Abstract
Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists grade the microscopic appearance of breast tissue using the Nottingham criteria, which are qualitative and do not account for noncancerous elements within the tumor microenvironment. Here we present the Histomic Prognostic Signature (HiPS), a comprehensive, interpretable scoring of the survival risk incurred by breast tumor microenvironment morphology. HiPS uses deep learning to accurately map cellular and tissue structures to measure epithelial, stromal, immune, and spatial interaction features. It was developed using a population-level cohort from the Cancer Prevention Study-II and validated using data from three independent cohorts, including the Prostate, Lung, Colorectal, and Ovarian Cancer trial, Cancer Prevention Study-3, and The Cancer Genome Atlas. HiPS consistently outperformed pathologists in predicting survival outcomes, independent of tumor–node–metastasis stage and pertinent variables. This was largely driven by stromal and immune features. In conclusion, HiPS is a robustly validated biomarker to support pathologists and improve patient prognosis.
Original language | English (US) |
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Pages (from-to) | 85-97 |
Number of pages | 13 |
Journal | Nature Medicine |
Volume | 30 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2024 |
Funding
We express sincere appreciation to all CPS-II and CPS-3 participants and to each member of the study and biospecimen management group. We would like to acknowledge the contributions to this study from central cancer registries supported through the Centers for Disease Control and Prevention’s National Program of Cancer Registries and cancer registries supported by the National Cancer Institute’s Surveillance Epidemiology and End Results Program. We thank the National Cancer Institute for access to NCI’s data collected by the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. We are grateful to the annotation team for the Breast Cancer Semantic Segmentation and NuCLS datasets. We would also like to acknowledge F.M. Howard and A.T. Pearson (University of Chicago) for providing us with the research-use Oncotype DX and MammaPrint scores for TCGA. Figures – and , and multiple supplementary figures, were created in part using BioRender.com . This work was supported by the US National Institutes of Health grants U01CA220401 and U24CA19436201. The ACS funds the creation, maintenance, and updating of the CPS-II and CPS-3 cohorts. We express sincere appreciation to all CPS-II and CPS-3 participants and to each member of the study and biospecimen management group. We would like to acknowledge the contributions to this study from central cancer registries supported through the Centers for Disease Control and Prevention’s National Program of Cancer Registries and cancer registries supported by the National Cancer Institute’s Surveillance Epidemiology and End Results Program. We thank the National Cancer Institute for access to NCI’s data collected by the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. We are grateful to the annotation team for the Breast Cancer Semantic Segmentation and NuCLS datasets. We would also like to acknowledge F.M. Howard and A.T. Pearson (University of Chicago) for providing us with the research-use Oncotype DX and MammaPrint scores for TCGA. Figures 1 –4 and 6 , and multiple supplementary figures, were created in part using BioRender.com. This work was supported by the US National Institutes of Health grants U01CA220401 and U24CA19436201. The ACS funds the creation, maintenance, and updating of the CPS-II and CPS-3 cohorts.
ASJC Scopus subject areas
- General Biochemistry, Genetics and Molecular Biology