Artificial intelligence for personalized therapy

Several randomized controlled trials have demonstrated the efficacy of anti-programmed death-1 (PD-1) antibodies (1-4), anti-HER2 therapy (5,6), and anti-claudin 18.2 antibody (7,8) for gastric cancer in recent years. Although their effectiveness is limited, pharmacotherapy for gastric cancer has gradually progressed (9). The efficacy of anti-PD-1 antibodies in microsatellite instability-high patients is particularly significant, and such a predictive biomarker is extremely valuable (10). While the administration of anti-PD-1 antibodies has led to a slight increase in long-term survival, it is also known that there are more cases of long-term survival with chemotherapy alone in programmed death ligand 1 (PD-L1)-positive patients than in PD-L1-negative patients (11).

In the 2000s, as global clinical trials expanded, researchers discovered that the efficacy of the same drug varied due to differences in the medical environment and patient status across regions (12-14). To provide effective drugs for patients in each region, it is essential to establish a system for collecting data on efficacy and safety from hospitals during clinical practice after approval. Additionally, continuous reviews should ensure that only truly effective drugs are administered to patients who need them. By appropriately collecting such data and utilizing artificial intelligence (AI), machine learning, it becomes possible to predict the survival of patients who have been treated for similar conditions in the past. For example, sex differences were observed in the effects of oxaliplatin plus S-1 (15,16), and it was confirmed that excision repair cross-complementing group 1 (ERCC1), gene alterations and ERCC1 gene expression are useful in predicting the effects of cisplatin plus S-1 and triplet therapy with docetaxel and cisplatin plus S-1 (17-20) (Figure 1). The chemotherapy-sensitive group without any ERCC1 genome alterations with high expression of ERCC1 showed better survival with the doublet therapy (20).

Figure 1 Predictive and prognostic value of ERCC1 in patients with advanced gastric cancer. SNP, single nucleotide polymorphism.

Regional variations in the effectiveness of perioperative chemotherapy exist due to differences in surgical methods, medical environment, and patient status. For instance, in China, Korea, Europe, and the United States, perioperative chemotherapy is a standard treatment (21-23). In contrast, postoperative adjuvant chemotherapy is the standard treatment in Japan because the usefulness of preoperative chemotherapy has not been demonstrated (24,25). Study results also differ based on the macroscopic and histological type of patients involved. While aiming to optimize treatment using AI and leveraging existing cancer registry databases, genome analysis for cytotoxic drugs to predict the efficacy, innovative approaches to optimal targets for peritoneal dissemination and type 4 gastric cancer will improve the overall prognosis for gastric cancer patients, as many researchers have taken up this challenge.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Chinese Clinical Oncology for the series “Progress and Future Direction to Treat Advanced Gastric Cancer”. The article did not undergo external peer review.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://cco.amegroups.com/article/view/10.21037/cco-24-66/coif). The series “Progress and Future Direction to Treat Advanced Gastric Cancer” was commissioned by the editorial office without any funding or sponsorship. Y.Y. served as the unpaid Guest Editor of the series. Y.Y. also reported payments from Taiho, Ono and Chugai. The author has no other conflicts of interest to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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Yasuhide Yamada