TME-guided deep learning predicts chemotherapy and immunotherapy response in gastric cancer with attention-enhanced residual Swin Transformer.
Authors
Affiliations (17)
Affiliations (17)
- Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA; Department of Computer Science, University of Miami, Miami, FL, USA; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston Salem, NC, USA.
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- The Reproductive Medical Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
- Department of Pathology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China.
- Department of Pathology, Wake Forest University School of Medicine, Winston Salem, NC, USA.
- Graduate Group of Epidemiology, University of California Davis, Davis, CA, USA.
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
- Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China.
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
- School of Clinical Medicine, Tsinghua University, Beijing Tsinghua Changgung Hospital, Beijing, China. Electronic address: [email protected].
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China. Electronic address: [email protected].
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston Salem, NC, USA. Electronic address: [email protected].
Abstract
Adjuvant chemotherapy and immune checkpoint blockade exert quite durable anti-tumor responses, but the lack of effective biomarkers limits the therapeutic benefits. Utilizing multi-cohorts of 3,095 patients with gastric cancer, we propose an attention-enhanced residual Swin Transformer network to predict chemotherapy response (main task), and two predicting subtasks (ImmunoScore and periostin [POSTN]) are used as intermediate tasks to improve the model's performance. Furthermore, we assess whether the model can identify which patients would benefit from immunotherapy. The deep learning model achieves high accuracy in predicting chemotherapy response and the tumor microenvironment (ImmunoScore and POSTN). We further find that the model can identify which patient may benefit from checkpoint blockade immunotherapy. This approach offers precise chemotherapy and immunotherapy response predictions, opening avenues for personalized treatment options. Prospective studies are warranted to validate its clinical utility.