Abstract 219P
Background
The well-established link between the gut microbiome and cancer immunotherapy faces the challenge of developing a reliable predictive model for clinical application. This challenge arises from several factors that can influence the gut microbiome, including host genetics, lifestyle, and geographic location. We aim to develop a robust yet practical model to predict immunotherapy response in non-small cell lung cancer (NSCLC).
Methods
We conducted a comprehensive analysis using metagenomic shotgun sequencing data from our study (Liu et al. Cancers 2022, Dataset 1: DS1) and published datasets (Routy et al. Science 2018, DS2; Derosa et al. Nat Med 2022, DS3), encompassing a total of 417 NSCLC patients who received immune check point inhibitors (ICIs). We classified clinical benefit (B) as achieving the best response of complete response, partial response, or stable disease, while disease progression was categorized as having no clinical benefit (NB). We employed various combinations of the datasets and data splitting, such as using a pooled set of DS1 and DS2 for both training and testing (train-test split ratio 7:3, randomly for 100 times; denoted as [1,2 | 1,2]). We also explored the train-test combinations of [3 | 3], [1,2,3 | 1,2,3], and [1,2,3 | 3]. Using neural network model, we compared the predictive potential of Akkermansia muciniphila, community-level taxonomic and functional profiles. In addition, we explored k-mer (k-long nucleotide) signature as a practical alternative as it is reference-free, and allow its signature built into microarray with low cost.
Results
Our k-mer signature using 91 selected 30-mers significantly outperformed Akkermansia muciniphila. Furthermore, it surpassed the taxonomic profile and approached the performance of the functional profile in predicting response to ICIs in NSCLC.
Conclusions
The predictive value of intestinal Akkermansia muciniphila can be substantially enhanced through the adoption of taxonomy or functional profiles. Due to the cost-effective synthesis of probes using k-mer sequences, especially when coupled with a microarray, the k-mer signature should be considered a practical and potent tool to predict immunotherapy response in NSCLC.
Legal entity responsible for the study
The authors.
Funding
University of Kansas Comprehensive Cancer Center Startup.
Disclosure
J. Zhang: Financial Interests, Personal, Speaker’s Bureau: AstraZeneca, Regeneron, Sanofi; Financial Interests, Personal, Research Grant: AstraZeneca, Mirati, Nilogen, Genentech; Financial Interests, Personal, Advisory Board: Novocure, Takeda; Financial Interests, Personal, Funding: BeiGene, Khar Medical. All other authors have declared no conflicts of interest.