Joshua D Ginzel, Henry Chapman, Joelle E Sills, Edwin J Allen, Lawrence S Barak, Robert D Cardiff, Alexander D Borowsky, Herbert Kim Lyerly, Bruce W Rogers, Joshua C Snyder
{"title":"Nonlinear progression during the occult transition establishes cancer lethality.","authors":"Joshua D Ginzel, Henry Chapman, Joelle E Sills, Edwin J Allen, Lawrence S Barak, Robert D Cardiff, Alexander D Borowsky, Herbert Kim Lyerly, Bruce W Rogers, Joshua C Snyder","doi":"10.1242/dmm.052113","DOIUrl":null,"url":null,"abstract":"<p><p>Cancer screening relies upon a linear model of neoplastic growth and progression. Yet, historical observations suggest that malignant progression is uncoupled from growth, which may explain the paradoxical increase in early-stage breast cancer detection without a dramatic reduction in metastasis. Here, we lineage trace millions of transformed cells and thousands of tumors using a cancer rainbow mouse model of HER2 (also known as ERBB2)-positive breast cancer. Transition rates from field cell to screen-detectable tumor to symptomatic tumor were estimated from a dynamical model of tumor development. Field cells were orders of magnitude less likely to transition to a screen-detectable tumor than the subsequent transition from screen-detectable tumor to symptomatic tumor. Our model supports a critical 'occult' transition in tumor development during which a transformed cell becomes a bona fide neoplasm. Lineage tracing and test by transplantation revealed that nonlinear progression during the occult transition gives rise to nascent lethal cancers at screen detection. Simulations illustrated how occult transition rates are a critical determinant of tumor growth and malignancy. Our data provide direct experimental evidence that cancers can deviate from the predictable linear progression model that is foundational to current screening paradigms.</p>","PeriodicalId":11144,"journal":{"name":"Disease Models & Mechanisms","volume":"18 3","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11957451/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Disease Models & Mechanisms","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1242/dmm.052113","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/19 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Cancer screening relies upon a linear model of neoplastic growth and progression. Yet, historical observations suggest that malignant progression is uncoupled from growth, which may explain the paradoxical increase in early-stage breast cancer detection without a dramatic reduction in metastasis. Here, we lineage trace millions of transformed cells and thousands of tumors using a cancer rainbow mouse model of HER2 (also known as ERBB2)-positive breast cancer. Transition rates from field cell to screen-detectable tumor to symptomatic tumor were estimated from a dynamical model of tumor development. Field cells were orders of magnitude less likely to transition to a screen-detectable tumor than the subsequent transition from screen-detectable tumor to symptomatic tumor. Our model supports a critical 'occult' transition in tumor development during which a transformed cell becomes a bona fide neoplasm. Lineage tracing and test by transplantation revealed that nonlinear progression during the occult transition gives rise to nascent lethal cancers at screen detection. Simulations illustrated how occult transition rates are a critical determinant of tumor growth and malignancy. Our data provide direct experimental evidence that cancers can deviate from the predictable linear progression model that is foundational to current screening paradigms.
期刊介绍:
Disease Models & Mechanisms (DMM) is an online Open Access journal focusing on the use of model systems to better understand, diagnose and treat human disease.