Prediction of response to neoadjuvant chemo-immunotherapy in patients with esophageal squamous cell carcinoma by a rapid breath test

Abstract

Background: Neoadjuvant chemo-immunotherapy combination has shown remarkable advances in the management of esophageal squamous cell carcinoma (ESCC). However, the identification of a reliable biomarker for predicting the response to this chemo-immunotherapy regimen remains elusive. While computed tomography (CT) is widely utilized for response evaluation, its inherent limitations in terms of accuracy are well recognized. Therefore, in this study, we present a novel technique to predict the response of ESCC patients before receiving chemo-immunotherapy by testing volatile organic compounds (VOCs) in exhaled breath.

Methods: This study employed a prospective-specimen-collection, retrospective-blinded-evaluation design. Patients' baseline breath samples were collected and analyzed using high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). Subsequently, patients were categorized as responders or non-responders based on the evaluation of therapeutic response using pathology (for patients who underwent surgery) or CT images (for patients who did not receive surgery).

Results: A total of 133 patients were included in this study, with 91 responders who achieved either a complete response (CR) or a partial response (PR), and 42 non-responders who had stable disease (SD) or progressive disease (PD). Among 83 participants who underwent both evaluations with CT and pathology, the paired t-test revealed significant differences between the two methods (p < 0.05). For the breath test prediction model using breath test data from all participants, the validation set demonstrated mean area under the curve (AUC) of 0.86 ± 0.06. For 83 patients with pathological reports, the breath test achieved mean AUC of 0.845 ± 0.123.

Conclusions: Since CT has inherent weakness in hollow organ assessment and no other ideal biomarker has been found, our study provided a noninvasive, feasible, and inexpensive tool that could precisely predict ESCC patients' response to neoadjuvant chemo-immunotherapy combination using breath test based on HPPI-TOFMS.

Fig. 1. The flow chart shows recruitment and allocation process.

*iRECIST modified Response Evaluation Criteria in Solid Tumours 1.1 for immune based therapeutics; TRG tumor regression grading.

Fig. 2. Sample collection and VOCs detection.

We collected and detected baseline exhaled breath from patients by HPPI-TOFMS. We illustrate two representative spectrum from a responder and a non-responder. The upper panel is the spectrum of a patient achieved complete response. The lower panel is the spectrum of a patient achieved stable disease. VOCs, volatile organic compounds.

Fig. 3. Prediction model construction based on breath test.

a The flow chart shows the data analysis process. All 133 patients’ data set recorded by HPPI-TOFMS were randomly allocated into training set and test set in a ratio of 7:3. b Receiver operator characteristic curve (ROC) curve (with 95%CI) for the selection of responders in the validation set of all patients, c patients with pathological reports. CI confidence interval.