Details

Autor(en) / Beteiligte

• Yang, Yi
• Yang, Jiancheng
• Shen, Lan
• Chen, Jiajun
• Xia, Liliang
• Ni, Bingbing
• Ge, Liang
• Wang, Ying
• Lu, Shun

Titel

A multi-omics-based serial deep learning approach to predict clinical outcomes of single-agent anti-PD-1/PD-L1 immunotherapy in advanced stage non-small-cell lung cancer

Ist Teil von

• American journal of translational research, 2021-01, Vol.13 (2), p.743-756

Ort / Verlag

United States: e-Century Publishing Corporation

Erscheinungsjahr

2021

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Only 20% NSCLC patients benefit from immunotherapy with a durable response. Current biomarkers are limited by the availability of samples and do not accurately predict who will benefit from immunotherapy. To develop a unified deep learning model to integrate multimodal serial information from CT with laboratory and baseline clinical information. We retrospectively analyzed 1633 CT scans and 3414 blood samples from 200 advanced stage NSCLC patients who received single anti-PD-1/PD-L1 agent between April 2016 and December 2019. Multidimensional information, including serial radiomics, laboratory data and baseline clinical data, was used to develop and validate deep learning models to identify immunotherapy responders and nonresponders. A Simple Temporal Attention (SimTA) module was developed to process asynchronous time-series imaging and laboratory data. Using cross-validation, the 90-day deep learning-based predicting model showed a good performance in distinguishing responders from nonresponders, with an area under the curve (AUC) of 0.80 (95% CI: 0.74-0.86). Before immunotherapy, we stratified the patients into high- and low-risk nonresponders using the model. The low-risk group had significantly longer progression-free survival (PFS) (8.4 months, 95% CI: 5.49-11.31 . 1.5 months, 95% CI: 1.29-1.71; HR 3.14, 95% CI: 2.27-4.33; log-rank test, <0.01) and overall survival (OS) (26.7 months, 95% CI: 18.76-34.64 . 8.6 months, 95% CI: 4.55-12.65; HR 2.46, 95% CI: 1.73-3.51; log-rank test, <0.01) than the high-risk group. An exploratory analysis of 93 patients with stable disease (SD) [after first efficacy assessment according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1] also showed that the 90-day model had a good prediction of survival and low-risk patients had significantly longer PFS (11.1 months, 95% CI: 10.24-11.96 . 3.3 months, 95% CI: 0.34-6.26; HR 2.93, 95% CI: 1.69-5.10; log-rank test, P<0.01) and OS (31.7 months, 95% CI: 23.64-39.76 . 17.2 months, 95% CI: 7.22-27.18; HR 2.22, 95% CI: 1.17-4.20; log-rank test, =0.01) than high-risk patients. In conclusion, the SimTA-based multi-omics serial deep learning provides a promising methodology for predicting response of advanced NSCLC patients to anti-PD-1/PD-L1 monotherapy. Moreover, our model could better differentiate survival benefit among SD patients than the traditional RECIST evaluation method.