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

Abstract

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 vs. 1.5 months, 95% CI: 1.29-1.71; HR 3.14, 95% CI: 2.27-4.33; log-rank test, P<0.01) and overall survival (OS) (26.7 months, 95% CI: 18.76-34.64 vs. 8.6 months, 95% CI: 4.55-12.65; HR 2.46, 95% CI: 1.73-3.51; log-rank test, P<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 vs. 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 vs. 17.2 months, 95% CI: 7.22-27.18; HR 2.22, 95% CI: 1.17-4.20; log-rank test, P=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.

Keywords: NSCLC; SimTA; multi-omics serial deep learning.

Figure 1

A deep learning model with Simple Temporal Attention (SimTA) modules. The SimTA modules process asynchronous clinical time series (i.e., the radiomics and blood tests) separately; the encoded features of these time series and static clinical information are then fused by a multiple-layer perceptron (MLP) to get the final output for the assessment prediction of responders/non-responders (R/non-R).

Figure 2

Model performance for response prediction in 200 patients. A. The AUC for the 60-day and 90-day response model. B. The AUC using the deep learning model incorporating baseline blood test data, baseline radiomics and using the RNN model. AUC, area under the ROC curve; ROC, receiver operating characteristic.

Figure 3

Deep learning prediction of PFS and OS in 200 patients. The prediction models stratify patients into high- and low-risk nonresponders using a default cutoff. A and B. PFS and OS according to risk stratification using the 60-day response model. C and D. PFS and OS according to risk stratification using the 90-day response model. HR, Hazards ratio; PFS, progression-free survival; OS, overall survival.

Figure 4

Deep learning prediction of survival in 93 patients with confirmed SD at the first efficacy assessment after anti-PD-1/PD-L1 treatment. The 90-day prediction model stratifies patients with SD into high- and low-risk nonresponders using a default cutoff. A. PFS in relation to risk stratification. B. OS in relation to risk stratification. Tumor response was evaluated according to RECIST 1.1. HR, Hazards ratio; SD, stable disease.