Evidence map›Paper›PMID 30627563›Full record

ReviewBioMed research international2018

The Adaptive Complexity of Cancer.

Youcef Derbal

Open access · hybridAbstract readReview
In one paragraph

Review in BioMed research international, 2018. 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
0.8field-weighted citation impact, top 22% 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, 35 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. The Yin-Yang Dynamics in Cancer Pharmacogenomics and Personalized Medicine.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  12. Review
  13. Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence.Cancer control : journal of the Moffitt Cancer Center
    Review
  14. Generative AI - Assisted Adaptive Cancer Therapy.Cancer control : journal of the Moffitt Cancer Center
    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

1 author at 1 institution in 1 country.

Youcef DerbalTed Rogers School of Information Technology Management, Ryerson University, Toronto, Canada.ORCID 0000-0003-3317-9417
Toronto Metropolitan University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer treatment options are expanding to the benefit of significant segments of patients. However, their therapeutic power is not equally realized for all cancer patients due to drug toxicity and disease resistance. Overcoming these therapeutic challenges would require a better understanding of the adaptive survival mechanisms of cancer. In this respect, an integrated view of the disease as a complex adaptive system is proposed as a framework to explain the dynamic coupling between the various drivers underlying tumor growth and cancer resistance to therapy. In light of this system view of cancer, the immune system is in principal the most appropriate and naturally available therapeutic instrument that can thwart the adaptive survival mechanisms of cancer. In this respect, new cancer therapies should aim at restoring immunosurveillance by priming the induction of an effective immune response through a judicious targeting of immunosuppression, inflammation, and the tumor nutritional lifeline extended by the tumor microenvironment.

Indexed as

Immune ToleranceImmunologic SurveillanceAdaptation, PhysiologicalAnimalsAntineoplastic AgentsDrug Resistance, NeoplasmHumansInflammationNeoplasmsTumor MicroenvironmentAntineoplastic Agents

Identifiers

PMID30627563
PMCPMC6304530
OpenAlexW2902540225

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.