Evidence map›Paper›PMID 38183141›Full record

ReviewEuropean journal of medical research2024

Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence.

Farida Zakariya, Fatma K Salem, Abdulwhhab Abu Alamrain, Vivek Sanker, Zainab G Abdelazeem, Mohamed Hosameldin, Joecelyn Kirani Tan, Rachel Howard, Helen Huang, Wireko Andrew Awuah

Abstract readReview
In one paragraph

Review in European journal of medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. 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

10 authors.

Farida ZakariyaFaculty of Pharmaceutical Sciences, Ahmadu Bello University, Zaria, Nigeria.ORCID http://orcid.org/0000-0002-2046-0253
Fatma K SalemFaculty of Veterinary Medicine, South Valley University, Qena, 83523, Egypt.ORCID http://orcid.org/0009-0001-7425-1174
Abdulwhhab Abu AlamrainFaculty of Medicine, Al-Quds University, Abu Dis, Palestine.ORCID http://orcid.org/0000-0002-0170-8903
Vivek SankerResearch Assistant, Dept. Of Neurosurgery, Trivandrum Medical College, Trivandrum, India.ORCID http://orcid.org/0000-0003-0615-8397
Zainab G AbdelazeemDivision of Molecular Biology, Department of Zoology, Faculty of Science, Alexandria University, Alexandria, Egypt.ORCID http://orcid.org/0009-0004-8733-3197
Mohamed HosameldinFaculty of Pharmacy, Zagazig University, Sharqia, Egypt.ORCID http://orcid.org/0009-0001-3102-6997
Joecelyn Kirani TanFaculty of Medicine, University of St Andrews, St Andrews, Scotland, UK.ORCID http://orcid.org/0009-0005-3648-6553
Rachel HowardSchool of Clinical Medicine, University of Cambridge, Cambridge, England.ORCID http://orcid.org/0000-0002-5118-7878
Helen Huang *Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, Dublin, Ireland.ORCID http://orcid.org/0000-0002-2809-0154
Wireko Andrew Awuah *Medical Institute, Sumy State University, Zamonstanksya 7, Sumy, 40007, Ukraine. andyvans36@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using the particular nature of melanoma mutanomes to develop medicines that activate the immune system against specific mutations is a game changer in immunotherapy individualisation. It offers a viable solution to the recent rise in resistance to accessible immunotherapy alternatives, with some patients demonstrating innate resistance to these drugs despite past sensitisation to these agents. However, various obstacles stand in the way of this method, most notably the practicality of sequencing each patient's mutanome, selecting immunotherapy targets, and manufacturing specific medications on a large scale. With the robustness and advancement in research techniques, artificial intelligence (AI) is a potential tool that can help refine the mutanome-based immunotherapy for melanoma. Mutanome-based techniques are being employed in the development of immune-stimulating vaccines, improving current options such as adoptive cell treatment, and simplifying immunotherapy responses. Although the use of AI in these approaches is limited by data paucity, cost implications, flaws in AI inference capabilities, and the incapacity of AI to apply data to a broad population, its potential for improving immunotherapy is limitless. Thus, in-depth research on how AI might help the individualisation of immunotherapy utilising knowledge of mutanomes is critical, and this should be at the forefront of melanoma management.

Indexed as

Artificial IntelligenceMelanomaHumansImmunotherapyKnowledgeMutationArtificial intelligenceImmunotherapyMelanomaMutanome

Identifiers

PMID38183141
PMCPMC10768232

What OpenQuestion holds

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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.