Javier Fernandez-Mateos, George D. Cresswell, Nicholas Trahearn, Katharine Webb, Chirine Sakr, Andrea Lampis, Christine Stuttle, Catherine M. Corbishley, Vasilis Stavrinides, Luis Zapata, Inmaculada Spiteri, Timon Heide, Lewis Gallagher, Chela James, Daniele Ramazzotti, Annie Gao, Zsofia Kote-Jarai, Ahmet Acar, Lesley Truelove, Paula Proszek, Julia Murray, Alison Reid, Anna Wilkins, Michael Hubank, Ros Eeles, David Dearnaley, Andrea Sottoriva
{"title":"肿瘤演变指标可预测局部晚期前列腺癌 10 年后的复发。","authors":"Javier Fernandez-Mateos, George D. Cresswell, Nicholas Trahearn, Katharine Webb, Chirine Sakr, Andrea Lampis, Christine Stuttle, Catherine M. Corbishley, Vasilis Stavrinides, Luis Zapata, Inmaculada Spiteri, Timon Heide, Lewis Gallagher, Chela James, Daniele Ramazzotti, Annie Gao, Zsofia Kote-Jarai, Ahmet Acar, Lesley Truelove, Paula Proszek, Julia Murray, Alison Reid, Anna Wilkins, Michael Hubank, Ros Eeles, David Dearnaley, Andrea Sottoriva","doi":"10.1038/s43018-024-00787-0","DOIUrl":null,"url":null,"abstract":"Cancer evolution lays the groundwork for predictive oncology. Testing evolutionary metrics requires quantitative measurements in controlled clinical trials. We mapped genomic intratumor heterogeneity in locally advanced prostate cancer using 642 samples from 114 individuals enrolled in clinical trials with a 12-year median follow-up. We concomitantly assessed morphological heterogeneity using deep learning in 1,923 histological sections from 250 individuals. Genetic and morphological (Gleason) diversity were independent predictors of recurrence (hazard ratio (HR) = 3.12 and 95% confidence interval (95% CI) = 1.34–7.3; HR = 2.24 and 95% CI = 1.28–3.92). Combined, they identified a group with half the median time to recurrence. Spatial segregation of clones was also an independent marker of recurrence (HR = 2.3 and 95% CI = 1.11–4.8). We identified copy number changes associated with Gleason grade and found that chromosome 6p loss correlated with reduced immune infiltration. Matched profiling of relapse, decades after diagnosis, confirmed that genomic instability is a driving force in prostate cancer progression. This study shows that combining genomics with artificial intelligence-aided histopathology leads to the identification of clinical biomarkers of evolution. Sottoriva and colleagues combine next-generation sequencing and AI-aided histopathology to assess tumor evolvability in patient samples with long-term follow-up and find that it can be a strong predictor of recurrence in high-risk prostate cancer.","PeriodicalId":18885,"journal":{"name":"Nature cancer","volume":null,"pages":null},"PeriodicalIF":23.5000,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43018-024-00787-0.pdf","citationCount":"0","resultStr":"{\"title\":\"Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer\",\"authors\":\"Javier Fernandez-Mateos, George D. Cresswell, Nicholas Trahearn, Katharine Webb, Chirine Sakr, Andrea Lampis, Christine Stuttle, Catherine M. Corbishley, Vasilis Stavrinides, Luis Zapata, Inmaculada Spiteri, Timon Heide, Lewis Gallagher, Chela James, Daniele Ramazzotti, Annie Gao, Zsofia Kote-Jarai, Ahmet Acar, Lesley Truelove, Paula Proszek, Julia Murray, Alison Reid, Anna Wilkins, Michael Hubank, Ros Eeles, David Dearnaley, Andrea Sottoriva\",\"doi\":\"10.1038/s43018-024-00787-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cancer evolution lays the groundwork for predictive oncology. Testing evolutionary metrics requires quantitative measurements in controlled clinical trials. We mapped genomic intratumor heterogeneity in locally advanced prostate cancer using 642 samples from 114 individuals enrolled in clinical trials with a 12-year median follow-up. We concomitantly assessed morphological heterogeneity using deep learning in 1,923 histological sections from 250 individuals. Genetic and morphological (Gleason) diversity were independent predictors of recurrence (hazard ratio (HR) = 3.12 and 95% confidence interval (95% CI) = 1.34–7.3; HR = 2.24 and 95% CI = 1.28–3.92). Combined, they identified a group with half the median time to recurrence. Spatial segregation of clones was also an independent marker of recurrence (HR = 2.3 and 95% CI = 1.11–4.8). We identified copy number changes associated with Gleason grade and found that chromosome 6p loss correlated with reduced immune infiltration. Matched profiling of relapse, decades after diagnosis, confirmed that genomic instability is a driving force in prostate cancer progression. This study shows that combining genomics with artificial intelligence-aided histopathology leads to the identification of clinical biomarkers of evolution. 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Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer
Cancer evolution lays the groundwork for predictive oncology. Testing evolutionary metrics requires quantitative measurements in controlled clinical trials. We mapped genomic intratumor heterogeneity in locally advanced prostate cancer using 642 samples from 114 individuals enrolled in clinical trials with a 12-year median follow-up. We concomitantly assessed morphological heterogeneity using deep learning in 1,923 histological sections from 250 individuals. Genetic and morphological (Gleason) diversity were independent predictors of recurrence (hazard ratio (HR) = 3.12 and 95% confidence interval (95% CI) = 1.34–7.3; HR = 2.24 and 95% CI = 1.28–3.92). Combined, they identified a group with half the median time to recurrence. Spatial segregation of clones was also an independent marker of recurrence (HR = 2.3 and 95% CI = 1.11–4.8). We identified copy number changes associated with Gleason grade and found that chromosome 6p loss correlated with reduced immune infiltration. Matched profiling of relapse, decades after diagnosis, confirmed that genomic instability is a driving force in prostate cancer progression. This study shows that combining genomics with artificial intelligence-aided histopathology leads to the identification of clinical biomarkers of evolution. Sottoriva and colleagues combine next-generation sequencing and AI-aided histopathology to assess tumor evolvability in patient samples with long-term follow-up and find that it can be a strong predictor of recurrence in high-risk prostate cancer.
期刊介绍:
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