Gene embedding: A novel machine learning approach to identify gene candidates related to immunotherapy responsiveness

Choy, Chi Tung; Wong, Clt; Chan, Sze Ling

doi:10.1093/annonc/mdy269.071

Cited by 1 publication

(1 citation statement)

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“…[68] Furthermore, analysis of over 13 000 samples parameterized into a 50dimensional space allowed the unsupervised network to identify an additional 18 genes that are correlated with the response of patients to immune checkpoint blockade. [177] Though these genes are not yet reliable biomarkers indicative of patient response to treatment, further validation of algorithms like these can lead to new treatment paradigms and personalized medicine. Another study expanded the scope of the data collected within the TCGA to develop a database related to the immune landscape surrounding a tumor.…”

Section: Artificial Intelligencementioning

confidence: 99%

Screening Cancer Immunotherapy: When Engineering Approaches Meet Artificial Intelligence

Zhou

Tebon

et al. 2020

Advanced Science

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Immunotherapy is a class of promising anticancer treatments that has recently gained attention due to surging numbers of FDA approvals and extensive preclinical studies demonstrating efficacy. Nevertheless, further clinical implementation has been limited by high variability in patient response to different immunotherapeutic agents. These treatments currently do not have reliable predictors of efficacy and may lead to side effects. The future development of additional immunotherapy options and the prediction of patient-specific response to treatment require advanced screening platforms associated with accurate and rapid data interpretation. Advanced engineering approaches ranging from sequencing and gene editing, to tumor organoids engineering, bioprinted tissues, and organs-on-a-chip systems facilitate the screening of cancer immunotherapies by recreating the intrinsic and extrinsic features of a tumor and its microenvironment. High-throughput platform development and progress in artificial intelligence can also improve the efficiency and accuracy of screening methods. Here, these engineering approaches in screening cancer immunotherapies are highlighted, and a discussion of the future perspectives and challenges associated with these emerging fields to further advance the clinical use of state-of-the-art cancer immunotherapies are provided.

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Section: Artificial Intelligencementioning

confidence: 99%