Zitong Jerry Wang

Zitong Jerry Wang, Alexander M. Xu, Aman Bhargava et al.

The tumor microenvironment (TME) significantly impacts cancer prognosis due to its immune composition. While therapies for altering the immune composition, including immunotherapies, have shown exciting results for treating hematological cancers, they are less effective for immunologically-cold, solid tumors. Spatial omics technologies capture the spatial organization of the TME with unprecedented molecular detail, revealing the relationship between immune cell localization and molecular signals. Here, we formulate T-cell infiltration prediction as a self-supervised machine learning problem and develop a counterfactual optimization strategy that leverages large scale spatial omics profiles of patient tumors to design tumor perturbations predicted to boost T-cell infiltration. A convolutional neural network predicts T-cell distribution based on signaling molecules in the TME provided by imaging mass cytometry. Gradient-based counterfactual generation, then, computes perturbations predicted to boost T-cell abundance. We apply our framework to melanoma, colorectal cancer liver metastases, and breast tumor data, discovering combinatorial perturbations predicted to support T-cell infiltration across tens to hundreds of patients. This work presents a paradigm for counterfactual-based prediction and design of cancer therapeutics using spatial omics data.

Kian Kenyon-Dean, Zitong Jerry Wang, John Urbanik et al.

Large-scale cell microscopy screens are used in drug discovery and molecular biology research to study the effects of millions of chemical and genetic perturbations on cells. To use these images in downstream analysis, we need models that can map each image into a feature space that represents diverse biological phenotypes consistently, in the sense that perturbations with similar biological effects have similar representations. In this work, we present the largest foundation model for cell microscopy data to date, a new 1.9 billion-parameter ViT-G/8 MAE trained on over 8 billion microscopy image crops. Compared to a previous published ViT-L/8 MAE, our new model achieves a 60% improvement in linear separability of genetic perturbations and obtains the best overall performance on whole-genome biological relationship recall and replicate consistency benchmarks. Beyond scaling, we developed two key methods that improve performance: (1) training on a curated and diverse dataset; and, (2) using biologically motivated linear probing tasks to search across each transformer block for the best candidate representation of whole-genome screens. We find that many self-supervised vision transformers, pretrained on either natural or microscopy images, yield significantly more biologically meaningful representations of microscopy images in their intermediate blocks than in their typically used final blocks. More broadly, our approach and results provide insights toward a general strategy for successfully building foundation models for large-scale biological data.