Abstract 1082P
Background
The appearance of immune checkpoint inhibitors (ICIs) has revolutionized treatment strategies in advanced NSCLC. The clinical efficacy and indications for ICIs are currently estimated, using the immunohistochemistry (IHC) profile of programmed death-ligand 1 (PD-L1) protein. Recent findings indicate that tumor mutational burden (TMB) is a unique feature that may improve response prediction to ICIs across multiple cancer types [1]. Nevertheless, only a minority of NSCLC patients will benefit from ICIs. Therefore, unraveling the genomics of NSCLC and the subsequent discovery of novel predictive biomarkers remains a high unmet need. Whole-genome sequencing (WGS) bounded with AI may identify biomarkers and composed signatures which will better than TMB or PD-L1 predict response to ICIs.
Methods
To develop the PD1Dx classifier with the highest predictive value for ICI response, WGS data analyses have been combined with machine learning approaches. The patient-derived WGS data has been labeled based on clinical response to treatment with pembrolizumab or nivolumab.
Results
As a result, the random forest-based model has selected 15 top predictive genomic features, achieving the best performance, with an accuracy of 0.72, a recall of 0.73, and a precision of 0.80. Using the same learning method and dataset, a model has been built based on a single TMB feature, whose performance determined by accuracy, recall and precision have been 0.67, 0.64, and 0.78, respectively. In addition to TMB, other predictive features have also been identified, such as those related to transcription-induced mutational strand bias, C>A/T single nucleotide variants correlating with mismatch repair deficiency and tumor neoantigen burden.
Conclusions
Our results confirm the predictive value of features other than TMB alone in predicting ICI responses in NSCLC. Further investigation is required to validate biomarkers of sensitivity and resistance to ICIs. References: 1. Krieger T, Pearson I, Bell J, Doherty J, Robbins P. Targeted literature review on use of tumor mutational burden status and programmed cell death ligand 1 expression to predict outcomes of checkpoint inhibitor treatment. Diagn Pathol 2020;15:6.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
MNM Diagnostics Sp. Z o.o.
Funding
MNM Diagnostics Sp. Z o. o.
Disclosure
M. Piernik: Financial Interests, Personal, Stocks/Shares: MNM Diagnostics Sp. z o.o. A. Wozna: Financial Interests, Personal, Stocks/Shares: MNM Diagnostics Sp. z o.o. P. Zawadzki: Financial Interests, Personal, Stocks/Shares: MNM Diagnostics Sp. z o.o. All other authors have declared no conflicts of interest.