Evidence map›Paper›PMID 40847227›Full record

ReviewCancer treatment and research2025

Artificial Intelligence and Machine Learning Approaches in Designing Immunotherapy in Cancer.

Lokesh Seth, Colton Ladbury, Arya Amini

Abstract readReview
PubMed Publisher
In one paragraph

Review in Cancer treatment and research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Lokesh SethDepartment of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA.
Colton LadburyDepartment of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA.
Arya AminiDepartment of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA. aamini@coh.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) and machine learning (ML) are revolutionizing cancer immunotherapy by addressing the complex interplay between cancer and the immune system. This chapter explores how AI technologies enhance immunotherapy development across multiple domains: antibody design, response prediction, biomarker identification, and T-cell target discovery. In therapeutic antibody design, AI improves efficiency through predictive modeling of antibody-antigen interactions, structure prediction tools, generative models that create novel antibody sequences, and developability optimization. Clinical applications include AI-powered systems that predict immunotherapy responses using multi-omics data integration, helping distinguish pseudoprogression from true disease progression. Beyond conventional biomarkers like programmed cell death protein 1, AI enables identification of additional markers including tumor mutational burden, microsatellite instability, immune cell infiltration patterns, and novel genomic alterations. Multi-omics approaches leverage AI to synthesize diverse data types, uncovering complex biomarker signatures that more accurately predict treatment outcomes. For T-cell target identification, next-generation immunoediting platforms like Gritstone's EDGE™ system exemplify AI-powered approaches that precisely identify neoantigens by integrating sequencing technologies with sophisticated prediction algorithms (Table 2.1). These platforms support both personalized and shared antigen approaches to immunotherapy, potentially enhanced through integration with innate immune pathways. Despite remarkable progress, challenges persist in addressing tumor heterogeneity, immune evasion mechanisms, and technical limitations in prediction algorithms. The continued refinement of AI approaches, expansion to diverse cancer types, and integration with complementary therapeutic modalities represent promising future directions. Overall, AI and ML are poised to transform cancer immunotherapy by enabling more precise, effective, and personalized treatment approaches that harness the immune system's power against cancer.

Indexed as

Artificial IntelligenceImmunotherapyMachine LearningNeoplasmsHumansArtificial intelligence (AI)Cancer immunotherapyImmune checkpoint inhibitorsImmunotherapyMachine learning (ML)

Identifiers

What OpenQuestion holds

Textmetadata
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.