Astha Lamichhane, Prasiddha Guragain, Jacob Heiss, Pouria Rafsanjani Nejad, Anju Rana Magar, Nicholas Ciavattone, Seema Agarwal, Gary D Luker, Hossein Tavana
{"title":"Targeting CAFs-Mediated Stromal Signaling in a Patient-Derived Organotypic Colorectal Tumor Model.","authors":"Astha Lamichhane, Prasiddha Guragain, Jacob Heiss, Pouria Rafsanjani Nejad, Anju Rana Magar, Nicholas Ciavattone, Seema Agarwal, Gary D Luker, Hossein Tavana","doi":"10.1158/1535-7163.MCT-24-0756","DOIUrl":null,"url":null,"abstract":"<p><p>Colorectal cancer, a significant cause of cancer-related mortality, often exhibits drug resistance, highlighting the need for improved tumor models to advance personalized drug testing and precision therapy. We generated organoids from primary colorectal cancer cells cultured through the conditional reprogramming technique, establishing a framework to perform short-term drug testing studies on patient-derived cells. To model interactions with stromal cells in the tumor microenvironment, we combined cancer cell organoids with carcinoma-associated fibroblasts (CAFs), a cell type implicated in disease progression and drug resistance. Our organotypic models revealed that CAFs promote cancer cell proliferation and stemness primarily through HGF-MET paracrine signaling and activation of Cyclin-Dependent Kinases (CDKs). Disrupting these tumor-stromal interactions reduced organoid size while limiting oncogenic signals and cancer stemness. Leveraging this tumor model, we identified effective drug combinations targeting colorectal cancer cells and their tumorigenic activities. Our study highlights a path to incorporate patient-derived cells and tumor-stromal interactions into a drug testing workflow that could identify effective therapies for individual patients.</p>","PeriodicalId":18791,"journal":{"name":"Molecular Cancer Therapeutics","volume":" ","pages":""},"PeriodicalIF":5.3000,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Cancer Therapeutics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1158/1535-7163.MCT-24-0756","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Colorectal cancer, a significant cause of cancer-related mortality, often exhibits drug resistance, highlighting the need for improved tumor models to advance personalized drug testing and precision therapy. We generated organoids from primary colorectal cancer cells cultured through the conditional reprogramming technique, establishing a framework to perform short-term drug testing studies on patient-derived cells. To model interactions with stromal cells in the tumor microenvironment, we combined cancer cell organoids with carcinoma-associated fibroblasts (CAFs), a cell type implicated in disease progression and drug resistance. Our organotypic models revealed that CAFs promote cancer cell proliferation and stemness primarily through HGF-MET paracrine signaling and activation of Cyclin-Dependent Kinases (CDKs). Disrupting these tumor-stromal interactions reduced organoid size while limiting oncogenic signals and cancer stemness. Leveraging this tumor model, we identified effective drug combinations targeting colorectal cancer cells and their tumorigenic activities. Our study highlights a path to incorporate patient-derived cells and tumor-stromal interactions into a drug testing workflow that could identify effective therapies for individual patients.
期刊介绍:
Molecular Cancer Therapeutics will focus on basic research that has implications for cancer therapeutics in the following areas: Experimental Cancer Therapeutics, Identification of Molecular Targets, Targets for Chemoprevention, New Models, Cancer Chemistry and Drug Discovery, Molecular and Cellular Pharmacology, Molecular Classification of Tumors, and Bioinformatics and Computational Molecular Biology. The journal provides a publication forum for these emerging disciplines that is focused specifically on cancer research. Papers are stringently reviewed and only those that report results of novel, timely, and significant research and meet high standards of scientific merit will be accepted for publication.