Evidence map›Paper›PMID 30483257›Full record

ReviewFrontiers in immunology2018

Single-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology.

Fatima Valdes-Mora, Kristina Handler, Andrew M K Law, Robert Salomon, Samantha R Oakes, Christopher J Ormandy, David Gallego-Ortega

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
3.4field-weighted citation impact, top 6% 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

39 citing papers in PubMed, 66 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Review
  9. Personalized analysis of human cancer multi-omics for precision oncology.Computational and structural biotechnology journal · 2024
    Review
  10. Review
  11. Review
  12. Single-Cell Analysis in Immuno-Oncology.International journal of molecular sciences · 2023
    Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. The Architecture of a Precision Oncology Platform.Advances in experimental medicine and biology · 2022
    Review
  19. Review
  20. 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

7 authors at 3 institutions in 1 country.

Fatima Valdes-MoraGenomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
Kristina HandlerThe Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
Andrew M K LawThe Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
Robert SalomonGarvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
Samantha R OakesSt. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
Christopher J OrmandySt. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
David Gallego-OrtegaSt. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
Garvan Institute of Medical Research · AUThe Kinghorn Cancer Centre · AUUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is a heterogeneous and complex disease. Tumors are formed by cancer cells and a myriad of non-cancerous cell types that together with the extracellular matrix form the tumor microenvironment. These cancer-associated cells and components contribute to shape the progression of cancer and are deeply involved in patient outcome. The immune system is an essential part of the tumor microenvironment, and induction of cancer immunotolerance is a necessary step involved in tumor formation and growth. Immune mechanisms are intimately associated with cancer progression, invasion, and metastasis; as well as to tumor dormancy and modulation of sensitivity to drug therapy. Transcriptome analyses have been extensively used to understand the heterogeneity of tumors, classifying tumors into molecular subtypes and establishing signatures that predict response to therapy and patient outcomes. However, the classification of the tumor cell diversity and specially the identification of rare populations has been limited in these transcriptomic analyses of bulk tumor cell populations. Massively-parallel single-cell RNAseq analysis has emerged as a powerful method to unravel heterogeneity and to study rare cell populations in cancer, through unsupervised sampling and modeling of transcriptional states in single cells. In this context, the study of the role of the immune system in cancer would benefit from single cell approaches, as it will enable the characterization and/or discovery of the cell types and pathways involved in cancer immunotolerance otherwise missed in bulk transcriptomic information. Thus, the analysis of gene expression patterns at single cell resolution holds the potential to provide key information to develop precise and personalized cancer treatment including immunotherapy. This review is focused on the latest single-cell RNAseq methodologies able to agnostically study thousands of tumor cells as well as targeted single-cell RNAseq to study rare populations within tumors. In particular, we will discuss methods to study the immune system in cancer. We will also discuss the current challenges to the study of cancer at the single cell level and the potential solutions to the current approaches.

Indexed as

Gene Expression ProfilingSingle-Cell AnalysisAnimalsHumansImmunotherapyMedical OncologyNeoplasmsPrecision MedicineMDSCsScRNA-seqsingle-cell transcriptomicsstromatumor heterogeneitytumor immunology

Identifiers

PMID30483257
PMCPMC6240655
OpenAlexW2900614390

What OpenQuestion 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 OpenQuestion graph.