1
Return

Sensing-Integrated Patient-Derived Tumorspheres Predict Chemotherapeutic Efficacy via Local Extracellular pH Dynamics

delete2026-08-13
delete0
delete
OA
AI
S
Stefania Forciniti
V
Valentina Onesto
A
Anna Chiara Siciliano
H
Helena Iuele
S
Stefano D'Ugo
N
Norma Depalma
C
Cinzia Fasano
A
Alessandro D'Amuri
G
Giuseppe Gigli
M
Marcello G. Spampinato *
L
Loretta L. del Mercato *
DOI:10.1002/smll.75149delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest malignancies, due to late diagnosis, poor therapeutic response, and high inter- and intra-patient heterogeneity. Metabolic alterations, particularly extracellular acidification, critically impair drug response yet remain underexplored in clinical stratification. We present a sensing-integrated 3D platform that enables non-invasive time-resolved mapping of pH dynamics in patient-derived tumorspheres. Primary PDAC cells from five patients were embedded in an alginate-based matrix incorporating ratiometric optical pH sensors, allowing real-time monitoring of local extracellular acidification associated with individual cells during treatment. In contrast to existing bulk or invasive methods, our platform captures extracellular acidification dynamics with high spatial and temporal resolution in a 3D model with potential clinical relevance. Distinct extracellular acidification profiles emerged in response to FOLFIRINOX, gemcitabine, and paclitaxel. These metabolic signatures correlated with treatment efficacy and highlighted patient-specific drug sensitivities. This platform offers a rapid, scalable tool for personalized drug screening and highlights a potential role of pH metabolic heterogeneity in PDAC chemoresistance. It may complement existing clinical workflows by providing early predictive insights into patient-specific therapeutic outcomes.
Keywords:
metabolic pH heterogeneity
pancreatic ductal adenocarcinoma
patient-derived tumorspheres
personalized drug screening
tumor microenvironment
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Small cover
Small
IF:
12.1
Papers:
3.0W
Citations:
16.4W

Organization

H
Hospital
Scholars:
547
Papers: 216
Citations: 0
N
national research council
Scholars:
1.6K
Papers: 658
Citations: 0
U
uoc of pathological anatomy vito fazzi hospital
Scholars:
3
Papers: 1
Citations: 0
Cited Papers

Cited Papers

Citing Papers

Citing Papers