# Pulmonary microbiome and metabolome signatures associate with chemotherapy response in lung cancer patients

## TL;DR

This study finds that the lung microbiome and metabolome can predict how well lung cancer patients respond to chemotherapy.

## Contribution

The study identifies specific microbial and metabolic signatures linked to chemotherapy response in lung cancer patients.

## Key findings

- 92 altered metabolites were found in bronchoalveolar lavage fluid between chemotherapy-sensitive and insensitive patients.
- Key bacterial genera like Caulobacter and Acinetobacter showed differential abundance in sensitive patients.
- Serum bile acids showed elevated levels in chemotherapy-sensitive patients with good predictive value.

## Abstract

Lung cancer is a leading cause of cancer-related mortality worldwide, with chemotherapy response varying significantly among patients. Emerging evidence suggests that the pulmonary microbiota and metabolome may influence treatment outcomes, but their roles remain unclear.

This study enrolled 25 lung cancer patients undergoing chemotherapy, categorized into chemotherapy-sensitive (n = 15) and chemotherapy-insensitive (n = 10) groups. Bronchoalveolar lavage fluid (BALF) was collected for 16S rDNA sequencing and untargeted metabolomics (LC-MS). Serum bile acids were also analyzed.

The study identified 92 significantly altered metabolites in BALF between the two groups. Trans-urocanate showed the highest increase, while phenylalanylphenylalanine exhibited the greatest decrease in sensitive patients. Key metabolic pathways, including ABC transporters, glutathione metabolism, and bile acid biosynthesis, were enriched. Microbiome analysis revealed differential abundances of specific bacterial genera, particularly increased Caulobacter and decreased Acinetobacter in sensitive patients. Notably, serum levels of four bile acids (chenodeoxycholic acid, cholic acid, deoxycholic acid, and ursodeoxycholic acid) were significantly elevated in chemotherapy-sensitive patients, demonstrating good predictive value with AUCs ranging from 0.633 to 0.830.

The study highlights distinct microbial and metabolic signatures associated with chemotherapy response, suggesting potential biomarkers for personalized therapy.