PhD Scientific Days 2023

Translational Medicine - Posters P

Bacterial Urine Proteome might Predict Immunotherapy Outcomes in Non-Small Cell Lung Cancer Patients

Előadó neve

Vágó-Szincsák, Sára, MSc

Neptun code

H1ZP61

Előadó munkahelye

Translational Medicine Institute, Semmelweis University, Budapest

Előadó telefonszáma

+36203335216

Előadó e-mail címe

vago.sara@phd.semmelweis.hu

Az előadás címe

Bacterial Urine Proteome might Predict Immunotherapy Outcomes in Non-Small Cell Lung Cancer Patients

Szerző(k) neve és munkahelye

Vágó-Szincsák Sára1, Dóra Dávid2, Lohinai Zoltán1
1 Translational Medicine Institute, Semmelweis University, Budapest
2 Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest

Bemutatás módja

Poszter

Szekció

Translational Medicine - Posters P

Language of the presentation

English

Preferred session

Theoretical and Translational Medicine

Összefoglaló szövege

Introduction
Urine samples are non-invasive approaches to studying potential circulating biomarkers. Microbiota-related metabolites can enter the systemic circulation and might regulate the host immune system by modulating the activities of various immune cell types. Our previous studies have investigated the associations between the gut microbiome and anti-Programmed death (PD) immunotherapy outcomes.
Aims
The primary aim is to identify bacteria-related proteins in human urine samples. The secondary aim is to evaluate the predictive role of bacterial urine metabolites in ICI-treated NSCLC patients.
Methods
We analyzed the urine proteome of 33 advanced-stage NSCLC patients treated with anti-PD1 immunotherapy using untargeted Liquid chromatography–mass spectrometry (LC-MS/MS). We stratified patients according to long (>6 months) and short (≤6 months) progression-free survival (PFS). Gut microbial communities on a subcohort of 23 patients were analyzed with shotgun metagenomics. We performed Random Forest Internal cross-validation using a machine-learning algorithm.
Results
A total of n=2647 bacterial proteins were detected in n=33 patients urine samples. Interestingly, when we compared the abundance of gut metagenome and the abundance of urine proteins of different microbial taxa, a significant correlation was detected in multiple cases, including E. coli and E. faecalis. We revealed that an increased bacterial/host protein ratio in the urine is more frequent in patients with long PFS. We found that multiple bacterial proteins show an association with PFS. Random forest machine learning model supported the reliability of our critical bacterial proteins in predicting PFS.
Conclusion
To our knowledge this is the first study to identify bacterial metabolites in cancer patients' urine samples with a potential predictive role for anti-PD immunotherapy.
Funding
The present work was supported by NKFIH OTKA FK 124652

University and Doctoral School

Semmelweis University, Károly Rácz Doctoral School of Clinical Medicine

Supervisor

Dr. Lohinai Zoltán

Publication of my abstract

I do not give consent to the publication of my abstract on the website of the congress.

Kind

Szabad

Status

elfogadva

Accepted presentation method

poszter

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

7486

Start

10:54

End

10:59

Authors (legacy)

Vágó-Szincsák Sára1, Dóra Dávid2, Lohinai Zoltán1
1 Translational Medicine Institute, Semmelweis University, Budapest
2 Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest