Patient-derived organoid modeling predicts personalized drug responses in prostate-metastatic mantle cell lymphoma: a case report

Tumor heterogeneity represents a significant challenge in cancer treatment. Current therapeutic strategies frequently rely on single biopsy assessments that may not fully capture tumor complexity. In this study, we developed prostate patient-derived organoids (PDOs) from a mantle cell lymphoma (MCL) case with prostatic metastasis. Monotherapy experiments revealed that the prostate organoids were sensitive to gemcitabine but resistant to rituximab and oxaliplatin. In combination therapy experiments, the half maximal inhibitory concentration value of gemcitabine increased, indicating that the combination regimen may attenuate its efficacy. In addition, the expression of prostate cancer markers prostate-specific membrane antigen and ETS-related gene was detected in the organoids. The research findings indicate that the PDO model not only dynamically monitors changes in drug sensitivity caused by heterogeneity but also serves as a powerful tool for predicting drug responses and optimizing precision treatment strategies. This is particularly applicable to clinical decision-making for highly heterogeneous tumors like MCL.

Authors

Institutions

Publication Details

Journal
Anti-Cancer Drugs
Published
2025-08-05
DOI
https://doi.org/10.1097/cad.0000000000001760
Primary Topic
Prostate Cancer Treatment and Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Patient-derived organoid modeling predicts personalized drug responses in prostate-metastatic mantle cell lymphoma: a case report

Sha Zhu, Ninghan Feng, Danyan Lin, Peng Jiang et al.
1 links
Anti-Cancer Drugs
Prostate Cancer Treatment and Research
article

Patient-derived organoid modeling predicts personalized drug responses in prostate-metastatic mantle cell lymphoma: a case report

Sha Zhu, Ninghan Feng, Danyan Lin, Peng Jiang, Xiaoting Wang, Gang Fu, Tingting Zhou, Jiayi Wan, Mingyi Shui
article en

Abstract

Tumor heterogeneity represents a significant challenge in cancer treatment. Current therapeutic strategies frequently rely on single biopsy assessments that may not fully capture tumor complexity. In this study, we developed prostate patient-derived organoids (PDOs) from a mantle cell lymphoma (MCL) case with prostatic metastasis. Monotherapy experiments revealed that the prostate organoids were sensitive to gemcitabine but resistant to rituximab and oxaliplatin. In combination therapy experiments, the half maximal inhibitory concentration value of gemcitabine increased, indicating that the combination regimen may attenuate its efficacy. In addition, the expression of prostate cancer markers prostate-specific membrane antigen and ETS-related gene was detected in the organoids. The research findings indicate that the PDO model not only dynamically monitors changes in drug sensitivity caused by heterogeneity but also serves as a powerful tool for predicting drug responses and optimizing precision treatment strategies. This is particularly applicable to clinical decision-making for highly heterogeneous tumors like MCL.

Anti-Cancer DrugsVol. 36(10)
Jiangnan University (CN), Wuxi No.2 People's Hospital (CN)
Good health and well-being
Openalex Percentile: Top 70%
Prostate Cancer Treatment and Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Patient-derived organoid modeling predicts personalized drug responses in prostate-metastatic mantle cell lymphoma: a case report — Sha Zhu, Ninghan Feng, et al. · Anti-Cancer Drugs (2025) | TGRS Research Map | TGRS