We present NaroNet, a Machine Learning framework that integrates the multiscale spatial, in situ analysis of the tumor microenvironment (TME) with patient-level predictions into a seamless end-to-end learning pipeline. Trained only with patient-level labels, NaroNetquantifies the phenotypes, neighborhoods, and neighborhood interactions that have the highest influence on the predictive task . We validate NaroNet using synthetic data simulating multiplex-immunostained images with adjustable probabilistic incidence of different TMEs . Then we apply our model to two real sets of patient tumors, one consisting of 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancers, and the other consisting of 372 35-plex mass cytometry images from 283 breast cancer patients . In both synthetic and real datasets, NaroNet provides outstanding predictions while associating those predictions to the presence of specific TMEs. This inherent interpretability could be of great value both in a clinical setting and as a tool to discover novel biomarker signatures.
Many efforts have been made to discover tumor-specific microenvironment elements (TMEs) from immunostained tissue sections. However, the identification of yet unknown but relevant TMEs from multiplex immunostained tissues remains a challenge, due to the number of markers involved (tens) and the complexity of their spatial interactions. We present NaroNet, which uses machine learning to identify and annotate known as well as novel TMEs from self-supervised embeddings of URLanized at different levels (local cell phenotypes and cellular neighborhoods). Then it uses the abundance of TMEs to classify patients based on biological or clinical features . We validate NaroNet using synthetic patient cohorts with adjustable incidence of different TMEs and two cancer patient datasets . In both synthetic and real datasets, NaroNet unsupervisedly identifies novel TMEs, relevant for the user-defined classification task. As NaroNet requires only patient-level information, it renders state-of-the-art computational methods accessible to a broad audience, accelerating the discovery of biomarker signatures.
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We present NaroNet, a Machine Learning framework that integrates the multiscale spatial, in situ analysis of
Many efforts have been made to discover tumor-specific microenvironment elements (TMEs) from immunostained tissue sections. However,
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tumor microenvironment (TME) with patient-level predictions into a seamless end-to-end learning pipeline. Trained only with patient-level labels, NaroNetquantifies the phenotypes, neighborhoods, and neighborhood interactions that have the highest influence on the predictive task
identification of yet unknown but relevant TMEs from multiplex immunostained tissues remains a challenge, due to the number of markers involved (tens) and the complexity of their spatial interactions. We present NaroNet, which uses machine learning to identify and annotate known as well as novel TMEs from self-supervised embeddings of URLanized at different levels (local cell phenotypes and cellular neighborhoods). Then it uses the abundance of TMEs to classify patients based on biological or clinical features
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data simulating multiplex-immunostained images with adjustable probabilistic
patient cohorts with adjustable
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. Then we apply our model to two real sets of patient tumors, one consisting of 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancers, and the other consisting of 372 35-plex mass cytometry images from 283 breast cancer patients
and two cancer patient datasets
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provides outstanding predictions while associating those predictions to the presence of specific TMEs. This inherent interpretability could be of great value both in a clinical setting and as a tool to discover novel
unsupervisedly identifies novel TMEs, relevant for the user-defined classification task. As NaroNet requires only patient-level information, it renders state-of-the-art computational methods accessible to a broad audience, accelerating the discovery of