Dataset Information

Precision Medicine in Pancreatic Cancer: Patient-Derived Organoid Pharmacotyping Is a Predictive Biomarker of Clinical Treatment Response.

ABSTRACT:

Purpose

Patient-derived organoids (PDO) are a promising technology to support precision medicine initiatives for patients with pancreatic ductal adenocarcinoma (PDAC). PDOs may improve clinical next-generation sequencing (NGS) and enable rapid ex vivo chemotherapeutic screening (pharmacotyping).

Experimental design

PDOs were derived from tissues obtained during surgical resection and endoscopic biopsies and studied with NGS and pharmacotyping. PDO-specific pharmacotype is assessed prospectively as a predictive biomarker of clinical therapeutic response by leveraging data from a randomized controlled clinical trial.

Results

Clinical sequencing pipelines often fail to detect PDAC-associated somatic mutations in surgical specimens that demonstrate a good pathologic response to previously administered chemotherapy. Sequencing the PDOs derived from these surgical specimens, after biomass expansion, improves the detection of somatic mutations and enables quantification of copy number variants. The detection of clinically relevant mutations and structural variants is improved following PDO biomass expansion. On clinical trial, PDOs were derived from biopsies of treatment-naïve patients prior to treatment with FOLFIRINOX (FFX). Ex vivo PDO pharmacotyping with FFX components predicted clinical therapeutic response in these patients with borderline resectable or locally advanced PDAC treated in a neoadjuvant or induction paradigm. PDO pharmacotypes suggesting sensitivity to FFX components were associated with longitudinal declines of tumor marker, carbohydrate-antigen 19-9 (CA-19-9), and favorable RECIST imaging response.

Conclusions

PDOs established from tissues obtained from patients previously receiving cytotoxic chemotherapies can be accomplished in a clinically certified laboratory. Sequencing PDOs following biomass expansion improves clinical sequencing quality. High in vitro sensitivity to standard-of-care chemotherapeutics predicts good clinical response to systemic chemotherapy in PDAC. See related commentary by Zhang et al., p. 3176.

SUBMITTER: Seppala TT

PROVIDER: S-EPMC9357072 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

Precision Medicine in Pancreatic Cancer: Patient-Derived Organoid Pharmacotyping Is a Predictive Biomarker of Clinical Treatment Response.

Seppälä Toni T TT Zimmerman Jacquelyn W JW Suri Reecha R Zlomke Haley H Ivey Gabriel D GD Szabolcs Annamaria A Shubert Christopher R CR Cameron John L JL Burns William R WR Lafaro Kelly J KJ He Jin J Wolfgang Christopher L CL Zou Ying S YS Zheng Lei L Tuveson David A DA Eshlemann James R JR Ryan David P DP Kimmelman Alec C AC Hong Theodore S TS Ting David T DT Jaffee Elizabeth M EM Burkhart Richard A RA

Clinical cancer research : an official journal of the American Association for Cancer Research 20220801 15

<h4>Purpose</h4>Patient-derived organoids (PDO) are a promising technology to support precision medicine initiatives for patients with pancreatic ductal adenocarcinoma (PDAC). PDOs may improve clinical next-generation sequencing (NGS) and enable rapid ex vivo chemotherapeutic screening (pharmacotyping).<h4>Experimental design</h4>PDOs were derived from tissues obtained during surgical resection and endoscopic biopsies and studied with NGS and pharmacotyping. PDO-specific pharmacotype is assessed ...[more]

PMID: 35363262

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Project description:For many years, cell lines and animal models have been essential to improve our understanding of the basis of cell metabolism, signaling, and genetics. They also provided an essential boost to cancer drug discovery. Nevertheless, these model systems failed to reproduce the tumor heterogeneity and the complex biological interactions between cancer cells and human hosts, making a high priority search for alternative methods that are able to export results from model systems to humans, which has become a major bottleneck in the drug development. The emergent human in vitro 3D cell culture technologies have attracted widespread attention because they seem to have the potential to overcome these limitations. Organoids are unique 3D culture models with the ability to self-organize in contained structures. Their versatility has offered an exceptional window of opportunity to approach human cancers. Pancreatic cancers (PCs) patient-derived-organoids (PDOs) preserve histological, genomic, and molecular features of neoplasms they originate from and therefore retain their heterogeneity. Patient-derived organoids can be established with a high success rate from minimal tissue core specimens acquired with endoscopic-ultrasound-guided techniques and assembled into platforms, representing tens to hundreds of cancers each conserving specific features, expanding the types of patient samples that can be propagated and analyzed in the laboratory. Because of their nature, PDO platforms are multipurpose systems that can be easily adapted in co-culture settings to perform a wide spectrum of studies, ranging from drug discovery to immune response evaluation to tumor-stroma interaction. This possibility to increase the complexity of organoids creating a hybrid culture with non-epithelial cells increases the interest in organoid-based platforms giving a pragmatic way to deeply study biological interactions in vitro. In this view, implementing organoid models in co-clinical trials to compare drug responses may represent the next step toward even more personalized medicine. In the present review, we discuss how PDO platforms are shaping modern-day oncology aiding to unravel the most complex aspects of PC.

Dataset Information

Precision Medicine in Pancreatic Cancer: Patient-Derived Organoid Pharmacotyping Is a Predictive Biomarker of Clinical Treatment Response.

Purpose

Experimental design

Results

Conclusions

Publications

Precision Medicine in Pancreatic Cancer: Patient-Derived Organoid Pharmacotyping Is a Predictive Biomarker of Clinical Treatment Response.

Similar Datasets

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