NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

TL;DR

NaroNet is a machine learning framework that identifies both known and novel tumor microenvironment elements from multiplexed images, aiding in patient classification and biomarker discovery without requiring detailed annotations.

Contribution

It introduces a self-supervised, multi-level approach to discover TMEs from multiplexed images, including unknown elements, and links them to clinical outcomes.

Findings

Successfully identifies novel TMEs in synthetic and real datasets.

Achieves accurate patient classification based on TME abundance.

Operates with only patient-level data, broadening accessibility.

Abstract

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 cells, organized 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.