-3437-4698
Mahidol University · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malignant ascites is an abnormal accumulation of fluid within the peritoneal cavity, caused by metastasis of several types of cancers, including colorectal cancer (CRC). Cancer cells in ascites reflect poor prognosis and serve as a good specimen to study tumour heterogeneity, as they represent a collection of multiple metastatic sites in the peritoneum. In the present study, we have employed single-cell RNA-sequencing (scRNA-seq) to explore and characterise ascites-derived cells from a CRC patient. The samples were prepared using mechanical and enzymatic dissociations, and obtained before and after a chemotherapy treatment. Unbiased clustering of 19,653 cells from four samples reveals 14 subclusters with unique transcriptomic patterns in four major cell types: epithelial cells, myeloid cells, fibroblasts, and lymphocytes. Interestingly, the percentages of cells recovered from different cell types appeared to be influenced by the preparation protocols, with more than 90% reduction in the number of myeloid cells recovered by enzymatic preparation. Analysis of epithelial cell subpopulations unveiled only three out of eleven subpopulations with clear contraction after the treatment, suggesting that the majority of the heterogeneous ascites-derived cells were resistant to the treatment, potentially reflecting the poor treatment outcome observed in the patient. Overall, our study showcases highly heterogeneous cancer subpopulations at single-cell resolution, which respond differently to a particular chemotherapy treatment. All in all, this work highlights the potential benefit of single-cell analyses in planning appropriate treatments and real-time monitoring of therapeutic response in cancer patients through routinely discarded ascites samples.

Indexed as

Gene Expression ProfilingGenetic HeterogeneityRNA-SeqSingle-Cell AnalysisTranscriptomeAntineoplastic Combined Chemotherapy ProtocolsAscitic FluidBiomarkers, TumorClinical Decision-MakingColorectal NeoplasmsDrug Resistance, NeoplasmFemaleHumansMiddle AgedPredictive Value of TestsRNA, NeoplasmBiomarkers, TumorRNA, Neoplasmchemotherapycolorectal cancermalignant ascitesscRNA-seqtumour heterogeneity

Identifiers

PMID34708245
PMCPMC8655500
OpenAlexW3210629269

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.