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Published on: February 12, 2017
A pathomics model for predicting response to chemo-immunotherapy in lung squamous cell carcinoma: A multicenter study
Dongying Wang1, Shuai Mu2, Minghui Zhang3
1Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Shanghai Key Laboratory of Thoracic Tumor Biotherapy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
Background:
The identification of lung squamous cell carcinoma (LUSC) patients who may benefit from first-line chemo-immunotherapy (CIT) remains a challenge. This study aimed to develop a pathomics model to predict the T cell-inflamed gene-expression profile (GEP) status and validate its utility in identifying patients who derive survival benefit from CIT.
Methods:
The pathomics model was developed using whole-slide images and RNA-sequencing data from The Cancer Genome Atlas (TCGA) LUSC cohort (n = 334) to predict the GEP status and generate a pathomics score (PS). The predictive value of PS was validated in a prospective, multicenter trial (AK105-302, n = 267) by assessing its interaction with treatment (CIT vs. chemotherapy) for progression-free survival (PFS) and overall survival (OS). Two additional independent cohorts (n = 82 and n = 50) were used for external validation.
Results:
The pathomics model accurately predicted GEP status, achieving area under the curve (AUC) values of 0.80 and 0.71 in the TCGA training and validation sets, respectively. In the AK105-302 cohort, significant interactions were identified between PS and treatment modalities for both PFS (interaction p = 0.011) and OS (interaction p < 0.001). Patients with high PS who received CIT exhibited significantly prolonged PFS (hazard ratio [HR]: 0.31, 95 % confidence interval [CI]: 0.21-0.48, p < 0.001) and OS (HR: 0.30, 95 % CI: 0.18-0.50, p < 0.001) compared to high PS patients receiving chemotherapy. However, this survival benefit was not observed in the low-PS patients. These findings were corroborated in two independent clinical cohorts. Furthermore, biological assessments revealed a significant association between high PS and an immune-hot tumor microenvironment.
Conclusion:
We developed a GEP-based pathomics model that provides a practical, cost-effective strategy to identify patients most likely to derive superior survival benefit from first-line CIT over chemotherapy alone.
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