Systems approach for congruence and selection of cancer models towards precision medicine

作者全名："Zou, Jian; Shah, Osama; Chiu, Yu-Chiao; Ma, Tianzhou; Atkinson, Jennifer M.; Oesterreich, Steffi; Lee, Adrian V.; Tseng, George C."

作者地址："[Zou, Jian] Chongqing Med Univ, Sch Publ Hlth, Dept Stat, Chongqing, Peoples R China; [Shah, Osama; Atkinson, Jennifer M.; Oesterreich, Steffi; Lee, Adrian V.] UPMC, Womens Canc Res Ctr, Hillman Canc Ctr HCC, Pittsburgh, PA 15219 USA; [Shah, Osama; Atkinson, Jennifer M.; Oesterreich, Steffi; Lee, Adrian V.] Magee Womens Res Inst, Pittsburgh, PA 15213 USA; [Shah, Osama; Atkinson, Jennifer M.; Oesterreich, Steffi; Lee, Adrian V.] Univ Pittsburgh, Dept Pharmacol & Chem Biol, Pittsburgh, PA 15260 USA; [Chiu, Yu-Chiao] UPMC, Canc Therapeut Program, Hillman Canc Ctr HCC, Pittsburgh, PA USA; [Chiu, Yu-Chiao] Univ Pittsburgh, Dept Med, Pittsburgh, PA USA; [Ma, Tianzhou] Univ Maryland, Dept Epidemiol & Biostat, College Pk, MD USA; [Tseng, George C.] Univ Pittsburgh, Dept Biostat, Pittsburgh, PA 15260 USA"

通信作者："Oesterreich, S; Lee, AV (通讯作者)，UPMC, Womens Canc Res Ctr, Hillman Canc Ctr HCC, Pittsburgh, PA 15219 USA.; Oesterreich, S; Lee, AV (通讯作者)，Magee Womens Res Inst, Pittsburgh, PA 15213 USA.; Oesterreich, S; Lee, AV (通讯作者)，Univ Pittsburgh, Dept Pharmacol & Chem Biol, Pittsburgh, PA 15260 USA.; Tseng, GC (通讯作者)，Univ Pittsburgh, Dept Biostat, Pittsburgh, PA 15260 USA."

来源：PLOS COMPUTATIONAL BIOLOGY

ESI学科分类：BIOLOGY & BIOCHEMISTRY

WOS号：WOS:001150652400003

JCR分区：Q1

影响因子：4.3

年份：2024

卷号：20

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：　

摘要："Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine. Cancer research relies on models, such as cell lines, patient-derived xenografts (PDX), and patient-derived organoids (PDO), as essential alternatives to human studies. However, it is crucial to determine how well these models mimic human patients and to quantify their congruence in disease-relevant genes and regulatory pathways. As the number of cancer models grows, researchers face the challenge of selecting the most representative model based on molecular profiles. Existing methods are machine learning based and are limited to prediction using genome-wide information without mechanistic insights. To address this, we developed a comprehensive suite of bioinformatics tools, namely Congruence Analysis and Selection of CAncer Models (CASCAM). The framework develops a multi-stage systems approach to quantify pathway and gene specific congruence and allow prioritization and selection of the most congruent cancer model(s), which provides a paradigm shift towards gene regulatory and systems investigations."

基金机构："Susan G. Komen Scholar awards [SAC110021, SAC160073]; Breast Cancer Research Foundation; Magee Foundation; National Cancer Institute [CA252378]; NIH [R01LM014142, R21LM012752, S10OD028483]; University of Pittsburgh Center for Research Computing [RRID:SCR_022735]; [P30CA047904]"

基金资助正文："Research funding for this project was provided in part by Susan G. Komen Scholar awards (SAC110021 to AVL and SAC160073 to SO), the Breast Cancer Research Foundation (to AVL and SO), the Magee Foundation, and the National Cancer Institute (CA252378). JZ and GCT were funded by NIH grant R01LM014142 and R21LM012752. This research was supported in part by the University of Pittsburgh Center for Research Computing, RRID:SCR_022735, through the resources provided. Specifically, this work used the HTC cluster, which is supported by NIH award number S10OD028483. The study is in part funded by P30CA047904. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."